Medicare Program; Inpatient Rehabilitation Facility (IRF) Prospective Payment System for Federal Fiscal Year 2020 and Updates to the IRF Quality Reporting Program
| Published date | 08 August 2019 |
| Citation | 84 FR 39054 |
| Record Number | 2019-16603 |
| Section | Rules and Regulations |
| Court | Centers For Medicare & Medicaid Services |
Federal Register, Volume 84 Issue 153 (Thursday, August 8, 2019)
[Federal Register Volume 84, Number 153 (Thursday, August 8, 2019)]
[Rules and Regulations]
[Pages 39054-39173]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2019-16603]
[[Page 39053]]
Vol. 84
Thursday,
No. 153
August 8, 2019
Part IIDepartment of Health and Human Services-----------------------------------------------------------------------Centers for Medicare & Medicaid Services-----------------------------------------------------------------------42 CFR Part 412Medicare Program; Inpatient Rehabilitation Facility (IRF) Prospective
Payment System for Federal Fiscal Year 2020 and Updates to the IRF
Quality Reporting Program; Final Rule
Federal Register / Vol. 84 , No. 153 / Thursday, August 8, 2019 /
Rules and Regulations
[[Page 39054]]
-----------------------------------------------------------------------
DEPARTMENT OF HEALTH AND HUMAN SERVICES
Centers for Medicare & Medicaid Services
42 CFR Part 412
[CMS-1710-F]
RIN 0938-AT67
Medicare Program; Inpatient Rehabilitation Facility (IRF)
Prospective Payment System for Federal Fiscal Year 2020 and Updates to
the IRF Quality Reporting Program
AGENCY: Centers for Medicare & Medicaid Services (CMS), HHS.
ACTION: Final rule.
-----------------------------------------------------------------------
SUMMARY: This final rule updates the prospective payment rates for
inpatient rehabilitation facilities (IRFs) for federal fiscal year (FY)
2020. As required by the statute, this final rule includes the
classification and weighting factors for the IRF prospective payment
system's (PPS) case-mix groups (CMGs) and a description of the
methodologies and data used in computing the prospective payment rates
for FY 2020. This final rule rebases and revises the IRF market basket
to reflect a 2016 base year rather than the current 2012 base year.
Additionally, this final rule revises the CMGs and updates the CMG
relative weights and average length of stay (LOS) values beginning with
FY 2020, based on analysis of 2 years of data (FYs 2017 and 2018).
Although we proposed to use a weighted motor score to assign patients
to CMGs, we are finalizing based on public comments the use of an
unweighted motor score to assign patients to CMGs beginning with FY
2020. Additionally, we are finalizing the removal of one item from the
motor score. We are updating the IRF wage index to use the concurrent
fiscal year inpatient prospective payment system (IPPS) wage index
beginning with FY 2020. We are amending the regulations to clarify that
the determination as to whether a physician qualifies as a
rehabilitation physician (that is, a licensed physician with
specialized training and experience in inpatient rehabilitation) is
made by the IRF. For the IRF Quality Reporting Program (QRP), we are
adopting two new measures, modifying an existing measure, and adopting
new standardized patient assessment data elements. We are also making
updates to reflect our migration to a new data submission system.
DATES:
Effective date: These regulations are effective on October 1, 2019.
Applicability dates: The updated IRF prospective payment rates are
applicable for IRF discharges occurring on or after October 1, 2019,
and on or before September 30, 2020 (FY 2020). The new and updated
quality measures and reporting requirements under the IRF QRP are
applicable for IRF discharges occurring on or after October 1, 2020.
FOR FURTHER INFORMATION CONTACT:
Gwendolyn Johnson, (410) 786-6954, for general information.
Catie Kraemer, (410) 786-0179, for information about the IRF
payment policies and payment rates.
Kadie Derby, (410) 786-0468, for information about the IRF coverage
policies.
Kate Brooks, (410) 786-7877, for information about the IRF quality
reporting program.
SUPPLEMENTARY INFORMATION:
Inspection of Public Comments: All comments received before the
close of the comment period are available for viewing by the public,
including any personally identifiable or confidential business
information that is included in a comment. We post all comments
received before the close of the comment period as soon as possible
after they have been received at http://www.regulations.gov. Follow the
search instructions on that website to view public comments.
The IRF PPS Addenda along with other supporting documents and
tables referenced in this final rule are available through the internet
on the CMS website at http://www.cms.hhs.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/.
Executive Summary
A. Purpose
This final rule updates the prospective payment rates for IRFs for
FY 2020 (that is, for discharges occurring on or after October 1, 2019,
and on or before September 30, 2020) as required under section
1886(j)(3)(C) of the Social Security Act (the Act). As required by
section 1886(j)(5) of the Act, this final rule includes the
classification and weighting factors for the IRF PPS's case-mix groups
(CMGs) and a description of the methodologies and data used in
computing the prospective payment rates for FY 2020. This final rule
also rebases and revises the IRF market basket to reflect a 2016 base
year, rather than the current 2012 base year. Additionally, this final
rule revises the CMGs and updates the CMG relative weights and average
LOS values beginning with FY 2020, based on analysis of 2 years of data
(FYs 2017 and 2018). Although we proposed to use a weighted motor score
to assign patients to CMGs, we are finalizing based on public comments
the use of an unweighted motor score to assign patients to CMGs
beginning with FY 2020. Additionally, we are finalizing the removal of
one item from the motor score. We are also updating the IRF wage index
to use the concurrent FY IPPS wage index for the IRF PPS beginning with
FY 2020. We are also amending the regulations at 42 CFR 412.622 to
clarify that the determination as to whether a physician qualifies as a
rehabilitation physician (that is, a licensed physician with
specialized training and experience in inpatient rehabilitation) is
made by the IRF. For the IRF QRP, we are adopting two new measures,
modifying an existing measure, and adopting new standardized patient
assessment data elements. We also include updates related to the system
used for the submission of data and related regulation text. We are not
finalizing our proposal requiring that IRFs submit data on measures and
standardized patient assessment data for which the source of the data
is the IRF-PAI to all patients, regardless of payer, but plan to
propose this policy in future rulemaking.
B. Summary of Major Provisions
In this final rule, we use the methods described in the FY 2019 IRF
PPS final rule (83 FR 38514) to update the prospective payment rates
for FY 2020 using updated FY 2018 IRF claims and the most recent
available IRF cost report data, which is FY 2017 IRF cost report data.
This final rule also rebases and revises the IRF market basket to
reflect a 2016 base year rather than the current 2012 base year.
Additionally, this final rule revises the CMGs and updates the CMG
relative weights and average LOS values beginning with FY 2020, based
on analysis of 2 years of data (FYs 2017 and 2018). Although we
proposed to use a weighted motor score to assign patients to CMGs, we
are finalizing based on public comments the use of an unweighted motor
score to assign patients to CMGs beginning with FY 2020. Additionally,
we are finalizing the removal of one item from the motor score. We are
also updating the IRF wage index to use the concurrent FY IPPS wage
index for the IRF PPS beginning in FY 2020. We are also amending the
regulations at Sec. 412.622 to clarify that the determination as to
whether a physician qualifies as a rehabilitation physician (that is, a
licensed physician with specialized
[[Page 39055]]
training and experience in inpatient rehabilitation) is made by the
IRF. We also update requirements for the IRF QRP.
C. Summary of Impacts
[GRAPHIC] [TIFF OMITTED] TR08AU19.000
I. Background
A. Historical Overview of the IRF PPS
Section 1886(j) of the Act provides for the implementation of a
per-discharge PPS for inpatient rehabilitation hospitals and inpatient
rehabilitation units of a hospital (collectively, hereinafter referred
to as IRFs). Payments under the IRF PPS encompass inpatient operating
and capital costs of furnishing covered rehabilitation services (that
is, routine, ancillary, and capital costs), but not direct graduate
medical education costs, costs of approved nursing and allied health
education activities, bad debts, and other services or items outside
the scope of the IRF PPS. Although a complete discussion of the IRF PPS
provisions appears in the original FY 2002 IRF PPS final rule (66 FR
41316) and the FY 2006 IRF PPS final rule (70 FR 47880), we are
providing a general description of the IRF PPS for FYs 2002 through
2019.
Under the IRF PPS from FY 2002 through FY 2005, the prospective
payment rates were computed across 100 distinct CMGs, as described in
the FY 2002 IRF PPS final rule (66 FR 41316). We constructed 95 CMGs
using rehabilitation impairment categories (RICs), functional status
(both motor and cognitive), and age (in some cases, cognitive status
and age may not be a factor in defining a CMG). In addition, we
constructed five special CMGs to account for very short stays and for
patients who expire in the IRF.
For each of the CMGs, we developed relative weighting factors to
account for a patient's clinical characteristics and expected resource
needs. Thus, the weighting factors accounted for the relative
difference in resource use across all CMGs. Within each CMG, we created
tiers based on the estimated effects that certain comorbidities would
have on resource use.
We established the federal PPS rates using a standardized payment
conversion factor (formerly referred to as the budget-neutral
conversion factor). For a detailed discussion of the budget-neutral
conversion factor, please refer to our FY 2004 IRF PPS final rule (68
FR 45684 through 45685). In the FY 2006 IRF PPS final rule (70 FR
47880), we discussed in detail the methodology for determining the
standard payment conversion factor.
We applied the relative weighting factors to the standard payment
conversion factor to compute the unadjusted prospective payment rates
under the IRF PPS from FYs 2002 through 2005. Within the structure of
the payment system, we then made adjustments to account for interrupted
stays, transfers, short stays, and deaths. Finally, we applied the
applicable adjustments to account for geographic variations in wages
(wage index), the percentage of low-income patients, location in a
rural area (if applicable), and outlier payments (if applicable) to the
IRFs' unadjusted prospective payment rates.
For cost reporting periods that began on or after January 1, 2002,
and before October 1, 2002, we determined the final prospective payment
amounts using the transition methodology prescribed in section
1886(j)(1) of the Act. Under this provision, IRFs transitioning into
the PPS were paid a blend of the federal IRF PPS rate and the payment
that the IRFs would have received had the IRF PPS not been implemented.
This provision also allowed IRFs to elect to bypass this blended
payment and immediately be paid 100 percent of the federal IRF PPS
rate. The transition methodology expired as of cost reporting periods
beginning on or after October 1, 2002 (FY 2003), and payments for all
IRFs now consist of 100 percent of the federal IRF PPS rate.
Section 1886(j) of the Act confers broad statutory authority upon
the Secretary to propose refinements to the IRF PPS. In the FY 2006 IRF
PPS final rule (70 FR 47880) and in correcting amendments to the FY
2006 IRF PPS final rule (70 FR 57166), we finalized a number of
refinements to the IRF PPS case-mix classification system (the CMGs and
the corresponding relative weights) and the case-level and facility-
level adjustments. These refinements included the adoption of the
Office of Management and Budget's (OMB) Core-Based Statistical Area
(CBSA) market definitions; modifications to the CMGs, tier
comorbidities; and CMG relative weights, implementation of a new
teaching status adjustment for IRFs; rebasing and revising the market
basket index used to update IRF payments, and updates to the rural,
low-income percentage (LIP), and high-cost outlier adjustments.
Beginning with the FY 2006 IRF PPS final rule (70 FR 47908 through
47917), the market basket index used to update IRF payments was a
market basket reflecting the operating and capital cost structures for
freestanding IRFs, freestanding inpatient psychiatric facilities
(IPFs), and long-term care hospitals (LTCHs) (hereinafter referred to
as the rehabilitation, psychiatric, and long-term care (RPL) market
basket). Any reference to the FY 2006 IRF PPS final rule in this final
rule also includes the provisions effective in the correcting
amendments. For a detailed discussion of the final key policy changes
for FY 2006, please refer to the FY 2006 IRF PPS final rule.
In the FY 2007 IRF PPS final rule (71 FR 48354), we further refined
the IRF PPS case-mix classification system (the CMG relative weights)
and the case-level adjustments, to ensure that IRF PPS payments would
continue to reflect as accurately as possible the costs of care. For a
detailed discussion of the FY 2007 policy revisions, please refer to
the FY 2007 IRF PPS final rule.
In the FY 2008 IRF PPS final rule (72 FR 44284), we updated the
prospective payment rates and the outlier threshold, revised the IRF
wage index policy, and clarified how we determine high-cost outlier
payments for transfer cases. For more information on the policy changes
[[Page 39056]]
implemented for FY 2008, please refer to the FY 2008 IRF PPS final
rule.
After publication of the FY 2008 IRF PPS final rule (72 FR 44284),
section 115 of the Medicare, Medicaid, and SCHIP Extension Act of 2007
(Pub. L. 110-173, enacted December 29, 2007) (MMSEA) amended section
1886(j)(3)(C) of the Act to apply a zero percent increase factor for
FYs 2008 and 2009, effective for IRF discharges occurring on or after
April 1, 2008. Section 1886(j)(3)(C) of the Act required the Secretary
to develop an increase factor to update the IRF prospective payment
rates for each FY. Based on the legislative change to the increase
factor, we revised the FY 2008 prospective payment rates for IRF
discharges occurring on or after April 1, 2008. Thus, the final FY 2008
IRF prospective payment rates that were published in the FY 2008 IRF
PPS final rule (72 FR 44284) were effective for discharges occurring on
or after October 1, 2007, and on or before March 31, 2008, and the
revised FY 2008 IRF prospective payment rates were effective for
discharges occurring on or after April 1, 2008, and on or before
September 30, 2008. The revised FY 2008 prospective payment rates are
available on the CMS website at http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Data-Files.html.
In the FY 2009 IRF PPS final rule (73 FR 46370), we updated the CMG
relative weights, the average LOS values, and the outlier threshold;
clarified IRF wage index policies regarding the treatment of ``New
England deemed'' counties and multi-campus hospitals; and revised the
regulation text in response to section 115 of the MMSEA to set the IRF
compliance percentage at 60 percent (the ``60 percent rule'') and
continue the practice of including comorbidities in the calculation of
compliance percentages. We also applied a zero percent market basket
increase factor for FY 2009 in accordance with section 115 of the
MMSEA. For more information on the policy changes implemented for FY
2009, please refer to the FY 2009 IRF PPS final rule.
In the FY 2010 IRF PPS final rule (74 FR 39762) and in correcting
amendments to the FY 2010 IRF PPS final rule (74 FR 50712), we updated
the prospective payment rates, the CMG relative weights, the average
LOS values, the rural, LIP, teaching status adjustment factors, and the
outlier threshold; implemented new IRF coverage requirements for
determining whether an IRF claim is reasonable and necessary; and
revised the regulation text to require IRFs to submit patient
assessments on Medicare Advantage (MA) (formerly called Medicare Part
C) patients for use in the 60 percent rule calculations. Any reference
to the FY 2010 IRF PPS final rule in this final rule also includes the
provisions effective in the correcting amendments. For more information
on the policy changes implemented for FY 2010, please refer to the FY
2010 IRF PPS final rule.
After publication of the FY 2010 IRF PPS final rule (74 FR 39762),
section 3401(d) of the Patient Protection and Affordable Care Act (Pub.
L. 111-148, enacted March 23, 2010), as amended by section 10319 of the
same Act and by section 1105 of the Health Care and Education
Reconciliation Act of 2010 (Pub. L. 111-152, enacted March 30, 2010)
(collectively, hereinafter referred to as ``PPACA''), amended section
1886(j)(3)(C) of the Act and added section 1886(j)(3)(D) of the Act.
Section 1886(j)(3)(C) of the Act requires the Secretary to estimate a
multifactor productivity (MFP) adjustment to the market basket increase
factor, and to apply other adjustments as defined by the Act. The
productivity adjustment applies to FYs from 2012 forward. The other
adjustments apply to FYs 2010 to 2019.
Sections 1886(j)(3)(C)(ii)(II) and 1886(j)(3)(D)(i) of the Act
defined the adjustments that were to be applied to the market basket
increase factors in FYs 2010 and 2011. Under these provisions, the
Secretary was required to reduce the market basket increase factor in
FY 2010 by a 0.25 percentage point adjustment. Notwithstanding this
provision, in accordance with section 3401(p) of the PPACA, the
adjusted FY 2010 rate was only to be applied to discharges occurring on
or after April 1, 2010. Based on the self-implementing legislative
changes to section 1886(j)(3) of the Act, we adjusted the FY 2010
prospective payment rates as required, and applied these rates to IRF
discharges occurring on or after April 1, 2010, and on or before
September 30, 2010. Thus, the final FY 2010 IRF prospective payment
rates that were published in the FY 2010 IRF PPS final rule (74 FR
39762) were used for discharges occurring on or after October 1, 2009,
and on or before March 31, 2010, and the adjusted FY 2010 IRF
prospective payment rates applied to discharges occurring on or after
April 1, 2010, and on or before September 30, 2010. The adjusted FY
2010 prospective payment rates are available on the CMS website at
https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html.
In addition, sections 1886(j)(3)(C) and (D) of the Act also
affected the FY 2010 IRF outlier threshold amount because they required
an adjustment to the FY 2010 RPL market basket increase factor, which
changed the standard payment conversion factor for FY 2010.
Specifically, the original FY 2010 IRF outlier threshold amount was
determined based on the original estimated FY 2010 RPL market basket
increase factor of 2.5 percent and the standard payment conversion
factor of $13,661. However, as adjusted, the IRF prospective payments
were based on the adjusted RPL market basket increase factor of 2.25
percent and the revised standard payment conversion factor of $13,627.
To maintain estimated outlier payments for FY 2010 equal to the
established standard of 3 percent of total estimated IRF PPS payments
for FY 2010, we revised the IRF outlier threshold amount for FY 2010
for discharges occurring on or after April 1, 2010, and on or before
September 30, 2010. The revised IRF outlier threshold amount for FY
2010 was $10,721.
Sections 1886(j)(3)(C)(ii)(II) and 1886(j)(3)(D)(i) of the Act also
required the Secretary to reduce the market basket increase factor in
FY 2011 by a 0.25 percentage point adjustment. The FY 2011 IRF PPS
notice (75 FR 42836) and the correcting amendments to the FY 2011 IRF
PPS notice (75 FR 70013) described the required adjustments to the FY
2010 and FY 2011 IRF PPS prospective payment rates and outlier
threshold amount for IRF discharges occurring on or after April 1,
2010, and on or before September 30, 2011. It also updated the FY 2011
prospective payment rates, the CMG relative weights, and the average
LOS values. Any reference to the FY 2011 IRF PPS notice in this final
rule also includes the provisions effective in the correcting
amendments. For more information on the FY 2010 and FY 2011 adjustments
or the updates for FY 2011, please refer to the FY 2011 IRF PPS notice.
In the FY 2012 IRF PPS final rule (76 FR 47836), we updated the IRF
prospective payment rates, rebased and revised the RPL market basket,
and established a new QRP for IRFs in accordance with section
1886(j)(7) of the Act. We also consolidated, clarified, and revised
existing policies regarding IRF hospitals and IRF units of hospitals to
eliminate unnecessary confusion and enhance consistency. For more
information on the policy changes implemented for FY 2012, please refer
to the FY 2012 IRF PPS final rule.
The FY 2013 IRF PPS notice (77 FR 44618) described the required
adjustments to the FY 2013 prospective
[[Page 39057]]
payment rates and outlier threshold amount for IRF discharges occurring
on or after October 1, 2012, and on or before September 30, 2013. It
also updated the FY 2013 prospective payment rates, the CMG relative
weights, and the average LOS values. For more information on the
updates for FY 2013, please refer to the FY 2013 IRF PPS notice.
In the FY 2014 IRF PPS final rule (78 FR 47860), we updated the
prospective payment rates, the CMG relative weights, and the outlier
threshold amount. We also updated the facility-level adjustment factors
using an enhanced estimation methodology, revised the list of diagnosis
codes that count toward an IRF's 60 percent rule compliance calculation
to determine ``presumptive compliance,'' revised sections of the IRF
patient assessment instrument (IRF-PAI), revised requirements for acute
care hospitals that have IRF units, clarified the IRF regulation text
regarding limitation of review, updated references to previously
changed sections in the regulations text, and updated requirements for
the IRF QRP. For more information on the policy changes implemented for
FY 2014, please refer to the FY 2014 IRF PPS final rule.
In the FY 2015 IRF PPS final rule (79 FR 45872) and the correcting
amendments to the FY 2015 IRF PPS final rule (79 FR 59121), we updated
the prospective payment rates, the CMG relative weights, and the
outlier threshold amount. We also revised the list of diagnosis codes
that count toward an IRF's 60 percent rule compliance calculation to
determine ``presumptive compliance,'' revised sections of the IRF-PAI,
and updated requirements for the IRF QRP. Any reference to the FY 2015
IRF PPS final rule in this final rule also includes the provisions
effective in the correcting amendments. For more information on the
policy changes implemented for FY 2015, please refer to the FY 2015 IRF
PPS final rule.
In the FY 2016 IRF PPS final rule (80 FR 47036), we updated the
prospective payment rates, the CMG relative weights, and the outlier
threshold amount. We also adopted an IRF-specific market basket that
reflects the cost structures of only IRF providers, a blended 1-year
transition wage index based on the adoption of new OMB area
delineations, a 3-year phase-out of the rural adjustment for certain
IRFs due to the new OMB area delineations, and updates for the IRF QRP.
For more information on the policy changes implemented for FY 2016,
please refer to the FY 2016 IRF PPS final rule.
In the FY 2017 IRF PPS final rule (81 FR 52056) and the correcting
amendments to the FY 2017 IRF PPS final rule (81 FR 59901), we updated
the prospective payment rates, the CMG relative weights, and the
outlier threshold amount. We also updated requirements for the IRF QRP.
Any reference to the FY 2017 IRF PPS final rule in this final rule also
includes the provisions effective in the correcting amendments. For
more information on the policy changes implemented for FY 2017, please
refer to the FY 2017 IRF PPS final rule.
In the FY 2018 IRF PPS final rule (82 FR 36238), we updated the
prospective payment rates, the CMG relative weights, and the outlier
threshold amount. We also revised the International Classification of
Diseases, 10th Revision, Clinical Modification (ICD-10-CM) diagnosis
codes that are used to determine presumptive compliance under the ``60
percent rule,'' removed the 25 percent payment penalty for IRF-PAI late
transmissions, removed the voluntary swallowing status item (Item 27)
from the IRF-PAI, summarized comments regarding the criteria used to
classify facilities for payment under the IRF PPS, provided for a
subregulatory process for certain annual updates to the presumptive
methodology diagnosis code lists, adopted the use of height/weight
items on the IRF-PAI to determine patient body mass index (BMI) greater
than 50 for cases of single-joint replacement under the presumptive
methodology, and updated requirements for the IRF QRP. For more
information on the policy changes implemented for FY 2018, please refer
to the FY 2018 IRF PPS final rule.
In the FY 2019 IRF PPS final rule (83 FR 38514), we updated the
prospective payment rates, the CMG relative weights, and the outlier
threshold amount. We also alleviated administrative burden for IRFs by
removing the FIMTM instrument and associated Function
Modifiers from the IRF-PAI beginning in FY 2020 and revised certain IRF
coverage requirements to reduce the amount of required paperwork in the
IRF setting beginning in FY 2019. Additionally, we incorporated certain
data items located in the Quality Indicators section of the IRF-PAI
into the IRF case-mix classification system using analysis of 2 years
of data (FYs 2017 and 2018) beginning in FY 2020. For the IRF QRP, we
adopted a new measure removal factor, removed two measures from the IRF
QRP measure set, and codified a number of program requirements in our
regulations. For more information on the policy changes implemented for
FY 2019, please refer to the FY 2019 IRF PPS final rule.
B. Provisions of the PPACA Affecting the IRF PPS in FY 2012 and Beyond
The PPACA included several provisions that affect the IRF PPS in
FYs 2012 and beyond. In addition to what was previously discussed,
section 3401(d) of the PPACA also added section 1886(j)(3)(C)(ii)(I) of
the Act (providing for a ``productivity adjustment'' for FY 2012 and
each subsequent fiscal year). The productivity adjustment for FY 2020
is discussed in section VI.D. of this final rule. Section
1886(j)(3)(C)(ii)(II) of the Act provides that the application of the
productivity adjustment to the market basket update may result in an
update that is less than 0.0 for a fiscal year and in payment rates for
a fiscal year being less than such payment rates for the preceding
fiscal year.
Sections 3004(b) of the PPACA and section 411(b) of the Medicare
Access and CHIP Reauthorization Act of 2015 (Pub. L. 114-10, enacted
April 16, 2015) (MACRA) also addressed the IRF PPS. Section 3004(b) of
PPACA reassigned the previously designated section 1886(j)(7) of the
Act to section 1886(j)(8) of the Act and inserted a new section
1886(j)(7) of the Act, which contains requirements for the Secretary to
establish a QRP for IRFs. Under that program, data must be submitted in
a form and manner and at a time specified by the Secretary. Beginning
in FY 2014, section 1886(j)(7)(A)(i) of the Act requires the
application of a 2 percentage point reduction to the market basket
increase factor otherwise applicable to an IRF (after application of
paragraphs (C)(iii) and (D) of section 1886(j)(3) of the Act) for a
fiscal year if the IRF does not comply with the requirements of the IRF
QRP for that fiscal year. Application of the 2 percentage point
reduction may result in an update that is less than 0.0 for a fiscal
year and in payment rates for a fiscal year being less than such
payment rates for the preceding fiscal year. Reporting-based reductions
to the market basket increase factor are not cumulative; they only
apply for the FY involved. Section 411(b) of MACRA amended section
1886(j)(3)(C) of the Act by adding paragraph (iii), which required us
to apply for FY 2018, after the application of section
1886(j)(3)(C)(ii) of the Act, an increase factor of 1.0 percent to
update the IRF prospective payment rates.
[[Page 39058]]
C. Operational Overview of the Current IRF PPS
As described in the FY 2002 IRF PPS final rule (66 FR 41316), upon
the admission and discharge of a Medicare Part A Fee-for-Service (FFS)
patient, the IRF is required to complete the appropriate sections of a
PAI, designated as the IRF-PAI. In addition, beginning with IRF
discharges occurring on or after October 1, 2009, the IRF is also
required to complete the appropriate sections of the IRF-PAI upon the
admission and discharge of each Medicare Advantage (MA) patient, as
described in the FY 2010 IRF PPS final rule (74 FR 39762 and 74 FR
50712). All required data must be electronically encoded into the IRF-
PAI software product. Generally, the software product includes patient
classification programming called the Grouper software. The Grouper
software uses specific IRF-PAI data elements to classify (or group)
patients into distinct CMGs and account for the existence of any
relevant comorbidities.
The Grouper software produces a five-character CMG number. The
first character is an alphabetic character that indicates the
comorbidity tier. The last four characters are numeric characters that
represent the distinct CMG number. Free downloads of the Inpatient
Rehabilitation Validation and Entry (IRVEN) software product, including
the Grouper software, are available on the CMS website at http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Software.html.
Once a Medicare Part A FFS patient is discharged, the IRF submits a
Medicare claim as a Health Insurance Portability and Accountability Act
of 1996 (Pub. L. 104-191, enacted August 21, 1996) (HIPAA) compliant
electronic claim or, if the Administrative Simplification Compliance
Act of 2002 (Pub. L. 107-105, enacted December 27, 2002) (ASCA)
permits, a paper claim (a UB-04 or a CMS-1450 as appropriate) using the
five-character CMG number and sends it to the appropriate Medicare
Administrative Contractor (MAC). In addition, once a MA patient is
discharged, in accordance with the Medicare Claims Processing Manual,
chapter 3, section 20.3 (Pub. 100-04), hospitals (including IRFs) must
submit an informational-only bill (Type of Bill (TOB) 111), which
includes Condition Code 04 to their MAC. This will ensure that the MA
days are included in the hospital's Supplemental Security Income (SSI)
ratio (used in calculating the IRF LIP adjustment) for fiscal year 2007
and beyond. Claims submitted to Medicare must comply with both ASCA and
HIPAA.
Section 3 of the ASCA amended section 1862(a) of the Act by adding
paragraph (22), which requires the Medicare program, subject to section
1862(h) of the Act, to deny payment under Part A or Part B for any
expenses for items or services for which a claim is submitted other
than in an electronic form specified by the Secretary. Section 1862(h)
of the Act, in turn, provides that the Secretary shall waive such
denial in situations in which there is no method available for the
submission of claims in an electronic form or the entity submitting the
claim is a small provider. In addition, the Secretary also has the
authority to waive such denial in such unusual cases as the Secretary
finds appropriate. For more information, see the ``Medicare Program;
Electronic Submission of Medicare Claims'' final rule (70 FR 71008).
Our instructions for the limited number of Medicare claims submitted on
paper are available at http://www.cms.gov/manuals/downloads/clm104c25.pdf.
Section 3 of the ASCA operates in the context of the administrative
simplification provisions of HIPAA, which include, among others, the
requirements for transaction standards and code sets codified in 45 CFR
part 160 and part 162, subparts A and I through R (generally known as
the Transactions Rule). The Transactions Rule requires covered
entities, including covered health care providers, to conduct covered
electronic transactions according to the applicable transaction
standards. (See the CMS program claim memoranda at http://www.cms.gov/ElectronicBillingEDITrans/ and listed in the addenda to the Medicare
Intermediary Manual, Part 3, section 3600).
The MAC processes the claim through its software system. This
software system includes pricing programming called the ``Pricer''
software. The Pricer software uses the CMG number, along with other
specific claim data elements and provider-specific data, to adjust the
IRF's prospective payment for interrupted stays, transfers, short
stays, and deaths, and then applies the applicable adjustments to
account for the IRF's wage index, percentage of low-income patients,
rural location, and outlier payments. For discharges occurring on or
after October 1, 2005, the IRF PPS payment also reflects the teaching
status adjustment that became effective as of FY 2006, as discussed in
the FY 2006 IRF PPS final rule (70 FR 47880).
D. Advancing Health Information Exchange
The Department of Health and Human Services (HHS) has a number of
initiatives designed to encourage and support the adoption of
interoperable health information technology and to promote nationwide
health information exchange to improve health care. The Office of the
National Coordinator for Health Information Technology (ONC) and CMS
work collaboratively to advance interoperability across settings of
care, including post-acute care.
To further interoperability in post-acute care, we developed a Data
Element Library (DEL) to serve as a publicly-available centralized,
authoritative resource for standardized data elements and their
associated mappings to health IT standards. The DEL furthers CMS' goal
of data standardization and interoperability. These interoperable data
elements can reduce provider burden by allowing the use and exchange of
healthcare data, support provider exchange of electronic health
information for care coordination, person-centered care, and support
real-time, data driven, clinical decision making. Standards in the Data
Element Library (https://del.cms.gov/) can be referenced on the CMS
website and in the ONC Interoperability Standards Advisory (ISA). The
2019 ISA is available at https://www.healthit.gov/isa.
The 21st Century Cures Act (Pub. L. 114-255, enacted December 13,
2016) (Cures Act), requires HHS to take new steps to enable the
electronic sharing of health information ensuring interoperability for
providers and settings across the care continuum. In another important
provision, Congress defined ``information blocking'' as practices
likely to interfere with, prevent, or materially discourage access,
exchange, or use of electronic health information, and established new
authority for HHS to discourage these practices. In March 2019, ONC and
CMS published the proposed rules, ``21st Century Cures Act:
Interoperability, Information Blocking, and the ONC Health IT
Certification Program,'' (84 FR 7424) and ``Interoperability and
Patient Access'' (84 FR 7610) to promote secure and more immediate
access to health information for patients and healthcare providers
through the implementation of information blocking provisions of the
Cures Act and the use of standardized application programming
interfaces (APIs) that enable easier access to electronic health
information. We solicited comment on the two proposed rules. We invited
providers to
[[Page 39059]]
learn more about these important developments and how they are likely
to affect IRFs.
II. Summary of Provisions of the Proposed Rule
In the FY 2020 IRF PPS proposed rule, we proposed to update the IRF
prospective payment rates for FY 2020 and to rebase and revise the IRF
market basket to reflect a 2016 base year rather than the current 2012
base year. We also proposed to replace the previously finalized
unweighted motor score with a weighted motor score to assign patients
to CMGs and remove one item from the score beginning with FY 2020 and
to revise the CMGs and update the CMG relative weights and average LOS
values beginning with FY 2020, based on analysis of 2 years of data
(FYs 2017 and 2018). We also proposed to use the concurrent FY IPPS
wage index for the IRF PPS beginning with FY 2020. We also solicited
comments on stakeholder concerns regarding the appropriateness of the
wage index used to adjust IRF payments. We proposed to amend the
regulations at Sec. 412.622 to clarify that the determination as to
whether a physician qualifies as a rehabilitation physician (that is, a
licensed physician with specialized training and experience in
inpatient rehabilitation) is made by the IRF.
The proposed policy changes and updates to the IRF prospective
payment rates for FY 2020 are as follows:
Describe a proposed weighted motor score to replace the
previously finalized unweighted motor score to assign a patient to a
CMG, the removal of one item from the score, and revisions to the CMGs
beginning on October 1, 2019, based on analysis of 2 years of data (FYs
2017 and 2018) using the Quality Indicator items in the IRF-PAI. This
includes proposed revisions to the CMG relative weights and average LOS
values for FY 2020, in a budget neutral manner, as discussed in section
III. of the FY 2020 IRF PPS proposed rule (84 FR 17244, 17249 through
17260).
Describe the proposed rebased and revised IRF market
basket to reflect a 2016 base year rather than the current 2012 base
year as discussed in section V. of the FY 2020 IRF PPS proposed rule
(84 FR 17244, 17261 through 17273).
Update the IRF PPS payment rates for FY 2020 by the
proposed market basket increase factor, based upon the most current
data available, with a proposed productivity adjustment required by
section 1886(j)(3)(C)(ii)(I) of the Act, as described in section V. of
the FY 2020 IRF PPS proposed rule (84 FR 17244, 17274 through 17275).
Describe the proposed update to the IRF wage index to use
the concurrent FY IPPS wage index and the FY 2020 proposed labor-
related share in a budget-neutral manner, as described in section V. of
the FY 2020 IRF PPS proposed rule (84 FR 17244, 17276 through 17279).
Describe the continued use of FY 2014 facility-level
adjustment factors, as discussed in section IV. of the FY 2020 IRF PPS
proposed rule (84 FR 17244, 17260 through 17261).
Describe the calculation of the IRF standard payment
conversion factor for FY 2020, as discussed in section V. of the FY
2020 IRF PPS proposed rule (84 FR 17244, 17280 through 17282).
Update the outlier threshold amount for FY 2020, as
discussed in section VI. of the FY 2020 IRF PPS proposed rule (84 FR
17244, 17283 through 17284).
Update the cost-to-charge ratio (CCR) ceiling and urban/
rural average CCRs for FY 2020, as discussed in section VI. of the FY
2020 IRF PPS proposed rule (84 FR 17244 at 17284).
Describe the proposed amendments to the regulations at
Sec. 412.622 to clarify that the determination as to whether a
physician qualifies as a rehabilitation physician (that is, a licensed
physician with specialized training and experience in inpatient
rehabilitation) is made by the IRF, as discussed in section VII. of the
FY 2020 IRF PPS proposed rule (84 FR 17244, 17284 through 17285).
Updates to the requirements for the IRF QRP, as discussed
in section VIII. of the FY 2020 IRF PPS proposed rule (84 FR 17244,
17285 through 17330).
III. Analysis and Response to Public Comments
We received 1,257 timely responses from the public, many of which
contained multiple comments on the FY 2020 IRF PPS proposed rule (84 FR
17244). The majority consisted of form letters, in which we received
multiple copies of two types of identically-worded letters that had
been signed and submitted by different individuals. We received
comments from various trade associations, IRFs, individual physicians,
therapists, clinicians, health care industry organizations, and health
care consulting firms. The following sections, arranged by subject
area, include a summary of the public comments that we received, and
our responses.
IV. Refinements to the Case-Mix Classification System Beginning With FY
2020
A. Background
Section 1886(j)(2)(A) of the Act requires the Secretary to
establish CMGs for payment under the IRF PPS and a method of
classifying specific IRF patients within these groups. Under section
1886(j)(2)(B) of the Act, the Secretary must assign each CMG an
appropriate weighting factor that reflects the relative facility
resources used for patients classified within the group as compared to
patients classified within other groups. Additionally, section
1886(j)(2)(C)(i) of the Act requires the Secretary from time to time to
adjust the established classifications and weighting factors as
appropriate to reflect changes in treatment patterns, technology, case-
mix, number of payment units for which payment is made under title
XVIII of the Act, and other factors which may affect the relative use
of resources. Such adjustments must be made in a manner so that changes
in aggregate payments under the classification system are a result of
real changes and are not a result of changes in coding that are
unrelated to real changes in case mix.
In the FY 2019 IRF PPS final rule (83 FR 38533 through 38549), we
finalized the removal of the Functional Independence Measure
(FIMTM) instrument and associated Function Modifiers from
the IRF-PAI and the incorporation of an unweighted additive motor score
derived from 19 data items located in the Quality Indicators section of
the IRF-PAI beginning with FY 2020 (83 FR 38535 through 38536, 38549).
As discussed in section IV.B of this final rule, based on further
analysis to examine the potential impact of weighting the motor score,
we proposed to replace the previously finalized unweighted motor score
with a weighted motor score and remove one item from the score
beginning with FY 2020.
Additionally, as noted in the FY 2019 IRF PPS final rule (83 FR
38534), the incorporation of the data items from the Quality Indicator
section of the IRF-PAI into the IRF case-mix classification system
necessitates revisions to the CMGs to ensure that IRF payments are
calculated accurately. We finalized the use of data items from the
Quality Indicators section of the IRF-PAI to construct the functional
status scores used to classify IRF patients in the IRF case-mix
classification system for purposes of establishing payment under the
IRF PPS beginning with FY 2020, but modified our proposal based on
[[Page 39060]]
public comments to incorporate 2 years of data (FYs 2017 and 2018) into
our analyses used to revise the CMG definitions (83 FR 38549). We
stated that any changes to the proposed CMG definitions resulting from
the incorporation of an additional year of data (FY 2018) into the
analysis would be addressed in future rulemaking prior to their
implementation beginning in FY 2020. As discussed in section III.C of
the FY 2020 IRF PPS proposed rule (84 FR 17244, 17250 through 17260),
we proposed to revise the CMGs based on analysis of 2 years of data
(FYs 2017 and 2018) beginning with FY 2020. We also proposed to update
the relative weights and average LOS values associated with the revised
CMGs beginning with FY 2020.
B. Proposed Use of a Weighted Motor Score Beginning With FY 2020
As noted in the FY 2019 IRF PPS final rule (83 FR 38535), the IRF
case-mix classification system currently uses a weighted motor score
based on FIMTM data items to assign patients to CMGs under
the IRF PPS through FY 2019. More information on the development and
implementation of this motor score can be found in the FY 2006 IRF PPS
final rule (70 FR 47896 through 47900). In the FY 2019 IRF PPS final
rule (83 FR 38535 through 38536, 38549), we finalized the incorporation
of an unweighted additive motor score derived from 19 data items
located in the Quality Indicators section of the IRF-PAI beginning with
FY 2020. We did not propose a weighted motor score at the time, because
we believed that the unweighted motor score would facilitate greater
understanding among the provider community, as it is less complex.
However, we also noted that we would take comments in favor of a
weighted motor score into consideration in future analysis. In response
to feedback we received from various stakeholders and professional
organizations regarding the use of an unweighted motor score and
requesting that we consider weighting the motor score, we extended our
contract with Research Triangle Institute, International (RTI) to
examine the potential impact of weighting the motor score. Based on
this analysis, discussed further below, we believed that a weighted
motor score would improve the accuracy of payments to IRFs and proposed
to replace the previously finalized unweighted motor score with a
weighted motor score to assign patients to CMGs beginning with FY 2020.
The previously finalized motor score is calculated by summing the
scores of the 19 data items, with equal weight applied to each item.
The 19 data items are (83 FR 38535):
GG0130A1 Eating.
GG0130B1 Oral hygiene.
GG0130C1 Toileting hygiene.
GG0130E1 Shower/bathe self.
GG0130F1 Upper-body dressing.
GG0130G1 Lower-body dressing.
GG0130H1 Putting on/taking off footwear.
GG0170A1 Roll left and right.
GG0170B1 Sit to lying.
GG0170C1 Lying to sitting on side of bed.
GG0170D1 Sit to stand.
GG0170E1 Chair/bed-to-chair transfer.
GG0170F1 Toilet transfer.
GG0170I1 Walk 10 feet.
GG0170J1 Walk 50 feet with two turns.
GG0170K1 Walk 150 feet.
GG0170M1 One step curb.
H0350 Bladder continence.
H0400 Bowel continence.
In response to feedback we received from various stakeholders and
professional organizations requesting that we consider applying weights
to the motor score, we extended our contract with RTI to explore the
potential of applying unique weights to each of the 19 items in the
motor score.
As part of their analysis, RTI examined the degree to which the
items used to construct the motor score were related to one another and
adjusted their weighting methodology to account for their findings. RTI
considered a number of different weighting methodologies to develop a
weighted index that would increase the predictive power of the IRF
case-mix classification system while at the same time maintaining
simplicity. RTI used regression analysis to explore the relationship of
the motor score items to costs. This analysis was undertaken to
determine the impact of each of the items on cost and then to weight
each item in the index according to its relative impact on cost. Based
on findings from this analysis, we proposed to remove the item GG0170A1
Roll left and right from the motor score as this item was found to have
a high degree of multicollinearity with other items in the motor score
and would have resulted in either a negative or non-significant
coefficient. As such, we did not believe it would be appropriate to
include this item in the motor score calculation. Using the revised
motor score composed of the remaining 18 items identified above, RTI
designed a weighting methodology for the motor score that could be
applied uniformly across all RICs. For a more detailed discussion of
the analysis used to construct the weighted motor score, we refer
readers to the March 2019 technical report entitled ``Analyses to
Inform the Use of Standardized Patient Assessment Data Elements in the
Inpatient Rehabilitation Facility Prospective Payment System'',
available at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Research.html. Findings from this analysis
suggested that the use of a weighted motor score index slightly
improves the ability of the IRF PPS to predict patient costs. Based on
this analysis, we proposed to use a weighted motor score for the
purpose of determining IRF payments.
Table 1 shows the proposed weights for each component of the motor
score, averaged to 1, obtained through the regression analysis.
[[Page 39061]]
[GRAPHIC] [TIFF OMITTED] TR08AU19.001
We proposed to determine the motor score by applying each of the
weights indicated in Table 1 to the score of each corresponding item,
as finalized in the FY 2019 IRF PPS final rule (83 FR 38535 through
38537), and then summing the weighted scores for each of the 18 items
that compose the motor score.
We received several comments on the proposal to replace the
previously finalized unweighted motor score with a weighted motor score
to assign patients to CMGs under the IRF PPS and our proposal to remove
the item GG0170A1 Roll left and right from the calculation of the motor
score beginning with FY 2020, that is, for all discharges beginning on
or after October 1, 2019. As summarized in more detail below, with the
exception of one comment from MedPAC, the commenters overwhelmingly
requested that CMS delay implementation of a weighted motor score and
use an unweighted motor score to assign patients to CMGs until we can
more fully analyze and work with stakeholders on developing a weighted
motor score methodology.
In response to public comments, we carefully considered whether to
finalize the proposed weighted motor score or go back to using an
unweighted motor score to assign patients to CMGs. Although the
proposed weighted motor score results in a slight improvement in the
ability of the IRF PPS to predict patient costs and thus the accuracy
of IRF PPS payments (less than 0.18 difference in accuracy between the
weighted and the unweighted motor scores), we acknowledge the
unweighted motor score is conceptually simpler and, as such, believe it
will ease providers' transition to the use of the data items located in
the Quality Indicators section of the IRF-PAI (also referred to as
section GG items). Thus, we are finalizing based on public comments the
use of an unweighted motor score to assign patients to CMGs beginning
with FY 2020. We appreciate the commenters' suggestions on the
weighting methodology and will take them into consideration as we
explore possible refinements to the case-mix classification system in
the future.
Comment: Although several commenters noted appreciation for the
fact that we analyzed a weighted motor score in response to their
comments on the FY 2019 IRF PPS proposed rule (83 FR 38546), these same
commenters expressed concerns with the actual weight values that CMS
proposed for FY 2020, as indicated in Table 1, and stated that we
should go back to an unweighted motor score so that we can do further
analysis and collaborate with stakeholders to further refine the
weighting methodology. Some commenters expressed concern that CMS might
be proposing higher weights for the self-care items than for the
mobility items, in contrast to the current weighted motor score, which
weights mobility items higher than self-care items. Some commenters
specifically requested that CMS explain why the weight for the eating
item increased from 0.6 under the current weighting methodology to 2.7
under the proposed methodology, and requested we explain what we
believe this change will mean for patients with eating deficits.
Commenters were also generally concerned by what they suggested were
large differences in the weight value assignments between the current
and proposed motor score.
Response: We used simple ordinary least squares regression analysis
of the data that IRFs submitted to us in FYs 2017 and 2018 to calculate
the proposed weight values for the motor score, in response to
stakeholder feedback on the FY 2019 IRF PPS proposed rule (83 FR
38546). Commenters are correct that the proposed weights for the motor
score items, in comparison with the current weights, shift some of the
weight from the mobility to the self-care items. We also note that the
proposed weights assigned to the bowel and bladder function items
increased compared with the current weights. These changes are all
reflective of the data the IRFs submitted to us in FYs 2017 and 2018.
Regarding the proposed increase in the weight for the eating item,
it is important to note key differences in the coding guidelines
between the FIMTM eating item and the section GG eating item
that may have contributed to the change in the relative importance of
this item for predicting IRF costs. For item GG0130A, Eating,
assistance with tube feedings is not considered when coding this item.
If a patient does not eat or drink by mouth but is instead tube fed,
item GG0130A must be coded as 88--``Not attempted due to medical
condition or safety concerns'' or 09--``Not applicable''. Both of these
responses would be recoded to a 01--``Dependent'' for the purposes of
assigning the patient to a CMG. This
[[Page 39062]]
differs from the coding instructions for the FIMTM eating
item used in the current motor score, which takes into consideration
assistance with tube feedings in scoring the item. For example,
according to the FIMTM instructions, a patient who could
administer the tube feeding completely independently could receive a
score of 7-Complete independence on the eating item.
In regards to the suggested differences in the weight value
assignments between the current and proposed methodologies, we note
that in certain cases the proposed weights were divided among multiple
items in the motor score that were found to be highly correlated to
avoid overweighting any particular measure of function. For instance,
the three items (GG0170I1, GG0170J1, and GG0170K1) that assess walking
function were each assigned a proposed weight of 0.8. When summed
together, the weight value for walking under the proposed methodology
is 2.4, which is slightly higher than the weight value of 1.6 for the
single walking item used in the current motor score.
Comment: One commenter disagreed with the removal of item GG0170A1
roll left and right from the motor score and noted it is an important
functional task in the IRF setting. Some commenters questioned the use
of averaging values across pairs of items that were correlated and
inquired why the roll left and right item was removed from the motor
score while other correlated items were not removed. Commenters also
inquired about the use of the item ``walk 10 feet'' to derive the
weights for the ``walk 50 feet'' and ``walk 150 feet'' items.
Response: We appreciate the commenter's concerns regarding the
removal of item GG0170A1 from the motor score. As described in detail
in the technical report, ``Analyses to Inform the Use of Standardized
Patient Assessment Data Elements in the Inpatient Rehabilitation
Facility Prospective Payment System,'' the roll left and right item was
found to have a high degree of multicollinearity with other
standardized patient assessment elements and to be inversely correlated
with costs after controlling for each of the other self-care and
mobility items. This relationship persisted when this item was paired
with the other correlated items. The continued inclusion of this item
in the motor score would have resulted in either a negative or non-
significant coefficient. As such, we do not believe it is appropriate
to include this item in the construction of the motor score. The other
item pairs that were found to be correlated did not generate negative
or non-significant coefficients, and were therefore maintained in the
calculation of the motor score.
Unlike the FIMTM instrument, the items from the quality
indicator section of the IRF-PAI sometimes use more than one item to
measure functional areas. As discussed in more detail in the technical
report, we noted that a few items were found to be highly correlated.
Because of the correlation, we proposed to use an average score for
some items so as to avoid introducing bias or inappropriately
overweighting any particular functional area. We note this methodology
is consistent with the methodology used under the Patient Driven
Payment Model (PDPM), as described in more detail in the FY 2019 SNF
final rule (83 FR 39204) and the accompanying technical report entitled
``Skilled Nursing Facilities Patient-Driven Payment Model Technical
Report'' available on the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/therapyresearch.html.
Regarding the ``walk 10 feet'' item, that item was used to derive
the weights for the ``walk 50 feet'' and ``walk 150 feet'' items as
these three items were found to be highly correlated and the ``walk 150
feet'' item had a high proportion of observations coded on admission
with ``activity not attempted'' codes.
Comment: Some commenters requested that CMS apply the current motor
score weights associated with the FIMTM items to the revised
motor score while other commenters requested that CMS postpone
weighting the motor score until additional data can be collected and
analyzed. While a few commenters were supportive of using a weighted
motor score, other commenters suggested that CMS use a 1-year payment
model or phase in the use of a weighted motor score.
Response: We do not believe it would be appropriate to apply the
weight values associated with the FIMTM items to the
components of the revised motor score, as these weights would not
accurately reflect how the various components of the revised motor
score contribute to predicting patient costs. We used simple ordinary
least squares regression analysis of the data that IRFs submitted to us
in FYs 2017 and 2018 to calculate the proposed weight values for the
revised motor score. Changes in patient demographics, treatment
practices, technology, and other factors that may affect the relative
use of resources in an IRF since the motor score weights were
originally calculated have likely contributed to changes in the weight
values applied across the self-care and mobility items. We proposed to
apply weights to the motor score items because RTI's analysis indicated
that a weighted motor score would improve the classification of
patients into CMGs, which in turn would improve the accuracy of
payments to IRFs. However, as discussed above, in response to public
comments, we carefully considered whether to finalize the proposed
weighted motor score or go back to using an unweighted motor score to
assign patients to CMGs. Although the proposed weighted motor score
results in a slight improvement in the ability of the IRF PPS to
predict patient costs and thus the accuracy of IRF PPS payments (less
than 0.18 difference in accuracy between the weighted and the
unweighted motor scores), we acknowledge the unweighted motor score is
conceptually simpler and, as such, believe it will ease providers'
transition to the use of the data items located in the Quality
Indicators section of the IRF-PAI (also referred to as section GG
items). Thus, we are finalizing based on public comments the use of an
unweighted motor score, in which each of the 18 items have a weight of
1, to assign patients to CMGs beginning with FY 2020.
Comment: Commenters expressed concern that the analysis performed
by RTI did not explicitly follow the analysis conducted by RAND when
the motor score weights were developed for FY 2006 (70 FR 47896 through
47900) and that RTI based their analyses on 2 years of data instead of
several years of data. Additionally, commenters requested more
information on the other weighting methodologies that RTI considered.
Response: We disagree with the commenters that the RAND analysis
for FY 2006 used more years of data than RTI's analysis for the FY 2020
proposed rule. As discussed in the FY 2006 IRF PPS final rule (70 FR
47897), RAND performed regression analysis on less than 2 full years of
data (calendar year (CY) 2002 and FY 2003) to derive the current motor
score weights. In contrast, RTI used 2 full years of data (FYs 2017 and
2018) to perform the analysis for the weighted motor score proposed in
the FY 2020 IRF PPS proposed rule. As the FYs 2017 and 2018 data
portrays the most recent and complete picture of patients under the IRF
PPS, we believe it was sufficient and appropriate to utilize for the
analysis for the proposed rule.
While RTI utilized a different weighting methodology than was used
by RAND in 2006, the overall model
[[Page 39063]]
prediction using the weighted motor score developed by RAND and the
weighted motor score developed by RTI is extremely similar. The model
using the CMGs based on the standardized patient assessment data
elements and comorbidity tiers to predict wage-adjusted costs of care
has an r-squared value is 0.3358, while the r-squared value is 0.3169
for the CMGs in the current IRF PPS. This is indicative of similar
model performance regardless of model specification. The item weights
that the RAND work notes as ``optimally weighted'' are weights that
were constructed separately for each RIC. These were not the weights
that were used in the final weights developed by RAND.
RTI also examined weighing methodologies utilizing a general linear
model (GLM) and log transformed ordinary least squares (OLS) regression
models, as well as the OLS model described in more detail in the
technical report. All three models had comparable model fit and
generated similar item weights. Based on the greater simplicity
achieved through the use of the OLS regression model we believe using
the OLS regression was appropriate to maintain simplicity and
transparency in the payment system.
Comment: Commenters disagreed with the omission of the wheelchair
mobility items from the items used to construct the motor score.
Response: We appreciate the commenters' concerns about wheelchair-
dependent patients. As most recently discussed in the FY 2019 IRF PPS
final rule (83 FR 38546) in response to similar stakeholder comments,
we explained our rationale for not including the wheelchair mobility
items in the construction of the finalized motor score. We continue to
believe that the higher resource needs of wheelchair dependent patients
in IRFs will be better accounted for by not including a wheelchair item
in the motor score at this time. Patients that are considered
wheelchair dependent or unable to walk will be accounted for through
the ``not attempted'' response codes captured through other items,
especially some of the walking items, that are included in the motor
score. In this way, we ensure that IRFs will be appropriately
compensated for the higher costs they incur in treating wheelchair-
dependent patients. We refer readers to the FY 2019 IRF PPS final rule
(83 FR 38546) and the technical report entitled ``Analyses to Inform
the Use of Standardized Patient Assessment Data Elements in the
Inpatient Rehabilitation Facility Prospective Payment System'' for more
information on the rationale as to why this item was not included in
the calculation of the motor score.
Comment: Commenters expressed concern with the weighted motor score
and questioned the reliability and validity of the weighted motor
score. Some commenters stated that they believe the weighted and
unweighted motor scores have shown little to no correlation with the
weighted motor score currently in use, and therefore, questioned if the
weighted motor score could accurately measure patient severity.
Response: We disagree with the commenters' suggestion that
unweighted and weighted motor scores have shown little to no
correlation with the weighted motor score currently in use as our
analysis shows a strong correlation between the scores. In addition,
each of the proposed Quality Indicators data items that were included
in the motor score were found to have statistically significant
correlation with IRF costs. As discussed in the technical report
``Analyses to Inform the Use of Standardized Patient Assessment Data
Elements in the Inpatient Rehabilitation Facility Prospective Payment
System'' the use of a weighted motor score was found to increase the
predictive ability of the payment model.
Comment: Commenters requested that CMS make available the data
utilized in the analyses including patient assessment data, matching
claims data, and additional facility and cost report data to enable
stakeholders to replicate the analyses.
Response: We appreciate the commenters' feedback regarding the
types of information that would be most useful to them in replicating
our analyses. We are unable to make patient assessment and claims data
publicly available on the CMS website because these data contain
personally identifiable information. However, we believe that we
released sufficient information in the proposed rule, the accompanying
data files, and the technical report entitled ``Analyses to Inform the
Use of Standardized Patient Assessment Data Elements in the Inpatient
Rehabilitation Facility Prospective Payment System,'' to enable
stakeholders to submit meaningful comments on the underlying analyses
and methodologies used to revise the IRF case-mix classification
system, to pose alternative approaches, and to assess the impacts of
the proposed revisions.
Comment: A few commenters noted that they did not believe that CMS
has performed the thorough data analyses and engagement with the
provider community that are necessary prior to making significant
changes to the existing IRF PPS. These commenters requested that we
solicit additional feedback from the stakeholder community, including
convening technical advisory panels (TEPs), to provide additional
transparency into the underlying analyses and to delay implementation
of a weighted motor score until we conduct additional engagements with
stakeholders.
Response: We value transparency in our processes and will continue
to engage stakeholders in future development of payment policies. We
appreciate the offers from stakeholders to assist in the development of
future revisions to payment policies and we recognize the value from
these partnerships. However, for something as analytically simple as
running a regression analysis to determine the weights for the motor
score items that best reflect patients' resource needs in the IRF, we
do not believe that a TEP is necessary.
As noted above, although the proposed weighted motor score results
in a slight improvement in the ability of the IRF PPS to predict
patient costs and thus the accuracy of IRF PPS payments (less than 0.18
difference in accuracy between the weighted and the unweighted motor
scores), we acknowledge the unweighted motor score is conceptually
simpler and, as such, believe it will ease providers' transition to the
use of the data items located in the Quality Indicators section of the
IRF-PAI (also referred to as section GG items). Thus, we are finalizing
based on public comments the use of an unweighted motor score to assign
patients to CMGs beginning with FY 2020. We appreciate the
stakeholders' comments on this topic and will take them into
consideration for future analysis.
Comment: A few commenters requested that CMS provide additional
information regarding the provider specific impact analysis file that
accompanied the rule, such as a data dictionary describing the data
used to calculate the impacts.
Response: In conjunction with the release of the FY 2020 IRF PPS
proposed rule, we posted a provider-specific impact analysis file that
compared estimated payments to providers for FY 2020 without the
proposed revisions to the CMGs with estimated payments to providers for
FY 2020 with the proposed revisions to the CMGs. We believe that this
file gives IRFs added information to enable them to see how their
individual payments would be affected by the proposed changes to the
CMGs. We updated this
[[Page 39064]]
provider specific impact analysis file shortly after it was initially
posted to include additional information regarding the underlying data
used to calculate the provider specific impacts, and we believe that
this additional information is responsive to commenters' requests. The
file can be downloaded from the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html. We appreciate the commenters' suggestions
regarding the additional types of information that would be most useful
to them to further facilitate understanding of our analyses.
As previously discussed, we proposed a weighted motor score as it
was found to slightly improve the predicative ability of the case-mix
system and thus the accuracy of IRF PPS payments. However, nearly all
of the comments we received requested that we revert to an unweighted
motor score for the various reasons discussed above. While we continue
to believe that a weighted motor score is slightly more accurate, the
difference is small, and in light of the conceptual simplicity achieved
through the use of an unweighted motor score, which we believe will
ease providers' transition to the use of the data items located in the
Quality Indicators section of the IRF-PAI, we are finalizing the use of
an unweighted motor score, in which each of the 18 items used in the
score have an equal weight of 1, to assign patients to CMGs beginning
with FY 2020. Additionally, we are finalizing the proposed removal of
one item (GG0170A1 Roll left to right) from the motor score beginning
with FY 2020. Effective for all discharges beginning on or after
October 1, 2019, we will use an unweighted motor score as indicated in
Table 2 to determine a beneficiary's CMG placement.
[GRAPHIC] [TIFF OMITTED] TR08AU19.002
C. Revisions to the CMGs and Updates to the CMG Relative Weights and
Average Length of Stay Values Beginning With FY 2020
In the FY 2019 IRF PPS final rule (83 FR 38549), we finalized the
use of data items from the Quality Indicators section of the IRF-PAI to
construct the functional status scores used to classify IRF patients in
the IRF case-mix classification system for purposes of establishing
payment under the IRF PPS beginning with FY 2020, but modified our
proposal based on public comments to incorporate 2 years of data (FYs
2017 and 2018) into our analyses used to revise the CMG definitions. We
stated that any changes to the proposed CMG definitions resulting from
the incorporation of an additional year of data (FY 2018) into the
analysis would be addressed in future rulemaking prior to their
implementation beginning in FY 2020. Additionally, we stated that we
would also update the relative weights and average LOS values
associated with any revised CMG definitions in future rulemaking.
As noted in the FY 2020 IRF PPS proposed rule (84 FR 17251), we
continued our contract with RTI to support us in developing proposed
revisions to the CMGs used under the IRF PPS based on analysis of 2
years of data (FYs 2017 and 2018). The process RTI uses for its
analysis, which is based on a Classification and Regression Tree (CART)
algorithm, is described in detail in the FY 2019 IRF PPS final rule (83
FR 38536 through 38540). RTI used this analysis to revise the CMGs
utilizing FYs 2017 and 2018 claim and assessment data and to develop
revised CMGs that reflect the use of the data items collected in the
Quality Indicators section of the IRF-PAI, incorporating the proposed
weighted motor score described in the FY 2020 IRF PPS proposed rule.
However, as discussed in section IV.B of this final rule, we are
finalizing based on public comments the use of an unweighted motor
score to assign patients to a CMGs beginning in with FY 2020.
To develop the proposed revised CMGs, RTI used CART analysis to
divide patients into payment groups based on similarities in their
clinical characteristics and relative costs. As part of this analysis,
RTI imposed some typically-used constraints on the payment group
divisions (for example, on the minimum number of cases that could be in
the resulting payment groups and the minimum dollar payment amount
differences between groups) to identify the optimal set of payment
groups. For a more detailed discussion of the analysis used to revise
[[Page 39065]]
the CMGs for FY 2020, we refer readers to the March 2019 technical
report entitled, ``Analyses to Inform the Use of Standardized Patient
Assessment Data Elements in the Inpatient Rehabilitation Facility
Prospective Payment System'' available at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Research.html.
Additionally, we refer readers to the FY 2020 IRF PPS proposed rule (84
FR 17250 through 17260) for more information on the proposed revisions
to the CMGs.
As noted above, we are finalizing the use of an unweighted motor
score beginning with FY 2020. As the motor score is a key input in the
CART analysis used to revise the CMGs, the use of the unweighted motor
score required that the CART analysis be rerun utilizing the unweighted
motor score. RTI utilized the same methodology described in the FY 2020
IRF PPS proposed rule (84 FR 17250 through 17260) to support us in
developing revisions to the CMGs, incorporating the unweighted motor
score, as described in section IV.B of this final rule. The revised
CMGs can be found in Table 3.
After developing the revised CMGs, RTI then calculated the relative
weights and average LOS values for each revised CMG using the same
methodologies that we have used to update the CMG relative weights and
average LOS values each fiscal year since 2009 (when we implemented an
update to this methodology). More information about the methodology
used to update the CMG relative weights can be found in the FY 2009 IRF
PPS final rule (73 FR 46372 through 46374). For FY 2020, we proposed to
use the FYs 2017 and 2018 IRF claims and FY 2017 IRF cost report data
to update the CMG relative weights and average LOS values. In
calculating the CMG relative weights, we use a hospital-specific
relative value method to estimate operating (routine and ancillary
services) and capital costs of IRFs. As noted in the FY 2019 IRF PPS
final rule (83 FR 38521), this is the same methodology that we have
used to update the CMG relative weights and average LOS values each
fiscal year since we implemented an update to the methodology in the FY
2009 IRF PPS final rule (73 FR 46372 through 46374). More information
on the methodology used to update calculate the CMG relative weights
and average LOS values can found in the March 2019 technical report
entitled ``Analyses to Inform the Use of Standardized Patient
Assessment Data Elements in the Inpatient Rehabilitation Facility
Prospective Payment System'' available at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Research.html.
Consistent with the methodology that we have used to update the IRF
classification system in each instance in the past, we proposed to
update the relative weights associated with the revised CMGs for FY
2020 in a budget neutral manner by applying a budget neutrality factor
to the standard payment amount. To calculate the appropriate budget
neutrality factor for use in updating the FY 2020 CMG relative weights,
we used the following steps:
Step 1. Calculate the estimated total amount of IRF PPS payments
for FY 2020 (with no changes to the CMG relative weights).
Step 2. Calculate the estimated total amount of IRF PPS payments
for FY 2020 by applying the changes to the CMGs and the associated CMG
relative weights (as described in this final rule).
Step 3. Divide the amount calculated in step 1 by the amount
calculated in step 2 to determine the budget neutrality factor (1.0016)
that would maintain the same total estimated aggregate payments in FY
2020 with and without the changes to the CMGs and the associated CMG
relative weights.
Step 4. Apply the budget neutrality factor (1.0016) to the FY 2019
IRF PPS standard payment amount after the application of the budget-
neutral wage adjustment factor.
We note that, as we typically do, we updated our data between the
FY 2020 IRF PPS proposed and final rules to ensure that we use the most
recent available data in calculating IRF PPS payments. Additionally, we
are finalizing the use of unweighted motor score beginning in with FY
2020 which generated revisions to the CMGs and relative weights. Based
on our analysis using this updated data and an unweighted motor score,
we now estimate a budget neutrality factor of (1.0010) to maintain the
same total estimated aggregate payments in FY 2020 with and without the
changes to the CMGs and the associated CMG relative weights. For FY
2020 we will apply the budget neutrality factor (1.0010) to the FY 2019
IRF PPS standard payment amount after the application of the budget-
neutral wage adjustment factor.
The relative weights and average LOS values for those revised CMGs
(found in Table 3) were calculated using the same methodology described
in the FY 2020 IRF PPS proposed rule, which is the same methodology
that we have used to update the CMG relative weights and average LOS
values each fiscal year since we implemented an update to the
methodology in FY 2009. The revised CMGs (reflecting the unweighted
motor score) and their respective descriptions, as well as the
comorbidity tiers, corresponding relative weights and the average LOS
values for each CMG and tier for FY 2020 are shown in Table 3. The
average LOS for each CMG is used to determine when an IRF discharge
meets the definition of a short-stay transfer, which results in a per
diem case level adjustment. In section V.H. of this final rule, we
discuss the proposed use of the existing methodology to calculate the
standard payment conversion factor for FY 2020.
We received a number of comments on the proposed revisions to the
CMGs based on analysis of 2 years of data (FYs 2017 and 2018) and the
proposed updates to the relative weights and average LOS values
associated with the revised CMGs beginning with FY 2020, that is, for
all discharges beginning on or after October 1, 2019, which are
summarized below.
Comment: A number of commenters were appreciative of the use of 2
years of data to revise the CMGs; however, commenters expressed concern
with the proposed CMG revisions and suggested that these changes could
result in payment rate compression or a misalignment between payments
and the costs of caring for patients. Commenters suggested payment
compression would result in reduced payments for higher acuity patients
and increased payments for lower acuity patients which could compromise
access to care for patients with certain impairments. Additionally,
some commenters questioned why there would be fewer CMGs within some
RICs and suggested having fewer CMGs would also contribute to payment
rate compression.
Response: We disagree with the commenters that revisions to CMGs
will lead to payment rate compression or could compromise access to
care for any particular group of patients. As the revised CMGs are
reflective of the data that IRFs submitted to us in FYs 2017 and 2018,
we believe the revised CMGs reflect the distinct resource needs of the
current Medicare IRF population. We believe the revised CMGs more
accurately predict resource use in IRFs and better align payments with
the expected costs of treating patients in the IRF setting. As such, we
believe that the revised CMGs may in fact improve access to and quality
of care for IRF patients by increasing the accuracy of IRF payments to
providers.
Regarding why some RICs would have fewer CMGs, we refer the
commenters to the Technical Report entitled ``Analyses
[[Page 39066]]
to Inform the Use of Standardized Patient Assessment Data Elements in
the Inpatient Rehabilitation Facility Prospective Payment System'' that
describes in detail the analysis used to derive the CMGs and the
criteria required to generate additional payment groups. As noted in
the FY 2020 IRF PPS proposed rule (84 FR 17250 through 17252), RTI
imposed some typically-used constraints in their analysis to identify
the proposed set of payment groups. These constraints consisted of a
minimum number of stays within a node, a 0.5 percentage point increase
of explanatory power, and monotonicity across the CMGs within each RIC.
We do not believe it would be appropriate to generate additional CMGs
that did not improve the predicative ability of the model beyond what
was produced through the CART analysis utilizing the constraints above.
We note that while the CART analysis generated fewer CMGs within some
RICs, it generated a greater number of CMGs within other RICs and that
the overall number of CMGs increases through these revisions to the
case-mix classification system. We do not believe having fewer CMGs
within any RIC will contribute to payment rate compression as we
believe these revisions better align payments with the expected costs
of treating patients in IRFs.
Additionally, we disagree with the commenters' statements that the
CMG revisions will result in higher payments for lower acuity patients
and reduced payments for higher acuity patients. Our analysis has found
that higher function is associated with a slight reduction in payment
under the revised CMGs and that lower function is associated with a
slight increase in payments. The purpose of the proposed revisions to
the CMGs is to align payments more appropriately with the costs of
caring for all types of patients in IRFs. As such, we do not believe
that the revisions will result in higher payments for lower acuity
patients. We appreciate the commenters' concerns and will continue to
monitor the IRF data closely to ensure that IRF payments are
appropriately aligned with costs of care and that Medicare patients
continue to have appropriate access to IRF services.
Comment: Several commenters expressed concerns that the proposed
CMG revisions could cause a significant redistribution of payments
among IRF provides. These commenters indicated that they believe the
section GG items make patients appear to be less severe and requested
additional information on how patients would be redistributed among the
revised CMGs. Additionally, commenters encouraged CMS to monitor the
data based on these changes and to update the model if necessary in the
future.
Response: We agree with the commenters that the revisions to the
CMGs may result in some redistribution of payments among providers. As
noted in the FY 2019 IRF PPS final rule (83 FR 38547), the scales and
coding instructions are slightly different between the item sets used
to derive the existing CMGs and those used to derive the revised CMGs.
As such, these differences may result in some patients grouping into
different CMGs that more accurately account for the expected resource
needs of the patient. While we cannot make individual Medicare
beneficiary data publically available, we believe we released adequate
information for stakeholders to determine how beneficiaries could be
distributed across the revised CMGs. We appreciate the commenters'
suggestions to conduct monitoring activities and make future updates to
the case-mix classification system and will take this into
consideration in the future.
Comment: Commenters expressed concern with the use of section GG
items to assign a patient to a CMG and suggested that these items are
not sensitive enough and do not capture patients' true burden of care.
Commenters also expressed concern with the reliability of the data
collected through these items and suggested that the data is not
accurate or valid.
Response: As discussed in detail in the FY 2019 IRF PPS final rule
(83 FR 38541), we believe that the data items located in the Quality
Indicators section of the IRF-PAI are sensitive and accurately capture
the functional and cognitive status of patients and can also be used to
accurately assess changes in patients' functional status. As noted
above, RTI found that the model predicting costs using the CMGs derived
from the items located in the Quality Indicators section of the IRF-PAI
had a slightly higher R-squared value than models using the current
CMGs which are derived from items in the FIMTM instrument,
indicating that the revised CMGs more accurately predict resource use
in IRFs than the CMGs that are currently utilized. As the data
collected in the Quality Indicators section of the IRF-PAI have been
collected nationally for all IRFs since October 1, 2016, we believe the
data to be accurate and valid at this time. We also believe it is the
responsibility of the IRF to submit accurate and valid data that
adheres to the coding guidelines detailed in the IRF-PAI training
manual.
Comment: Commenters expressed concern with the cognition items
collected on the IRF-PAI and their omission from the revised CMGs. A
few commenters noted the importance of cognitive impairment in the IRF
setting and encouraged CMS to conduct further analysis of the
relationship between cognitive function and resource use in the IRF
setting and to improve the items that are used to measure cognitive
function.
Response: We appreciate the commenters' concerns with the cognitive
items that are collected on the IRF-PAI. As we discussed in the FY 2019
IRF PPS final rule (83 FR 38546), the cognitive items that we used for
this analysis are the best ones that we have for use at the present
time. Unfortunately, we found that including these cognitive items in
generating the CMGs would have resulted in lower payments for patients
with higher cognitive deficits. This result does not make sense from a
clinical perspective, and could have the unintended consequence of
reducing access to IRF care for more cognitively impaired
beneficiaries. Thus, we determined that it would be better at this time
to remove the CMG splits that were generated by the cognitive items. We
appreciate the commenters' suggestion to incorporate improved cognition
measures into the IRF-PAI and will take this into consideration in the
future.
Comment: Commenters suggested that CMS has not provided sufficient
education, training materials, or supporting documentation regarding
the functional items to support their use in developing a payment
model. Some commenters suggested revisions to the existing training
materials while other commenters requested that CMS provide additional
training, monitor the data, and modify the case mix groupings as
needed.
Response: We disagree with the commenters that we have provided
insufficient training or guidance on proper coding of this data. We
believe we have provided adequate training opportunities for IRFs on
coding the Quality Indicator data items, including multiple in-person
training opportunities, webinars, on-line training and on-going help
desk guidance. We are committed to providing information and support
that will allow providers to accurately interpret and complete quality
reporting items and we will continue to provide these types of
opportunities to the IRF community. We thank the commenters for their
suggestions to improve the training materials and we appreciate the
commenters' suggestions to continue to monitor the data and make
updates to
[[Page 39067]]
the case-mix classification system when necessary.
After careful consideration of the comments received, we are
finalizing revisions to the CMGs based on analysis of 2 years of data
(FYs 2017 and 2018) and the incorporation of the unweighted motor score
described in section IV.B of this final rule. The revised CMGs that
will be effective October 1, 2019 are presented below in Table 3. We
refer readers to Table 20 in section XIII.C of this final rule for more
information on the distributional effects of revisions to the CMGs. For
a provider specific impact analysis for this change, we refer readers
to the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html.
We are also updating the relative weights and average LOS values
associated with the revised CMGs (reflecting an unweighted motor score)
beginning with FY 2020.
BILLING CODE 4120-01-P
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BILLING CODE 4120-01-C
V. Facility-Level Adjustment Factors
Section 1886(j)(3)(A)(v) of the Act confers broad authority upon
the Secretary to adjust the per unit payment rate by such factors as
the Secretary determines are necessary to properly reflect variations
in necessary costs of treatment among rehabilitation facilities. Under
this authority, we currently adjust the prospective payment amount
associated with a CMG to account for facility-level characteristics
such as an IRF's LIP, teaching status, and location in a rural area, if
applicable, as described in Sec. 412.624(e).
