Medicare Program; Prospective Payment System and Consolidated Billing for Skilled Nursing Facilities; Updates to the Quality Reporting Program and Value-Based Purchasing Program for Federal Fiscal Year 2022
| Citation | 86 FR 19954 |
| Record Number | 2021-07556 |
| Published date | 15 April 2021 |
| Court | Centers For Medicare & Medicaid Services |
Federal Register, Volume 86 Issue 71 (Thursday, April 15, 2021)
[Federal Register Volume 86, Number 71 (Thursday, April 15, 2021)]
[Proposed Rules]
[Pages 19954-20022]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2021-07556]
[[Page 19953]]
Vol. 86
Thursday,
No. 71
April 15, 2021
Part IIDepartment of Health and Human Services-----------------------------------------------------------------------Centers for Medicare & Medicaid Services-----------------------------------------------------------------------42 CFR Parts 411, 413 and 489Medicare Program; Prospective Payment System and Consolidated Billing
for Skilled Nursing Facilities; Updates to the Quality Reporting
Program and Value-Based Purchasing Program for Federal Fiscal Year
2022; Proposed Rule
Federal Register / Vol. 86 , No. 71 / Thursday, April 15, 2021 /
Proposed Rules
[[Page 19954]]
DEPARTMENT OF HEALTH AND HUMAN SERVICES
Centers for Medicare & Medicaid Services
42 CFR Parts 411, 413, and 489
[CMS-1746-P]
RIN 0938-AU36
Medicare Program; Prospective Payment System and Consolidated
Billing for Skilled Nursing Facilities; Updates to the Quality
Reporting Program and Value-Based Purchasing Program for Federal Fiscal
Year 2022
AGENCY: Centers for Medicare & Medicaid Services (CMS), HHS.
ACTION: Proposed rule.
-----------------------------------------------------------------------
SUMMARY: This proposed rule would update the payment rates used under
the prospective payment system (PPS) for skilled nursing facilities
(SNFs) for fiscal year (FY) 2022. In addition, the proposed rule
includes a proposed forecast error adjustment for FY 2022, proposes
updates to the diagnosis code mappings used under the Patient Driven
Payment Model (PDPM), proposes to rebase and revise the SNF market
basket, proposes to implement a recently-enacted SNF consolidated
billing exclusion along with the required proportional reduction in the
SNF PPS base rates, and includes a discussion of a methodology to
recalibrate the PDPM parity adjustment. In addition, the proposed rule
includes proposals for the SNF Quality Reporting Program (QRP) and the
SNF Value-Based Purchasing (VBP) Program, including a proposal to
suppress the use of the SNF readmission measure for scoring and payment
adjustment purposes in the FY 2022 SNF VBP program because we have
determined that circumstances caused by the public health emergency for
COVID-19 have significantly affected the validity and reliability of
the measure and resulting performance scores.
DATES: To be assured consideration, comments must be received at one of
the addresses provided below, no later than 5 p.m. on June 7, 2021.
ADDRESSES: In commenting, please refer to file code CMS-1746-P.
Comments, including mass comment submissions, must be submitted in
one of the following three ways (please choose only one of the ways
listed):
1. Electronically. You may submit electronic comments on this
regulation to http://www.regulations.gov. Follow the ``Submit a
comment'' instructions.
2. By regular mail. You may mail written comments to the following
address ONLY: Centers for Medicare & Medicaid Services, Department of
Health and Human Services, Attention: CMS-1746-P, P.O. Box 8016,
Baltimore, MD 21244-8016.
Please allow sufficient time for mailed comments to be received
before the close of the comment period.
3. By express or overnight mail. You may send written comments to
the following address ONLY: Centers for Medicare & Medicaid Services,
Department of Health and Human Services, Attention: CMS-1746-P, Mail
Stop C4-26-05, 7500 Security Boulevard, Baltimore, MD 21244-1850.
For information on viewing public comments, see the beginning of
the SUPPLEMENTARY INFORMATION section.
FOR FURTHER INFORMATION CONTACT:
Penny Gershman, (410) 786-6643, for information related to SNF PPS
clinical issues.
Anthony Hodge, (410) 786-6645, for information related to
consolidated billing, and payment for SNF-level swing-bed services.
John Kane, (410) 786-0557, for information related to the
development of the payment rates and case-mix indexes, and general
information.
Kia Burwell, (410) 786-7816, for information related to the wage
index.
Heidi Magladry, (410) 786-6034, for information related to the
skilled nursing facility quality reporting program.
Lang Le, (410) 786-5693, for information related to the skilled
nursing facility value-based purchasing 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 on the following
website as soon as possible after they have been received: http://www.regulations.gov. Follow the search instructions on that website to
view public comments. CMS will not post on Regulations.gov public
comments that make threats to individuals or institutions or suggest
that the individual will take actions to harm the individual. CMS
continues to encourage individuals not to submit duplicative comments.
We will post acceptable comments from multiple unique commenters even
if the content is identical or nearly identical to other comments.
Availability of Certain Tables Exclusively Through the Internet on the
CMS Website
As discussed in the FY 2014 SNF PPS final rule (78 FR 47936),
tables setting forth the Wage Index for Urban Areas Based on CBSA Labor
Market Areas and the Wage Index Based on CBSA Labor Market Areas for
Rural Areas are no longer published in the Federal Register. Instead,
these tables are available exclusively through the internet on the CMS
website. The wage index tables for this proposed rule can be accessed
on the SNF PPS Wage Index home page, at http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/WageIndex.html.
Readers who experience any problems accessing any of these online
SNF PPS wage index tables should contact Kia Burwell at (410) 786-7816.