Based on the substantive changes to the facility-level adjustment
factors that were adopted in the FY 2014 IRF PPS final rule (78 FR
47860, 47868 through 47872), in the FY 2015 IRF PPS final rule (79 FR
45872, 45882 through 45883), we froze the facility-level adjustment
factors at the FY 2014 levels for FY 2015 and all subsequent years
(unless and until we propose to update them again through future
notice-and-comment rulemaking). For FY 2020, we will continue to hold
the adjustment factors at the FY 2014 levels as we continue to monitor
the most current IRF claims data available and continue to evaluate and
monitor the effects of the FY 2014 changes.
VI. FY 2020 IRF PPS Payment Update
A. Background
Section 1886(j)(3)(C) of the Act requires the Secretary to
establish an increase factor that reflects changes over time in the
prices of an appropriate mix of goods and services included in the
covered IRF services. According to section 1886(j)(3)(A)(i) of the Act,
the increase factor shall be used to update the IRF prospective payment
rates for each FY. Section 1886(j)(3)(C)(ii)(I) of the Act requires the
application of a productivity adjustment. Thus, in the FY 2020 IRF
proposed rule, we proposed to update the IRF PPS payments for FY 2020
by a market basket increase factor as required by section 1886(j)(3)(C)
of the Act based upon the most current data available, with a
productivity adjustment as required by section 1886(j)(3)(C)(ii)(I) of
the Act (84 FR 17261).
We have utilized various market baskets through the years in the
IRF PPS. For a discussion of these market baskets, we refer readers to
the FY 2016 IRF PPS final rule (80 FR 47046).
Beginning with FY 2016, we finalized the use of a 2012-based IRF
market basket, using Medicare cost report (MCR) data for both
freestanding and hospital-based IRFs (80 FR 47049 through 47068).
Beginning with FY 2020, we proposed to rebase and revise the IRF market
basket to reflect a 2016 base year. In the following discussion, we
provide an overview of the proposed market basket and describe the
methodologies used to determine the operating and capital portions of
the proposed 2016-based IRF market basket.
B. Overview of the 2016-Based IRF Market Basket
The 2016-based IRF market basket is a fixed-weight, Laspeyres-type
price index. A Laspeyres price index measures the change in price, over
time, of the same mix of goods and services purchased in the base
period. Any changes in the quantity or mix of goods and services (that
is, intensity) purchased over time relative to a base period are not
measured.
The index itself is constructed in three steps. First, a base
period is selected (for the proposed IRF market basket, the base period
is 2016), total base period costs are estimated for a set of mutually
exclusive and exhaustive cost categories, and each category is
calculated as a proportion of total costs. These proportions are called
cost weights. Second, each cost category is matched to an appropriate
price or wage variable, referred to as a price proxy. In nearly every
instance where we have selected price proxies for the various market
baskets, these price proxies are derived from publicly available
statistical series that are published on a consistent schedule
(preferably at least on a quarterly basis). In cases where a publicly
available price series is not available (for example, a price index for
malpractice insurance), we have collected price data from other sources
and subsequently developed our own index to capture changes in prices
for these types of costs. Finally, the cost weight for each cost
category is multiplied by the established price proxy. The sum of these
products (that is, the cost weights multiplied by their price levels)
for all cost categories yields the composite index level of the market
basket for the given time period. Repeating this step for other periods
produces a series of market basket levels over time. Dividing the
composite index level of one period by the composite index level for an
earlier period produces a rate of growth in the input price index over
that timeframe.
[[Page 39072]]
As previously noted, the market basket is described as a fixed-
weight index because it represents the change in price over time of a
constant mix (quantity and intensity) of goods and services needed to
furnish IRF services. The effects on total costs resulting from changes
in the mix of goods and services purchased after the base period are
not measured. For example, an IRF hiring more nurses after the base
period to accommodate the needs of patients would increase the volume
of goods and services purchased by the IRF, but would not be factored
into the price change measured by a fixed-weight IRF market basket.
Only when the index is rebased would changes in the quantity and
intensity be captured, with those changes being reflected in the cost
weights. Therefore, we rebase the market basket periodically so that
the cost weights reflect recent changes in the mix of goods and
services that IRFs purchase to furnish inpatient care between base
periods.
C. Rebasing and Revising of the IRF PPS Market Basket
As discussed in the FY 2016 IRF PPS final rule (80 FR 47050), the
2012-based IRF market basket reflects the Medicare cost reports for
both freestanding and hospital-based facilities.
Beginning with FY 2020, we proposed to rebase and revise the 2012-
based IRF market basket to a 2016 base year reflecting both
freestanding and hospital-based IRFs. Below we provide a detailed
description of our methodology used to develop the proposed 2016-based
IRF market basket. This proposed methodology is generally similar to
the methodology used to develop the 2012-based IRF market basket with
the exception of the proposed derivation of the Home Office Contract
Labor cost weight using the MCR data as described in section VI.C.a.(6)
of this final rule.
1. Development of Cost Categories and Weights for the 2016-Based IRF
Market Basket
a. Use of Medicare Cost Report Data
We proposed a 2016-based IRF market basket that consists of seven
major cost categories and a residual derived from the 2016 Medicare
cost reports (CMS Form 2552-10) for freestanding and hospital-based
IRFs. The seven cost categories are Wages and Salaries, Employee
Benefits, Contract Labor, Pharmaceuticals, Professional Liability
Insurance (PLI), Home Office Contract Labor, and Capital. The residual
category reflects all remaining costs not captured in the seven cost
categories. The 2016 cost reports include providers whose cost
reporting period began on or after October 1, 2015, and prior to
September 30, 2016. We selected 2016 as the base year because we
believe that the Medicare cost reports for this year represent the most
recent, complete set of MCR data available for developing the IRF
market basket at the time of the proposed rule.
Since our goal is to establish cost weights that were reflective of
case mix and practice patterns associated with the services IRFs
provide to Medicare beneficiaries, as we did for the 2012-based IRF
market basket, we proposed to limit the cost reports used to establish
the 2016-based IRF market basket to those from facilities that had a
Medicare average LOS that was relatively similar to their facility
average LOS. We believe that this requirement eliminates statistical
outliers and ensures a more accurate market basket that reflects the
costs generally incurred during a Medicare-covered stay. The Medicare
average LOS for freestanding IRFs is calculated from data reported on
line 14 of Worksheet S-3, part I. The Medicare average LOS for
hospital-based IRFs is calculated from data reported on line 17 of
Worksheet S-3, part I. We proposed to include the cost report data from
IRFs with a Medicare average LOS within 15 percent (that is, 15 percent
higher or lower) of the facility average LOS to establish the sample of
providers used to estimate the 2016-based IRF market basket cost
weights. We proposed to apply this LOS edit to the data for IRFs to
exclude providers that serve a population whose LOS would indicate that
the patients served are not consistent with a LOS of a typical Medicare
patient. We note that this is the same LOS edit that we applied to
develop the 2012-based IRF market basket. This process resulted in the
exclusion of about eight percent of the freestanding and hospital-based
IRF Medicare cost reports. Of those excluded, about 18 percent were
freestanding IRFs and 82 percent were hospital-based IRFs. This ratio
is relatively consistent with the ratio of the universe of freestanding
to hospital-based IRF providers.
We then used the cost reports for IRFs that met this requirement to
calculate the costs for the seven major cost categories (Wages and
Salaries, Employee Benefits, Contract Labor, Professional Liability
Insurance, Pharmaceuticals, Home Office Contract Labor, and Capital)
for the market basket. For comparison, the 2012-based IRF market basket
utilized the Bureau of Economic Analysis Benchmark Input-Output data
rather than MCR data to derive the Home Office Contract Labor cost
weight. A more detailed discussion of this methodological change is
provided in section VI.C.1.a.(6). of this final rule.
Similar to the 2012-based IRF market basket major cost weights, the
proposed 2016-based IRF market basket cost weights reflect Medicare
allowable costs (routine, ancillary and capital)--costs that are
eligible for reimbursement through the IRF PPS.
For freestanding IRFs, total Medicare allowable costs would be
equal to the total costs as reported on Worksheet B, part I, column 26,
lines 30 through 35, 50 through 76 (excluding 52 and 75), 90 through
91, and 93. For hospital-based IRFs, total Medicare allowable costs
would be equal to the total costs for the IRF inpatient unit after the
allocation of overhead costs (Worksheet B, part I, column 26, line 41)
and a proportion of total ancillary costs reported on Worksheet B, part
I, column 26, lines 50 through 76 (excluding 52 and 75), 90 through 91,
and 93. We proposed to calculate the portion of ancillary costs
attributable to the hospital-based IRF for a given ancillary cost
center by multiplying total facility ancillary costs for the specific
cost center (as reported on Worksheet B, part I, column 26) by the
ratio of IRF Medicare ancillary costs for the cost center (as reported
on Worksheet D-3, column 3 for hospital-based IRFs) to total Medicare
ancillary costs for the cost center (equal to the sum of Worksheet D-3,
column 3 for all relevant PPS [that is, IPPS, IRF, IPF and skilled
nursing facility (SNF)]). We proposed to use these methods to derive
levels of total costs for IRF providers. This is the same methodology
used for the 2012-based IRF market basket. With this work complete, we
then set about deriving cost levels for the seven major cost categories
and then derive a residual cost weight reflecting all other costs not
classified.
(1) Wages and Salaries Costs
For freestanding IRFs, we proposed to derive Wages and Salaries
costs as the sum of routine inpatient salaries, ancillary salaries, and
a proportion of overhead (or general service cost centers in the
Medicare cost reports) salaries as reported on Worksheet A, column 1.
Since overhead salary costs are attributable to the entire IRF, we only
include the proportion attributable to the Medicare allowable cost
centers. We proposed to estimate the proportion of overhead salaries
that are attributed to Medicare allowable costs centers by multiplying
the ratio of Medicare allowable area salaries (Worksheet A, column 1,
lines 50 through 76
[[Page 39073]]
(excluding 52 and 75), 90 through 91, and 93) to total salaries
(Worksheet A, column 1, line 200) times total overhead salaries
(Worksheet A, column 1, lines 4 through 18). This is the same
methodology used in the 2012-based IRF market basket.
For hospital-based IRFs, we proposed to derive Wages and Salaries
costs as the sum of inpatient routine salary costs (Worksheet A, column
1, line 41) for the hospital-based IRF and the overhead salary costs
attributable to this IRF inpatient unit; and ancillary salaries plus a
portion of overhead salary costs attributable to the ancillary
departments utilized by the hospital-based IRF.
We proposed to calculate hospital-based ancillary salary costs for
a specific cost center (Worksheet A, column 1, lines 50 through 76
(excluding 52 and 75), 90 through 91, and 93) using salary costs from
Worksheet A, column 1, multiplied by the ratio of IRF Medicare
ancillary costs for the cost center (as reported on Worksheet D-3,
column 3, for IRF subproviders) to total Medicare ancillary costs for
the cost center (equal to the sum of Worksheet D-3, column 3, for all
relevant PPS units [that is, IPPS, IRF, IPF and a SNF]). For example,
if hospital-based IRF Medicare physical therapy costs represent 30
percent of the total Medicare physical therapy costs for the entire
facility, then 30 percent of total facility physical therapy salaries
(as reported in Worksheet A, column 1, line 66) would be attributable
to the hospital-based IRF. We believe it is appropriate to use only a
portion of the ancillary costs in the market basket cost weight
calculations since the hospital-based IRF only utilizes a portion of
the facility's ancillary services. We believe the ratio of reported IRF
Medicare costs to reported total Medicare costs provides a reasonable
estimate of the ancillary services utilized, and costs incurred, by the
hospital-based IRF.
We proposed to calculate the portion of overhead salary costs
attributable to hospital-based IRFs by first calculating total
noncapital overhead costs (Worksheet B, part I, columns 4-18, line 41,
less Worksheet B, part II, columns 4-18, line 41). We then multiply
total noncapital overhead costs by an overhead ratio equal to the ratio
of total facility overhead salaries (as reported on Worksheet A, column
1, lines 4-18) to total facility noncapital overhead costs (as reported
on Worksheet A, column 1 and 2, lines 4-18). This methodology assumes
the proportion of total costs related to salaries for the overhead cost
center is similar for all inpatient units (that is, acute inpatient or
inpatient rehabilitation).
We proposed to calculate the portion of overhead salaries
attributable to each ancillary department by first calculating total
noncapital overhead costs attributable to each specific ancillary
department (Worksheet B, part I, columns 4-18 less, Worksheet B, part
II, columns 4-18). We then identify the portion of these noncapital
overhead costs attributable to Wages and Salaries by multiplying these
costs by the overhead ratio defined as the ratio of total facility
overhead salaries (as reported on Worksheet A, column 1, lines 4-18) to
total overhead costs (as reported on Worksheet A, column 1 & 2, lines
4-18). Finally, we identified the portion of these overhead salaries
for each ancillary department that is attributable to the hospital-
based IRF by multiplying by the ratio of IRF Medicare ancillary costs
for the cost center (as reported on Worksheet D-3, column 3, for
hospital-based IRFs) to total Medicare ancillary costs for the cost
center (equal to the sum of Worksheet D-3, column 3, for all relevant
PPS units [that is, IPPS, IRF, IPF and SNF]). This is the same
methodology used to derive the 2012-based IRF market basket.
(2) Employee Benefits Costs
Effective with the implementation of CMS Form 2552-10, we began
collecting Employee Benefits and Contract Labor data on Worksheet S-3,
part V.
For 2016 MCR data, the majority of providers did not report data on
Worksheet S-3, part V; particularly, approximately 48 percent of
freestanding IRFs and 40 percent of hospital-based IRFs reported data
on Worksheet S-3, part V. However, we believe we have a large enough
sample to enable us to produce a reasonable Employee Benefits cost
weight. Again, we continue to encourage all providers to report these
data on the Medicare cost report.
For freestanding IRFs, we proposed Employee Benefits costs would be
equal to the data reported on Worksheet S-3, part V, column 2, line 2.
We note that while not required to do so, freestanding IRFs also may
report Employee Benefits data on Worksheet S-3, part II, which is
applicable to only IPPS providers. For those freestanding IRFs that
report Worksheet S-3, part II, data, but not Worksheet S-3, part V, we
proposed to use the sum of Worksheet S-3, part II, lines 17, 18, 20,
and 22, to derive Employee Benefits costs. This proposed method allows
us to obtain data from about 30 more freestanding IRFs than if we were
to only use the Worksheet S-3, part V, data as was done for the 2012-
based IRF market basket.
For hospital-based IRFs, we proposed to calculate total benefit
costs as the sum of inpatient unit benefit costs, a portion of
ancillary benefits, and a portion of overhead benefits attributable to
the routine inpatient unit and a portion of overhead benefits
attributable to the ancillary departments. We proposed inpatient unit
benefit costs be equal to Worksheet S-3, part V, column 2, line 4. We
proposed that the portion of overhead benefits attributable to the
routine inpatient unit and ancillary departments be calculated by
multiplying ancillary salaries for the hospital-based IRF and overhead
salaries attributable to the hospital-based IRF (determined in the
derivation of hospital-based IRF Wages and Salaries costs as described
above) by the ratio of total facility benefits to total facility
salaries. Total facility benefits is equal to the sum of Worksheet S-3,
part II, column 4, lines 17-25, and total facility salaries is equal to
Worksheet S-3, part II, column 4, line 1.
(3) Contract Labor Costs
Contract Labor costs are primarily associated with direct patient
care services. Contract labor costs for other services such as
accounting, billing, and legal are calculated separately using other
government data sources as described in section VI.C.3. of this final
rule. To derive contract labor costs using Worksheet S-3, part V, data,
for freestanding IRFs, we proposed Contract Labor costs be equal to
Worksheet S-3, part V, column 1, line 2. As we noted for Employee
Benefits, freestanding IRFs also may report Contract Labor data on
Worksheet S-3, part II, which is applicable to only IPPS providers. For
those freestanding IRFs that report Worksheet S-3, part II data, but
not Worksheet S-3, part V, we proposed to use the sum of Worksheet S-3,
part II, lines 11 and 13, to derive Contract Labor costs.
For hospital-based IRFs, we proposed that Contract Labor costs
would be equal to Worksheet S-3, part V, column 1, line 4. As
previously noted, for 2016 MCR data, while there were providers that
did report data on Worksheet S-3, part V, many providers did not
complete this worksheet. However, we believe we have a large enough
sample to enable us to produce a reasonable Contract Labor cost weight.
We continue to encourage all providers to report these data on the
Medicare cost report.
(4) Pharmaceuticals Costs
For freestanding IRFs, we proposed to calculate pharmaceuticals
costs using
[[Page 39074]]
non-salary costs reported on Worksheet A, column 7, less Worksheet A,
column 1, for the pharmacy cost center (line 15) and drugs charged to
patients cost center (line 73).
For hospital-based IRFs, we proposed to calculate pharmaceuticals
costs as the sum of a portion of the non-salary pharmacy costs and a
portion of the non-salary drugs charged to patient costs reported for
the total facility. We proposed that non-salary pharmacy costs
attributable to the hospital-based IRF would be calculated by
multiplying total pharmacy costs attributable to the hospital-based IRF
(as reported on Worksheet B, part I, column 15, line 41) by the ratio
of total non-salary pharmacy costs (Worksheet A, column 2, line 15) to
total pharmacy costs (sum of Worksheet A, columns 1 and 2 for line 15)
for the total facility. We proposed that non-salary drugs charged to
patient costs attributable to the hospital-based IRF would be
calculated by multiplying total non-salary drugs charged to patient
costs (Worksheet B, part I, column 0, line 73 plus Worksheet B, part I,
column 15, line 73, less Worksheet A, column 1, line 73) for the total
facility by the ratio of Medicare drugs charged to patient ancillary
costs for the IRF unit (as reported on Worksheet D-3 for hospital-based
IRFs, column 3, line 73) to total Medicare drugs charged to patient
ancillary costs for the total facility (equal to the sum of Worksheet
D-3, column 3, line 73 for all relevant PPS [that is, IPPS, IRF, IPF
and SNF]).
(5) Professional Liability Insurance Costs
For freestanding IRFs, we proposed that Professional Liability
Insurance (PLI) costs (often referred to as malpractice costs) would be
equal to premiums, paid losses and self-insurance costs reported on
Worksheet S-2, part I, columns 1 through 3, line 118. For hospital-
based IRFs, we proposed to assume that the PLI weight for the total
facility is similar to the hospital-based IRF unit since the only data
reported on this worksheet is for the entire facility, as we currently
have no means to identify the proportion of total PLI costs that are
only attributable to the hospital-based IRF. Therefore, hospital-based
IRF PLI costs are equal to total facility PLI (as reported on Worksheet
S-2, part I, columns 1 through 3, line 118) divided by total facility
costs (as reported on Worksheet A, columns 1 and 2, line 200) times
hospital-based IRF Medicare allowable total costs. Our assumption is
that the same proportion of expenses are used among each unit of the
hospital.
(6) Home Office/Related Organization Contract Labor Costs
For the 2016-based IRF market basket, we proposed to determine the
home office/related organization contract labor costs using MCR data.
The 2012-based IRF market basket used the 2007 Benchmark Input-Output
(I-O) expense data published by the Bureau of Economic Analysis (BEA)
to derive these costs (80 FR 47057). A more detailed explanation of the
general methodology using the BEA I-O data is provided in section
VI.C.3. of this final rule. For freestanding and hospital-based IRFs,
we proposed to calculate the home office contract labor cost weight
(using data reported on Worksheet S-3, part II, column 4, lines 14,
1401, 1402, 2550, and 2551) and total facility costs (Worksheet B, part
I, column 26, line 202). We proposed to use total facility costs as the
denominator for calculating the home office contract labor cost weight
as these expenses reported on Worksheet S-3, part II reflect the entire
hospital facility. Our assumption is that the same proportion of
expenses are used among each unit of the hospital. For the 2012-based
IRF market basket, we calculated the home office cost weight using
expense data for North American Industry Classification System (NAICS)
code 55, Management of Companies and Enterprises (80 FR 47067).
(7) Capital Costs
For freestanding IRFs, we proposed that capital costs would be
equal to Medicare allowable capital costs as reported on Worksheet B,
part II, column 26, lines 30 through 35, 50 through 76 (excluding 52
and 75), 90 through 91, and 93.
For hospital-based IRFs, we proposed that capital costs would be
equal to IRF inpatient capital costs (as reported on Worksheet B, part
II, column 26, line 41) and a portion of IRF ancillary capital costs.
We calculate the portion of ancillary capital costs attributable to the
hospital-based IRF for a given cost center by multiplying total
facility ancillary capital costs for the specific ancillary cost center
(as reported on Worksheet B, part II, column 26) by the ratio of IRF
Medicare ancillary costs for the cost center (as reported on Worksheet
D-3, column 3 for hospital-based IRFs) to total Medicare ancillary
costs for the cost center (equal to the sum of Worksheet D-3, column 3
for all relevant PPS [that is, IPPS, IRF, IPF and SNF]). For example,
if hospital-based IRF Medicare physical therapy costs represent 30
percent of the total Medicare physical therapy costs for the entire
facility, then 30 percent of total facility physical therapy capital
costs (as reported in Worksheet B, part II, column 26, line 66) would
be attributable to the hospital-based IRF.
b. Final Major Cost Category Computation
After we derive costs for the major cost categories for each
provider using the MCR data as previously described, we proposed to
trim the data for outliers. For the Wages and Salaries, Employee
Benefits, Contract Labor, Pharmaceuticals, Professional Liability
Insurance, and Capital cost weights, we first divide the costs for each
of these six categories by total Medicare allowable costs calculated
for the provider to obtain cost weights for the universe of IRF
providers. We then remove those providers whose derived cost weights
fall in the top and bottom 5 percent of provider specific derived cost
weights to ensure the exclusion of outliers. After the outliers have
been excluded, we sum the costs for each category across all remaining
providers. We then divide this by the sum of total Medicare allowable
costs across all remaining providers to obtain a cost weight for the
2016-based IRF market basket for the given category.
The proposed trimming methodology for the Home Office Contract
Labor cost weight is slightly different than the proposed trimming
methodology for the other six cost categories as described above. For
the Home Office Contract Labor cost weight, since we are using total
facility data rather than Medicare-allowable costs associated with IRF
services, we proposed to trim the freestanding and hospital-based IRF
cost weights separately. For each of the providers, we first divide the
home office contract labor costs by total facility costs to obtain a
Home Office Contract Labor cost weight for the universe of IRF
providers. We then proposed to trim only the top 1 percent of providers
to exclude outliers while also allowing providers who have reported
zero home office costs to remain in the Home Office Contract Labor cost
weight calculations as not all providers will incur home office costs.
After removing these outliers, we are left with a trimmed data set for
both freestanding and hospital-based providers. We then proposed to sum
the costs for each category (freestanding and hospital-based) across
all remaining providers. We next divide this by the sum of total
facility costs across all remaining providers to obtain a freestanding
and hospital-based cost weight. Lastly, we proposed to weight these two
cost weights together using
[[Page 39075]]
the Medicare-allowable costs to derive a Home Office Contract Labor
cost weight for the 2016-based IRF market basket.
Finally, we proposed to calculate the residual ``All Other'' cost
weight that reflects all remaining costs that are not captured in the
seven cost categories listed.
We received a few comments on our proposed derivation of the Home
Office Contract Labor cost weight from the Medicare cost reports, which
are summarized below.
Comment: Commenters expressed concern with the proposed methodology
change to the Home Office Contract Labor cost weight. These commenters
stated that CMS had not provided sufficient rationale for this change
in methodology nor has CMS provided a discussion of how these data
points were reasonably validated and tested. One commenter requested
that CMS provide stakeholders with more information on the rationale
and the data validation methodologies employed in the final rule.
The commenters expressed concern with the sample of IRFs reporting
the home office cost data and found based on their analysis that
reporting was between 50 to 65 percent. These commenters suggested that
this was due to these cost report line items being an optional category
for IRFs under Medicare cost reporting requirements. One of the
commenters further expressed concern with the methodology and approach
that CMS applied in determining IRF unit Home Office Contract Labor
amounts, specifically the assumption that hospital-based IRFs utilize
the same proportion of home office expenses as the rest of the acute
care hospital in which it is located. The commenter stated that
typically IRF units are a very small part of the larger parent acute
care hospital and that the larger systems do not spend the same
proportional time and resources on these units compared to hospital
system as a whole. They stated that this assumption likely overstates
the Home Office Contract Labor cost weight.
Based on these concerns, the commenters requested that CMS not
finalize its proposed changes to the Home Office Contract Labor cost
category and instead finalize use of the previous methodology relating
to this category that was used for the 2012-based market basket. One
commenter also requested that CMS revisit this potential change with
adequate explanation and data in future rulemaking.
Response: We appreciate the commenters' concerns on the proposed
methodological change for the Home Office Contract Labor cost weight.
We proposed to revise our methodology and use the 2016 IRF MCR data to
calculate the Home Office Contract Labor costs rather than the 2012
Benchmark I-O data because it reflected more up-to-date data and we
believe it to be an improvement over the use of the BEA Benchmark I-O
data that is not specific to IRFs. The MCR data allows us to calculate
Home Office Contract Labor Costs for freestanding and IRF hospital-
based facilities.
We disagree with the commenters' concern that the MCR data
completion rates for the Home Office Contract Labor costs are
inadequate to obtain a cost weight. When developing the proposed 2016-
based IRF market basket, we conducted a thorough analysis of the MCR
data and our proposed Home Office Contract Labor cost weight
methodology. We found that approximately 90 percent of freestanding
IRFs reported having a home office, of which over 50 percent reported
home office compensation data on Worksheet S-3, part II. The
composition of the providers (by ownership-type and region) that
reported both wage index data (including those who do not have a home
office) and home office contract labor cost data were similarly
representative to all freestanding IRFs. A sensitivity analysis of
calculating a reweighted Home Office Contract Labor cost weight based
on ownership-type and region produced a Home Office Contract Labor cost
weight similar to the proposed 3.7 percent weight.
For additional sensitivity testing, recognizing that some of the
freestanding IRFs with home offices may not have completed the
applicable fields on the MCR, we calculated a weight using only
freestanding IRFs that reported having a home office (Worksheet S-2,
part I, line 140). This produced a Home Office Contract Labor cost
weight nearly identical to the freestanding IRF 2016 cost weight using
our proposed methodology. Based on this analysis, we believe that the
sample of providers included in the Home Office Contract Labor cost
weight are a technically representative sample of all IRF providers.
Regarding IRF units, we recognize the commenter's concern that they
represent a small proportion of the total facility. We believe that the
assumption that IRFs utilize the same proportion of home office
expenses as the rest of the acute care hospital is reasonable. The use
of total facility data assumes the facility Home Office Contract Labor
cost weight is equal to the Home Office Contract Labor cost weight for
the IRF unit. Further analysis of the MCR data shows IRF unit direct
patient care costs (as reported on Worksheet B, part I, column 0, line
41) account for about one percent of total facility costs (excluding
capital, Administrative and General (A&G), and Employee Benefit
department costs). Similarly, A&G costs (Worksheet B, part I, column 0,
line 5), where Home Office Contract Labor costs are likely captured,
allocated to the IRF unit account for a similar proportion of direct
patient care costs with about one percent of total A&G costs. We also
found the proportion of allocated A&G costs for other larger, more
medically-complex hospital units (such as the intensive care, surgical
care, and operating room) were consistent with direct patient care cost
proportions and the proportions for these units were higher than the
proportion of the A&G expenses allocated to the IRF unit. This supports
the commenter's claim that hospitals allocate less A&G costs to less
medically-complex services (as measured by costs). Our proposed
calculation would adhere to this assumption as well since the facility
level cost weight is applied to the IRF Medicare allowable total costs
representing these relatively less medically-complex services.
Furthermore, the Benchmark I-O methodology used in the 2012-based IRF
market basket also assumes that the IRF relative costs are the same as
those of the hospital total facility. We invite the commenters to
submit additional data that would help in this area for consideration
in future rulemaking.
We disagree with the commenters' request to use the Benchmark I-O
data to calculate the Home Office Contract Labor cost weight rather
than the proposed 2016 MCR data. We believe the proposed methodology is
a technical improvement over the prior methodology because it
represents more recent data that is representative compositionally and
geographically of IRFs. It is also is the same data used to determine
the other major cost weights in the 2016-based market basket and the
proportion of the Home Office Contract Labor cost weight that is
allocated to the Professional Fees: Labor-related and Professional
Fees: Nonlabor-related cost weights. We believe the assumptions made by
using the total facility data for the hospital-based IRFs are
reasonable and supported by the MCR data on A&G cost allocation.
Finally, we note that the methodological change accounts for only 0.2
percentage point of the 2.0 percentage points change in the labor-
related share.
[[Page 39076]]
After careful consideration of comments, we are finalizing our
methodology for deriving the major cost weights as proposed.
Table 4 presents the cost weights for these major cost categories
calculated from the Medicare cost reports for the 2016-based IRF market
basket, as well as for the 2012-based IRF market basket.
[GRAPHIC] [TIFF OMITTED] TR08AU19.007
As we did for the 2012-based IRF market basket, we proposed to
allocate the Contract Labor cost weight to the Wages and Salaries and
Employee Benefits cost weights based on their relative proportions
under the assumption that contract labor costs are comprised of both
wages and salaries and employee benefits. The Contract Labor allocation
proportion for Wages and Salaries is equal to the Wages and Salaries
cost weight as a percent of the sum of the Wages and Salaries cost
weight and the Employee Benefits cost weight. For the proposed rule,
this rounded percentage is 81 percent; therefore, we proposed to
allocate 81 percent of the Contract Labor cost weight to the Wages and
Salaries cost weight and 19 percent to the Employee Benefits cost
weight. The 2012-based IRF market basket percentage was also 81 percent
(80 FR 47056). We did not receive any specific public comments on our
proposed allocation of Contract Labor. Therefore, we are finalizing our
method of allocating Contract Labor as proposed.
Table 5 shows the Wages and Salaries and Employee Benefit cost
weights after Contract Labor cost weight allocation for both the 2016-
based IRF market basket and 2012-based IRF market basket.
[GRAPHIC] [TIFF OMITTED] TR08AU19.008
c. Derivation of the Detailed Operating Cost Weights
To further divide the ``All Other'' residual cost weight estimated
from the 2016 MCR data into more detailed cost categories, we proposed
to use the 2012 Benchmark I-O ``Use Tables/Before Redefinitions/
Purchaser Value'' for NAICS 622000, Hospitals, published by the BEA.
This data is publicly available at http://www.bea.gov/industry/io_annual.htm. For the 2012-based IRF market basket, we used the 2007
Benchmark I-O data, the most recent data available at the time (80 FR
47057).
The BEA Benchmark I-O data are scheduled for publication every 5
years with the most recent data available for 2012. The 2007 Benchmark
I-O data are derived from the 2012 Economic Census and are the building
blocks for BEA's economic accounts. Thus, they represent the most
comprehensive and complete set of data on the economic processes or
mechanisms by which output is produced and distributed.\1\ BEA also
produces Annual I-O estimates; however, while based on a similar
methodology, these estimates reflect less comprehensive and less
detailed data sources and are subject to revision when benchmark data
becomes available. Instead of using the less detailed Annual I-O data,
we proposed to inflate the 2012 Benchmark I-O data forward to 2016 by
applying the annual price changes from the respective price proxies to
the appropriate market basket cost categories that are obtained from
the 2012 Benchmark I-O data. We repeat this practice for each year. We
then proposed to calculate the cost shares that each cost category
represents of the inflated 2012 data. These resulting 2016 cost shares
are applied to the All Other residual cost weight to obtain the
detailed cost weights for the 2016-based IRF market basket. For
example, the cost for Food: Direct Purchases represents 5.0 percent of
the sum of the ``All Other'' 2012 Benchmark I-O Hospital Expenditures
inflated to 2016; therefore, the Food: Direct Purchases cost weight
represents 5.0 percent of the 2016-based IRF market basket's ``All
Other'' cost category (22.2 percent), yielding a ``final'' Food: Direct
Purchases cost weight of 1.1 percent in the 2016-based IRF market
basket (0.05 * 22.2 percent = 1.1 percent).
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\1\ http://www.bea.gov/papers/pdf/IOmanual_092906.pdf.
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Using this methodology, we proposed to derive seventeen detailed
IRF market
[[Page 39077]]
basket cost category weights from the 2016-based IRF market basket
residual cost weight (22.2 percent). These categories are: (1)
Electricity; (2) Fuel, Oil, and Gasoline; (3) Food: Direct Purchases;
(4) Food: Contract Services; (5) Chemicals; (6) Medical Instruments;
(7) Rubber & Plastics; (8) Paper and Printing Products; (9)
Miscellaneous Products; (10) Professional Fees: Labor-related; (11)
Administrative and Facilities Support Services; (12) Installation,
Maintenance, and Repair; (13) All Other Labor-related Services; (14)
Professional Fees: Nonlabor-related; (15) Financial Services; (16)
Telephone Services; and (17) All Other Nonlabor-related Services. We
note that for the 2012-based IRF market basket, we had a Water and
Sewerage cost weight. For the 2016-based IRF market basket, we proposed
to include Water and Sewerage costs in the Electricity cost weight due
to the small amount of costs in this category.
For the 2012-based IRF market basket, we used the I-O data for
NAICS 55 Management of Companies to derive the Home Office Contract
Labor cost weight, which were classified in the Professional Fees:
Labor-related and Professional Fees: Nonlabor-related cost weights. As
previously discussed, we proposed to use the MCR data to derive the
Home Office Contract Labor cost weight, which we would further classify
into the Professional Fees: Labor-related or Professional Fees:
Nonlabor-related categories.
We did not receive any specific comments on the derivation of the
detailed operating cost weights. In this final rule, we are finalizing
our methodology for deriving the detailed operating cost weights as
proposed.
d. Derivation of the Detailed Capital Cost Weights
As described in section VI.C.1.a.(6) of this final rule, we
proposed a Capital-Related cost weight of 9.0 percent as obtained from
the 2016 Medicare cost reports for freestanding and hospital-based IRF
providers. We proposed to then separate this total Capital-Related cost
weight into more detailed cost categories.
Using 2016 Medicare cost reports, we were able to group Capital-
Related costs into the following categories: Depreciation, Interest,
Lease, and Other Capital-Related costs. For each of these categories,
we proposed to determine separately for hospital-based IRFs and
freestanding IRFs what proportion of total capital-related costs the
category represents.
For freestanding IRFs, we proposed to derive the proportions for
Depreciation, Interest, Lease, and Other Capital-related costs using
the data reported by the IRF on Worksheet A-7, which is similar to the
methodology used for the 2012-based IRF market basket.
For hospital-based IRFs, data for these four categories were not
reported separately for the hospital-based IRF; therefore, we proposed
to derive these proportions using data reported on Worksheet A-7 for
the total facility. We assumed the cost shares for the overall hospital
are representative for the hospital-based IRF unit. For example, if
depreciation costs make up 60 percent of total capital costs for the
entire facility, we believe it is reasonable to assume that the
hospital-based IRF would also have a 60 percent proportion because it
is a unit contained within the total facility. This is the same
methodology used for the 2012-based IRF market basket (80 FR 47057).
To combine each detailed capital cost weight for freestanding and
hospital-based IRFs into a single capital cost weight for the 2016-
based IRF market basket, we proposed to weight together the shares for
each of the categories (Depreciation, Interest, Lease, and Other
Capital-related costs) based on the share of total capital costs each
provider type represents of the total capital costs for all IRFs for
2016. Applying this methodology results in proportions of total
capital-related costs for Depreciation, Interest, Lease and Other
Capital-related costs that are representative of the universe of IRF
providers. This is the same methodology used for the 2012-based IRF
market basket (80 FR 47057 through 47058).
Lease costs are unique in that they are not broken out as a
separate cost category in the 2016-based IRF market basket. Rather, we
proposed to proportionally distribute these costs among the cost
categories of Depreciation, Interest, and Other Capital-Related,
reflecting the assumption that the underlying cost structure of leases
is similar to that of capital-related costs in general. As was done
under the 2012-based IRF market basket, we proposed to assume that 10
percent of the lease costs as a proportion of total capital-related
costs represents overhead and assign those costs to the Other Capital-
Related cost category accordingly. We proposed to distribute the
remaining lease costs proportionally across the three cost categories
(Depreciation, Interest, and Other Capital-Related) based on the
proportion that these categories comprise of the sum of the
Depreciation, Interest, and Other Capital-related cost categories
(excluding lease expenses). This resulted in three primary capital-
related cost categories in the 2016-based IRF market basket:
Depreciation, Interest, and Other Capital-Related costs. This is the
same methodology used for the 2012-based IRF market basket (80 FR
47058). The allocation of these lease expenses are shown in Table 6.
Finally, we proposed to further divide the Depreciation and
Interest cost categories. We proposed to separate Depreciation into the
following two categories: (1) Building and Fixed Equipment; and (2)
Movable Equipment. We proposed to separate Interest into the following
two categories: (1) Government/Nonprofit; and (2) For-profit.
To disaggregate the Depreciation cost weight, we need to determine
the percent of total Depreciation costs for IRFs that are attributable
to Building and Fixed Equipment, which we hereafter refer to as the
``fixed percentage.'' For the 2016-based IRF market basket, we proposed
to use slightly different methods to obtain the fixed percentages for
hospital-based IRFs compared to freestanding IRFs.
For freestanding IRFs, we proposed to use depreciation data from
Worksheet A-7 of the 2016 Medicare cost reports. However, for hospital-
based IRFs, we determined that the fixed percentage for the entire
facility may not be representative of the hospital-based IRF unit due
to the entire facility likely employing more sophisticated movable
assets that are not utilized by the hospital-based IRF. Therefore, for
hospital-based IRFs, we proposed to calculate a fixed percentage using:
(1) Building and fixture capital costs allocated to the hospital-based
IRF unit as reported on Worksheet B, part I, line 41; and (2) building
and fixture capital costs for the top five ancillary cost centers
utilized by hospital-based IRFs. We proposed to weight these two fixed
percentages (inpatient and ancillary) using the proportion that each
capital cost type represents of total capital costs in the 2016-based
IRF market basket. We proposed to then weight the fixed percentages for
hospital-based and freestanding IRFs together using the proportion of
total capital costs each provider type represents. For both
freestanding and hospital-based IRFs, this is the same methodology used
for the 2012-based IRF market basket (80 FR 47058).
To disaggregate the Interest cost weight, we determined the percent
of total interest costs for IRFs that are attributable to government
and nonprofit facilities, which is hereafter referred to as the
``nonprofit percentage,'' as price pressures associated with these
types of interest
[[Page 39078]]
costs tend to differ from those for for-profit facilities. For the
2016-based IRF market basket, we proposed to use interest costs data
from Worksheet A-7 of the 2016 Medicare cost reports for both
freestanding and hospital-based IRFs. We proposed to determine the
percent of total interest costs that are attributed to government and
nonprofit IRFs separately for hospital-based and freestanding IRFs. We
then proposed to weight the nonprofit percentages for hospital-based
and freestanding IRFs together using the proportion of total capital
costs that each provider type represents.
We did not receive any specific public comments on the derivation
of the detailed capital cost weights. In this final rule, we are
finalizing our methodology for deriving the detailed capital cost
weights as proposed. Table 6 provides the detailed capital cost share
composition estimated from the 2016 IRF Medicare cost reports. These
detailed capital cost share composition percentages are applied to the
total Capital-Related cost weight of 9.0 percent explained in detail in
section VI.C.1.a.(6) of this final rule.
[GRAPHIC] [TIFF OMITTED] TR08AU19.009
e. 2016-Based IRF Market Basket Cost Categories and Weights
Table 7 compares the cost categories and weights for the final
2016-based IRF market basket compared to the 2012-based IRF market
basket.
[[Page 39079]]
[GRAPHIC] [TIFF OMITTED] TR08AU19.010
2. Selection of Price Proxies
After developing the cost weights for the 2016-based IRF market
basket, we selected the most appropriate wage and price proxies
currently available to represent the rate of price change for each
expenditure category. For the majority of the cost weights, we base the
price proxies on U.S. Bureau of Labor Statistics (BLS) data and group
them into one of the following BLS categories:
Employment Cost Indexes. Employment Cost Indexes (ECIs)
measure the rate of change in employment wage rates and employer costs
for employee benefits per hour worked. These indexes are fixed-weight
indexes and strictly measure the change in wage rates and employee
benefits per hour. ECIs are superior to Average Hourly Earnings (AHE)
as price proxies for input price indexes because they are not affected
by shifts in occupation or industry mix, and because they measure pure
price change and are available by both occupational group and by
industry. The industry ECIs are based on the NAICS and the occupational
ECIs are based on the Standard Occupational Classification System
(SOC).
Producer Price Indexes. Producer Price Indexes (PPIs)
measure the average change over time in the selling prices received by
domestic producers for their output. The prices included in the PPI
[[Page 39080]]
are from the first commercial transaction for many products and some
services (https://www.bls.gov/ppi/).
Consumer Price Indexes. Consumer Price Indexes (CPIs)
measure the average change over time in the prices paid by urban
consumers for a market basket of consumer goods and services (https://www.bls.gov/cpi/). CPIs are only used when the purchases are similar to
those of retail consumers rather than purchases at the producer level,
or if no appropriate PPIs are available.
We evaluate the price proxies using the criteria of reliability,
timeliness, availability, and relevance:
Reliability. Reliability indicates that the index is based
on valid statistical methods and has low sampling variability. Widely
accepted statistical methods ensure that the data were collected and
aggregated in a way that can be replicated. Low sampling variability is
desirable because it indicates that the sample reflects the typical
members of the population. (Sampling variability is variation that
occurs by chance because only a sample was surveyed rather than the
entire population.)
Timeliness. Timeliness implies that the proxy is published
regularly, preferably at least once a quarter. The market baskets are
updated quarterly, and therefore, it is important for the underlying
price proxies to be up-to-date, reflecting the most recent data
available. We believe that using proxies that are published regularly
(at least quarterly, whenever possible) helps to ensure that we are
using the most recent data available to update the market basket. We
strive to use publications that are disseminated frequently, because we
believe that this is an optimal way to stay abreast of the most current
data available.
Availability. Availability means that the proxy is
publicly available. We prefer that our proxies are publicly available
because this will help ensure that our market basket updates are as
transparent to the public as possible. In addition, this enables the
public to be able to obtain the price proxy data on a regular basis.
Relevance. Relevance means that the proxy is applicable
and representative of the cost category weight to which it is applied.
The CPIs, PPIs, and ECIs that we have selected meet these criteria.
Therefore, we believe that they continue to be the best measure of
price changes for the cost categories to which they would be applied.
Table 10 lists all price proxies that we proposed to use for the
2016-based IRF market basket. Below is a detailed explanation of the
price proxies we proposed for each cost category weight. We did not
receive any specific comments on our proposed price proxies for the
2016-based IRF market basket. Therefore, in this final rule, we are
finalizing the price proxies as proposed.
a. Price Proxies for the Operating Portion of the 2016-Based IRF Market
Basket
(1) Wages and Salaries
We proposed to continue to use the ECI for Wages and Salaries for
All Civilian workers in Hospitals (BLS series code CIU1026220000000I)
to measure the wage rate growth of this cost category. This is the same
price proxy used in the 2012-based IRF market basket (80 FR 47060).
(2) Benefits
We proposed to continue to use the ECI for Total Benefits for All
Civilian workers in Hospitals to measure price growth of this category.
This ECI is calculated using the ECI for Total Compensation for All
Civilian workers in Hospitals (BLS series code CIU1016220000000I) and
the relative importance of wages and salaries within total
compensation. This is the same price proxy used in the 2012-based IRF
market basket (80 FR 47060).
(3) Electricity
We proposed to continue to use the PPI Commodity Index for
Commercial Electric Power (BLS series code WPU0542) to measure the
price growth of this cost category. This is the same price proxy used
in the 2012-based IRF market basket (80 FR 47060).
(4) Fuel, Oil, and Gasoline
Similar to the 2012-based IRF market basket, for the 2016-based IRF
market basket, we proposed to use a blend of the PPI for Petroleum
Refineries and the PPI Commodity for Natural Gas. Our analysis of the
Bureau of Economic Analysis' 2012 Benchmark Input-Output data (use
table before redefinitions, purchaser's value for NAICS 622000
[Hospitals]), shows that Petroleum Refineries expenses account for
approximately 90 percent and Natural Gas expenses account for
approximately 10 percent of Hospitals' (NAICS 622000) total Fuel, Oil,
and Gasoline expenses. Therefore, we proposed to use a blend of 90
percent of the PPI for Petroleum Refineries (BLS series code
PCU324110324110) and 10 percent of the PPI Commodity Index for Natural
Gas (BLS series code WPU0531) as the price proxy for this cost
category. The 2012-based IRF market basket used a 70/30 blend of these
price proxies, reflecting the 2007 I-O data (80 FR 47060). We believe
that these two price proxies continue to be the most technically
appropriate indices available to measure the price growth of the Fuel,
Oil, and Gasoline cost category in the 2016-based IRF market basket.
(5) Professional Liability Insurance
We proposed to continue to use the CMS Hospital Professional
Liability Index to measure changes in PLI premiums. To generate this
index, we collect commercial insurance premiums for a fixed level of
coverage while holding non-price factors constant (such as a change in
the level of coverage). This is the same proxy used in the 2012-based
IRF market basket (80 FR 47060).
(6) Pharmaceuticals
We proposed to continue to use the PPI for Pharmaceuticals for
Human Use, Prescription (BLS series code WPUSI07003) to measure the
price growth of this cost category. This is the same proxy used in the
2012-based IRF market basket (80 FR 47060).
(7) Food: Direct Purchases
We proposed to continue to use the PPI for Processed Foods and
Feeds (BLS series code WPU02) to measure the price growth of this cost
category. This is the same proxy used in the 2012-based IRF market
basket (80 FR 47060).
(8) Food: Contract Purchases
We proposed to continue to use the CPI for Food Away From Home (BLS
series code CUUR0000SEFV) to measure the price growth of this cost
category. This is the same proxy used in the 2012-based IRF market
basket (80 FR 47060 through 47061).
(9) Chemicals
Similar to the 2012-based IRF market basket, we proposed to use a
four part blended PPI as the proxy for the chemical cost category in
the 2016-based IRF market basket. The proposed blend is composed of the
PPI for Industrial Gas Manufacturing, Primary Products (BLS series code
PCU325120325120P), the PPI for Other Basic Inorganic Chemical
Manufacturing (BLS series code PCU32518-32518-), the PPI for Other
Basic Organic Chemical Manufacturing (BLS series code PCU32519-32519-),
and the PPI for Other Miscellaneous Chemical Product Manufacturing (BLS
series code PCU325998325998). We note that the four part blended PPI
used in the 2012-based IRF market basket is composed of the PPI for
Industrial Gas Manufacturing (BLS series code
[[Page 39081]]
PCU325120325120P), the PPI for Other Basic Inorganic Chemical
Manufacturing (BLS series code PCU32518-32518-), the PPI for Other
Basic Organic Chemical Manufacturing (BLS series code PCU32519-32519-),
and the PPI for Soap and Cleaning Compound Manufacturing (BLS series
code PCU32561-32561-). For the 2016-based IRF market basket, we
proposed to derive the weights for the PPIs using the 2012 Benchmark I-
O data. The 2012-based IRF market basket used the 2007 Benchmark I-O
data to derive the weights for the four PPIs (80 FR 47061).
Table 8 shows the weights for each of the four PPIs used to create
the proposed blended Chemical proxy for the 2016 IRF market basket
compared to the 2012-based blended Chemical proxy.
[GRAPHIC] [TIFF OMITTED] TR08AU19.011
(10) Medical Instruments
We proposed to continue to use a blend of two PPIs for the Medical
Instruments cost category. The 2012 Benchmark Input-Output data shows
an approximate 57/43 split between Surgical and Medical Instruments and
Medical and Surgical Appliances and Supplies for this cost category.
Therefore, we proposed a blend composed of 57 percent of the commodity-
based PPI for Surgical and Medical Instruments (BLS series code
WPU1562) and 43 percent of the commodity-based PPI for Medical and
Surgical Appliances and Supplies (BLS series code WPU1563). The 2012-
based IRF market basket used a 50/50 blend of these PPIs based on the
2007 Benchmark I-O data (80 FR 47061).
(11) Rubber and Plastics
We proposed to continue to use the PPI for Rubber and Plastic
Products (BLS series code WPU07) to measure price growth of this cost
category. This is the same proxy used in the 2012-based IRF market
basket (80 FR 47061).
(12) Paper and Printing Products
We proposed to continue to use the PPI for Converted Paper and
Paperboard Products (BLS series code WPU0915) to measure the price
growth of this cost category. This is the same proxy used in the 2012-
based IRF market basket (80 FR 47061).
(13) Miscellaneous Products
We proposed to continue to use the PPI for Finished Goods Less Food
and Energy (BLS series code WPUFD4131) to measure the price growth of
this cost category. This is the same proxy used in the 2012-based IRF
market basket (80 FR 47061).
(14) Professional Fees: Labor-Related
We proposed to continue to use the ECI for Total Compensation for
Private Industry workers in Professional and Related (BLS series code
CIU2010000120000I) to measure the price growth of this category. This
is the same proxy used in the 2012-based IRF market basket (80 FR
47061).
(15) Administrative and Facilities Support Services
We proposed to continue to use the ECI for Total Compensation for
Private Industry workers in Office and Administrative Support (BLS
series code CIU2010000220000I) to measure the price growth of this
category. This is the same proxy used in the 2012-based IRF market
basket (80 FR 47061).
(16) Installation, Maintenance, and Repair
We proposed to continue to use the ECI for Total Compensation for
Civilian workers in Installation, Maintenance, and Repair (BLS series
code CIU1010000430000I) to measure the price growth of this cost
category. This is the same proxy used in the 2012-based IRF market
basket (80 FR 47061).
(17) All Other: Labor-Related Services
We proposed to continue to use the ECI for Total Compensation for
Private Industry workers in Service Occupations (BLS series code
CIU2010000300000I) to measure the price growth of this cost category.
This is the same proxy used in the 2012-based IRF market basket (80 FR
47061).
(18) Professional Fees: Nonlabor-Related
We proposed to continue to use the ECI for Total Compensation for
Private Industry workers in Professional and Related (BLS series code
CIU2010000120000I) to measure the price growth of this category. This
is the same proxy used in the 2012-based IRF market basket (80 FR
47061).
(19) Financial Services
We proposed to continue to use the ECI for Total Compensation for
Private Industry workers in Financial Activities (BLS series code
CIU201520A000000I) to measure the price growth of this cost category.
This is the same proxy used in the 2012-based IRF market basket (80 FR
47061).
(20) Telephone Services
We proposed to continue to use the CPI for Telephone Services (BLS
series code CUUR0000SEED) to measure the price growth of this cost
category. This is the same proxy used in the 2012-based IRF market
basket (80 FR 47061).
(21) All Other: Nonlabor-Related Services
We proposed to continue to use the CPI for All Items Less Food and
Energy (BLS series code CUUR0000SA0L1E) to measure the price growth of
this cost category. This is the same proxy used in the 2012-based IRF
market basket (80 FR 47061).
b. Price Proxies for the Capital Portion of the 2016-Based IRF Market
Basket
(1) Capital Price Proxies Prior to Vintage Weighting
We proposed to continue to use the same price proxies for the
capital-related cost categories in the 2016-based
[[Page 39082]]
IRF market basket as were used in the 2012-based IRF market basket (80
FR 47062), which are provided in Table 10 and described below.
Specifically, we proposed to proxy:
Depreciation: Building and Fixed Equipment cost category
by BEA's Chained Price Index for Nonresidential Construction for
Hospitals and Special Care Facilities (BEA Table 5.4.4. Price Indexes
for Private Fixed Investment in Structures by Type).
Depreciation: Movable Equipment cost category by the PPI
for Machinery and Equipment (BLS series code WPU11).
Nonprofit Interest cost category by the average yield on
domestic municipal bonds (Bond Buyer 20-bond index).
For-profit Interest cost category by the average yield on
Moody's Aaa bonds (Federal Reserve).
Other Capital-Related cost category by the CPI-U for Rent
of Primary Residence (BLS series code CUUS0000SEHA).
We believe these are the most appropriate proxies for IRF capital-
related costs that meet our selection criteria of relevance,
timeliness, availability, and reliability. We proposed to continue to
vintage weight the capital price proxies for Depreciation and Interest
to capture the long-term consumption of capital. This vintage weighting
method is similar to the method used for the 2012-based IRF market
basket (80 FR 47062) and is described below.
(2) Vintage Weights for Price Proxies
Because capital is acquired and paid for over time, capital-related
expenses in any given year are determined by both past and present
purchases of physical and financial capital. The vintage-weighted
capital-related portion of the 2016-based IRF market basket is intended
to capture the long-term consumption of capital, using vintage weights
for depreciation (physical capital) and interest (financial capital).
These vintage weights reflect the proportion of capital-related
purchases attributable to each year of the expected life of building
and fixed equipment, movable equipment, and interest. We proposed to
use vintage weights to compute vintage-weighted price changes
associated with depreciation and interest expenses.
Capital-related costs are inherently complicated and are determined
by complex capital-related purchasing decisions, over time, based on
such factors as interest rates and debt financing. In addition, capital
is depreciated over time instead of being consumed in the same period
it is purchased. By accounting for the vintage nature of capital, we
are able to provide an accurate and stable annual measure of price
changes. Annual non-vintage price changes for capital are unstable due
to the volatility of interest rate changes, and therefore, do not
reflect the actual annual price changes for IRF capital-related costs.
The capital-related component of the 2016-based IRF market basket
reflects the underlying stability of the capital-related acquisition
process.
The methodology used to calculate the vintage weights for the 2016-
based IRF market basket is the same as that used for the 2012-based IRF
market basket (80 FR 47062 through 47063) with the only difference
being the inclusion of more recent data. To calculate the vintage
weights for depreciation and interest expenses, we first need a time
series of capital-related purchases for building and fixed equipment
and movable equipment. We found no single source that provides an
appropriate time series of capital-related purchases by hospitals for
all of the above components of capital purchases. The early Medicare
cost reports did not have sufficient capital-related data to meet this
need. Data we obtained from the American Hospital Association (AHA) do
not include annual capital-related purchases. However, we are able to
obtain data on total expenses back to 1963 from the AHA. Consequently,
we proposed to use data from the AHA Panel Survey and the AHA Annual
Survey to obtain a time series of total expenses for hospitals. We then
proposed to use data from the AHA Panel Survey supplemented with the
ratio of depreciation to total hospital expenses obtained from the
Medicare cost reports to derive a trend of annual depreciation expenses
for 1963 through 2016. We proposed to separate these depreciation
expenses into annual amounts of building and fixed equipment
depreciation and movable equipment depreciation as determined earlier.
From these annual depreciation amounts, we derive annual end-of-year
book values for building and fixed equipment and movable equipment
using the expected life for each type of asset category. While data is
not available that is specific to IRFs, we believe this information for
all hospitals serves as a reasonable alternative for the pattern of
depreciation for IRFs.
To continue to calculate the vintage weights for depreciation and
interest expenses, we also need to account for the expected lives for
Building and Fixed Equipment, Movable Equipment, and Interest for the
2016-based IRF market basket. We proposed to calculate the expected
lives using MCR data from freestanding and hospital-based IRFs. The
expected life of any asset can be determined by dividing the value of
the asset (excluding fully depreciated assets) by its current year
depreciation amount. This calculation yields the estimated expected
life of an asset if the rates of depreciation were to continue at
current year levels, assuming straight-line depreciation. We proposed
to determine the expected life of building and fixed equipment
separately for hospital-based IRFs and freestanding IRFs, and then
weight these expected lives using the percent of total capital costs
each provider type represents. We proposed to apply a similar method
for movable equipment. Using these methods, we determined the average
expected life of building and fixed equipment to be equal to 22 years,
and the average expected life of movable equipment to be equal to 11
years. For the expected life of interest, we believe vintage weights
for interest should represent the average expected life of building and
fixed equipment because, based on previous research described in the FY
1997 IPPS final rule (61 FR 46198), the expected life of hospital debt
instruments and the expected life of buildings and fixed equipment are
similar. We note that for the 2012-based IRF market basket, the
expected life of building and fixed equipment is 23 years, and the
expected life of movable equipment is 11 years (80 FR 47062).
Multiplying these expected lives by the annual depreciation amounts
results in annual year-end asset costs for building and fixed equipment
and movable equipment. We then calculate a time series, beginning in
1964, of annual capital purchases by subtracting the previous year's
asset costs from the current year's asset costs.
For the building and fixed equipment and movable equipment vintage
weights, we proposed to use the real annual capital-related purchase
amounts for each asset type to capture the actual amount of the
physical acquisition, net of the effect of price inflation. These real
annual capital-related purchase amounts are produced by deflating the
nominal annual purchase amount by the associated price proxy as
provided earlier in this final rule. For the interest vintage weights,
we proposed to use the total nominal annual capital-related purchase
amounts to capture the value of the debt instrument (including, but not
limited to, mortgages and bonds). Using these capital-related purchase
time series specific to each asset type, we proposed to calculate the
vintage weights for
[[Page 39083]]
building and fixed equipment, for movable equipment, and for interest.
The vintage weights for each asset type are deemed to represent the
average purchase pattern of the asset over its expected life (in the
case of building and fixed equipment and interest, 22 years, and in the
case of movable equipment, 11 years). For each asset type, we used the
time series of annual capital-related purchase amounts available from
2016 back to 1964. These data allow us to derive 32, 22-year periods of
capital-related purchases for building and fixed equipment and
interest, and 43, 11-year periods of capital-related purchases for
movable equipment. For each 22-year period for building and fixed
equipment and interest, or 11-year period for movable equipment, we
calculate annual vintage weights by dividing the capital-related
purchase amount in any given year by the total amount of purchases over
the entire 22-year or 11-year period. This calculation is done for each
year in the 22-year or 11-year period and for each of the periods for
which we have data. We then calculate the average vintage weight for a
given year of the expected life by taking the average of these vintage
weights across the multiple periods of data.
We did not receive any specific public comments on our proposed
calculation of the vintage weights for the 2016-based IRF market
basket. Therefore, in this final rule, we are finalizing the vintage
weights as proposed. The vintage weights for the capital-related
portion of the 2016-based IRF market basket and the 2012-based IRF
market basket are presented in Table 9.
[GRAPHIC] [TIFF OMITTED] TR08AU19.012
The process of creating vintage-weighted price proxies requires
applying the vintage weights to the price proxy index where the last
applied vintage weight in Table 8 is applied to the most recent data
point. We have provided on the CMS website an example of how the
vintage weighting price proxies are calculated, using example vintage
weights and example price indices. The example can be found at http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/MedicareProgramRatesStats/MarketBasketResearch.html in the zip
file titled ``Weight Calculations as described in the IPPS FY 2010
Proposed Rule.''
c. Summary of Price Proxies of the 2016-Based IRF Market Basket
Table 10 shows both the operating and capital price proxies for the
2016-based IRF market basket.
BILLING CODE 4120-01-P
[[Page 39084]]
[GRAPHIC] [TIFF OMITTED] TR08AU19.013
[[Page 39085]]
BILLING CODE 4120-01-C
D. FY 2020 Market Basket Update and Productivity Adjustment
1. FY 2020 Market Basket Update
For FY 2020 (that is, beginning October 1, 2019 and ending
September 30, 2020), we proposed to use the 2016-based IRF market
basket increase factor described in section V.C. of the proposed rule
to update the IRF PPS base payment rate. Consistent with historical
practice, we proposed to estimate the market basket update for the IRF
PPS based on IHS Global Inc.'s (IGI's) forecast using the most recent
available data. IGI is a nationally-recognized economic and financial
forecasting firm with which we contract to forecast the components of
the market baskets and MFP. In the FY 2020 IRF PPS proposed rule (84 FR
17274), we proposed a market basket increase factor of 3.0 percent for
FY 2020, which was based on IGI's first quarter 2019 forecast with
historical data through fourth quarter 2018.
In the FY 2020 IRF PPS proposed rule, we also proposed that if more
recent data were subsequently available (for example, a more recent
estimate of the market basket and MFP adjustment), we would use such
data to determine the FY 2020 update in the final rule. Incorporating
more recent data, the projected 2016-based IRF market basket increase
factor for FY 2020 is 2.9 percent, which is based on IGI's second
quarter 2019 forecast with historical data through first quarter 2019.
We received several comments on our proposed market basket update
and productivity adjustment, which are summarized below.
Comment: Commenters supported the proposal to update the market
basket and MFP adjustment using the latest available data, and
encouraged CMS to update these factors using the latest available data
as part of the release of the FY 2020 IRF PPS final rule.
Response: We appreciate the commenters' support for updating the
market basket and MFP adjustments using the latest available data.
Comment: A few commenters expressed concern about the lack of
transparency of the market basket and MFP payment updates. The
commenters stated that the IGI forecast appears to be procured
specifically for the purpose of CMS updating the IRF market basket and
productivity adjustment. The commenters also noted that it is
concerning that CMS does not provide IGI's analyses or report to the
public given the key role the market basket and productivity adjustment
play in updating the payment system each year and that without such
information stakeholders are unable to evaluate the accuracy of the
update. The commenters also mentioned that the same comment was
submitted in the FY 2019 rulemaking process but they do not believe
that the response was adequate since the actual analysis or report used
to create the forecasts was not provided (83 FR 38525). The commenters
requested that CMS release an IGI report and analysis used to update
the IRF market basket and standard payment conversion factor.
Response: IGI regularly produces and publishes a wide variety of
forecasted series on a monthly or quarterly basis. These forecasts are
derived using a framework of proprietary economic models that are
created and updated regularly by IGI. IGI provides these forecasts to a
wide array of clients in addition to CMS. We use a contractor for the
price forecasts so that the forecasts are independent and reflect a
complete economic forecasting model, a capability that we do not have.
IGI has received multiple awards for their macroeconomic forecast
accuracy of major economic indicators. We use IGI's price forecasts in
all of the FFS market baskets used for payment updates and has used the
forecasts produced by this company for many years.
We select approximately 30 individual price proxies as inputs to
the IRF market basket calculation. The price series are discussed in
detail as part of the rulemaking process. In order to derive a forecast
of the IRF market basket index, we contract with IGI to procure the
forecasts of these individual price proxies on a quarterly basis. We
then combine these price proxies with the market basket base year cost
weights to derive the levels of the IRF market basket. The data sources
and methods used to derive these cost weights are discussed in detail
as part of the rulemaking process.
As provided in our previous response to this comment in the FY 2019
IRF PPS final rule (83 FR 38525), the market basket update is derived
using: (1) The market basket base year cost weights as finalized by CMS
through rulemaking; and (2) the most up-to-date forecast of the price
proxies used in the market basket as forecasted by IGI. Specifically,
for each cost category in the market basket (for example, Wages and
Salaries, Pharmaceuticals), the level of each of these price proxies
are multiplied by the cost weight for that cost category. The sum of
these products (that is, weights multiplied by proxied index levels)
for all cost categories yields the composite index level in the market
basket in a given year.
As acknowledged by the commenters, we provided a link from the CMS
website to the top-line market basket updates. We also indicated that
more detailed forecasts of the IRF market basket calculations are
readily available by request by sending an email to [email protected]
to request this information (83 FR 38525). Using these detailed data,
the commenter would be able to replicate the levels of the IRF market
basket update in the history and the forecast period. We encourage
stakeholders to utilize these data, which we believe will address the
commenters' concerns.
Incorporating more recent data, the projected 2016-based IRF market
basket update for FY 2020 is 2.9 percent. After careful consideration
of the comments, consistent with our historical practice of estimating
market basket increases based on the best available data, we are
finalizing a market basket increase factor of 2.9 percent for FY 2020.
For comparison, the current 2012-based IRF market basket is also
projected to increase by 2.9 percent in FY 2020 based on IGI's second
quarter 2019 forecast.
Table 11 compares the 2016-based IRF market basket and the 2012-
based IRF market basket percent changes.
[[Page 39086]]
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2. Productivity Adjustment
According to section 1886(j)(3)(C)(i) of the Act, the Secretary
shall establish an increase factor based on an appropriate percentage
increase in a market basket of goods and services. As described in
sections VI.C and VI.D.1. of this final rule, we are finalizing an
estimate of the IRF PPS increase factor for FY 2020 based on the 2016-
based IRF market basket. Section 1886(j)(3)(C)(ii) of the Act then
requires that, after establishing the increase factor for a FY, the
Secretary shall reduce such increase factor for FY 2012 and each
subsequent FY, by the productivity adjustment described in section
1886(b)(3)(B)(xi)(II) of the Act. Section 1886(b)(3)(B)(xi)(II) of the
Act sets forth the definition of this productivity adjustment. The
statute defines the productivity adjustment to be equal to the 10-year
moving average of changes in annual economy-wide private nonfarm
business MFP (as projected by the Secretary for the 10-year period
ending with the applicable FY, year, cost reporting period, or other
annual period) (the ``MFP adjustment''). The BLS publishes the official
measure of private nonfarm business MFP. Please see http://www.bls.gov/mfp for the BLS historical published MFP data.
MFP is derived by subtracting the contribution of labor and capital
input growth from output growth. The projections of the components of
MFP are currently produced by IGI, a nationally recognized economic
forecasting firm with which CMS contracts to forecast the components of
the market basket and MFP. For more information on the productivity
adjustment, we refer reader to the discussion in the FY 2016 IRF PPS
final rule (80 FR 47065).
Using IGI's first quarter 2019 forecast, the proposed MFP
adjustment for FY 2020 (the 10-year moving average of MFP for the
period ending FY 2020) was 0.5 percent (84 FR 17274). Thus, in
accordance with section 1886(j)(3)(C) of the Act, we proposed to base
the FY 2020 market basket update, which is used to determine the
applicable percentage increase for the IRF payments, on the most recent
estimate of the 2016-based IRF market basket. We proposed to then
reduce this percentage increase by the current estimate of the proposed
MFP adjustment for FY 2020 of 0.5 percentage point (the 10-year moving
average of MFP for the period ending FY 2020 based on IGI's first
quarter 2019 forecast). Therefore, the proposed FY 2020 IRF update was
2.5 percent (3.0 percent market basket update, less 0.5 percentage
point MFP adjustment). Furthermore, we proposed that if more recent
data are subsequently available (for example, a more recent estimate of
the market basket and MFP adjustment), we would use such data to
determine the FY 2020 market basket update and MFP adjustment in the
final rule.
We received a few comments on the application of the productivity
adjustment, which are summarized below.
Comment: Commenters continue to be concerned about the application
of the productivity adjustment to IRFs. One of the commenters stated
that they understood CMS is bound by statute to reduce the market
basket update by a productivity adjustment factor in accordance with
the PPACA, but they believe that IRFs are unable to generate additional
productivity gains at a pace matching the productivity of the economy
at large on an ongoing, consistent basis. The commenter noted that the
services provided in IRFs are labor-intensive and the services do not
lend themselves to continuous productivity improvements. The commenter
also noted that IRFs are bound by unchanging labor-intensive standards
such as the 3-hour therapy rule and other regulatory requirements that
reduce flexibility and restrict the pursuit of certain efficiencies.
The commenter noted that continued application of a productivity
adjustment to payments could results in decreased beneficiary access to
IRF services. The commenter requested that CMS continue to monitor the
impact that the multi-factor productivity adjustments have on the IRF
sector, provide feedback to Congress as appropriate, and reduce the
productivity adjustment. One commenter requested that, in addition to
monitoring its effects on overall payments, CMS should evaluate whether
IRFs are able to achieve the same level of productivity improvement as
workers across the U.S. economy.
Response: We acknowledge the commenters' concerns regarding
productivity growth at the economy-wide level and its application to
IRFs. As the commenter acknowledges, section 1886(j)(3)(C)(ii)(I) of
the Act requires the application of a productivity adjustment to the
IRF PPS market basket increase factor.
We will continue to monitor the impact of the payment updates,
including the effects of the productivity
[[Page 39087]]
adjustment, on IRF finances, as well as beneficiary access to care.
We note that each year, MedPAC makes an annual update
recommendation to Congress based on a variety of measures related to
payment adequacy, including a detailed margin analysis and analysis of
beneficiary access to care for IRF services. For FY 2020, MedPAC
recommended that Congress reduce the IRF PPS base rate by 5 percent and
found that beneficiary access to care was not a concern. The ``March
2019 Report to the Congress: Medicare Payment Policy'', chapter 10 is
publicly available at http://www.medpac.gov/-documents-/reports.
We would be very interested in better understanding IRF-specific
productivity; however, the data elements required to estimate IRF
specific multi-factor productivity are not produced at the level of
detail that would allow this analysis. We have estimated hospital-
sector multi-factor productivity and have published the findings on the
CMS website at https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/ReportsTrustFunds/Downloads/ProductivityMemo2016.pdf.
After careful consideration of comments, we are incorporating more
recent data to determine the market basket update and MFP adjustment
for FY 2020. Using IGI's second quarter 2019 forecast, the current
estimate of the MFP adjustment for FY 2020 (the 10-year moving average
of MFP for the period ending FY 2020) is 0.4 percent. Thus, in
accordance with section 1886(j)(3)(C) of the Act, we are finalizing a
FY 2020 market basket update of 2.9 percent. We then reduce this
percentage increase by the most recent estimate of the MFP adjustment
for FY 2020 of 0.4 percentage point (the 10-year moving average of MFP
for the period ending FY 2020 based on IGI's second quarter 2019
forecast). Therefore, the final FY 2020 IRF productivity-adjusted
market basket update is equal to 2.5 percent (2.9 percent market basket
update, less 0.4 percentage point MFP adjustment).
For FY 2020, the Medicare Payment Advisory Commission (MedPAC)
recommends that a decrease of 5 percent be applied to IRF PPS payment
rates. As discussed, and in accordance with section 1886(j)(3)(C) of
the Act, we are finalizing an update to IRF PPS payment rates for FY
2020 by a productivity-adjusted market basket increase factor of 2.5
percent, as section 1886(j)(3)(C) of the Act does not provide the
Secretary with the authority to apply a different update factor to IRF
PPS payment rates for FY 2020.
Comment: One commenter (MedPAC) stated that they understand that
CMS is required to implement the statutory update of market basket less
productivity adjustment, but that their analysis of beneficiary access
to rehabilitative services, the supply of providers, and aggregate IRF
Medicare margins, which have been above 11 percent since 2012,
indicates that the Congress should reduce the IRF payment rate by 5
percent for FY 2020.
Response: We appreciate MedPAC's interest in the IRF increase
factor. However, we are required to update IRF PPS payments by the
market basket reduced by the productivity adjustment, as directed by
section 1886(j)(3)(C) of the Act.
E. Labor-Related Share for FY 2020
Section 1886(j)(6) of the Act specifies that the Secretary is to
adjust the proportion (as estimated by the Secretary from time to time)
of rehabilitation facilities' costs which are attributable to wages and
wage-related costs, of the prospective payment rates computed under
section 1886(j)(3) of the Act for area differences in wage levels by a
factor (established by the Secretary) reflecting the relative hospital
wage level in the geographic area of the rehabilitation facility
compared to the national average wage level for such facilities. The
labor-related share is determined by identifying the national average
proportion of total costs that are related to, influenced by, or vary
with the local labor market. We proposed to continue to classify a cost
category as labor-related if the costs are labor-intensive and vary
with the local labor market. As stated in the FY 2016 IRF PPS final
rule (80 FR 47068), the labor-related share was defined as the sum of
the relative importance of Wages and Salaries, Employee Benefits,
Professional Fees: Labor-related Services, Administrative and
Facilities Support Services, Installation, Maintenance, and Repair, All
Other: Labor-related Services, and a portion of the Capital Costs from
the 2012-based IRF market basket.
Based on our definition of the labor-related share and the cost
categories in the 2016-based IRF market basket, we proposed to include
in the labor-related share for FY 2020 the sum of the FY 2020 relative
importance of Wages and Salaries, Employee Benefits, Professional Fees:
Labor-related, Administrative and Facilities Support Services,
Installation, Maintenance, and Repair, All Other: Labor-related
Services, and a portion of the Capital-Related cost weight from the
2016-based IRF market basket.
Similar to the 2012-based IRF market basket (80 FR 47067), the
2016-based IRF market basket includes two cost categories for
nonmedical Professional Fees (including, but not limited to, expenses
for legal, accounting, and engineering services). These are
Professional Fees: Labor-related and Professional Fees: Nonlabor-
related. For the 2016-based IRF market basket, we proposed to estimate
the labor-related percentage of non-medical professional fees (and
assign these expenses to the Professional Fees: Labor-related services
cost category) based on the same method that was used to determine the
labor-related percentage of professional fees in the 2012-based IRF
market basket.
As was done in the 2012-based IRF market basket (80 FR 47067), we
proposed to determine the proportion of legal, accounting and auditing,
engineering, and management consulting services that meet our
definition of labor-related services based on a survey of hospitals
conducted by us in 2008, a discussion of which can be found in the FY
2010 IPPS/LTCH PPS final rule (74 FR 43850 through 43856). Based on the
weighted results of the survey, we determined that hospitals purchase,
on average, the following portions of contracted professional services
outside of their local labor market:
34 percent of accounting and auditing services.
30 percent of engineering services.
33 percent of legal services.
42 percent of management consulting services.
We proposed to apply each of these percentages to the respective
Benchmark I-O cost category underlying the professional fees cost
category to determine the Professional Fees: Nonlabor-related costs.