To assist readers in referencing sections contained in this
document, we are providing the following Table of Contents.
Table of Contents
I. Executive Summary
A. Purpose
B. Summary of Major Provisions
C. Summary of Cost and Benefits
D. Advancing Health Information Exchange
II. Background on SNF PPS
A. Statutory Basis and Scope
B. Initial Transition for the SNF PPS
C. Required Annual Rate Updates
III. Proposed SNF PPS Rate Setting Methodology and FY 2022 Update
A. Federal Base Rates
B. SNF Market Basket Update
C. Case-Mix Adjustment
D. Wage Index Adjustment
E. SNF Value-Based Purchasing Program
F. Adjusted Rate Computation Example
IV. Additional Aspects of the SNF PPS
A. SNF Level of Care--Administrative Presumption
B. Consolidated Billing
C. Payment for SNF-Level Swing-Bed Services
D. Revisions to the Regulation Text
V. Other SNF PPS Issues
A. Proposed Changes to SNF PPS Wage Index
B. Technical Updates to PDPM ICD-10 Mappings
C. Recalibrating the PDPM Parity Adjustment
VI. Skilled Nursing Facility (SNF) Quality Reporting Program (QRP)
VII. Skilled Nursing Facility Value-Based Purchasing Program (SNF
VBP)
VIII. Collection of Information Requirements
IX. Response to Comments
X. Economic Analyses
A. Regulatory Impact Analysis
B. Regulatory Flexibility Act Analysis
C. Unfunded Mandates Reform Act Analysis
D. Federalism Analysis
E. Reducing Regulation and Controlling Regulatory Costs
[[Page 19955]]
F. Congressional Review Act
G. Regulatory Review Costs
I. Executive Summary
A. Purpose
This proposed rule would update the SNF prospective payment rates
for fiscal year (FY) 2022 as required under section 1888(e)(4)(E) of
the Social Security Act (the Act). It also responds to section
1888(e)(4)(H) of the Act, which requires the Secretary to provide for
publication of certain specified information relating to the payment
update (see section II.C. of this proposed rule) in the Federal
Register, before the August 1 that precedes the start of each FY. As
discussed in section V.A. of this proposed rule, it would also rebase
and revise the SNF market basket index, including updating the base
year from 2014 to 2018. As discussed in section IV.D. of this proposed
rule, it would also make revisions in the regulation text to exclude
from SNF consolidated billing certain blood clotting factors and items
and services related to the furnishing of such factors effective for
items and services furnished on or after October 1, 2021, as required
by the Consolidated Appropriations Act, 2021 (Pub. L. 116-260, enacted
December 27, 2020), as well as certain other conforming revisions. In
addition, as required under section 1888(e)(4)(G)(iii) of the Act, as
added by section 103(b) of the BBRA 1999, we propose to provide for a
proportional reduction in the Part A SNF PPS base rates to account for
this exclusion, as described in section III.B.6. of this proposed rule.
We also propose to make changes to the code mappings used under the SNF
PPS for classifying patients into case-mix groups. Additionally, this
proposed rule includes a proposed forecast error adjustment for FY
2022. This proposed rule also includes a discussion of a methodology to
recalibrate the PDPM parity adjustment. Finally, this proposed rule
would also update requirements for the Skilled Nursing Facility Quality
Reporting Program (SNF QRP) and the Skilled Nursing Facility Value-
Based Purchasing Program (SNF VBP), including a proposal to suppress
the use of the SNF readmission measure for scoring and payment
adjustment purposes in the FY 2022 SNF VBP program because we have
determined that circumstances caused by the public health emergency for
COVID-19 have significantly affected the validity and reliability of
the measure and resulting performance scores.
B. Summary of Major Provisions
In accordance with sections 1888(e)(4)(E)(ii)(IV) and (e)(5) of the
Act, the Federal rates in this proposed rule would reflect an update to
the rates that we published in the SNF PPS final rule for FY 2021 (85
FR47594, August 5, 2020). We also propose to rebase and revise the SNF
market basket index, including updating the base year from 2014 to
2018. This proposed rule proposes revisions to the regulation text to
exclude from SNF consolidated billing certain blood clotting factors
and items and services related to the furnishing of such factors
effective for items and services furnished on or after October 1, 2021,
as required by the Consolidated Appropriations Act, 2021, as well as
certain conforming revisions. We also propose to make a required
reduction in the SNF PPS base rates to account for this new exclusion.
This proposed rule also proposes revisions to the International
Classification of Diseases, Version 10 (ICD-10) code mappings used
under PDPM to classify patients into case-mix groups. Additionally,
this proposed rule includes a proposed forecast error adjustment for FY
2022. This proposed rule also includes a discussion of a methodology to
recalibrate the PDPM parity adjustment, used to implement PDPM in a
budget neutral manner.
This proposed rule proposes to update requirements for the SNF QRP,
including the proposal of two new quality measures beginning with the
FY 2023 SNF QRP: The SNF Healthcare Associated Infections (HAI)
Requiring Hospitalization measure; and the COVID-19 Vaccination
Coverage among Healthcare Personnel (HCP) measure. We are proposing
that SNFs use the Centers for Disease Control and Prevention (CDC)/
National Healthcare Safety Network (NHSN) as the method of data
submission for the proposed COVID-19 Vaccination Coverage among
Healthcare Personnel (HCP) measure. We are also proposing to modify the
denominator for the Transfer of Health Information to the Patient--Post
Acute Care (PAC) Measure. We are proposing a revision to the number of
quarters used for publicly reporting certain SNF QRP measures due to
the public health emergency (PHE). Finally, we are seeking comment on
the use of Health Level Seven International (HL7[supreg]) Fast
Healthcare Interoperability Resources[supreg] (FHIR) in quality
programs, specifically the SNF QRP, and on our continued efforts to
close the health equity gap.