The Professional Fees: Labor-related costs were determined to be the
difference between the total costs for each Benchmark I-O category and
the Professional Fees: Nonlabor-related costs. This is the same
methodology that we used to separate the 2012-based IRF market basket
professional fees category into Professional Fees: Labor-related and
Professional Fees: Nonlabor-related cost categories (80 FR 47067).
In the 2016-based IRF market basket, nonmedical professional fees
that are subject to allocation based on these survey results represent
4.4 percent of total costs (and are limited to those fees related to
Accounting & Auditing, Legal, Engineering, and Management Consulting
services). Based on our survey results, we proposed to apportion 2.8
percentage points of the
[[Page 39088]]
4.4 percentage point figure into the Professional Fees: Labor-related
share cost category and designate the remaining 1.6 percentage point
into the Professional Fees: Nonlabor-related cost category.
In addition to the professional services listed, for the 2016-based
IRF market basket, we proposed to allocate a proportion of the Home
Office Contract Labor cost weight, calculated using the Medicare cost
reports as stated above, into the Professional Fees: Labor-related and
Professional Fees: Nonlabor-related cost categories. We proposed to
classify these expenses as labor-related and nonlabor-related as many
facilities are not located in the same geographic area as their home
office, and therefore, do not meet our definition for the labor-related
share that requires the services to be purchased in the local labor
market. For the 2012-based IRF market basket, we used the BEA I-O
expense data for NAICS 55, Management of Companies and Enterprises, to
estimate the Home Office Contract Labor cost weight (80 FR 47067). We
then allocated these expenses into the Professional Fess: Labor-related
and Professional Fees: Nonlabor-related cost categories.
Similar to the 2012-based IRF market basket, we proposed for the
2016-based IRF market basket to use the Medicare cost reports for both
freestanding IRF providers and hospital-based IRF providers to
determine the home office labor-related percentages. The MCR requires a
hospital to report information regarding their home office provider.
For the 2016-based IRF market basket, we proposed to start with the
sample of IRF providers that passed the top 1 percent trim used to
derive the Home Office Contract Labor cost weight as described in
section VI.B. of this final rule. For both freestanding and hospital-
based providers, we proposed to multiply each provider's Home Office
Contract Labor cost weight (calculated using data from the total
facility) by Medicare allowable total costs. This results in an amount
of Medicare allowable home office compensation costs for each IRF.
Using information on the Medicare cost report, we then compare the
location of the IRF with the location of the IRF's home office. We
proposed to classify an IRF with a home office located in their
respective local labor market if the IRF and its home office are
located in the same Metropolitan Statistical Area. We then calculate
the proportion of Medicare allowable home office compensation costs
that these IRFs represent of total Medicare allowable home office
compensation costs. We proposed to multiply this percentage (42
percent) by the Home Office Contract Labor cost weight (3.7 percent) to
determine the proportion of costs that should be allocated to the
labor-related share. Therefore, we allocated 1.6 percentage points of
the Home Office Contract Labor cost weight (3.7 percent times 42
percent) to the Professional Fees: Labor-related cost weight and 2.1
percentage points of the Home Office Contract Labor cost weight to the
Professional Fees: Nonlabor-related cost weight (3.7 percent times 58
percent). For the 2012-based IRF market basket, we used a similar
methodology but we relied on provider counts rather than home office/
related organization contract labor compensation costs to determine the
labor-related percentage (80 FR 47067).
In summary, we apportioned 2.8 percentage points of the non-medical
professional fees and 1.6 percentage points of the home office/related
organization contract labor cost weights into the Professional Fees:
Labor-related cost category. This amount was added to the portion of
professional fees that was identified to be labor-related using the I-O
data such as contracted advertising and marketing costs (approximately
0.6 percentage point of total costs) resulting in a Professional Fees:
Labor-related cost weight of 5.0 percent.
We received several comments on the proposed labor-related share,
which are summarized below.
Comment: A few commenters noted that the cost weight for Home
Office Contract Labor costs is 3.7 percent of all IRFs' costs and
influences changes in other payment areas, such as the total labor-
related share. The commenters stated that they believe the proposed
changes to the methodology are responsible, at least in large part, to
the notable proposed increase of approximately 2 percent of the labor-
related share. Some of the commenters also stated that the increase in
the labor-related share will adversely impact rural IRFs and IRFs with
a wage index below 1.0.
Response: The labor-related share for IRFs is derived from the
relative importance of the labor-related cost categories. The relative
importance for FY 2020 reflects the different rates of price change for
each of the individual cost categories between the base year and FY
2020. For the FY 2020 final rule, as proposed, the final labor-related
share for FY 2020 is based on a more recent forecast of the 2016-based
IRF market basket. Using the more recent forecast, the total difference
between the FY 2020 labor-related share using the 2016-based IRF market
basket and 2012-based IRF market basket is 2.0 percentage points (72.7
percent using 2016-based IRF market basket and 70.7 percent using 2012-
based IRF market basket). This difference can be separated into two
primary components: (1) Revision to the base year cost weights (1.4
percentage points); and (2) revision to starting point of calculation
of relative importance (base year) from 2012 to 2016 (0.6 percentage
point). Of the 1.4-percentage points difference in the base year cost
weights, just 0.2 percentage point is attributable to deriving the Home
Office Contract Labor cost weight using the MCR data rather than the I-
O data; the remainder is due to the increase in Compensation and
Capital cost weights (calculated using the MCR data) and the
incorporation of the 2012 Benchmark I-O data.
The impact of using the MCR data to calculate the Home Office
Contract Labor cost weight is minimal because it also lowers the
residual ``All Other'' cost weight from 25.8 percent (using the I-O
data to calculate the Home Office Contract Labor cost weight) to 22.2
percent (using the MCR data to calculate the Home Office Contract labor
cost weight). The lower residual ``All Other'' cost weight then leads
to relatively lower cost weights for Administrative and Business
Support Services, Installation, Maintenance and Repair Services, and
All Other: Labor-related Services (which are calculated using the
Benchmark I-O data), each of which is also reflected in the labor-
related share.
After careful consideration of comments, in this final rule, we are
finalizing the 2016-based IRF market basket labor-related share cost
weights as proposed.
As stated previously, we proposed to include in the labor-related
share the sum of the relative importance of Wages and Salaries,
Employee Benefits, Professional Fees: Labor-Related, Administrative and
Facilities Support Services, Installation, Maintenance, and Repair, All
Other: Labor-related Services, and a portion of the Capital-Related
cost weight from the 2016-based IRF market basket. The relative
importance reflects the different rates of price change for these cost
categories between the base year (2016) and FY 2020. Based on IGI's 2nd
quarter 2019 forecast for the 2016-based IRF market basket, the sum of
the FY 2020 relative importance for Wages and Salaries, Employee
Benefits, Professional Fees: Labor-related, Administrative and
Facilities Support Services, Installation Maintenance & Repair
Services, and All Other: Labor-related Services is 68.7 percent. The
portion of Capital costs that are influenced by the local labor market
is estimated to be 46 percent, which is the same percentage applied to
[[Page 39089]]
the 2012-based IRF market basket (80 FR 47068). Since the relative
importance for Capital is 8.6 percent of the 2016-based IRF market
basket in FY 2020, we took 46 percent of 8.6 percent to determine the
labor-related share of Capital for FY 2020 of 4.0 percent. Therefore,
we are finalizing a total labor-related share for FY 2020 of 72.7
percent (the sum of 68.7 percent for the operating costs and 4.0
percent for the labor-related share of Capital).
Table 12 shows the FY 2020 labor-related share using the final
2016-based IRF market basket relative importance and the FY 2019 labor-
related share which was based on the 2012-based IRF market basket
relative importance.
[GRAPHIC] [TIFF OMITTED] TR08AU19.015
F. Update to the IRF Wage Index To Use Concurrent IPPS Wage Index
Beginning With FY 2020
1. Background
Section 1886(j)(6) of the Act requires the Secretary to adjust the
proportion of rehabilitation facilities' costs attributable to wages
and wage-related costs (as estimated by the Secretary from time to
time) by a factor (established by the Secretary) reflecting the
relative hospital wage level in the geographic area of the
rehabilitation facility compared to the national average wage level for
those facilities. The Secretary is required to update the IRF PPS wage
index on the basis of information available to the Secretary on the
wages and wage-related costs to furnish rehabilitation services. Any
adjustment or updates made under section 1886(j)(6) of the Act for a FY
are made in a budget-neutral manner.
2. Update to the IRF Wage Index To Use Concurrent IPPS Wage Index
Beginning with FY 2020
When the IRF PPS was implemented in the FY 2002 IRF PPS final rule
(66 FR 41358), we finalized the use of the FY IPPS wage data in the
creation of an IRF wage index. We believed that a wage index based on
FY IPPS wage data was the best proxy and most appropriate wage index to
use in adjusting payments to IRFs, since both IPPS hospitals and IRFs
compete in the same labor markets. For this reason, we believed, and
continue to believe, that the wage data of IPPS hospitals accurately
captures the relationship of wages and wage-related costs of IRFs in an
area as compared with the national average. Therefore, in the FY 2002
IRF PPS final rule, we finalized use of the FY 1997 IPPS wage data to
develop the wage index for the IRF PPS, as that was the most recent
final data available.
For all subsequent years in which the IRF PPS wage index has been
updated, we have continued to use the most recent final IPPS data
available, which has led us to use the pre-floor, pre-reclassified FY
IPPS wage index values from the prior fiscal year.
In the FY 2018 IRF PPS proposed rule (82 FR 20742 through 20743),
we included a request for information (RFI) to solicit comments from
stakeholders requesting information on CMS flexibilities and
efficiencies. The purpose of the RFI was to receive feedback regarding
ways in which we could reduce burden for hospitals and physicians,
improve quality of care, decrease costs and ensure that patients
receive the best care. We received comments from IRF industry
associations, state and national hospital associations, industry
groups, representing hospitals, and individual IRF providers in
response to the solicitation. One of the responses we received to the
RFI suggested that there is concern among IRF stakeholders about the
different wage index data used in the different post-acute care (PAC)
settings. For the IRF PPS, we use a 1-year lag of the pre-floor, pre-
reclassified FY IPPS wage index, meaning that for the IRF PPS for FY
2019, we finalized use of the FY 2018 IPPS wage index (83 FR 38527).
However, we base the wage indexes for the SNF PPS and the LTCH PPS on
the concurrent IPPS wage index ((83 FR 39172 through 39178) and (83 FR
41731), respectively).
As we look towards a more unified PACpayment system, we believe
that standardizing the wage index data across PAC settings is
necessary. Therefore, we proposed to change the IRF wage index
methodology to align with other PAC settings. Specifically, we proposed
changing from our established policy of using the pre-floor, pre-
reclassified FY IPPS wage index (that is, for FY 2020 we proposed to
use the concurrent FY 2020 pre-floor, pre-reclassified IPPS wage index
under the IRF PPS). This proposed change would use the concurrent IPPS
pre-floor, pre-reclassified wage index for the IRF wage index beginning
with FY 2020 and continuing for all subsequent years. Thus, for the FY
2020 IRF wage index, we proposed to use the FY 2020 pre-floor, pre-
reclassified IPPS wage index, which is based on data submitted for
hospital cost reporting periods beginning in FY 2016. We proposed to
implement these revisions in a budget neutral manner. For more
information
[[Page 39090]]
on the distributional impacts of this proposal, we refer readers to the
FY 2020 IRF PPS proposed rule (84 FR 17278).
Using the current pre-floor, pre-reclassified FY IPPS wage index
would result in the most up-to-date wage data being the basis for the
IRF wage index. It would also result in more consistency and equity in
the wage index methodology used by Medicare.
We received 7 comments on this proposal to align the data
timeframes with that of the IPPS by using the FY 2020 pre-floor, pre-
reclassified FY IPPS wage index as the basis for the FY 2020 IRF wage
index, which are summarized below.
Comment: All of the commenters supported CMS' proposal to use the
FY 2020 pre-floor, pre-reclassified FY IPPS wage index for the FY 2020
IRF wage index. Commenters agreed that the proposed change to use the
concurrent FY IPPS wage index data would align the wage index data
across PAC settings and move in the direction of unified PAC payment. A
few commenters recommended that CMS adopt other wage index policies for
IRFs that apply to or have been proposed for IPPS hospitals, such as
geographic reclassifications, suggesting that this would increase
consistency and alignment across settings.
Response: We appreciate the commenter's support for the proposal.
We agree that finalizing this proposal is necessary as we move towards
a more unified PAC payment system. We plan to monitor the use of the
concurrent FY IPPS wage index data before we consider any other
potential wage index policy changes.
After careful consideration of the comments we received, we are
finalizing our proposal to align the data timeframes with that of the
IPPS by using the concurrent pre-floor, pre-reclassified IPPS wage
index for the IRF wage index beginning with FY 2020 and continuing for
all subsequent years. Thus, we will use the FY 2020 pre-floor, pre-
reclassified IPPS wage index as the basis for the FY 2020 IRF wage
index (that is, for all IRF discharges beginning on or after October 1,
2019). We will implement these revisions in a budget neutral manner. We
refer readers to Table 20 in section XIII.C of this final rule for more
information on the distributional effects of this change.
3. Wage Adjustment for FY 2020 Using Concurrent IPPS Wage Index Labor
Market Area Definitions and the
Due to our proposal to use the concurrent IPPS wage index beginning
with FY 2020, for FY 2020, we proposed using the policy and
methodologies described in section VI. of this final rule related to
the labor market area definitions and the wage index methodology for
areas with wage data. Thus, we proposed using the CBSA labor market
area definitions and the FY 2020 pre-reclassification and pre-floor
IPPS wage index data. In accordance with section 1886(d)(3)(E) of the
Act, the FY 2020 pre-reclassification and pre-floor IPPS wage index is
based on data submitted for hospital cost reporting periods beginning
on or after October 1, 2015 and before October 1, 2016 (that is, FY
2016 cost report data).
The labor market designations made by the OMB include some
geographic areas where there are no hospitals and, thus, no hospital
wage index data on which to base the calculation of the IRF PPS wage
index. We proposed to continue to use the same methodology discussed in
the FY 2008 IRF PPS final rule (72 FR 44299) to address those
geographic areas where there are no hospitals and, thus, no hospital
wage index data on which to base the calculation for the FY 2020 IRF
PPS wage index.
We received one comment on this proposal, which is summarized
below.
Comment: One commenter requested that, until a new wage index
system is implemented, CMS should establish a smoothing variable to be
applied to the current IRF wage index to reduce the fluctuations IRFs
experience annually.
Response: Under section 1886(j)(6) of the Act, we adjust IRF PPS
rates to account for differences in area wage levels. Any perceived
volatility in the wage index is predicated upon volatility in actual
wages in that area and reflects real differences in area wage levels.
As we believe that the application of a smoothing variable would make
the wage index values less reflective of the area wage levels, we do
not believe it would be appropriate to implement such a change to the
IRF wage index policy.
After careful consideration of the comments we received, we are
finalizing our proposal to use the policy and methodologies described
in section VI. of this final rule related to the labor market area
definitions and the wage index methodology for areas with wage data.
Thus, we are finalizing the use of the CBSA labor market area
definitions and the FY 2020 pre-reclassification and pre-floor IPPS
wage index data. We are finalizing the continued use of the same
methodology discussed in the FY 2008 IRF PPS final rule (72 FR 44299)
to address those geographic areas where there are no hospitals and,
thus, no hospital wage index data on which to base the calculation for
the FY 2020 IRF PPS wage index.
4. Core-Based Statistical Areas (CBSAs) for the FY 2020 IRF Wage Index
The wage index used for the IRF PPS is calculated using the pre-
reclassification and pre-floor IPPS wage index data and is assigned to
the IRF on the basis of the labor market area in which the IRF is
geographically located. IRF labor market areas are delineated based on
the CBSAs established by the OMB. The current CBSA delineations (which
were implemented for the IRF PPS beginning with FY 2016) are based on
revised OMB delineations issued on February 28, 2013, in OMB Bulletin
No. 13-01. OMB Bulletin No. 13-01 established revised delineations for
Metropolitan Statistical Areas, Micropolitan Statistical Areas, and
Combined Statistical Areas in the United States and Puerto Rico based
on the 2010 Census, and provided guidance on the use of the
delineations of these statistical areas using standards published in
the June 28, 2010 Federal Register (75 FR 37246 through 37252). We
refer readers to the FY 2016 IRF PPS final rule (80 FR 47068 through
47076) for a full discussion of our implementation of the OMB labor
market area delineations beginning with the FY 2016 wage index.
Generally, OMB issues major revisions to statistical areas every 10
years, based on the results of the decennial census. However, OMB
occasionally issues minor updates and revisions to statistical areas in
the years between the decennial censuses. On July 15, 2015, OMB issued
OMB Bulletin No. 15-01, which provides minor updates to and supersedes
OMB Bulletin No. 13-01 that was issued on February 28, 2013. The
attachment to OMB Bulletin No. 15-01 provides detailed information on
the update to statistical areas since February 28, 2013. The updates
provided in OMB Bulletin No. 15-01 are based on the application of the
2010 Standards for Delineating Metropolitan and Micropolitan
Statistical Areas to Census Bureau population estimates for July 1,
2012 and July 1, 2013.
In the FY 2018 IRF PPS final rule (82 FR 36250 through 36251), we
adopted the updates set forth in OMB Bulletin No. 15-01 effective
October 1, 2017, beginning with the FY 2018 IRF wage index. For a
complete discussion of the adoption of the updates set forth in OMB
Bulletin No. 15-01, we refer readers to the FY 2018 IRF PPS final rule.
In the FY 2019 IRF PPS final rule (83 FR 38527), we continued to use
the OMB delineations that were adopted
[[Page 39091]]
beginning with FY 2016 to calculate the area wage indexes, with updates
set forth in OMB Bulletin No. 15-01 that we adopted beginning with the
FY 2018 wage index.
On August 15, 2017, OMB issued OMB Bulletin No. 17-01, which
provided updates to and superseded OMB Bulletin No. 15-01 that was
issued on July 15, 2015. The attachments to OMB Bulletin No. 17-01
provide detailed information on the update to statistical areas since
July 15, 2015, and are based on the application of the 2010 Standards
for Delineating Metropolitan and Micropolitan Statistical Areas to
Census Bureau population estimates for July 1, 2014 and July 1, 2015.
In OMB Bulletin No. 17-01, OMB announced that one Micropolitan
Statistical Area now qualifies as a Metropolitan Statistical Area. The
new urban CBSA is as follows:
Twin Falls, Idaho (CBSA 46300). This CBSA is comprised of
the principal city of Twin Falls, Idaho in Jerome County, Idaho and
Twin Falls County, Idaho.
The OMB bulletin is available on the OMB website at https://www.whitehouse.gov/sites/whitehouse.gov/files/omb/bulletins/2017/b-17-01.pdf.
As we indicated in the FY 2019 IRF PPS final rule (83 FR 38528), we
believe that it is important for the IRF PPS to use the latest labor
market area delineations available as soon as is reasonably possible to
maintain a more accurate and up-to-date payment system that reflects
the reality of population shifts and labor market conditions. As
discussed in the FY 2019 IPPS and LTCH PPS final rule (83 FR 20591),
these updated labor market area definitions were implemented under the
IPPS beginning on October 1, 2018. Therefore, we proposed to implement
these revisions for the IRF PPS beginning October 1, 2019, consistent
with our historical practice of modeling IRF PPS adoption of the labor
market area delineations after IPPS adoption of these delineations.
We received 2 comments on this proposal, which are summarized
below.
Comment: Commenters expressed concern that the IRF wage index
values published in the FY 2020 IRF PPS proposed rule were not
consistent with the values published in the FY 2020 IPPS proposed rule
wage index public use file. These commenters suggested that CMS examine
these wage index values and correct them if we find that they are in
error prior to finalizing the use of the concurrent IPPS wage index
data for the IRF PPS.
Response: We identified a slight error in the proposed rule wage
index values after the FY 2020 IRF PPS proposed rule was published. A
programming error caused the data for all providers in a single county
to be included twice, which affected the national average hourly rate,
and therefore, affected nearly all wage index values. We have corrected
the programming logic so this error cannot occur again. We also
standardized our procedures for rounding, to ensure consistency. The
correction to the proposed rule wage index data was not completed until
after the comment period closed on June 17, 2019. This final rule
reflects the corrected and updated wage index data.
We are finalizing and implementing these revisions for the IRF PPS
beginning October 1, 2019, consistent with our historical practice of
modeling IRF PPS adoption of the labor market area delineations after
IPPS adoption of these delineations.
5. Wage Adjustment
The FY 2020 wage index tables (which, as discussed in section VI.F
above, we base on the FY 2020 pre-reclassified, pre-floor FY 2020 IPPS
wage index) are available on the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html. Table A is for urban areas, and Table B
is for rural areas.
To calculate the wage-adjusted facility payment for the payment
rates set forth in this final rule, we would multiply the unadjusted
federal payment rate for IRFs by the FY 2020 labor-related share based
on the 2016-based IRF market basket (72.7 percent) to determine the
labor-related portion of the standard payment amount. A full discussion
of the calculation of the labor-related share is located in section
VI.E of this final rule. We would then multiply the labor-related
portion by the applicable IRF wage index from the tables in the
addendum to this final rule. These tables are available on the CMS
website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html.
Adjustments or updates to the IRF wage index made under section
1886(j)(6) of the Act must be made in a budget-neutral manner. We
proposed to calculate a budget-neutral wage adjustment factor as
established in the FY 2004 IRF PPS final rule (68 FR 45689), codified
at Sec. 412.624(e)(1), as described in the steps below. We proposed to
use the listed steps to ensure that the FY 2020 IRF standard payment
conversion factor reflects the updates to the IRF wage index (based on
the FY 2020 IPPS wage index) and the labor-related share in a budget-
neutral manner:
Step 1. Determine the total amount of the estimated FY 2019 IRF PPS
payments, using the FY 2019 standard payment conversion factor and the
labor-related share and the wage indexes from FY 2019 (as published in
the FY 2019 IRF PPS final rule (83 FR 38514)).
Step 2. Calculate the total amount of estimated IRF PPS payments
using the FY 2020 standard payment conversion factor and the FY 2020
labor-related share and CBSA urban and rural wage indexes.
Step 3. Divide the amount calculated in step 1 by the amount
calculated in step 2. The resulting quotient is the FY 2020 budget-
neutral wage adjustment factor of 1.0076.
Step 4. Apply the FY 2020 budget-neutral wage adjustment factor
from step 3 to the FY 2020 IRF PPS standard payment conversion factor
after the application of the increase factor to determine the FY 2020
standard payment conversion factor.
We note that we have updated our data between the FY 2020 IRF PPS
proposed and final rules to ensure that we use the most recent
available data in calculating IRF PPS payments. This updated data
includes a more complete set of claims for FY 2018 and updated wage
index data. Based on our analysis using this updated data, we now
estimate a budget-neutral wage adjustment factor of 1.0031 for FY 2020.
We discuss the calculation of the standard payment conversion
factor for FY 2020 in section VI.H. of this final rule.
We invited public comments on this proposal. However, we did not
receive any comments on the proposed methodology for calculating the
budget-neutral wage adjustment factor.
As we did not receive any comments on the proposed methodology for
calculating the budget-neutral wage adjustment factor, we are
finalizing this policy as proposed for FY 2020.
G. Wage Index Comment Solicitation
Historically, we have calculated the IRF wage index values using
unadjusted wage index values from another provider setting.
Stakeholders have frequently commented on certain aspects of the IRF
wage index values and their impact on payments. Therefore, we solicited
public comments in the FY 2020 IRF PPS proposed rule (84 FR 17280) on
concerns stakeholders may have regarding the wage index used to adjust
[[Page 39092]]
IRF payments and suggestions for possible updates and improvements to
the geographic adjustment of IRF payments.
We appreciate the commenters' responses to this solicitation and
will take them into consideration for possible future policy
development.
H. Description of the IRF Standard Payment Conversion Factor and
Payment Rates for FY 2020
To calculate the standard payment conversion factor for FY 2020, as
illustrated in Table 13, we begin by applying the increase factor for
FY 2020, as adjusted in accordance with sections 1886(j)(3)(C) of the
Act, to the standard payment conversion factor for FY 2019 ($16,021).
Applying the 2.5 percent increase factor for FY 2020 to the standard
payment conversion factor for FY 2019 of $16,021 yields a standard
payment amount of $16,422. Then, we apply the budget neutrality factor
for the FY 2020 wage index and labor-related share of 1.0031, which
results in a standard payment amount of $16,472. We next apply the
budget neutrality factor for the revised CMGs and CMG relative weights
of 1.0010, which results in the standard payment conversion factor of
$16,489 for FY 2020.
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We received one comment on the proposed FY 2020 standard payment
conversion factor, which is summarized below.
Comment: One commenter stated that the proposed rate update fails
to cover the cost of medical inflation or payment reductions due to
sequestration. As a result, this commenter expressed concern that their
hospitals' financial viability and their ability to care for their
patients will be threatened.
Response: We appreciate this commenter's concerns. However, we note
that the IRF PPS payment rates are updated annually by an increase
factor that reflects changes over time in the prices of an appropriate
mix of goods and services included in the covered IRF services, as
required by section 1886(j)(3)(C) of the Act.
After careful consideration of the comment we received, we are
finalizing the IRF standard payment conversion factor of $16,489 for FY
2020.
After the application of the CMG relative weights described in
section IV. of this final rule to the FY 2020 standard payment
conversion factor ($16,489), the resulting unadjusted IRF prospective
payment rates for FY 2020 are shown in Table 14.
BILLING CODE 4120-01-P
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BILLING CODE 4120-01-C
H. Example of the Methodology for Adjusting the Prospective Payment
Rates
Table 15 illustrates the methodology for adjusting the prospective
payments (as described in section VI. of this final rule). The
following examples are based on two hypothetical Medicare
beneficiaries, both classified into CMG 0104 (without comorbidities).
The unadjusted prospective payment rate for
[[Page 39095]]
CMG 0104 (without comorbidities) appears in Table 14.
Example: One beneficiary is in Facility A, an IRF located in rural
Spencer County, Indiana, and another beneficiary is in Facility B, an
IRF located in urban Harrison County, Indiana. Facility A, a rural non-
teaching hospital has a Disproportionate Share Hospital (DSH)
percentage of 5 percent (which would result in a LIP adjustment of
1.0156), a wage index of 0.8319, and a rural adjustment of 14.9
percent. Facility B, an urban teaching hospital, has a DSH percentage
of 15 percent (which would result in a LIP adjustment of 1.0454
percent), a wage index of 0.8844, and a teaching status adjustment of
0.0784.
To calculate each IRF's labor and non-labor portion of the
prospective payment, we begin by taking the unadjusted prospective
payment rate for CMG 0104 (without comorbidities) from Table 14. Then,
we multiply the labor-related share for FY 2020 (72.7 percent)
described in section VI.E. of this final rule by the unadjusted
prospective payment rate. To determine the non-labor portion of the
prospective payment rate, we subtract the labor portion of the federal
payment from the unadjusted prospective payment.
To compute the wage-adjusted prospective payment, we multiply the
labor portion of the federal payment by the appropriate wage index
located in Tables A and B. These tables are available on the CMS
website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html.
The resulting figure is the wage-adjusted labor amount. Next, we
compute the wage-adjusted federal payment by adding the wage-adjusted
labor amount to the non-labor portion of the federal payment.
Adjusting the wage-adjusted federal payment by the facility-level
adjustments involves several steps. First, we take the wage-adjusted
prospective payment and multiply it by the appropriate rural and LIP
adjustments (if applicable). Second, to determine the appropriate
amount of additional payment for the teaching status adjustment (if
applicable), we multiply the teaching status adjustment (0.0784, in
this example) by the wage-adjusted and rural-adjusted amount (if
applicable). Finally, we add the additional teaching status payments
(if applicable) to the wage, rural, and LIP-adjusted prospective
payment rates. Table 15 illustrates the components of the adjusted
payment calculation.
[GRAPHIC] [TIFF OMITTED] TR08AU19.019
Thus, the adjusted payment for Facility A would be $28,327.82, and
the adjusted payment for Facility B would be $28,467.16.
VII. Update to Payments for High-Cost Outliers Under the IRF PPS for FY
2020
A. Update to the Outlier Threshold Amount for FY 2020
Section 1886(j)(4) of the Act provides the Secretary with the
authority to make payments in addition to the basic IRF prospective
payments for cases incurring extraordinarily high costs. A case
qualifies for an outlier payment if the estimated cost of the case
exceeds the adjusted outlier threshold. We calculate the adjusted
outlier threshold by adding the IRF PPS payment for the case (that is,
the CMG payment adjusted by all of the relevant facility-level
adjustments) and the adjusted threshold amount (also adjusted by all of
the relevant facility-level adjustments). Then, we calculate the
estimated cost of a case by multiplying the IRF's overall CCR by the
Medicare allowable covered charge. If the estimated cost of the case is
higher than the adjusted outlier threshold, we make an outlier payment
for the case equal to 80 percent of the difference between the
estimated cost of the case and the outlier threshold.
In the FY 2002 IRF PPS final rule (66 FR 41362 through 41363), we
discussed our rationale for setting the outlier threshold amount for
the IRF PPS so that estimated outlier payments would equal 3 percent of
total estimated payments. For the 2002 IRF PPS final rule, we analyzed
various outlier policies using 3, 4, and 5 percent of the total
estimated payments, and we concluded that an outlier policy set at 3
percent of total estimated payments would optimize the extent to which
we could reduce the financial risk to IRFs
[[Page 39096]]
of caring for high-cost patients, while still providing for adequate
payments for all other (non-high cost outlier) cases.
Subsequently, we updated the IRF outlier threshold amount in the
FYs 2006 through 2019 IRF PPS final rules and the FY 2011 and FY 2013
notices (70 FR 47880, 71 FR 48354, 72 FR 44284, 73 FR 46370, 74 FR
39762, 75 FR 42836, 76 FR 47836, 76 FR 59256, 77 FR 44618, 78 FR 47860,
79 FR 45872, 80 FR 47036, 81 FR 52056, 82 FR 36238, and 83 FR 38514,
respectively) to maintain estimated outlier payments at 3 percent of
total estimated payments. We also stated in the FY 2009 final rule (73
FR 46370 at 46385) that we would continue to analyze the estimated
outlier payments for subsequent years and adjust the outlier threshold
amount as appropriate to maintain the 3 percent target.
To update the IRF outlier threshold amount for FY 2020, we proposed
to use FY 2018 claims data and the same methodology that we used to set
the initial outlier threshold amount in the FY 2002 IRF PPS final rule
(66 FR 41316 and 41362 through 41363), which is also the same
methodology that we used to update the outlier threshold amounts for
FYs 2006 through 2019. The outlier threshold is calculated by
simulating aggregate payments and using an iterative process to
determine a threshold that results in outlier payments being equal to 3
percent of total payments under the simulation. To determine the
outlier threshold for FY 2020, we estimate the amount of FY 2020 IRF
PPS aggregate and outlier payments using the most recent claims
available (FY 2018) and the FY 2020 standard payment conversion factor,
labor-related share, and wage indexes, incorporating any applicable
budget-neutrality adjustment factors. The outlier threshold is adjusted
either up or down in this simulation until the estimated outlier
payments equal 3 percent of the estimated aggregate payments. Based on
an analysis of the preliminary data used for the proposed rule, we
estimated that IRF outlier payments as a percentage of total estimated
payments would be approximately 3.2 percent in FY 2019. Therefore, we
proposed to update the outlier threshold amount from $9,402 for FY 2019
to $9,935 for FY 2020 to maintain estimated outlier payments at
approximately 3 percent of total estimated aggregate IRF payments for
FY 2020.
We note that, as we typically do, we updated our data between the
FY 2020 IRF PPS proposed and final rules to ensure that we use the most
recent available data in calculating IRF PPS payments. This updated
data includes a more complete set of claims for FY 2018. Based on our
analysis using this updated data, we now estimate that IRF outlier
payments as a percentage of total estimated payments are approximately
3.0 percent in FY 2019. Although our analysis shows that we achieved
our goal to have estimated outlier payments equal 3.0 percent of total
estimated aggregate IRF payments for FY 2019, we still need to adjust
the IRF outlier threshold to reflect changes in estimated costs and
payments for IRFs in FY 2020. That is, as discussed in section VI. of
this final rule, we are finalizing our proposal to increase IRF PPS
payment rates by 2.5 percent, in accordance with section 1886(j)(3)(C)
of the Act to account for changes over time in the prices of an
appropriate mix of goods and services included in the covered IRF
services. Similarly, we estimate costs for IRFs in FY 2020 are expected
to increase to account for changes over time in the prices of goods and
services included in the covered IRF services. Therefore, we will
update the outlier threshold amount from $9,402 for FY 2019 to $9,300
for FY 2020 to account for the increases in IRF PPS payments and
estimated costs and to maintain estimated outlier payments at
approximately 3 percent of total estimated aggregate IRF payments for
FY 2020.
We received three comments on the proposed update to the FY 2020
outlier threshold, which are summarized below.
Comment: Commenters suggested that historical outlier
reconciliation dollars should be included in the calculation of the
fixed loss threshold under the IRF PPS.
Response: As we did not propose a change to the methodology used to
establish an outlier threshold for IRF PPS payments, these comments are
outside the scope of this rule. However, we will continue to monitor
our IRF outlier policies to ensure that they continue to compensate
IRFs appropriately for treating unusually high-cost patients and do not
limit access to care for patients who are likely to require unusually
high-cost care.
Comment: A few commenters suggested that CMS consider implementing
a cap on the amount of outlier payments an individual IRF can receive
under the IRF PPS. One commenter was supportive of maintaining
estimated payments for outlier payments at approximately 3 percent
while other commenters expressed concern with maintaining the 3 percent
target and suggested reducing the outlier pool below 3 percent.
Response: As we did not propose to implement a cap on the amount of
outlier payments an individual IRF can receive under the IRF PPS, these
comments are outside the scope of this rule. However, we note that any
future consideration given to imposing a limit on outlier payments
would have to carefully analyze and take into consideration the effect
on access to IRF care for certain high-cost populations.
As most recently discussed in the FY 2019 IRF PPS final rule (83 FR
38532), we analyzed various outlier policies using 3, 4, and 5 percent
of the total estimated payments for the FY 2002 IRF PPS final rule, and
we concluded that an outlier policy set at 3 percent of total estimated
payments would optimize the extent to which we could reduce the
financial risk to IRFs of caring for high-cost patients, while still
providing for adequate payments for all other (non-high cost outlier)
cases. We continue to believe that the outlier policy of 3 percent of
total estimated aggregate payments accomplishes this objective. We
refer readers to the FY 2002 IRF PPS final rule (66 FR 41316, 41362
through 41363) for more information regarding the rationale for setting
the outlier threshold amount for the IRF PPS so that estimated outlier
payments would equal 3 percent of total estimated payments.
Comment: One commenter requested that CMS update the outlier
threshold amount in the final rule using the latest available data.
Response: We agree that we should use the most recent data
available to calculate the outlier threshold. Therefore, as previously
stated, we updated the data used to calculate the outlier threshold
between the FY 2020 IRF PPS proposed and final rules.
Having carefully considered the public comments received and also
taking into account the most recent available data, we are finalizing
the outlier threshold amount of $9,300 to maintain estimated outlier
payments at approximately 3 percent of total estimated aggregate IRF
payments for FY 2020.
B. Update to the IRF Cost-to-Charge Ratio Ceiling and Urban/Rural
Averages for FY 2020
Cost-to-charge ratios are used to adjust charges from Medicare
claims to costs and are computed annually from facility-specific data
obtained from Medicare cost reports. IRF specific cost-to-charge ratios
are used in the development of the CMG relative weights and the
calculation of outlier
[[Page 39097]]
payments under the IRF prospective payment system. In accordance with
the methodology stated in the FY 2004 IRF PPS final rule (68 FR 45674,
45692 through 45694), we proposed to apply a ceiling to IRFs' CCRs.
Using the methodology described in that final rule, we proposed to
update the national urban and rural CCRs for IRFs, as well as the
national CCR ceiling for FY 2020, based on analysis of the most recent
data that is available. We apply the national urban and rural CCRs in
the following situations:
New IRFs that have not yet submitted their first Medicare
cost report.
IRFs whose overall CCR is in excess of the national CCR
ceiling for FY 2020, as discussed below in this section.
Other IRFs for which accurate data to calculate an overall
CCR are not available.
Specifically, for FY 2020, we proposed to estimate a national
average CCR of 0.500 for rural IRFs, which we calculated by taking an
average of the CCRs for all rural IRFs using their most recently
submitted cost report data. Similarly, we proposed to estimate a
national average CCR of 0.406 for urban IRFs, which we calculated by
taking an average of the CCRs for all urban IRFs using their most
recently submitted cost report data. We apply weights to both of these
averages using the IRFs' estimated costs, meaning that the CCRs of IRFs
with higher total costs factor more heavily into the averages than the
CCRs of IRFs with lower total costs. For this final rule, we have used
the most recent available cost report data (FY 2017). This includes all
IRFs whose cost reporting periods begin on or after October 1, 2016,
and before October 1, 2017. If, for any IRF, the FY 2017 cost report
was missing or had an ``as submitted'' status, we used data from a
previous fiscal year's (that is, FY 2004 through FY 2016) settled cost
report for that IRF. We do not use cost report data from before FY 2004
for any IRF because changes in IRF utilization since FY 2004 resulting
from the 60 percent rule and IRF medical review activities suggest that
these older data do not adequately reflect the current cost of care.
Using updated FY 2017 cost report data for this final rule, we estimate
a national average CCR of 0.500 for rural IRFs, and a national average
CCR of 0.405 for urban IRFs.
In accordance with past practice, we proposed to set the national
CCR ceiling at 3 standard deviations above the mean CCR. Using this
method, we proposed a national CCR ceiling of 1.31 for FY 2020. This
means that, if an individual IRF's CCR were to exceed this ceiling of
1.31 for FY 2020, we would replace the IRF's CCR with the appropriate
proposed national average CCR (either rural or urban, depending on the
geographic location of the IRF). We calculated the proposed national
CCR ceiling by:
Step 1. Taking the national average CCR (weighted by each IRF's
total costs, as previously discussed) of all IRFs for which we have
sufficient cost report data (both rural and urban IRFs combined).
Step 2. Estimating the standard deviation of the national average
CCR computed in step 1.
Step 3. Multiplying the standard deviation of the national average
CCR computed in step 2 by a factor of 3 to compute a statistically
significant reliable ceiling.
Step 4. Adding the result from step 3 to the national average CCR
of all IRFs for which we have sufficient cost report data, from step 1.
Using the updated FY 2017 cost report data for this final rule, we
estimate a national average CCR ceiling of 1.31, using the same
methodology.
We did not receive comments on the proposed update to the IRF CCR
ceiling and the urban/rural averages for FY 2020.
As we did not receive any comments on the proposed update to the
IRF CCR ceiling and the urban/rural averages for FY 2020, we are
finalizing the national average urban CCR at 0.405, the national
average rural CCR at 0.500, and the national average CCR ceiling at
1.31 for FY 2020.
VIII. Amendments to Sec. 412.622 To Clarify the Definition of a
Rehabilitation Physician
Under Sec. 412.622(a)(3)(iv), a rehabilitation physician is
defined as ``a licensed physician with specialized training and
experience in inpatient rehabilitation.'' The term rehabilitation
physician is used in several other places in Sec. 412.622, with
corresponding references to Sec. 412.622(a)(3)(iv). The definition at
Sec. 412.622(a)(3)(iv) does not specify the level or type of training
and experience required for a licensed physician to be designated as a
rehabilitation physician because we believe that the IRFs are in the
best position to make this determination for purposes of Sec. 412.622.
Therefore, we proposed to amend the definition of a rehabilitation
physician to clarify that the determination as to whether a physician
qualifies as a rehabilitation physician (that is, a licensed physician
with specialized training and experience in inpatient rehabilitation)
is made by the IRF (84 FR 17284 through 17285). For clarity, we also
proposed to remove this definition from Sec. 412.622(a)(3)(iv) and
move it to a new paragraph (Sec. 412.622(c)). We also proposed to make
corresponding technical corrections elsewhere in Sec.
412.622(a)(3)(iv), (a)(4)(i)(A), (a)(4)(iii)(A), and (a)(5)(i) to
remove the references to Sec. 412.622(a)(3)(iv) in those paragraphs,
so as to reflect the new location of the definition.
We received 1,163 comments on the proposal to clarify the
definition of a rehabilitation physician, to move the definition from
Sec. 412.622(a)(3)(iv) to Sec. 412.622(c), and to make corresponding
technical corrections elsewhere in Sec. 412.622 to remove references
to the current location of the definition in Sec. 412.622(a)(3)(iv).
The majority of these comments consisted of form letters, in which we
received multiple copies of two types of identically-worded letters
that had been signed and submitted by different individuals. The
comments we received on this are summarized below.
Comment: Many of the commenters noted appreciation and support for
the proposal to amend the definition of a rehabilitation physician to
clarify that the determination as to whether a physician qualifies as a
rehabilitation physician (that is, a licensed physician with
specialized training and experience in inpatient rehabilitation) is
made by the IRF. One commenter stated that while board-certified
physiatrists play a crucial caregiver and leadership role in
rehabilitation hospitals, they are not alone in doing so. Physicians
representing other specialties can and do also display the leadership
and caregiving skills and experience that clearly qualify them as a
rehabilitation physician. One commenter indicated that CMS' proposal is
consistent with CMS' previously stated position from 2010. Some
commenters also stated that clarifying the regulation would reduce the
number of claims denials by promoting a shared understanding of the
requirements between IRFs and Medicare contractors.
Response: We appreciate the commenters' support and agree that this
clarification in our regulations supports our longstanding position
that the responsibility is, and always has been, on the IRF to ensure
that the rehabilitation physician(s) who are making the admission
decisions and treating the patients have the necessary training and
experience.
Comment: Many commenters stated that they do not support CMS'
proposal and suggested that CMS not finalize the proposed amendments to
Sec. 412.622. These commenters requested that CMS delay any changes to
current regulations
[[Page 39098]]
until CMS and stakeholders can work together to develop a consensus
approach for protecting the quality and integrity of IRF care. These
commenters stated that they believe that allowing the IRF to determine
whether an individual physician meets the regulatory standards for a
rehabilitation physician could increase the risks that some IRFs will
hire or contract with unqualified or underqualified physicians, reduce
the quality of care that patients receive in IRFs, and reduce the value
of physiatrists. These commenters also stated that reducing the value
of physiatrists could also deter students from wanting to pursue this
specialty in the future. Some commenters also indicated that CMS'
proposal, if finalized, would undermine CMS' ability to engage in
appropriate program integrity oversight by not reviewing an IRF's
decision to hire a particular physician to fill a rehabilitation
physician role.
Response: While we appreciate and share the commenters' desire to
ensure that Medicare beneficiaries in IRFs receive the highest-quality
care from trained and qualified physicians, we do not believe that
merely clarifying our existing policy would reduce quality of care. The
regulation will continue to require a rehabilitation physician to be a
licensed physician with specialized training and experience in
inpatient rehabilitation. We are not lowering these requirements.
However, we continue to believe that we need to clarify our existing
policy that the IRF makes the determination as to whether a given
physician qualifies as a rehabilitation physician in order to eliminate
any unnecessary uncertainty on this issue. Over the past year, we have
received questions regarding how this provision can be enforced, and we
believe that this clarification will promote a shared understanding of
how we intend the enforcement to occur. We expect that IRFs will
continue to ensure that the rehabilitation physicians treating patients
in their facilities have the necessary training and experience in
inpatient rehabilitation. To this end, we will continue to work with
stakeholders to refine Medicare's IRF payment policies in the future so
that they support IRFs in providing the highest quality care to
beneficiaries.
After careful consideration of the comments we received, we are
finalizing our proposal to amend the definition of a rehabilitation
physician to clarify that the determination as to whether a physician
qualifies as a rehabilitation physician (that is, a licensed physician
with specialized training and experience in inpatient rehabilitation)
is made by the IRF. However, based on the stakeholder feedback, we will
continue to assess whether future refinements to this policy may be
needed.
For clarity, we are also removing this definition from Sec.
412.622(a)(3)(iv) and moving it to a new paragraph (Sec. 412.622(c)).
We are also making corresponding technical corrections elsewhere in
Sec. 412.622(a)(3)(iv), (a)(4)(i)(A), (a)(4)(iii)(A), and (a)(5)(i) to
remove the references to Sec. 412.622(a)(3)(iv) in those paragraphs,
so as to reflect the new location of the definition.
IX. Updates to the IRF Quality Reporting Program (QRP)
A. Background
The IRF QRP is authorized by section 1886(j)(7) of the Act, and it
applies to freestanding IRFs, as well as inpatient rehabilitation units
of hospitals or critical access hospitals (CAHs) paid by Medicare under
the IRF PPS. Under the IRF QRP, the Secretary must reduce the annual
increase factor for discharges occurring during such fiscal year by 2
percentage points for any IRF that does not submit data in accordance
with the requirements established by the Secretary. For more
information on the background and statutory authority for the IRF QRP,
we refer readers to the FY 2012 IRF PPS final rule (76 FR 47873 through
47874), the CY 2013 Hospital Outpatient Prospective Payment System/
Ambulatory Surgical Center (OPPS/ASC) Payment Systems and Quality
Reporting Programs final rule (77 FR 68500 through 68503), the FY 2014
IRF PPS final rule (78 FR 47902), the FY 2015 IRF PPS final rule (79 FR
45908), the FY 2016 IRF PPS final rule (80 FR 47080 through 47083), the
FY 2017 IRF PPS final rule (81 FR 52080 through 52081), the FY 2018 IRF
PPS final rule (82 FR 36269 through 36270), and the FY 2019 IRF PPS
final rule (83 FR 38555 through 38556).
While we did not solicit comments on previously finalized IRF QRP
policies, we received comments, which are summarized below.
Comment: A few commenters stated that the IRF QRP compliance
threshold of 95 percent for assessment-based items is too high given
the number of data elements that have been added to the IRF-PAI, and
requested that CMS lower it to 80 percent in alignment with other
programs.
Response: We did not propose any changes to the compliance
threshold, which has been codified at Sec. 412.634(f). While these
comments were out of scope for this rule, we will take these comments
under consideration.
B. General Considerations Used for the Selection of Measures for the
IRF QRP
For a detailed discussion of the considerations we use for the
selection of IRF QRP quality, resource use, and other measures, we
refer readers to the FY 2016 IRF PPS final rule (80 FR 47083 through
47084).
C. Quality Measures Currently Adopted for the FY 2021 IRF QRP
The IRF QRP currently has 15 measures for the FY 2020 program year,
which are set out in Table 16.
[[Page 39099]]
[GRAPHIC] [TIFF OMITTED] TR08AU19.020
While we did not solicit comments on currently adopted measures
(with the exception of the Discharge to Community Measure discussed in
section IX.D.3 of this rule and the policies regarding public display
of the Drug Regimen Review Conducted With Follow-Up for Identified
Issues--PAC IRF QRP in section IX.I of this rule), we received several
comments.
Comment: A few commenters had suggestions for removing measures
they believe were ``topped out'' according to the Hospital Inpatient
Quality Reporting (IQR) Program definition (83 FR 20408) and did not
demonstrate variation across facilities, including Application of
Percent of Residents Experiencing One or More Falls with Major Injury
(Long Stay) (NQF #0674) and Application of Percent of Long-Term Care
Hospital Patients with an Admission and Discharge Functional Assessment
and a Care Plan That Addresses Function (NQF #2631), and Changes in
Skin Integrity Post-Acute Care: Pressure Ulcer/Injury. One commenter
had suggestions for improving the training manual for the Drug Regimen
Review measure in terms of considered clinically significant medication
issue.
Response: We did not propose any changes to these previously
finalized measures, nor did we propose measure removals from the IRF
QRP. We wish to clarify that the IRF QRP has not adopted the Hospital
Inpatient Quality Reporting (IQR) definition of ``topped out'' in the
measure removal criteria finalized for the IRF QRP at Sec. 412.634(2).
We also note that we do not automatically remove high performing
measures, and wish to reiterate that such measures may be retained for
other specified reasons. For example, a particular measure with high
performance rates may be retained if the measure addresses a topic
related to quality that is so significant that we do not want to risk a
decline in quality that could result if we removed the measure, or if
the measure addresses a topic that is statutorily required. We will
continue to monitor and evaluate the data from all IRF QRP measures.
With regard to the commenter's suggestions about the Drug Regimen
Review measure, we interpret that the commenter is requesting
additional clarification for coding. We will take these comments into
account as we develop training materials for the IRF QRP.
D. Adoption of Two New Quality Measures and Updated Specifications for
a Third Quality Measure Beginning With the FY 2022 IRF QRP
In the FY 2020 IRF PPS proposed rule (84 FR 17286 through 17291),
we proposed to adopt two process measures for the IRF QRP that would
satisfy section 1899B(c)(1)(E)(ii) of the Act, which requires that the
quality measures specified by the Secretary include measures with
respect to the quality measure domain titled ``Accurately communicating
the existence of and providing for the transfer of health information
and care preferences of an individual to the individual, family
caregiver of the individual, and providers of services
[[Page 39100]]
furnishing items and services to the individual when the individual
transitions from a PAC provider to another applicable setting,
including a different PAC provider, a hospital, a critical access
hospital, or the home of the individual.'' Given the length of this
domain title, hereafter, we will refer to this quality measure domain
as ``Transfer of Health Information.''
The two measures we proposed to adopt are: (1) Transfer of Health
Information to the Provider--Post-Acute Care (PAC); and (2) Transfer of
Health Information to the Patient--Post-Acute Care (PAC). Both of these
measures support our Meaningful Measures priority of promoting
effective communication and coordination of care, specifically the
Meaningful Measure area of the transfer of health information and
interoperability.
In addition to the two measure proposals, we proposed to update the
specifications for the Discharge to Community--Post Acute Care (PAC)
IRF QRP measure to exclude baseline nursing facility (NF) residents
from the measure.
We sought public comment on each of these proposals. These comments
are summarized after each proposal below.
1. Transfer of Health Information to the Provider--Post-Acute Care
(PAC) Measure
The Transfer of Health Information to the Provider--Post-Acute Care
(PAC) Measure that we proposed to adopt beginning with the FY 2022 IRF
QRP is a process-based measure that assesses whether or not a current
reconciled medication list is given to the subsequent provider when a
patient is discharged or transferred from his or her current PAC
setting.
a. Background
In 2013, 22.3 percent of all acute hospital discharges were
discharged to PAC settings, including 11 percent who were discharged to
home under the care of a home health agency, and 9 percent who were
discharged to SNFs.\2\ The proportion of patients being discharged from
an acute care hospital to a PAC setting was greater among beneficiaries
enrolled in Medicare FFS. Among Medicare FFS patients discharged from
an acute hospital, 42 percent went directly to PAC settings. Of that 42
percent, 20 percent were discharged to a SNF, 18 percent were
discharged to a home health agency (HHA), 3 percent were discharged to
an IRF, and 1 percent were discharged to an LTCH.\3\ Of the Medicare
FFS beneficiaries with an IRF stay in FYs 2016 and 2017, an estimated
10 percent were discharged or transferred to an acute care hospital, 51
percent discharged home with home health services, 16 percent
discharged or transferred to a SNF, and one percent discharged or
transferred to another PAC setting (for example, another IRF, a
hospice, or an LTCH).\4\
---------------------------------------------------------------------------
\2\ Tian, W. ``An all-payer view of hospital discharge to post-
acute care,'' May 2016. Available at https://www.hcup-us.ahrq.gov/reports/statbriefs/sb205-Hospital-Discharge-Postacute-Care.jsp.
\3\ Ibid.
\4\ RTI International analysis of Medicare claims data for index
stays in IRF 2016/2017. (RTI program reference: MM150).
\5\ Kwan, J.L., Lo, L., Sampson, M., & Shojania, K.G.,
``Medication reconciliation during transitions of care as a patient
safety strategy: a systematic review,'' Annals of Internal Medicine,
2013, Vol. 158(55), pp. 397-403.
\6\ Boockvar, K.S., Blum, S., Kugler, A., Livote, E.,
Mergenhagen, K.A., Nebeker, J.R., & Yeh, J., ``Effect of admission
medication reconciliation on adverse drug events from admission
medication changes,'' Archives of Internal Medicine, 2011, Vol.
171(99), pp. 860-861.
\7\ Bell, C.M., Brener, S.S., Gunraj, N., Huo, C., Bierman,
A.S., Scales, D.C., & Urbach, D.R., ``Association of ICU or hospital
admission with unintentional discontinuation of medications for
chronic diseases,'' JAMA, 2011, Vol. 306(88), pp. 840-847.
\8\ Basey, A.J., Krska, J., Kennedy, T.D., & Mackridge, A.J.,
``Prescribing errors on admission to hospital and their potential
impact: a mixed-methods study,'' BMJ Quality & Safety, 2014, Vol.
23(11), pp. 17-25.
\9\ Desai, R., Williams, C.E., Greene, S.B., Pierson, S., &
Hansen, R.A., ``Medication errors during patient transitions into
nursing homes: characteristics and association with patient harm,''
The American Journal of Geriatric Pharmacotherapy, 2011, Vol. 9(66),
pp. 413-422.
\10\ Boling, P.A., ``Care transitions and home health care,''
Clinical Geriatric Medicine, 2009, Vol.25(11), pp. 135-48.
\11\ Barnsteiner, J.H., ``Medication Reconciliation: Transfer of
medication information across settings--keeping it free from
error,'' The American Journal of Nursing, 2005, Vol. 105(3), pp. 31-
36.
\12\ Arbaje, A.I., Kansagara, D.L., Salanitro, A.H., Englander,
H.L., Kripalani, S., Jencks, S.F., & Lindquist, L.A., ``Regardless
of age: incorporating principles from geriatric medicine to improve
care transitions for patients with complex needs,'' Journal of
General Internal Medicine, 2014, Vol. 29(66), pp. 932-939.
\13\ Jencks, S.F., Williams, M.V., & Coleman, E.A.,
``Rehospitalizations among patients in the Medicare fee-for-service
program,'' New England Journal of Medicine, 2009, Vol. 360(114), pp.
1418-1428.
\14\ Institute of Medicine. ``Preventing medication errors:
quality chasm series,'' Washington, DC: The National Academies Press
2007. Available at https://www.nap.edu/read/11623/chapter/1.
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The transfer and/or exchange of health information from one
provider to another can be done verbally (for example, clinician-to-
clinician communication in-person or by telephone), paper-based (for
example, faxed or printed copies of records), and via electronic
communication (for example, through a health information exchange
network using an electronic health/medical record, and/or secure
messaging). Health information, such as medication information, that is
incomplete or missing increases the likelihood of a patient or resident
safety risk, and is often life-threatening.5 6 7 8 9 10 Poor
communication and coordination across health care settings contributes
to patient complications, hospital readmissions, emergency department
visits, and medication errors.11 12 13 14 15 16 17 18 19 20
Communication has been cited as the third most frequent root cause in
sentinel events, which The Joint Commission defines \21\ as a patient
safety event that results in death, permanent harm, or severe temporary
harm. Failed or ineffective patient handoffs are estimated to play a
role in 20 percent of serious preventable adverse events.\22\ When care
transitions are enhanced through care coordination activities, such as
expedited patient information flow, these activities can reduce
duplication of care services and costs of care, resolve conflicting
care plans, and prevent medical errors.23 24 25 26 27
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\15\ Kitson, N.A., Price, M., Lau, F.Y., & Showler, G.,
``Developing a medication communication framework across continuums
of care using the Circle of Care Modeling approach,'' BMC Health
Services Research, 2013, Vol. 13(11), pp. 1-10.
\16\ Mor, V., Intrator, O., Feng, Z., & Grabowski, D.C., ``The
revolving door of rehospitalization from skilled nursing
facilities,'' Health Affairs, 2010, Vol. 29(11), pp. 57-64.
\17\ Institute of Medicine. ``Preventing medication errors:
quality chasm series,'' Washington, DC: The National Academies Press
2007. Available at https://www.nap.edu/read/11623/chapter/1.
\18\ Kitson, N.A., Price, M., Lau, F.Y., & Showler, G.,
``Developing a medication communication framework across continuums
of care using the Circle of Care Modeling approach,'' BMC Health
Services Research, 2013, Vol. 13(11), pp. 1-10.
\19\ Forster, A.J., Murff, H.J., Peterson, J.F., Gandhi, T.K., &
Bates, D.W., ``The incidence and severity of adverse events
affecting patients after discharge from the hospital.'' Annals of
Internal Medicine, 2003,138(33), pp. 161-167.
\20\ King, B.J., Gilmore-Bykovskyi, A.L., Roiland, R.A.,
Polnaszek, B.E., Bowers, B.J., & Kind, A.J. ``The consequences of
poor communication during transitions from hospital to skilled
nursing facility: a qualitative study,'' Journal of the American
Geriatrics Society, 2013, Vol. 61(7), 1095-1102.
\21\ The Joint Commission, ``Sentinel Event Policy'' available
at https://www.jointcommission.org/sentinel_event_policy_and_procedures/.
\22\ The Joint Commission. ``Sentinel Event Data Root Causes by
Event Type 2004-2015.'' 2016. Available at https://www.jointcommission.org/assets/1/23/jconline_Mar_2_2016.pdf.
\23\ Mor, V., Intrator, O., Feng, Z., & Grabowski, D.C., ``The
revolving door of rehospitalization from skilled nursing
facilities,'' Health Affairs, 2010, Vol. 29(11), pp. 57-64.
\24\ Institute of Medicine, ``Preventing medication errors:
quality chasm series,'' Washington, DC: The National Academies
Press, 2007. Available at https://www.nap.edu/read/11623/chapter/1.
\25\ Starmer, A.J., Sectish, T.C., Simon, D.W., Keohane, C.,
McSweeney, M.E., Chung, E.Y., Yoon, C.S., Lipsitz, S.R., Wassner,
A.J., Harper, M.B., & Landrigan, C.P., ``Rates of medical errors and
preventable adverse events among hospitalized children following
implementation of a resident handoff bundle,'' JAMA, 2013, Vol.
310(221), pp. 2262-2270.
\26\ Pronovost, P., M.M.E. Johns, S. Palmer, R.C. Bono, D.B.
Fridsma, A. Gettinger, J. Goldman, W. Johnson, M. Karney, C. Samitt,
R.D. Sriram, A. Zenooz, and Y.C. Wang, Editors. Procuring
Interoperability: Achieving High-Quality, Connected, and Person-
Centered Care. Washington, DC, 2018 National Academy of Medicine.
Available at https://nam.edu/wp-content/uploads/2018/10/Procuring-Interoperability_web.pdf.
\27\ Balaban RB, Weissman JS, Samuel PA, & Woolhandler, S.,
``Redefining and redesigning hospital discharge to enhance patient
care: a randomized controlled study,'' J Gen Intern Med, 2008, Vol.
23(8), pp. 1228-33.
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[[Page 39101]]
Care transitions across health care settings have been
characterized as complex, costly, and potentially hazardous, and may
increase the risk for multiple adverse outcomes.28 29 The
rising incidence of preventable adverse events, complications, and
hospital readmissions have drawn attention to the importance of the
timely transfer of health information and care preferences at the time
of transition. Failures of care coordination, including poor
communication of information, were estimated to cost the U.S. health
care system between $25 billion and $45 billion in wasteful spending in
2011.\30\ The communication of health information and patient care
preferences is critical to ensuring safe and effective transitions from
one health care setting to another.31 32
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\28\ Arbaje, A.I., Kansagara, D.L., Salanitro, A.H., Englander,
H.L., Kripalani, S., Jencks, S.F., & Lindquist, L.A., ``Regardless
of age: incorporating principles from geriatric medicine to improve
care transitions for patients with complex needs,'' Journal of
General Internal Medicine, 2014, Vol 29(66), pp. 932-939.
\29\ Simmons, S., Schnelle, J., Slagle, J., Sathe, N.A.,
Stevenson, D., Carlo, M., & McPheeters, M.L., ``Resident safety
practices in nursing home settings.'' Technical Brief No. 24
(Prepared by the Vanderbilt Evidence-based Practice Center under
Contract No. 290-2015-00003-I.) AHRQ Publication No. 16-EHC022-EF.
Rockville, MD: Agency for Healthcare Research and Quality. May 2016.
Available at https://www.ncbi.nlm.nih.gov/books/NBK384624/.
\30\ Berwick, D.M. & Hackbarth, A.D. ``Eliminating Waste in US
Health Care,'' JAMA, 2012, Vol. 307(114), pp.1513-1516.
\31\ McDonald, K.M., Sundaram, V., Bravata, D.M., Lewis, R.,
Lin, N., Kraft, S.A. & Owens, D.K. Care Coordination. Vol. 7 of:
Shojania K.G., McDonald K.M., Wachter R.M., Owens D.K., editors.
``Closing the quality gap: A critical analysis of quality
improvement strategies.'' Technical Review 9 (Prepared by the
Stanford University-UCSF Evidence-based Practice Center under
contract 290-02-0017). AHRQ Publication No. 04(07)-0051-7.
Rockville, MD: Agency for Healthcare Research and Quality. June
2006. Available at https://www.ncbi.nlm.nih.gov/books/NBK44015/.
\32\ Lattimer, C., ``When it comes to transitions in patient
care, effective communication can make all the difference,''
Generations, 2011, Vol. 35(1), pp. 69-72.
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Patients in PAC settings often have complicated medication regimens
and require efficient and effective communication and coordination of
care between settings, including detailed transfer of medication
information.33 34 35 Individuals in PAC settings may be
vulnerable to adverse health outcomes due to insufficient medication
information on the part of their health care providers, and the higher
likelihood for multiple comorbid chronic conditions, polypharmacy, and
complicated transitions between care settings.36 37
Preventable adverse drug events (ADEs) may occur after hospital
discharge in a variety of settings including PAC.\38\ A 2014 Office of
Inspector General report found that 10 percent of Medicare patients in
IRFs experienced adverse events, with most of those events being
medication related. Over 45 percent of the adverse events and temporary
harm events were clearly or likely preventable.\39\ Medication errors
and one-fifth of ADEs occur during transitions between settings,
including admission to or discharge from a hospital to home or a PAC
setting, or transfer between hospitals.40 41
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\33\ Starmer A.J, Spector N.D., Srivastava R., West, D.C.,
Rosenbluth, G., Allen, A.D., Noble, E.L., & Landrigen, C.P.,
``Changes in medical errors after implementation of a handoff
program,'' N Engl J Med, 2014, Vol. 37(1), pp. 1803-1812.
\34\ Kruse, C.S. Marquez, G., Nelson, D., & Polomares, O., ``The
use of health information exchange to augment patient handoff in
long-term care: a systematic review,'' Applied Clinical Informatics,
2018, Vol. 9(44), pp. 752-771.
\35\ Brody, A.A., Gibson, B., Tresner-Kirsch, D., Kramer, H.,
Thraen, I., Coarr, M.E., & Rupper, R., ``High prevalence of
medication discrepancies between home health referrals and Centers
for Medicare and Medicaid Services home health certification and
plan of care and their potential to affect safety of vulnerable
elderly adults,'' Journal of the American Geriatrics Society, 2016,
Vol. 64(11), pp. e166-e170.
\36\ Chhabra, P.T., Rattinger, G.B., Dutcher, S.K., Hare, M.E.,
Parsons, K., L., & Zuckerman, I.H., ``Medication reconciliation
during the transition to and from long-term care settings: a
systematic review,'' Res Social Adm Pharm, 2012, Vol. 8(1), pp. 60-
75.
\37\ Levinson, D.R., & General, I., ``Adverse events in skilled
nursing facilities: national incidence among Medicare
beneficiaries.'' Washington, DC: U.S. Department of Health and Human
Services, Office of the Inspector General, February 2014. Available
at https://oig.hhs.gov/oei/reports/oei-06-11-00370.pdf.
\38\ Battles J., Azam I., Grady M., & Reback K., ``Advances in
patient safety and medical liability,'' AHRQ Publication No. 17-
0017-EF. Rockville, MD: Agency for Healthcare Research and Quality,
August 2017. Available at https://www.ahrq.gov/sites/default/files/publications/files/advances-complete_3.pdf.
\39\ Health and Human Services Office of Inspector General.
Adverse Events in Rehabilitation Hospitals: National Incidence Among
Medicare Beneficiaries. (OEI-06-14-00110). 2018. Available at
https://oig.hhs.gov/oei/reports/oei-06-14-00110.asp.
\40\ Barnsteiner, J.H., ``Medication Reconciliation: Transfer of
medication information across settings--keeping it free from
error,'' The American Journal of Nursing, 2005, Vol. 105(3), pp. 31-
36.
\41\ Gleason, K.M., Groszek, J.M., Sullivan, C., Rooney, D.,
Barnard, C., Noskin, G.A., ``Reconciliation of discrepancies in
medication histories and admission orders of newly hospitalized
patients,'' American Journal of Health System Pharmacy, 2004, Vol.
61(16), pp. 1689-1694.
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Patients in PAC settings are often taking multiple medications.
Consequently, PAC providers regularly are in the position of starting
complex new medication regimens with little knowledge of the patients
or their medication history upon admission. Furthermore, inter-facility
communication barriers delay resolving medication discrepancies during
transitions of care.\42\ Medication discrepancies are common \43\ and
found to occur in 86 percent of all transitions, increasing the
likelihood of ADEs.44 45 46 Up to 90 percent of patients
experience at least one medication discrepancy in the transition from
hospital to home care, and discrepancies occur within all therapeutic
classes of medications.47 48
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\42\ Patterson M., Foust J.B., Bollinger, S., Coleman, C.,
Nguyen, D., ``Inter-facility communication barriers delay resolving
medication discrepancies during transitions of care,'' Research in
Social & Administrative Pharmacy (2018), doi: 10.1016/
j.sapharm.2018.05.124.
\43\ Manias, E., Annaikis, N., Considine, J., Weerasuriya, R., &
Kusljic, S. ``Patient-, medication- and environment-related factors
affecting medication discrepancies in older patients,'' Collegian,
2017, Vol. 224, pp. 571-577.
\44\ Tjia, J., Bonner, A., Briesacher, B.A., McGee, S., Terrill,
E., Miller, K., ``Medication discrepancies upon hospital to skilled
nursing facility transitions,'' J Gen Intern Med, 2009, Vol. 24(55),
pp. 630-635.
\45\ Sinvani, L.D., Beizer, J., Akerman, M., Pekmezaris, R.,
Nouryan, C., Lutsky, L., Cal, C., Dlugacz, Y., Masick, K., Wolf-
Klein, G.,''Medication reconciliation in continuum of care
transitions: a moving target,'' J Am Med Dir Assoc, 2013, Vol.
14(9), 668-672.
\46\ Coleman E.A., Parry C., Chalmers S., & Min, S.J., ``The
Care Transitions Intervention: results of a randomized controlled
trial,'' Arch Intern Med, 2006, Vol. 1166, pp. 1822-28.
\47\ Corbett C.L., Setter S.M., Neumiller J.J., & Wood, l.D.,
``Nurse identified hospital to home medication discrepancies:
implications for improving transitional care,'' Geriatr Nurs, 2011,
Vol. 31(33), pp. 188-96.
\48\ Setter S.M., Corbett C.F., Neumiller J.J., Gates, B.J.,
Sclar, D.A., & Sonnett, T.E., ``Effectiveness of a pharmacist-nurse
intervention on resolving medication discrepancies in older patients
transitioning from hospital to home care: impact of a pharmacy/
nursing intervention,'' Am J Health Syst Pharm, 2009, Vol. 66, pp.
2027-31.
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Transfer of a medication list between providers is necessary for
medication reconciliation interventions, which have been shown to be a
cost-effective way to avoid ADEs by reducing errors 49 50 51
[[Page 39102]]
especially when medications are reviewed by a pharmacist using
electronic medical records.\52\
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\49\ Boockvar, K.S., Blum, S., Kugler, A., Livote, E.,
Mergenhagen, K.A., Nebeker, J.R., & Yeh, J., ``Effect of admission
medication reconciliation on adverse drug events from admission
medication changes,'' Archives of Internal Medicine, 2011, Vol.
171(99), pp. 860-861.
\50\ Kwan, J.L., Lo, L., Sampson, M., & Shojania, K.G.,
``Medication reconciliation during transitions of care as a patient
safety strategy: a systematic review,'' Annals of Internal Medicine,
2013, Vol. 158(55), pp. 397-403.
\51\ Chhabra, P.T., Rattinger, G.B., Dutcher, S.K., Hare, M.E.,
Parsons, K., L., & Zuckerman, I.H., ``Medication reconciliation
during the transition to and from long-term care settings: a
systematic review,'' Res Social Adm Pharm, 2012, Vol. 8(1), pp. 60-
75.
\52\ Agrawal A, Wu WY. ``Reducing medication errors and
improving systems reliability using an electronic medication
reconciliation system,'' The Joint Commission Journal on Quality and
Patient Safety, 2009, Vol. 35(2), pp. 106-114.
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b. Stakeholder and Technical Expert Panel (TEP) Input
The proposed measure was developed after consideration of feedback
we received from stakeholders and four TEPs convened by our
contractors. Further, the proposed measure was developed after
evaluation of data collected during two pilot tests we conducted in
accordance with the CMS Measures Management System Blueprint.
Our measure development contractors constituted a TEP which met on
September 27, 2016,\53\ January 27, 2017,\54\ and August 3, 2017 \55\
to provide input on a prior version of this measure. Based on this
input, we updated the measure concept in late 2017 to include the
transfer of a specific component of health information--medication
information. Our measure development contractors reconvened this TEP on
April 20, 2018 for the purpose of obtaining expert input on the
proposed measure, including the measure's reliability, components of
face validity, and feasibility of being implemented across PAC
settings. Overall, the TEP was supportive of the proposed measure,
affirming that the measure provides an opportunity to improve the
transfer of medication information. A summary of the April 20, 2018 TEP
proceedings titled ``Transfer of Health Information TEP Meeting 4--June
2018'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
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\53\ Technical Expert Panel Summary Report: Development of two
quality measures to satisfy the Improving Medicare Post-Acute Care
Transformation Act of 2014 (IMPACT Act) Domain of Transfer of health
Information and Care Preferences When an Individual Transitions to
Skilled Nursing Facilities (SNFs), Inpatient Rehabilitation
Facilities (IRFs), Long Term Care Hospitals (LTCHs) and Home Health
Agencies (HHAs). Available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/Transfer-of-Health-Information-TEP_Summary_Report_Final-June-2017.pdf.
\54\ Technical Expert Panel Summary Report: Development of two
quality measures to satisfy the Improving Medicare Post-Acute Care
Transformation Act of 2014 (IMPACT Act) Domain of Transfer of health
Information and Care Preferences When an Individual Transitions to
Skilled Nursing Facilities (SNFs), Inpatient Rehabilitation
Facilities (IRFs), Long Term Care Hospitals (LTCHs) and Home Health
Agencies (HHAs). Available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/Transfer-of-Health-Information-TEP-Meetings-2-3-Summary-Report_Final_Feb2018.pdf.
\55\ Ibid.
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Our measure development contractors solicited stakeholder feedback
on the proposed measure by requesting comment on the CMS Measures
Management System Blueprint website, and accepted comments that were
submitted from March 19, 2018 to May 3, 2018. The comments received
noted overall support for the measure. Several commenters suggested
ways to improve the measure, primarily related to what types of
information should be included at transfer. We incorporated this input
into development of the proposed measure. The summary report for the
March 19 to May 3, 2018 public comment period titled ``IMPACT
Medication Profile Transferred Public Comment Summary Report'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
c. Pilot Testing
The proposed measure was tested between June and August 2018 in a
pilot test that involved 24 PAC facilities/agencies, including five
IRFs, six SNFs, six LTCHs, and seven HHAs. The 24 pilot sites submitted
a total of 801 records. Analysis of agreement between coders within
each participating facility (266 qualifying pairs) indicated a 93
percent agreement for this measure. Overall, pilot testing enabled us
to verify its reliability, components of face validity, and feasibility
of being implemented across PAC settings. Further, more than half of
the sites that participated in the pilot test stated during the
debriefing interviews that the measure could distinguish facilities or
agencies with higher quality medication information transfer from those
with lower quality medication information transfer at discharge. The
pilot test summary report titled ``Transfer of Health Information 2018
Pilot Test Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
d. Measure Applications Partnership (MAP) Review and Related Measures
We included the proposed measure in the IRF QRP section of the 2018
Measures Under Consideration (MUC) List. The MAP conditionally
supported this measure pending NQF endorsement, noting that the measure
can promote the transfer of important medication information. The MAP
also suggested that we consider a measure that can be adapted to
capture bi-directional information exchange, and recommended that the
medication information transferred include important information about
supplements and opioids. More information about the MAP's
recommendations for this measure is available at http://www.qualityforum.org/Publications/2019/02/MAP_2019_Considerations_for_Implementing_Measures_Final_Report_-_PAC-LTC.aspx.
As part of the measure development and selection process, we also
identified one NQF-endorsed quality measure similar to the proposed
measure, titled Documentation of Current Medications in the Medical
Record (NQF #0419, CMS eCQM ID: CMS68v8). This measure was adopted as
one of the recommended adult core clinical quality measures for
eligible professionals for the EHR Incentive Program beginning in 2014
and was also adopted under the Merit-based Incentive Payment System
(MIPS) quality performance category beginning in 2017. The measure is
calculated based on the percentage of visits for patients aged 18 years
and older for which the eligible professional or eligible clinician
attests to documenting a list of current medications using all
resources immediately available on the date of the encounter.