Additionally, we are proposing several updates for the SNF VBP
Program including a proposal to suppress the Skilled Nursing Facility
30-Day All-Cause Readmission Measure (SNFRM) for the FY 2022 SNF VBP
Program Year and other proposals for scoring and adjusting payments to
SNFs for that program year if the SNFRM is suppressed. We are also
proposing to update the Phase One Review and Corrections policy to
implement a claims ``snapshot'' policy which would align the review and
corrections policy for the SNF VBP Program with the review and
corrections policy we use in other value-based purchasing programs and
to codify the proposed policy at Sec. 413.338(e)(1) of our
regulations. We are further proposing to make a technical update to the
instructions for a SNF to request an extraordinary circumstance
exception and to codify that update at Sec. 413.338(d)(4)(ii) of our
regulations. Finally, we are seeking comments on measures and measure
concepts we are considering for an expanded SNF VBP Program measure
set.
C. Summary of Cost and Benefits
Table 1--Cost and Benefits
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Provision description Total transfers/costs
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Proposed FY 2022 SNF PPS The overall economic impact of this
payment rate update. proposed rule is an estimated increase
of $444 million in aggregate payments to
SNFs during FY 2022.
Proposed FY 2022 SNF QRP The overall economic impact of this
changes. proposed rule is an estimated increase
in cost to SNFs of $6.63 million.
Proposed FY 2022 SNF VBP The overall economic impact of the SNF
changes. VBP Program is an estimated reduction of
$191.64 million in aggregate payments to
SNFs during FY 2022.
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[[Page 19956]]
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 and patient access
to their health information.
To further interoperability in post-acute care settings, CMS and
the Office of the National Coordinator for Health Information
Technology (ONC) participate in the Post-Acute Care Interoperability
Workgroup (PACIO) (https://pacioproject.org/) to facilitate
collaboration with industry stakeholders to develop FHIR standards.
These standards could support the exchange and reuse of patient
assessment data derived from the minimum data set (MDS), inpatient
rehabilitation facility patient assessment instrument (IRF-PAI), long
term care hospital continuity assessment record and evaluation (LCDS),
outcome and assessment information set (OASIS), and other sources. The
PACIO Project has focused on FHIR implementation guides for functional
status, cognitive status and new use cases on advance directives and
speech, and language pathology. We encourage post-acute care (PAC)
provider and health information technology (IT) vendor participation as
these efforts advance.
The CMS Data Element Library (DEL) continues to be updated and
serves as the authoritative resource for PAC assessment data elements
and their associated mappings to health IT standards such as Logical
Observation Identifiers Names and Codes (LOINC) and Systematized
Nomenclature of Medicine Clinical Terms (SNOMED). The DEL furthers CMS'
goal of data standardization and interoperability. When combined with
digital information systems that capture and maintain these coded
elements, their standardized clinical content can reduce provider
burden by supporting and exchange of standardized healthcare data;
supporting provider exchange of electronic health information for care
coordination, person-centered care; and supporting real-time, data
driven, clinical decision making. Standards in the Data Element Library
(https://del.cms.gov/DELWeb/pubHome) can be referenced on the CMS
website and in the ONC Interoperability Standards Advisory (ISA). The
2021 ISA is available at https://www.healthit.gov/isa.
The 21st Century Cures Act (Cures Act) (Pub. L. 114-255, enacted
December 13, 2016) requires HHS to take new steps to enable the
electronic sharing of health information ensuring interoperability for
providers and settings across the care continuum. The Cures Act
includes a trusted exchange framework and common agreement (TEFCA)
provision \1\ that will enable the nationwide exchange of electronic
health information across health information networks and provide an
important way to enable bi-directional health information exchange in
the future. For more information on current developments related to
TEFCA, we refer readers to https://www.healthit.gov/topic/interoperability/trusted-exchange-framework-and-common-agreement and
https://rce.sequoiaproject.org/.
---------------------------------------------------------------------------
\1\ ONC, Draft 2 Trusted Exchange Framework and Common
Agreement, https://www.healthit.gov/sites/default/files/page/2019-04/FINALTEFCAQTF41719508version.pdf.
---------------------------------------------------------------------------
The ONC final rule entitled ``21st Century Cures Act:
Interoperability, Information Blocking, and the ONC Health IT
Certification Program'' (85 FR 25642) published in the May 1, 2020
Federal Register (hereinafter referred to as ``ONC Cures Act Final
Rule'') established policies related to information blocking as
authorized under section 4004 of the 21st Century Cures Act.
Information blocking is generally defined as a practice by a health IT
developer of certified health IT, health information network, health
information exchange, or health care provider that, except as required
by law or specified by the HHS Secretary as a reasonable and necessary
activity, is likely to interfere with access, exchange, or use of
electronic health information. The definition of information blocking
includes a knowledge standard, which is different for health care
providers than for health IT developers of certified health IT and
health information networks or health information exchanges. A
healthcare provider must know that the practice is unreasonable, as
well as likely to interfere with access, exchange, or use of electronic
health information. To deter information blocking, health IT developers
of certified health IT, health information networks and health
information exchanges whom the HHS Inspector General determines,
following an investigation, have committed information blocking, are
subject to civil monetary penalties of up to $1 million per violation.
Appropriate disincentives for health care providers are expected to be
established by the Secretary through future rulemaking. Stakeholders
can learn more about information blocking at https://www.healthit.gov/curesrule/final-rule-policy/information-blocking. ONC has posted
information resources including fact sheets (https://www.healthit.gov/curesrule/resources/fact-sheets), frequently asked questions (https://www.healthit.gov/curesrule/resources/information-blocking-faqs), and
recorded webinars (https://www.healthit.gov/curesrule/resources/webinars).
We invite providers to learn more about these important
developments and how they are likely to affect SNFs.
II. Background on SNF PPS
A. Statutory Basis and Scope
As amended by section 4432 of the Balanced Budget Act of 1997 (BBA
1997) (Pub. L. 105-33, enacted August 5, 1997), section 1888(e) of the
Act provides for the implementation of a PPS for SNFs. This methodology
uses prospective, case-mix adjusted per diem payment rates applicable
to all covered SNF services defined in section 1888(e)(2)(A) of the
Act. The SNF PPS is effective for cost reporting periods beginning on
or after July 1, 1998, and covers all costs of furnishing covered SNF
services (routine, ancillary, and capital-related costs) other than
costs associated with approved educational activities and bad debts.
Under section 1888(e)(2)(A)(i) of the Act, covered SNF services include
post-hospital extended care services for which benefits are provided
under Part A, as well as those items and services (other than a small
number of excluded services, such as physicians' services) for which
payment may otherwise be made under Part B and which are furnished to
Medicare beneficiaries who are residents in a SNF during a covered Part
A stay. A comprehensive discussion of these provisions appears in the
May 12, 1998 interim final rule (63 FR 26252). In addition, a detailed
discussion of the legislative history of the SNF PPS is available
online at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/Downloads/Legislative_History_2018-10-01.pdf.
Section 215(a) of the Protecting Access to Medicare Act of 2014
(PAMA) (Pub. L. 113-93, enacted April 1, 2014) added section 1888(g) to
the Act requiring the Secretary to specify an all-cause all-condition
hospital readmission measure and an all-condition risk-adjusted
potentially preventable hospital readmission measure for the SNF
setting. Additionally, section 215(b) of PAMA added section 1888(h)
[[Page 19957]]
to the Act requiring the Secretary to implement a VBP program for SNFs.
Finally, section 2(c)(4) of the IMPACT Act amended section 1888(e)(6)
of the Act, which requires the Secretary to implement a QRP for SNFs
under which SNFs report data on measures and resident assessment data.
B. Initial Transition for the SNF PPS
Under sections 1888(e)(1)(A) and (e)(11) of the Act, the SNF PPS
included an initial, three-phase transition that blended a facility-
specific rate (reflecting the individual facility's historical cost
experience) with the Federal case-mix adjusted rate. The transition
extended through the facility's first 3 cost reporting periods under
the PPS, up to and including the one that began in FY 2001. Thus, the
SNF PPS is no longer operating under the transition, as all facilities
have been paid at the full Federal rate effective with cost reporting
periods beginning in FY 2002. As we now base payments for SNFs entirely
on the adjusted Federal per diem rates, we no longer include adjustment
factors under the transition related to facility-specific rates for the
upcoming FY.
C. Required Annual Rate Updates
Section 1888(e)(4)(E) of the Act requires the SNF PPS payment rates
to be updated annually. The most recent annual update occurred in a
final rule that set forth updates to the SNF PPS payment rates for FY
2021 (85 FR 47594, August 5, 2020).
Section 1888(e)(4)(H) of the Act specifies that we provide for
publication annually in the Federal Register the following:
The unadjusted Federal per diem rates to be applied to
days of covered SNF services furnished during the upcoming FY.
The case-mix classification system to be applied for these
services during the upcoming FY.
The factors to be applied in making the area wage
adjustment for these services.
Along with other revisions discussed later in this preamble, this
proposed rule provides the required annual updates to the per diem
payment rates for SNFs for FY 2022.
III. Proposed SNF PPS Rate Setting Methodology and FY 2022 Update
A. Federal Base Rates
Under section 1888(e)(4) of the Act, the SNF PPS uses per diem
Federal payment rates based on mean SNF costs in a base year (FY 1995)
updated for inflation to the first effective period of the PPS. We
developed the Federal payment rates using allowable costs from
hospital-based and freestanding SNF cost reports for reporting periods
beginning in FY 1995. The data used in developing the Federal rates
also incorporated a Part B add-on, which is an estimate of the amounts
that, prior to the SNF PPS, would be payable under Part B for covered
SNF services furnished to individuals during the course of a covered
Part A stay in a SNF.
In developing the rates for the initial period, we updated costs to
the first effective year of the PPS (the 15-month period beginning July
1, 1998) using a SNF market basket index, and then standardized for
geographic variations in wages and for the costs of facility
differences in case mix. In compiling the database used to compute the
Federal payment rates, we excluded those providers that received new
provider exemptions from the routine cost limits, as well as costs
related to payments for exceptions to the routine cost limits. Using
the formula that the BBA 1997 prescribed, we set the Federal rates at a
level equal to the weighted mean of freestanding costs plus 50 percent
of the difference between the freestanding mean and weighted mean of
all SNF costs (hospital-based and freestanding) combined. We computed
and applied separately the payment rates for facilities located in
urban and rural areas, and adjusted the portion of the Federal rate
attributable to wage-related costs by a wage index to reflect
geographic variations in wages.
B. SNF Market Basket Update
1. SNF Market Basket Index
Section 1888(e)(5)(A) of the Act requires us to establish a SNF
market basket index that reflects changes over time in the prices of an
appropriate mix of goods and services included in covered SNF services.