The proposed Transfer of Health Information to the Provider--Post-
Acute Care (PAC) measure addresses the transfer of information whereas
the NQF-endorsed measure #0419 assesses the documentation of
medications, but not the transfer of such information. This is
important as the proposed measure assesses for the transfer of
medication information for the proposed measure calculation. Further,
the proposed measure utilizes standardized patient assessment data
elements (SPADEs), which is a requirement for measures specified under
the Transfer of Health Information measure domain under section
1899B(c)(1)(E) of the Act, whereas NQF #0419 does not.
[[Page 39103]]
After review of the NQF-endorsed measure, we determined that the
proposed Transfer of Health Information to the Provider--Post-Acute
Care (PAC) measure better addresses the Transfer of Health Information
measure domain, which requires that at least some of the data used to
calculate the measure be collected as standardized patient assessment
data through the post-acute care assessment instruments. Section
1886(j)(7)(D)(i) of the Act requires that any measure specified by the
Secretary be endorsed by the entity with a contract under section
1890(a) of the Act, which is currently the National Quality Form (NQF).
However, when a feasible and practical measure has not been NQF
endorsed for a specified area or medical topic determined appropriate
by the Secretary, section 1886(j)(7)(D)(ii) of the Act allows the
Secretary to specify a measure that is not NQF endorsed as long as due
consideration is given to the measures that have been endorsed or
adopted by a consensus organization identified by the Secretary. For
the reasons discussed previously, we believe that there is currently no
feasible NQF-endorsed measure that we could adopt under section
1886(j)(7)(D)(ii) of the Act. However, we note that we intend to submit
the proposed measure to the NQF for consideration of endorsement when
feasible.
e. Quality Measure Calculation
The proposed Transfer of Health Information to the Provider--Post-
Acute Care (PAC) quality measure is calculated as the proportion of
patient stays with a discharge assessment indicating that a current
reconciled medication list was provided to the subsequent provider at
the time of discharge. The proposed measure denominator is the total
number of IRF patient stays ending in discharge to a subsequent
provider, which is defined as a short-term general acute-care hospital,
intermediate care (intellectual and developmental disabilities
providers), home under care of an organized home health service
organization or hospice, hospice in an institutional facility, a SNF,
an LTCH, another IRF, an IPF, or a CAH. These health care providers
were selected for inclusion in the denominator because they are
identified as subsequent providers on the discharge destination item
that is currently included on the IRF PAI. The proposed measure
numerator is the number of IRF patient stays with an IRF-PAI discharge
assessment indicating a current reconciled medication list was provided
to the subsequent provider at the time of discharge. For additional
technical information about this proposed measure, we refer readers to
the document titled, ``Final Specifications for IRF QRP Quality
Measures and Standardized Patient Assessment Data Elements,'' available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html. The data source for the proposed
quality measure is the IRF-PAI assessment instrument for IRF patients.
For more information about the data submission requirements we
proposed for this measure, we refer readers to section VIII.G.3. of
this final rule.
Commenters submitted the following comments related to the proposed
rule's discussion of the IRF QRP Quality Measure Proposals beginning
with the FY 2022 IRF QRP. A discussion of these comments, along with
our responses, appears below. We also address comments on the proposed
Transfer of Health Information to the Patient--Post-Acute Care measure
(discussed further in a subsequent section of this final rule) in this
section because commenters frequently addressed both Transfer of Health
Information measures together.
Response: We thank the commenters for their support of the Transfer
of Health Information measures.
Comment: One commenter suggested that other providers, such as
outpatient physical therapists, should be included in the definition of
a subsequent provider for the Transfer of Health Information to the
Provider--Post-Acute Care measure.
Response: We appreciate the suggestion to expand the Transfer of
Health Information to the Provider--Post-Acute Care measure outcome to
assess the transfer of health information to other providers such as
outpatient physical therapists. We recognize that sharing medication
information with outpatient providers is important, and will take into
consideration additional providers in future measure modifications.
Through our measure development and pilot testing we learned that
outpatient providers cannot always be readily identified by the PAC
provider. For this process measure, which serves as a building block
for improving the transfer of medication information, we specified
providers who will be involved in the care of the patient and
medication management after discharge and can be readily identified
through the discharge location item on the IRF-PAI. The clear
delineation of the recipient of the medication list in the measure
specifications will improve measure reliability and validity.
Comment: A commenter recommended that the Transfer of Health
Information to the Provider--Post-Acute Care measure be expanded to
include the transfer of information that would help prevent infections
and facilitate appropriate infection prevention and control
interventions during care transitions in addition to the medication
information in the finalized measure.
Response: The Transfer of Health Information to the Provider--Post-
Acute Care measure focuses on the transfer of a reconciled medication
list. The measure was designed after input from TEPs, public comment,
and other stakeholders that suggested the quality measures focus on the
transfer of the most critical pieces of information to support patient
safety and care coordination. However, we acknowledge that the transfer
of many other forms of health information is important, and while the
focus of this measure is on a reconciled medication list, we hope to
expand our measures in the future.
Comment: Several commenters raised concerns about both of the
Transfer of Health Information measures not being endorsed by the
National Quality Forum (NQF). A few commenters requested that we
consider delaying rollout of these two new measures until endorsed by
NQF. A few commenters recommended that we only adopt measures that have
NQF approval. One commenter was opposed to the measures because they
have not been endorsed by NQF.
Response: While this measure is not currently NQF-endorsed, we
recognize that the NQF endorsement process is an important part of
measure development. As discussed in the FY 2020 IRF PPS proposed rule
(84 FR 17286 through 17291), we believe the measures better address the
Transfer of Health Information measure domain, which requires that at
least some of the data used to calculate the measure be collected as
standardized patient assessment data through the post-acute care
assessment instruments, than any endorsed measures. While section
1886(j)(7)(D)(i) of the Act requires that any measure specified by the
Secretary be endorsed by the entity with a contract under section
1890(a) of the Act, which is currently the National Quality Form (NQF),
when a feasible and practical measure has not been NQF endorsed for a
specified area or medical topic determined appropriate by the
[[Page 39104]]
Secretary, section 1886(j)(7)(D)(ii) of the Act allows the Secretary to
specify a measure that is not NQF endorsed as long as due consideration
is given to the measures that has been endorsed or adopted by a
consensus organization identified by the Secretary. We plan to submit
the measure for NQF endorsement consideration as soon as feasible.
Comment: Several commenters stated that the Transfer of Health
Information measures will add burden. Two commenters did not support
the measures for this reason. One commenter stated that achieving high
performance on the measures will add administrative burden. Another
commenter stated that the measures will add burden with no added value.
Another commenter stated that while there will be additional burden on
IRFs to collect and report data for these new measures, the benefit to
patients and the CMS program outweighs the additional burden on
providers.
Response: We agree that the benefit to patients outweighs any
additional burden on providers. We are also very mindful of burden that
may occur from the collection and reporting of our measures, as
supported by the Meaningful Measures and Patients over Paperwork
initiatives. We emphasize that both measures are comprised of one item,
and further, the activities associated with the measure align with
existing requirements related to transferring information at the time
of discharge to safeguard patients. Additionally, TEP feedback and
pilot test found that the burden of reporting will not be significant.
We believe that these measures will likely drive improvements in the
transfer of medication information between providers and with patients,
families, and caregivers.
Comment: One commenter stated that there will be no additional
burden to IRFs, because providing medication information as part of
discharge planning is a Condition of Participation requirement for
Medicaid and Medicare, and the medication list can be generated from
the electronic medical record.
Response: We believe that the Transfer of Health Information
measures will not substantially increase burden because we understand
that many hospitals already generate medication lists as a best
practice.
Comment: We received comments related to the validity and
reliability of both Transfer of Health Information measures. One
commenter suggested that CMS should ensure accuracy of these measures.
Other commenters suggested that additional testing is needed to ensure
that these measures will be able to differentiate among IRF providers.
Another commenter questioned if the measures would be topped out
shortly after adoption, since medication reconciliation is already
completed by facilities at discharge.
Response: Elements of validity and reliability were analyzed during
pilot testing of these measures, with good results, including inter-
rater reliability of at least 87 percent for all tested items. Pilot
testing also indicated that there is room for improvement for IRFs and
other settings, so we do not expect the measure to be topped out
shortly after adoption. As we monitor the outcomes of these measures,
we will ensure that reliability and validity of the measures meet
acceptable standards.
Comment: Some commenters recommended ways in which the Transfer of
Health Information measures specifications could be updated or changed.
A few commenters suggested that the ``not applicable'' (NA) answer
choice available in the home health version of the measure be made
available in all settings, including IRFs. A few commenters also
requested clarification about why patients discharged home under the
care of an organized home health service or hospice would be captured
in the denominators of both Transfer of Health information measures.
Response: We are appreciative of the measure modification
suggestions and clarify why the response option of N/A was considered
only for the HH version of this measure. The coding response N/A, or
``not applicable'' is used when the HHA was not made aware of the
transfer in a timely manner and, therefore, the HHA is not able to
provide the medication list at the time of transfer to the subsequent
provider. For example, a HHA may not be immediately aware when a
patient is taken to the emergency room. For facility settings, such as
the IRF setting, where 24-hour care is being provided, the facility
should always be aware and actively involved in the discharge of the
patient, and therefore, able to provide the current reconciled
medication list at the time of discharge. Therefore, we believed the
coding option of ``N/A'' would not be useful in the facility-based
measure as the facility is aware and involved in the discharge. We wish
to note that while the N/A option is considered for the HHA version of
the measure, the measure specifications indicate that these patients
are not removed from the denominator. In addition, discharge to home
under the care of an organized HHA or hospice is captured in the
denominator of both the Transfer of Health Information to Provider and
Transfer of Health Information to Patient measures because this type of
discharge represents two opportunities to transfer the medication list.
These measures aim to assure that each of these transfers is taking
place. We refer readers to the measure specifications where updates or
changes can be found and are available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Comment: One commenter suggested that the Transfer of Health
Information measures should include a measure of the timeliness of the
transfer. The commenter stated that, as currently specified, the
measures give equal credit for information that is sent immediately and
information sent days later.
Response: We appreciate the suggestions that CMS develop and adopt
measures that assess for the timeliness of transfer. We agree that
measure concepts of this type are important and would complement the
measures that focus for whether information was transferred at the time
the patient leaves the facility. We clarify that the measures do not
give credit for when information was sent, whether immediately or days
later. This is because there may be circumstances where information may
not be sent at the immediate time of discharge. However, the measures
do require that information be shared with the subsequent provider and/
or the patient as close to the time of discharge as this is actionable,
allows for shared decision making, and will increase coordinated care.
We are not establishing a new standard of transfer at discharge; we are
simply assessing if information was sent at the time a patient leaves
the facility. As we move through future measure development work, we
will consider a ``timeliness'' component for these measure concepts.
Comment: A commenter noted that although CMS provided guidelines
regarding what should be included in the transfer of medication
information, the data collection on this measure does not require that
these guidelines be met. The commenter questioned if CMS intends to
audit IRFs to ensure that the measure values are consistent with the
information being shared.
Response: The Transfer of Health Information measures serve as a
check to ensure that a reconciled medication list is provided as the
patient changes care settings. Defining the completeness of that
medication list is left to the discretion of the providers and patient
[[Page 39105]]
who are coordinating this care. We interpret the comment about audits
to be referring to data validation. While we do not have a data
validation program in place at this time, we are exploring such a
program akin to that of the hospital QRPs. For all measures and data
collected for the IRF QRP, we monitor and evaluate our data to assess
for coding patterns, errors, reliability, and soundness of the data.
Through data monitoring, we are able to assess if measure outcomes are
consistent with the information that is collected. We note that all
data are subject to review and audit.
Comment: A few comments included concerns that the Transfer of
Health Information measures are not indicative of provider quality and
questioned the ability of the measures to improve patient outcomes. Two
commenters did not support the measures for this reason. Commenters
noted that the measures assess whether a medication list was
transferred and not whether that medication list was accurate and
received by the subsequent provider.
Response: The Transfer of Health Information to the Provider--Post-
Acute Care and Transfer of Health Information to the Patient--Post-
Acute Care measures are process measures designed to address and
improve an important aspect of care quality. Lack of timely transfer of
medication information at transitions has been demonstrated to lead to
increased risk of adverse events, medication errors, and
hospitalizations. In addition, public commenters and our TEP members
identified many problems and gaps in the timely transfer of medication
information at transitions. Process measures, such as these, are
building blocks toward improved coordinated care and discharge
planning, providing information that will improve shared decision
making and coordination. Further, process measures hold a lot of value
as they delineate negative and/or positive aspects of the health care
process. These measures will capture the quality of the process of
medication information transfer and, we believe, help to improve those
processes. When developing future measures, we will take into
consideration suggestions about measures that assess the accuracy of
the medication list and whether it was received by the subsequent
provider.
Comment: One commenter suggested that CMS work to identify
interoperability solutions as a means of decreasing opportunities for
errors by providing clinicians and patients secure access to the most
up-to-date medication-related information. The commenter also suggests
that if CMS is required by the IMPACT Act to adopt these measures, that
we do so as an interim step, within a defined timeframe, while
interoperability solutions are explored and tested.
Response: We agree with the comments on the importance of
interoperability solutions to support health information transfer. CMS
and ONC are focused on improving interoperability and the timely
sharing of information between providers, patients, families and
caregivers. We believe that PAC provider health information exchange
supports the goals of high quality, personalized, efficient healthcare,
care coordination, person-centered care, and supports real-time, data
driven, clinical decision making. We are optimistic that this measure
will encourage the electronic transfer of current and important
medication information at transitions. These measures and related
efforts may help accelerate interoperability solutions. The Transfer of
Health Information measures assess the process of medication transfer,
which can occur through both electronic and non-electronic means. We
clarify that these measures are an interim step in improving
coordinated care, and we also believe that other interoperable
solutions should be explored. Finalizing these Transfer of Health
measures will be a first step in measuring the transfer of this
medication-related information.
After consideration of the public comments, we are finalizing our
proposal to adopt the Transfer of Health Information to the Provider--
Post Acute Care (PAC) measure, under section 1899B(c)(1)(E) of the Act,
with data collection for discharges beginning October 1, 2020.
2. Transfer of Health Information to the Patient--Post-Acute Care (PAC)
Measure
Beginning with the FY 2022 IRF QRP, we proposed to adopt the
Transfer of Health Information to the Patient--Post Acute Care (PAC)
measure, a measure that satisfies the IMPACT Act domain of Transfer of
Health Information, with data collection for discharges beginning
October 1, 2020. This process-based measure assesses whether or not a
current reconciled medication list was provided to the patient, family,
or caregiver when the patient was discharged from a PAC setting to a
private home/apartment, a board and care home, assisted living, a group
home, transitional living or home under care of an organized home
health service organization, or a hospice.
a. Background
In 2013, 22.3 percent of all acute hospital discharges were
discharged to PAC settings, including 11 percent who were discharged to
home under the care of a home health agency.\56\ Of the Medicare FFS
beneficiaries with an IRF stay in FYs 2016 and 2017, an estimated 51
percent were discharged home with home health services, 21 percent were
discharged home with self-care, and 0.5 percent were discharged with
home hospice services.\57\
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\56\ Tian, W. ``An all-payer view of hospital discharge to
postacute care,'' May 2016. Available at https://www.hcup-us.ahrq.gov/reports/statbriefs/sb205-Hospital-Discharge-Postacute-Care.jsp.
\57\ RTI International analysis of Medicare claims data for
index stays in IRF 2016/2017. (RTI program reference: MM150).
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The communication of health information, such as a reconciled
medication list, is critical to ensuring safe and effective patient
transitions from health care settings to home and/or other community
settings. Incomplete or missing health information, such as medication
information, increases the likelihood of a patient safety risk, often
life-threatening.58 59 60 61 62 Individuals who use PAC care
services are particularly vulnerable to adverse health outcomes due to
their higher likelihood of having multiple comorbid chronic conditions,
polypharmacy, and complicated transitions between care
settings.63 64 Upon discharge to home,
[[Page 39106]]
individuals in PAC settings may be faced with numerous medication
changes, new medication regimes, and follow-up
details.65 66 67 The efficient and effective communication
and coordination of medication information may be critical to prevent
potentially deadly adverse effects. When care coordination activities
enhance care transitions, these activities can reduce duplication of
care services and costs of care, resolve conflicting care plans, and
prevent medical errors.68 69
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\58\ Kwan, J.L., Lo, L., Sampson, M., & Shojania, K.G.,
``Medication reconciliation during transitions of care as a patient
safety strategy: A systematic review,'' Annals of Internal Medicine,
2013, Vol. 158(55), pp. 397-403.
\59\ Boockvar, K.S., Blum, S., Kugler, A., Livote, E.,
Mergenhagen, K.A., Nebeker, J.R., & Yeh, J., ``Effect of admission
medication reconciliation on adverse drug events from admission
medication changes,'' Archives of Internal Medicine, 2011, Vol.
171(99), pp. 860-861.
\60\ Bell, C.M., Brener, S.S., Gunraj, N., Huo, C., Bierman,
A.S., Scales, D.C., & Urbach, D.R., ``Association of ICU or hospital
admission with unintentional discontinuation of medications for
chronic diseases,'' JAMA, 2011, Vol. 306(88), pp. 840-847.
\61\ Basey, A.J., Krska, J., Kennedy, T.D., & Mackridge, A.J.,
``Prescribing errors on admission to hospital and their potential
impact: A mixed-methods study,'' BMJ Quality & Safety, 2014, Vol.
23(11), pp. 17-25.
\62\ Desai, R., Williams, C.E., Greene, S.B., Pierson, S., &
Hansen, R.A., ``Medication errors during patient transitions into
nursing homes: Characteristics and association with patient harm,''
The American Journal of Geriatric Pharmacotherapy, 2011, Vol. 9(6),
pp. 413-422.
\63\ Brody, A.A., Gibson, B., Tresner-Kirsch, D., Kramer, H.,
Thraen, I., Coarr, M.E., & Rupper, R. ``High prevalence of
medication discrepancies between home health referrals and Centers
for Medicare and Medicaid Services home health certification and
plan of care and their potential to affect safety of vulnerable
elderly adults,'' Journal of the American Geriatrics Society, 2016,
Vol. 64(11), pp. e166-e170.
\64\ Chhabra, P.T., Rattinger, G.B., Dutcher, S.K., Hare, M.E.,
Parsons, K., L., & Zuckerman, I.H., ``Medication reconciliation
during the transition to and from long-term care settings: A
systematic review,'' Res Social Adm Pharm, 2012, Vol. 8(1), pp. 60-
75.
\65\ Brody, A.A., Gibson, B., Tresner-Kirsch, D., Kramer, H.,
Thraen, I., Coarr, M.E., & Rupper, R. ``High prevalence of
medication discrepancies between home health referrals and Centers
for Medicare and Medicaid Services home health certification and
plan of care and their potential to affect safety of vulnerable
elderly adults,'' Journal of the American Geriatrics Society, 2016,
Vol. 64(11), pp. e166-e170.
\66\ Bell, C.M., Brener, S.S., Gunraj, N., Huo, C., Bierman,
A.S., Scales, D.C., & Urbach, D.R., ``Association of ICU or hospital
admission with unintentional discontinuation of medications for
chronic diseases,'' JAMA, 2011, Vol. 306(88), pp. 840-847.
\67\ Sheehan, O.C., Kharrazi, H., Carl, K.J., Leff, B., Wolff,
J.L., Roth, D.L., Gabbard, J., & Boyd, C.M., ``Helping older adults
improve their medication experience (HOME) by addressing medication
regimen complexity in home healthcare,'' Home Healthcare Now. 2018,
Vol. 36(11) pp. 10-19.
\68\ Mor, V., Intrator, O., Feng, Z., & Grabowski, D. C., ``The
revolving door of rehospitalization from skilled nursing
facilities,'' Health Affairs, 2010, Vol. 29(11), pp. 57-64.
\69\ Starmer, A.J., Sectish, T.C., Simon, D.W., Keohane, C.,
McSweeney, M.E., Chung, E.Y., Yoon, C.S., Lipsitz, S.R., Wassner,
A.J., Harper, M.B., & Landrigan, C.P., ``Rates of medical errors and
preventable adverse events among hospitalized children following
implementation of a resident handoff bundle,'' JAMA, 2013, Vol.
310(21), pp. 2262-2270.
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Finally, the transfer of a patient's discharge medication
information to the patient, family, or caregiver is common practice and
supported by discharge planning requirements for participation in
Medicare and Medicaid programs.\70\ \71\ Most PAC EHR systems generate
a discharge medication list to promote patient participation in
medication management, which has been shown to be potentially useful
for improving patient outcomes and transitional care.\72\
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\70\ CMS, ``Revision to state operations manual (SOM), Hospital
Appendix A--Interpretive Guidelines for 42 CFR 482.43, Discharge
Planning'' May 17, 2013. Available at https://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/SurveyCertificationGenInfo/Downloads/Survey-and-Cert-Letter-13-32.pdf.
\71\ The State Operations Manual Guidance to Surveyors for Long
Term Care Facilities (Guidance Sec. 483.21(c)(1) Rev. 11-22-17) for
discharge planning process. Available at https://www.cms.gov/Regulations-and-Guidance/Guidance/Manuals/downloads/som107ap_pp_guidelines_ltcf.pdf.
\72\ Toles, M., Colon-Emeric, C., Naylor, M.D., Asafu-Adjei, J.,
Hanson, L.C., ``Connect-home: Transitional care of skilled nursing
facility patients and their caregivers,'' Am Geriatr Soc., 2017,
Vol. 65(10), pp. 2322-2328.
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b. Stakeholder and Technical Expert Panel (TEP) Input
The proposed measure was developed after consideration of feedback
we received from stakeholders and four TEPs convened by our
contractors. Further, the proposed measure was developed after
evaluation of data collected during two pilot tests we conducted in
accordance with the CMS Measures Management System Blueprint.
Our measure development contractors constituted a TEP which met on
September 27, 2016,\73\ January 27, 2017,\74\ and August 3, 2017 \75\
to provide input on a prior version of this measure. Based on this
input, we updated the measure concept in late 2017 to include the
transfer of a specific component of health information--medication
information. Our measure development contractors reconvened this TEP on
April 20, 2018 to seek expert input on the measure. Overall, the TEP
members supported the proposed measure, affirming that the measure
provides an opportunity to improve the transfer of medication
information. Most of the TEP members believed that the measure could
improve the transfer of medication information to patients, families,
and caregivers. Several TEP members emphasized the importance of
transferring information to patients and their caregivers in a clear
manner using plain language. A summary of the April 20, 2018 TEP
proceedings titled ``Transfer of Health Information TEP Meeting 4--June
2018'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
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\73\ Technical Expert Panel Summary Report: Development of two
quality measures to satisfy the Improving Medicare Post-Acute Care
Transformation Act of 2014 (IMPACT Act) Domain of Transfer of health
Information and Care Preferences When an Individual Transitions to
Skilled Nursing Facilities (SNFs), Inpatient Rehabilitation
Facilities (IRFs), Long Term Care Hospitals (LTCHs) and Home Health
Agencies (HHAs). Available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/Transfer-of-Health-Information-TEP_Summary_Report_Final-June-2017.pdf.
\74\ Technical Expert Panel Summary Report: Development of two
quality measures to satisfy the Improving Medicare Post-Acute Care
Transformation Act of 2014 (IMPACT Act) Domain of Transfer of health
Information and Care Preferences When an Individual Transitions to
Skilled Nursing Facilities (SNFs), Inpatient Rehabilitation
Facilities (IRFs), Long Term Care Hospitals (LTCHs) and Home Health
Agencies (HHAs). Available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/Transfer-of-Health-Information-TEP-Meetings-2-3-Summary-Report_Final_Feb2018.pdf.
\75\ Ibid.
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Our measure development contractors solicited stakeholder feedback
on the proposed measure by requesting comment on the CMS Measures
Management System Blueprint website, and accepted comments that were
submitted from March 19, 2018 to May 3, 2018. Several commenters noted
the importance of ensuring that the instruction provided to patients
and caregivers is clear and understandable to promote transparent
access to medical record information and meet the goals of the IMPACT
Act. The summary report for the March 19 to May 3, 2018 public comment
period titled ``IMPACT--Medication Profile Transferred Public Comment
Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
c. Pilot Testing
Between June and August 2018, we held a pilot test involving 24 PAC
facilities/agencies, including five IRFs, six SNFs, six LTCHs, and
seven HHAs. The 24 pilot sites submitted a total of 801 assessments.
Analysis of agreement between coders within each participating facility
(241 qualifying pairs) indicated an 87 percent agreement for this
measure. Overall, pilot testing enabled us to verify its reliability,
components of face validity, and feasibility of being implemented
across PAC settings. Further, more than half of the sites that
participated in the pilot test stated, during debriefing interviews,
that the measure could distinguish facilities or agencies with higher
quality medication information transfer from those with lower quality
medication information transfer at discharge. The pilot test summary
report titled ``Transfer of Health Information 2018 Pilot Test Summary
Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
[[Page 39107]]
d. Measure Applications Partnership (MAP) Review and Related Measures
We included the proposed measure in the IRF QRP section of the 2018
MUC list. The MAP conditionally supported this measure pending NQF
endorsement, noting that the measure can promote the transfer of
important medication information to the patient. The MAP recommended
that providers transmit medication information to patients that is easy
to understand because health literacy can impact a person's ability to
take medication as directed. More information about the MAP's
recommendations for this measure is available at http://www.qualityforum.org/Publications/2019/02/MAP_2019_Considerations_for_Implementing_Measures_Final_Report_-_PAC-LTC.aspx.
Section 1886(j)(7)(D)(i) of the Act, requires that any measure
specified by the Secretary be endorsed by the entity with a contract
under section 1890(a) of the Act, which is currently the NQF. However,
when a feasible and practical measure has not been NQF endorsed for a
specified area or medical topic determined appropriate by the
Secretary, section 1886(j)(7)(D)(ii) of the Act allows the Secretary to
specify a measure that is not NQF endorsed as long as due consideration
is given to the measures that have been endorsed or adopted by a
consensus organization identified by the Secretary. Therefore, in the
absence of any NQF-endorsed measures that address the proposed Transfer
of Health Information to the Patient--Post-Acute Care (PAC), which
requires that at least some of the data used to calculate the measure
be collected as standardized patient assessment data through PAC
assessment instruments, we believe that there is currently no feasible
NQF-endorsed measure that we could adopt under section
1886(j)(7)(D)(ii) of the Act. However, we note that we intend to submit
the proposed measure to the NQF for consideration of endorsement when
feasible.
e. Quality Measure Calculation
The calculation of the proposed Transfer of Health Information to
the Patient--Post-Acute Care (PAC) measure would be based on the
proportion of patient stays with a discharge assessment indicating that
a current reconciled medication list was provided to the patient,
family, or caregiver at the time of discharge.
The proposed measure denominator is the total number of IRF patient
stays ending in discharge to a private home/apartment, a board and care
home, assisted living, a group home, transitional living or home under
care of an organized home health service organization, or a hospice.
These locations were selected for inclusion in the denominator because
they are identified as home locations on the discharge destination item
that is currently included on the IRF-PAI. The proposed measure
numerator is the number of IRF patient stays with an IRF-PAI discharge
assessment indicating a current reconciled medication list was provided
to the patient, family, or caregiver at the time of discharge. For
technical information about this proposed measure, we refer readers to
the document titled ``Final Specifications for IRF QRP Quality Measures
and Standardized Patient Assessment Data Elements,'' available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html. Data for the proposed quality
measure would be calculated using data from the IRF-PAI assessment
instrument for IRF patients.
For more information about the data submission requirements we
proposed for this measure, we refer readers to section VIII.G.3. of
this rule.
Commenters submitted the following comments related to the proposed
rule's discussion of the IRF QRP Quality Measure Proposals Beginning
with the FY 2022 IRF QRP. A discussion of these comments, along with
our responses, appears below. We received many comments that addressed
both of the Transfer of Health Information measures. Comments that
applied to both measures are discussed above in IX.D.1 of this rule.
Comment: One commenter suggested that CMS use the field's
experience with transferring information to patients and reporting on
this measure to disseminate best practices about how to best convey the
medication list and suggested this include formats and informational
elements helpful to patients and families.
Response: We have interpreted ``the field'' to mean PAC providers.
Facilities and clinicians should use clinical judgement to guide their
practices around transferring information to patients and how to best
convey the medication list, including identifying the best formats and
informational elements. This may be determined by the patient's
individualized needs in response to their medical condition. We do not
determine clinical best practices standards and facilities are advised
to refer to other sources, such as professional guidelines.
Comment: One commenter suggested that the Transfer of Health
Information to the Patient--Post-Acute Care (PAC) Measure require
transfer of the medication list to both the patient and family or
caregiver.
Response: We agree there are times when it is appropriate for the
IRF to provide the medication list to the patient and family and this
decision should be based on clinical judgement. However, because it is
not always necessary or appropriate to provide the medication list to
both the patient and family, we are not requiring this for the measure.
After consideration of the public comments, we are finalizing our
proposal to adopt the Transfer of Health Information to the Patient--
Post Acute Care (PAC) measure, under section 1899B(c)(1)(E) of the Act,
with data collection for discharges beginning October 1, 2020.
3. Update to the Discharge to Community--Post Acute Care (PAC)
Inpatient Rehabilitation Facility (IRF) Quality Reporting Program (QRP)
Measure
In the FY 2020 IRF PPS proposed rule (84 FR 17291), we proposed to
update the specifications for the Discharge to Community--PAC IRF QRP
measure to exclude baseline nursing facility (NF) residents from the
measure. This measure reports an IRF's risk-standardized rate of
Medicare FFS patients who are discharged to the community following an
IRF stay, do not have an unplanned readmission to an acute care
hospital or LTCH in the 31 days following discharge to community, and
who remain alive during the 31 days following discharge to community.
We adopted this measure in the FY 2017 IRF PPS final rule (81 FR 52095
through 52103).
In the FY 2017 IRF PPS final rule (81 FR 52099), we addressed
public comments recommending exclusion of IRF patients who were
baseline NF residents, as these patients lived in a NF prior to their
IRF stay, as these patients may not be expected to return to the
community following their IRF stay. In the FY 2018 IRF PPS final rule
(82 FR 36285), we addressed public comments expressing support for a
potential future modification of the measure that would exclude
baseline NF residents; commenters stated that the exclusion would
result in the measure more accurately portraying quality of care
provided by IRFs, while controlling for factors outside of IRF control.
[[Page 39108]]
We assessed the impact of excluding baseline NF residents from the
measure using CY 2015 and CY 2016 data, and found that this exclusion
impacted both patient- and facility-level discharge to community rates.
We defined baseline NF residents as IRF patients who had a long-term NF
stay in the 180 days preceding their hospitalization and IRF stay, with
no intervening community discharge between the NF stay and qualifying
hospitalization for measure inclusion. Baseline NF residents
represented 0.3 percent of the measure population after all measure
exclusions were applied. Observed patient-level discharge to community
rates were significantly lower for baseline NF residents (20.82
percent) compared with non-NF residents (64.52 percent). The national
observed patient-level discharge to community rate was 64.41 percent
when baseline NF residents were included in the measure, increasing to
64.52 percent when they were excluded from the measure. After excluding
baseline NF residents, 26.9 percent of IRFs had an increase in their
risk-standardized discharge to community rate that exceeded the
increase in the national observed patient-level discharge to community
rate.
Based on public comments received and our impact analysis, we
proposed to exclude baseline NF residents from the Discharge to
Community--PAC IRF QRP measure beginning with the FY 2020 IRF QRP, with
baseline NF residents defined as IRF patients who had a long-term NF
stay in the 180 days preceding their hospitalization and IRF stay, with
no intervening community discharge between the NF stay and
hospitalization.
For additional technical information regarding the Discharge to
Community--PAC IRF QRP measure, including technical information about
the proposed exclusion, we refer readers to the document titled ``Final
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We sought public comment on this proposal and received several
comments. A discussion of these comments, along with our responses,
appears below.
Comment: Several commenters supported the proposed exclusion of
baseline NF residents from the Discharge to Community--PAC IRF QRP
measure. Commenters referred to their recommendation of this exclusion
in prior years and appreciated CMS' willingness to consider and
implement stakeholder feedback. One commenter stated they did not
foresee any negative impacts of the exclusion. One commenter suggested
that CMS instead consider other quality measures for NF residents, such
as functional status measures, to determine whether residents receive
the appropriate standard of care they need in a long-term NF stay.
Response: We thank the commenters for their support of the proposed
exclusion of baseline nursing facility residents from this measure and
for recommending other measures for consideration for baseline NF
residents.
Comment: MedPAC did not support the proposed exclusion of baseline
nursing facility residents from the Discharge to Community--PAC IRF QRP
measure. They suggested that CMS instead expand their definition of
``return to the community'' to include baseline nursing home residents
returning to the nursing home where they live, as this represents their
home or community. MedPAC also stated that providers should be held
accountable for the quality of care they provide for as much of their
Medicare patient population as feasible.
Response: We agree that providers should be accountable for quality
of care for as much of their Medicare population as feasible; we
endeavor to do this as much as possible, only specifying exclusions we
believe are necessary for measure validity. We also believe that
monitoring quality of care and outcomes is important for all PAC
patients, including baseline NF residents who return to a NF after
their PAC stay. We publicly report several long-stay resident quality
measures on Nursing Home Compare including measures of hospitalization
and emergency department visits.
Community is traditionally understood as representing non-
institutional settings by policy makers, providers, and other
stakeholders. Including long-term care NF in the definition of
community would confuse this long-standing concept of community and
would misalign with CMS' definition of community in patient assessment
instruments. We conceptualized this measure using the traditional
definition of ``community'' and specified the measure as a discharge to
community measure, rather than a discharge to baseline residence
measure.
Baseline NF residents represent an inherently different patient
population with not only a significantly lower likelihood of discharge
to community settings, but also a higher likelihood of post-discharge
readmissions and death compared with PAC patients who did not live in a
NF at baseline. The inherent differences in patient characteristics and
PAC processes and goals of care for baseline NF residents and non-NF
residents are significant enough that we do not believe risk adjustment
using a NF flag would provide adequate control. While we acknowledge
that a return to nursing home for baseline NF residents represents a
return to their home, this outcome does not align with our measure
concept. Thus, we have chosen to exclude baseline NF residents from the
measure.
Comment: One commenter suggested the definition of ``long-term'' NF
stay in the proposed measure exclusion, requesting further
clarification in the measure specifications.
Response: We have further clarified the definition of long-term NF
stay in the final measure specifications, Final Specifications for IRF
QRP Quality Measures and Standardized Patient Assessment Data
Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html. A
long-term NF stay is identified by the presence of a non-SNF PPS MDS
assessment in the 180 days preceding the qualifying prior acute care
admission and index SNF stay.
Comment: One commenter questioned whether the methodology for
calculating confidence intervals for performance categories used in
public display of the Discharge to Community--PAC measures has been
updated.
Response: On May 31, 2019, we announced an update to the
methodology used for calculating confidence intervals for provider
assignment to performance categories for public display of the
Discharge to Community--PAC measures. For more information, we refer
readers to the ``Fact Sheet for Discharge to Community Post-Acute Care
Measures'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/LTCH-Quality-Reporting/Downloads/Fact-Sheet-for-Discharge-to-Community-Post-Acute-Care-Measures.pdf and the ``FAQ for Discharge to Community Post-Acute Care
Measures'' available at https://www.cms.gov/Medicare/Quality-
Initiatives-Patient-Assessment-Instruments/LTCH-Quality-Reporting/
Downloads/FAQ-for-Discharge-to-
[[Page 39109]]
Community-Post-Acute-Care-Measures.pdf.
After consideration of the public comments, we are finalizing our
proposal to exclude baseline NF residents from the Discharge to
Community--PAC IRF QRP measure as proposed beginning with the FY 2020
IRF QRP.
E. IRF QRP Quality Measures, Measure Concepts, and Standardized Patient
Assessment Data Elements Under Consideration for Future Years: Request
for Information
We sought input on the importance, relevance, appropriateness, and
applicability of each of the measures, standardized patient assessment
data elements (SPADEs), and concepts under consideration listed in the
Table 17 for future years in the IRF QRP.
[GRAPHIC] [TIFF OMITTED] TR08AU19.021
While we will not be responding to specific comments submitted in
response to this Request for Information, we intend to use this input
to inform our future measure and SPADE development efforts.
We received several comments on this RFI, which are summarized
below.
Comment: Several commenters supported the inclusion of all of the
proposed measures and SPADEs listed in Table 17. One commenter agreed
that the SPADE categories will provide a fuller picture of the patients
in the IRF setting and could be used for creating and risk adjusting
quality measures.
Many commenters supported the dementia SPADE, since dementia can
affect a beneficiary's ability to participate in his or her care in the
PAC setting, in addition to managing chronic conditions and medications
after discharge. One commenter also agreed that regularly assessing
cognitive function and mental health status presents opportunities for
better care and quality of life.
One commenter did not support the cognitive complexity SPADEs,
since there is no singular assessment tool designed to assess executive
function and memory, and it would be overly burdensome for IRFs to
conduct testing on every patient. The commenter recommended that CMS
work with stakeholders to prioritize which patient conditions would
benefit from a cognitive complexity assessment and screen for those
cases.
Many commenters supported the caregiver status SPADE; one commenter
stated that regular assessment of caregivers will result in better care
for the beneficiary and quality of life for both individuals. Another
commenter encouraged CMS to capture caregiver status, along with the
caregiver's willingness and ability, and account for it in discharge
disposition outcomes.
With regard to an opioids-based quality measure, providers had some
concerns about unintended consequences of reporting of opioid use,
including the over- or under-prescribing of opioids or limiting
patients access to critical treatments for pain management.
Many commenters were supportive of SPADEs focused on bowel and
bladder continence. One commenter noted that this is already collected
on admission and did not support a bowel and bladder SPADE on
discharge, citing that IRFs already communicate continence needs at
discharge and this would be duplicative. A few commenters had concerns
about the burden of future measures and SPADEs. One commenter
recommended that prior to adding measures or data elements, CMS
reassess and analyze all of the measures and data elements currently
collected to limit administrative burden and create a meaningful set of
measures and data elements. Another commenter supported utilization of
data from the suggested measures and SPADEs and suggested using
existing data sources, such a Medicare claims data. One commenter did
not support any future SPADE concepts that were not required by the
IMPACT Act. Another commenter suggested that CMS should explore
beneficiary-matching methods with the Department of Veteran's Affairs
to collect veteran status without additional IRF data collection
burden.
Response: We appreciate the input provided by commenters. While we
will not be responding to specific comments submitted in response to
this Request for Information, we intend to use this input to inform our
future measure and SPADE development efforts.
F. Standardized Patient Assessment Data Reporting Beginning With the FY
2022 IRF QRP
Section 1886(j)(7)(F)(ii) of the Act requires that, for FY 2019 and
each subsequent fiscal year, IRFs must report standardized patient
assessment data required under section 1899B(b)(1) of the Act. Section
1899B(a)(1)(C) of the Act requires, in part, the Secretary to modify
the PAC assessment instruments in order for PAC providers, including
IRFs, to submit SPADEs under the Medicare program. Section
1899B(b)(1)(A) of the Act requires PAC providers to submit SPADEs under
applicable reporting provisions (which, for IRFs, is the IRF QRP) with
respect to the admission and discharge of an
[[Page 39110]]
individual (and more frequently as the Secretary deems appropriate),
and section 1899B(b)(1)(B) of the Act defines standardized patient
assessment data as data required for at least the quality measures
described in section 1899B(c)(1) of the Act and that is with respect to
the following categories: (1) Functional status, such as mobility and
self-care at admission to a PAC provider and before discharge from a
PAC provider; (2) cognitive function, such as ability to express ideas
and to understand, and mental status, such as depression and dementia;
(3) special services, treatments, and interventions, such as need for
ventilator use, dialysis, chemotherapy, central line placement, and
total parenteral nutrition; (4) medical conditions and comorbidities,
such as diabetes, congestive heart failure, and pressure ulcers; (5)
impairments, such as incontinence and an impaired ability to hear, see,
or swallow; and (6) other categories deemed necessary and appropriate
by the Secretary.
In the FY 2018 IRF PPS proposed rule (82 FR 20722 through 20739),
we proposed to adopt SPADEs that would satisfy the first five
categories. In the FY 2018 IRF PPS final rule (82 FR 36287 through
36289), we summarized comments that supported our adoption of SPADEs,
including support for our broader standardization goal and support for
the clinical usefulness of specific proposed SPADEs. However, we did
not finalize the majority of our SPADE proposals in recognition of the
concern raised by many commenters that we were moving too fast to adopt
the SPADEs and modify our assessment instruments in light of all of the
other requirements we were also adopting under the IMPACT Act at that
time (82 FR 36292 through 36294). In addition, commenters noted that we
should conduct further testing of the data elements we have proposed
(82 FR 36288).
However, we finalized the adoption of SPADEs for two of the
categories described in section 1899B(b)(1)(B) of the Act: (1)
Functional status: Data elements currently reported by IRFs to
calculate the measure Application of Percent of Long-Term Care Hospital
Patients with an Admission and Discharge Functional Assessment and a
Care Plan That Addresses Function (NQF #2631); and (2) Medical
conditions and comorbidities: The data elements used to calculate the
pressure ulcer measures, Percent of Residents or Patients with Pressure
Ulcers That Are New or Worsened (Short Stay) (NQF #0678) and the
replacement measure, Changes in Skin Integrity Post-Acute Care:
Pressure Ulcer/Injury. We stated that these data elements were
important for care planning, known to be valid and reliable, and
already being reported by IRFs for the calculation of quality measures.
Since we issued the FY 2018 IRF PPS final rule, IRFs have had an
opportunity to familiarize themselves with other new reporting
requirements that we have adopted under the IMPACT Act. We have also
conducted further testing of the SPADEs, as described more fully below,
and believe that this testing supports the use of the SPADEs in our PAC
assessment instruments. Therefore, we proposed to adopt many of the
same SPADEs that we previously proposed to adopt, along with other
SPADEs.
We proposed that IRFs would be required to report these SPADEs
beginning with the FY 2022 IRF QRP. If finalized as proposed, IRFs
would be required to report these data with respect to admission and
discharge for Medicare Part A and Medicare Advantage patients
discharged between October 1, 2020, and December 31, 2020 for the FY
2022 IRF QRP. Beginning with the FY 2023 IRF QRP, we proposed that IRFs
must report data with respect to Medicare Part A and Medicare Advantage
admissions and discharges that occur during the subsequent calendar
year (for example, CY 2021 for the FY 2023 IRF QRP, CY 2022 for the FY
2024 IRF QRP).
We also proposed that IRFs that submit the Hearing, Vision, Race,
and Ethnicity SPADEs with respect to admission will be deemed to have
submitted those SPADEs with respect to both admission and discharge,
because it is unlikely that the assessment of those SPADEs at admission
will differ from the assessment of the same SPADEs at discharge.
In selecting the proposed SPADEs below, we considered the burden of
assessment-based data collection and aimed to minimize additional
burden by evaluating whether any data that is currently collected
through one or more PAC assessment instruments could be collected as
SPADEs. In selecting the SPADEs below, we also took into consideration
the following factors with respect to each data element:
(1) Overall clinical relevance;
(2) Interoperable exchange to facilitate care coordination during
transitions in care;
(3) Ability to capture medical complexity and risk factors that can
inform both payment and quality; and
(4) Scientific reliability and validity, general consensus
agreement for its usability.
In identifying the SPADEs proposed below, we additionally drew on
input from several sources, including TEPs held by our data element
contractor, public input, and the results of a recent National Beta
Test of candidate data elements conducted by our data element
contractor (hereafter ``National Beta Test'').
The National Beta Test collected data from 3,121 patients and
residents across 143 PAC facilities (26 LTCHs, 60 SNFs, 22 IRFs, and 35
HHAs) from November 2017 to August 2018 to evaluate the feasibility,
reliability, and validity of the candidate data elements across PAC
settings. The 3,121 patients and residents with an admission assessment
included 507 in LTCHs, 1,167 in SNFs, 794 in IRFs, and 653 in HHAs. The
National Beta Test also gathered feedback on the candidate data
elements from staff who administered the test protocol in order to
understand usability and workflow of the candidate data elements. More
information on the methods, analysis plan, and results for the National
Beta Test can be found in the document titled, ``Development and
Evaluation of Candidate Standardized Patient Assessment Data Elements:
Findings from the National Beta Test (Volume 2),'' available in the
document titled, ``Development and Evaluation of Candidate Standardized
Patient Assessment Data Elements: Findings from the National Beta Test
(Volume 2),'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Further, to inform the proposed SPADEs, we took into account
feedback from stakeholders, as well as from technical and clinical
experts, including feedback on whether the candidate data elements
would support the factors described above. Where relevant, we also took
into account the results of the Post-Acute Care Payment Reform
Demonstration (PAC PRD) that took place from 2006 to 2012.
Comment: Several commenters were supportive of the SPADE proposals.
A commenter recognized that the proposed SPADEs may influence care,
impact case mix and risk adjustment scores, and drive planning for
future management. Other commenters supported the proposals to add the
proposed SPADEs to the IRF-PAI, with one noting that many of the data
elements are already collected and reported on, and the other stating
that the items are important to describing current IRF patients and are
applicable to determining patient acuity. Another
[[Page 39111]]
commenter stated that data standardization as accomplished by the
SPADEs will help facilitate appropriate payment reforms and appropriate
quality measures.
Response: We thank the commenters for their support. We selected
the proposed SPADEs in part because of the attributes that the
commenters noted, such as their ability to describe IRF patients and to
support future quality measurement.
Comment: Some commenters stated support but noted reservations. One
commenter described the SPADEs as an appropriate start, but noted that
the SPADEs cannot stand alone, and must be built upon in order to be
useful for risk adjustment and quality measurement. Similarly, another
commenter suggested CMS continue working with clinicians and
researchers to ensure that the SPADEs are collecting valid, reliable,
and useful data, and to continue to refine and explore new data
elements for standardization.
Response: We agree with the commenter's statement that the SPADEs
are an appropriate start for standardization, but we disagree that they
cannot stand alone. While we intend to evaluate the SPADEs as they are
submitted and explore additional opportunities for standardization, we
also believe that the SPADEs as proposed represent an important core
set of information about clinical status and patient characteristics
and they will be useful for quality measurement. We will continue to
explore the use of the SPADEs across our PAC setting, continuing our
efforts to explore the feasibility, reliability, validity, and
usability of the data elements in our measure models and QRPs. We would
welcome continued input, recommendations, and feedback from
stakeholders about ways to improve assessment and quality measurement
for PAC providers, including ways that the SPADEs could be used in the
IRF QRP. Input can be shared with CMS through our PAC Quality
Initiatives email address [email protected].
Comment: One commenter noted support for the goals of the IMPACT
Act, but expressed concern about the scope and timing of proposed
changes, including the SPADEs. The same commenter suggested that CMS
share with the public a data use strategy and analysis plan for the
SPADEs so that providers better understand how CMS will assess the
potential usability of the SPADEs to support changes to payment and
quality programs.
Response: We thank the commenter for the support and appreciate
their concern about the proposed changes. We intend to monitor and
evaluate SPADEs as they are submitted, and to continue to engage
stakeholders around ways the SPADEs could be best used in the PAC
quality programs. We will continue to communicate and collaborate with
stakeholders by soliciting input on use of the SPADEs in the IRF QRP
through future rulemaking.
Comment: One commenter was generally critical of the set of SPADEs
proposed, stating they fail to adequately describe a patient's clinical
situation with regard to their level of independence, including
swallowing function, communication, and cognitive function.
Response: The proposed SPADEs were selected based on their overall
clinical relevance to PAC providers, including IRFs, their ability to
facilitate care coordination during transitions, their ability to
capture medical complexity and risk factors, and their scientific
reliability and validity. We have strived to balance the scope and
level of detail of the data elements against the potential burden
placed on patients and providers. At this time, SPADEs focused on
impairments are limited to sensory impairments (that is, hearing and
vision) and do not include swallowing. The patient's ability to
communicate is also not captured with a SPADE, although we note that
the IRF-PAI includes two data elements on communication: Expression of
Ideas and Wants, and Understanding Verbal and Non-Verbal Content.
However, in combination with other sections of the IRF-PAI that have
been standardized across PAC providers, we believe the proposed SPADEs
capture key clinical information (for example, cognitive function for
patients who are able to communicate, as collected by the BIMS) and
form an important foundation of standardized assessment on which to
build.
Comment: One commenter described several concerns about the scope
and implementation of the National Beta Test, including the
representativeness of IRFs included in the sample, the share of total
IRF patients included in the National Beta Test, the reported exclusion
of patients with communication and cognitive impairments, and the
exclusion of non-English speaking patients, and described how these
concerns compromise their confidence in the findings of the National
Beta Test.
Response: In a supplementary document to the proposed rule, we
described key findings from the National Beta Test related to the
proposed SPADEs. We also referred readers to an initial volume of the
National Beta Test report that details the methodology of the field
test (``Development and Evaluation of Candidate Standardized Patient
Assessment Data Elements: Findings from the National Beta Test (Volume
2),'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html). Additional
volumes of the National Beta Test report will be available in late
2019.
To address the commenter's specific concerns, we note that the
National Beta Test was designed to generate valid and robust national
SPADE performance estimates for each of the four PAC provider types,
which required acceptable geographic diversity, sufficient sample size,
and reasonable coverage of the range of clinical characteristics. To
meet these requirements, the National Beta Test was carefully designed
so that data could be collected from a wide range of environments,
allowing for thorough evaluation of candidate SPADE performance in all
PAC settings. The approach included a stratified random sample, to
maximize generalizability, and subsequent analyses included extensive
checks on the sampling design.
The commenter further implied that the small share of overall IRF
admissions included in the Beta test is indicative of inadequate
representativeness. The objective of the National Beta Test was to
evaluate the performance of candidate SPADEs for cross-setting use. It
is true that the proportion of IRFs may not reflect actual proportion
in the United States, but our sampling design ensured that sufficient
spread of IRFs across randomly selected markets, and adequate numbers
to provide ample data with which to evaluate SPADE performance in IRFs
relative to other settings.
The National Beta Test did not exclude non-communicative patients/
residents; rather, it had two distinct samples, one of which focused on
patients/residents who were able to communicate, and one of which
focused on patient/residents who were not able to communicate. The
assessment of non-communicative patients/residents differed primarily
in that observational assessments were substituted for some interview
assessments. Non-English-speaking patients were excluded from the
National Beta Test due to feasibility constraints during the field
test. Including limited English proficiency patients/residents in the
sample would
[[Page 39112]]
have required the Beta test facilities to engage or involve translators
during the test assessments. We anticipated that this would have added
undue complexity to what facilities/agencies were being requested to
do, and would have undermined the ability of facility/agency staff to
complete the requested number of assessments during the study period.
Moreover, there is strong existing evidence for the feasibility of all
clinical patient/resident interview SPADEs included in this final rule
(BIMS [section IX.G.1 in this final rule], Pain Interference [section
IX.G.3 in this final rule], PHQ [section IX.G.1 in this final rule])
when administered in other languages, either through standard PAC
workflow, as tested and currently collected in the MDS 3.0, or through
rigorous translation and testing, such as the PHQ. For all these
reasons, we determined that the performance of translated versions of
these patient/resident interview SPADEs did not need to be further
evaluated. In addition, because their exclusion did not threaten our
ability to achieve acceptable geographic diversity, sufficient sample
size, and reasonable coverage of the range of PAC patient/resident
clinical characteristics, the exclusion of limited English proficiency
patients/residents was not considered a limitation to interpretation of
the National Beta Test results.
Comment: Two commenters wanted CMS to share more information from
the National Beta Test. One of the commenters remarked on the lack of
information about clinical characteristics that has been shared with
stakeholders, limiting their ability to draw conclusions about the
data, and requested that CMS release the data from the National Beta
Test to be analyzed by third parties. The other commenter noted that
CMS has not shared quantitative results of the National Beta Test which
has limited the ability of stakeholders to determine if these items
will yield useful information for quality and/or payment purposes, and
suggested CMS release additional information, such as response
frequencies, and analysis from the field test to provide evidence of
the validity and utility of the SPADEs for quality and payment.
Response: We shared both quantitative and qualitative findings from
the National Beta Test with stakeholders at a public meeting on
November 27, 2018. For each SPADE proposed in this rule within the
clinical categories in the IMPACT Act, we provided information in the
supplementary documents to the proposed rule (the document titled
``Proposed Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html) on the feasibility and reliability based on findings from
the National Beta Test.
We are in the process of writing the final report for the National
Beta Test, which includes the clinical SPADEs in this rule as well as
additional data elements. Volume 2 of that report (``Development and
Evaluation of Candidate Standardized Patient Assessment Data Elements.
Findings from the National Beta Test (Volume 2)'') was posted on CMS'
website in March 2019. The other volumes will be available in late
2019. In addition, we are committed to making data available for
researchers and the public to analyze, and to doing so in a way that
protects the privacy of patients and providers who participated in the
National Beta Test. We are in the process of creating research
identifiable files that we anticipate will be available through a data
use agreement sometime in 2019.
Comment: Many commenters expressed concerns with respect to the
standardized patient assessment data proposals. Several commenters
stated that the standardized patient assessment data reporting
requirements will impose significant burden on providers, given the
volume of new standardized patient assessment data elements, and
corresponding sub-elements, that were proposed to be added to the IRF-
PAI. One commenter noted that the addition of the proposed standardized
patient assessment data elements would require an expanded timeline to
implement to ensure necessary operational and workflow revisions.
Response: We acknowledge the additional burden that the SPADEs will
impose on providers and patients. Our development and selection process
for the SPADEs we are adopting in this final rule prioritized data
elements that are essential to comprehensive patient care. We maintain
that there will be significant benefit associated with each of the
SPADEs to providers and patients, in that they are clinically useful
(for example, for care planning), they support patient-centered care,
and they will promote interoperability and data exchange between
providers. During the SPADE development process, we were cognizant of
the changes that providers will need to make to implement these
additions to the IRF-PAI. In the last two rules (82 FR 36287 through
36289, 83 FR 38555), we provided information about goals, scope, and
timeline for implementing SPADEs, as well as updated IRFs about ongoing
development and testing of data elements through other public forums.
We believe that IRFs have had an opportunity to familiarize themselves
with other new reporting requirements that we have adopted under the
IMPACT Act and prepare for additional changes.
Comment: Some commenters expressed concern that this additional
burden was not justified because, in their view, there was limited or
no evidence for the SPADEs to describe case mix, measure quality, or
improve care. One of these commenters noted that CMS has provided
evidence of validity, reliability, and feasibility through documents
related to the National Beta Test, but stated that CMS has not provided
any evidence that the proposed SPADEs have the ``potential for
improving quality'' or ``utility for describing case mix.''
Response: The clinical SPADEs proposed in this rule were the result
of an extensive consensus vetting process in which experts and
stakeholders were engaged through Technical Expert Panels, Special Open
Door Forums, and posting of interim reports and other documents on the
CMS website. Results of these activities provide evidence that experts
and providers believe that the proposed SPADEs have the potential for
measuring quality, for describing case mix, and improving care. We
refer the commenter to the most recent TEP report: A summary of the
September 17, 2018 TEP meeting titled ``SPADE Technical Expert Panel
Summary (Third Convening)'', which is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html. In this report, we summarize the TEP's discussion of
individual SPADEs in which they reflect on the clinical usefulness and
importance of the SPADEs for describing patient acuity (case mix) and
providing high-quality clinical care (improving quality). Therefore, we
have provided evidence that the SPADEs have the potential for improving
quality and utility for describing case mix.
Comment: One commenter believes that the expansion of the IRF-PAI
assessment will prove to be intrusive and prove challenging for
patients who are elderly, frail, in pain, or have cognitive deficits,
causing the patients
[[Page 39113]]
to lose focus, and thus, impact the accuracy of the data.
Response: We acknowledge that several SPADEs in this rule require
the patient to be asked questions directly. We believe that direct
patient assessment and patient-reported outcomes on these topics have
benefits for providers and patients. These data elements support
patient-centered care by soliciting the patient's perspective, and
better information on a patient's status is expected to improve the
care the patient receives.76 77 78 The burden the patient-
interview data elements place on patients is necessary for accurate
assessment of the patient's status. Regarding the validity and
performance of interview-based data elements, we note that many of
these data elements (for example, the BIMS, PHQ, and Pain Interference
data elements) are currently used in the MDS in SNFs. Evidence from
that setting, as well as from the National Beta Test, demonstrates
feasibility of these data elements for even very sick patients, such as
many patients receiving care from IRFs.
---------------------------------------------------------------------------
\76\ Boyce MB, Browne JP, Greenhalgh J The experiences of
professionals with using information from patient-reported outcome
measures to improve the quality of healthcare: A systematic review
of qualitative research BMJ Quality & Safety 2014;23:508-518.
\77\ Chen J, Ou L, Hollis SJ. A systematic review of the impact
of routine collection of patient reported outcome measures on
patients, providers and health organizations in an oncologic
setting. BMC Health Services Research 2013;13:211.
\78\ Marshall, S., Haywood, K. and Fitzpatrick, R. (2006),
Impact of patient[hyphen]reported outcome measures on routine
practice: A structured review. Journal of Evaluation in Clinical
Practice, 12: 559-568. doi:10.1111/j.1365-2753.2006.00650.x.
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Comment: Commenters also stated that the time burden (as in,
``time-to-complete'') associated with the clinical SPADEs was
underestimated, with some commenters noting that it did not account for
clinician time to review charts and update treatment plans or that test
conditions do not represent conditions of day-to-day operation. One
commenter stated that the estimated time to complete reported in the
National Beta Test was based only on the time needed to enter a value
on a tablet and did not include the time to evaluate the patient on
each item. Another commenter stated that because testing conditions
focused on cognitively intact, English-speaking patients with no speech
or language deficits, the estimates of impact to providers' time and
resources is inadequate.
Response: We disagree with the commenters that the National Beta
Test time-to-complete estimates are underestimates. Contrary to what
one commenter noted, we wish to clarify that time-to-complete estimates
from the National Beta Test included the time spent both to collect
data, including the review of the medical record, if needed, and to
enter the data elements into a tablet. We note that time-to-complete
estimates were calculated using the data from Facility/Agency Staff
only, and not Research Nurses, who completed more training and
conducted more assessments overall than the Facility/Agency staff. This
decision to calculate time-to-complete estimates from Facility/Agency
Staff only supports our claim that the time-to-complete estimates are
accurate reflections of the time the SPADEs will require when
implemented by PAC providers in day-to-day operations. Contrary to
another commenter's statement, we also wish to clarify that National
Beta Test did exclude patients/residents who were not able to
communicate in English, but did not categorically exclude patients with
cognitive impairment or patients with speech or language deficits.
Therefore, we believe that our estimates of time-to-complete capture
the general population of IRF patients, including those with
communication impairments.
Comment: Some commenters recommended changes to when and how SPADEs
would be collected in order to reduce administrative burden. These
recommendations included collecting data only at admission when answers
are unlikely to change between admission and discharge, adopting a
staged implementation or only a subset of the proposed data elements,
and that CMS explore options for obtaining these data via claims or
voluntary reporting only, particularly as many of the proposed SPADEs
are not relevant to IRF patients.
Response: We appreciate the commenters' recommendations. To support
data exchange between settings, and to support quality measurement,
section 1899B(b)(1)(A) of the Act requires that the SPADEs be collected
with respect to both admission and discharge. In the FY 2020 IRF PPS
proposed rule (84 FR 17292), we proposed that IRFs that submit four
SPADEs with respect to admission will be deemed to have submitted those
SPADEs with respect to both admission and discharge, because we stated
that it is unlikely that the assessment of those SPADEs at admission
would differ from the assessment of the same SPADEs at discharge. We
note that a patient's ability to hear or ability to see are more likely
to change between admission and discharge than, for example, a
patient's self-report of his or her race, ethnicity, preferred
language, or need for interpreter services. The Hearing and Vision
SPADEs are also different from the other SPADEs (that is, Race,
Ethnicity, Preferred Language, and Interpreter Services) because
evaluation of sensory status is a fundamental part of the ongoing
nursing assessment conducted for IRF patients. Therefore, clinically
significant changes that occur in a patient's hearing or vision status
during the IRF stay would be captured as part of the clinical record
and communicated to the next setting of care, as well as taken into
account during discharge planning as a part of standard best practice.
After consideration of public comments discussed in sections IX.G.4
and IX.G.4.b in this final rule, we will deem IRFs that submit the
Hearing, Vision, Race, Ethnicity, Preferred Language, and Interpreter
Services SPADEs with respect to admission to have submitted with
respect to both admission and discharge. We will take into
consideration the recommendation to obtain patient data from claims
data in future work.
Comment: A commenter recommended that CMS limit the number and type
of data elements implemented in the coming year, continue ongoing
dialogue with stakeholders, and develop and implement a process to
assess the value of specific indicators for all patient types. Another
commenter recommended that CMS conduct a thorough analysis of SPADEs
currently collected to determine if any current data elements could be
eliminated. One commenter believed that CMS should not finalize the
implementation of the SPADEs until they evaluate alternative means of
data collection (such as via billing/claims data), or measures to
reduce burden (such as removal of duplicative data elements and
elimination of data collection at discharge).
Response: We note that we adopted SPADEs in the last two rule
cycles to support the adoption of the IRF Functional Outcomes Measures
(Application of Percent of Long-Term Care Hospital Patients with an
Admission and Discharge Functional Assessment and a Care Plan That
Addresses Function (80 FR 47111); Change in Self-Care for Medical
Rehabilitation Patients (80 FR 47117); Change in Mobility Score for
Medical Rehabilitation Patients (80 FR 47118); Discharge Self-Care
Score for Medical Rehabilitation Patients (80 FR 47119); Discharge
Mobility Score for Medical Rehabilitation Patients (80 FR 47120)) and
drug regimen review (Drug Regimen Review Conducted with Follow-Up for
[[Page 39114]]
Identified Issues (81 FR 52111)). We have also communicated about the
SPADE development work with stakeholders over the last 2 years through
SODFs held on June 20, 2017, September 28, 2017, December 12, 2017,
March 28, 2018, June 19, 2018, and July 25, 2018, and at a public
meeting of stakeholders on November 27, 2018. Therefore, our
implementation to date has been incremental while we have strived to
keep stakeholders apprised as to the status of ongoing SPADE
development. We have also conducted a large-scale test of feasibility
and reliability--the National Beta Test, described in the proposed rule
(84 FR 17293)--which, along with the consensus vetting activities
described in the proposals for each SPADE, provide evidence of the
value of the SPADEs for patients across PAC settings, including IRF
patients. We will monitor and conduct analysis on the SPADEs as they
are submitted in order to identify any problems and to identify any
unnecessary burden or duplication.
Comment: One commenter recommended that CMS focus on providing
funding and administrative support to allow improvements and
standardization to the electronic medical record to allow effective
interoperability across all post-acute sites.
Response: We appreciate the commenter's recommendation. At this
time, funding for electronic medical record adoption and support is not
currently authorized for PAC providers.
Final decisions on the SPADEs are given below, following more
detailed comments on each SPADE proposal.
G. Standardized Patient Assessment Data by Category
1. Cognitive Function and Mental Status Data
A number of underlying conditions, including dementia, stroke,
traumatic brain injury, side effects of medication, metabolic and/or
endocrine imbalances, delirium, and depression, can affect cognitive
function and mental status in PAC patient and resident populations.\79\
The assessment of cognitive function and mental status by PAC providers
is important because of the high percentage of patients and residents
with these conditions,\80\ and because these assessments provide
opportunity for improving quality of care.
---------------------------------------------------------------------------
\79\ National Institute on Aging. (2014). Assessing Cognitive
Impairment in Older Patients. A Quick Guide for Primary Care
Physicians. Retrieved from https://www.nia.nih.gov/alzheimers/publication/assessing-cognitive-impairment-older-patients.
\80\ Gage B., Morley M., Smith L., et al. (2012). Post-Acute
Care Payment Reform Demonstration (Final report, Volume 4 of 4).
Research Triangle Park, NC: RTI International.
---------------------------------------------------------------------------
Symptoms of dementia may improve with pharmacotherapy, occupational
therapy, or physical activity,81 82 83 and promising
treatments for severe traumatic brain injury are currently being
tested.\84\ For older patients and residents diagnosed with depression,
treatment options to reduce symptoms and improve quality of life
include antidepressant medication and
psychotherapy,85 86 87 88 and targeted services, such as
therapeutic recreation, exercise, and restorative nursing, to increase
opportunities for psychosocial interaction.\89\
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\81\ Casey D.A., Antimisiaris D., O'Brien J. (2010). Drugs for
Alzheimer's Disease: Are They Effective? Pharmacology &
Therapeutics, 35, 208-11.
\82\ Graff M.J., Vernooij-Dassen M.J., Thijssen M., Dekker J.,
Hoefnagels W.H., Rikkert M.G.O. (2006). Community Based Occupational
Therapy for Patients with Dementia and their Care Givers: Randomised
Controlled Trial. BMJ, 333(7580): 1196.
\83\ Bherer L., Erickson K.I., Liu-Ambrose T. (2013). A Review
of the Effects of Physical Activity and Exercise on Cognitive and
Brain Functions in Older Adults. Journal of Aging Research, 657508.
\84\ Giacino J.T., Whyte J., Bagiella E., et al. (2012).
Placebo-controlled trial of amantadine for severe traumatic brain
injury. New England Journal of Medicine, 366(9), 819-826.
\85\ Alexopoulos G.S., Katz I.R., Reynolds C.F. 3rd, Carpenter
D., Docherty J.P., Ross R.W. (2001). Pharmacotherapy of depression
in older patients: A summary of the expert consensus guidelines.
Journal of Psychiatric Practice, 7(6), 361-376.
\86\ Arean P.A., Cook B.L. (2002). Psychotherapy and combined
psychotherapy/pharmacotherapy for late life depression. Biological
Psychiatry, 52(3), 293-303.
\87\ Hollon S.D., Jarrett R.B., Nierenberg A.A., Thase M.E.,
Trivedi M., Rush A.J. (2005). Psychotherapy and medication in the
treatment of adult and geriatric depression: Which monotherapy or
combined treatment? Journal of Clinical Psychiatry, 66(4), 455-468.
\88\ Wagenaar D, Colenda CC, Kreft M, Sawade J, Gardiner J,
Poverejan E. (2003). Treating depression in nursing homes: Practice
guidelines in the real world. J Am Osteopath Assoc. 103(10), 465-
469.
\89\ Crespy SD, Van Haitsma K, Kleban M, Hann CJ. Reducing
Depressive Symptoms in Nursing Home Residents: Evaluation of the
Pennsylvania Depression Collaborative Quality Improvement Program. J
Healthc Qual. 2016. Vol. 38, No. 6, pp. e76-e88.
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In alignment with our Meaningful Measures Initiative, accurate
assessment of cognitive function and mental status of patients and
residents in PAC is expected to make care safer by reducing harm caused
in the delivery of care; promote effective prevention and treatment of
chronic disease; strengthen person and family engagement as partners in
their care; and promote effective communication and coordination of
care. For example, standardized assessment of cognitive function and
mental status of patients and residents in PAC will support
establishing a baseline for identifying changes in cognitive function
and mental status (for example, delirium), anticipating the patient's
or resident's ability to understand and participate in treatments
during a PAC stay, ensuring patient and resident safety (for example,
risk of falls), and identifying appropriate support needs at the time
of discharge or transfer. Standardized patient assessment data elements
will enable or support clinical decision-making and early clinical
intervention; person-centered, high quality care through facilitating
better care continuity and coordination; better data exchange and
interoperability between settings; and longitudinal outcome analysis.
Therefore, reliable standardized patient assessment data elements
assessing cognitive function and mental status are needed to initiate a
management program that can optimize a patient's or resident's
prognosis and reduce the possibility of adverse events.
The data elements related to cognitive function and mental status
were first proposed as standardized patient assessment data elements in
the FY 2018 IRF PPS proposed rule (82 FR 20723 through 20726). In
response to our proposals, a few commenters noted that the proposed
data elements did not capture some dimensions of cognitive function and
mental status, such as functional cognition, communication, attention,
concentration, and agitation. One commenter also suggested that other
cognitive assessments should be considered for standardization. Another
commenter stated support for the standardized assessment of cognitive
function and mental status, because it could support appropriate use of
skilled therapy for beneficiaries with degenerative conditions, such as
dementia, and appropriate use of medications for behavioral and
psychological symptoms of dementia.
We sought comment on our proposals to collect as standardized
patient assessment data the following data with respect to cognitive
function and mental status.
Commenters submitted the following comments related to the proposed
rule's discussion of the cognitive function and mental status data
elements.
Comment: A few commenters were supportive of the proposal to adopt
the BIMS, CAM, and PHQ-2 to 9 as SPADEs on the topic of cognitive
function and mental status. One commenter agreed that standardizing
cognitive assessments will allow providers to identify changes in
status, support clinical decision-making, and improve care continuity
and interventions.
Response: We thank the commenters for their support. We selected
the
[[Page 39115]]
Cognitive Function and Mental Status data elements for proposal as
standardized data in part because of the attributes that the commenters
noted.
Comment: A few commenters noted limitations of these SPADEs to
fully assess all areas of cognition and mental status, particularly
mild to moderate cognitive impairment, and performance deficits that
may be related to cognitive impairment. Some commenters suggested CMS
continue exploring assessment tools on the topic of cognition and to
include a more comprehensive assessment of cognitive function for use
in PAC settings, noting that highly vulnerable patients with a mild
cognitive impairment cannot be readily identified through the current
SPADEs.
Response: We have strived to balance the scope and level of detail
of the data elements against the potential burden placed on patients
and providers. In our past work, we evaluated the potential of several
different cognition assessments for use as standardized data elements
in PAC settings. We ultimately decided on the BIMS, CAM, and PHQ-2 to 9
data elements in our proposal as a starting point. We would welcome
continued input, recommendations, and feedback from stakeholders about
additional data elements for standardization, which can be shared with
CMS through our PAC Quality Initiatives email address:
[email protected].
Comment: A commenter stated that cognitive assessment should be
individualized, rather than standardized, and performed as determined
by patient needs.
Response: We believe that the standardized assessment of cognitive
function is essential to achieving the goals of the IMPACT Act. We also
wish to clarify that the proposed SPADEs are not intended to replace
comprehensive clinical evaluation and in no way preclude providers from
conducting further patient evaluation or assessments in their settings
as they believe are necessary and useful.
Comment: Regarding future use of these data elements, one commenter
recommended that CMS monitor the use of the cognition and mental status
SPADEs as risk adjustors and make appropriate adjustments to
methodology as needed.
Response: We intend to monitor data submitted via the proposed
SPADEs and will consider these uses in the future. We will also
continue to review recommendation and feedback from stakeholders
regarding data elements that would both satisfy the categories listed
in the IMPACT Act and provide meaningful data.
Final decisions on the SPADEs are given below, following more
detailed comments on each SPADE proposal.
Brief Interview for Mental Status (BIMS)
In the FY 2020 IRF PPS proposed rule (84 FR 17294 through 17295),
we proposed that the data elements that comprise the BIMS meet the
definition of standardized patient assessment data with respect to
cognitive function and mental status under section 1899B(b)(1)(B)(ii)
of the Act.
As described in the FY 2018 IRF PPS Proposed Rule (82 FR 20723
through 20724), dementia and cognitive impairment are associated with
long-term functional dependence and, consequently, poor quality of life
and increased healthcare costs and mortality.\90\ This makes assessment
of mental status and early detection of cognitive decline or impairment
critical in the PAC setting. The intensity of routine nursing care is
higher for patients and residents with cognitive impairment than those
without, and dementia is a significant variable in predicting
readmission after discharge to the community from PAC providers.\91\
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\90\ Ag[uuml]ero-Torres, H., Fratiglioni, L., Guo, Z., Viitanen,
M., von Strauss, E., & Winblad, B. (1998). ``Dementia is the major
cause of functional dependence in the elderly: 3-year follow-up data
from a population-based study.'' Am J of Public Health 88(10): 1452-
1456.
\91\ RTI International. Proposed Measure Specifications for
Measures Proposed in the FY 2017 IRF QRP NPRM. Research Triangle
Park, NC. 2016.