Accordingly, we have developed a SNF market basket index that
encompasses the most commonly used cost categories for SNF routine
services, ancillary services, and capital-related expenses. In the SNF
PPS final rule for FY 2018 (82 FR 36548 through 36566), we rebased and
revised the market basket index, which included updating the base year
from FY 2010 to 2014. In this year's rule, we propose to rebase and
revise the market basket index and update the base year from 2014 to
2018. See section V.A. of this proposed rule for more information.
The SNF market basket index is used to compute the market basket
percentage change that is used to update the SNF Federal rates on an
annual basis, as required by section 1888(e)(4)(E)(ii)(IV) of the Act.
This market basket percentage update is adjusted by a forecast error
correction, if applicable, and then further adjusted by the application
of a productivity adjustment as required by section 1888(e)(5)(B)(ii)
of the Act and described in section III.B.2.d. of this proposed rule.
In the FY 2021 SNF PPS final rule (85 FR 47597), the SNF market basket
percentage was estimated to be 2.2 percent for FY 2021 based on IHS
Global Inc's (IGI's) second quarter 2020 forecast of the 2014-based SNF
market basket with historical data through first quarter 2020.
For this proposed rule, we propose a FY 2022 SNF market basket
percentage of 2.3 percent based on IGI's fourth quarter 2020 forecast
of the proposed 2018-based SNF market basket (before application of the
forecast error adjustment and multifactor productivity (MFP)
adjustment). We also propose that if more recent data subsequently
become available (for example, a more recent estimate of the market
basket and/or the MFP), we would use such data, if appropriate, to
determine the FY 2022 SNF market basket percentage change, labor-
related share relative importance, forecast error adjustment, or MFP
adjustment in the SNF PPS final rule.
In section III.B.2.e. of this proposed rule, we discuss the 2
percent reduction applied to the market basket update for those SNFs
that fail to submit measures data as required by section 1888(e)(6)(A)
of the Act.
2. Use of the SNF Market Basket Percentage
Section 1888(e)(5)(B) of the Act defines the SNF market basket
percentage as the percentage change in the SNF market basket index from
the midpoint of the previous FY to the midpoint of the current FY. For
the Federal rates set forth in this proposed rule, we use the
percentage change in the SNF market basket index to compute the update
factor for FY 2022. This factor is based on the FY 2022 percentage
increase in the proposed 2018-based SNF market basket index reflecting
routine, ancillary, and capital-related expenses. As stated previously,
in this proposed rule, the SNF market basket percentage update is
estimated to be 2.3 percent for FY 2022 based on IGI's fourth quarter
2020 forecast.
3. Forecast Error Adjustment
As discussed in the June 10, 2003 supplemental proposed rule (68 FR
34768) and finalized in the August 4, 2003 final rule (68 FR 46057
through 46059), Sec. 413.337(d)(2) provides for an
[[Page 19958]]
adjustment to account for market basket forecast error. The initial
adjustment for market basket forecast error applied to the update of
the FY 2003 rate for FY 2004, and took into account the cumulative
forecast error for the period from FY 2000 through FY 2002, resulting
in an increase of 3.26 percent to the FY 2004 update. Subsequent
adjustments in succeeding FYs take into account the forecast error from
the most recently available FY for which there is final data, and apply
the difference between the forecasted and actual change in the market
basket when the difference exceeds a specified threshold. We originally
used a 0.25 percentage point threshold for this purpose; however, for
the reasons specified in the FY 2008 SNF PPS final rule (72 FR 43425),
we adopted a 0.5 percentage point threshold effective for FY 2008 and
subsequent FYs. As we stated in the final rule for FY 2004 that first
issued the market basket forecast error adjustment (68 FR 46058), the
adjustment will reflect both upward and downward adjustments, as
appropriate.
For FY 2020 (the most recently available FY for which there is
final data), the forecasted or estimated increase in the SNF market
basket index was 2.8 percentage points, and the actual increase for FY
2020 is 2.0 percentage points, resulting in the actual increase being
0.8 percentage point lower than the estimated increase. Accordingly, as
the difference between the estimated and actual amount of change in the
market basket index exceeds the 0.5 percentage point threshold, under
the policy previously described (comparing the forecasted and actual
increase in the market basket), the FY 2022 market basket percentage
change of 2.3 percent would be adjusted downward to account for the
forecast error correction of 0.8 percentage point, resulting in a SNF
market basket percentage change of 1.5 percent.
We note that we may consider modifying this forecast error
methodology in future rulemaking. We invite comments and feedback on
this issue, in particular on the possibility of, in future rulemaking,
either eliminating the forecast error adjustment, or raising the
threshold for the forecast error from 0.5 percent to 1.0 percent.
Table 2 shows the forecasted and actual market basket increases for
FY 2020.
Table 2--Difference Between the Actual and Forecasted Market Basket Increases for FY 2020
----------------------------------------------------------------------------------------------------------------
Forecasted FY 2020 Actual FY 2020 FY 2020
Index Increase* Increase** difference
----------------------------------------------------------------------------------------------------------------
SNF................................................. 2.8 2.0 -0.8
----------------------------------------------------------------------------------------------------------------
* Published in Federal Register; based on second quarter 2019 IGI forecast (2014-based index).
** Based on the fourth quarter 2020 IGI forecast (2014-based index).
4. Multifactor Productivity Adjustment
Section 1888(e)(5)(B)(ii) of the Act, as added by section 3401(b)
of the Patient Protection and Affordable Care Act (Affordable Care Act)
(Pub. L. 111-148, enacted March 23, 2010) requires that, in FY 2012 and
in subsequent FYs, the market basket percentage under the SNF payment
system (as described in section 1888(e)(5)(B)(i) of the Act) is to be
reduced annually by the MFP adjustment described in section
1886(b)(3)(B)(xi)(II) of the Act. Section 1886(b)(3)(B)(xi)(II) of the
Act, in turn, defines the MFP adjustment to be equal to the 10-year
moving average of changes in annual economy-wide private nonfarm
business multi-factor productivity (as projected by the Secretary for
the 10-year period ending with the applicable FY, year, cost-reporting
period, or other annual period). The Bureau of Labor Statistics (BLS)
is the agency that publishes the official measure of private nonfarm
business MFP. We refer readers to the BLS website at http://www.bls.gov/mfp for the BLS historical published MFP data.
MFP is derived by subtracting the contribution of labor and capital
inputs 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 baskets and MFP. To generate a forecast of MFP, IGI
replicates the MFP measure calculated by the BLS, using a series of
proxy variables derived from IGI's U.S. macroeconomic models. For a
discussion of the MFP projection methodology, we refer readers to the
FY 2012 SNF PPS final rule (76 FR 48527 through 48529) and the FY 2016
SNF PPS final rule (80 FR 46395). A complete description of the MFP
projection methodology is available on our website at http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/MedicareProgramRatesStats/MarketBasketResearch.html.
a. Incorporating the MFP Into the Market Basket Update
Per section 1888(e)(5)(A) of the Act, the Secretary shall establish
a SNF market basket index that reflects changes over time in the prices
of an appropriate mix of goods and services included in covered SNF
services. Section 1888(e)(5)(B)(ii) of the Act, added by section
3401(b) of the Affordable Care Act, requires that for FY 2012 and each
subsequent FY, after determining the market basket percentage described
in section 1888(e)(5)(B)(i) of the Act, the Secretary shall reduce such
percentage by the productivity adjustment described in section
1886(b)(3)(B)(xi)(II) of the Act (which we refer to as the MFP
adjustment). Section 1888(e)(5)(B)(ii) of the Act further states that
the reduction of the market basket percentage by the MFP adjustment may
result in the market basket percentage being less than zero for a FY,
and may result in payment rates under section 1888(e) of the Act being
less than such payment rates for the preceding fiscal year. Thus, if
the application of the MFP adjustment to the market basket percentage
calculated under section 1888(e)(5)(B)(i) of the Act results in an MFP-
adjusted market basket percentage that is less than zero, then the
annual update to the unadjusted Federal per diem rates under section
1888(e)(4)(E)(ii) of the Act would be negative, and such rates would
decrease relative to the prior FY.
Based on the data available for this FY 2022 SNF PPS proposed rule,
the current estimate of the 10-year moving average of changes in MFP
for the period ending September 30, 2022 would be 0.2 percentage point.
Consistent with section 1888(e)(5)(B)(i) of the Act and Sec.
413.337(d)(2), as discussed previously, the market basket percentage
for FY 2022 for the SNF PPS is based on IGI's fourth quarter 2020
forecast of the SNF market basket percentage, which is estimated to be
2.3 percent. As discussed above, we are applying a 0.2 percentage point
MFP adjustment to the FY 2022 SNF market basket percentage.
[[Page 19959]]
The resulting MFP-adjusted FY 2022 SNF market basket update is,
therefore, equal to 2.1 percent, or 2.3 percent less 0.2 percentage
point.
5. Market Basket Update Factor for FY 2022
Sections 1888(e)(4)(E)(ii)(IV) and (e)(5)(i) of the Act require
that the update factor used to establish the FY 2022 unadjusted Federal
rates be at a level equal to the market basket index percentage change.
Accordingly, we determined the total growth from the average market
basket level for the period of October 1, 2020 through September 30,
2021 to the average market basket level for the period of October 1,
2021 through September 30, 2022. This process yields a percentage
change in the proposed 2018-based SNF market basket of 2.3 percent.
As further explained in section III.B.2.c. of this proposed rule,
as applicable, we adjust the market basket percentage change by the
forecast error from the most recently available FY for which there is
final data and apply this adjustment whenever the difference between
the forecasted and actual percentage change in the market basket
exceeds a 0.5 percentage point threshold. Since the forecasted FY 2020
SNF market basket percentage change exceeded the actual FY 2020 SNF
market basket percentage change (FY 2020 is the most recently available
FY for which there is historical data) by more than the 0.5 percentage
point threshold, we propose to adjust the FY 2022 market basket
percentage change downward by the forecast error correction. Applying
the -0.8 percent forecast error correction results in an adjusted FY
2022 SNF market basket percentage change of 1.5 percent (2.3 percent
market basket update less 0.8 percentage point forecast error
adjustment).
Section 1888(e)(5)(B)(ii) of the Act requires us to reduce the
market basket percentage change by the MFP adjustment (10-year moving
average of changes in MFP for the period ending September 30, 2022)
which is estimated to be 0.2 percent, as described in section
III.B.2.d. of this proposed rule. Thus, we propose to apply a net SNF
market basket update factor of 1.3 percent in our determination of the
FY 2022 SNF PPS unadjusted Federal per diem rates, which reflects a
market basket increase factor of 2.3 percent, less the 0.8 percent
forecast error correction and less the projected 0.2 percentage point
MFP adjustment.