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The BIMS is a performance-based cognitive assessment screening tool
that assesses repetition, recall with and without prompting, and
temporal orientation. The data elements that make up the BIMS are seven
questions on the repetition of three words, temporal orientation, and
recall that result in a cognitive function score. The BIMS was
developed to be a brief, objective screening tool, with a focus on
learning and memory. As a brief screener, the BIMS was not designed to
diagnose dementia or cognitive impairment, but rather to be a
relatively quick and easy to score assessment that could identify
cognitively impaired patients, as well as those who may be at risk for
cognitive decline and require further assessment. It is currently in
use in two of the PAC assessments: The MDS used by SNFs and the IRF-PAI
used by IRFs. For more information on the BIMS, we refer readers to the
document titled ``Final Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The data elements that comprise the BIMS were first proposed as
standardized patient assessment data elements in the FY 2018 IRF PPS
proposed rule (82 FR 20723 through 20724). In that proposed rule, we
stated that the proposal was informed by input we received through a
call for input published on the CMS Measures Management System
Blueprint website. Input submitted from August 12 to September 12,
2016, noted support for use of the BIMS, noting that it is reliable,
feasible to use across settings, and will provide useful information
about patients and residents. We also stated that the data collected
through the BIMS will provide a clearer picture of patient or resident
complexity, help with the care planning process, and be useful during
care transitions and when coordinating across providers. A summary
report for the August 12 to September 12, 2016 public comment period
titled ``SPADE August 2016 Public Comment Summary Report'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the use of the BIMS,
especially in its capacity to inform care transitions, but other
commenters were critical, noting the limitations of the BIMS to assess
mild cognitive impairment and ``functional'' cognition, and that the
BIMS cannot be completed by patients and residents who are unable to
communicate. They also stated that other cognitive assessments
available in the public domain should be considered for
standardization. One commenter suggested that CMS require use of the
BIMS with respect to discharge, as well as admission.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
BIMS was included in the National Beta Test of candidate data elements
conducted by our data element contractor from November 2017 to August
2018. Results of this test found the BIMS to be feasible and reliable
for use with PAC patients and residents. More information about the
performance of the BIMS in the National Beta Test can be found in the
document titled ``Final Specifications for IRF QRP Quality Measures and
[[Page 39116]]
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements and the TEP supported the
assessment of patient or resident cognitive status with respect to both
admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums (SODFs) and small-group
discussions with PAC providers and other stakeholders in 2018 for the
purpose of updating the public about our ongoing SPADE development
efforts. Finally, on November 27, 2018, our data element contractor
hosted a public meeting of stakeholders to present the results of the
National Beta Test and solicit additional comments. General input on
the testing and item development process and concerns about burden were
received from stakeholders during this meeting and via email through
February 1, 2019. Some commenters also expressed concern that the BIMS,
if used alone, may not be sensitive enough to capture the range of
cognitive impairments, including mild cognitive impairment. A summary
of the public input received from the November 27, 2018 stakeholder
meeting titled ``Input on Standardized Patient Assessment Data Elements
(SPADEs) Received After November 27, 2018 Stakeholder Meeting'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We understand the concerns raised by stakeholders that BIMS, if
used alone, may not be sensitive enough to capture the range of
cognitive impairments, including functional cognition and MCI, but note
that the purpose of the BIMS data elements as SPADEs is to screen for
cognitive impairment in a broad population. We also acknowledge that
further cognitive tests may be required based on a patient's condition
and will take this feedback into consideration in the development of
future standardized patient assessment data elements. However, taking
together the importance of assessing for cognitive status, stakeholder
input, and strong test results, we proposed that the BIMS data elements
meet the definition of standardized patient assessment data with
respect to cognitive function and mental status under section
1899B(b)(1)(B)(ii) of the Act and to adopt the BIMS data elements as
standardized patient assessment data for use in the IRF QRP.
Commenters submitted the following comments related to the proposed
rule's discussion of the BIMS data elements.
Comment: One commenter supported the collection of BIMS at both
admission and discharge and believes it will result in more complete
data and better care.
Response: We thank the commenter for the support of the BIMS data
element.
Comment: One commenter stated that the BIMS fails to detect mild
cognitive impairment, differentiate cognitive impairment from a
language impairment, link impairment to functional limitation, or
identify issues with problem solving and executive function. This
commenter recommended use of the Development of Outpatient Therapy
Payment Alternatives (DOTPA) items for PAC, as well as a screener
targeting functional cognition. Another commenter also recommended CMS
identify a better cognitive assessment and not to move forward with the
proposal.
Response: We recognize that the BIMS assesses components of
cognition and does not, alone, provide a comprehensive assessment of
potential cognitive impairment. We clarify that any SPADE is intended
as a minimum assessment and does not limit the ability of providers to
conduct a more comprehensive assessment of cognition to identify the
complexities or potential impacts of cognitive impairment that the
commenter describes.
We evaluated the suitability of the DOTPA, as well as other
screening tools that targeted functional cognition, by engaging our
TEP, through ``alpha'' feasibility testing, and through soliciting
input from stakeholders. At the second meeting of TEP in March 2017,
members questioned the use of data elements that rely on assessor
observation and judgment, such as DOTPA CARE tool items, and favored
other assessments of cognition that required patient interview or
patient actions. The TEP also discussed performance-based assessment of
functional cognition. These are assessments that require patients to
respond by completing a simulated task, such as ordering from a menu,
or reading medication instructions and simulating the taking of
medications, as required by the Performance Assessment of Self-Care
Skills (PASS) items.
In Alpha 2 feasibility testing, which was conducted between April
and July 2017, we included a subset of items from the DOTPA as well as
the PASS. Findings of that test identified several limitations of the
DOTPA items for use as SPADEs, such as relatively long to administer (5
to 7 minutes), especially in the LTCH setting. Assessors also indicated
that these items had low relevance for SNF and LTCH patients. In
addition, interrater reliability was highly variable among the DOTPA
items, both overall and across settings, with some items showing very
low agreement (as low as 0.34) and others showing excellent agreement
(as high as 0.81). Similarly, findings of the Alpha 2 feasibility test
identified several limitations of the PASS for use as SPADEs. The PASS
was relatively time-intensive to administer (also 5 to 7 minutes), many
patients in HHAs and IRFs needed assistance completing the PASS tasks,
and missing data were prevalent. Unlike the DOTPA items, interrater
reliability was consistently high overall for PASS (ranging from 0.78
to 0.92), but the high reliability was not deemed to outweigh
fundamental feasibility concerns related to administration challenges.
A summary report for the Alpha 2 feasibility testing titled
``Development and Maintenance of Standardized Cross Setting Patient
Assessment Data for Post-Acute Care: Summary Report of Findings from
Alpha 2 Pilot Testing'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/Alpha-2-SPADE-Pilot-Summary-Document.pdf.
Feedback was obtained on the DOTPA and other assessments of
functional cognition through a call for input that was open from April
26, 2017 to June 26, 2017. While we received support for the DOTPA,
PASS, and other assessments of functional cognition, commenters also
raised concerns about the reliability of the DOTPA, given that it is
based on staff evaluation, and the feasibility of the PASS, given that
the simulated medication task requires props, such as a medication
bottle with printed label and pill box, which may not be accessible in
all settings. A summary report for the April 26 to June 26, 2017 public
comment period titled
[[Page 39117]]
``Public Comment Summary Report 2'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/Public-Comment-Summary-Report_Standardized-Patient-Assessment-Data-Element-Work_PC2_Jan-2018.pdf.
Based on the input from our TEP, results of alpha feasibility
testing, and input from stakeholders, we decided to propose the BIMS
for standardization at this time due to the body of research literature
supporting its feasibility and validity, its relative brevity, and its
existing use in the MDS and IRF-PAI.
Comment: A few commenters noted that BIMS is currently collected by
IRFs and has not been demonstrated to predict costs or differentiate
case-mix and believes that CMS has not provided any evidence that the
BIMS is capable of being utilized for quality purposes to support the
collection of these data elements at discharge. Another commenter
stated that CMS has not provided quantitative evidence that the BIMS
data elements are capable of measuring provider performance for quality
or of differentiating case-mix for payment.
Response: We reiterate that the purpose of standardizing data
elements, in accordance with the IMPACT Act, is to support care
planning, clinical decision support, inform case-mix and quality
measurement, support care transitions, and enable interoperable data
exchange and data sharing between PAC settings. Before being identified
as a SPADE, the BIMS underwent an extensive consensus vetting process
in which experts and stakeholders were engaged through TEPs, SODFs, and
posting of interim reports and other documents on the CMS.gov website.
A summary of the most recent TEP meeting (September 17, 2018) titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html. Results of these activities
provide evidence that experts and providers believe that the BIMS data
elements have the potential for measuring quality, describing case mix,
and improving care.
Comment: A commenter believes that assessing BIMS at discharge
would not be clinically useful and would not contribute to improved
patient care or outcomes. The commenter noted that assessing BIMS at
discharge was not evaluated during the National Beta Test, and objected
to the BIMS being proposed for use at discharge.
Response: We maintain that a standardized cognitive assessment
using the BIMS is clinically useful and has the potential to improve
patient care and outcomes. The commenter stated that the BIMS was not
administered at discharge in the National Beta Test. However, the BIMS
was in fact assessed at both admission and discharge in the National
Beta Test. Moreover, to support data exchange between settings, and to
support quality measurement, the IMPACT Act requires that the SPADEs be
collected with respect to both admission and discharge. After careful
consideration of the public comments we received, we are finalizing our
proposal to adopt the BIMS as standardized patient assessment data
beginning with the FY 2022 IRF QRP as proposed.
Confusion Assessment Method (CAM)
In the FY 2020 IRF PPS proposed rule (84 FR 17295), we proposed
that the data elements that comprise the Confusion Assessment Method
(CAM) meet the definition of standardized patient assessment data with
respect to cognitive function and mental status under section
1899B(b)(1)(B)(ii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20724),
the CAM was developed to identify the signs and symptoms of delirium.
It results in a score that suggests whether a patient or resident
should be assigned a diagnosis of delirium. Because patients and
residents with multiple comorbidities receive services from PAC
providers, it is important to assess delirium, which is associated with
a high mortality rate and prolonged duration of stay in hospitalized
older adults.\92\ Assessing these signs and symptoms of delirium is
clinically relevant for care planning by PAC providers.
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\92\ Fick, D.M., Steis, M.R., Waller, J.L., & Inouye, S.K.
(2013). ``Delirium superimposed on dementia is associated with
prolonged length of stay and poor outcomes in hospitalized older
adults.'' J of Hospital Med 8(9): 500-505.
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The CAM is a patient assessment that screens for overall cognitive
impairment, as well as distinguishes delirium or reversible confusion
from other types of cognitive impairment. The CAM is currently in use
in two of the PAC assessments: A four-item version of the CAM is used
in the MDS in SNFs; and a six-item version of the CAM is used in the
LTCH CARE Data Set (LCDS) in LTCHs. We proposed the four-item version
of the CAM that assesses acute change in mental status, inattention,
disorganized thinking, and altered level of consciousness. For more
information on the CAM, we refer readers to the document titled ``Final
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The data elements that comprise the CAM were first proposed as
standardized patient assessment data elements in the FY 2018 IRF PPS
proposed rule (82 FR 20724). In that proposed rule, we stated that the
proposal was informed by public input we received on the CAM through a
call for input published on the CMS Measures Management System
Blueprint website. Input submitted from August 12 to September 12, 2016
noted support for use of the CAM, noting that it would provide
important information for care planning and care coordination, and
therefore, contribute to quality improvement. We also stated that those
commenters had noted the CAM is particularly helpful in distinguishing
delirium and reversible confusion from other types of cognitive
impairment. A summary report for the August 12 to September 12, 2016
public comment period titled ``SPADE August 2016 Public Comment Summary
Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
one commenter supported use of the CAM for standardized patient
assessment data. However, some commenters expressed concerns that the
CAM data elements assess: The presence of behavioral symptoms, but not
the cause; the possibility of a false positive for delirium due to
patient cognitive or communication impairments; and the lack of
specificity of the assessment specifications. In addition, other
commenters noted that the CAM is not necessary because: Delirium is
easily diagnosed without a tool; the CAM and BIMS assessments are
redundant; and some CAM response options are not meaningful.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
CAM was included in the National Beta Test of candidate data elements
conducted by our data element contractor from November 2017 to August
2018. Results of this test found the CAM to be feasible
[[Page 39118]]
and reliable for use with PAC patients and residents. More information
about the performance of the CAM in the National Beta Test can be found
in the document titled ``Final Specifications for IRF QRP Quality
Measures and Standardized Patient Assessment Data Elements,'' available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although they did not
specifically discuss the CAM data elements, the TEP supported the
assessment of patient or resident cognitive status with respect to both
admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for delirium,
stakeholder input, and strong test results, we proposed that the CAM
data elements meet the definition of standardized patient assessment
data with respect to cognitive function and mental status under section
1899B(b)(1)(B)(ii) of the Act and to adopt the CAM data elements as
standardized patient assessment data for use in the IRF QRP.
Commenters submitted the following comments related to the proposed
rule's discussion of the proposed CAM data elements.
Comment: A few commenters stated that the CAM would be redundant
with other cognitive assessments, such as BIMS. One commenter stated
that delirium would be assessed prior to discharge from the acute care
setting, making the assessment of delirium at admission to the IRF
redundant. Another commenter stated that concerns about burden
outweighed the value that the CAM might have for some populations, and
noted that daily physician visits and daily assessments of patients by
the interdisciplinary team were sufficient to assess cognitive needs.
Response: The CAM specifically screens for change in mental status,
inattention, disorganized thinking and altered level of consciousness,
which can indicate symptoms of delirium. These symptoms are not
assessed by other cognitive assessments in the IRF-PAI. We believe the
assessment of delirium at admission and discharge is important to
informing patient care. Delirium occurs in up to half of patients/
residents receiving PAC services,\93\ and signs and symptoms of
delirium are associated with poor functional recovery,\94\ re-
hospitalization, and mortality.\95\ Because the majority of delirium
episodes are transient,\96\ we would not expect assessment of delirium
prior to discharge from the acute care setting to capture all cases of
delirium in PAC, as there may be an acute change in mental status from
the patient's baseline or fluctuations in the patient's behaviors that
are identified after PAC admission.
---------------------------------------------------------------------------
\93\ Dan K. Kiely et al., ``Characteristics Associated with
Delirium Persistence Among Newly Admitted Post-Acute Facility
Patients,'' Journals of Gerontology: Series A (Biological Sciences
and Medical Sciences), Vol. 59, No. 4, April 2004; Edward R.
Marcantonio et al., ``Delirium Symptoms in Post-Acute Care:
Prevalent, Persistent, and Associated with Poor Functional
Recovery,'' Journal of the American Geriatrics Society, Vol. 51, No.
1, January 2003.
\94\ Marcantonio, Edward R., Samuel E. Simon, Margaret A.
Bergmann, Richard N. Jones, Katharine M. Murphy, and John N. Morris,
``Delirium Symptoms in Post-Acute Care: Prevalent, Persistent, and
Associated with Poor Functional Recovery,'' Journal of the American
Geriatrics Society, Vol. 51, No. 1, January 2003, pp. 4-9.
\95\ Edward R. Marcantonio et al., Outcomes of Older People
Admitted to Postacute Facilities with Delirium,'' Journal of the
American Geratrics Society, Vol. 53, No. 6, June 2005.
\96\ Cole MG, Ciampi A, Belzile E, Zhong L. Persistent delirium
in older hospital patients: A systematic review of frequency and
prognosis. Age Ageing 2009;38:19-26.
---------------------------------------------------------------------------
Comment: Several commenters noted doubts about the usefulness of
the CAM. One commenter was unsure if CAM will identify differences in
cognitive status or measure changes during the stay resulting from
therapeutic interventions. A few commenters stated that the CAM would
not provide information that would be useful clinically, that it was
not specific enough or too narrowly focused, and that it should not be
required at discharge. Another commenter suggested that CMS not include
the CAM as SPADE because they believe delirium is clinically apparent,
and therefore, doubt that a standardized assessment of delirium will
contribute to improving patient care or outcomes. Another commenter
expressed concern that the CAM data elements would not identify
cognitive needs that would impact quality in therapeutic intervention
across facilities.
Response: As with any brief screening tool, we believe that the CAM
has value as a universal assessment to identify patients in need of
further clinical evaluation. Delirium occurs in up to 50 percent of
patients/residents in PAC \97\ and is associated with poor
outcomes.98 99 Hyperactive delirium--the type of delirium
that manifests with agitation--makes up only a quarter of delirium
cases.100 101 Delirium more commonly manifests as
hypoactive, or ``quiet'' delirium,\102\ suggesting that brief,
universal screening is appropriate. Moreover, because there are
treatments for delirium that can be developed based on medication
review, physical examination, laboratory tests, and evaluation of
environmental factors,\103\
[[Page 39119]]
we believe that screening for delirium would support care planning and
care transitions for these patients.
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\97\ Kiely DK, Jones RN, Bergmann MA, Marcantonio ER.
Association between psychomotor activity delirium subtypes and
mortality among newly admitted post-acute facility patients. J
Gerontol A Biol Sci Med Sci 2007;62:174-179.
\98\ Marcantonio, Edward R., Samuel E. Simon, Margaret A.
Bergmann, Richard N. Jones, Katharine M. Murphy, and John N. Morris,
``Delirium Symptoms in Post-Acute Care: Prevalent, Persistent, and
Associated with Poor Functional Recovery,'' Journal of the American
Geriatrics Society, Vol. 51, No. 1, January 2003, pp. 4-9.
\99\ Edward R. Marcantonio et al., Outcomes of Older People
Admitted to Postacute Facilities with Delirium,'' Journal of the
American Geratrics Society, Vol. 53, No. 6, June 2005.
\100\ Inouye SK, Westendorp RG, Saczynski JS. Delirium in
elderly people. Lancet 2014;383:911-922.
\101\ Marcantonio ER. In the clinic: Delirium. Ann Intern Med
2011;154:ITC6-1-ITC6-1.
\102\ Yang FM, Marcantonio ER, Inouye SK, et al.
Phenomenological subtypes of delirium in older persons: Patterns,
prevalence, and prognosis. Psychosomatics 2009;50:248-254.
\103\ Marcantonio ER. Delirium in Hospitalized Older Adults. N
Engl J Med. 2017 Oct 12;377(15):1456-1466.
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Comment: A few commenters believe the CAM would be difficult to
administer and raised concerns about the training that staff would
receive in order to ensure that administration is consistent and valid.
Response: We appreciate the commenters' recommendation to provide
clear training for administering the CAM, and will take it into
consideration as we revise the current training for the IRF-PAI. We
intend to reinforce assessment tips and item rationale through
training, open door forums, and future rulemaking efforts.
Comment: One commenter disagreed that delirium assesses a dimension
of cognitive function.
Response: The CAM data elements were proposed to meet the
definition of the standardized patient assessment data with respect to
cognitive function and mental status. Section 1899B(b)(1)(B)(ii) of the
Act specifies that PAC providers shall be required to submit
standardized patient assessment data for the category of cognitive
function, such as the ability to express ideas and to understand, and
mental status, such as depression and dementia. A recent deterioration
in cognitive function or present and fluctuating behaviors of
inattention, disorganized thinking, or altered level of consciousness
may indicate delirium.\104\ Delirium can also be misdiagnosed as
dementia.\105\
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\104\ Inouye SK, van Dyck CH, Alessi CA, Balkin S, Siegal AP,
Horwitz RI. Clarifying confusion: The confusion assessment method. A
new method for detection of delirium. Ann Intern Med. 1990 Dec
15;113(12):941-8.
\105\ Marcantonio ER. Delirium in Hospitalized Older Adults. N
Engl J Med. 2017 Oct 12;377(15):1456-1466.
---------------------------------------------------------------------------
Comment: A commenter stated that CMS has not provided quantitative
evidence that the CAM data elements are capable of measuring provider
performance for quality or of differentiating case-mix for payment.
Response: The clinical SPADEs proposed in this rule, including CAM,
were the result of an extensive consensus vetting process. Over the
past several years, we have engaged experts and a wide range of
stakeholders through TEPs, Special Open Door Forums, and documents made
available on the CMS.gov website. A summary of the most recent TEP
meeting (September 17, 2018) titled ``SPADE Technical Expert Panel
Summary (Third Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html. Results of these activities provide evidence that experts
and providers believe that the proposed SPADEs, including the CAM data
elements, have the potential for measuring quality, describing case
mix, and improving care.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the CAM as standardized patient
assessment data beginning with the FY 2022 IRF QRP as proposed.
Patient Health Questionnaire-2 to 9 (PHQ-2 to 9)
In the FY 2020 IRF PPS proposed rule (84 FR 17296 through 17297),
we proposed that the Patient Health Questionnaire-2 to 9 (PHQ-2 to 9)
data elements meet the definition of standardized patient assessment
data with respect to cognitive function and mental status under section
1899B(b)(1)(B)(ii) of the Act. The proposed data elements are based on
the PHQ-2 mood interview, which focuses on only the two cardinal
symptoms of depression, and the longer PHQ-9 mood interview, which
assesses presence and frequency of nine signs and symptoms of
depression. The name of the data element, the PHQ-2 to 9, refers to an
embedded skip pattern that transitions patients with a threshold level
of symptoms in the PHQ-2 to the longer assessment of the PHQ-9. The
skip pattern is described further below. As described in the FY 2018
IRF PPS proposed rule (82 FR 20725 through 20726), depression is a
common and under-recognized mental health condition. Assessments of
depression help PAC providers better understand the needs of their
patients and residents by: Prompting further evaluation after
establishing a diagnosis of depression; elucidating the patient's or
resident's ability to participate in therapies for conditions other
than depression during their stay; and identifying appropriate ongoing
treatment and support needs at the time of discharge.
The proposed PHQ-2 to 9 is based on the PHQ-9 mood interview. The
PHQ-2 consists of questions about only the first two symptoms addressed
in the PHQ-9: Depressed mood and anhedonia (inability to pleasure),
which are the cardinal symptoms of depression. The PHQ-2 has performed
well as both a screening tool for identifying depression, to assess
depression severity, and to monitor patient mood over
time.106 107 If a patient demonstrates signs of depressed
mood and anhedonia under the PHQ-2, then the patient is administered
the lengthier PHQ-9. This skip pattern (also referred to as a gateway)
is designed to reduce the length of the interview assessment for
patients who fail to report the cardinal symptoms of depression. The
design of the PHQ-2 to 9 reduces the burden that would be associated
with requiring the full PHQ-9, while ensuring that patients and
residents with indications of depressive symptoms based on the PHQ-2
receive the longer assessment.
---------------------------------------------------------------------------
\106\ Li, C., Friedman, B., Conwell, Y., & Fiscella, K. (2007).
``Validity of the Patient Health Questionnaire 2 (PHQ-2) in
identifying major depression in older people.'' J of the A
Geriatrics Society, 55(4): 596-602.
\107\ L[ouml]we, B., Kroenke, K., & Gr[auml]fe, K. (2005).
``Detecting and monitoring depression with a two-item questionnaire
(PHQ-2).'' J of Psychosomatic Research, 58(2): 163-171.
---------------------------------------------------------------------------
Components of the proposed data elements are currently used in the
OASIS for HHAs (PHQ-2) and the MDS for SNFs (PHQ-9). For more
information on the PHQ-2 to 9, we refer readers to the document titled
``Final Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We proposed the PHQ-2 data elements as SPADEs in the FY 2018 IRF
proposed rule (82 FR 20725 through 20726). In that proposed rule, we
stated that the proposal was informed by input we received from the TEP
convened by our data element contractor on April 6 and 7, 2016. The TEP
members particularly noted that the brevity of the PHQ-2 made it
feasible to administer with low burden for both assessors and PAC
patients or residents. A summary of the April 6 and 7, 2016 TEP meeting
titled ``SPADE Technical Expert Panel Summary (First Convening)'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The rule proposal was also informed by public input that we
received through a call for input published on the CMS Measures
Management System Blueprint website. Input was submitted from August 12
to September 12, 2016 on three versions of the PHQ depression screener:
The PHQ-2; the PHQ-9; and
[[Page 39120]]
the PHQ-2 to 9 with the skip pattern design. Many commenters were
supportive of the standardized assessment of mood in PAC settings,
given the role that depression plays in well-being. Several commenters
noted support for an approach that would use PHQ-2 as a gateway to the
longer PHQ-9 while still potentially reducing burden on most patients
and residents, as well as test administrators, and ensuring the
administration of the PHQ-9, which exhibits higher specificity,\108\
for patients and residents who showed signs and symptoms of depression
on the PHQ-2. A summary report for the August 12 to September 12, 2016
public comment period titled ``SPADE August 2016 Public Comment Summary
Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
---------------------------------------------------------------------------
\108\ Arroll B, Goodyear-Smith F, Crengle S, Gunn J, Kerse N,
Fishman T, et al. Validation of PHQ-2 and PHQ-9 to screen for major
depression in the primary care population. Annals of family
medicine. 2010;8(4):348-53. doi: 10.1370/afm.1139 pmid:20644190;
PubMed Central PMCID: PMC2906530.
---------------------------------------------------------------------------
In response to our proposal to use the PHQ-2 in the FY 2018 IRF PPS
proposed rule (82 FR 20725 through 20726), we received comments
agreeing to the importance of a standardized assessment of depression
in patients and residents receiving PAC services. Commenters also
raised concerns about the ability of the PHQ-2 to correctly identify
all patients and residents with signs and symptoms of depression. One
commenter supported using the PHQ-2 as a gateway assessment and
conducting a more thorough evaluation of depression symptoms with the
PHQ-9 if the PHQ-2 is positive. Another commenter expressed concern
that standardized assessment of signs and symptoms of depression via
the PHQ-2 is not appropriate in the IRF setting, as patients may have
recently experienced acute illness or injury, and routine screening may
lead to overprescribing of antidepressant medications. Another
commenter expressed concern about potential conflicts between the
results of screening assessments and documented diagnoses based on the
expertise of physicians and other clinicians. In response to these
comments, we carried out additional testing, and we provide our
findings below.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
PHQ-2 to 9 was included in the National Beta Test of candidate data
elements conducted by our data element contractor from November 2017 to
August 2018. Results of this test found the PHQ-2 to 9 to be feasible
and reliable for use with PAC patients and residents. More information
about the performance of the PHQ-2 to 9 in the National Beta Test can
be found in the document titled ``Final Specifications for IRF QRP
Quality Measures and Standardized Patient Assessment Data Elements,''
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the PHQ-2 to
9. The TEP was supportive of the PHQ-2 to 9 data element set as a
screener for signs and symptoms of depression. The TEP's discussion
noted that symptoms evaluated by the full PHQ-9 (for example,
concentration, sleep, appetite) had relevance to care planning and the
overall well-being of the patient or resident, but that the gateway
approach of the PHQ-2 to 9 would be appropriate as a depression
screening assessment, as it depends on the well-validated PHQ-2 and
focuses on the cardinal symptoms of depression. A summary of the
September 17, 2018 TEP meeting titled ``SPADE Technical Expert Panel
Summary (Third Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our on-going SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for depression,
stakeholder input, and test results, we proposed that the PHQ-2 to 9
data elements meet the definition of standardized patient assessment
data with respect to cognitive function and mental status under section
1899B(b)(1)(B)(ii) of the Act and to adopt the PHQ-2 to 9 data elements
as standardized patient assessment data for use in the IRF QRP.
Commenters submitted the following comments related to the proposed
rule's discussion of the PHQ-2 to 9 data elements.
Comment: Some commenters supported the inclusion of the PHQ-2 to 9.
One of these commenters was particularly supportive of the use of the
2-item gateway in the PHQ-2 to 9 approach to improve efficiency.
Response: We thank the commenters for their support of the PHQ-2 to
9, including the gateway approach as a way to decrease burden for
providers and patients.
Comment: One commenter was unsure if PHQ-2 to 9 will identify
differences in cognitive status or measure changes during the stay
resulting from therapeutic interventions. Another commenter expressed
concern that the PHQ-2 to 9 data elements would not identify cognitive
needs that would impact quality in therapeutic intervention across
facilities.
Response: As with any brief screening tool, we believe that the
PHQ-2 to 9 has value as a universal assessment to identify patients in
need of further clinical evaluation. We believe that applying a brief,
standardized assessment of depression across PAC settings, including
IRFs, will improve detection based on the PHQ-2 to 9 interview. A
universal depression screening is expected to improve patient outcomes
by increasing the likelihood that depression will be identified and
treated in IRF patients. The proposal of the PHQ-2 to 9 was the result
of an extensive consensus vetting process in which experts and
stakeholders were engaged through TEPs, SODFs, and posting of interim
reports and other documents on CMS.gov. These experts and stakeholders
were supportive of the clinical usefulness of the PHQ-2 to 9
assessment. A summary of the most recent TEP meeting (September 17,
2018) titled ``SPADE Technical Expert Panel Summary (Third Convening)''
is available at https://www.cms.gov/Medicare/Quality-Initiatives-
Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/
IMPACT-Act-of-
[[Page 39121]]
2014/IMPACT-Act-Downloads-and-Videos.html.
Comment: A few commenters raised concerns about administration of
the PHQ-2 to 9 to IRF patients. One commenter noted that patients in
acute rehabilitation may have limited attention and working memory that
affects their ability to complete the PHQ-2 to 9. Another commenter
noted doubts that PHQ-9 is a good tool for IRFs because of the
likelihood of false positives, given patients who are adjusting to
recent injuries, surgeries, conditions, and various disabilities.
Rather, the commenter believes that assessment by rehabilitation
psychologists, who have specialty training in working with
rehabilitation populations, would provide a comprehensive evaluation
and informed treatment plan. Another commenter expressed concerns about
the use of the PHQ in short-stay IRF patients, suggesting that being
assessed for depression, especially if assessed multiple times, will
affect the patient's perception of how they should be experiencing
their situation.
Response: We recognize the challenges faced by patients receiving
care from IRF providers. We believe that the PHQ-2 to 9 is the most
accurate and appropriate depression screening for the PAC population,
including patients in IRFs, and that assessing for depression is
necessary for high-quality clinical care. As stated in our proposal
above, the PHQ-2 has performed well as a screening tool for identifying
depression, to assess depression severity, and to monitor patient mood
over time.\109\ \110\ Additionally, the PHQ-2 and PHQ-9 instruments
have been validated in primary care populations against a gold standard
diagnostic interview.\111\ We believe this prior validation research
generalizes to the IRF population. We also note that, regardless of the
LOS of patients, the timeframe over which they may have been
experiencing signs and symptoms of depression, and the types of
circumstances that have led to their IRF stay, it is the responsibility
of the IRF to deliver high quality care for all the symptoms or
conditions a patient may have. The expectation that the episode of care
will be short does not exempt an IRF from screening and treating
patients for the full range of physical and mental health problems.
Similarly, if a patient self-reports a significant number of depressive
symptoms, we do not believe that they should be considered to be a
``false positive'' because of, for example, a recent trauma or acute
care stay. As a screening tool, the PHQ-2 to 9 is intended to capture
likely depression to have those patients referred for further
evaluation, which will ascertain if their condition is consistent with
the full diagnostic criteria for a major depressive disorder. Moreover,
standardized screening for the signs and symptoms of depression with
the PHQ-2 to 9 does not preclude or provide a substitute for assessment
by rehabilitation psychologist or other clinicians, as deemed
appropriate by a patient's care team.
---------------------------------------------------------------------------
\109\ Li, C., Friedman, B., Conwell, Y., & Fiscella, K. (2007).
``Validity of the Patient Health Questionnaire 2 (PHQ-2) in
identifying major depression in older people.'' J of the A
Geriatrics Society, 55(4): 596-602.
\110\ L[ouml]we, B., Kroenke, K., & Gr[auml]fe, K. (2005).
``Detecting and monitoring depression with a two-item questionnaire
(PHQ-2).'' J of Psychosomatic Research, 58(2): 163-171.
\111\ Arroll B, Goodyear-Smith F, Crengle S, Gunn J, Kerse N,
Fishman T, et al. Validation of PHQ-2 and PHQ-9 to screen for major
depression in the primary care population. Annals of family
medicine. 2010;8(4):348-353.
---------------------------------------------------------------------------
Comment: Several commenters cited concerns related to the findings
from the National Beta Test related to the PHQ-2 to 9, namely, that
testing found it to be burdensome for staff and patients and the
wording difficult to understand.
Response: We acknowledge that some assessors in the National Beta
Test noted concerns regarding the burden of the PHQ-2 to 9 for staff
and patients and that the wording of some items was challenging for
patients to understand. In the National Beta Test, the PHQ-2 to 9 was
one of a collection of mood assessments, meaning that assessors and
patients completed additional questions about depressed mood and well-
being immediately before and after the PHQ-2 to 9. We believe that the
perception of burden of the PHQ-2 to 9 was in part due to the larger
mood assessment section included in the National Beta Test. Despite the
burden and administration challenges noted by National Beta Test
assessors, assessors generally appreciated the clinical utility and
relevance of the PHQ-2 to 9 and noted the importance of standardizing
the assessment of depressive symptoms.
Comment: Additional concerns about administration focused on the
patient interview format of the PHQ-2 to 9. Some commenters raised
concerns about administering the PHQ-2 to 9 to patients with severe
cognitive deficits, prior mental health issues, or non-communicative
conditions. One commenter suggested that CMS develop exemptions from
repeated screenings for short stay patients, and for patients whose
medical or cognitive status make it inappropriate to administer the
PHQ-2 to 9. Another commenter suggested that the PHQ-2 to 9 have an
option to be self-administered by the patient via a patient-friendly
paper and pencil layout, which would reduce time burden placed on
assessors.
Response: We appreciate commenters' concerns that administering the
PHQ-2 to 9 to patients whose medical or cognitive status make it
inappropriate to administer. The guidance for completing the data
elements will include instructions that if the patient is rarely or
never understood verbally, in writing, or using another method, the
PHQ-2 to 9 interview will not be completed and the assessor code the
responses to the first two items (Little interest or pleasure in doing
things; Feeling down, depressed, or hopeless) as 9 (no response). We
will take the suggestion to explore the possibility for patient self-
administration of the PHQ-2 to 9 into consideration in future SPADE
development work.
Comment: One commenter noted confusion about how depression relates
to cognitive function.
Response: Section 1899(b)(1)(B)(ii) of the Act specifies the
category of ``cognitive function, such as ability to express ideas and
to understand, and mental status, such as depression and dementia.'' We
proposed the PHQ-2 to 9 data elements to meet the definition of the
standardized patient assessment data with respect to cognitive function
and mental status, particularly the ``mental status'' topic within that
category.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the PHQ-2 to 9 data elements as
standardized patient assessment data beginning with the FY 2022 IRF QRP
as proposed.
2. Special Services, Treatments, and Interventions Data
Special services, treatments, and interventions performed in PAC
can have a major effect on an individual's health status, self-image,
and quality of life. The assessment of these special services,
treatments, and interventions in PAC is important to ensure the
continuing appropriateness of care for the patients and residents
receiving them, and to support care transitions from one PAC provider
to another, an acute care hospital, or discharge. In alignment with our
Meaningful Measures Initiative, accurate assessment of special
services, treatments, and interventions of patients and residents
served by PAC providers is expected to make care safer by reducing harm
caused in the delivery of care; promote effective prevention and
treatment of chronic disease; strengthen person and
[[Page 39122]]
family engagement as partners in their care; and promote effective
communication and coordination of care.
For example, standardized assessment of special services,
treatments, and interventions used in PAC can promote patient and
resident safety through appropriate care planning (for example,
mitigating risks such as infection or pulmonary embolism associated
with central intravenous access), and identifying life-sustaining
treatments that must be continued, such as mechanical ventilation,
dialysis, suctioning, and chemotherapy, at the time of discharge or
transfer. Standardized assessment of these data elements will enable or
support: Clinical decision-making and early clinical intervention;
person-centered, high quality care through, for example, facilitating
better care continuity and coordination; better data exchange and
interoperability between settings; and longitudinal outcome analysis.
Therefore, reliable data elements assessing special services,
treatments, and interventions are needed to initiate a management
program that can optimize a patient's or resident's prognosis and
reduce the possibility of adverse events.
A TEP convened by our data element contractor provided input on the
proposed data elements for special services, treatments, and
interventions. In a meeting held on January 5 and 6, 2017, this TEP
found that these data elements are appropriate for standardization
because they would provide useful clinical information to inform care
planning and care coordination. The TEP affirmed that assessment of
these services and interventions is standard clinical practice, and
that the collection of these data by means of a list and checkbox
format would conform with common workflow for PAC providers. A summary
of the January 5 and 6, 2017 TEP meeting titled ``SPADE Technical
Expert Panel Summary (Second Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Comments on the category of special services, treatments, and
interventions were also submitted by stakeholders during the FY 2018
IRF PPS proposed rule (82 FR 20726 through 20736) public comment
period. One commenter supported adding the SPADEs for special services,
treatments, and interventions. Others stated labor costs and staff
burden would increase for data collection. The Medicare Payment
Advisory Commission (MedPAC) suggested that a few other high-cost
services, such as cardiac monitoring and specialty bed/surfaces, may
warrant consideration for inclusion in future collection efforts. One
commenter believes that the low frequency of the special services,
treatments, and interventions in the IRF setting makes them not worth
assessing for patients given the cost of data collection and reporting.
A few commenters noted that many of these data elements should be
obtainable from administrative data (that is, coding and Medicare
claims), and therefore, assessing them through patient record review
would be duplicated effort.
Information on data element performance in the National Beta Test,
which collected data between November 2017 and August 2018, is reported
within each data element proposal below. Clinical staff who
participated in the National Beta Test supported these data elements
because of their importance in conveying patient or resident
significant health care needs, complexity, and progress. However,
clinical staff also noted that, despite the simple ``check box'' format
of these data element, they sometimes needed to consult multiple
information sources to determine a patient's or resident's treatments.
We sought comment on our proposals to collect as standardized
patient assessment data the following data with respect to special
services, treatments, and interventions.
Commenters submitted the following comments related to the proposed
rule's discussion of special services, treatments, and interventions
data elements.
Comment: One commenter was supportive of collecting these data
elements, noting that collection will help to better inform CMS and IRF
providers on the severity and needs of patients in this setting.
Response: We thank the commenter for the support of these items. We
selected the Special Services, Treatments, and Interventions data
elements for proposal as standardized data in part because of the
attributes noted.
Comment: Some commenters were concerned about the reliability of
the Special Services, Treatments, and Interventions data elements,
noting that the results of the National Beta Test indicated that these
data elements had a low interrater reliability kappa statistic relative
to other data elements in the test.
Response: In the category of Special Services, Treatments, and
Interventions, for SPADEs where kappas could be calculated, 1 data
element and 2 sub-elements demonstrated overall reliabilities in the
moderate range (0.41-0.60) and only 1 sub-element demonstrated an
overall reliability in the slight/poor range (0.00-0.20). These overall
reliabilities were as follows: 0.60 for the Therapeutic Diet data
element; 0.55 for the ``Continuous'' sub-element of Oxygen Therapy;
0.46 for the ``Other'' sub-element of IV Medications; and 0.13 for the
``Anticoagulant'' sub-element of IV Medications. However, the overall
reliabilities for all other data elements and sub-elements where kappas
could be calculated were substantial/good or excellent/almost perfect.
When looking at percent agreement--an alternative measure of interrater
agreement--values of overall percent agreement for all Special
Services, Treatments, and Interventions SPADEs and sub-elements ranged
from 80 to 100 percent.
Comment: Commenters also noted concern around the burden of
completing these data elements, in particular because of their low
frequency of occurrence in IRF settings. To reduce burden around
collection of this information, commenters recommended that CMS explore
obtaining this data via claims. Additionally, one commenter added that
if these data elements are finalized, they should be collected at
discharge only, to reduce administrative burden.
Response: We appreciate the commenters' concern for burden on
clinical staff due to completing assessments with respect to both
admission and discharge. We believe that assessment of various special
services, treatments, and interventions received by patients in the IRF
setting will provide important information for care planning and
resource use in IRFs. The assessments of the special services,
treatments, and interventions with multiple responses are formatted as
a ``check all that apply'' format. Therefore, when treatments do not
apply--as the commenters note, this is the case for many IRF patients--
the assessor need only check one row for ``None of the Above.'' We will
take under consideration the commenters' recommendation to explore the
feasibility of collecting information on special services, treatments,
and interventions through claims-based data. Regarding the
recommendation to collect these SPADEs at discharge only, we state that
it is clinically appropriate and important to the ultimate usefulness
of these SPADEs that they are collected with respect to both admission
and
[[Page 39123]]
discharge. For example, for patients coming from acute care or from the
community, the admission assessment establishes a baseline for the IRF
stay. For all patients, the admission assessment ensures that each
patient is systematically assessed for a broad range of health and
well-being issues, which we expect to inform care planning.
Comment: One commenter expressed concern that the Special Services,
Treatments, and Interventions data elements assess the presence or
absence of something rather than the clinical rationale or patient
outcomes. This commenter stressed the importance of bringing this
assessment to ``the next level'' in order to determine impact of these
treatments on patients' outcomes.
Response: We agree with commenter's concern that recording the
presence or absence of certain treatments is only a first step in
characterizing the complexity that is often the cause of a patient's
receipt of special services, treatments, and interventions. We clarify
that all the SPADEs we proposed were intended as a minimum assessment
and do not limit the ability of providers to conduct a more
comprehensive evaluation of a patient's situation to identify the
potential impacts on outcomes that the commenter describes.
Comment: One commenter noted that the item numbering in the Special
Services, Treatments, and Interventions data elements is extremely
confusing and needs to be reworked.
Response: Several patient assessment tools have traditionally
combined letters and numbers, along with labels, to distinguish between
data elements. The proposed data elements in the Special Services,
Treatments, and Interventions section follow the conventions
established by CMS. However, we will take this feedback into
consideration in our evaluation and refinement of patient assessment
instruments.
Final decisions on the SPADEs are given below, following more
detailed comments on each SPADE proposal.
Cancer Treatment: Chemotherapy (IV, Oral, Other)
In the FY 2020 IRF PPS proposed rule (84 FR 17297 through 17299),
we proposed that the Chemotherapy (IV, Oral, Other) data element meets
the definition of standardized patient assessment data with respect to
special services, treatments, and interventions under section
1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20726
through 20727), chemotherapy is a type of cancer treatment that uses
drugs to destroy cancer cells. It is sometimes used when a patient has
a malignancy (cancer), which is a serious, often life-threatening or
life-limiting condition. Both intravenous (IV) and oral chemotherapy
have serious side effects, including nausea/vomiting, extreme fatigue,
risk of infection due to a suppressed immune system, anemia, and an
increased risk of bleeding due to low platelet counts. Oral
chemotherapy can be as potent as chemotherapy given by IV and can be
significantly more convenient and less resource-intensive to
administer. Because of the toxicity of these agents, special care must
be exercised in handling and transporting chemotherapy drugs. IV
chemotherapy is administered either peripherally, or more commonly,
given via an indwelling central line, which raises the risk of
bloodstream infections. Given the significant burden of malignancy, the
resource intensity of administering chemotherapy, and the side effects
and potential complications of these highly-toxic medications,
assessing the receipt of chemotherapy is important in the PAC setting
for care planning and determining resource use. The need for
chemotherapy predicts resource intensity, both because of the
complexity of administering these potent, toxic drug combinations under
specific protocols, and because of what the need for chemotherapy
signals about the patient's underlying medical condition. Furthermore,
the resource intensity of IV chemotherapy is higher than for oral
chemotherapy, as the protocols for administration and the care of the
central line (if present) for IV chemotherapy require significant
resources.
The Chemotherapy (IV, Oral, Other) data element consists of a
principal data element (Chemotherapy) and three response option sub-
elements: IV chemotherapy, which is generally resource-intensive; Oral
chemotherapy, which is less invasive and generally requires less
intensive administration protocols; and a third category, Other,
provided to enable the capture of other less common chemotherapeutic
approaches. This third category is potentially associated with higher
risks and is more resource intensive due to delivery by other routes
(for example, intraventricular or intrathecal). If the assessor
indicates that the patient is receiving chemotherapy on the principal
Chemotherapy data element, the assessor would then indicate by which
route or routes (for example, IV, Oral, Other) the chemotherapy is
administered.
A single Chemotherapy data element that does not include the
proposed three sub-elements is currently in use in the MDS in SNFs. For
more information on the Chemotherapy (IV, Oral, Other) data element, we
refer readers to the document titled ``Final Specifications for IRF QRP
Quality Measures and Standardized Patient Assessment Data Elements,''
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Chemotherapy data element was first proposed as a standardized
patient assessment data element in the FY 2018 IRF PPS proposed rule
(82 FR 20726 through 20727). In that proposed rule, we stated that the
proposal was informed by input we received through a call for input
published on the CMS Measures Management System Blueprint website.
Input submitted from August 12 to September 12, 2016 noted support for
the IV Chemotherapy data element and suggested it be included as
standardized patient assessment data. We also stated that those
commenters had noted that assessing the use of chemotherapy services is
relevant to share across the care continuum to facilitate care
coordination and care transitions and noted the validity of the data
element. Commenters also noted the importance of capturing all types of
chemotherapy, regardless of route, and stated that collecting data only
on patients and residents who received chemotherapy by IV would limit
the usefulness of this standardized data element. A summary report for
the August 12 to September 12, 2016 public comment period titled
``SPADE August 2016 Public Comment Summary Report'' is available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general; no additional
comments were received that were specific to the Chemotherapy data
element.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Chemotherapy data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results
[[Page 39124]]
of this test found the Chemotherapy data element to be feasible and
reliable for use with PAC patients and residents. More information
about the performance of the Chemotherapy data element in the National
Beta Test can be found in the document titled ``Final Specifications
for IRF QRP Quality Measures and Standardized Patient Assessment Data
Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP members
did not specifically discuss the Chemotherapy data element, the TEP
members supported the assessment of the special services, treatments,
and interventions included in the National Beta Test with respect to
both admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for chemotherapy,
stakeholder input, and strong test results, we proposed that the
Chemotherapy (IV, Oral, Other) data element with a principal data
element and three sub-elements meet the definition of standardized
patient assessment data with respect to special services, treatments,
and interventions under section 1899B(b)(1)(B)(iii) of the Act and to
adopt the Chemotherapy (IV, Oral, Other) data element as standardized
patient assessment data for use in the IRF QRP.
A commenter submitted the following comment related to the proposed
rule's discussion of the Chemotherapy data element.
Comment: One commenter agreed that it is important to know if a
patient is receiving chemotherapy for cancer and the method of
administration, but also expressed concern about the lack of an
association with a patient outcome. This commenter noted that
implications of chemotherapy for patients needing speech-language
pathology services include chemotherapy-related cognitive impairment,
dysphagia, and speech- and voice-related deficits.
Response: We appreciate the commenter's concern. We agree with the
commenter that chemotherapy can create related treatment needs for
patients, such as the examples noted by the commenter. However, we
believe that it is not feasible for SPADEs to capture all of a
patient's needs related to any given treatment, and we maintain that
the Special Services, Treatments, and Interventions SPADEs provide a
common foundation of clinical assessment, which can be built on by the
individual provider or a patient's care team.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the Chemotherapy (IV, Oral, Other)
data element as standardized patient assessment data beginning with the
FY 2022 IRF QRP as proposed.
Cancer Treatment: Radiation
In the FY 2020 IRF PPS proposed rule (84 FR 17299), we proposed
that the Radiation data element meets the definition of standardized
patient assessment data with respect to special services, treatments,
and interventions under section 1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20727
through 20728), radiation is a type of cancer treatment that uses high-
energy radioactivity to stop cancer by damaging cancer cell DNA, but it
can also damage normal cells. Radiation is an important therapy for
particular types of cancer, and the resource utilization is high, with
frequent radiation sessions required, often daily for a period of
several weeks. Assessing whether a patient or resident is receiving
radiation therapy is important to determine resource utilization
because PAC patients and residents will need to be transported to and
from radiation treatments, and monitored and treated for side effects
after receiving this intervention. Therefore, assessing the receipt of
radiation therapy, which would compete with other care processes given
the time burden, would be important for care planning and care
coordination by PAC providers.
The proposed data element consists of the single Radiation data
element. The Radiation data element is currently in use in the MDS in
SNFs. For more information on the Radiation data element, we refer
readers to the document titled ``Final Specifications for IRF QRP
Quality Measures and Standardized Patient Assessment Data Elements,''
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Radiation data element was first proposed as a standardized
patient assessment data element in the FY 2018 IRF PPS proposed rule
(82 FR 20727 through 20728). In that proposed rule, we stated that the
proposal was informed by input we received through a call for input
published on the CMS Measures Management System Blueprint website.
Input submitted from August 12 to September 12, 2016 noted support for
the Radiation data element, noting its importance and clinical
usefulness for patients and residents in PAC settings, due to the side
effects and consequences of radiation treatment on patients and
residents that need to be considered in care planning and care
transitions, the feasibility of the item, and the potential for it to
improve quality. A summary report for the August 12 to September 12,
2016 public comment period titled ``SPADE August 2016 Public Comment
Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general; no additional
comments were received
[[Page 39125]]
that were specific to the Radiation data element.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Radiation data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the Radiation
data element to be feasible and reliable for use with PAC patients and
residents. More information about the performance of the Radiation data
element in the National Beta Test can be found in the document titled
``Final Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP members
did not specifically discuss the Radiation data element, the TEP
members supported the assessment of the special services, treatments,
and interventions included in the National Beta Test with respect to
both admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present results of the National Beta Test and solicit
additional comments. General input on the testing and item development
process and concerns about burden were received from stakeholders
during this meeting and via email through February 1, 2019. A summary
of the public input received from the November 27, 2018 stakeholder
meeting titled ``Input on Standardized Patient Assessment Data Elements
(SPADEs) Received After November 27, 2018 Stakeholder Meeting'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for radiation,
stakeholder input, and strong test results, we proposed that the
Radiation data element meets the definition of standardized patient
assessment data with respect to special services, treatments, and
interventions under section 1899B(b)(1)(B)(iii) of the Act and to adopt
the Radiation data element as standardized patient assessment data for
use in the IRF QRP.
A commenter submitted the following comment related to the proposed
rule's discussion of the Radiation data element.
Comment: One commenter expressed concern that the Radiation data
element assesses whether a patient is receiving radiation for cancer
treatment, but does not identify the rationale for and outcomes
associated with radiation. The commenter noted that implications of
radiation for patients needing speech-language pathology services
include reduced head and neck range of motion due to radiation or
severe fibrosis, scar bands, and reconstructive surgery complications
and that these can impact both communication and swallowing abilities.
Response: We appreciate the commenter's concern. We agree with the
commenter that radiation can create related treatment needs for
patients, such as the examples noted by the commenter. However, we
believe that it is not feasible for SPADEs to capture all of a
patient's needs related to any given treatment, and we maintain that
the Special Services, Treatments, and Interventions SPADEs provide a
common foundation of clinical assessment, which can be built on by the
individual provider or a patient's care team.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the Radiation data element as
standardized patient assessment data beginning with the FY 2022 IRF QRP
as proposed.
Respiratory Treatment: Oxygen Therapy (Intermittent,
Continuous, High-concentration Oxygen Delivery System)
In the FY 2020 IRF PPS proposed rule (84 FR 17299 through 17300),
we proposed that the Oxygen Therapy (Intermittent, Continuous, High-
concentration Oxygen Delivery System) data element meets the definition
of standardized patient assessment data with respect to special
services, treatments, and interventions under section
1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20728), we
proposed a similar data element related to oxygen therapy. Oxygen
therapy provides a patient or resident with extra oxygen when medical
conditions such as chronic obstructive pulmonary disease, pneumonia, or
severe asthma prevent the patient or resident from getting enough
oxygen from breathing. Oxygen administration is a resource-intensive
intervention, as it requires specialized equipment such as a source of
oxygen, delivery systems (for example, oxygen concentrator, liquid
oxygen containers, and high-pressure systems), the patient interface
(for example, nasal cannula or mask), and other accessories (for
example, regulators, filters, tubing). The data element proposed here
captures patient or resident use of three types of oxygen therapy
(intermittent, continuous, and high-concentration oxygen delivery
system), which reflects the intensity of care needed, including the
level of monitoring and bedside care required. Assessing the receipt of
this service is important for care planning and resource use for PAC
providers.
The proposed data element, Oxygen Therapy, consists of the
principal Oxygen Therapy data element and three response option sub-
elements: Continuous (whether the oxygen was delivered continuously,
typically defined as > =14 hours per day); Intermittent; or High-
concentration Oxygen Delivery System. Based on public comments and
input from expert advisors about the importance and clinical usefulness
of documenting the extent of oxygen use, we added a third sub-element,
high-concentration oxygen delivery system, to the sub-elements, which
previously included only intermittent and continuous. If the assessor
indicates that the patient is receiving oxygen therapy on the principal
oxygen therapy data element, the assessor then would indicate the type
of oxygen the patient receives (for example, Intermittent, Continuous,
High-concentration oxygen delivery system).
These three proposed sub-elements were developed based on similar
data elements that assess oxygen therapy, currently in use in the MDS
in SNFs (``Oxygen Therapy''), previously used in the OASIS (``Oxygen
(intermittent or continuous)''), and a data element tested in the PAC
PRD that focused on intensive oxygen therapy (``High O2 Concentration
Delivery System with FiO2 > 40 percent''). For more information on the
proposed Oxygen
[[Page 39126]]
Therapy (Continuous, Intermittent, High-concentration oxygen delivery
system) data element, we refer readers to the document titled ``Final
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Oxygen Therapy (Intermittent, Continuous) data element was
first proposed as standardized patient assessment data in the FY 2018
IRF PPS proposed rule (82 FR 20728). In that proposed rule, we stated
that the proposal was informed by input we received on the single data
element, Oxygen (inclusive of intermittent and continuous oxygen use),
through a call for input published on the CMS Measures Management
System Blueprint website. Input submitted from August 12 to September
12, 2016, noted the importance of the Oxygen data element, noting
feasibility of this item in PAC, and the relevance of it to
facilitating care coordination and supporting care transitions, but
suggesting that the extent of oxygen use be documented. A summary
report for the August 12 to September 12, 2016 public comment period
titled ``SPADE August 2016 Public Comment Summary Report'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general; no additional
comments were received that were specific to the Oxygen Therapy
(Intermittent, Continuous) data element.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Oxygen Therapy data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the Oxygen
Therapy data element to be feasible and reliable for use with PAC
patients and residents. More information about the performance of the
Oxygen Therapy data element in the National Beta Test can be found in
the document titled ``Final Specifications for IRF QRP Quality Measures
and Standardized Patient Assessment Data Elements,'' available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Oxygen Therapy data element, the TEP supported
the assessment of the special services, treatments, and interventions
included in the National Beta Test with respect to both admission and
discharge. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing oxygen therapy,
stakeholder input, and strong test results, we proposed that the Oxygen
Therapy (Intermittent, Continuous, High-concentration Oxygen Delivery
System) data element with a principal data element and three sub-
elements meets the definition of standardized patient assessment data
with respect to special services, treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act and to adopt the Oxygen Therapy
(Intermittent, Continuous, High-concentration Oxygen Delivery System)
data element as standardized patient assessment data for use in the IRF
QRP.
We invited public comment on this proposal. While we received
support from some commenters on the Special Services, Treatments, and
Interventions section (IX.G.2 in this final rule) and its proposals as
a whole (section IX.F in this final rule), we did not receive any
specific comments on the Oxygen Therapy (Intermittent, Continuous,
High-concentration Oxygen Delivery System) data element in particular.
After careful consideration of the public comments we received on
the category of Special Services, Treatments, and Interventions, we are
finalizing our proposal to adopt the Oxygen Therapy (Intermittent,
Continuous, High-Concentration Oxygen Delivery System) data element as
standardized patient assessment data beginning with the FY 2022 IRF QRP
as proposed.
Respiratory Treatment: Suctioning (Scheduled, as Needed)
In the FY 2020 IRF PPS proposed rule (84 FR 17300 through 17302),
we proposed that the Suctioning (Scheduled, As needed) data element
meets the definition of standardized patient assessment data with
respect to special services, treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20728
through 20729), suctioning is a process used to clear secretions from
the airway when a person cannot clear those secretions on his or her
own. It is done by aspirating secretions through a catheter connected
to a suction source. Types of suctioning include oropharyngeal and
nasopharyngeal suctioning, nasotracheal suctioning, and suctioning
through an artificial airway such as a tracheostomy tube. Oropharyngeal
and nasopharyngeal suctioning are a key part of many patients' or
residents' care plans, both to prevent the accumulation of secretions
than can lead to aspiration pneumonias (a common condition in patients
and residents with inadequate gag reflexes), and to relieve
obstructions from mucus plugging during an acute or chronic respiratory
infection, which often lead to desaturations and increased respiratory
effort. Suctioning can be done on a scheduled basis if the patient is
judged to clinically benefit from regular interventions, or can be done
as needed when secretions become so prominent that gurgling or choking
is noted, or a sudden desaturation occurs from a mucus plug. As
suctioning is generally performed by a care provider
[[Page 39127]]
rather than independently, this intervention can be quite resource
intensive if it occurs every hour, for example, rather than once a
shift. It also signifies an underlying medical condition that prevents
the patient from clearing his/her secretions effectively (such as after
a stroke, or during an acute respiratory infection). Generally,
suctioning is necessary to ensure that the airway is clear of
secretions which can inhibit successful oxygenation of the individual.
The intent of suctioning is to maintain a patent airway, the loss of
which can lead to death or complications associated with hypoxia.
The Suctioning (Scheduled, As needed) data element consists of a
principal data element, and two sub-elements: Scheduled and As needed.
These sub-elements capture two types of suctioning. Scheduled indicates
suctioning based on a specific frequency, such as every hour. As needed
means suctioning only when indicated. If the assessor indicates that
the patient is receiving suctioning on the principal Suctioning data
element, the assessor would then indicate the frequency (for example,
Scheduled, As needed). The proposed data element is based on an item
currently in use in the MDS in SNFs which does not include our proposed
two sub-elements, as well as data elements tested in the PAC PRD that
focused on the frequency of suctioning required for patients and
residents with tracheostomies (``Trach Tube with Suctioning: Specify
most intensive frequency of suctioning during stay [Every __hours]'').
For more information on the Suctioning data element, we refer readers
to the document titled ``Final Specifications for IRF QRP Quality
Measures and Standardized Patient Assessment Data Elements,'' available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Suctioning data element was first proposed as standardized
patient assessment data elements in the FY 2018 IRF PPS proposed rule
(82 FR 20728 through 20729). In that proposed rule, we stated that the
proposal was informed by input we received through a call for input
published on the CMS Measures Management System Blueprint website.
Input submitted from August 12 to September 12, 2016 noted support for
the Suctioning data element. The input noted the feasibility of this
item in PAC, and the relevance of this data element to facilitating
care coordination and supporting care transitions.
We also stated that those commenters had suggested that we examine
the frequency of suctioning to better understand the use of staff time,
the impact on a patient or resident's capacity to speak and swallow,
and intensity of care required. Based on these comments, we decided to
add two sub-elements (Scheduled and As needed) to the suctioning
element. The proposed Suctioning data element includes both the
principal Suctioning data element that is included on the MDS in SNFs
and two sub-elements, Scheduled and As needed. A summary report for the
August 12 to September 12, 2016 public comment period titled ``SPADE
August 2016 Public Comment Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general; no additional
comments were received that were specific to the Suctioning data
element. Subsequent to receiving comments on the FY 2018 IRF PPS rule,
the Suctioning data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the Suctioning
data element to be feasible and reliable for use with PAC patients and
residents. More information about the performance of the Suctioning
data element in the National Beta Test can be found in the document
titled ``Final Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Suctioning data element, the TEP supported the
assessment of the special services, treatments, and interventions
included in the National Beta Test with respect to both admission and
discharge. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicited additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for suctioning,
stakeholder input, and strong test results, we proposed that the
Suctioning (Scheduled, As needed) data element with a principal data
element and two sub-elements meets the definition of standardized
patient assessment data with respect to special services, treatments,
and interventions under section 1899B(b)(1)(B)(iii) of the Act and to
adopt the Suctioning (Scheduled, As needed) data element as
standardized patient assessment data for use in the IRF QRP.
A commenter submitted the following comment related to the proposed
rule's discussion of the Suctioning data element.
Comment: One commenter requested that this data element also assess
the frequency of suctioning, as it can impact resource utilization and
potential medication changes in the plan of care.
Response: We appreciate the commenter's feedback that the response
options for this data element may not fully capture impacts to resource
utilization and care plans. The Suctioning data element does include
sub-elements to identify if suctioning is performed on a ``Scheduled''
or ``As Needed'' basis, but it does not directly
[[Page 39128]]
assess the frequency of suctioning by, for example, asking an assessor
to specify how often suctioning is scheduled. As finalized, this data
element differentiates between patients who only occasionally need
suctioning, and patients for whom assessment of suctioning needs is a
frequent and routine part of the care (that is, where suctioning is
performed on a schedule according to physician instructions). In our
work to identify standardized data elements, we have strived to balance
the scope and level of detail of the data elements against the
potential burden placed on patients and providers. However, we clarify
that any SPADE is intended as a minimum assessment and does not limit
the ability of providers to conduct a more comprehensive evaluation of
a patient's situation to identify the potential impacts on outcomes
that the commenter describes.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the Suctioning (Scheduled, As
needed) data element as standardized patient assessment data beginning
with the FY 2022 IRF QRP as proposed.
Respiratory Treatment: Tracheostomy Care
In the FY 2020 IRF PPS proposed rule (84 FR 17302), we proposed
that the Tracheostomy Care data element meets the definition of
standardized patient assessment data with respect to special services,
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the
Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20729
through 20730), a tracheostomy provides an air passage to help a
patient or resident breathe when the usual route for breathing is
obstructed or impaired. Generally, in all of these cases, suctioning is
necessary to ensure that the tracheostomy is clear of secretions, which
can inhibit successful oxygenation of the individual. Often,
individuals with tracheostomies are also receiving supplemental
oxygenation. The presence of a tracheostomy, albeit permanent or
temporary, warrants careful monitoring and immediate intervention if
the tracheostomy becomes occluded or if the device used becomes
dislodged. While in rare cases the presence of a tracheostomy is not
associated with increased care demands (and in some of those instances,
the care of the ostomy is performed by the patient) in general the
presence of such as device is associated with increased patient risk,
and clinical care services will necessarily include close monitoring to
ensure that no life-threatening events occur as a result of the
tracheostomy. In addition, tracheostomy care, which primarily consists
of cleansing, dressing changes, and replacement of the tracheostomy
cannula (tube), is a critical part of the care plan. Regular cleansing
is important to prevent infection, such as pneumonia, and to prevent
any occlusions with which there are risks for inadequate oxygenation.
The proposed data element consists of the single Tracheostomy Care
data element. The proposed data element is currently in use in the MDS
in SNFs (``Tracheostomy care''). For more information on the
Tracheostomy Care data element, we refer readers to the document titled
``Final Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Tracheostomy Care data element was first proposed as a
standardized patient assessment data element in the FY 2018 IRF PPS
proposed rule (82 FR 20729 through 20730). In that proposed rule, we
stated that the proposal was informed by input we received on the
Tracheostomy Care data element through a call for input published on
the CMS Measures Management System Blueprint website. Input submitted
from August 12 to September 12, 2016 noted support for this data
element, noting the feasibility of this item in PAC, and the relevance
of this data element to facilitating care coordination and supporting
care transitions. A summary report for the August 12 to September 12,
2016 public comment period titled ``SPADE August 2016 Public Comment
Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general; no additional
comments were received that were specific to the Tracheostomy Care data
element.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Tracheostomy Care data element was included in the National Beta Test
of candidate data elements conducted by our data element contractor
from November 2017 to August 2018. Results of this test found the
Tracheostomy Care data element to be feasible and reliable for use with
PAC patients and residents. More information about the performance of
the Tracheostomy Care data element in the National Beta Test can be
found in the document titled ``Final Specifications for IRF QRP Quality
Measures and Standardized Patient Assessment Data Elements,'' at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Tracheostomy Care data element, the TEP
supported the assessment of the special services, treatments, and
interventions included in the National Beta Test with respect to both
admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for tracheostomy care,
stakeholder input, and strong test results, we proposed that the
[[Page 39129]]
Tracheostomy Care data element meets the definition of standardized
patient assessment data with respect to special services, treatments,
and interventions under section 1899B(b)(1)(B)(iii) of the Act and to
adopt the Tracheostomy Care data element as standardized patient
assessment data for use in the IRF QRP.
We invited public comment on this proposal. While we received
support from some commenters on Special Services, Treatments, and
Interventions as a whole (section IX.G.2 in this final rule), we did
not receive any specific comments on Tracheostomy Care data element.
After careful consideration of the public comments we received on
the category of Special Services, Treatments, and Interventions, we are
finalizing our proposal to adopt the Tracheostomy Care data element as
standardized patient assessment data beginning with the FY 2022 IRF QRP
as proposed.
Respiratory Treatment: Non-Invasive Mechanical Ventilator
(BiPAP, CPAP)
In the FY 2020 IRF PPS proposed rule (84 FR 17303), we proposed
that the Non-invasive Mechanical Ventilator (Bilevel Positive Airway
Pressure [BiPAP], Continuous Positive Airway Pressure [CPAP]) data
element meets the definition of standardized patient assessment data
with respect to special services, treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20730),
BiPAP and CPAP are respiratory support devices that prevent the airways
from closing by delivering slightly pressurized air via electronic
cycling throughout the breathing cycle (BiPAP) or through a mask
continuously (CPAP). Assessment of non-invasive mechanical ventilation
is important in care planning, as both CPAP and BiPAP are resource-
intensive (although less so than invasive mechanical ventilation) and
signify underlying medical conditions about the patient or resident who
requires the use of this intervention. Particularly when used in
settings of acute illness or progressive respiratory decline,
additional staff (for example, respiratory therapists) are required to
monitor and adjust the CPAP and BiPAP settings and the patient or
resident may require more nursing resources.
The proposed data element, Non-invasive Mechanical Ventilator
(BiPAP, CPAP), consists of the principal Non-invasive Mechanical
Ventilator data element and two response option sub-elements: BiPAP and
CPAP. If the assessor indicates that the patient is receiving non-
invasive mechanical ventilation on the principal Non-invasive
Mechanical Ventilator data element, the assessor would then indicate
which type (for example, BiPAP, CPAP). Data elements that assess non-
invasive mechanical ventilation are currently included on LCDS for the
LTCH setting (``Non-invasive Ventilator (BiPAP, CPAP)''), and the MDS
for the SNF setting (``Non-invasive Mechanical Ventilator (BiPAP/
CPAP)''). For more information on the Non-invasive Mechanical
Ventilator (BiPAP, CPAP) data element, we refer readers to the document
titled ``Final Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Non-invasive Mechanical Ventilator data element was first
proposed as standardized patient assessment data elements in the FY
2018 IRF PPS proposed rule (82 FR 20730). In that proposed rule, we
stated that the proposal was informed by input we received through a
call for input published on the CMS Measures Management System
Blueprint website. Input submitted from August 12 to September 12, 2016
on a single data element, BiPAP/CPAP, that captures equivalent clinical
information but uses a different label than the data element currently
used in the MDS in SNFs and LCDS, noted support for this data element,
noting the feasibility of these items in PAC, and the relevance of this
data element for facilitating care coordination and supporting care
transitions. In addition, we also stated that some commenters supported
separating out BiPAP and CPAP as distinct sub-elements, as they are
therapies used for different types of patients and residents. A summary
report for the August 12 to September 12, 2016 public comment period
titled ``SPADE August 2016 Public Comment Summary Report'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general. One commenter
noted appreciation of the revisions to the Non-invasive Mechanical
Ventilator data element in response to comments submitted during a
public input period held from August 12 to September 12, 2016.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Non-invasive Mechanical Ventilator data element was included in the
National Beta Test of candidate data elements conducted by our data
element contractor from November 2017 to August 2018. Results of this
test found the Non-invasive Mechanical Ventilator data element to be
feasible and reliable for use with PAC patients and residents. More
information about the performance of the Non-invasive Mechanical
Ventilator data element in the National Beta Test can be found in the
document titled ``Final Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Non-invasive Mechanical Ventilator data
element, the TEP supported the assessment of the special services,
treatments, and interventions included in the National Beta Test with
respect to both admission and discharge. A summary of the September 17,
2018 TEP meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized
[[Page 39130]]
Patient Assessment Data Elements (SPADEs) Received After November 27,
2018 Stakeholder Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for non-invasive
mechanical ventilation, stakeholder input, and strong test results, we
proposed that the Non-invasive Mechanical Ventilator (BiPAP, CPAP) data
element with a principal data element and two sub-elements meets the
definition of standardized patient assessment data with respect to
special services, treatments, and interventions under section
1899B(b)(1)(B)(iii) of the Act and to adopt the Non-invasive Mechanical
Ventilator (BiPAP, CPAP) data element as standardized patient
assessment data for use in the IRF QRP.
We invited public comment on this proposal. While we received
support from some commenters on Special Services, Treatments, and
Interventions as a whole (section IX.G.2 in this final rule), we did
not receive any specific comments on the Non-invasive Mechanical
Ventilator (BiPAP, CPAP) data element.
After careful consideration of the public comments we received on
the category of Special Services, Treatments, and Interventions, we are
finalizing our proposal to adopt the Non-invasive Mechanical Ventilator
(BiPAP, CPAP) data element as standardized patient assessment data
beginning with the FY 2022 IRF QRP as proposed.
Respiratory Treatment: Invasive Mechanical Ventilator
In the FY 2020 IRF PPS proposed rule (84 FR 17304), we proposed
that the Invasive Mechanical Ventilator data element meets the
definition of standardized patient assessment data with respect to
special services, treatments, and interventions under section
1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20730
through 20731), invasive mechanical ventilation includes ventilators
and respirators that ventilate the patient through a tube that extends
via the oral airway into the pulmonary region or through a surgical
opening directly into the trachea. Thus, assessment of invasive
mechanical ventilation is important in care planning and risk
mitigation. Ventilation in this manner is a resource-intensive therapy
associated with life-threatening conditions without which the patient
or resident would not survive. However, ventilator use has inherent
risks requiring close monitoring. Failure to adequately care for the
patient or resident who is ventilator dependent can lead to iatrogenic
events such as death, pneumonia, and sepsis. Mechanical ventilation
further signifies the complexity of the patient's underlying medical or
surgical condition. Of note, invasive mechanical ventilation is
associated with high daily and aggregate costs.\112\
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\112\ Wunsch, H., Linde-Zwirble, W.T., Angus, D.C., Hartman,
M.E., Milbrandt, E.B., & Kahn, J.M. (2010). ``The epidemiology of
mechanical ventilation use in the United States.'' Critical Care Med
38(10): 1947-1953.
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The proposed data element, Invasive Mechanical Ventilator, consists
of a single data element. Data elements that capture invasive
mechanical ventilation are currently in use in the MDS in SNFs and LCDS
in LTCHs. For more information on the Invasive Mechanical Ventilator
data element, we refer readers to the document titled ``Final
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Invasive Mechanical Ventilator data element was first proposed
as a standardized patient assessment data element in the FY 2018 IRF
PPS proposed rule (82 FR 20730 through 20731). In that proposed rule,
we stated that the proposal was informed by input we received on data
elements that assess invasive ventilator use and weaning status that
were tested in the PAC PRD (``Ventilator--Weaning'' and ``Ventilator--
Non-Weaning'') through a call for input published on the CMS Measures
Management System Blueprint website. Input submitted from August 12 to
September 12, 2016, noted support for this data element, highlighting
the importance of this information in supporting care coordination and
care transitions. We also stated that some commenters had expressed
concern about the appropriateness for standardization given: The
prevalence of ventilator weaning across PAC providers; the timing of
administration; how weaning is defined; and how weaning status in
particular relates to quality of care. These public comments guided our
decision to propose a single data element focused on current use of
invasive mechanical ventilation only, which does not attempt to capture
weaning status. A summary report for the August 12 to September 12,
2016 public comment period titled ``SPADE August 2016 Public Comment
Summary Report'' we received is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general. Two commenters
noted their appreciation of the revisions to the Invasive Mechanical
Ventilator data element in response to comments submitted during a
public input period held from August 12 to September 12, 2016.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Invasive Mechanical Ventilator data element was included in the
National Beta Test of candidate data elements conducted by our data
element contractor from November 2017 to August 2018. Results of this
test found the Invasive Mechanical Ventilator data element to be
feasible and reliable for use with PAC patients and residents. More
information about the performance of the Invasive Mechanical Ventilator
data element in the National Beta Test can be found in the document
titled ``Final Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data element. Although the TEP did not
specifically discuss the Invasive Mechanical Ventilator data element,
the TEP supported the assessment of the special services, treatments,
and interventions included in the National Beta Test with respect to
both admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
[[Page 39131]]
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present results of the National Beta Test and solicit
additional comments. General input on the testing and item development
process and concerns about burden were received from stakeholders
during this meeting and via email through February 1, 2019. A summary
of the public input received from the November 27, 2018 stakeholder
meeting titled ``Input on Standardized Patient Assessment Data Elements
(SPADEs) Received After November 27, 2018 Stakeholder Meeting'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for invasive mechanical
ventilation, stakeholder input, and strong test results, we proposed
that the Invasive Mechanical Ventilator data element that assesses the
use of an invasive mechanical ventilator meets the definition of
standardized patient assessment data with respect to special services,
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the
Act and to adopt the Invasive Mechanical Ventilator data element as
standardized patient assessment data for use in the IRF QRP.
A commenter submitted the following comment related to the proposed
rule's discussion of the Invasive Mechanical Ventilator data element.
Comment: One commenter noted disappointment over seeing that the
SPADE for invasive mechanical ventilator only assesses whether or not a
patient is on a mechanical ventilator. The commenter suggested CMS
consider collecting data to track functional outcomes related to
progress towards independence in communication and swallowing.
Response: We have attempted to balance the scope and level of
detail of the data elements against the potential burden placed on
patients and providers. We believe that assessing the use of an
invasive mechanical ventilator will be a useful point of information to
inform care planning and further assessment, such as related to
functional outcomes, as the commenter suggests.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the Invasive Mechanical Ventilator
data element as standardized patient assessment data beginning with the
FY 2022 IRF QRP as proposed.
Intravenous (IV) Medications (Antibiotics, Anticoagulants,
Vasoactive Medications, Other)
In the FY 2020 IRF PPS proposed rule (84 FR 17305 through 17306),
we proposed that the IV Medications (Antibiotics, Anticoagulants,
Vasoactive Medications, Other) data element meets the definition of
standardized patient assessment data with respect to special services,
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the
Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20731
through 20732), when we proposed a similar data element related to IV
medications, IV medications are solutions of a specific medication (for
example, antibiotics, anticoagulants) administered directly into the
venous circulation via a syringe or intravenous catheter. IV
medications are administered via intravenous push, single,
intermittent, or continuous infusion through a catheter placed into the
vein. Further, IV medications are more resource intensive to administer
than oral medications, and signify a higher patient complexity (and
often higher severity of illness).
The clinical indications for each of the sub-elements of the IV
Medications data element (Antibiotics, Anticoagulants, Vasoactive
Medications, and Other) are very different. IV antibiotics are used for
severe infections when the bioavailability of the oral form of the
medication would be inadequate to kill the pathogen or an oral form of
the medication does not exist. IV anticoagulants refer to anti-clotting
medications (that is, ``blood thinners''). IV anticoagulants are
commonly used for hospitalized patients who have deep venous
thrombosis, pulmonary embolism, or myocardial infarction, as well as
those undergoing interventional cardiac procedures. Vasoactive
medications refer to the IV administration of vasoactive drugs,
including vasopressors, vasodilators, and continuous medication for
pulmonary edema, which increase or decrease blood pressure or heart
rate. The indications, risks, and benefits of each of these classes of
IV medications are distinct, making it important to assess each
separately in PAC. Knowing whether or not patients and residents are
receiving IV medication and the type of medication provided by each PAC
provider will improve quality of care.
The IV Medications (Antibiotics, Anticoagulants, Vasoactive
Medications, and Other) data element we proposed consists of a
principal data element (IV Medications) and four response option sub-
elements: Antibiotics, Anticoagulants, Vasoactive Medications, and
Other. The Vasoactive Medications sub-element was not proposed in the
FY 2018 IRF PPS proposed rule (82 FR 20731 through 20732). We added the
Vasoactive Medications sub-element to our proposal in order to
harmonize the proposed IV Mediciations element with the data currently
collected in the LCDS.
If the assessor indicates that the patient is receiving IV
medications on the principal IV Medications data element, the assessor
would then indicate which types of medications (for example,
Antibiotics, Anticoagulants, Vasoactive Medications, Other). An IV
Medications data element is currently in use on the MDS in SNFs and
there is a related data element in OASIS that collects information on
Intravenous and Infusion Therapies. For more information on the IV
Medications (Antibiotics, Anticoagulants, Vasoactive Medications,
Other) data element, we refer readers to the document titled ``Final
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
An IV Medications data element was first proposed as standardized
patient assessment data element in the FY 2018 IRF PPS proposed rule
(82 FR 20731 through 20732). In that proposed rule, we stated that the
proposal was informed by input we received on Vasoactive Medications
through a call for input published on the CMS Measures Management
System Blueprint website. Input submitted from August 12 to September
12, 2016 supported this data element with one noting the importance of
this data element in supporting care transitions. We also stated that
those commenters had criticized the need for collecting specifically
Vasoactive Medications, giving feedback that the data element was too
narrowly focused. In addition, public comment received indicated that
the clinical significance of vasoactive medications administration
alone was not high enough in PAC to merit mandated assessment, noting
that related and more useful information could be captured in an item
that assessed all IV medication use. A
[[Page 39132]]
summary report for the August 12 to September 12, 2016 public comment
period titled ``SPADE August 2016 Public Comment Summary Report'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general; no additional
comments were received that were specific to the IV Medications data
element.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
IV Medications data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the IV
Medications data element to be feasible and reliable for use with PAC
patients and residents. More information about the performance of the
IV Medications data element in the National Beta Test can be found in
the document titled ``Final Specifications for IRF QRP Quality Measures
and Standardized Patient Assessment Data Elements,'' available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the IV Medications data element, the TEP supported
the assessment of the special services, treatments, and interventions
included in the National Beta Test with respect to both admission and
discharge. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for IV medications,
stakeholder input, and strong test results, we proposed that the IV
Medications (Antibiotics, Anticoagulants, Vasoactive Medications,
Other) data element with a principal data element and four sub-elements
meets the definition of standardized patient assessment data with
respect to special services, treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act and to adopt the IV Medications
(Antibiotics, Anticoagulants, Vasoactive Medications, Other) data
element as standardized patient assessment data for use in the IRF QRP.
Commenters submitted the following comments related to the proposed
rule's discussion of the IV Medications data elements.
Comment: One commenter noted that the IV Medications data elements
seem redundant of the proposed High-Risk Drug Classes: Use and
Indication data elements.
Response: We wish to clarify that the IV Medications data element
collects information on medications received by IV only, with sub-
elements specific to antibiotics, anticoagulants, and vasoactive
medications only. In contrast, the High Risk Drug Classes: Use and
Indication data element collects information on medications received by
any route, only for six specific drug classes, and collects information
on the presence of an indication. We believe the overlap between these
SPADEs is minimal, as it would only occur when a medication in a high-
risk drug class is delivered by IV. Additionally, in this case, the
High-Risk Drug Classes: Use and Indication data element would assess
the presence of an indication in the patient's medical record, which
the IV Medications data element does not do.
Comment: Commenters were concerned about the performance of the IV
Medications data element in the National Beta Test, noting that its
reliability was only fair to good and poor for the anticoagulation sub-
element.
Response: The kappa for the overarching IV Medications data element
was 0.70 across settings, which falls in the range of ``substantial/
good'' agreement. The IV Medications sub-element that had a ``slight/
poor'' reliability (in the range of 0.00-0.20) was the IV
Anticoagulants sub-element (kappa = 0.13). The Other IV Medications
sub-element had ``moderate'' reliability (kappa = 0.46). Consultation
with assessors suggested that the low kappa for the IV Anticoagulants
sub-element was likely due to inconsistent interpretation of the coding
instructions. Having identified the likely source of the relatively
lower interrater reliability, we are confident that with proper
training of IRFs on how to report the data elements, the reliability of
these sub-elements will be improved.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the IV Medications (Antibiotics,
Anticoagulants, Vasoactive Medications, Other) data element as
standardized patient assessment data beginning with the FY 2022 IRF QRP
as proposed.
Transfusions
In the FY 2020 IRF PPS proposed rule (84 FR 17306), we proposed
that the Transfusions data element meets the definition of standardized
patient assessment data with respect to special services, treatments,
and interventions under section 1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20732),
transfusion refers to introducing blood or blood products into the
circulatory system of a person. Blood transfusions are based on
specific protocols, with multiple safety checks and monitoring required
during and after the infusion in case of adverse events. Coordination
with the provider's blood bank is necessary, as well as documentation
by clinical staff to ensure compliance with regulatory requirements. In
addition, the need for transfusions signifies underlying patient
complexity that is likely to require care coordination and patient
monitoring, and impacts planning for transitions of care, as
transfusions are not performed by all PAC providers.
The proposed data element consists of the single Transfusions data
element. A
[[Page 39133]]
data element on transfusion is currently in use in the MDS in SNFs
(``Transfusions'') and a data element tested in the PAC PRD (``Blood
Transfusions'') was found feasible for use in each of the four PAC
settings. For more information on the Transfusions data element, we
refer readers to the document titled ``Final Specifications for IRF QRP
Quality Measures and Standardized Patient Assessment Data Elements,''
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Transfusions data element was first proposed as a standardized
patient assessment data element in the FY 2018 IRF PPS proposed rule
(82 FR 20732). In response to our proposal in the FY 2018 IRF PPS
proposed rule, we received public comments in support of the special
services, treatments, and interventions data elements in general; no
additional comments were received that were specific to the
Transfusions data element.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Transfusions data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the
Transfusions data element to be feasible and reliable for use with PAC
patients and residents. More information about the performance of the
Transfusions data element in the National Beta Test can be found in the
document titled ``Final Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Transfusions data element, the TEP supported
the assessment of the special services, treatments, and interventions
included in the National Beta Test with respect to both admission and
discharge. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for transfusions,
stakeholder input, and strong test results, we proposed that the
Transfusions data element meets the definition of standardized patient
assessment data with respect to special services, treatments, and
interventions under section 1899B(b)(1)(B)(iii) of the Act and to adopt
the Transfusions data element as standardized patient assessment data
for use in the IRF QRP.
Commenters submitted the following comments related to the proposed
rule's discussion of the Transfusions data element.
Comment: One commenter applauded CMS for including the Transfusions
data element, noting that it will provide information on care planning,
clinical decision making, patient safety, care transitions, and
resource use in IRFs and will contribute to higher quality and
coordinated care for patients who rely on these life-saving treatments.
Response: We thank the commenter for their support. We selected the
Transfusions data element for proposal as standardized data in part
because of the attributes that the commenter noted.
Comment: One commenter was concerned that IRFs will not have the
resources needed to provide patients with access to blood transfusions
and requested that CMS consider whether payments to IRFs are adequate
to cover the cost of this resource intensive, specialized service.
Response: We wish to clarify that this item is finalized only to
collect information on the complexity of the patient and resources the
patient requires. At this time, this item will not be used for any
payment purposes, and thus we are not able to comment on cost of this
service. We wish to clarify that this SPADE is not intended to measure
the ability of an IRF to provide in-house transfusions, only to capture
the services a given patient may be receiving. Further, for patients
who require services related to blood transfusions, information
collected by this data element is a part of common clinical workflow,
and thus, we believe that burden on resource intensity would not be
affected by the standardization of this data element.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the Transfusions data element as
standardized patient assessment data beginning with the FY 2022 IRF QRP
as proposed.
Dialysis (Hemodialysis, Peritoneal Dialysis)
In the FY 2020 IRF PPS proposed rule (84 FR 17306 through 17307),
we proposed that the Dialysis (Hemodialysis, Peritoneal dialysis) data
element meets the definition of standardized patient assessment data
with respect to special services, treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20732
through 20733), dialysis is a treatment primarily used to provide
replacement for lost kidney function. Both forms of dialysis
(hemodialysis and peritoneal dialysis) are resource intensive, not only
during the actual dialysis process but before, during, and following.
Patients and residents who need and undergo dialysis procedures are at
high risk for physiologic and hemodynamic instability from fluid shifts
and electrolyte disturbances, as well as infections that can lead to
sepsis. Further, patients or residents receiving hemodialysis are often
transported to a different facility, or at a minimum, to a different
location in the same facility for treatment. Close monitoring for fluid
shifts, blood pressure abnormalities, and other adverse effects is
required prior to, during, and following each dialysis session. Nursing
staff typically perform peritoneal dialysis at the bedside, and as with
hemodialysis, close monitoring is required.
[[Page 39134]]
The proposed data element, Dialysis (Hemodialysis, Peritoneal
dialysis) consists of the principal Dialysis data element and two
response option sub-elements: Hemodialysis and Peritoneal dialysis. If
the assessor indicates that the patient is receiving dialysis on the
principal Dialysis data element, the assessor would then indicate which
type (Hemodialysis or Peritoneal dialysis). The principal Dialysis data
element is currently included on the MDS in SNFs and the LCDS for LTCHs
and assesses the overall use of dialysis.
As the result public feedback described below, in the proposed
rule, we proposed a data element that includes the principal Dialysis
data element and two sub-elements (Hemodialysis and Peritoneal
dialysis). For more information on the Dialysis data element, we refer
readers to the document titled ``Final Specifications for IRF QRP
Quality Measures and Standardized Patient Assessment Data Elements,''
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Dialysis data element was first proposed as standardized
patient assessment data in the FY 2018 IRF PPS proposed rule (82 FR
20732 through 20733). In that proposed rule, we stated that the
proposal was informed by input we received on a singular Hemodialysis
data element through a call for input published on the CMS Measures
Management System Blueprint website. Input submitted from August 12 to
September 12, 2016 supported the assessment of hemodialysis and
recommended that the data element be expanded to include peritoneal
dialysis. We also stated that those commenters had supported the
singular Hemodialysis data element, noting the relevance of this
information for sharing across the care continuum to facilitate care
coordination and care transitions, the potential for this data element
to be used to improve quality, and the feasibility for use in PAC. In
addition, we received comments that the item would be useful in
improving patient and resident transitions of care. We also noted that
several commenters had stated that peritoneal dialysis should be
included in a standardized data element on dialysis and recommended
collecting information on peritoneal dialysis in addition to
hemodialysis. The rationale for including peritoneal dialysis from
commenters included the fact that patients and residents receiving
peritoneal dialysis will have different needs at post-acute discharge
compared to those receiving hemodialysis or not having any dialysis.
Based on these comments, the Hemodialysis data element was expanded to
include a principal Dialysis data element and two sub-elements,
Hemodialysis and Peritoneal dialysis. We proposed the version of the
Dialysis element that includes two types of dialysis. A summary report
for the August 12 to September 12, 2016 public comment period titled
``SPADE August 2016 Public Comment Summary Report'' is available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received comments in support of the special services, treatments,
and interventions data elements in general. One commenter noted that
they appreciated the revisions to the Dialysis data element in response
to comments submitted during a public input period held from August 12
to September 12, 2016.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Dialysis data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the Dialysis
data element to be feasible and reliable for use with PAC patients and
residents. More information about the performance of the Dialysis data
element in the National Beta Test can be found in the document titled
``Final Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although they did not
specifically discuss the Dialysis data element, the TEP supported the
assessment of the special services, treatments, and interventions
included in the National Beta Test with respect to both admission and
discharge. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for dialysis,
stakeholder input, and strong test results, we proposed that the
Dialysis (Hemodialysis, Peritoneal dialysis) data element with a
principal data element and two sub-elements meets the definition of
standardized patient assessment data with respect to special services,
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the
Act and to adopt the Dialysis (Hemodialysis, Peritoneal dialysis) data
element as standardized patient assessment data for use in the IRF QRP.
We invited public comment on this proposal. While we received
support from some commenters on this Special Services, Treatments, and
Interventions as a whole (section IX.G.2 in this final rule), we did
not receive any specific comments on the Dialysis (Hemodialysis,
Peritoneal dialysis) data element.
After careful consideration of the public comments we received on
the category of Special Services, Treatments, and Interventions, we are
finalizing our proposal to adopt the Dialysis (Hemodialysis, Peritoneal
dialysis) data element as standardized patient assessment data
beginning with the FY 2022 IRF QRP as proposed.
[[Page 39135]]
Intravenous (IV) Access (Peripheral IV, Midline, Central Line)
In the FY 2020 IRF PPS proposed rule (84 FR 17307 through 17308),
we proposed that the IV Access (Peripheral IV, Midline, Central line)
data element meets the definition of standardized patient assessment
data with respect to special services, treatments, and interventions
under section 1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20733
through 20734), patients or residents with central lines, including
those peripherally inserted or who have subcutaneous central line
``port'' access, always require vigilant nursing care to keep patency
of the lines and ensure that such invasive lines remain free from any
potentially life-threatening events such as infection, air embolism, or
bleeding from an open lumen. Clinically complex patients and residents
are likely to be receiving medications or nutrition intravenously. The
sub-elements included in the IV Access data elements distinguish
between peripheral access and different types of central access. The
rationale for distinguishing between a peripheral IV and central IV
access is that central lines confer higher risks associated with life-
threatening events such as pulmonary embolism, infection, and bleeding.
The proposed data element, IV Access (Peripheral IV, Midline,
Central line), consists of the principal IV Access data element and
three response option sub-elements: Peripheral IV, Midline, and Central
line. The proposed IV Access data element is not currently included on
any of the PAC assessment instruments. For more information on the IV
Access data element, we refer readers to the document titled ``Final
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The IV Access data element was first proposed as standardized
patient assessment data elements in the FY 2018 IRF PPS proposed rule
(82 FR 20733 through 20734). In that proposed rule, we stated that the
proposal was informed by input we received on one of the PAC PRD data
elements, Central Line Management, through a call for input published
on the CMS Measures Management System Blueprint website. A central line
is a type of IV access. Input submitted from August 12 to September 12,
2016 supported the assessment of central line management and
recommended that the data element be broadened to also include other
types of IV access. Several commenters noted feasibility and importance
for facilitating care coordination and care transitions. However, a few
commenters recommended that the definition of this data element be
broadened to include peripherally inserted central catheters (``PICC
lines'') and midline IVs. Based on public comment feedback and in
consultation with expert input, described below, we created an
overarching IV Access data element with sub-elements for other types of
IV access in addition to central lines (that is, peripheral IV and
midline). This expanded version of IV Access is the data element being
proposed. A summary report for the August 12 to September 12, 2016
public comment period titled ``SPADE August 2016 Public Comment Summary
Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general. One commenter
noted appreciation of the revisions to the IV Access data element in
response to comments submitted during a public input period held from
August 12 to September 12, 2016.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
IV Access data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the IV Access
data element to be feasible and reliable for use with PAC patients and
residents. More information about the performance of the IV Access data
element in the National Beta Test can be found in the document titled
``Final Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the IV Access data element, the TEP supported the
assessment of the special services, treatments, and interventions
included in the National Beta Test with respect to both admission and
discharge. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present results of the National Beta Test and solicit
additional comments. General input on the testing and item development
process and concerns about burden were received from stakeholders
during this meeting and via email through February 1, 2019. A summary
of the public input received from the November 27, 2018 stakeholder
meeting titled ``Input on Standardized Patient Assessment Data Elements
(SPADEs) Received After November 27, 2018 Stakeholder Meeting'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for IV access,
stakeholder input, and strong test results, we proposed that the IV
access (Peripheral IV, Midline, Central line) data element with a
principal data element and three sub-elements meets the definition of
standardized patient assessment data with respect to special services,
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the
Act and to adopt the IV Access (Peripheral IV, Midline, Central line)
data element as standardized patient assessment data for use in the IRF
QRP.
We invited public comment on this proposal. While we received
support from some commenters on this Special Services, Treatments, and
Interventions as a whole (section IX.G.2 in this final rule), we did
not receive any specific comments on the IV Access (Peripheral IV,
Midline, Central line) data element.
After careful consideration of the public comments we received on
the
[[Page 39136]]
category of Special Services, Treatments, and Interventions, we are
finalizing our proposal to adopt the IV Access (Peripheral IV, Midline,
Central line) data element as standardized patient assessment data
beginning with the FY 2022 IRF QRP as proposed.
Nutritional Approach: Parenteral/IV Feeding
In the FY 2020 IRF PPS proposed rule (84 FR 17308 through 17309),
we proposed that the Parenteral/IV Feeding data element meets the
definition of standardized patient assessment data with respect to
special services, treatments, and interventions under section
1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20734),
parenteral nutrition/IV feeding refers to a patient or resident being
fed intravenously using an infusion pump, bypassing the usual process
of eating and digestion. The need for IV/parenteral feeding indicates a
clinical complexity that prevents the patient or resident from meeting
his or her nutritional needs enterally, and is more resource intensive
than other forms of nutrition, as it often requires monitoring of blood
chemistries and the maintenance of a central line. Therefore, assessing
a patient's or resident's need for parenteral feeding is important for
care planning and resource use. In addition to the risks associated
with central and peripheral intravenous access, total parenteral
nutrition is associated with significant risks, such as air embolism
and sepsis.
The proposed data element consists of the single Parenteral/IV
Feeding data element. The proposed Parenteral/IV Feeding data element
is currently in use in the MDS in SNFs, and equivalent or related data
elements are in use in the LCDS, IRF-PAI, and OASIS. We proposed to
rename the existing Tube/Parenteral feeding item in the IRF-PAI to be
the Parenteral/IV Feeding data element. For more information on the
Parenteral/IV Feeding data element, we refer readers to the document
titled ``Final Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Parenteral/IV Feeding data element was first proposed as a
standardized patient assessment data element in the FY 2018 IRF PPS
proposed rule (82 FR 20734). In that proposed rule, we stated that the
proposal was informed by input we received on Total Parenteral
Nutrition (an item with nearly the same meaning as the proposed data
element, but with the label used in the PAC PRD), through a call for
input published on the CMS Measures Management System Blueprint
website. Input submitted from August 12 to September 12, 2016 supported
this data element, noting its relevance to facilitating care
coordination and supporting care transitions. After the public comment
period, the Total Parenteral Nutrition data element was renamed
Parenteral/IV Feeding, to be consistent with how this data element is
referred to in the MDS in SNFs. A summary report for the August 12 to
September 12, 2016 public comment period titled ``SPADE August 2016
Public Comment Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received comments in support of the special services, treatments,
and interventions data elements in general; no additional comments were
received that were specific to the Parenteral/IV Feeding data element.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Parenteral/IV Feeding data element was included in the National Beta
Test of candidate data elements conducted by our data element
contractor from November 2017 to August 2018. Results of this test
found the Parenteral/IV Feeding data element to be feasible and
reliable for use with PAC patients and residents. More information
about the performance of the Parenteral/IV Feeding data element in the
National Beta Test can be found in the document titled ``Final
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Parenteral/IV Feeding data element, the TEP
supported the assessment of the special services, treatments, and
interventions included in the National Beta Test with respect to both
admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for parenteral/IV
feeding, stakeholder input, and strong test results, we proposed that
the Parenteral/IV Feeding data element meets the definition of
standardized patient assessment data with respect to special services,
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the
Act and to adopt the Parenteral/IV Feeding data element as standardized
patient assessment data for use in the IRF QRP.
A commenter submitted the following comment related to the proposed
rule's discussion of the Parenteral/IV Feeding data element.
Comment: One commenter was supportive of collecting this data
element, but noted that it should not be a substitute for capturing
information related to swallowing which reflects additional patient
complexity and resource use.
Response: We thank the commenter for their support and appreciate
the concerns raised. We agree that the Parenteral/IV Feeding SPADE
should not be used as a substitute for an assessment of a patient's
swallowing
[[Page 39137]]
function. The proposed SPADEs are not intended to replace comprehensive
clinical evaluation and in no way preclude providers from conducting
further patient evaluation or assessments in their settings as they
believe are necessary and useful. We agree that information related to
swallowing can capture patient complexity. However, we also note that
Parenteral/IV Feeding data element captures a different construct than
an evaluation of swallowing. That is, the Parenteral/IV Feeding data
element captures a patient's need to receive calories and nutrients
intravenously, while an assessment of swallowing would capture a
patient's functional ability to safely consume food/liquids orally for
digestion in their gastrointestinal tract.
After careful consideration of the public comments we received on
the category of Special Services, Treatments, and Interventions, we are
finalizing our proposal to adopt the Parenteral/IV Feeding data element
as standardized patient assessment data beginning with the FY 2022 IRF
QRP as proposed.
Nutritional Approach: Feeding Tube
In the FY 2020 IRF PPS proposed rule (84 FR 17309 through 17310),
we proposed that the Feeding Tube data element meets the definition of
standardized patient assessment data with respect to special services,
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the
Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20734
through 20735), the majority of patients admitted to acute care
hospitals experience deterioration of their nutritional status during
their hospital stay, making assessment of nutritional status and method
of feeding if unable to eat orally very important in PAC. A feeding
tube can be inserted through the nose or the skin on the abdomen to
deliver liquid nutrition into the stomach or small intestine. Feeding
tubes are resource intensive, and therefore, are important to assess
for care planning and resource use. Patients with severe malnutrition
are at higher risk for a variety of complications.\113\ In PAC
settings, there are a variety of reasons that patients and residents
may not be able to eat orally (including clinical or cognitive status).
---------------------------------------------------------------------------
\113\ Dempsey, D.T., Mullen, J.L., & Buzby, G.P. (1988). ``The
link between nutritional status and clinical outcome: can
nutritional intervention modify it?'' Am J of Clinical Nutrition,
47(2): 352-356.
---------------------------------------------------------------------------
The proposed data element consists of the single Feeding Tube data
element. The Feeding Tube data element is currently included in the MDS
for SNFs, and in the OASIS for HHAs, where it is labeled Enteral
Nutrition. A related data element, collected in the IRF-PAI for IRFs
(Tube/Parenteral Feeding), assesses use of both feeding tubes and
parenteral nutrition. We proposed to rename the existing Tube/
Parenteral feeding item in the IRF-PAI to the Feeding Tube data
element. For more information on the Feeding Tube data element, we
refer readers to the document titled ``Final Specifications for IRF QRP
Quality Measures and Standardized Patient Assessment Data Elements,''
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Feeding Tube data element was first proposed as a standardized
patient assessment data element in the FY 2018 IRF PPS proposed rule
(82 FR 20734 through 20735). In that proposed rule, we stated that the
proposal was informed by input we received on an Enteral Nutrition data
element (the Enteral Nutrition data item is the same as the data
element we proposed, but is used in the OASIS under a different name)
through a call for input published on the CMS Measures Management
System Blueprint website. Input submitted from August 12 to September
12, 2016 supported the data element, noting the importance of assessing
enteral nutrition status for facilitating care coordination and care
transitions. After the public comment period, the Enteral Nutrition
data element used in public comment was renamed Feeding Tube,
indicating the presence of an assistive device. A summary report for
the August 12 to September 12, 2016 public comment period titled
``SPADE August 2016 Public Comment Summary Report'' is available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general. In addition, a
commenter recommended that the term ``enteral feeding'' be used instead
of ``feeding tube''.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Feeding Tube data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the Feeding
Tube data element to be feasible and reliable for use with PAC patients
and residents. More information about the performance of the Feeding
Tube data element in the National Beta Test can be found in the
document titled ``Final Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Feeding Tube data element, the TEP supported
the assessment of the special services, treatments, and interventions
included in the National Beta Test with respect to both admission and
discharge. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for feeding tubes,
stakeholder input, and strong test results, we
[[Page 39138]]
proposed that the Feeding Tube data element meets the definition of
standardized patient assessment data with respect to special services,
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the
Act and to adopt the Feeding Tube data element as standardized patient
assessment data for use in the IRF QRP.
Commenters submitted the following comments related to the proposed
rule's discussion of the Feeding Tube data element.
Comment: One commenter noted that in addition to identifying if the
patient is on a feeding tube or not, it would be important to assess
the patient's progression towards oral feeding within this data
element, as this impacts the tube feeding regimen.
Response: We agree that progression to oral feeding is important
for care planning and transfer. At this time, we are finalizing a
singular Feeding Tube SPADE, which assesses the nutritional approach
only and does not capture the patient's prognosis with regard to oral
feeding. We wish to clarify that the proposed SPADEs are not intended
to replace comprehensive clinical evaluation and in no way preclude
providers from conducting further patient evaluation or assessments in
their settings as they believe are necessary and useful. We will take
this recommendation into consideration in future work on standardized
data elements.
Comment: One commenter noted that this data element should
designate between percutaneous endoscopic gastrostomy (PEG) tube and
nasogastric (NG) tube because the different routes of access have
different levels of resource requirements.
Response: We appreciate the commenter's suggestion, but we have
decided to maintain the singular Feeding Tube SPADE. We agree that
different routes of access may have different levels of resource
requirements. However, we do not believe collecting this level of
information about nutritional therapies via a SPADE would be
significantly more clinically useful or supportive of care transitions
than the singular Feeding Tube SPADE. However, we will take this
suggestion into consideration in future refinement of the clinical
SPADEs.
After careful consideration of the public comments we received on
the category of Special Services, Treatments, and Interventions, we are
finalizing our proposal to adopt the Feeding Tube data element as
standardized patient assessment data beginning with the FY 2022 IRF QRP
as proposed.
Nutritional Approach: Mechanically Altered Diet
In the FY 2020 IRF PPS proposed rule (84 FR 17310 through 17311),
we proposed that the Mechanically Altered Diet data element meets the
definition of standardized patient assessment data with respect to
special services, treatments, and interventions under section
1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20735
through 20736), the Mechanically Altered Diet data element refers to
food that has been altered to make it easier for the patient or
resident to chew and swallow, and this type of diet is used for
patients and residents who have difficulty performing these functions.
Patients with severe malnutrition are at higher risk for a variety of
complications.\114\
---------------------------------------------------------------------------
\114\ Dempsey, D.T., Mullen, J.L., & Buzby, G.P. (1988). ``The
link between nutritional status and clinical outcome: can
nutritional intervention modify it?'' Am J of Clinical Nutrition,
47(2): 352-356.
---------------------------------------------------------------------------
In PAC settings, there are a variety of reasons that patients and
residents may have impairments related to oral feedings, including
clinical or cognitive status. The provision of a mechanically altered
diet may be resource intensive, and can signal difficulties associated
with swallowing/eating safety, including dysphagia. In other cases, it
signifies the type of altered food source, such as ground or puree that
will enable the safe and thorough ingestion of nutritional substances
and ensure safe and adequate delivery of nourishment to the patient.
Often, patients and residents on mechanically altered diets also
require additional nursing support, such as individual feeding or
direct observation, to ensure the safe consumption of the food product.
Therefore, assessing whether a patient or resident requires a
mechanically altered diet is important for care planning and resource
identification.
The proposed data element consists of the single Mechanically
Altered Diet data element. The proposed data element is currently
included on the MDS for SNFs. A related data element (``Modified food
consistency/supervision'') is currently included on the IRF-PAI for
IRFs. Another related data element is included in the OASIS for HHAs
that collects information about independent eating that requires ``a
liquid, pureed or ground meat diet.'' We proposed to replace the
existing Modified food consistency/supervision data element in the IRF-
PAI to the Mechanically Altered Diet data element. For more information
on the Mechanically Altered Diet data element, we refer readers to the
document titled ``Final Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Mechanically Altered Diet data element was first proposed as a
standardized patient assessment data element in the FY 2018 IRF PPS
proposed rule (82 FR 20735 through 20736). In response to our proposal
in the FY 2018 IRF PPS proposed rule, we received public comments in
support of the special services, treatments, and interventions data
elements in general; no additional comments were received that were
specific to the Mechanically Altered Diet data element.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Mechanically Altered Diet data element was included in the National
Beta Test of candidate data elements conducted by our data element
contractor from November 2017 to August 2018. Results of this test
found the Mechanically Altered Diet data element to be feasible and
reliable for use with PAC patients and residents. More information
about the performance of the Mechanically Altered Diet data element in
the National Beta Test can be found in the document titled ``Final
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Mechanically Altered Diet data element, the
TEP supported the assessment of the special services, treatments, and
interventions included in the National Beta Test with respect to both
admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-
Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-
Initiatives/IMPACT-Act-of-2014/
[[Page 39139]]
IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for mechanically
altered diet, stakeholder input, and strong test results, we proposed
that the Mechanically Altered Diet data element meets the definition of
standardized patient assessment data with respect to special services,
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the
Act and to adopt the Mechanically Altered Diet data element as
standardized patient assessment data for use in the IRF QRP.
Commenters submitted the following comments related to the proposed
rule's discussion of the Mechanically Altered Diet data element.
Comment: Commenters were concerned about the performance of this
data element in the National Beta Test, noting that its reliability was
only moderate in IRF settings.
Response: We provided supplementary information with the proposed
rule on the reliability of the SPADEs, described by the kappa statistic
and by the ``percent agreement'' between assessor, another measure of
reliability that is in some cases more accurate than the kappa
statistic, depending on the underlying distribution. (The document
titled ``Proposed Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html). In this document, we stated that
the interrater reliability for Mechanically Altered Diet data element,
as measured by kappa, was ``substantial/good'' across the four PAC
provider types (LTCH, SNF, HHA, and IRF) in which it was tested (kappa
= 0.65) and ``moderate'' in the IRF setting (kappa = 0.53). However,
percent agreement for the data element was 93 percent across all PAC
settings in the National Beta Test (that is, HHA, IRF, LTCH, and SNF)
and 89 percent in the IRF setting. That is, when assessing if patients
required a mechanically altered diet, the facility staff and the
external research nurse agreed 89 percent of the time for IRF patients.
Comment: One commenter was concerned that the Mechanically Altered
Diet data element does not capture clinical complexity and does not
provide any insight into resource allocation because it only measures
whether the patient needs a mechanically altered diet and not, for
example, the extent of help a patient needs in consuming his or her
meal.
Response: We believe that assessing patients' needs for
mechanically altered diets captures one piece of information about
resource intensity. That is, patients with this special nutritional
requirement may require additional nutritional planning services,
special meals, and staff to ensure that meals are prepared and served
in the way the patient needs. Additional factors that would affect
resource allocation, such as those noted by the commenter, are not
captured by this data element. We have attempted to balance the scope
and level of detail of the data elements against the potential burden
placed on providers who must complete the assessment. We will take this
suggestion into consideration in future refinement of the clinical
SPADEs.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the Mechanically Altered Diet data
element as standardized patient assessment data beginning with the FY
2022 IRF QRP as proposed.
Nutritional Approach: Therapeutic Diet
In the FY 2020 IRF PPS proposed rule (84 FR 17311 through 17312),
we proposed that the Therapeutic Diet data element meets the definition
of standardized patient assessment data with respect to special
services, treatments, and interventions under section
1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20736), a
therapeutic diet refers to meals planned to increase, decrease, or
eliminate specific foods or nutrients in a patient's or resident's
diet, such as a low-salt diet, for the purpose of treating a medical
condition. The use of therapeutic diets among patients and residents in
PAC provides insight on the clinical complexity of these patients and
residents and their multiple comorbidities. Therapeutic diets are less
resource intensive from the bedside nursing perspective, but do signify
one or more underlying clinical conditions that preclude the patient
from eating a regular diet. The communication among PAC providers about
whether a patient is receiving a particular therapeutic diet is
critical to ensure safe transitions of care.
The proposed data element consists of the single Therapeutic Diet
data element. This data element is currently in use in the MDS in SNFs.
For more information on the Therapeutic Diet data element, we refer
readers to the document titled ``Final Specifications for IRF QRP
Quality Measures and Standardized Patient Assessment Data Elements,''
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Therapeutic Diet data element was first proposed as a
standardized patient assessment data element in the FY 2018 IRF PPS
proposed rule (82 FR 20736). In response to our proposal in the FY 2018
IRF PPS proposed rule, we received public comments in support of the
special services, treatments, and interventions data elements in
general. One commenter recommended that the definition of Therapeutic
Diet be aligned with the Academy of Nutrition and Dietetics' definition
and that ``medically altered diet'' be added to the list of nutritional
approaches.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Therapeutic Diet data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the
Therapeutic Diet data element to be feasible and reliable for use with
PAC patients and residents. More information about the performance of
the Therapeutic Diet data element in the National Beta Test can be
found in the document titled ``Final Specifications for IRF QRP Quality
Measures and Standardized Patient Assessment Data Elements,'' available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-
[[Page 39140]]
Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-
of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Therapeutic Diet data element, the TEP
supported the assessment of the special services, treatments, and
interventions included in the National Beta Test with respect to both
admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for therapeutic diet,
stakeholder input, and strong test results, we proposed that the
Therapeutic Diet data element meets the definition of standardized
patient assessment data with respect to special services, treatments,
and interventions under section 1899B(b)(1)(B)(iii) of the Act and to
adopt the Therapeutic Diet data element as standardized patient
assessment data for use in the IRF QRP.
We invited public comment on this proposal. While we received
support from some commenters on Special Services, Treatments, and
Interventions as a whole (section IX.G.2 in this final rule), we did
not receive any specific comments on the Therapeutic Diet data element.
After careful consideration of the public comments we received on
the category of Special Services, Treatments, and Interventions, we are
finalizing our proposal to adopt the Therapeutic Diet data element as
standardized patient assessment data beginning with the FY 2022 IRF QRP
as proposed.
High-Risk Drug Classes: Use and Indication
In the FY 2020 IRF PPS proposed rule (84 FR 17312 through 17314),
we proposed that the High-Risk Drug Classes: Use and Indication data
element meets the definition of standardized patient assessment data
with respect to special services, treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act.
Most patients and residents receiving PAC services depend on short-
and long-term medications to manage their medical conditions. However,
as a treatment, medications are not without risk; medications are, in
fact, a leading cause of adverse events. A study by the U.S. Department
of Health and Human Services found that 31 percent of adverse events
that occurred in 2008 among hospitalized Medicare beneficiaries were
related to medication.\115\ Moreover, changes in a patient's condition,
medications, and transitions between care settings put patients at risk
of medication errors and adverse drug events (ADEs). ADEs may be caused
by medication errors such as drug omissions, errors in dosage, and
errors in dosing frequency.\116\
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\115\ U.S. Department of Health and Human Services. Office of
Inspector General. Daniel R. Levinson. Adverse Events in Hospitals:
National Incidence Among Medicare Beneficiaries. OEI-06-09-00090.
November 2010.
\116\ Boockvar KS, Liu S, Goldstein N, Nebeker J, Siu A, Fried
T. Prescribing discrepancies likely to cause adverse drug events
after patient transfer. Qual Saf Health Care. 2009;18(1):32-6.
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ADEs are known to occur across different types of healthcare
settings. For example, the incidence of ADEs in the outpatient setting
has been estimated at 1.15 ADEs per 100 person-months,\117\ while the
rate of ADEs in the long-term care setting is approximately 9.80 ADEs
per 100 resident-months.\118\ In the hospital setting, the incidence
has been estimated at 15 ADEs per 100 admissions.\119\ In addition,
approximately half of all hospital-related medication errors and 20
percent of ADEs occur during transitions within, admission to, transfer
to, or discharge from a hospital.\120\ \121\ \122\ ADEs are more common
among older adults, who make up most patients receiving PAC services.
The rate of emergency department visits for ADEs is three times higher
among adults 65 years of age and older compared to that among those
younger than age 65.\123\
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\117\ Gandhi TK, Seger AC, Overhage JM, et al. Outpatient
adverse drug events identified by screening electronic health
records. J Patient Saf 2010;6:91-6.doi:10.1097/PTS.0b013e3181dcae06.
\118\ Gurwitz JH, Field TS, Judge J, Rochon P, Harrold LR,
Cadoret C, et al. The incidence of adverse drug events in two large
academic long-term care facilities. Am J Med. 2005; 118(3):2518. Epub 2005/03/05. https://doi.org/10.1016/j.amjmed.2004.09.018 PMID: 15745723.
\119\ Hug BL, Witkowski DJ, Sox CM, Keohane CA, Seger DL, Yoon
C, Matheny ME, Bates DW. Occurrence of adverse, often preventable,
events in community hospitals involving nephrotoxic drugs or those
excreted by the kidney. Kidney Int. 2009; 76:1192-1198. [PubMed:
19759525].
\120\ Barnsteiner JH. Medication reconciliation: transfer of
medication information across settings-keeping it free from error. J
Infus Nurs. 2005;28(2 Suppl):31-36.
\121\ Rozich J, Roger, R. Medication safety: one organization's
approach to the challenge. Journal of Clinical Outcomes Management.
2001(8):27-34.
\122\ Gleason KM, Groszek JM, Sullivan C, Rooney D, Barnard C,
Noskin GA. Reconciliation of discrepancies in medication histories
and admission orders of newly hospitalized patients. Am J Health
Syst Pharm. 2004;61(16):1689-1695.
\123\ Shehab N, Lovegrove MC, Geller AI, Rose KO, Weidle NJ,
Budnitz DS. US emergency department visits for outpatient adverse
drug events, 2013-2014. JAMA. doi: 10.1001/jama.2016.16201.
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Understanding the types of medication a patient is taking, and the
reason for its use, are key facets of a patient's treatment with
respect to medication. Some classes of drugs are associated with more
risk than others.\124\ We proposed one High-Risk Drug Class data
element with six sub-elements. The response options that correspond to
the six medication classes are: Anticoagulants, antiplatelets,
hypoglycemics (including insulin), opioids, antipsychotics, and
antibiotics. These drug classes are high-risk due to the adverse
effects that may result from use. In particular, bleeding risk is
associated with anticoagulants and antiplatelets; \125\ \126\ fluid
retention, heart failure, and lactic acidosis are associated with
hypoglycemics; \127\
[[Page 39141]]
misuse is associated with opioids; \128\ fractures and strokes are
associated with antipsychotics; \129\ \130\ and various adverse events,
such as central nervous systems effects and gastrointestinal
intolerance, are associated with antimicrobials,\131\ the larger
category of medications that include antibiotics. Moreover, some
medications in five of the six drug classes included in this data
element are included in the 2019 Updated Beers Criteria[supreg] list as
potentially inappropriate medications for use in older adults.\132\
Finally, although a complete medication list should record several
important attributes of each medication (for example, dosage, route,
stop date), recording an indication for the drug is of crucial
importance.\133\
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\124\ Ibid.
\125\ Shoeb M, Fang MC. Assessing bleeding risk in patients
taking anticoagulants. J Thromb Thrombolysis. 2013;35(3):312-319.
doi: 10.1007/s11239-013-0899-7.
\126\ Melkonian M, Jarzebowski W, Pautas E. Bleeding risk of
antiplatelet drugs compared with oral anticoagulants in older
patients with atrial fibrillation: a systematic review and
meta[hyphen]analysis. J Thromb Haemost. 2017;15:1500-1510. DOI:
10.1111/jth.13697.
\127\ Hamnvik OP, McMahon GT. Balancing Risk and Benefit with
Oral Hypoglycemic Drugs. The Mount Sinai journal of medicine, New
York. 2009; 76:234-243.
\128\ Naples JG, Gellad WF, Hanlon JT. The Role of Opioid
Analgesics in Geriatric Pain Management. Clin Geriatr Med.
2016;32(4):725-735.
\129\ Rigler SK, Shireman TI, Cook-Wiens GJ, Ellerbeck EF,
Whittle JC, Mehr DR, Mahnken JD. Fracture risk in nursing home
residents initiating antipsychotic medications. J Am Geriatr Soc.
2013; 61(5):715-722. [PubMed: 23590366].
\130\ Wang S, Linkletter C, Dore D et al. Age, antipsychotics,
and the risk of ischemic stroke in the Veterans Health
Administration. Stroke 2012;43:28-31. doi:10.1161/
STROKEAHA.111.617191.
\131\ Faulkner CM, Cox HL, Williamson JC. Unique aspects of
antimicrobial use in older adults. Clin Infect Dis. 2005;40(7):997-
1004.
\132\ American Geriatrics Society 2019 Beers Criteria Update
Expert Panel. American Geriatrics Society 2019 Updated Beers
Criteria for Potentially Inappropriate Medication Use in Older
Adults. J Am Geriatr Soc 2019; 00:1-21.
\133\ Li Y, Salmasian H, Harpaz R, Chase H, Friedman C.
Determining the reasons for medication prescriptions in the EHR
using knowledge and natural language processing. AMIA Annu Symp
Proc. 2011; 2011:768-76.
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The High-Risk Drug Classes: Use and Indication data element
requires an assessor to record whether or not a patient is taking any
medications within the six drug classes. The six response options for
this data element are high-risk drug classes with particular relevance
to PAC patients and residents, as identified by our data element
contractor. The six data element response options are Anticoagulants,
Antiplatelets, Hypoglycemics, Opioids, Antipsychotics, and Antibiotics.
For each drug class, the assessor is required to indicate if the
patient is taking any medications within the class, and, for drug
classes in which medications were being taken, whether indications for
all drugs in the class are noted in the medical record. For example,
for the response option Anticoagulants, if the assessor indicates that
the patient has received anticoagulant medication, the assessor would
then indicate if an indication is recorded in the medication record for
the anticoagulant(s).
The High-Risk Drug Classes: Use and Indication data element that is
being proposed as a SPADE was developed as part of a larger set of data
elements to assess medication reconciliation, the process of obtaining
a patient's multiple medication lists and reconciling any
discrepancies. For more information on the High-Risk Drug Classes: Use
and Indication data element, we refer readers to the document titled
``Final Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We sought public input on the relevance of conducting assessments
on medication reconciliation and specifically on the proposed High-Risk
Drug Classes: Use and Indication data element. Our data element
contractor presented data elements related to medication reconciliation
to the TEP convened on April 6 and 7, 2016. The TEP supported a focus
on high-risk drugs, because of higher potential for harm to patients
and residents, and were in favor of a data element to capture whether
or not indications for medications were recorded in the medical record.
A summary of the April 6 and 7, 2016 TEP meeting titled ``SPADE
Technical Expert Panel Summary (First Convening)'' is available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html. Medication reconciliation data
elements were also discussed at a second TEP meeting on January 5 and
6, 2017, convened by our data element contractor. At this meeting, the
TEP agreed about the importance of evaluating the medication
reconciliation process, but disagreed about how this could be
accomplished through standardized assessment. The TEP also disagreed
about the usability and appropriateness of using the Beers Criteria to
identify high-risk medications.\134\ A summary of the January 5 and 6,
2017 TEP meeting titled ``SPADE Technical Expert Panel Summary (Second
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
---------------------------------------------------------------------------
\134\ American Geriatrics Society 2015 Beers Criteria Update
Expert Panel. American Geriatrics Society. Updated Beers Criteria
for Potentially Inappropriate Medication Use in Older Adults. J Am
Geriatr Soc 2015; 63:2227-2246.
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We also solicited public input on data elements related to
medication reconciliation during a public input period from April 26 to
June 26, 2017. Several commenters noted support for the medication
reconciliation data elements that were put on display, noting the
importance of medication reconciliation in preventing medication errors
and stated that the items seemed feasible and clinically useful. A few
commenters were critical of the choice of 10 drug classes posted during
that comment period, stating that ADEs are not limited to high-risk
drugs, and raised issues related to training assessors to correctly
complete a valid assessment of medication reconciliation. A summary
report for the April 26 to June 26, 2017 public comment period titled
``SPADE May-June 2017 Public Comment Summary Report'' is available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The High-Risk Drug Classes: Use and Indication data element was
included in the National Beta Test of candidate data elements conducted
by our data element contractor from November 2017 to August 2018.
Results of this test found the High-Risk Drug Classes: Use and
Indication data element to be feasible and reliable for use with PAC
patients and residents. More information about the performance of the
High-Risk Drug Classes: Use and Indication data element in the National
Beta Test can be found in the document titled ``Final Specifications
for IRF QRP Quality Measures and Standardized Patient Assessment Data
Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. The TEP acknowledged the
challenges of assessing medication safety, but were supportive of some
of the data elements focused on medication reconciliation that were
tested in the National Beta Test. The TEP was especially supportive of
the focus on the six high-risk drug classes and using these classes to
assess
[[Page 39142]]
whether the indication for a drug is recorded. A summary of the
September 17, 2018 TEP meeting titled ``SPADE Technical Expert Panel
Summary (Third Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. These activities provided
updates on the field-testing work and solicited feedback on data
elements considered for standardization, including the High-Risk Drug
Classes: Use and Indication data element. One stakeholder group was
critical of the six drug classes included as response options in the
High-Risk Drug Classes: Use and Indication data element, noting that
potentially risky medications (for example, muscle relaxants) are not
included in this list; that there may be important differences between
drugs within classes (for example, more recent versus older style
antidepressants); and that drug allergy information is not captured.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present the results of the National
Beta Test and solicit additional comments. General input on the testing
and item development process and concerns about burden were received
from stakeholders during this meeting and via email through February 1,
2019. Additionally, one commenter questioned whether the time to
complete the High-Risk Drug Classes: Use and Indication data element
would differ across settings. A summary of the public input received
from the November 27, 2018 stakeholder meeting titled ``Input on
Standardized Patient Assessment Data Elements (SPADEs) Received After
November 27, 2018 Stakeholder Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing high-risk drugs and for
whether or not indications are noted for high-risk drugs, stakeholder
input, and strong test results, we proposed that the High-Risk Drug
Classes: Use and Indication data element meets the definition of
standardized patient assessment data with respect to special services,
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the
Act and to adopt the High-Risk Drug Classes: Use and Indication data
element as standardized patient assessment data for use in the IRF QRP.
Commenters submitted the following comments related to the proposed
rule's discussion of the High-Risk Drug Classes: Use and Indication
data element.
Comment: Some commenters noted that the proposed High-Risk Drug
Classes: Use and Indication data elements are redundant of the existing
standards in the Hospital Conditions of Participation (CoPs) and that
requiring the collection of these data elements would be duplicative,
unnecessary, and at odds with the Meaningful Measures framework.
Response: We disagree that assessing the extent to which
medications from certain drug classes are being taken and the extent to
which indications are recorded for medications in these classes is
redundant with the existing CoPs. The CoPs provide guidance on clinical
practice, while the proposed SPADEs attempt to collect information
about individual patients in order to understand clinical acuity and to
populate a core set of information that can be exchanged with the
patient across care transitions.
Comment: Commenters noted that because adverse drug events (ADEs)
are not limited to high-risk drugs, this data element has limited
utility.
Response: We acknowledge that not all ADEs are associated with
``high-risk'' drugs, and we also note that medications in the named
drug classes are mostly used in a safe manner. Prescribed high-risk
medications are defined as a ``proximate factor'' to preventable ADEs
by the Joint Commission.\135\ However, the Joint Commission's
conceptual model of preventable ADEs also includes provider, patient,
health care system, organization, and technical factors, all of which
present many opportunities for disrupting preventable ADEs. We have
decided to focus on a selection of drug classes that are commonly used
by older adults and are related to ADEs which are clinically
significant, preventable, and measurable. Anticoagulants, antibiotics,
and diabetic agents have been implicated in an estimated 46.9 percent
(95 percent CI, 44.2 percent-49.7 percent) of emergency department
visits for adverse drug events.\136\ Among older adults (aged >=65
years), three drug classes (anticoagulants, diabetic agents, and opioid
analgesics) have been implicated in an estimated 59.9 percent (95
percent CI, 56.8 percent-62.9 percent) of ED visits for adverse drug
events.\137\ Further, antipsychotic medications have been identified as
a drug class for which there is a need for increased outreach and
educational efforts to reduce use among older adults.
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\135\ Chang A, Schyve PM, Croteau RJ, O'Leary DS, Loeb JM. The
JCAHO patient safety event taxonomy: A standardized terminology and
classification schema for near misses and adverse events. Int J Qual
Health Care. 2005;17(2):95-105.
\136\ Shehab N, Lovegrove MC, Geller AI, Rose KO, Weidle NJ,
Budnitz DS. US emergency department visits for outpatient adverse
drug events, 2013-2014. JAMA 2016;316(2):2115-2125.
\137\ Shehab N, Lovegrove MC, Geller AI, Rose KO, Weidle NJ,
Budnitz DS. US emergency department visits for outpatient adverse
drug events, 2013-2014. JAMA 2016;316(2):2115-2125.
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Comment: One commenter was concerned with the addition of the High-
Risk Drug Classes: Use and Indication data elements, noting that
providers should be granted clinical judgment to effectively treat
patients without CMS monitoring of medications used for treatment.
Response: The proposed SPADEs attempt to collect information about
individual patients to understand clinical acuity and to populate a
core set of information that can be exchanged with the patient across
care transitions. The intent of these data elements is not to monitor
prescribing practices, but rather to assess the extent to which
indications are noted for medications in certain drug classes.
Comment: A few commenters noted that the High-Risk Drug Class: Use
and Indication data elements seemed redundant with other SPADEs (that
is, IV Medications) and measures (that is, Provision of Current
Reconciled Medication List to Subsequent Provider at Discharge), or
duplicative of existing standards in the Hospital CoPs related to
procurement, preparation, and administration of drugs, which creates
unnecessary burden.
Response: The High-Risk Drugs: Use and Indications data element
captures unique information compared to the other SPADEs and measures
to which the commenters referred. With regard to the reference to the
measure Provision of Current Reconciled Medication List to Subsequent
Provider at Discharge, we wish to clarify that the High-Risk Drug
Classes: Use and Indication data elements capture medications taken by
any route and focuses on a select set of drug classes, not the act of
communicating a complete medication list. To the extent that the
activities captured by the High-Risk Drugs: Use and Indications data
element are already being performed by providers as part of
[[Page 39143]]
the Hospital CoPs, we believe that reporting of this data elements
should be easily integrated into existing workflow.
Comment: One commenter noted that medication indications are
typically documented in narrative notes by the medical staff and would
therefore be difficult to collect.
Response: We maintain that collecting information on the presence
of indications in the medical record is clinically important
information that can inform care planning and support care transitions.
It is the responsibility of IRF providers to record patient data in a
way that is useful and appropriate to meet clinical and administrative
needs. It is possible that the adoption of this SPADE and related
reporting requirement will promote a more efficient method for
documenting the clinical indication for each medication.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the High-Risk Drug Classes: Use
and Indication data element as standardized patient assessment data
beginning with the FY 2022 IRF QRP as proposed.
3. Medical Condition and Comorbidity Data
Assessing medical conditions and comorbidities is critically
important for care planning and safety for patients and residents
receiving PAC services, and the standardized assessment of selected
medical conditions and comorbidities across PAC providers is important
for managing care transitions and understanding medical complexity.
In this section we discuss our proposals for data elements related
to the medical condition of pain as standardized patient assessment
data. Appropriate pain management begins with a standardized
assessment, and thereafter establishing and implementing an overall
plan of care that is person-centered, multi-modal, and includes the
treatment team and the patient. Assessing and documenting the effect of
pain on sleep, participation in therapy, and other activities may
provide information on undiagnosed conditions and comorbidities and the
level of care required, and do so more objectively than subjective
numerical scores. With that, we assess that taken separately and
together, these proposed data elements are essential for care planning,
consistency across transitions of care, and identifying medical
complexities including undiagnosed conditions. We also conclude that it
is the standard of care to always consider the risks and benefits
associated with a personalized care plan, including the risks of any
pharmacological therapy, especially opioids.\138\ We also conclude that
in addition to assessing and appropriately treating pain through the
optimum mix of pharmacologic, non-pharmacologic, and alternative
therapies, while being cognizant of current prescribing guidelines,
clinicians in partnership with patients are best able to mitigate
factors that contribute to the current opioid crisis.\139\ \140\ \141\
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\138\ Department of Health and Human Services: Pain Management
Best Practices Inter-Agency Task Force. Draft Report on Pain
Management Best Practices: Updates, Gaps, Inconsistencies, and
Recommendations. Accessed April 1, 2019. https://www.hhs.gov/sites/default/files/final-pmtf-draft-report-on-pain-management%20-best-practices-2018-12-12-html-ready-clean.pdf.
\139\ Department of Health and Human Services: Pain Management
Best Practices Inter-Agency Task Force. Draft Report on Pain
Management Best Practices: Updates, Gaps, Inconsistencies, and
Recommendations. Accessed April 1, 2019. https://www.hhs.gov/sites/default/files/final-pmtf-draft-report-on-pain-management%20-best-practices-2018-12-12-html-ready-clean.pdf.
\140\ Fishman SM, Carr DB, Hogans B, et al. Scope and Nature of
Pain- and Analgesia-Related Content of the United States Medical
Licensing Examination (USMLE). Pain Med Malden Mass. 2018;19(3):449-
459. doi:10.1093/pm/pnx336.
\141\ Fishman SM, Young HM, Lucas Arwood E, et al. Core
competencies for pain management: results of an interprofessional
consensus summit. Pain Med Malden Mass. 2013;14(7):971-981.
doi:10.1111/pme.12107.
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In alignment with our Meaningful Measures Initiative, accurate
assessment of medical conditions and comorbidities of patients and
residents in PAC is expected to make care safer by reducing harm caused
in the delivery of care; promote effective prevention and treatment of
chronic disease; strengthen person and family engagement as partners in
their care; and promote effective communication and coordination of
care. The SPADEs will enable or support: Clinical decision-making and
early clinical intervention; person-centered, high quality care
through: facilitating better care continuity and coordination; better
data exchange and interoperability between settings; and longitudinal
outcome analysis. Therefore, reliable data elements assessing medical
conditions and comorbidities are needed to initiate a management
program that can optimize a patient's or resident's prognosis and
reduce the possibility of adverse events.
We sought comment that applies specifically to the standardized
patient assessment data for the category of medical conditions and co-
morbidities. We did not receive any comments on the category of medical
conditions and co-morbidities.
Final decisions on the SPADEs are given below, following more
detailed comments on each SPADE proposal.
Pain Interference (Pain Effect on Sleep, Pain Interference
With Therapy Activities, and Pain Interference With Day-to-Day
Activities)
In acknowledgement of the opioid crisis, we specifically sought
comment on whether or not we should add these pain items in light of
those concerns. Commenters were asked to address to what extent the
collection of the SPADEs described below through patient queries might
encourage providers to prescribe opioids.
In the FY 2020 IRF PPS proposed rule (84 FR 17314 through 17316),
we proposed that a set of three data elements on the topic of Pain
Interference (Pain Effect on Sleep, Pain Interference with Therapy
Activities, and Pain Interference with Day-to-Day Activities) meet the
definition of standardized patient assessment data with respect to
medical condition and comorbidity data under section 1899B(b)(1)(B)(iv)
of the Act.
The practice of pain management began to undergo significant
changes in the 1990s because the inadequate, non-standardized, non-
evidence-based assessment and treatment of pain became a public health
issue.\142\ In pain management, a critical part of providing
comprehensive care is performance of a thorough initial evaluation,
including assessment of both the medical and any biopsychosocial
factors causing or contributing to the pain, with a treatment plan to
address the causes of pain and to manage pain that persists over
time.\143\ Quality pain management, based on current guidelines and
evidence-based practices, can minimize unnecessary opioid prescribing
both by offering alternatives or supplemental treatment to opioids and
by clearly stating when they may be appropriate, and how to utilize
risk-benefit analysis for opioid and non-opioid treatment
modalities.\144\
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\142\ Institute of Medicine. Relieving Pain in America: A
Blueprint for Transforming Prevention, Care, Education, and
Research. Washington (DC): National Academies Press (US); 2011.
http://www.ncbi.nlm.nih.gov/books/NBK91497/.
\143\ Department of Health and Human Services: Pain Management
Best Practices Inter-Agency Task Force. Draft Report on Pain
Management Best Practices: Updates, Gaps, Inconsistencies, and
Recommendations. Accessed April 1, 2019. https://www.hhs.gov/sites/default/files/final-pmtf-draft-report-on-pain-management%20-best-practices-2018-12-12-html-ready-clean.pdf.
\144\ National Academies. Pain Management and the Opioid
Epidemic: Balancing Societal and Individual Benefits and Risks of
Prescription Opioid Use. Washington DC: National Academies of
Sciences, Engineering, and Medicine.; 2017.
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[[Page 39144]]
Pain is not a surprising symptom in PAC patients and residents,
where healing, recovery, and rehabilitation often require regaining
mobility and other functions after an acute event. Standardized
assessment of pain that interferes with function is an important first
step towards appropriate pain management in PAC settings. The National
Pain Strategy called for refined assessment items on the topic of pain,
and describes the need for these improved measures to be implemented in
PAC assessments.\145\ Further, the focus on pain interference, as
opposed to pain intensity or pain frequency, was supported by the TEP
convened by our data element contractor as an appropriate and
actionable metric for assessing pain. A summary of the September 17,
2018 TEP meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
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\145\ National Pain Strategy: A Comprehensive Population-Health
Level Strategy for Pain. https://iprcc.nih.gov/sites/default/files/HHSNational_Pain_Strategy_508C.pdf.
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We appreciate the important concerns related to the misuse and
overuse of opioids in the treatment of pain and to that end we note
that in the proposed rule we have also proposed a SPADE that assess for
the use of, as well as importantly the indication for the use of, high-
risk drugs, including opioids. Further, in the FY 2017 IRF PPS final
rule (81 FR 52111) we adopted the Drug Regimen Review Conducted With
Follow-Up for Identified Issues--Post Acute Care (PAC) IRF QRP measure
which assesses whether PAC providers were responsive to potential or
actual clinically significant medication issue(s), which includes
issues associated with use and misuse of opioids for pain management,
when such issues were identified.
We also note that the proposed SPADE related to pain assessment are
not associated with any particular approach to management. Since the
use of opioids is associated with serious complications, particularly
in the elderly,\146\ \147\ \148\ an array of successful non-
pharmacologic and non-opioid approaches to pain management may be
considered. PAC providers have historically used a range of pain
management strategies, including non-steroidal anti-inflammatory drugs,
ice, transcutaneous electrical nerve stimulation (TENS) therapy,
supportive devices, acupuncture, and the like. In addition, non-
pharmacological interventions for pain management include, but are not
limited to, biofeedback, application of heat/cold, massage, physical
therapy, stretching and strengthening exercises, chiropractic,
electrical stimulation, radiotherapy, and ultrasound.\149\ \150\ \151\
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\146\ Chau, D. L., Walker, V., Pai, L., & Cho, L. M. (2008).
Opiates and elderly: use and side effects. Clinical interventions in
aging, 3(2), 273-8.
\147\ Fine, P. G. (2009). Chronic Pain Management in Older
Adults: Special Considerations. Journal of Pain and Symptom
Management, 38(2): S4-S14.
\148\ Solomon, D. H., Rassen, J. A., Glynn, R. J., Garneau, K.,
Levin, R., Lee, J., & Schneeweiss, S. (2010). Archives Internal
Medicine, 170(22):1979-1986.
\149\ Byrd L. Managing chronic pain in older adults: a long-term
care perspective. Annals of Long-Term Care: Clinical Care and Aging.
2013;21(12):34-40.
\150\ Kligler, B., Bair, M.J., Banerjea, R. et al. (2018).
Clinical Policy Recommendations from the VHA State-of-the-Art
Conference on Non-Pharmacological Approaches to Chronic
Musculoskeletal Pain. Journal of General Internal Medicine, 33(Suppl
1): 16. https://doi.org/10.1007/s11606-018-4323-z.
\151\ Chou, R., Deyo, R., Friedly, J., et al. (2017).
Nonpharmacologic Therapies for Low Back Pain: A Systematic Review
for an American College of Physicians Clinical Practice Guideline.
Annals of Internal Medicine, 166(7):493-505.
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We believe that standardized assessment of pain interference will
support PAC clinicians in applying best-practices in pain management
for chronic and acute pain, consistent with current clinical
guidelines. For example, the standardized assessment of both opioids
and pain interference would support providers in successfully tapering
the dosage regimens in patients/residents who arrive in the PAC setting
with long-term opioid use off of opioids onto non-pharmacologic
treatments and non-opioid medications, as recommended by the Society
for Post-Acute and Long-Term Care Medicine,\152\ and consistent with
HHS's 5-Point Strategy To Combat the Opioid Crisis \153\ which includes
``Better Pain Management.''
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\152\ Society for Post-Acute and Long-Term Care Medicine (AMDA).
(2018). Opioids in Nursing Homes: Position Statement. https://paltc.org/opioids%20in%20nursing%20homes.
\153\ https://www.hhs.gov/opioids/about-the-epidemic/hhs-response/index.html.
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The Pain Interference data elements consist of three data elements:
Pain Effect on Sleep, Pain Interference with Therapy Activities, and
Pain Interference with Day-to-Day Activities. Pain Effect on Sleep
assesses the frequency with which pain affects a resident's sleep. Pain
Interference with Therapy Activities assesses the frequency with which
pain interferes with a resident's ability to participate in therapies.
The Pain Interference with Day-to-Day Activities assesses the extent to
which pain interferes with a resident's ability to participate in day-
to-day activities excluding therapy.
A similar data element on the effect of pain on activities is
currently included in the OASIS. A similar data element on the effect
on sleep is currently included in the MDS instrument. For more
information on the Pain Interference data elements, we refer readers to
the document titled ``Final Specifications for IRF QRP Quality Measures
and Standardized Patient Assessment Data Elements,'' available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We sought public input on the relevance of conducting assessments
on pain and specifically on the larger set of Pain Interview data
elements included in the National Beta Test. The proposed data elements
were supported by comments from the TEP meeting held by our data
element contractor on April 7 to 8, 2016. The TEP affirmed the
feasibility and clinical utility of pain as a concept in a standardized
assessment. The TEP agreed that data elements on pain interference with
ability to participate in therapies versus other activities should be
addressed. Further, during a more recent convening of the same TEP on
September 17, 2018, the TEP supported the interview-based pain data
elements included in the National Beta Test. The TEP members were
particularly supportive of the items that focused on how pain
interferes with activities (that is, Pain Interference data elements),
because understanding the extent to which pain interferes with function
would enable clinicians to determine the need for appropriate pain
treatment. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We held a public input period in 2016 to solicit feedback on the
standardization of pain and several other items that were under
development in prior efforts. From the prior public comment period, we
included several pain data elements (Pain Effect on Sleep; Pain
Interference--Therapy Activities; Pain Interference--Other Activities)
in a second call for public input, open from
[[Page 39145]]
April 26 to June 26, 2017. The items we sought comment on were modified
from all stakeholder and test efforts. Commenters provided general
comments about pain assessment in general in addition to feedback on
the specific pain items. A few commenters shared their support for
assessing pain, the potential for pain assessment to improve the
quality of care, and for the validity and reliability of the data
elements. Commenters affirmed that the item of pain and the effect on
sleep would be suitable for PAC settings. Commenters' main concerns
included redundancy with existing data elements, feasibility and
utility for cross-setting use, and the applicability of interview-based
items to patients and residents with cognitive or communication
impairments, and deficits. A summary report for the April 26 to June
26, 2017 public comment period titled ``SPADE May-June 2017 Public
Comment Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Pain Interference data elements were included in the National
Beta Test of candidate data elements conducted by our data element
contractor from November 2017 to August 2018. Results of this test
found the Pain Interference data elements to be feasible and reliable
for use with PAC patients and residents. More information about the
performance of the Pain Interference data elements in the National Beta
Test can be found in the document titled ``Final Specifications for IRF
QRP Quality Measures and Standardized Patient Assessment Data
Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018 for the purpose of soliciting input on the
standardized patient assessment data elements. The TEP supported the
interview-based pain data elements included in the National Beta Test.
The TEP members were particularly supportive of the items that focused
on how pain interferes with activities (that is, Pain Interference data
elements), because understanding the extent to which pain interferes
with function would enable clinicians to determine the need for pain
treatment. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our on-going SPADE development efforts.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present the results of the National
Beta Test and solicit additional comments. General input on the testing
and item development process and concerns about burden were received
from stakeholders during this meeting and via email through February 1,
2019. Additionally, one commenter noted strong support for the Pain
data elements and was encouraged by the fact that this portion of the
assessment goes beyond merely measuring the presence of pain. A summary
of the public input received from the November 27, 2018 stakeholder
meeting titled ``Input on Standardized Patient Assessment Data Elements
(SPADEs) Received After November 27, 2018 Stakeholder Meeting'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for the effect of pain
on function, stakeholder input, and strong test results, we proposed
that the three Pain Interference data elements (Pain Effect on Sleep,
Pain Interference with Therapy Activities, and Pain Interference with
Day-to-Day Activities) meet the definition of standardized patient
assessment data with respect to medical conditions and comorbidities
under section 1899B(b)(1)(B)(iv) of the Act and to adopt the Pain
Interference data elements (Pain Effect on Sleep; Pain Interference
with Therapy Activities; and Pain Interference with Day-to-Day
Activities) as standardized patient assessment data for use in the IRF
QRP.
Commenters submitted the following comments related to our proposal
to adopt the Pain Interference (Pain Effect on Sleep, Pain Interference
with Therapy Activities, and Pain Interference with Day-to-Day
Activities) data elements.
Comment: A few commenters noted support for the Pain Interference
data element, noting that the data element will provide a useful and
more accurate assessment of a patient's ability to function, and that
understanding the impact of pain on therapy and other activities,
including sleep, can improve the quality of care, which in turn will
support providers in their ability to provide effective pain management
services.
Response: We thank the commenters for their support of the Pain
Interference data element.
Comment: A commenter noted that the proposed Pain Interference
SPADEs document pain frequency, but stated that it is important to
identify both pain frequency and pain intensity.
Response: We wish to clarify, the Pain Interference interview data
elements question the patient on the frequency with which pain
interferes with sleep, therapy, or non-therapy activities. These data
elements therefore combine the concepts of frequency and intensity,
with the measure of intensity being interference with the named
activities. Self-reported measures of pain intensity are often
criticized for being infeasible to standardize. In these data elements,
we use interference with activities as an alternative to inquiring
about intensity.
Comment: A commenter expressed concerns about the suitability of
the Pain Interference data elements for use in patients with cognitive
and communication deficits and recommended CMS consider the use of non-
verbal means to allow patients to respond to SPADEs related to pain.
Response: We appreciate the commenter's concern surrounding pain
assessment with patients with cognitive and communication deficits. The
Pain Interference interview SPADEs require that a patient be able to
communicate, whether verbally, in writing, or using another method;
assessors may use non-verbal means to administer the questions (for
example, providing the questions and response in writing for a patient
with severe hearing impairment). Patients who are unable to communicate
by any means would not be required to complete the Pain Interference
interview SPADEs. However, evidence suggests that pain presence can be
reliably assessed in non-communicative patients through structural
observational protocols. To that end, we tested observational pain
presence elements in the National Beta Test, but have chosen not to
propose those data elements as SPADEs at this time. We will take the
commenter's concern into consideration as the SPADEs are monitored and
refined in the future.
[[Page 39146]]
Comment: A commenter expressed concerns about how CMS might use
these data elements, noting particular concern that collection of these
data elements may inappropriately translate into an assessment of
quality, and that data collection on this topic could create incentives
that directly or indirectly interfere with treatment decisions.
Response: We appreciate the commenter's concern related to wanting
to understand how we will use the SPADEs in the future. We intend to
continue to communicate and collaborate with stakeholders about how the
SPADEs will be used in the IRF QRP, as those plans are developed, by
soliciting input during the development process and establishing use of
the SPADEs in payment and quality programs through future rulemaking.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the Pain Interference (Pain Effect
on Sleep, Pain Interference with Therapy Activities, and Pain
Interference with Day-to-Day Activities) data elements as standardized
patient assessment data beginning with the FY 2022 IRF QRP as proposed.
4. Impairment Data
Hearing and vision impairments are conditions that, if unaddressed,
affect activities of daily living, communication, physical functioning,
rehabilitation outcomes, and overall quality of life. Sensory
limitations can lead to confusion in new settings, increase isolation,
contribute to mood disorders, and impede accurate assessment of other
medical conditions. Failure to appropriately assess, accommodate, and
treat these conditions increases the likelihood that patients and
residents will require more intensive and prolonged treatment. Onset of
these conditions can be gradual, so individualized assessment with
accurate screening tools and follow-up evaluations are essential to
determining which patients and residents need hearing- or vision-
specific medical attention or assistive devices and accommodations,
including auxiliary aids and/or services, and to ensure that person-
directed care plans are developed to accommodate a patient's or
resident's needs. Accurate diagnosis and management of hearing or
vision impairment would likely improve rehabilitation outcomes and care
transitions, including transition from institutional-based care to the
community. Accurate assessment of hearing and vision impairment would
be expected to lead to appropriate treatment, accommodations, including
the provision of auxiliary aids and services during the stay, and
ensure that patients and residents continue to have their vision and
hearing needs met when they leave the facility.
In alignment with our Meaningful Measures Initiative, we expect
accurate and individualized assessment, treatment, and accommodation of
hearing and vision impairments of patients and residents in PAC to make
care safer by reducing harm caused in the delivery of care; promote
effective prevention and treatment of chronic disease; strengthen
person and family engagement as partners in their care; and promote
effective communication and coordination of care. For example,
standardized assessment of hearing and vision impairments used in PAC
will support ensuring patient safety (for example, risk of falls),
identifying accommodations needed during the stay, and appropriate
support needs at the time of discharge or transfer. Standardized
assessment of these data elements will: Enable or support clinical
decision-making and early clinical intervention; person-centered, high
quality care (for example, facilitating better care continuity and
coordination); better data exchange and interoperability between
settings; and longitudinal outcome analysis. Therefore, reliable data
elements assessing hearing and vision impairments are needed to
initiate a management program that can optimize a patient's or
resident's prognosis and reduce the possibility of adverse events.
Comments on the category of impairments were also submitted by
stakeholders during the FY 2018 IRF PPS proposed rule (82 FR 20737
through 20739) public comment period. A commenter stated hearing and
vision assessments should be administered at the beginning of the
assessment process to provide evidence about any sensory deficits that
may affect the patient's ability to participate in the assessment and
to allow the assessor to offer an assistive device.
We sought comment on our proposals to collect as standardized
patient assessment data the following data with respect to impairments.
We did not receive any comments on the category of impairments.
Final decisions on the SPADEs are given below, following more
detailed comments on each SPADE proposal.
Hearing
In the FY 2020 IRF PPS proposed rule (84 FR 17317 through 17318),
we proposed that the Hearing data element meets the definition of
standardized patient assessment data with respect to impairments under
section 1899B(b)(1)(B)(v) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20737
through 20738), accurate assessment of hearing impairment is important
in the PAC setting for care planning and resource use. Hearing
impairment has been associated with lower quality of life, including
poorer physical, mental, social functioning, and emotional
health.154 155 Treatment and accommodation of hearing
impairment led to improved health outcomes including, but not limited
to, quality of life.\156\ For example, hearing loss in elderly
individuals has been associated with depression and cognitive
impairment,157 158 159 higher rates of incident cognitive
impairment and cognitive decline,\160\ and less time in occupational
therapy.\161\ Accurate assessment of hearing impairment is important in
the PAC setting for care planning and defining resource use.
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\154\ Dalton DS, Cruickshanks KJ, Klein BE, Klein R, Wiley TL,
Nondahl DM. The impact of hearing loss on quality of life in older
adults. Gerontologist. 2003;43(5):661-668.
\155\ Hawkins K, Bottone FG, Jr., Ozminkowski RJ, et al. The
prevalence of hearing impairment and its burden on the quality of
life among adults with Medicare Supplement Insurance. Qual Life Res.
2012;21(7):1135-1147.
\156\ Horn KL, McMahon NB, McMahon DC, Lewis JS, Barker M,
Gherini S. Functional use of the Nucleus 22-channel cochlear implant
in the elderly. The Laryngoscope. 1991;101(3):284-288.
\157\ Sprinzl GM, Riechelmann H. Current trends in treating
hearing loss in elderly people: a review of the technology and
treatment options--a mini-review. Gerontology. 2010;56(3):351-358.