We note that if more recent data become available (for example, a
more recent estimate of the SNF market basket and/or MFP), we would use
such data, if appropriate, to determine the FY 2022 SNF market basket
percentage change, labor-related share relative importance, forecast
error adjustment, or MFP adjustment in the FY 2022 SNF PPS final rule.
We also note that section 1888(e)(6)(A)(i) of the Act provides
that, beginning with FY 2018, SNFs that fail to submit data, as
applicable, in accordance with sections 1888(e)(6)(B)(i)(II) and (III)
of the Act for a fiscal year will receive a 2.0 percentage point
reduction to their market basket update for the fiscal year involved,
after application of section 1888(e)(5)(B)(ii) of the Act (the MFP
adjustment) and section 1888(e)(5)(B)(iii) of the Act (the 1 percent
market basket increase for FY 2018). In addition, section
1888(e)(6)(A)(ii) of the Act states that application of the 2.0
percentage point reduction (after application of section
1888(e)(5)(B)(ii) and (iii) of the Act) may result in the market basket
index percentage change being less than zero for a fiscal year, and may
result in payment rates for a fiscal year being less than such payment
rates for the preceding fiscal year. Section 1888(e)(6)(A)(iii) of the
Act further specifies that the 2.0 percentage point reduction is
applied in a noncumulative manner, so that any reduction made under
section 1888(e)(6)(A)(i) of the Act applies only to the fiscal year
involved, and that the reduction cannot be taken into account in
computing the payment amount for a subsequent fiscal year.
6. Unadjusted Federal per Diem Rates for FY 2022
As discussed in the FY 2019 SNF PPS final rule (83 FR 39162), in FY
2020 we implemented a new case-mix classification system to classify
SNF patients under the SNF PPS, the PDPM. As discussed in section V.B.
of that final rule, under PDPM, the unadjusted Federal per diem rates
are divided into six components, five of which are case-mix adjusted
components (Physical Therapy (PT), Occupational Therapy (OT), Speech-
Language Pathology (SLP), Nursing, and Non-Therapy Ancillaries (NTA)),
and one of which is a non-case-mix component, as existed under the
previous RUG-IV model. We propose to use the SNF market basket,
adjusted as described previously, to adjust each per diem component of
the Federal rates forward to reflect the change in the average prices
for FY 2022 from the average prices for FY 2021. We propose to further
adjust the rates by a wage index budget neutrality factor, described
later in this section. Further, in the past, we used the revised OMB
delineations adopted in the FY 2015 SNF PPS final rule (79 FR 45632,
45634), with updates as reflected in OMB Bulletin Nos. 15-01 and 17-01,
to identify a facility's urban or rural status for the purpose of
determining which set of rate tables would apply to the facility. As
discussed in the FY 2021 SNF PPS proposed and final rules, we adopted
the revised OMB delineations identified in OMB Bulletin No. 18-04
(available at https://www.whitehouse.gov/wp-content/uploads/2018/09/Bulletin-18-04.pdf) to identify a facility's urban or rural status
effective beginning with FY 2021.
For FY 2022, we note an additional adjustment to the unadjusted per
diem base rates. Specifically, section 134 in Division CC of the
Consolidated Appropriations Act, 2021 included a provision amending
section 1888(e)(2)(A)(iii) of the Act so as to add ``blood clotting
factors indicated for the treatment of patients with hemophilia and
other bleeding disorders . . . and items and services related to the
furnishing of such factors under section 1842(o)(5)(C)'' to the list of
items and services excludable from the Part A SNF PPS per diem payment,
effective for items and services furnished on or after October 1, 2021.
We discuss this provision further in section IV.B. of this proposed
rule.
Section 1888(e)(4)(G)(iii) of the Act further requires that the
Secretary ``provide for an appropriate proportional reduction in
payments so that . . . the aggregate amount of such reductions is equal
to the aggregate increase in payments attributable to the exclusion''
of the services from the Part A PPS per diem rates under section
1888(e)(2)(A)(iii) of the Act.
In the FY 2001 rulemaking cycle (65 FR 19202 and 46792), we
established a methodology for computing such offsets in response to
similar targeted consolidated billing exclusions added to section
1888(e)(2)(A)(iii) Act by section 103 of BBRA 1999. This methodology
resulted in a reduction of 5 cents ($0.05) in the unadjusted urban and
rural rates, using the identical data as used to establish the Part B
add-on for a sample of approximately 1,500 SNFs from the 1995 base
period. However, because this methodology relied on data from 1995, we
propose a new methodology based on updated data (as discussed below) to
apply the offsets required for the exclusion of the blood clotting
factors and items and services related to the furnishing of such
factors under section 1842(o)(5)(C) of the Act (referred to
collectively as the blood clotting factor exclusion), as specified
under the
[[Page 19960]]
Consolidated Appropriations Act, 2021. We believe the use of the
updated data will more accurately capture the actual cost of these
factors, as using updated utilization data would reflect new types of
blood clotting factors introduced in recent years and changes in
utilization patterns of blood clotting factors since 1995.