\158\ Lin FR, Thorpe R, Gordon-Salant S, Ferrucci L. Hearing
Loss Prevalence and Risk Factors Among Older Adults in the United
States. The Journals of Gerontology Series A: Biological Sciences
and Medical Sciences. 2011;66A(5):582-590.
\159\ Hawkins K, Bottone FG, Jr., Ozminkowski RJ, et al. The
prevalence of hearing impairment and its burden on the quality of
life among adults with Medicare Supplement Insurance. Qual Life Res.
2012;21(7):1135-1147.
\160\ Lin FR, Metter EJ, O'Brien RJ, Resnick SM, Zonderman AB,
Ferrucci L. Hearing Loss and Incident Dementia. Arch Neurol.
2011;68(2):214-220.
\161\ Cimarolli VR, Jung S. Intensity of Occupational Therapy
Utilization in Nursing Home Residents: The Role of Sensory
Impairments. J Am Med Dir Assoc. 2016;17(10):939-942.
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The proposed data element consists of the single Hearing data
element. This data consists of one question that assesses level of
hearing impairment. This data element is currently in use in the MDS in
SNFs. For more information on the Hearing data element, we refer
readers to the document titled ``Final Specifications for IRF QRP
Quality Measures and Standardized Patient Assessment Data Elements,''
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-
Assessment-
[[Page 39147]]
Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/
IMPACT-Act-Downloads-and-Videos.html.
The Hearing data element was first proposed as a standardized
patient assessment data element in the FY 2018 IRF PPS proposed rule
(82 FR 20737 through 20738). In that proposed rule, we stated that the
proposal was informed by input we received on the PAC PRD form of the
data element (``Ability to Hear'') through a call for input published
on the CMS Measures Management System Blueprint website. Input
submitted from August 12 to September 12, 2016 recommended that
hearing, vision, and communication assessments be administered at the
beginning of patient assessment process. A summary report for the
August 12 to September 12, 2016 public comment period titled ``SPADE
August 2016 Public Comment Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of adopting the Hearing data
element for standardized cross-setting use, noting that it would help
address the needs of patient and residents with disabilities and that
failing to identify impairments during the initial assessment can
result in inaccurate diagnoses of impaired language or cognition and
can invalidate other information obtained from patient assessment.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Hearing data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the Hearing
data element to be feasible and reliable for use with PAC patients and
residents. More information about the performance of the Hearing data
element in the National Beta Test can be found in the document titled
``Final Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on January
5 and 6, 2017 for the purpose of soliciting input on all the SPADEs,
including the Hearing data element. The TEP affirmed the importance of
standardized assessment of hearing impairment in PAC patients and
residents. A summary of the January 5 and 6, 2017 TEP meeting titled
``SPADE Technical Expert Panel Summary (Second Convening)'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our ongoing SPADE development efforts.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present the results of the National
Beta Test and solicit additional comments. General input on the testing
and item development process and concerns about burden were received
from stakeholders during this meeting and via email through February 1,
2019. Additionally, a commenter noted support for the Hearing data
element and suggested administration at the beginning of the patient
assessment to maximize utility. A summary of the public input received
from the November 27, 2018 stakeholder meeting titled ``Input on
Standardized Patient Assessment Data Elements (SPADEs) Received After
November 27, 2018 Stakeholder Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Due to the relatively stable nature of hearing impairment, it is
unlikely that a patient's score on this assessment would change between
the start and end of the IRF stay. Therefore, we proposed that IRFs
that submit the Hearing data element with respect to admission will be
deemed to have submitted with respect to both admission and discharge.
Taking together the importance of assessing for hearing,
stakeholder input, and strong test results, we proposed that the
Hearing data element meets the definition of standardized patient
assessment data with respect to impairments under section
1899B(b)(1)(B)(v) of the Act and to adopt the Hearing data element as
standardized patient assessment data for use in the IRF QRP.
Commenters submitted the following comments related to our proposal
for the Hearing data element.
Comment: A few commenters supported the collection of information
on hearing impairment. One of these commenters also suggested that CMS
consider how hearing impairment impacts a patient's ability to respond
to the assessment tool in general.
Response: We thank the commenters for their support of the Hearing
data element. We intend to reinforce assessment tips and item rationale
through training, open door forums, and future rulemaking efforts.
In the existing guidance manual for the IRF-PAI, we offer tips for
administration that direct assessors to take appropriate steps to
accommodate sensory and communication impairments when conducting the
assessment.
Comment: Some commenters expressed concern that severely impaired
hearing occurs infrequently in IRF patients, thereby limiting the
utility of the data collected.
Response: The Hearing SPADE consists of one data element completed
by the assessor based primarily on interacting with the patient and
reviewing the medical record. Given the low burden of reporting the
Hearing data element, and despite severe hearing impairment occurring
in a small proportion of IRF patients, we believe it is important to
systematically assess for hearing impairment in order to improve
clinical care and care transitions.
Comment: One commenter recommended adding ``unable to assess'' as a
response option, which the commenter believes would be the appropriate
choice if the patient is comatose or is unable to effectively answer
questions related to an assessment of their hearing.
Response: We appreciate the commenter's recommendation. The
assessment of hearing is completed based on observing the patient
during assessment, patient interactions with others, reviewing medical
record documentation, and consulting with patient's family and other
staff, in addition to interviewing the patient, so it can be completed
when the patient is unable to effectively answer questions related to
an assessment of their hearing.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the Hearing data element as
standardized patient assessment data beginning with the FY 2022 IRF QRP
as proposed.
Vision
In the FY 2020 IRF PPS proposed rule (84 FR 17318 through 17319),
we proposed that the Vision data element
[[Page 39148]]
meets the definition of standardized patient assessment data with
respect to impairments under section 1899B(b)(1)(B)(v) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20738
through 20739), evaluation of an individual's ability to see is
important for assessing for risks such as falls and provides
opportunities for improvement through treatment and the provision of
accommodations, including auxiliary aids and services, which can
safeguard patients and residents and improve their overall quality of
life. Further, vision impairment is often a treatable risk factor
associated with adverse events and poor quality of life. For example,
individuals with visual impairment are more likely to experience falls
and hip fracture, have less mobility, and report depressive
symptoms.162 163 164 165 166 167 168 Individualized initial
screening can lead to life-improving interventions such as
accommodations, including the provision of auxiliary aids and services,
during the stay and/or treatments that can improve vision and prevent
or slow further vision loss.
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\162\ Colon-Emeric CS, Biggs DP, Schenck AP, Lyles KW. Risk
factors for hip fracture in skilled nursing facilities: Who should
be evaluated? Osteoporos Int. 2003;14(6):484-489.
\163\ Freeman EE, Munoz B, Rubin G, West SK. Visual field loss
increases the risk of falls in older adults: The Salisbury eye
evaluation. Invest Ophthalmol Vis Sci. 2007;48(10):4445-4450.
\164\ Keepnews D, Capitman JA, Rosati RJ. Measuring patient-
level clinical outcomes of home health care. J Nurs Scholarsh.
2004;36(1):79-85.
\165\ Nguyen HT, Black SA, Ray LA, Espino DV, Markides KS.
Predictors of decline in MMSE scores among older Mexican Americans.
J Gerontol A Biol Sci Med Sci. 2002;57(3):M181-185.
\166\ Prager AJ, Liebmann JM, Cioffi GA, Blumberg DM. Self-
reported Function, Health Resource Use, and Total Health Care Costs
Among Medicare Beneficiaries With Glaucoma. JAMA ophthalmology.
2016;134(4):357-365.
\167\ Rovner BW, Ganguli M. Depression and disability associated
with impaired vision: The MoVies Project. J Am Geriatr Soc.
1998;46(5):617-619.
\168\ Tinetti ME, Ginter SF. The nursing home life-space
diameter. A measure of extent and frequency of mobility among
nursing home residents. J Am Geriatr Soc. 1990;38(12):1311-1315.
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In addition, vision impairment is often a treatable risk factor
associated with adverse events which can be prevented and accommodated
during the stay. Accurate assessment of vision impairment is important
in the IRF setting for care planning and defining resource use.
The proposed data element consists of the single Vision data
element (Ability To See in Adequate Light) that consists of one
question with five response categories. The Vision data element that we
proposed for standardization was tested as part of the development of
the MDS and is currently in use in that assessment in SNFs. Similar
data elements, but with different wording and fewer response option
categories, are in use in the OASIS. For more information on the Vision
data element, we refer readers to the document titled ``Final
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Vision data element was first proposed as a standardized
patient assessment data element in the FY 2018 IRF PPS proposed rule
(82 FR 20738 through 20739).
In that proposed rule, we stated that the proposal was informed by
input we received on the Ability to See in Adequate Light data element
(version tested in the PAC PRD with three response categories) through
a call for input published on the CMS Measures Management System
Blueprint website. Although the data element in public comment differed
from the proposed data element, input submitted from August 12 to
September 12, 2016 supported assessing vision in PAC settings and the
useful information a vision data element would provide.
We also stated that commenters had noted that the Ability to See
item would provide important information that would facilitate care
coordination and care planning, and consequently improve the quality of
care. Other commenters suggested it would be helpful as an indicator of
resource use and noted that the item would provide useful information
about the abilities of patients and residents to care for themselves.
Additional commenters noted that the item could feasibly be implemented
across PAC providers and that its kappa scores from the PAC PRD support
its validity. Some commenters noted a preference for MDS version of the
Vision data element in SNFs over the form put forward in public
comment, citing the widespread use of this data element. A summary
report for the August 12 to September 12, 2016 public comment period
titled ``SPADE August 2016 Public Comment Summary Report'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received a comment supporting having a standardized patient
assessment data element for vision across PAC settings, but it stated
the proposed data element captures only basic information for risk
adjustment, and more detailed information would need to be collected to
use it as an outcome measure.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Vision data element was included in the National Beta Test of candidate
data elements conducted by our data element contractor from November
2017 to August 2018. Results of this test found the Vision data element
to be feasible and reliable for use with PAC patients and residents.
More information about the performance of the Vision data element in
the National Beta Test can be found in the document titled ``Final
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on January
5 and 6, 2017 for the purpose of soliciting input on all the SPADEs
including the Vision data element. The TEP affirmed the importance of
standardized assessment of vision impairment in PAC patients and
residents. A summary of the January 5 and 6, 2017 TEP meeting titled
``SPADE Technical Expert Panel Summary (Second Convening)'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held SODFs and small-group discussions with PAC providers
and other stakeholders in 2018 for the purpose of updating the public
about our ongoing SPADE development efforts. Finally, on November 27,
2018, our data element contractor hosted a public meeting of
stakeholders to present the results of the National Beta Test and
solicit additional comments. General input on the testing and item
development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. Additionally, a commenter noted support for the Vision data
element and suggested administration at the beginning of the patient
assessment to maximize utility. A summary of the public input received
from the November 27, 2018 stakeholder meeting
[[Page 39149]]
titled ``Input on Standardized Patient Assessment Data Elements
(SPADEs) Received After November 27, 2018 Stakeholder Meeting'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Due to the relatively stable nature of vision impairment, it is
unlikely that a patient's score on this assessment would change between
the start and end of the IRF stay. Therefore, we proposed that IRFs
that submit the Vision data element with respect to admission will be
deemed to have submitted with respect to both admissions and discharge.
Taking together the importance of assessing for vision, stakeholder
input, and strong test results, we proposed that the Vision data
element meets the definition of standardized patient assessment data
with respect to impairments under section 1899B(b)(1)(B)(v) of the Act
and to adopt the Vision data element as standardized patient assessment
data for use in the IRF QRP.
Commenters submitted the following comments related to the proposed
rule's discussion of the Vision data element.
Comment: A few commenters supported the collection of information
on vision impairment. One of the commenters noted that the collection
of information on vision impairment would support the identification
and appropriate treatment of vision problems, which they stated were
prevalent and undertreated.
Response: We thank the commenters for their support.
Comment: One commenter recommended that a doctor of optometry
should play a lead role in conducting vision assessments, and that
vision assessments done by other clinicians should also obtain the
patient's own assessment of his or her vision, such as used by the
Centers for Disease Control and Prevention (CDC) Behavioral Risk
Factors Surveillance System survey, which questions patients ``Do you
have serious difficulty seeing, even when wearing glasses?'' This
commenter expressed concerns about the proposed SPADE being subjective
and risks of mis-categorizing patients.
Response: We appreciate the commenter's recommendation about how to
assess for vision impairment. We do not require that a certain type of
clinician complete assessments; the SPADEs have been developed so that
any clinician who is trained in the administration of the assessment
will be able to administer it correctly. The proposed item relies on
the assessor's evaluation of the patient's vision, which has the
advantage of reducing burden placed on the patient. We will take the
recommendation to use patient-reported vision impairment assessment
into consideration in the development of future assessments.
Comment: Some commenters expressed concern that severely impaired
vision occurs infrequently in IRF patients, thereby limiting the
utility of the data collected.
Response: The Vision SPADE consists of one data element completed
by the assessor based primarily on interacting with the patient and
reviewing the medical record. Given the low burden of the Vision data
element, and despite severe vision impairment occurring in a small
proportion of IRF patients, we believe it is important to
systematically assess for vision impairment in order to improve
clinical care and care transitions.
Comment: A commenter recommended that CMS require a vision
assessment at discharge, noting that vision impairment could be related
to challenges in medication management and compliance with written
follow-up instructions for care.
Response: We appreciate the commenter's feedback. We agree that
adequate vision--or the accommodations and assistive technology needed
to compensate for vision impairment--is important to patient safety in
the community, in part for the reasons the commenter mentions. In the
FY 2020 IRF PPS proposed rule (84 FR 17292), we proposed that IRFs that
submitted the Vision SPADE with respect to admission will be deemed to
have submitted with respect to both admission and discharge; we stated
that it is unlikely that the assessment of this SPADEs at admission
would differ from the assessment at discharge. Vision assessment,
collected via the Vision SPADE with respect to admission, will provide
information that will support the patient's care while in the IRF. Out
of consideration for the burden of data collection, and with an
understanding that significant clinical changes to a patient's vision
will be documented in the medical record as part of routine clinical
practice, we are finalizing our proposal that IRFs that submit the
Vision SPADE with respect to admission will be deemed to have submitted
with respect to both admission and discharge. We note that during the
discharge planning process, it is incumbent on IRF providers to make
reasonable assurances that the patient's needs will be met in the next
care setting, including in the home.
Comment: One commenter recommended adding ``unable to assess'' as a
response option, which the commenter believes would be the appropriate
choice if the patient is comatose or is unable to effectively answer
questions related to an assessment of their vision.
Response: We appreciate the commenter's recommendation. However,
the assessment of vision is completed based on consulting with
patient's family and other staff, observing the patient including
requesting the patient to read text or examine pictures or numbers in
addition to interviewing the patient about their vision abilities.
These other sources/methods can be used to complete the assessment of
vision when the patient is unable to effectively answer questions
related to an assessment of their vision.
After careful consideration of the public comments we received, we
are finalizing our proposal to adopt the Vision data element as
standardized patient assessment data beginning with the FY 2022 IRF QRP
as proposed.
4. New Category: Social Determinants of Health
a. Social Determinants of Health Data Collection To Inform Measures and
Other Purposes
Section 2(d)(2)(A) of the IMPACT Act requires CMS to assess
appropriate adjustments to quality measures, resource measures and
other measures, and to assess and implement appropriate adjustments to
payment under Medicare, based on those measures, after taking into
account studies conducted by ASPE on social risk factors (described
below) and other information, and based on an individual's health
status and other factors. Paragraph (C) of section 2(d)(2) of the
IMPACT Act further requires the Secretary to carry out periodic
analyses, at least every 3 years, based on the factors referred to
paragraph (A) so as to monitor changes in possible relationships.
Paragraph (B) of section 2(d)(2) of the IMPACT Act requires CMS to
collect or otherwise obtain access to data necessary to carry out the
requirement of the paragraph (both assessing adjustments described
above in such paragraph (A) and for periodic analyses in such paragraph
(C)). Accordingly we proposed to use our authority under paragraph (B)
of section 2(d)(2) of the IMPACT Act to establish a new data source for
information to
[[Page 39150]]
meet the requirements of paragraphs (A) and (C) of section 2(d)(2) of
the IMPACT Act. In this rule, we proposed to collect and access data
about social determinants of health (SDOH) in order to perform CMS'
responsibilities under paragraphs (A) and (C) of section 2(d)(2) of the
IMPACT Act, as explained in more detail below. Social determinants of
health, also known as social risk factors, or health-related social
needs, are the socioeconomic, cultural and environmental circumstances
in which individuals live that impact their health. We proposed to
collect information on seven proposed SDOH SPADE data elements relating
to race, ethnicity, preferred language, interpreter services, health
literacy, transportation, and social isolation; a detailed discussion
of each of the proposed SDOH data elements is found in section
VII.G.5.b. of this rule.
We also proposed to use the assessment instrument for the IRF QRP,
the IRF-PAI, described as a PAC assessment instrument under section
1899B(a)(2)(B) of the Act, to collect these data via an existing data
collection mechanism. We believe this approach will provide CMS with
access to data with respect to the requirements of section 2(d)(2) of
the IMPACT Act, while minimizing the reporting burden on PAC health
care providers by relying on a data reporting mechanism already used
and an existing system to which PAC health care providers are already
accustomed.
The IMPACT Act includes several requirements applicable to the
Secretary, in addition to those imposing new data reporting obligations
on certain PAC providers as discussed in IX.G.4.b. of this final rule.
Paragraphs (A) and (B) of sections 2(d)(1) of the IMPACT Act require
the Secretary, acting through the Office of the Assistant Secretary for
Planning and Evaluation (ASPE), to conduct two studies that examine the
effect of risk factors, including individuals' socioeconomic status, on
quality, resource use and other measures under the Medicare program.
The first ASPE study was completed in December 2016 and is discussed
below, and the second study is to be completed in the fall of 2019. We
recognize that ASPE, in its studies, is considering a broader range of
social risk factors than the SDOH data elements in this proposal, and
address both PAC and non-PAC settings. We acknowledge that other data
elements may be useful to understand, and that some of those elements
may be of particular interest in non-PAC settings. For example, for
beneficiaries receiving care in the community, as opposed to an in-
patient facility, housing stability and food insecurity may be more
relevant. We will continue to take into account the findings from both
of ASPE's reports in future policy making.
One of the ASPE's first actions under the IMPACT Act was to
commission the National Academies of Sciences, Engineering, and
Medicine (NASEM) to define and conceptualize socioeconomic status for
the purposes of ASPE's two studies under section 2(d)(1) of the IMPACT
Act. The NASEM convened a panel of experts in the field and conducted
an extensive literature review. Based on the information collected, the
2016 NASEM panel report titled, ``Accounting for Social Risk Factors in
Medicare Payment: Identifying Social Risk Factors'', concluded that the
best way to assess how social processes and social relationships
influence key health-related outcomes in Medicare beneficiaries is
through a framework of social risk factors instead of socioeconomic
status. Social risk factors discussed in the NASEM report include
socioeconomic position, race, ethnicity, gender, social context, and
community context. These factors are discussed at length in chapter 2
of the NASEM report, titled ``Social Risk Factors.'' \169\ Consequently
NASEM framed the results of its report in terms of ``social risk
factors'' rather than ``socioeconomic status'' or ``sociodemographic
status.'' The full text of the ``Social Risk Factors'' NASEM report is
available for reading on the website at https://www.nap.edu/read/21858/chapter/1.
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\169\ National Academies of Sciences, Engineering, and Medicine.
2016. Accounting for social risk factors in Medicare payment:
Identifying social risk factors. Chapter 2. Washington, DC: The
National Academies Press.
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Each of the data elements we proposed to collect and access under
our authority under section 2(d)(2)(B) of the IMPACT Act is identified
in the 2016 NASEM report as a social risk factor that has been shown to
impact care use, cost and outcomes for Medicare beneficiaries. CMS uses
the term social determinants of health (SDOH) to denote social risk
factors, which is consistent with the objectives of Healthy People
2020.\170\
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\170\ Social Determinants of Health. Healthy People 2020.
https://www.healthypeople.gov/2020/topics-objectives/topic/social-determinants-of-health. (February 2019).
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ASPE issued its first Report to Congress, titled ``Social Risk
Factors and Performance Under Medicare's Value-Based Purchasing
Programs,'' under section 2(d)(1)(A) of the IMPACT Act on December 21,
2016.\171\ Using NASEM's social risk factors framework, ASPE focused on
the following social risk factors, in addition to disability: (1) Dual
enrollment in Medicare and Medicaid as a marker for low income; (2)
residence in a low-income area; (3) Black race; (4) Hispanic ethnicity;
and (5) residence in a rural area. ASPE acknowledged that the social
risk factors examined in its report were limited due to data
availability. The report also noted that the data necessary to
meaningfully attempt to reduce disparities and identify and reward
improved outcomes for beneficiaries with social risk factors have not
been collected consistently on a national level in PAC settings. Where
these data have been collected, the collection frequently involves
lengthy questionnaires. More information on the Report to Congress on
Social Risk Factors and Performance under Medicare's Value-Based
Purchasing Programs, including the full report, is available on the
website at https://aspe.hhs.gov/social-risk-factors-and-medicares-value-based-purchasing-programs-reports.
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\171\ U.S. Department of Health and Human Services, Office of
the Assistant Secretary for Planning and Evaluation. 2016. Report to
Congress: Social Risk Factors and Performance Under Medicare's
Value-Based Payment Programs. Washington, DC.
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Section 2(d)(2) of the IMPACT Act relates to CMS activities and
imposes several responsibilities on the Secretary relating to quality,
resource use, and other measures under Medicare. As mentioned
previously, under paragraph (A) of section 2(d)(2) of the IMPACT Act,
the Secretary is required, on an ongoing basis, taking into account the
ASPE studies and other information, and based on an individual's health
status and other factors, to assess appropriate adjustments to quality,
resource use, and other measures, and to assess and implement
appropriate adjustments to Medicare payments based on those measures.
Section 2(d)(2)(A)(i) of the IMPACT Act applies to measures adopted
under sections (c) and (d) of section 1899B of the Act and to other
measures under Medicare. However, CMS' ability to perform these
analyses, and assess and make appropriate adjustments is hindered by
limits of existing data collections on SDOH data elements for Medicare
beneficiaries. In its first study in 2016, in discussing the second
study, ASPE noted that information relating to many of the specific
factors listed in the IMPACT Act, such as health literacy, limited
English proficiency, and Medicare beneficiary activation, are not
available in Medicare data.
[[Page 39151]]
Paragraph 2(d)(2)(A) of the IMPACT Act specifically requires the
Secretary to take the studies and considerations from ASPE's reports to
Congress, as well as other information as appropriate, into account in
assessing and implementing adjustments to measures and related payments
based on measures in Medicare. The results of the ASPE's first study
demonstrated that Medicare beneficiaries with social risk factors
tended to have worse outcomes on many quality measures, and providers
who treated a disproportionate share of beneficiaries with social risk
factors tended to have worse performance on quality measures. As a
result of these findings, ASPE suggested a three-pronged strategy to
guide the development of value-based payment programs under which all
Medicare beneficiaries receive the highest quality healthcare services
possible. The three components of this strategy are to: (1) Measure and
report quality of care for beneficiaries with social risk factors; (2)
set high, fair quality standards for care provided to all
beneficiaries; and (3) reward and support better outcomes for
beneficiaries with social risk factors. In discussing how measuring and
reporting quality for beneficiaries with social risk factors can be
applied to Medicare quality payment programs, the report offered nine
considerations across the three-pronged strategy, including enhancing
data collection and developing statistical techniques to allow
measurement and reporting of performance for beneficiaries with social
risk factors on key quality and resource use measures.
Congress, in section 2(d)(2)(B) of the IMPACT Act, required the
Secretary to collect or otherwise obtain access to the data necessary
to carry out the provisions of paragraph (2) of section 2(d) of the
IMPACT Act through both new and existing data sources. Taking into
consideration NASEM's conceptual framework for social risk factors
discussed above, ASPE's study, and considerations under section
2(d)(1)(A) of the IMPACT Act, as well as the current data constraints
of ASPE's first study and its suggested considerations, we proposed to
collect and access data about SDOH under section 2(d)(2) of the IMPACT
Act. Our collection and use of the SDOH data described in section
IX.G.4.b. of this final rule, under section 2(d)(2) of the IMPACT Act
would be independent of our proposal below (in section IX.G.4.b. of
this final rule) and our authority to require submission of that data
for use as SPADE under section 1899B(a)(1)(B) of the Act.
Accessing standardized data relating to the SDOH data elements on a
national level is necessary to permit CMS to conduct periodic analyses,
to assess appropriate adjustments to quality measures, resource use
measures, and other measures, and to assess and implement appropriate
adjustments to Medicare payments based on those measures. We agree with
ASPE's observations, in the value-based purchasing context, that the
ability to measure and track quality, outcomes, and costs for
beneficiaries with social risk factors over time is critical as
policymakers and providers seek to reduce disparities and improve care
for these groups. Collecting the data as proposed will provide the
basis for our periodic analyses of the relationship between an
individual's health status and other factors and quality, resource use,
and other measures, as required by section 2(d)(2) of the IMPACT Act,
and to assess appropriate adjustments. These data will also permit us
to develop the statistical tools necessary to maximize the value of
Medicare data, reduce costs and improve the quality of care for all
beneficiaries. Collecting and accessing SDOH data in this way also
supports the three-part strategy put forth in the first ASPE report,
specifically ASPE's consideration to enhance data collection and
develop statistical techniques to allow measurement and reporting of
performance for beneficiaries with social risk factors on key quality
and resource use measures.
For the reasons discussed above, we proposed under section 2(d)(2)
of the IMPACT Act, to collect the data on the following SDOH: (1) Race,
as described in section VII.G.4.b.(1) of this rule; (2) Ethnicity, as
described in section VII.G.4.b.(1) of this rule; (3) Preferred
Language, as described in section VII.G.4.b.(2) of this rule; (4)
Interpreter Services, as described in section VII.G.4.b.(2) of this
rule; (5) Health Literacy, as described in section VII.G.4.b.(3) of
this rule; (6) Transportation, as described in section VII.G.4.b.(4) of
this rule; and (7) Social Isolation, as described in section
VII.G.4.b.(5) of this rule. These data elements are discussed in more
detail below in section VII.G.4.b of this rule. A detailed discussion
of the comments we received, along with our responses is included in
each section.
b. Standardized Patient Assessment Data
Section 1899B(b)(1)(B)(vi) of the Act authorizes the Secretary to
collect SPADEs with respect to other categories deemed necessary and
appropriate. Below we proposed to create a Social Determinants of
Health SPADE category under section 1899B(b)(1)(B)(vi) of the Act. In
addition to collecting SDOH data for the purposes outlined above under
section 2(d)(2)(B), we also proposed to collect as SPADE these same
data elements (race, ethnicity, preferred language, interpreter
services, health literacy, transportation, and social isolation) under
section 1899B(b)(1)(B)(vi) of the Act. We believe that this proposed
new category of Social Determinants of Health will inform provider
understanding of individual patient risk factors and treatment
preferences, facilitate coordinated care and care planning, and improve
patient outcomes. We proposed to deem this category necessary and
appropriate, for the purposes of SPADE, because using common standards
and definitions for PAC data elements is important in ensuring
interoperable exchange of longitudinal information between PAC
providers and other providers to facilitate coordinated care,
continuity in care planning, and the discharge planning process from
PAC settings.
All of the Social Determinants of Health data elements we proposed
under section 1899B(b)(1)(B)(vi) of the Act have the capacity to take
into account treatment preferences and care goals of patients, and to
inform our understanding of patient complexity and risk factors that
may affect care outcomes. While acknowledging the existence and
importance of additional social determinants of health, we proposed to
assess some of the factors relevant for patients receiving PAC that PAC
settings are in a position to impact through the provision of services
and supports, such as connecting patients with identified needs with
transportation programs, certified interpreters, or social support
programs.
We proposed to adopt the following seven data elements as SPADE
under the proposed Social Determinants of Health category: Race,
ethnicity, preferred language, interpreter services, health literacy,
transportation, and social isolation. To select these data elements, we
reviewed the research literature, a number of validated assessment
tools and frameworks for addressing SDOH currently in use (for example,
Health Leads,\172\ NASEM, Protocol for Responding to and Assessing
Patients' Assets, Risks, and Experiences (PRAPARE), and ICD-10), and we
engaged in discussions with stakeholders. We also prioritized balancing
the reporting burden for PAC providers with our policy objective to
collect SPADEs that will inform care
[[Page 39152]]
planning and coordination and quality improvement across care settings.
Furthermore, incorporating SDOH data elements into care planning has
the potential to reduce readmissions and help beneficiaries achieve and
maintain their health goals.
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\172\ Health Leads. Available at https://healthleadsusa.org/.
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We also considered feedback received during a listening session
that we held on December 13, 2018. The purpose of the listening session
was to solicit feedback from health systems, research organizations,
advocacy organizations and state agencies and other members of the
public on collecting patient-level data on SDOH across care settings,
including consideration of race, ethnicity, spoken language, health
literacy, social isolation, transportation, sex, gender identity, and
sexual orientation. We also gave participants an option to submit
written comments. A full summary of the listening session, titled
``Listening Session on Social Determinants of Health Data Elements:
Summary of Findings,'' includes a list of participating stakeholders
and their affiliations, and is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We solicited comment on these proposals.
Commenters submitted the following comments related to the proposed
rule's discussion of SDOH SPADEs. A discussion of these comments, along
with our responses, appears below.
Comment: One commenter supported the incorporation of SDOH in the
IRF QRP, in the interest of promoting access and assuring high-quality
care for all beneficiaries. The commenter also encouraged CMS to be
mindful of meaningful data collection and the potential impact for data
overload. Since SDOH have impacts far beyond the post[hyphen]acute care
setting, the commenter cautioned data collection that cannot be readily
gathered, shared, or replicated beyond the PAC setting.
The commenter also encouraged CMS to consider leveraging data
points collected during primary care visits by using social risk factor
data captured during those encounters. They pointed out that the
ability to have a hospital's or physician's EHR also collect, capture,
and exchange segments of this information is powerful. The commenter
recommended that CMS take a holistic view of SDOH across the care
continuum so that all care settings may gather, collect or leverage
this data efficiently and in way that maximizes its impact.
Response: We agree that collecting SDOH data elements can be useful
in identifying and addressing health disparities. We also agree that
CMS should be mindful that data elements selected are useful. The
proposed SDOH SPADEs are aligned with SDOH identified in the 2016 NASEM
report, which was commissioned by ASPE. Regarding the commenter's
suggestion that CMS consider how it can align existing and future SDOH
data collection to minimize burden on providers, we agree that it is
important to minimize duplication of effort and will take this under
advisement for future policy development.
Comment: One commenter recommended that CMS consider admission
assessment for certain SPADEs as also fulfilling the discharge
assessment requirement. The commenter supported the inclusion of the
SDOH SPADEs and recommended that CMS require these items be assessed at
some point during the patient's stay instead of during the admission
assessment time window. The commenter recommended that any SDOH SPADES
finalized should be assessed at any point during the stay.
Response: We disagree with the commenters regarding SDOH SPADES
should be assessed at any point during the stay. Each of the SDOH SPADE
data elements will assist with care planning when the patient is
admitted. It is important for providers to identify a patient's needs
in order to better inform the patient's care decisions made during and
after the stay, including a patient's unique risk factors and treatment
preferences.
Comment: Commenters were generally in favor of the concept of
collecting SDOH data elements and provided that, if implemented
appropriately, the data could be useful in identifying and addressing
health care disparities, as well as refining the risk adjustment of
outcome measures. However, some of the commenters suggested CMS not to
finalize the proposed policy until CMS can address important issues
around the potential future uses of these elements and the requirements
around data collection for certain elements. The commenters provided
that CMS did not state explicitly in the rule whether it anticipates
the SDOH SPADEs will be used in adjusting measures and believe that the
IMPACT Act's requirements make it likely the SPADEs will be considered
for use in future adjustments. The commenters recommended CMS to be
circumspect and transparent in its approaches to incorporating the data
elements proposed in payment and quality adjustments, such as by
collecting stakeholder feedback before implementing any adjustments.
Response: We appreciate the commenters for recognizing that
collecting SDOH data elements can be useful in identifying and address
health disparities. We intend to use this data to assess the impact
that the social determinants of health have on health outcomes. We will
continue to work with stakeholders to promote transparency and support
providers who serve vulnerable populations, promote high quality care,
and refine and further implement SDOH SPADE. We appreciate the comment
on collecting stakeholder feedback before implementing any adjustments
to measures based on the SDOH SPADE. Collection of this data will help
us in identifying potential disparities, conducting analyses, and
assessing whether any adjustments are needed. Any future policy
development based on this data would be done transparently, and involve
solicitation of stakeholder feedback through the notice and comment
rulemaking process as appropriate.
Comment: Several commenters recommended that CMS include disability
status as a SDOH that contributes to overall patient access to care,
health status, outcomes, and many other determinants of health since it
is already included in some Medicare risk adjustment. The commenters
stated that ASPE's report to Congress entitled ``Social Risk Factors
and Performance Under Medicare's Value-Based Purchasing Programs''
reported that disability is an independent predictor of poor mental and
physical health outcomes and that individuals with disabilities may
receive lower-quality preventive care.
Response: We appreciate the comments and suggestions provided by
the commenters. We agree that it is important to understand and meet
the needs of patients with disabilities. While disability is not being
currently assessed through the SPADE, it is comprehensively assessed as
part of existing protocols around care plans and health goals. However,
as we continue to evaluate SDOH SPADEs, we will keep commenters'
feedback in mind and may consider these suggestions in future
rulemaking.
Comment: One commenter supported CMS's proposal to collect SDOH
data within SPADEs but was concerned that all of these new elements may
be burdensome. The commenter recommended that CMS require data
collection on race, ethnicity, preferred
[[Page 39153]]
language, and interpreter services, and make data collection on health
literacy, transportation, and social isolation voluntary for now and
have the requirement phased into future rulemaking. The commenter noted
that this would give IRFs an opportunity to adjust to the new data
collection methods, while signaling their importance as entities that
are currently collecting information on SDOH are experiencing various
workflow, privacy, and other challenges. The commenter recommended that
CMS consider including the collection of housing status in the future
as individuals with unmet housing needs, such as homelessness or
substandard housing, have higher health care costs and can be at risk
for readmissions.
Response: We thank the commenter for their comment. As discussed
above, section 2(d)(2)(B) of the IMPACT Act requires the Secretary to
collect or otherwise obtain access to the data necessary to carry out
the provisions of paragraph (2) of section 2(d) of the IMPACT Act
through both new and existing data sources. Accessing standardized data
relating to the SDOH data elements on a national level is necessary to
permit CMS to conduct periodic analyses, to assess appropriate
adjustments to quality measures, resource use measures, and other
measures, and to assess and implement appropriate adjustments to
Medicare payments based on those measures. Collecting the data as
proposed will provide the basis for our periodic analyses of the
relationship between an individual's health status and other factors
and quality, resource use, and other measures, as required by section
2(d)(2) of the IMPACT Act, and to assess appropriate adjustments.
Regarding the suggestion that CMS consider a housing status SPADE data
element in future rulemaking efforts, we appreciate this feedback and
will consider this suggestion in future rulemaking efforts on SPADE
SDOH data elements.
(1) Race and Ethnicity
The persistence of racial and ethnic disparities in health and
health care is widely documented, including in PAC
settings.173 174 175 176 177 Despite the trend toward
overall improvements in quality of care and health outcomes, the Agency
for Healthcare Research and Quality, in its National Healthcare Quality
and Disparities Reports, consistently indicates that racial and ethnic
disparities persist, even after controlling for factors such as income,
geography, and insurance.\178\ For example, racial and ethnic
minorities tend to have higher rates of infant mortality, diabetes and
other chronic conditions, and visits to the emergency department, and
lower rates of having a usual source of care and receiving
immunizations such as the flu vaccine.\179\ Studies have also shown
that African Americans are significantly more likely than white
Americans to die prematurely from heart disease and stroke.\180\
However, our ability to identify and address racial and ethnic health
disparities has historically been constrained by data limitations,
particularly for smaller populations groups such as Asians, American
Indians and Alaska Natives, and Native Hawaiians and other Pacific
Islanders.\181\
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\173\ 2017 National Healthcare Quality and Disparities Report.
Rockville, MD: Agency for Healthcare Research and Quality; September
2018. AHRQ Pub. No. 18-0033-EF.
\174\ Fiscella, K. and Sanders, M.R. Racial and Ethnic
Disparities in the Quality of Health Care. (2016). Annual Review of
Public Health. 37:375-394.
\175\ 2018 National Impact Assessment of the Centers for
Medicare & Medicaid Services (CMS) Quality Measures Reports.
Baltimore, MD: U.S. Department of Health and Human Services, Centers
for Medicare and Medicaid Services; February 28, 2018.
\176\ Smedley, B.D., Stith, A.Y., & Nelson, A.R. (2003). Unequal
treatment: Confronting racial and ethnic disparities in health care.
Washington, DC, National Academy Press.
\177\ Chase, J., Huang, L. and Russell, D. (2017). Racial/ethnic
disparities in disability outcomes among post-acute home care
patients. J of Aging and Health. 30(9):1406-1426.
\178\ National Healthcare Quality and Disparities Reports.
(December 2018). Agency for Healthcare Research and Quality,
Rockville, MD. http://www.ahrq.gov/research/findings/nhqrdr/index.html.
\179\ National Center for Health Statistics. Health, United
States, 2017: With special feature on mortality. Hyattsville,
Maryland. 2018.
\180\ HHS. Heart disease and African Americans. 2016b. (October
24, 2016). http://minorityhealth.hhs.gov/omh/browse.aspx?lvl=4&lvlid=19.
\181\ National Academies of Sciences, Engineering, and Medicine;
Health and Medicine Division; Board on Population Health and Public
Health Practice; Committee on Community-Based Solutions to Promote
Health Equity in the United States; Baciu A, Negussie Y, Geller A,
et al., editors. Communities in Action: Pathways to Health Equity.
Washington (DC): National Academies Press (US); 2017 Jan 11. 2, The
State of Health Disparities in the United States. Available at
https://www.ncbi.nlm.nih.gov/books/NBK425844/.
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The ability to improve understanding of and address racial and
ethnic disparities in PAC outcomes requires the availability of better
data. There is currently a Race and Ethnicity data element, collected
in the MDS, LCDS, IRF-PAI, and OASIS, that consists of a single
question, which aligns with the 1997 Office of Management and Budget
(OMB) minimum data standards for federal data collection efforts.\182\
The 1997 OMB Standard lists five minimum categories of race: (1)
American Indian or Alaska Native; (2) Asian; (3) Black or African
American; (4) Native Hawaiian or Other Pacific Islander; (5) and White.
The 1997 OMB Standard also lists two minimum categories of ethnicity:
(1) Hispanic or Latino; and (2) Not Hispanic or Latino. The 2011 HHS
Data Standards requires a two-question format when self-identification
is used to collect data on race and ethnicity. Large federal surveys
such as the National Health Interview Survey, Behavioral Risk Factor
Surveillance System, and the National Survey on Drug Use and Health,
have implemented the 2011 HHS race and ethnicity data standards. CMS
has similarly updated the Medicare Current Beneficiary Survey, Medicare
Health Outcomes Survey, and the Health Insurance Marketplace
Application for Health Coverage with the 2011 HHS data standards. More
information about the HHS Race and Ethnicity Data Standards are
available on the website at https://minorityhealth.hhs.gov/omh/browse.aspx?lvl=3&lvlid=54.
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\182\ ``Revisions to the Standards for the Classification of
Federal Data on Race and Ethnicity (Notice of Decision)''. Federal
Register 62:210 (October 30, 1997) pp. 58782-58790. Available at
https://www.govinfo.gov/content/pkg/FR-1997-10-30/pdf/97-28653.pdf.
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We proposed to revise the current Race and Ethnicity data element
for purposes of this proposal to conform to the 2011 HHS Data Standards
for person-level data collection, while also meeting the 1997 OMB
minimum data standards for race and ethnicity. Rather than one data
element that assesses both race and ethnicity, we proposed two separate
data elements: One for Race and one for Ethnicity, that would conform
with the 2011 HHS Data Standards and the 1997 OMB Standard. In
accordance with the 2011 HHS Data Standards a two-question format would
be used for the proposed race and ethnicity data elements.
The proposed Race data element asks, ``What is your race? We
proposed to include fourteen response options under the race data
element: (1) White; (2) Black or African American; (3) American Indian
or Alaska Native; (4) Asian Indian; (5) Chinese; (6) Filipino; (7)
Japanese; (8) Korean; (9) Vietnamese; (10) Other Asian; (11) Native
Hawaiian; (12) Guamanian or Chamorro; (13) Samoan; and (14) Other
Pacific Islander.
The proposed Ethnicity data element asks, ``Are you Hispanic,
Latino/a, or Spanish origin?'' We proposed to include five response
options under the ethnicity data element: (1) Not of Hispanic, Latino/
a, or Spanish origin; (2) Mexican, Mexican American,
[[Page 39154]]
Chicano/a; (3) Puerto Rican; (4) Cuban; and (5) Another Hispanic,
Latino, or Spanish Origin. We are including the addition of ``of'' to
the Ethnicity data element to read, ``Are you of Hispanic, Latino/a, or
Spanish origin?''
We believe that the two proposed data elements for race and
ethnicity conform to the 2011 HHS Data Standards for person-level data
collection, while also meeting the 1997 OMB minimum data standards for
race and ethnicity, because under those standards, more detailed
information on population groups can be collected if those additional
categories can be aggregated into the OMB minimum standard set of
categories.
In addition, we received stakeholder feedback during the December
13, 2018 SDOH listening session on the importance of improving response
options for race and ethnicity as a component of health care
assessments and for monitoring disparities. Some stakeholders
emphasized the importance of allowing for self-identification of race
and ethnicity for more categories than are included in the 2011 HHS
Standard to better reflect state and local diversity, while
acknowledging the burden of coding an open-ended health care assessment
question across different settings.
We believe that the proposed modified race and ethnicity data
elements more accurately reflect the diversity of the U.S. population
than the current race/ethnicity data element included in MDS, LCDS,
IRF-PAI, and OASIS.183 184 185 186 We believe, and research
consistently shows, that improving how race and ethnicity data are
collected is an important first step in improving quality of care and
health outcomes. Addressing disparities in access to care, quality of
care, and health outcomes for Medicare beneficiaries begins with
identifying and analyzing how SDOH, such as race and ethnicity, align
with disparities in these areas.\187\ Standardizing self-reported data
collection for race and ethnicity allows for the equal comparison of
data across multiple healthcare entities.\188\ By collecting and
analyzing these data, CMS and other healthcare entities will be able to
identify challenges and monitor progress. The growing diversity of the
U.S. population and knowledge of racial and ethnic disparities within
and across population groups supports the collection of more granular
data beyond the 1997 OMB minimum standard for reporting categories. The
2011 HHS race and ethnicity data standard includes additional detail
that may be used by PAC providers to target quality improvement efforts
for racial and ethnic groups experiencing disparate outcomes. For more
information on the Race and Ethnicity data elements, we refer readers
to the document titled ``Final Specifications for IRF QRP Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
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\183\ Penman-Aguilar, A., Talih, M., Huang, D., Moonesinghe, R.,
Bouye, K., Beckles, G. (2016). Measurement of Health Disparities,
Health Inequities, and Social Determinants of Health to Support the
Advancement of Health Equity. J Public Health Manag Pract. 22 Suppl
1: S33-42.
\184\ Ramos, R., Davis, J.L., Ross, T., Grant, C.G., Green, B.L.
(2012). Measuring health disparities and health inequities: Do you
have REGAL data? Qual Manag Health Care. 21(3):176-87.
\185\ IOM (Institute of Medicine). 2009. Race, Ethnicity, and
Language Data: Standardization for Health Care Quality Improvement.
Washington, DC: The National Academies Press.
\186\ ``Revision of Standards for Maintaining, Collecting, and
Presenting Federal Data on Race and Ethnicity: Proposals From
Federal Interagency Working Group (Notice and Request for
Comments).'' Federal Register 82: 39 (March 1, 2017) p. 12242.
\187\ National Academies of Sciences, Engineering, and Medicine;
Health and Medicine Division; Board on Population Health and Public
Health Practice; Committee on Community-Based Solutions to Promote
Health Equity in the United States; Baciu A, Negussie Y, Geller A,
et al., editors. Communities in Action: Pathways to Health Equity.
Washington (DC): National Academies Press (US); 2017 Jan 11. 2, The
State of Health Disparities in the United States. Available at
https://www.ncbi.nlm.nih.gov/books/NBK425844/.
\188\ IOM (Institute of Medicine). 2009. Race, Ethnicity, and
Language Data: Standardization for Health Care Quality Improvement.
Washington, DC: The National Academies Press.
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In an effort to standardize the submission of race and ethnicity
data among IRFs, HHAs, SNFs and LTCHs, for the purposes outlined in
section 1899B(a)(1)(B) of the Act, while minimizing the reporting
burden, we proposed to adopt the Race and Ethnicity data elements
described above as SPADEs with respect to the proposed Social
Determinants of Health category.
Specifically, we proposed to replace the current Race/Ethnicity
data element with the proposed Race and Ethnicity data elements on the
IRF-PAI. We also proposed that IRFs that submit the Race and Ethnicity
data elements with respect to admission will be considered to have
submitted with respect to discharge as well, because it is unlikely
that the results of these assessment findings will change between the
start and end of the IRF stay, making the information submitted with
respect to a patient's admission the same with respect to a patient's
discharge.
We solicited comment on these proposals.
Commenters submitted the following comments related to the proposed
rule's discussion of the Race and Ethnicity SPADEs. A discussion of
these comments, along with our responses, appears below.
Comment: Some commenters noted that the response options for race
do not align with those used in other government data, such as the U.S.
Census or the Office of Management and Budget (OMB). The commenters
also stated these responses are not consistent with the recommendations
made in the 2009 Institute of Medicine report. The commenters pointed
out that IOM report recommended using broader OMB race categories and
granular ethnicities chosen from a national standard set that can be
``rolled up'' into the broader categories. The commenters stated that
it is unclear how CMS chose the 14 response options under the race data
element and the five options under the ethnicity element and worried
that these response options would add to the confusion that already may
exist for patients about what terms like ``race'' and ``ethnicity''
mean for the purposes of health care data collection. The commenters
also noted that CMS should confer directly with experts on the issue to
ensure patient assessments are collecting the right data in the right
way before these SDOH SPADEs are finalized.
Response: The proposed Race and Ethnicity categories align with and
are rolled up into the 1997 OMB minimum data standards and conforming
with the 2011 HHS Data Standards as described in the implementation
guidance titled ``U.S. Department of Health and Human Services
Implementation Guidance on Data Collection Standards for Race,
Ethnicity, Sex, Primary Language, and Disability Status'' at https://aspe.hhs.gov/basic-report/hhs-implementation-guidance-data-collection-standards-race-ethnicity-sex-primary-language-and-disability-status. As
stated in the proposed rule, the 14 race categories and the 5 ethnicity
categories conform with the 2011 HHS Data Standards for person-level
data collection, which were developed in fulfillment of section 4302 of
the Affordable Care Act that required the Secretary of HHS to establish
data collection standards for race, ethnicity, sex, primary language,
and disability status. Through the HHS Data Council, which is the
principal, senior internal Departmental forum and advisory body to the
Secretary on health and human
[[Page 39155]]
services data policy and coordinates HHS data collection and analysis
activities, the Section 4302 Standards Workgroup was formed. The
Workgroup included representatives from HHS, the OMB, and the Census
Bureau. The Workgroup examined current federal data collection
standards, adequacy of prior testing, and quality of the data produced
in prior surveys; consulted with statistical agencies and programs;
reviewed OMB data collection standards and the Institute of Medicine
(IOM) Report Race, Ethnicity, and Language Data Collection:
Standardization for Health Care Quality Improvement; sought input from
national experts; and built on its members' experience with collecting
and analyzing demographic data. As a result of this Workgroup, a set of
data collection standards were developed, and then published for public
comment. This set of data collection standards is referred to as the
2011 HHS Data Standards.\189\ As described in the implementation
guidance provided above, the categories of race and ethnicity under the
2011 HHS Data Standards allow for more detailed information to be
collected and the additional categories under the 2011 HHS Data
Standards can be aggregated into the OMB minimum standards set of
categories.
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\189\ HHS Data Standards. Available at https://aspe.hhs.gov/basic-report/hhs-implementation-guidance-data-collection-standards-race-ethnicity-sex-primary-language-and-disability-status.
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As noted in the FY 2020 IRF PPS proposed rule (84 FR 17321 through
17323), we conferred with experts by conducting a listening session
regarding the proposed SDOH data elements regarding the importance of
improving response options for race and ethnicity as a component of
health care assessments and for monitoring disparities. Some
stakeholders emphasized the importance of allowing for self-
identification of race and ethnicity for more categories than are
included in the 2011 HHS Data Standards to better reflect state and
local diversity.
Comment: A commenter recommended that CMS consider the implications
of having PAC providers collect Race and Ethnicity codes that vary from
the Race and Ethnicity codes collected by other healthcare providers,
specifically acute-care hospitals. The commenter noted that unless all
care providers are expected to utilize the uniform 2011 HHS Data
Standards, the consistency and accuracy of race and ethnicity data
across settings will likely be unreliable and problematic. Another
commenter provided that the proposed list of response options for Race
may not include all races that should be reflected, for example, Native
African and Middle Eastern. In addition, the item should include
``check all that apply'' to ensure accurate and complete data
collection. The commenter encouraged CMS to refine the list of response
options for Race and provide a rationale for the final list of response
options.
Response: We thank the commenter and agree that it is important to
collect race and ethnicity data in a consistent way. The race and
ethnicity categories that were proposed align with the 2011 HHS Data
Standards and are rolled up into the 1997 OMB minimum data standards,
which can be found at https://aspe.hhs.gov/basic-report/hhs-implementation-guidance-data-collection-standards-race-ethnicity-sex-primary-language-and-disability-status. For example, the 1997 OMB
minimum data standard for Hispanic is the roll up category for the
following response options on the 2011 HHS Data Standards: Mexican,
Mexican American, Chicano/a; Puerto Rican; Cuban; another Hispanic,
Latino, or Spanish origin. However, we will take the comment under
advisement for future consideration. We also note that the option for
``check all that apply'' is available for providers to choose from the
list of response options.
Comment: A commenter supported the opportunities to better account
for SDOH in the diagnosis and treatment of patients but is concerned by
the specificity of several of the seven proposed element for data
collection for example, collection of race by Japanese, Chinese,
Korean, etc. The commenter's concern is with the added burden in
collecting the level of specificity outlined, and the commenter
requested that CMS provide more detailed guidance in the final rule
regarding how this information should be collected and shared in
compliance with HIPAA. Further, the commenter asked that the agency
outlines its expectations for how this newly collected information will
be used by Medicare for payment and public reporting.
Response: For the Race and Ethnicity SPADE, this data should be
completed based on the response of the patient. It is important to ask
the patient to select the category or categories that most closely
correspond to their race and ethnicity. Respondents should be offered
the option of selecting one or more race and ethnicity categories.
Observer identification or medical record documentation may not be
used.
The SDOH data elements that will be collected will assist with care
coordination and with evaluating the impact of disparities. With
respect to how the data will be used for payment and public reporting,
any potential future use of the data for these purposes would be done
through future rulemaking.
SDOH data elements should be treated the same as other data
collected on the assessment tool. As to any specific HIPAA questions,
we appreciate the commenter's commitment to compliance with the HIPAA
requirements, but note that the Office for Civil Rights (OCR) is tasked
with implementing and enforcing HIPAA, not CMS. Commenters should
consult appropriate counsel in instances in which they are unsure of
their HIPAA status, or the permissibility of a disclosure under the
HIPAA Privacy Rule. In doing so, commenters may wish to consult 45 CFR
164.103 (definition of ``required by law'') and Sec. 164.512(a)
(allowing ``required by law'' disclosures).
(2) Preferred Language and Interpreter Services
More than 64 million Americans speak a language other than English
at home, and nearly 40 million of those individuals have limited
English proficiency (LEP).\190\ Individuals with LEP have been shown to
receive worse care and have poorer health outcomes, including higher
readmission rates.191 192 193 Communication with individuals
with LEP is an important component of high quality health care, which
starts by understanding the population in need of language services.
Unaddressed language barriers between a patient and provider care team
negatively affects the ability to identify and address individual
medical and non-medical care needs, to convey and understand clinical
information, as well as discharge and follow up instructions, all of
which are necessary for providing high quality care. Understanding the
communication assistance needs of patients with LEP, including
individuals who are Deaf or hard of
[[Page 39156]]
hearing, is critical for ensuring good outcomes.
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\190\ U.S. Census Bureau, 2013-2017 American Community Survey 5-
Year Estimates.
\191\ Karliner LS, Kim SE, Meltzer DO, Auerbach AD. Influence of
language barriers on outcomes of hospital care for general medicine
inpatients. J Hosp Med. 2010 May-Jun;5(5):276-82. doi: 10.1002/
jhm.658.
\192\ Kim EJ, Kim T, Paasche-Orlow MK, et al. Disparities in
Hypertension Associated with Limited English Proficiency. J Gen
Intern Med. 2017 Jun;32(6):632-639. doi: 10.1007/s11606-017-3999-9.
\193\ National Academies of Sciences, Engineering, and Medicine.
2016. Accounting for social risk factors in Medicare payment:
Identifying social risk factors. Washington, DC: The National
Academies Press.
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Presently, the preferred language of patients and residents and
need for interpreter services are assessed in two PAC assessment tools.
The LCDS and the MDS use the same two data elements to assess preferred
language and whether a patient or resident needs or wants an
interpreter to communicate with health care staff. The MDS initially
implemented preferred language and interpreter services data elements
to assess the needs of SNF residents and patients and inform care
planning. For alignment purposes, the LCDS later adopted the same data
elements for LTCHs. The 2009 NASEM (formerly Institute of Medicine)
report on standardizing data for health care quality improvement
emphasizes that language and communication needs should be assessed as
a standard part of health care delivery and quality improvement
strategies.\194\
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\194\ IOM (Institute of Medicine). 2009. Race, Ethnicity, and
Language Data: Standardization for Health Care Quality Improvement.
Washington, DC: The National Academies Press.
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In developing our proposal for a standardized language data element
across PAC settings, we considered the current preferred language and
interpreter services data elements that are in LCDS and MDS. We also
considered the 2011 HHS Primary Language Data Standard and peer-
reviewed research. The current preferred language data element in LCDS
and MDS asks, ``What is your preferred language?'' Because the
preferred language data element is open-ended, the patient or resident
is able to identify their preferred language, including American Sign
Language (ASL). Finally, we considered the recommendations from the
2009 NASEM (formerly Institute of Medicine) report, ``Race, Ethnicity,
and Language Data: Standardization for Health Care Quality
Improvement.'' In it, the committee recommended that organizations
evaluating a patient's language and communication needs for health care
purposes, should collect data on the preferred spoken language and on
an individual's assessment of his/her level of English proficiency.
A second language data element in LCDS and MDS asks, ``Do you want
or need an interpreter to communicate with a doctor or health care
staff?'' and includes yes or no response options. In contrast, the 2011
HHS Primary Language Data Standard recommends either a single question
to assess how well someone speaks English or, if more granular
information is needed, a two-part question to assess whether a language
other than English is spoken at home and if so, identify that language.
However, neither option allows for a direct assessment of a patient's
or resident's preferred spoken or written language nor whether they
want or need interpreter services for communication with a doctor or
care team, both of which are an important part of assessing patient/
resident needs and the care planning process. More information about
the HHS Data Standard for Primary Language is available on the website
at https://minorityhealth.hhs.gov/omh/browse.aspx?lvl=3&lvlid=54.
Research consistently recommends collecting information about an
individual's preferred spoken language and evaluating those responses
for purposes of determining language access needs in health care.\195\
However, using ``preferred spoken language'' as the metric does not
adequately account for people whose preferred language is ASL, which
would necessitate adopting an additional data element to identify
visual language. The need to improve the assessment of language
preferences and communication needs across PAC settings should be
balanced with the burden associated with data collection on the
provider and patient. Therefore we proposed to retain the Preferred
Language and Interpreter Services data elements currently in use on the
MDS and LCDS on the IRF-PAI.
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\195\ Guerino, P. and James, C. Race, Ethnicity, and Language
Preference in the Health Insurance Marketplaces 2017 Open Enrollment
Period. Centers for Medicare & Medicaid Services, Office of Minority
Health. Data Highlight: Volume 7--April 2017. Available at https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/Data-Highlight-Race-Ethnicity-and-Language-Preference-Marketplace.pdf.
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In addition, we received feedback during the December 13, 2018
listening session on the importance of evaluating and acting on
language preferences early to facilitate communication and allowing for
patient self-identification of preferred language. Although the
discussion about language was focused on preferred spoken language,
there was general consensus among participants that stated language
preferences may or may not accurately indicate the need for interpreter
services, which supports collecting and evaluating data to determine
language preference, as well as the need for interpreter services. An
alternate suggestion was made to inquire about preferred language
specifically for discussing health or health care needs. While this
suggestion does allow for ASL as a response option, we do not have data
indicating how useful this question might be for assessing the desired
information and thus we are not including this question in our
proposal.
Improving how preferred language and need for interpreter services
data are collected is an important component of improving quality by
helping PAC providers and other providers understand patient needs and
develop plans to address them. For more information on the Preferred
Language and Interpreter Services data elements, we refer readers to
the document titled ``Final Specifications for IRF QRP Measures and
Standardized Patient Assessment Data Elements,'' available on the
website at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In an effort to standardize the submission of language data among
IRFs, HHAs, SNFs and LTCHs, for the purposes outlined in section
1899B(a)(1)(B) of the Act, while minimizing the reporting burden, we
proposed to adopt the Preferred Language and Interpreter Services data
elements currently used on the MDS and LCDS, and described above, as
SPADEs with respect to the Social Determinants of Health category. We
proposed to add the current Preferred Language and Interpreter Services
data elements from the MDS and LCDS to the IRF-PAI.
We solicited comment on these proposals.
Commenters submitted the following comments related to the proposed
rule's discussion of Preferred Language and Interpreter Services
SPADEs. A discussion of these comments, along with our responses,
appears below.
Comment: Some commenters noted that, if finalized, IRFs should only
need to submit data on the race and ethnicity SPADEs with respect to
admission and would not need to collect and report again at discharge,
as it is unlikely that patient status for these elements will change.
The commenters believe that a patient's preferred language and need for
an interpreter also are unlikely to change between admission and
discharge; thus, the commenter urged CMS to require collection of these
SDOH SPADEs with respect to admission only.
Response: We thank the commenters for the comment. With regard to
the submission of the Preferred Language SPADE and the Interpreter
Services SPADE, we agree with the commenters that it is unlikely that
the assessment of Preferred Language and Interpreter
[[Page 39157]]
Services at admission would differ from assessment at discharge. As
discussed in previous response for Vision and Hearing, we believe that
the submission of preferred language and the need for an interpreter is
similar to the submission of Race, Ethnicity, Hearing, and Vision
SPADES.
We account for this change to the Collection of Information
requirements for the IRF QRP in XIV.C of this final rule. Based on the
comments received, and for the reasons discussed, we are finalizing
that the Preferred Language and Interpreter Services SPADEs be
collected as proposed with the modification that we will deem IRFs that
submit these two SPADEs with respect to admission to have submitted
with respect to both admission and discharge.
(3) Health Literacy
The Department of Health and Human Services defines health literacy
as ``the degree to which individuals have the capacity to obtain,
process, and understand basic health information and services needed to
make appropriate health decisions.'' \196\ Similar to language
barriers, low health literacy can interfere with communication between
the provider and patient and the ability for patients or their
caregivers to understand and follow treatment plans, including
medication management. Poor health literacy is linked to lower levels
of knowledge about health, worse health outcomes, and the receipt of
fewer preventive services, but higher medical costs and rates of
emergency department use.\197\
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\196\ U.S. Department of Health and Human Services, Office of
Disease Prevention and Health Promotion. National action plan to
improve health literacy. Washington (DC): Author; 2010.
\197\ National Academies of Sciences, Engineering, and Medicine.
2016. Accounting for social risk factors in Medicare payment:
Identifying social risk factors. Washington, DC: The National
Academies Press.
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Health literacy is prioritized by Healthy People 2020 as an
SDOH.\198\ Healthy People 2020 is a long-term, evidence-based effort
led by the Department of Health and Human Services that aims to
identify nationwide health improvement priorities and improve the
health of all Americans. Although not designated as a social risk
factor in NASEM's 2016 report on accounting for social risk factors in
Medicare payment, the NASEM noted that health literacy is impacted by
other social risk factors and can affect access to care, as well as
quality of care and health outcomes.\199\ Assessing for health literacy
across PAC settings would facilitate better care coordination and
discharge planning. A significant challenge in assessing the health
literacy of individuals is avoiding excessive burden on patients and
health care providers. The majority of existing, validated health
literacy assessment tools use multiple screening items, generally with
no fewer than four, which would make them burdensome if adopted in MDS,
LCDS, IRF-PAI, and OASIS. The Single Item Literacy Screener (SILS)
question questions, ``How often do you need to have someone help you
when you read instructions, pamphlets, or other written material from
your doctor or pharmacy?'' Possible response options are: (1) Never;
(2) Rarely; (3) Sometimes; (4) Often; and (5) Always. The SILS
question, which assesses reading ability, (a primary component of
health literacy), tested reasonably well against the 36 item Short Test
of Functional Health Literacy in Adults (S-TOFHLA), a thoroughly vetted
and widely adopted health literacy test, in assessing the likelihood of
low health literacy in an adult sample from primary care practices
participating in the Vermont Diabetes Information
System.200 201 The S-TOFHLA is a more complex assessment
instrument developed using actual hospital related materials such as
prescription bottle labels and appointment slips, and often considered
the instrument of choice for a detailed evaluation of health
literacy.\202\ Furthermore, the S-TOFHLA instrument is proprietary and
subject to purchase for individual entities or users.\203\ Given that
SILS is publicly available, shorter and easier to administer than the
full health literacy screen, and research found that a positive result
on the SILS demonstrates an increased likelihood that an individual has
low health literacy, we proposed to use the single-item reading
question for health literacy in the standardized data collection across
PAC settings. We believe that use of this data element will provide
sufficient information about the health literacy of IRF patients to
facilitate appropriate care planning, care coordination, and
interoperable data exchange across PAC settings.
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\198\ Social Determinants of Health. Healthy People 2020.
https://www.healthypeople.gov/2020/topics-objectives/topic/social-determinants-of-health. (February 2019).
\199\ U.S. Department of Health & Human Services, Office of the
Assistant Secretary for Planning and Evaluation. Report to Congress:
Social Risk Factors and Performance Under Medicare's Value-Based
Purchasing Programs. Available at https://aspe.hhs.gov/pdf-report/report-congress-social-risk-factors-and-performance-under-medicares-value-based-purchasing-programs. Washington, DC: 2016.
\200\ Morris, N.S., MacLean, C.D., Chew, L.D., & Littenberg, B.
(2006). The Single Item Literacy Screener: evaluation of a brief
instrument to identify limited reading ability. BMC family practice,
7, 21. doi:10.1186/1471-2296-7-21.
\201\ Brice, J.H., Foster, M.B., Principe, S., Moss, C., Shofer,
F.S., Falk, R.J., Ferris, M.E., DeWalt, D.A. (2013). Single-item or
two-item literacy screener to predict the S-TOFHLA among adult
hemodialysis patients. Patient Educ Couns. 94(1):71-5.
\202\ University of Miami, School of Nursing & Health Studies,
Center of Excellence for Health Disparities Research. Test of
Functional Health Literacy in Adults (TOFHLA). (March 2019).
Available at https://elcentro.sonhs.miami.edu/research/measures-library/tofhla/index.html.
\203\ Nurss, J.R., Parker, R.M., Williams, M.V., &Baker, D.W.
David W. (2001). TOFHLA. Peppercorn Books & Press. Available at
http://www.peppercornbooks.com/catalog/information.php?info_id=5.
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In addition, we received feedback during the December 13, 2018 SDOH
listening session on the importance of recognizing health literacy as
more than understanding written materials and filling out forms, as it
is also important to evaluate whether patients understand their
conditions. However, the NASEM recently recommended that health care
providers implement health literacy universal precautions instead of
taking steps to ensure care is provided at an appropriate literacy
level based on individualized assessment of health literacy.\204\ Given
the dearth of Medicare data on health literacy and gaps in addressing
health literacy in practice, we recommend the addition of a health
literacy data element.
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\204\ Hudson, S., Rikard, R.V., Staiculescu, I. & Edison, K.
(2017). Improving health and the bottom line: The case for health
literacy. In Building the case for health literacy: Proceedings of a
workshop. Washington, DC: The National Academies Press.
---------------------------------------------------------------------------
The proposed Health Literacy data element is consistent with
considerations raised by NASEM and other stakeholders and research on
health literacy, which demonstrates an impact on health care use, cost,
and outcomes.\205\ For more information on the proposed Health Literacy
data element, we refer readers to the document titled ``Final
Specifications for IRF QRP Measures and Standardized Patient Assessment
Data Elements,'' available on the website at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
---------------------------------------------------------------------------
\205\ National Academies of Sciences, Engineering, and Medicine.
2016. Accounting for Social Risk Factors in Medicare Payment:
Identifying Social Risk Factors. Washington, DC: The National
Academies Press.
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In an effort to standardize the submission of health literacy data
among IRFs, HHAs, SNFs and LTCHs, for the purposes outlined in section
[[Page 39158]]
1899B(a)(1)(B) of the Act, while minimizing the reporting burden, we
proposed to adopt SILS question described above for the Health Literacy
data element as SPADE under the Social Determinants of Health Category.
We proposed to add the Health Literacy data element to the IRF-PAI.
We solicited comment on this proposals. A discussion of these
comments, along with our responses, appears below.
Comment: Some commenters noted that, if finalized, IRFs should only
need to submit data on the race and ethnicity SPADEs with respect to
admission and would not need to collect and report again at discharge,
as it is unlikely that patient status for these elements will change.
The commenters believe that a patient's health literacy is unlikely to
change between admission and discharge; thus, the commenter urged CMS
to require collection of all SDOH SPADEs with respect to admission
only.
Response: We disagree with the commenters that it is unlikely
patient status for health literacy will change from admission to
discharge. Unlike the Vision, Hearing, Race, Ethnicity, Preferred
Language, and Interpreter Services SPADEs, we believe that the response
to this data element may change from admission to discharge for some
patients. Health literacy can impact a patient's ability to manage
their conditions, and it something that should be taken into account
when developing care plans. The collection of the Health Literacy SPADE
at discharge is to support patients, whose circumstances may have
changed over the duration of their admission, in having the appropriate
supports post-discharge. Therefore, the health literacy data element
should be collected at both admission and discharge given the impact
this could have on health outcomes and care planning.
Comment: One commenter stated that the health literacy question
could be improved to capture whether the patient can read, understand,
and implement/respond to the information. In addition, the commenter
stated that the question does not take into account whether a patient's
need for help is due to limited vision, which is different from the
purpose of the separate Vision Impairment data element. Another
possible question the commenter suggested was ``How often do you have
difficulty?'' The commenter suggested that a single construct may not
be sufficient for this area, depending on the aspect of health literacy
that CMS intends to identify.
Response: We thank the commenters for the comment on the health
literacy data element. We agree that knowing whether a patient has a
reading or comprehension challenge, or limited vision would be helpful.
However, we specifically proposed data elements that have been tested.
We were also mindful to try and limit the potential burden of asking
additional questions related to health literacy. The SILS Health
Literacy data element that we proposed performed well when tested, and
it minimizes concerns related to burden by requiring one instead of
multiple questions on health literacy.206 207 If commenters
have examples of SDOH questions that have been cognitively tested, we
would welcome that feedback as we seek to refine SDOH SPADE data
elements in future rulemaking.
---------------------------------------------------------------------------
\206\ Morris, N.S., MacLean, C.D., Chew, L.D., & Littenberg, B.
(2006). The Single Item Literacy Screener: Evaluation of a brief
instrument to identify limited reading ability. BMC family practice,
7, 21. doi:10.1186/1471-2296-7-21.
\207\ Brice, J.H., Foster, M.B., Principe, S., Moss, C., Shofer,
F.S., Falk, R.J., Ferris, M.E., DeWalt, D.A. (2013). Single-item or
two-item literacy screener to predict the S-TOFHLA among adult
hemodialysis patients. Patient Educ Couns. 94(1):71-5.
---------------------------------------------------------------------------
(4) Transportation
Transportation barriers commonly affect access to necessary health
care, causing missed appointments, delayed care, and unfilled
prescriptions, all of which can have a negative impact on health
outcomes.\208\ Access to transportation for ongoing health care and
medication access needs, particularly for those with chronic diseases,
is essential to successful chronic disease management. Adopting a data
element to collect and analyze information regarding transportation
needs across PAC settings would facilitate the connection to programs
that can address identified needs. We therefore proposed to adopt as
SPADE a single transportation data element that is from the Protocol
for Responding to and Assessing Patients' Assets, Risks, and
Experiences (PRAPARE) assessment tool and currently part of the
Accountable Health Communities (AHC) Screening Tool.
---------------------------------------------------------------------------
\208\ Syed, S.T., Gerber, B.S., and Sharp, L.K. (2013).
Traveling Towards Disease: Transportation Barriers to Health Care
Access. J Community Health. 38(5): 976-993.
---------------------------------------------------------------------------
The proposed Transportation data element from the PRAPARE tool
questions, ``Has lack of transportation kept you from medical
appointments, meetings, work, or from getting things needed for daily
living?'' The three response options are: (1) Yes, it has kept me from
medical appointments or from getting my medications; (2) Yes, it has
kept me from non-medical meetings, appointments, work, or from getting
things that I need; and (3) No. The patient would be given the option
to select all responses that apply. We proposed to use the
transportation data element from the PRAPARE Tool, with permission from
National Association of Community Health Centers (NACHC), after
considering research on the importance of addressing transportation
needs as a critical SDOH.\209\
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\209\ Health Research & Educational Trust. (2017, November).
Social determinants of health series: Transportation and the role of
hospitals. Chicago, IL. Available at www.aha.org/transportation.www.aha.org/transportation.
---------------------------------------------------------------------------
The proposed data element is responsive to research on the
importance of addressing transportation needs as a critical SDOH and
would adopt the Transportation item from the PRAPARE tool.\210\ This
data element comes from the national PRAPARE social determinants of
health assessment protocol, developed and owned by NACHC, in
partnership with the Association of Asian Pacific Community Health
Organization, the Oregon Primary Care Association, and the Institute
for Alternative Futures. Similarly the Transportation data element used
in the AHC Screening Tool was adapted from the PRAPARE tool. The AHC
screening tool was implemented by the Center for Medicare and Medicaid
Innovation's AHC Model and developed by a panel of interdisciplinary
experts that looked at evidence-based ways to measure SDOH, including
transportation. While the transportation access data element in the AHC
screening tool serves the same purposes as our proposed SPADE
collection about transportation barriers, the AHC tool has binary yes
or no response options that do not differentiate between challenges for
medical versus non-medical appointments and activities. We believe that
this is an important nuance for informing PAC discharge planning to a
community setting, as transportation needs for non-medical activities
may differ than for medical activities and should be taken into
account.\211\ We believe that use of this data element will provide
sufficient information about transportation barriers to medical and
non-medical care for IRF patients to facilitate appropriate discharge
planning and care coordination across PAC settings. As such, we
proposed to adopt the Transportation data element from PRAPARE. More
information about
[[Page 39159]]
development of the PRAPARE tool is available on the website at https://protect2.fireeye.com/url?k=7cb6eb44-20e2f238-7cb6da7b-0cc47adc5fa2-1751cb986c8c2f8c&u=http://www.nachc.org/prapare.
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\210\ Health Research & Educational Trust. (2017, November).
Social determinants of health series: Transportation and the role of
hospitals. Chicago, IL. Available at www.aha.org/transportation.
\211\ Northwestern University. (2017). PROMIS Item Bank v. 1.0--
Emotional Distress--Anger--Short Form 1.
---------------------------------------------------------------------------
In addition, we received stakeholder feedback during the December
13, 2018 SDOH listening session on the impact of transportation
barriers on unmet care needs. While recognizing that there is no
consensus in the field about whether providers should have
responsibility for resolving patient transportation needs, discussion
focused on the importance of assessing transportation barriers to
facilitate connections with available community resources.
Adding a Transportation data element to the collection of SPADE
would be an important step to identifying and addressing SDOH that
impact health outcomes and patient experience for Medicare
beneficiaries. For more information on the Transportation data element,
we refer readers to the document titled ``Final Specifications for IRF
QRP Measures and Standardized Patient Assessment Data Elements,''
available on the website at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In an effort to standardize the submission of transportation data
among IRFs, HHAs, SNFs and LTCHs, for the purposes outlined in section
1899B(a)(1)(B) of the Act, while minimizing the reporting burden, we
proposed to adopt the Transportation data element described above as
SPADE with respect to the proposed Social Determinants of Health
category. If finalized as proposed, we would add the Transportation
data element to the IRF-PAI.
We solicited comment on these proposals. A discussion of these
comments, along with our responses, appears below.
Comment: One commenter supported the collection of data to capture
the reason(s) transportation affects a patient's access to health care.
The commenter appreciated the inclusion of these items on the IRF-PAI
and encouraged exploration of quality measures in this area as
transportation is an extremely important instrumental activity of daily
living to effectively transition to the community.
Response: We thank the commenter and we will consider this feedback
as we continue to improve and refine the SPADEs.
Comment: Some commenters noted that, if finalized, IRFs should only
need to submit data on the race and ethnicity SPADEs with respect to
admission and would not need to collect and report again at discharge,
as it is unlikely that patient status for these elements will change.
The commenters believe that a patient's access to transportation is
unlikely to change between admission and discharge; thus, the commenter
suggested CMS to require collection of all SDOH SPADEs with respect to
admission only.
Response: We disagree with the commenters that stated that access
to transportation will always be the same from admission to discharge.
Unlike the Vision, Hearing, Race, Ethnicity, Preferred Language, and
Interpreter Services SPADEs, we believe that the response to this data
element is likely to change from admission to discharge for some
patients. For example, a patient could lose a family member or
caregiver between admission and discharge, which could impact his or
her access to transportation and impact how the patient responds to the
access to transportation SPADE data element. Therefore, we believe that
the response to this SDOH data element is likely to change from
admission to discharge for some patients and we proposed to collect
this SPADE data element with respect to both admission and discharge.
As outlined in the FY 2020 IRF PPS proposed rule, multiple studies
have demonstrated that access to transportation has an impact on the
health of patients (84 FR 17325). Therefore, it is important for
providers to be able to identify a patient's needs when the patient is
admitted and when the patient is discharged in order to better inform
the patient's care decisions made during and after the stay, including
understanding the patient's unique risk factors and treatment
preferences. Because of this, we are requiring that the Access to
Transportation data element be assessed with respect to both admission
and discharge.
(5) Social Isolation
Distinct from loneliness, social isolation refers to an actual or
perceived lack of contact with other people, such as living alone or
residing in a remote area.212 213 Social isolation tends to
increase with age, is a risk factor for physical and mental illness,
and a predictor of mortality.214 215 216 PAC providers are
well-suited to design and implement programs to increase social
engagement of patients, while also taking into account individual needs
and preferences. Adopting a data element to collect and analyze
information about social isolation in IRFs and across PAC settings
would facilitate the identification of patients who are socially
isolated and who may benefit from engagement efforts.
---------------------------------------------------------------------------
\212\ Tomaka, J., Thompson, S., and Palacios, R. (2006). The
Relation of Social Isolation, Loneliness, and Social Support to
Disease Outcomes Among the Elderly. J of Aging and Health. 18(3):
359-384.
\213\ Social Connectedness and Engagement Technology for Long-
Term and Post-Acute Care: A Primer and Provider Selection Guide.
(2019). Leading Age. Available at https://www.leadingage.org/white-papers/social-connectedness-and-engagement-technology-long-term-and-post-acute-care-primer-and#1.1.
\214\ Landeiro, F., Barrows, P., Nuttall Musson, E., Gray, A.M.,
and Leal, J. (2017). Reducing Social Loneliness in Older People: A
Systematic Review Protocol. BMJ Open. 7(5): e013778.
\215\ Ong, A.D., Uchino, B.N., and Wethington, E. (2016).
Loneliness and Health in Older Adults: A Mini-Review and Synthesis.
Gerontology. 62:443-449.
\216\ Leigh-Hunt, N., Bagguley, D., Bash, K., Turner, V.,
Turnbull, S., Valtorta, N., and Caan, W. (2017). An overview of
systematic reviews on the public health consequences of social
isolation and loneliness. Public Health. 152:157-171.
---------------------------------------------------------------------------
We proposed to adopt as SPADE a single social isolation data
element that is currently part of the AHC Screening Tool. The AHC item
was selected from the Patient-Reported Outcomes Measurement Information
System (PROMIS[supreg]) Item Bank on Emotional Distress and questions,
``How often do you feel lonely or isolated from those around you?'' The
five response options are: (1) Never; (2) Rarely; (3) Sometimes; (4)
Often; and (5) Always.\217\ The AHC Screening Tool was developed by a
panel of interdisciplinary experts that looked at evidence-based ways
to measure SDOH, including social isolation. More information about the
AHC Screening Tool is available on the website at https://innovation.cms.gov/Files/worksheets/ahcm-screeningtool.pdf.
---------------------------------------------------------------------------
\217\ Northwestern University. (2017). PROMIS Item Bank v. 1.0--
Emotional Distress--Anger--Short Form 1.
---------------------------------------------------------------------------
In addition, we received stakeholder feedback during the December
13, 2018 SDOH listening session on the value of receiving information
on social isolation for purposes of care planning. Some stakeholders
also recommended assessing social isolation as an SDOH as opposed to
social support.
The proposed Social Isolation data element is consistent with NASEM
considerations about social isolation as a function of social
relationships that impacts health outcomes and increases mortality
risk, as well as the current work of a NASEM committee examining how
social isolation and loneliness
[[Page 39160]]
impact health outcomes in adults 50 years and older. We believe that
adding a Social Isolation data element would be an important component
of better understanding patient complexity and the care goals of
patients, thereby facilitating care coordination and continuity in care
planning across PAC settings. For more information on the Social
Isolation data element, we refer readers to the document titled ``Final
Specifications for IRF QRP Measures and Standardized Patient Assessment
Data Elements,'' available on the website at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In an effort to standardize the submission of social isolation data
among IRFs, HHAs, SNFs and LTCHs, for the purposes outlined in section
1899B(a)(1)(B) of the Act, while minimizing the reporting burden, we
proposed to adopt the Social Isolation data element described above as
SPADE with respect to the proposed Social Determinants of Health
category. We proposed to add the Social Isolation data element to the
IRF-PAI.
We sought public comment on this proposal. A discussion of these
comments, along with our responses, appears below.
Comment: Commenters agreed with CMS that SDOH data could provide
Medicare with valuable information about the role that non-clinical
factors play in PAC patient outcomes and that the addition of the SDOH
SPADEs will facilitate communication between PAC settings and other
health care providers. A commenter noted that common standards and
definitions are important for interoperability and communication across
providers and encouraged CMS to ensure that the SDOH elements collected
in IRF settings are aligned with future proposed SDOH data collection
requirements in other settings. One commenter stated that there is
increasing attention on the critical role that social factors play in
individual and population health and that addressing health-related
social needs through enhanced clinical-community linkages can improve
health outcomes and reduce costs. Another commenter was also pleased
that CMS is looking at SDOH and believes it is a positive step toward
identifying disparities in health care.
Response: We thank the commenters for the comments.
Comment: Some commenters noted that, if finalized, IRFs should only
need to submit data on the race and ethnicity SPADEs with respect to
admission and would not need to collect and report again at discharge,
as it is unlikely that patient status for these elements will change.
The commenters believe that a patient's response to social isolation is
unlikely to change between admission and discharge; thus, the commenter
suggested CMS to require collection of all SDOH SPADEs with respect to
admission only.
Response: We disagree with the commenters that stated that the
response to the Social Isolation data element will be the same from
admission to discharge. Unlike the Vision, Hearing, Race, Ethnicity,
Preferred Language, and Interpreter Services SPADEs, we believe that
the response to this data element is likely to change from admission to
discharge for some patients. For example, a patient could lose a family
member or caregiver between admission and discharge, which could impact
their response to the Social Isolation data element. Therefore, we
proposed to collect this SPADE data element with respect to both
admission and discharge. As outlined in the FY 2020 IRF PPS proposed
rule, multiple studies have demonstrated that social isolation has an
impact on the health of patients (84 FR 17325 through 17326).
Therefore, it is important for providers to be able to identify a
patient's needs when the patient is admitted and when the patient is
discharged in order to better inform the patient's care decisions made
during and after the stay, including understanding the patient's unique
risk factors and treatment preferences. Because of this, we are
requiring that the Social Isolation data element be assessed at both
admission and discharge.
Comment: One commenter stated that the proposed question on social
isolation may have a very different answer based on the time horizon
considered by the beneficiary as beneficiaries who are newly admitted
to an IRF may have experienced differing levels of social isolation
over the preceding week due to interactions with health care providers,
emergency providers, and friends or family visiting due to
hospitalization. The commenter believes this question could be improved
by adding a timeframe to the question. For example, ``How often have
you felt lonely or isolated from those around you in the past 6
months?''
Response: We thank the commenter for this comment. The Social
Isolation data element assesses whether a patient has experienced
social isolation in the past 6 months to a year. The social isolation
question proposed is currently part of the Accountable Health
Communities (AHC) Screening Tool. The AHC item was selected from the
Patient-Reported Outcomes Measurement Information System
(PROMIS[supreg]) Item Bank on Emotional Distress.
Comment: A commenter suggested that collecting SDOH SPADEs that
have no clinical value, such as transportation and social isolation
during an assigned period of either admission or discharge, is a
significant concern. The commenter stated that at admission, the focus
should be on assessing the patient's medical needs and plan of care,
and at discharge, the focus shifts to patient's transition plan and
caregiver education. As there are already multiple required assessments
on the IRF-PAI, the SDOH SPADEs would add burden and recommended that
any SDOH SPADEs finalized should be assessed at any point during the
stay.
Response: We disagree with the commenters that the Social Isolation
and Transportation data elements have no value. As proposed in the
transportation and social isolation section, multiple studies have
demonstrated that access to transportation and social isolation have an
impact on the health of patients.218 219 For example, access
to transportation is important to medication access. Similarly, social
isolation is a predictor of mortality. Therefore, it is important for
providers to identify a patient's needs both at admission and discharge
in order to better inform the patient's care decisions made during and
after the stay, including a patient's unique risk factors and treatment
preferences. To minimize burden, we proposed to collect this data
element with respect to admission and discharge, rather than more
frequently.
---------------------------------------------------------------------------
\218\ Syed, S.T., Gerber, B.S., and Sharp, L.K. (2013).
Traveling Towards Disease: Transportation Barriers to Health Care
Access. J Community Health. 38(5): 976-993.
\219\ Leigh-Hunt, N., Bagguley, D., Bash, K., Turner, V.,
Turnbull, S., Valtorta, N., and Caan, W. (2017). An overview of
systematic reviews on the public health consequences of social
isolation and loneliness. Public Health. 152:157-171.
---------------------------------------------------------------------------
After consideration of the public comments, we are finalizing our
proposals to collect SDOH data for the purposes of section 2(d)(2)(B)
of the IMPACT Act and section 1899B(b)(1)(B)(vi) of the Act as follows.
With regard to Race, Ethnicity, Health Literacy, Transportation, and
Social Isolation, we are finalizing our proposals as proposed. In
response to stakeholder comments, we are revising our proposed policies
and finalizing
[[Page 39161]]
that IRFs that submit the Preferred Language and Interpreter Services
SPADEs with respect to admission will be deemed to have submitted with
respect to both admission and discharge.
H. Form, Manner, and Timing of Data Submission Under the IRF QRP
1. Background
We refer readers to Sec. 412.634(b) for information regarding the
current policies for reporting IRF QRP data.
2. Update to the CMS System for Reporting Quality Measures and
Standardized Patient Assessment Data and Associated Procedural
Proposals
IRFs are currently required to submit IRF-PAI data to CMS using the
Quality Improvement and Evaluation System (QIES) Assessment and
Submission Processing (ASAP) system. We will be migrating to a new
internet Quality Improvement and Evaluation System (iQIES) that will
enable real-time upgrades, and we proposed to designate that system as
the data submission system for the IRF QRP beginning October 1, 2019.
We proposed to revise Sec. 412.634(a)(1) by replacing ``Certification
and Survey Provider Enhanced Reports (CASPER)'' with ``CMS designated
data submission''. We proposed to revise Sec. 412.634(d)(1) by
replacing the reference to ``Quality Improvement and Evaluation System
Assessment Submission and Processing (QIES ASAP) system'' with ``CMS
designated data submission system''. We proposed to revise Sec.
412.634(d)(5) by replacing reference to the ``QIES ASAP'' with ``CMS
designated data submission''. We proposed to revise Sec. 412.634(f)(1)
by replacing ``QIES'' with ``CMS designated data submission system''.
In addition, we proposed to notify the public of any future changes to
the CMS designated system using subregulatory mechanisms, such as
website postings, listserv messaging, and webinars.
We invited public comment on our proposals.
Comment: One commenter supported this proposal and recommended that
CMS begin educating and preparing IRFs for the transition as soon as
possible.
Response: We thank the commenter for their support and appreciate
the importance of educating for this transition. Information regarding
the transition to iQIES and instructions for onboarding has been
provided to IRFs and will be ongoing. Training resources are currently
available on You-Tube at https://go.cms.gov/iQIES_Training and
additional help content for users is available within iQIES. Ongoing
technical support via email is also available at [email protected].
After consideration of the public comments, we are finalizing our
proposal to revise Sec. 412.634(a)(1), Sec. 412.634(d)(1), Sec.
412.634(d)(5), and Sec. 412.634(f)(1) as proposed. We are also
finalizing our proposal to notify the public of any future changes to
the CMS designated system using subregulatory mechanisms, such as
website postings, listserv messaging, and webinars.
3. Schedule for Reporting the Transfer of Health Information Quality
Measures Beginning With the FY 2022 IRF QRP
As discussed in section VIII.D. of this final rule, we proposed to
adopt the Transfer of Health Information to the Provider--Post-Acute
Care (PAC) and Transfer of Health Information to the Patient--Post-
Acute Care (PAC) quality measures beginning with the FY 2022 IRF QRP.
We also proposed that IRFs would report the data on those measures
using the IRF-PAI. IRFs would be required to collect data on both
measures for Medicare Part A and Medicare Advantage patients beginning
with patients discharged on or after October 1, 2020. We refer readers
to the FY 2018 IRF PPS final rule (82 FR 36291 through 36292) for the
data collection and submission timeframes that we finalized for the IRF
QRP.
We sought public comment on this proposal and did not receive any
comments.
We are finalizing our proposal that IRFs report the data on
Transfer of Health Information to the Provider--Post-Acute Care (PAC)
and Transfer of Health Information to the Patient--Post-Acute Care
(PAC) quality measures using the IRF-PAI as proposed. IRFs will be
required to collect data on both measures for Medicare Part A and
Medicare Advantage patients beginning with patients discharged on or
after October 1, 2020.
4. Schedule for Reporting Standardized Patient Assessment Data Elements
Beginning With the FY 2022 IRF QRP
As discussed in section IV.F. of the proposed rule, we proposed to
adopt SPADEs beginning with the FY 2022 IRF QRP. We proposed that IRFs
would report the data using the IRF-PAI. Similar to the proposed
schedule for reporting the Transfer of Health Information to the
Provider--Post-Acute Care (PAC) and Transfer of Health Information to
the Patient--Post-Acute Care (PAC) quality measures, IRFs would be
required to collect the SPADEs for all Medicare Part A and Medicare
Advantage patients discharged on or after October 1, 2020, at both
admission and discharge. IRFs that submit data with respect to
admission for the Hearing, Vision, Race, and Ethnicity SPADEs would be
considered to have submitted data with respect to discharges. We refer
readers to the FY 2018 IRF PPS final rule (82 FR 36291 through 36292)
for the data collection and submission timeframes that we finalized for
the IRF QRP.
We sought public comment on this proposal and did not receive any
comments.
We are finalizing our proposal that IRFs must submit the SPADEs for
all Medicare Part A and Medicare Advantage patients discharged on or
after October 1, 2020, with respect to both admission and discharge,
using the IRF-PAI. IRFs that submit data with respect to admission for
the Hearing, Vision, Preferred Language, Interpreter Services, Race,
and Ethnicity SPADEs will be considered to have submitted data with
respect to discharges.
5. Data Reporting on Patients for the IRF Quality Reporting Program
Beginning With the FY 2022 IRF QRP
We received public input suggesting that the quality measures used
in the IRF QRP should be calculated using data collected from all IRF
patients, regardless of the patients' payer. This input was provided to
us via comments requested about quality measure development on the CMS
Measures Management System Blueprint website,\220\ as well as through
comments we received from stakeholders via our IRF QRP mailbox, and
feedback received from the NQF-convened MAP as part of their
recommendations on Coordination Strategy for Post-Acute Care and Long-
Term Care Performance Measurement.\221\ Further, in the FY 2018 IRF PPS
proposed rule (82 FR 20740), we sought input on expanding the reporting
of quality measures to include all patients, regardless of payer, so as
to ensure that the IRF QRP makes publicly available information
regarding the quality of the services furnished to the IRF population
as a whole, rather
[[Page 39162]]
than just those patients who have Medicare.
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\220\ Public Comment Summary Report Posting for Transfer of
Health Information and Care Preferences. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/Development-of-Cross-Setting-Transfer-of-Health-Information-Quality-Meas.pdf.
\221\ MAP Coordination Strategy for Post-Acute Care and Long-
Term Care Performance Measurement. Feb 2012. http://www.qualityforum.org/Publications/2012/02/MAP_Coordination_Strategy_for_Post-Acute_Care_and_Long-Term_Care_Performance_Measurement.aspx.
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In response to that request for public input, several commenters,
including MedPAC, submitted comments stating that they would be
supportive of an effort to collect data specified under the IRF QRP
from all IRF patients regardless of their payer. Many commenters noted
that this would not be overly burdensome, as most of their
organizations' members currently complete the IRF-PAI on all patients,
regardless of their payer. A few commenters had concerns, including
recommending that CMS continue to align the patient assessment
instruments across PAC settings and whether the use of the data would
outweigh any additional reporting burden. For a more detailed
discussion, we refer readers to the FY 2018 IRF final rule (82 FR
36292). We have taken these concerns under consideration in proposing
this policy.
Further, given that we do not have access to other payer claims, we
believe that the most accurate representation of the quality provided
in IRFs would be best conveyed using data collected via the IRF-PAI on
all IRF patients, regardless of payer, for the purposes of the IRF QRP.
Medicare is the primary payer for approximately 60 percent of IRF
patients.\222\
---------------------------------------------------------------------------
\222\ National Academies of Sciences, Engineering, and Medicine.
2016. Accounting for social risk factors in Medicare payment:
Identifying social risk factors. Washington, DC: The National
Acadiemies Press.
---------------------------------------------------------------------------
We also believe that data reporting on standardized patient
assessment data elements using IRF-PAI should include all IRF patients
for the same reasons for collecting data on all residents for the IRF
QRP's quality measures: To promote higher quality and more efficient
health care for Medicare beneficiaries and all patients receiving IRF
services, for example through the exchange of information and
longitudinal analysis of the data. With that, we believe that
collecting quality measure and standardized patient assessment data via
the IRF-PAI on all IRF patients ensures that quality care is provided
for Medicare beneficiaries, and patients receiving IRF services as a
whole. While we appreciate that collecting quality data on all patients
regardless of payer may create additional burden, we also note that the
effort to separate out Medicare beneficiaries from other patients is
also burdensome.
Collecting data on all IRF patients will provide us with the most
robust, accurate reflection of the quality of care delivered to
Medicare beneficiaries as compared with non-Medicare patients and
residents, and we intend to display the calculation of this data on IRF
Compare in the future. Accordingly, we proposed that IRFs collect data
on all IRF patients to ensure that all patients, regardless of their
payer, are receiving the same care and that provider metrics measure
performance across the spectrum of patients.
Therefore, to meet the quality reporting requirements for IRFs for
the FY 2022 payment determination and each subsequent year, we proposed
to expand the reporting of IRF-PAI data used for the IRF QRP to include
data on all patients, regardless of their payer, beginning with
patients discharged on or after October 1, 2020 for the FY 2022 IRF QRP
and the IRF-PAI V4.0, effective October 1, 2020.
We sought public comment on this proposal and received several
comments, which are discussed below.
Comment: Many commenters, including MedPAC, supported the proposal
to expand the reporting of quality measures to all patients regardless
of payer, agreeing that quality care should be a goal for all patients.
Several commenters agreed that most providers already complete an IRF-
PAI for all patients. MedPAC also cautioned that any future Medicare
payment adjustments related to performance should be based only on
outcomes for Medicare beneficiaries. One commenter stated that this
approach is consistent with other quality programs and offers consumers
a fuller picture of quality of care. One commenter recommended
including quality data about all payers on IRF Compare, and another
commenter supported the proposal but suggested CMS to allow adequate
time to review and validate data before it is made public and allow
data on IRF Compare to be analyzed by payer.
Response: We thank commenters for their support and appreciate
suggestions for implementing this policy.
Comment: A few commenters requested additional details about how
this proposal would be implemented. One commenter suggested that CMS
verify comprehensive data submission on all patients to avoid ``cherry-
picking'' patients. A few commenters recommended that CMS delay this
proposal and study how this additional data affects quality measure
performance.
Response: We appreciate the commenters' request for more details
regarding the implementation of this proposal, how data submission will
be verified to avoid cherry-picking, and how this data will affect
quality measure performance. We acknowledge the commenters' concerns
about the proposal's implementation timeline and the request to delay
the proposal; however instead of delaying, we plan to use the comments
received during this rulemaking cycle to bring a new all-payer policy
proposal in the future. Therefore, after consideration of the public
comments we received on these issues, we have decided that at this
time, we will not finalize this proposal. We agree that it would be
useful to assess further how to best implement the collection of data
for all payers for the IRF QRP.
Comment: Many commenters had concerns about the burden of
collecting quality data on all patients regardless of payer, citing
that it contradicted the Patients over Paperwork initiative. One
commenter suggested that CMS make this requirement voluntary and to
conduct an analysis on the administrative burden on IRFs. Another
commenter suggested that the Collection of Information section should
contain an estimate of burden required for this reporting.
Response: We do not believe that that the intent of this policy
contradicts the Patients over Paperwork initiative, which aims to
simplify the documentation required for our programs. However, the all
payer proposal would have imposed a new reporting burden on IRFs. We
are sensitive to the issue of burden associated with data collection
and acknowledge the commenters' concerns about the additional burden
required to collect quality data on all patients. Although we believe
that the reporting of all-payer data under the IRF QRP would add value
to the program and provide a more accurate representation of the
quality provided by IRFs, we believe we need to better quantify the new
reporting burden on IRFs from this proposal for stakeholders to submit
comments. Therefore, after consideration of the public comments, we
received on these issues, we have decided that at this time, we will
not finalize this proposal. We agree that this burden should be
accounted for and we will estimate this burden in future rulemaking.
Comment: One commenter questioned whether IRFs support this
proposal. Another commenter was concerned that this proposal would add
complexity to CMS' administration of the IRF QRP compliance
determination process. One commenter was concerned that quality data
would be skewed because younger, non-Medicare patients have more room
for improvement compared to older patients.
Response: We do not believe this will add complexity to the IRF QRP
[[Page 39163]]
compliance determination process, since adding more patients will not
change the overall process that we follow with regard to determining
compliance. With regard to IRF support for this proposal, we sought
input on this topic in the FY 2018 IRF PPS proposed rule (82 FR 20740)
and we received several supportive comments. With regard to the
commenter's concerns that quality data would be skewed because younger
non-Medicare patients have more room for improvement, we note that risk
adjustment is currently used for many quality measures, including
measures that focus on improvement, such as the functional outcome
measures. We take patient characteristics, such as age, into
consideration when developing measures, and these are included as risk
adjustors for the functional outcome measures.
Comment: Several commenters did not support the proposal, citing
concerns about patient privacy. Some commenters suggested that
collecting quality data from non-Medicare beneficiaries would be a
violation of the Health Insurance Portability and Accountability Act of
1996 (HIPAA) since it is not required for reimbursement purposes.
Another commenter was concerned that CMS' collection of, and possible
disclosing of, sensitive health information from non-Medicare patients
without consent may violate the Privacy Act of 1974, the E-Government
Act of 2002, and other state level privacy acts. The commenter suggests
amending Sec. 412.608(a) to require the clinician at the IRF to
provide the Privacy Act Statement and other information to non-Medicare
patients.
Other commenters questioned how CMS would keep this non-Medicare
data secure and were concerned that CMS could work with other payers to
de-identify this data. A few commenters recommended informing non-
Medicare beneficiaries of this reporting and to use only de-identified
data. A few commenters requested more details from CMS about the scope
of data collection, including non-quality information on the IRF-PAI.
Response: We appreciate the commenters' concerns but disagree that
this proposal is a violation of HIPAA, Privacy Act of 1974, and e-
Government Act of 2002. IRF-PAI data is collected under an existing
system of records notice (66 FR 56682). Any disclosure of the data will
be made in accordance with the Privacy Act and those routine uses
outlined in the SORN. Medicare patients are currently given a Privacy
Act Statement and would be given to every patient under the IRF QRP.
Section 208 of the e-Government Act of 2002 requires federal agencies
to perform Privacy Impact Assessments when acquiring or developing new
information technology or making substantial changes to existing
information technology that involves the collection maintenance, or
dissemination of information in identifiable form. Because we are not
acquiring or developing new information technology, or making
substantial changes to existing information technology under this
proposal, we disagree that this policy violates the e-Government Act.
With regard to questions about how CMS would keep data non-Medicare
data secure, we safeguard the IRF-PAI data in a secure data system. The
system limits data access to authorized users and monitors such users
to ensure against unauthorized data access or disclosures. This system
conforms to all applicable federal laws and regulations as well as
federal government, Department of Health & Human Services (HHS), and
CMS policies and standards as they relate to information security and
data privacy. The applicable laws and regulations include, but are not
limited to: The Privacy Act of 1974; the Federal Information Security
Management Act of 2002; the Computer Fraud and Abuse Act of 1986; the
Health Insurance Portability and Accountability Act of 1996; the E-
Government Act of 2002; the Clinger-Cohen Act of 1996; the Medicare
Modernization Act of 2003; and the corresponding implementing
regulations. With regard to the scope of data collection, IRFs would be
required to submit quality measure and standardized patient assessment
data elements required by the IRF QRP. After consideration of the
public comments we received on these issues, we have decided that at
this time, we will not finalize this proposal. We appreciate concerns
raised by providers and will take them into consideration for future
rulemaking.
Comment: One commenter questioned whether CMS has the statutory
authority to require IRFs to submit IRF-PAI data for the IRF QRP for
all patients, regardless of payer, citing that it is inconsistent with
section 1886(j)(2)(D) of the Act because data from non-Medicare IRF
patients are not ``necessary'' for administering the IRF PPS. The
commenter further noted that Sec. 412.604(c) currently requires IRFs
to complete an IRF-PAI for all Medicare Part A and Part C patients that
an IRF admits or discharges and does not address reporting for non-
Medicare patients.
Response: We believe that we generally have authority to collect
all payer data for the IRF QRP under section 1886(j)(7) of the Act. We
also note that with respect to the data submitted in accordance with
section 1886(j)(7)(F) of the Act, the statute expressly requires that
data on quality measures specified under section 1899B(c)(1) of the Act
be submitted using the IRF PAI, to the extent possible, and that SPADE
required under section 1899B(b)(1) of the Act be submitted using the
IRF PAI. No all payer data collected for the IRF QRP would be used for
purposes of administering the IRF PPS.
We appreciate the support offered by some commenters for our
proposal to collect data on all IRF patients regardless of payer so as
to ensure that the IRF QRP makes publicly available information
regarding the quality of the services furnished to Medicare
beneficiaries, as well as to the IRF population as a whole. However, we
also acknowledge the concerns raised by some commenters with respect to
the administrative challenges of implementing all payer data
collection, the need to account for the burden related to this policy,
as well as the need for us to provide further detail and training to
IRFs. We continue to believe that the collection of quality data to
include all patients would help to ensure that Medicare patients
receive the same quality of care as other patients who are treated by
IRFs.
Therefore, after careful consideration of the public comments we
received, we will not finalize the proposal to expand the reporting of
IRF quality data to include all patients, regardless of payer, at this
time. We plan to use the comments we received on this proposal to help
inform a future all payer proposal.
I. Policies Regarding Public Display of Measure Data for the IRF QRP
Section 1886(j)(7)(E) of the Act requires the Secretary to
establish procedures for making the IRF QRP data available to the
public after ensuring that IRFs have the opportunity to review their
data prior to public display. Measure data are currently displayed on
the Inpatient Rehabilitation Facility Compare website, an interactive
web tool that assists individuals by providing information on IRF
quality of care. For more information on IRF Compare, we refer readers
to the website at https://www.medicare.gov/inpatientrehabilitationfacilitycompare/. For a more detailed discussion
about our
[[Page 39164]]
policies regarding public display of IRF QRP measure data and
procedures for the opportunity to review and correct data and
information, we refer readers to the FY 2017 IRF PPS final rule (81 FR
52125 through 52131).
In the proposed rule, we proposed to begin publicly displaying data
for the Drug Regimen Review Conducted With Follow-Up for Identified
Issues--PAC IRF QRP measure beginning CY 2020 or as soon as technically
feasible. We finalized the Drug Regimen Review Conducted With Follow-Up
for Identified Issues--PAC IRF QRP measure in the FY 2017 IRF PPS final
rule (81 FR 52111 through 52116).
Data collection for this assessment-based measure began with
patients discharged on or after October 1, 2018. We proposed to display
data based on four rolling quarters, initially using discharges from
January 1, 2019 through December 31, 2019 (Quarter 1 2019 through
Quarter 4 2019). To ensure the statistical reliability of the data, we
proposed that we would not publicly report an IRF's performance on the
measure if the IRF had fewer than 20 eligible cases in any four
consecutive rolling quarters. IRFs that have fewer than 20 eligible
cases would be distinguished with a footnote that states, ``The number
of cases/patient stays is too small to publicly report.''
We sought public comment on these proposals and received several,
which are summarized below.
Comment: Several commenters supported the proposal to begin
publicly displaying data for the Drug Regimen Review Conducted With
Follow-Up for Identified Issues--PAC IRF QRP measure in CY 2020 or as
soon as technically feasible, including the exception for IRFs with
fewer than 20 eligible cases. One commenter clarified that its support
is contingent on the measure not utilizing performance categories.
Response: We appreciate the commenter's support.
After consideration of the public comments, we are finalizing our
proposal to begin publicly displaying data for the Drug Regimen Review
Conducted With Follow-Up for Identified Issues--PAC IRF QRP measure
beginning CY 2020 or as soon as technically feasible.
J. Removal of the List of Compliant IRFs
In the FY 2016 IRF PPS final rule (80 FR 47125 through 47127), we
finalized that we would publish a list of IRFs that successfully met
the reporting requirements for the applicable payment determination on
the IRF QRP website and update the list on an annual basis. We have
received feedback from stakeholders that this list offers minimal
benefit. Although the posting of successful providers was the final
step in the applicable payment determination process, it does not
provide new information or clarification to the providers regarding
their annual payment update status. Therefore, we proposed that we will
no longer publish a list of compliant IRFs on the IRF QRP website,
effective beginning with the FY 2020 payment determination.
We sought public comment on this proposal and received several
comments.
Comment: One commenter supported this proposal, but suggested that
CMS make this information available to stakeholders upon request in the
interest of transparency.
Response: We thank commenters for their support. At this time, we
do not plan to make the list of compliant IRFs available upon request,
in alignment with other QRPs that do not provide this list. We believe
stakeholders can find sufficient quality information about IRFs on the
IRF compare website.
Comment: Several commenters did not support the proposal removal of
the list of compliant IRFs. One commenter agreed that the list was not
relevant to IRF providers in reviewing their own compliance status, but
stated that it could be of interest to patients and other IRFs. Other
commenters recommended posting the list because it is helpful for large
health systems to quickly determine which hospitals are compliant. One
commenter further suggested that the list continue to be posted in a
standardized manner across the various QRPs to improve transparency.
Response: We acknowledge commenters' concerns about removing the
requirement to post the list of compliant IRFs. Patients and consumers
can still find information about IRF quality on the IRF Compare
website. We do not believe that removing this list will have a negative
impact for IRFs, since the list does not give any new information to
IRF providers or health providers about their own compliance status. We
also note that other QRPs do not require posting of a list of compliant
facilities.
After consideration of the comments, we are finalizing our proposal
and will no longer publish a list of compliant IRFs on the IRF QRP
website, beginning with the FY 2020 payment determination.
K. Method for Applying the Reduction to the FY 2020 IRF Increase Factor
for IRFs That Fail To Meet the Quality Reporting Requirements
As previously noted, section 1886(j)(7)(A)(i) of the Act requires
the application of a 2-percentage point reduction of the applicable
market basket increase factor for payments for discharges occurring
during such fiscal year for IRFs that fail to comply with the quality
data submission requirements.
We proposed to apply a 2-percentage point reduction to the
applicable FY 2020 proposed market basket increase factor in
calculating an adjusted FY 2020 proposed standard payment conversion
factor to apply to payments for only those IRFs that failed to comply
with the data submission requirements. As previously noted, application
of the 2-percentage point reduction may result in an update that is
less than 0.0 for a fiscal year and in payment rates for a fiscal year
being less than such payment rates for the preceding fiscal year. Also,
reporting-based reductions to the market basket increase factor will
not be cumulative; they will only apply for the FY involved.
We invited public comment on the proposed method for applying the
reduction to the FY 2020 IRF increase factor for IRFs that fail to meet
the quality reporting requirements, which are summarized below.
Comment: Some commenters suggested that CMS provide flexibility in
its application of the IRF QRP payment penalty for IRFs who make a
good-faith effort to comply and submit quality reporting data.
Response: We interpret the commenter's suggestion that we take into
consideration case by case exceptions and apply leniency for providers
have attempted but failed to submit their quality reporting data for
the IRF QRP. We are unable to provide flexibility with respect to the 2
percent payment penalty; as noted previously, section 1886(j)(7) of the
Act requires the Secretary to reduce the annual increase factor for
IRFs that fail to comply with the quality data submission requirements.
While we did not seek comment on flexibilities on which the penalty is
applied, we note that we have provided flexibility where the failure of
the IRF to comply with the requirements of the IRF QRP stemmed from
circumstances beyond its control. For example, we have finalized
policies that grant exceptions or extensions for IRFs if we determine
that a systemic problem with one of our data collection systems
affected the ability of IRFs to submit data (79 FR 45920). We have also
[[Page 39165]]
adopted policies (78 FR 47920) that allow us to grant exemptions or
extensions to an IRF if it has experienced an extraordinary
circumstance beyond its control. In addition, we set the reporting
compliance threshold at 95 percent rather than at 100 percent to data
to for account for the rare instances when assessment data collection
and submission maybe impossible, such as when patients have been
discharged emergently, or against medical advice.
Table 18 shows the calculation of the adjusted FY 2020 standard
payment conversion factor that will be used to compute IRF PPS payment
rates for any IRF that failed to meet the quality reporting
requirements for the applicable reporting period.
[GRAPHIC] [TIFF OMITTED] TR08AU19.022
After consideration of the comments, we are finalizing our proposal
to apply a 2-percentage point reduction to the applicable FY 2020
proposed market basket increase factor in calculating an adjusted FY
2020 proposed standard payment conversion factor to apply to payments
for only those IRFs that failed to comply with the data submission
requirements.
X. Miscellaneous Comments
We received several comments that were outside the scope of the FY
2020 IRF PPS proposed rule. Specifically, we received comments
regarding the processes for updating the IRF facility-level adjustment
factors and the transparency of these updates, the application of a
cost-of-living adjustment for IRFs located in Alaska and Hawaii, the
need for CMS education and instruction on the appropriate IGC/ICD
coding on the IRF-PAI, re-evaluating and phasing out the 60 percent
rule as criteria for IRF admission, and federal funding for universal
health care. We thank commenters for bringing these issues to our
attention, and we will take these comments into consideration for
potential policy refinements.
XI. Provisions of the Final Regulations
In this final rule, we are adopting the provisions set forth in the
FY 2020 IRF PPS proposed rule (84 FR 17244).
Specifically:
We will adopt an unweighted motor score to assign patients
to CMGs, the removal of one item from the score, and revisions to the
CMGs beginning on October 1, 2019, based on analysis of 2 years of data
(FYs 2017 and 2018) using the Quality Indicator items in the IRF-PAI.
This includes revisions to the CMG relative weights and average LOS
values for FY 2020, in a budget neutral manner, as discussed in section
IV. of this final rule.
We will rebase and revise the IRF market basket to reflect
a 2016 base year rather than the current 2012 base year as discussed in
section VI. of this FY 2020 IRF PPS final rule.
We will update the IRF PPS payment rates for FY 2020 by
the market basket increase factor, based upon the most current data
available, with a productivity adjustment required by section
1886(j)(3)(C)(ii)(I) of the Act, as described in section VI. of this
final rule.
We will update to the IRF wage index to use the concurrent
FY IPPS wage index and the FY 2020 labor-related share in a budget-
neutral manner, as described in section VI. of this final rule.
The facility-level adjustments will remain frozen at the
FY 2014 levels for FY 2015 and all subsequent years, as discussed in
section V. of this final rule.
We will calculate the final IRF standard payment
conversion factor for FY 2020, as discussed in section VI. of this
final rule.
We will update the outlier threshold amount for FY 2020,
as discussed in section VII. of this final rule.
We will update the CCR ceiling and urban/rural average
CCRs for FY 2020, as discussed in section VII. of this final rule.
We will amend the regulations at Sec. 412.622 to clarify
that the determination as to whether a physician qualifies as a
rehabilitation physician (that is, a licensed physician with
specialized training and experience in inpatient rehabilitation) is
made by the IRF, as discussed in section VIII. of this final rule.
We will adopt updates requirements to the IRF QRP, as
discussed in section IX. of this final rule.
XII. Collection of Information Requirements
A. Statutory Requirement for Solicitation of Comments
Under the Paperwork Reduction Act of 1995 (PRA), we are required to
provide 60-day notice in the Federal Register and solicit public
comment before a collection of information requirement is submitted to
the OMB for review and approval. To fairly evaluate whether an
information collection should be approved by OMB, section 3506(c)(2)(A)
of the PRA requires that we solicit comment on the following issues:
The need for the information collection and its usefulness
in carrying out the proper functions of our agency.
The accuracy of our estimate of the information collection
burden.
The quality, utility, and clarity of the information to be
collected.
Recommendations to minimize the information collection
burden on the affected public, including automated collection
techniques.
This final rule makes reference to associated information
collections that are not discussed in the regulation text contained in
this document.
B. Collection of Information Requirements for Updates Related to the
IRF QRP
An IRF that does not meet the requirements of the IRF QRP for a
fiscal year will receive a 2 percentage point reduction to its
otherwise applicable
[[Page 39166]]
annual increase factor for that fiscal year. Information is not
currently available to determine the precise number of IRFs that will
receive less than the full annual increase factor for FY 2020 due to
non-compliance with the requirements of the IRF QRP.
We believe that the burden associated with the IRF QRP is the time
and effort associated with complying with the requirements of the IRF
QRP. As of July 15, 2019, there are approximately 1,122 IRFs reporting
quality data to CMS. For the purposes of calculating the costs
associated with the collection of information requirements, we obtained
mean hourly wages for these staff from the U.S. Bureau of Labor
Statistics' May 2018 National Occupational Employment and Wage
Estimates (http://www.bls.gov/oes/current/oes_nat.htm). To account for
overhead and fringe benefits, we have doubled the hourly wage. These
amounts are detailed in Table 19.
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As discussed in section VIII.D. of this final rule, we are adopting
two new measures, (1) Transfer of Health Information to the Provider--
Post-Acute Care (PAC); and (2) Transfer of Health Information to the
Patient--Post-Acute Care (PAC), beginning with the FY 2022 IRF QRP. As
a result, the estimated burden and cost for IRFs for complying with
requirements of the FY 2022 IRF QRP will increase. Specifically, we
believe that there will be a 1.2 minute addition in clinical staff time
to report data per patient stay. We estimate 411,622 discharges from
1,122 IRFs annually. This equates to an increase of 8,232 hours in
burden for all IRFs (0.02 hours per assessment x 411,622 discharges).
Given 0.7 minutes of RN time at $70.72 per hour and 0.5 minutes of LVN
time at $43.96 per hour, we estimate that the total cost will be
increased by $437 per IRF annually, or $490,314 for all IRFs annually.
This increase in burden will be accounted for in the information
collection under OMB control number (0938-0842), which expires December
31, 2021.
In addition, we are finalizing our proposal to add the standardized
patient assessment data elements described in section VIII.F of this
final rule beginning with the FY 2022 IRF QRP. As a result, the
estimated burden and cost for IRFs for complying with requirements of
the FY 2022 IRF QRP will be increased. Specifically, we believe that
there will be an addition of 7.8 minutes on admission, and 10.95
minutes on discharge, for a total of 18.8 minutes of additional
clinical staff time to report data per patient stay. Note that this is
a decrease from the proposed 11.1 minutes at discharge because of the
changes in section XIII.G.4.2 of this final rule. We estimate 411,622
discharges from 1,122 IRFs annually. This equates to an increase of
122,995 hours in burden for all IRFs (0.3 hours per assessment x
409,982 discharges). Given 11.3 minutes of RN time at $70.72 per hour
and 7.5 minutes of LVN time at $43.96 per hour, we estimate that the
total cost will be increased by $6,902 per IRF annually, or $7,744,044
for all IRFs. This increase in burden will be accounted for in the
information collection under OMB control number (0938-0842), which
expires December 31, 2021.
In summary, the newly adopted IRF QRP quality measures and
standardized patient assessment data elements will result in a burden
addition of $7,339 per IRF annually, and $8,234,450 for all IRFs
annually.
XIII. Regulatory Impact Analysis
A. Statement of Need
This final rule updates the IRF prospective payment rates for FY
2020 as required under section 1886(j)(3)(C) of the Act. It responds to
section 1886(j)(5) of the Act, which requires the Secretary to publish
in the Federal Register on or before the August 1 that precedes the
start of each fiscal year, the classification and weighting factors for
the IRF PPS's CMGs, and a description of the methodology and data used
in computing the prospective payment rates for that fiscal year.
This final rule also implements sections 1886(j)(3)(C) of the Act.
Section 1886(j)(3)(C)(ii)(I) of the Act requires the Secretary to apply
a MFP adjustment to the market basket increase factor. The productivity
adjustment applies to FYs from 2012 forward.
Furthermore, this final rule also adopts policy changes under the
statutory discretion afforded to the Secretary under section 1886(j)(7)
of the Act. Specifically, we are rebasing and revising the IRF market
basket to reflect a 2016 base year rather than the current 2012 base
year, revising the CMGs, making a technical correction to the
regulatory language to indicate that the determination of whether a
treating physician has specialized training and experience in inpatient
rehabilitation is made by the IRF and updating regulatory language
related to IRF QRP data collection.
B. Overall Impact
We have examined the impacts of this rule as required by Executive
Order 12866 on Regulatory Planning and Review (September 30, 1993),
Executive Order 13563 on Improving Regulation and Regulatory Review
(January 18, 2011), the Regulatory Flexibility Act (RFA) (September 19,
1980, Pub. L. 96-354), section 1102(b) of the Act, section 202 of the
Unfunded Mandates Reform Act of 1995 (March 22, 1995; Pub. L. 104-4),
Executive Order 13132 on Federalism (August 4, 1999), the Congressional
Review Act (5 U.S.C. 804(2) and Executive Order 13771 on Reducing
Regulation and Controlling Regulatory Costs (January 30, 2017).
Executive Orders 12866 and 13563 direct agencies to assess all
costs and benefits of available regulatory alternatives and, if
regulation is necessary, to select regulatory approaches that maximize
net benefits (including potential economic, environmental, public
health and safety effects, distributive impacts, and equity). Section
3(f) of Executive Order 12866 defines a ``significant regulatory
action'' as an action that is likely to result in a rule: (1) Having an
annual effect on the economy of $100 million or more in any 1 year, or
adversely and materially affecting a sector of the economy,
productivity, competition, jobs, the environment, public health or
safety, or state, local or tribal governments or communities (also
[[Page 39167]]
referred to as ``economically significant''); (2) creating a serious
inconsistency or otherwise interfering with an action taken or planned
by another agency; (3) materially altering the budgetary impacts of
entitlement grants, user fees, or loan programs or the rights and
obligations of recipients thereof; or (4) raising novel legal or policy
issues arising out of legal mandates, the President's priorities, or
the principles set forth in the Executive Order.
A regulatory impact analysis (RIA) must be prepared for major rules
with economically significant effects ($100 million or more in any 1
year). We estimate the total impact of the policy updates described in
this final rule by comparing the estimated payments in FY 2020 with
those in FY 2019. This analysis results in an estimated $210 million
increase for FY 2020 IRF PPS payments. Additionally we estimate that
costs associated with the proposals to update the reporting
requirements under the IRF QRP result in an estimated $8.2 million
addition in costs in FY 2020 for IRFs. We estimate that this rulemaking
is ``economically significant'' as measured by the $100 million
threshold, and hence also a major rule under the Congressional Review
Act. Also, the rule has been reviewed by OMB. Accordingly, we have
prepared a Regulatory Impact Analysis that, to the best of our ability,
presents the costs and benefits of the rulemaking.
C. Anticipated Effects
1. Effects on IRFs
The RFA requires agencies to analyze options for regulatory relief
of small entities, if a rule has a significant impact on a substantial
number of small entities. For purposes of the RFA, small entities
include small businesses, nonprofit organizations, and small
governmental jurisdictions. Most IRFs and most other providers and
suppliers are small entities, either by having revenues of $7.5 million
to $38.5 million or less in any 1 year depending on industry
classification, or by being nonprofit organizations that are not
dominant in their markets. (For details, see the Small Business
Administration's final rule that set forth size standards for health
care industries, at 65 FR 69432 at http://www.sba.gov/sites/default/files/files/Size_Standards_Table.pdf, effective March 26, 2012 and
updated on February 26, 2016.) Because we lack data on individual
hospital receipts, we cannot determine the number of small proprietary
IRFs or the proportion of IRFs' revenue that is derived from Medicare
payments. Therefore, we assume that all IRFs (an approximate total of
1,120 IRFs, of which approximately 55 percent are nonprofit facilities)
are considered small entities and that Medicare payment constitutes the
majority of their revenues. The HHS generally uses a revenue impact of
3 to 5 percent as a significance threshold under the RFA. As shown in
Table 20, we estimate that the net revenue impact of this final rule on
all IRFs is to increase estimated payments by approximately 2.5
percent. The rates and policies set forth in this final rule will not
have a significant impact (not greater than 3 percent) on a substantial
number of small entities. Medicare Administrative Contractors are not
considered to be small entities. Individuals and states are not
included in the definition of a small entity.
In addition, section 1102(b) of the Act requires us to prepare a
regulatory impact analysis if a rule may have a significant impact on
the operations of a substantial number of small rural hospitals. This
analysis must conform to the provisions of section 604 of the RFA. For
purposes of section 1102(b) of the Act, we define a small rural
hospital as a hospital that is located outside of a Metropolitan
Statistical Area and has fewer than 100 beds. As discussed in detail
below in this section, the rates and policies set forth in this final
rule will not have a significant impact (not greater than 3 percent) on
a substantial number of rural hospitals based on the data of the 136
rural units and 11 rural hospitals in our database of 1,122 IRFs for
which data were available.
Section 202 of the Unfunded Mandates Reform Act of 1995 (Pub. L.
104-04, enacted March 22, 1995) (UMRA) also requires that agencies
assess anticipated costs and benefits before issuing any rule whose
mandates require spending in any 1 year of $100 million in 1995
dollars, updated annually for inflation. In 2019, that threshold is
approximately $154 million. This final rule does not mandate any
requirements for State, local, or tribal governments, or for the
private sector.
Executive Order 13132 establishes certain requirements that an
agency must meet when it issues a proposed rule (and subsequent final
rule) that imposes substantial direct requirement costs on state and
local governments, preempts state law, or otherwise has federalism
implications. As stated, this final rule will not have a substantial
effect on state and local governments, preempt state law, or otherwise
have a federalism implication.
Executive Order 13771, titled Reducing Regulation and Controlling
Regulatory Costs, was issued on January 30, 2017 and requires that the
costs associated with significant new regulations ``shall, to the
extent permitted by law, be offset by the elimination of existing costs
associated with at least two prior regulations.'' This final rule is
considered an E.O. 13771 regulatory action. We estimate that this rule
would generate $6.18 million in annualized cost, discounted at 7
percent relative to year 2016, over a perpetual time horizon. Details
on the estimated costs of this rule can be found in the preceding
analyses.
2. Detailed Economic Analysis
This final rule updates to the IRF PPS rates contained in the FY
2019 IRF PPS final rule (83 FR 38514). Specifically, this final rule
updates the CMG relative weights and average LOS values, the wage
index, and the outlier threshold for high-cost cases. This final rule
applies a MFP adjustment to the FY 2020 IRF market basket increase
factor in accordance with section 1886(j)(3)(C)(ii)(I) of the Act.
Further, this final rule rebases and revises the IRF market basket to
reflect a 2016 base year rather than the current 2012 base year,
revises the CMGs based on FYs 2017 and 2018 data and amends the
regulatory language to clarify that the determination of whether a
treating physician has specialized training and experience in inpatient
rehabilitation is made by the IRF.
We estimate that the impact of the changes and updates described in
this final rule will be a net estimated increase of $210 million in
payments to IRF providers. This estimate does not include the
implementation of the required 2 percentage point reduction of the
market basket increase factor for any IRF that fails to meet the IRF
quality reporting requirements (as discussed in section IX.K. of this
final rule). The impact analysis in Table 20 of this final rule
represents the projected effects of the updates to IRF PPS payments for
FY 2020 compared with the estimated IRF PPS payments in FY 2019. We
determine the effects by estimating payments while holding all other
payment variables constant. We use the best data available, but we do
not attempt to predict behavioral responses to these changes, and we do
not make adjustments for future changes in such variables as number of
discharges or case-mix.
We note that certain events may combine to limit the scope or
accuracy of our impact analysis, because such an analysis is future-
oriented and, thus,
[[Page 39168]]
susceptible to forecasting errors because of other changes in the
forecasted impact time period. Some examples could be legislative
changes made by the Congress to the Medicare program that would impact
program funding, or changes specifically related to IRFs. Although some
of these changes may not necessarily be specific to the IRF PPS, the
nature of the Medicare program is such that the changes may interact,
and the complexity of the interaction of these changes could make it
difficult to predict accurately the full scope of the impact upon IRFs.
In updating the rates for FY 2020, we are adopting standard annual
revisions described in this final rule (for example, the update to the
wage and market basket indexes used to adjust the federal rates). We
are also implementing a productivity adjustment to the FY 2020 IRF
market basket increase factor in accordance with section
1886(j)(3)(C)(ii)(I) of the Act. We estimate the total increase in
payments to IRFs in FY 2020, relative to FY 2019, will be approximately
$210 million.
This estimate is derived from the application of the FY 2020 IRF
market basket increase factor, as reduced by a productivity adjustment
in accordance with section 1886(j)(3)(C)(ii)(I) of the Act, which
yields an estimated increase in aggregate payments to IRFs of $210
million. Outlier payments are estimated to remain at 3 percent in FY
2020. Therefore, we estimate that these updates will result in a net
increase in estimated payments of $210 million from FY 2019 to FY 2020.
The effects of the updates that impact IRF PPS payment rates are
shown in Table 20. The following updates that affect the IRF PPS
payment rates are discussed separately below:
The effects of the update to the outlier threshold amount,
from approximately 3.0 percent to 3.0 percent of total estimated
payments for FY 2020, consistent with section 1886(j)(4) of the Act.
The effects of the annual market basket update (using the
IRF market basket) to IRF PPS payment rates, as required by sections
1886(j)(3)(A)(i) and (j)(3)(C) of the Act, including a productivity
adjustment in accordance with section 1886(j)(3)(C)(i)(I) of the Act.
The effects of applying the budget-neutral labor-related
share and wage index adjustment, as required under section 1886(j)(6)
of the Act.
The effects of the budget-neutral changes to the CMGs,
relative weights and average LOS values, under the authority of section
1886(j)(2)(C)(i) of the Act.
The total change in estimated payments based on the FY
2020 payment changes relative to the estimated FY 2019 payments.
3. Description of Table 20
Table 20 shows the overall impact on the 1,122 IRFs included in the
analysis.
The next 12 rows of Table 20 contain IRFs categorized according to
their geographic location, designation as either a freestanding
hospital or a unit of a hospital, and by type of ownership; all urban,
which is further divided into urban units of a hospital, urban
freestanding hospitals, and by type of ownership; and all rural, which
is further divided into rural units of a hospital, rural freestanding
hospitals, and by type of ownership. There are 975 IRFs located in
urban areas included in our analysis. Among these, there are 697 IRF
units of hospitals located in urban areas and 278 freestanding IRF
hospitals located in urban areas. There are 147 IRFs located in rural
areas included in our analysis. Among these, there are 136 IRF units of
hospitals located in rural areas and 11 freestanding IRF hospitals
located in rural areas. There are 393 for-profit IRFs. Among these,
there are 357 IRFs in urban areas and 36 IRFs in rural areas. There are
616 non-profit IRFs. Among these, there are 526 urban IRFs and 90 rural
IRFs. There are 113 government-owned IRFs. Among these, there are 92
urban IRFs and 21 rural IRFs.
The remaining four parts of Table 20 show IRFs grouped by their
geographic location within a region, by teaching status, and by DSH PP.
First, IRFs located in urban areas are categorized for their location
within a particular one of the nine Census geographic regions. Second,
IRFs located in rural areas are categorized for their location within a
particular one of the nine Census geographic regions. In some cases,
especially for rural IRFs located in the New England, Mountain, and
Pacific regions, the number of IRFs represented is small. IRFs are then
grouped by teaching status, including non-teaching IRFs, IRFs with an
intern and resident to average daily census (ADC) ratio less than 10
percent, IRFs with an intern and resident to ADC ratio greater than or
equal to 10 percent and less than or equal to 19 percent, and IRFs with
an intern and resident to ADC ratio greater than 19 percent. Finally,
IRFs are grouped by DSH PP, including IRFs with zero DSH PP, IRFs with
a DSH PP less than 5 percent, IRFs with a DSH PP between 5 and less
than 10 percent, IRFs with a DSH PP between 10 and 20 percent, and IRFs
with a DSH PP greater than 20 percent.
The estimated impacts of each policy described in this rule to the
facility categories listed are shown in the columns of Table 20. The
description of each column is as follows:
Column (1) shows the facility classification categories.
Column (2) shows the number of IRFs in each category in
our FY 2020 analysis file.
Column (3) shows the number of cases in each category in
our FY 2020 analysis file.
Column (4) shows the estimated effect of the adjustment to
the outlier threshold amount.
Column (5) shows the estimated effect of the update to the
IRF labor-related share and wage index, in a budget-neutral manner.
Column (6) shows the estimated effect of the update to the
CMGs, relative weights, and average LOS values, in a budget-neutral
manner.
Column (7) compares our estimates of the payments per
discharge, incorporating all of the policies reflected in this final
rule for FY 2020 to our estimates of payments per discharge in FY 2019.
The average estimated increase for all IRFs is approximately 2.5
percent. This estimated net increase includes the effects of the IRF
market basket increase factor for FY 2020 of 2.9 percent, reduced by a
productivity adjustment of 0.4 percentage point in accordance with
section 1886(j)(3)(C)(ii)(I) of the Act. There is no change in
estimated IRF outlier payments from the update to the outlier threshold
amount. Since we are making the updates to the IRF wage index and the
CMG relative weights in a budget-neutral manner, they will not be
expected to affect total estimated IRF payments in the aggregate.
However, as described in more detail in each section, they will be
expected to affect the estimated distribution of payments among
providers.
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4. Impact of the Update to the Outlier Threshold Amount
The estimated effects of the update to the outlier threshold
adjustment are presented in column 4 of Table 20. In the FY 2019 IRF
PPS final rule (83 FR 38531 through 38532), we used FY 2017 IRF claims
data (the best, most complete data available at that time) to set the
outlier threshold amount for FY 2019 so that estimated outlier payments
would equal 3 percent of total estimated payments for FY 2019.
For the FY 2020 IRF PPS proposed rule (84 FR 17244), we used
preliminary FY 2018 IRF claims data, and, based on that preliminary
analysis, we estimated that IRF outlier payments as a percentage of
total estimated IRF payments would be 3.2 percent in FY 2019. As we
typically do between the proposed and final rules each year, we updated
our FY 2018 IRF claims data to ensure that we are using the most recent
available data in setting IRF payments. Therefore, based on updated
analysis of the most recent IRF claims data for this final rule, we now
estimate that IRF outlier payments as a percentage of total IRF
payments as 3.0 in FY 2019. Thus, we are adjusting the outlier
threshold amount in this final rule to maintain total estimated outlier
payments equal to 3 percent of total estimated payments in FY 2020.
The impact of this outlier adjustment update (as shown in column 4
of Table 20) is to maintain estimated overall payments to IRFs at 3
percent.
5. Impact of the CBSA Wage Index and Labor-Related Share
In column 5 of Table 20, we present the effects of the budget-
neutral update of the wage index and labor-related share. The changes
to the wage index and the labor-related share are discussed together
because the wage index is applied to the labor-related share portion of
payments, so the changes in the two have a combined effect on payments
to providers. As discussed in section VI.E. of this final rule, we are
updating the labor-related share from 70.5 percent in FY 2019 to 72.7
percent in FY 2020.
6. Impact of the Update to the CMG Relative Weights and Average LOS
Values
In column 6 of Table 20, we present the effects of the budget-
neutral update of the CMGs, relative weights and average LOS values. In
the aggregate, we do not estimate that these updates will affect
overall estimated payments of IRFs. However, we do expect these updates
to have small distributional effects.
7. Effects of the Requirements for the IRF QRP for FY 2020
In accordance with section 1886(j)(7)(A) of the Act, the Secretary
must reduce by 2 percentage points the market basket increase factor
otherwise applicable to an IRF for a fiscal year if the IRF does not
comply with the requirements of the IRF QRP for that fiscal year. In
section VIII.J of this final rule, we discuss the method for applying
the 2 percentage point reduction to IRFs that fail to meet the IRF QRP
requirements.
As discussed in section VIII.D. of this final rule, we are
finalizing our proposal to add two measures to the IRF QRP: (1)
Transfer of Health Information to the Provider--Post-Acute Care (PAC);
and (2) Transfer of Health Information to the Patient--Post-Acute Care
(PAC), beginning with the FY 2022 IRF QRP. We are also finalizing our
proposal to add standardized patient assessment data elements, as
discussed in section IV.G of this final rule. We describe the estimated
burden and cost reductions for both of these measures in section VIII.C
of this final rule. In summary, the changes to the IRF QRP will result
in a burden addition of $7,339 per IRF annually, and $8,234,450 for all
IRFs annually.
We intend to continue to closely monitor the effects of the IRF QRP
on IRFs and to help perpetuate successful reporting outcomes through
ongoing stakeholder education, national trainings, IRF announcements,
website postings, CMS Open Door Forums, and general and technical help
desks.
8. Effects of the Amending Sec. 412.622(a)(3)(iv) To Clarify the
Definition of a Rehabilitation Physician
As discussed in section VIII. of this final rule, we are amending
Sec. 412.622(a)(3)(iv) to clarify that the determination as to whether
a physician qualifies as a rehabilitation physician (that is, a
licensed physician with specialized training and experience in
inpatient rehabilitation) is made by the IRF. We do not expect this to
have any effect on the quality of care that beneficiaries receive in
IRFs because we continue to require that the rehabilitation physicians
caring for patients in IRFs be licensed physicians with specialized
training and experience in inpatient rehabilitation. We expect IRFs to
continue ensuring that the rehabilitation physicians meet these
requirements. Although we do not currently collect data from IRFs on
the physicians specialties that are providing care to patients in IRFs,
we do not expect this to change as a result of the amendments we are
making to Sec. 412.622(a)(3)(iv). However, we will continue to monitor
the quality of care beneficiaries receive in IRFs, and will initiate
appropriate actions through future rulemaking if we observe any
declines in quality of care in IRFs.
As this is merely clarifying our existing policy regarding the
definition of a rehabilitation physician in Sec. 412.622(a)(3)(iv), we
do not expect this to result in any financial impacts for the Medicare
contractors, IRFs, other providers, or for the Medicare program.
However, we expect that this clarification may ease some administrative
burden for IRFs and for Medicare contractors by making it easier for
IRF providers to document their decisions regarding the licensed
physicians in their facilities that meet the regulatory definition of a
rehabilitation physician and for the Medicare contractors to continue
to accept the IRFs' decisions in this regard. We are unable at this
time to quantify how much administrative burden may have existed
because of the previous ambiguity surrounding the definition of a
rehabilitation physician, but we are hopeful that this clarification
will alleviate any administrative burden that might have existed
before.
We expect this clarification to enhance Medicare's program
integrity efforts in this area by eliminating uncertainty surrounding
the definition of a rehabilitation physician.
D. Alternatives Considered
The following is a discussion of the alternatives considered for
the IRF PPS updates contained in this final rule.
Section 1886(j)(3)(C) of the Act requires the Secretary to update
the IRF PPS payment rates by an increase factor that reflects changes
over time in the prices of an appropriate mix of goods and services
included in the covered IRF services.
We are adopting a market basket increase factor for FY 2020 that is
based on a rebased and revised market basket reflecting a 2016 base
year. We considered the alternative of continuing to use the IRF market
basket without rebasing to determine the market basket increase factor
for FY 2020. However, we typically rebase and revise the market baskets
for the various PPS every 4 to 5 years so that the cost weights and
price proxies reflect more recent data. Therefore, we believe it is
more technically appropriate to use a 2016-based IRF market basket
since it allows for the FY 2020 market basket increase
[[Page 39171]]
factor to reflect a more up-to-date cost structure experienced by IRFs.
As noted previously in this final rule, section
1886(j)(3)(C)(ii)(I) of the Act requires the Secretary to apply a
productivity adjustment to the market basket increase factor for FY
2020. Thus, in accordance with section 1886(j)(3)(C) of the Act, we are
updating the IRF prospective payments in this final rule by 2.5 percent
(which equals the 2.9 percent estimated IRF market basket increase
factor for FY 2020 reduced by a 0.4 percentage point productivity
adjustment as determined under section 1886(b)(3)(B)(xi)(II) of the Act
(as required by section 1886(j)(3)(C)(ii)(I) of the Act)).
As we finalized in the FY 2019 IRF PPS final rule (83 FR 38514) use
of the Quality Indicators items in determining payment and the
associated CMG and CMG relative weight revisions using 2 years of data
(FYs 2017 and 2018) beginning with FY 2020, we did not consider any
alternative to proposing these changes.
However, we did consider whether or not to apply a weighting
methodology to the IRF motor score that was finalized in the FY 2019
IRF PPS final rule (83 FR 38514) to assign patients to CMGs beginning
in FY 2020. As described in the FY 2020 IRF PPS proposed rule (84 FR
17244, 17249 through 17260), we explored the use of a weighted motor
score, as requested by stakeholders. Our analysis showed that weighting
the motor score would improve the accuracy of payments under the IRF
PPS. The improved accuracy combined with the requests from stakeholders
to explore a weighted methodology led us to propose to use a weighted
motor score to assign patients to CMGs beginning on October 1, 2019.
However, in light of the many concerned stakeholder comments on the FY
2020 IRF PPS proposed rule that requested that we go back to an
unweighted motor score methodology until we can more fully analyze a
weighted motor score, the fact that the improvement in accuracy using
the weighted motor score is small, and the greater simplicity achieved
through the use of an unweighted motor score, we are finalizing an
unweighted motor score, in which each of the 18 items have a weight of
1, beginning October 1, 2019. We will continue to analyze weighted
motor score approaches and will consider possible revisions to the
motor score for future rulemaking.
We considered not removing the item GG0170A1 Roll left and right
from the composition of the motor score. However, this item was found
to be very collinear with other items in the motor score and did not
behave as expected in the models. Therefore, we believe it is
appropriate to remove this item from the construction of the motor
score.
We considered updating facility-level adjustment factors for FY
2020. However, as discussed in more detail in the FY 2015 final rule
(79 FR 45872), we believe that freezing the facility-level adjustments
at FY 2014 levels for FY 2015 and all subsequent years (unless and
until the data indicate that they need to be further updated) will
allow us an opportunity to monitor the effects of the substantial
changes to the adjustment factors for FY 2014, and will allow IRFs time
to adjust to the previous changes.
We considered not updating the IRF wage index to use the concurrent
fiscal year's IPPS wage index and instead continuing to use a 1-year
lag of the IPPS wage index. However, we believe that updating the IRF
wage index based on the concurrent fiscal year's IPPS wage index will
better align the data across acute and PAC settings in support of our
efforts to move toward more unified Medicare payments across PAC
settings.
We considered maintaining the existing outlier threshold amount for
FY 2020. However, the outlier threshold must be adjusted to reflect
changes in estimated costs and payments for IRFs in FY 2020.
Consequently, we are adjusting the outlier threshold amount in this
final rule to maintain total outlier payments equal to 3 percent of
aggregate estimated payments in FY 2020.
We considered not amending Sec. 412.622(a)(3)(iv) to clarify that
the determination as to whether a physician qualifies as a
rehabilitation physician (that is, a licensed physician with
specialized training and experience in inpatient rehabilitation) is
made by the IRF. Instead, we considered addressing this issue through
subregulatory means, such as issuing guidance to the Medicare
contractors. However, we believe that it is important to clarify this
definition in regulation to ensure that IRF providers and Medicare
contractors have a shared understanding of these regulatory
requirements and to make certain that there is no room for further
ambiguity on this point.
In addition, we considered addressing this issue by amending Sec.
412.622(a)(3)(iv) to add further specificity to the definition of a
rehabilitation physician. However, we did not take this approach
because we continue to believe that the IRFs are in the best position
to make the determination as to which licensed physicians meet the
requirements for purposes of Sec. 412.622, and we did not want to
inadvertently affect access to IRF care for beneficiaries. However, we
will continue to monitor this policy and engage with stakeholders to
determine if further specificity of these requirements may be warranted
in the future.
E. Regulatory Review Costs
If regulations impose administrative costs on private entities,
such as the time needed to read and interpret this final rule, we
should estimate the cost associated with regulatory review. Due to the
uncertainty involved with accurately quantifying the number of entities
that will review the rule, we assume that the total number of unique
commenters on the FY 2020 IRF PPS proposed rule will be the number of
reviewers of this final rule. We acknowledge that this assumption may
understate or overstate the costs of reviewing this final rule. It is
possible that not all commenters reviewed the FY 2020 IRF PPS proposed
rule in detail, and it is also possible that some reviewers chose not
to comment on the proposed rule. For these reasons we thought that the
number of past commenters would be a fair estimate of the number of
reviewers of this final rule.
We also recognize that different types of entities are in many
cases affected by mutually exclusive sections of this final rule, and
therefore, for the purposes of our estimate we assume that each
reviewer reads approximately 50 percent of the rule. We sought comments
on this assumption.
Using the wage information from the BLS for medical and health
service managers (Code 11-9111), we estimate that the cost of reviewing
this rule is $107.38 per hour, including overhead and fringe benefits
(https://www.bls.gov/oes/current/oes_nat.htm). Assuming an average
reading speed, we estimate that it would take approximately 2 hours for
the staff to review half of this final rule. For each IRF that reviews
the rule, the estimated cost is $218.72 (2 hours x $109.36). Therefore,
we estimate that the total cost of reviewing this regulation is
$274,931.04 ($218.72 x 1,257 reviewers).
We received one comment on the proposed methodology for estimating
the total cost of reviewing this regulation which is summarized below.
Comment: One commenter suggested that CMS should take into
consideration the number of times the proposed rule has been downloaded
in estimating the cost of reviewing this regulation.
Response: The regulatory review cost is an estimate that makes
several assumptions such as average reading speed and number of the
people who
[[Page 39172]]
read the document, etc. For more than 2 years, we have used the number
of comments received as a proxy for the number of staff members who
review the document. This assumption is well accepted by the general
public. The number of comments received is a more reasonable proxy than
the number of downloads since those who provide comments must actually
read the rule, as those that download the rule may not read the rule.
F. Accounting Statement and Table
As required by OMB Circular A-4 (available at http://www.whitehouse.gov/sites/default/files/omb/assets/omb/circulars/a004/a-4.pdf), in Table 21, we have prepared an accounting statement showing
the classification of the expenditures associated with the provisions
of this final rule. Table 21 provides our best estimate of the increase
in Medicare payments under the IRF PPS as a result of the updates
presented in this final rule based on the data for 1,122 IRFs in our
database. In addition, Table 21 presents the costs associated with the
new IRF QRP requirements for FY 2020.
[GRAPHIC] [TIFF OMITTED] TR08AU19.025
G. Conclusion
Overall, the estimated payments per discharge for IRFs in FY 2020
are projected to increase by 2.5 percent, compared with the estimated
payments in FY 2019, as reflected in column 7 of Table 20.
IRF payments per discharge are estimated to increase by 2.4 percent
in urban areas and 4.4 percent in rural areas, compared with estimated
FY 2019 payments. Payments per discharge to rehabilitation units are
estimated to increase 5.0 percent in urban areas and 5.7 percent in
rural areas. Payments per discharge to freestanding rehabilitation
hospitals are estimated to increase 0.2 percent in urban areas and
decrease 2.1 percent in rural areas.
Overall, IRFs are estimated to experience a net increase in
payments as a result of the policies in this final rule. The largest
payment increase is estimated to be a 6.8 percent increase for rural
government IRFs and rural IRFs located in the West South Central
region. The analysis above, together with the remainder of this
preamble, provides a Regulatory Impact Analysis.
In accordance with the provisions of Executive Order 12866, this
regulation was reviewed by the Office of Management and Budget.
List of Subjects in 42 CFR Part 412
Administrative practice and procedure, Health facilities, Medicare,
Puerto Rico, Reporting and recordkeeping requirements.
For the reasons set forth in the preamble, the Centers for Medicare
& Medicaid Services amends 42 CFR chapter IV as set forth below:
PART 412--PROSPECTIVE PAYMENT SYSTEMS FOR INPATIENT HOSPITAL
SERVICES
0
1. The authority citation for part 412 is revised to read as follows:
Authority: 42 U.S.C. 1302 and 1395hh.
0
2. Section 412.622 is amended by revising paragraphs (a)(3)(iv),
(a)(4)(i)(A), (a)(4)(iii)(A), and (a)(5)(i) and adding paragraph (c) to
read as follows:
Sec. 412.622 Basis of payment.
(a) * * *
(3) * * *
(iv) Requires physician supervision by a rehabilitation physician.
The requirement for medical supervision means that the rehabilitation
physician must conduct face-to-face visits with the patient at least 3
days per week throughout the patient's stay in the IRF to assess the
patient both medically and functionally, as well as to modify the
course of treatment as needed to maximize the patient's capacity to
benefit from the rehabilitation process. The post-admission physician
evaluation described in paragraph (a)(4)(ii) of this section may count
as one of the face-to-face visits.
(4) * * *
(i) * * *
(A) It is conducted by a licensed or certified clinician(s)
designated by a rehabilitation physician within the 48 hours
immediately preceding the IRF admission. A preadmission screening that
includes all of the required elements, but that is conducted more than
48 hours immediately preceding the IRF admission, will be accepted as
long as an update is conducted in person or by telephone to update the
patient's medical and functional status within the 48 hours immediately
preceding the IRF admission and is documented in the patient's medical
record.
* * * * *
(iii) * * *
(A) It is developed by a rehabilitation physician with input from
the interdisciplinary team within 4 days of the patient's admission to
the IRF.
* * * * *
(5) * * *
(i) The team meetings are led by a rehabilitation physician and
further consist of a registered nurse with specialized training or
experience in rehabilitation; a social worker or case manager (or
both); and a licensed or certified therapist from each therapy
discipline involved in treating the patient. All team members must have
current knowledge of the patient's medical and functional status. The
rehabilitation physician may lead the interdisciplinary team meeting
remotely via a mode of communication such as video or telephone
conferencing.
* * * * *
(c) Definitions. As used in this section--
Rehabilitation physician means a licensed physician who is
determined by the IRF to have specialized training and experience in
inpatient rehabilitation.
0
3. Section 412.634 is amended by revising paragraphs (a)(1), (d)(1) and
(5), and (f)(1) to read as follows:
[[Page 39173]]
Sec. 412.634 Requirements under the Inpatient Rehabilitation
Facility (IRF) Quality Reporting Program (QRP).
(a) * * *
(1) For the FY 2018 payment determination and subsequent years, an
IRF must begin reporting data under the IRF QRP requirements no later
than the first day of the calendar quarter subsequent to 30 days after
the date on its CMS Certification Number (CCN) notification letter,
which designates the IRF as operating in the CMS designated data
submission system.
* * * * *
(d) * * *
(1) IRFs that do not meet the requirement in paragraph (b) of this
section for a program year will receive a written notification of non-
compliance through at least one of the following methods: The CMS
designated data submission system, the United States Postal Service, or
via an email from the Medicare Administrative Contractor (MAC).
* * * * *
(5) CMS will notify IRFs, in writing, of its final decision
regarding any reconsideration request through at least one of the
following methods: CMS designated data submission system, the United
States Postal Service, or via an email from the Medicare Administrative
Contractor (MAC).
* * * * *
(f) * * *
(1) IRFs must meet or exceed two separate data completeness
thresholds: One threshold set at 95 percent for completion of required
quality measures data and standardized patient assessment data
collected using the IRF-PAI submitted through the CMS designated data
submission system; and a second threshold set at 100 percent for
measures data collected and submitted using the CDC NHSN.
* * * * *
Dated: July 23, 2019.
Seema Verma,
Administrator, Centers for Medicare & Medicaid Services.
Dated: July 25, 2019.
Alex M. Azar II,
Secretary, Department of Health and Human Services.
[FR Doc. 2019-16603 Filed 7-31-19; 4:15 pm]
BILLING CODE 4120-01-P