The proposed methodology for calculating the blood clotting factor
exclusion offset consists of five steps. In the first step, we begin
with the total number of SNF utilization days for beneficiaries who
have any amount of blood clotting factor (BCF) use in FY 2020. While we
recognize the potential effects of the PHE for COVID-19 on SNF
utilization during 2020, we believe we should use FY 2020 data because
it is the most recent data available, and thus would best reflect the
latest types of blood clotting factors and the most recent changes in
utilization patterns; also, the FY 2020 data is the only data available
that reflects utilization under the PDPM model rather than the RUG-IV
model. However, in light of the potential impact of the PHE for COVID-
19 on SNF utilization, particularly as it relates to those patients
admitted with COVID-19 or whose stays utilized a PHE-related waiver
(for example, the waiver which removes the requirement for a three-day
prior inpatient hospital stay in order to receive SNF Part A coverage),
we believe it would be appropriate to use a subset of the full FY 2020
SNF population which excludes patients diagnosed with COVID-19 and
those stays which utilized a PHE-related waiver. We discuss this
concept in more detail in relation to the recalibration of the PDPM
parity adjustment, discussed in section V.C. of this proposed rule. As
further explained below, we would note that using this subset
population has very little impact on the result of the methodology
described below. Throughout the discussion below, the term ``SNF
beneficiary'' refers to beneficiaries in the FY 2020 subset population
described above.
Since BCF use has historically been subject to SNF consolidated
billing and its usage cannot be observed on billed SNF claims, this
methodology resorts to claims from other settings to approximate BCF
utilization in SNFs. Specifically, BCF use as well as items and
services related to the furnishing of such factors under section
1842(o)(5)(C) of the Act are identified by checking if any of the HCPCS
codes listed in the Act, including J7170, J7175, J7177-J7183, J7185-
J7190, J7192-J7195, J7198-J7203, J7205, and J7207-J7211, are recorded
on outpatient claims, which are claims submitted by institutional
outpatient providers (such as a hospital outpatient department), or
carrier claims, which are fee-for-service claims submitted by
professional practitioners, such as physicians, physician assistants,
clinical social workers, and nurse practitioners, and by some
organizational providers, such as free-standing facilities. A SNF
beneficiary with any BCF use is defined as a SNF beneficiary with at
least one matched outpatient or carrier claim for blood clotting
factors in FY 2020. To calculate the number of SNF utilization days for
beneficiaries who have any amount of BCF use in FY 2020, we sum up the
corresponding SNF utilization days of SNF beneficiaries with BCF use in
FY 2020 (84 beneficiaries), which is 3,317 total utilization days.
In the second step, we estimate the BCF payment per day per SNF
beneficiary with any BCF use in FY 2020, which would include payment
for the BCFs and items and services related to the furnishing of such
factors under section 1842(o)(5)(C) of the Act. There is no direct
payment data to track BCF use in SNFs since BCF use is bundled within
the Part A per diem payment. Therefore, we rely on payment in
outpatient and carrier claims as a proxy for this step. Instead of
calculating BCF payment per day for SNF beneficiaries in a SNF stay, we
estimate the BCF payment per day for SNF beneficiaries outside of their
SNF and inpatient stays, under the assumption that BCF payment per day
for SNF beneficiaries is similar during and outside of SNF stays.
Outpatient or carrier claims for BCF use that overlap with a SNF stay
or an inpatient stay of a SNF beneficiary are excluded to ensure that
BCF-related payment is fully captured in Part B claims instead of
partially paid through Part A. Overlapping claims are identified when
the outpatient claim ``From'' date or the carrier claim expense date
fall within a SNF or inpatient stay's admission and discharge date
window. The total BCF payment for SNF beneficiaries' BCF use observed
through Part B claims in FY 2020 was $4,843,551. Next, to determine the
corresponding utilizations days for SNF beneficiaries' BCF use, we need
to carve out their utilization days in a SNF or inpatient setting for
these target beneficiaries. We first determine the total SNF and
inpatient utilization days for these beneficiaries in FY 2020, which
totals 5,408. Next, we determine the total days that the beneficiaries
with BCF use were not in a SNF or inpatient stay, which is 365 (for
days in the year) multiplied by the number of SNF beneficiaries with
BCF use (84), less the total SNF and inpatient utilization days for
these beneficiaries (5,408), which is 20,142. Finally, we estimated the
BCF payment per day, which is the total BCF payment observed in
outpatient and carrier claims ($4,843,551) divided by the total days
the beneficiaries were not in a SNF or inpatient (20,142). Thus, we
calculate the BCF payment per day per SNF beneficiary to be $240.
In the third step, we calculate the percentage of SNF payment
associated with BCF usage. We multiply the estimated BCF payment per
day ($240 as determined in step 2) by the total SNF utilization days
for SNF beneficiaries with BCF use in FY 2020 (3,317 as determined in
step 1). This yields an estimated BCF payment for SNF beneficiaries in
the study population of $797,640. Next, we divide this by the total SNF
payment for the study population during FY 2020 ($22,636,345,868) to
yield the percentage of SNF payment associated with BCF use, which we
estimate to be 0.00352 percent.
In the fourth step, we calculate the urban and rural base rate
reductions, by multiplying the proposed FY 2022 urban/rural base rates
by the percentage of SNF payment associated with clotting factor use
determined in step 3 (0.00352 percent). In the case of the proposed
urban base rate of $434.79, this yields an urban base rate deduction of
$0.02, which we would apply as a $0.01 reduction to the proposed FY
2022 NTA base rate and a $0.01 reduction to the proposed FY 2022
nursing base rate. In the case of the proposed rural base rate of
$444.79, this yields a rural base rate deduction of $0.02, which we
would apply as a $0.01 reduction to the proposed FY 2022 NTA base rates
and a $0.01 reduction to the proposed FY 2022 nursing base rate. We
would apply the reduction to the NTA and nursing base rates because BCF
is a type of NTA and nursing resources are required to furnish this
medication.
In step five, for purposes of impact analysis, we calculate the
budget impact of the base rate reductions to be $782,785. We estimate
the budget impact by multiplying the total FY2022 SNF baseline
($34,211,000,000) by the percentage of SNF payment for clotting factor
(0.00352 percent). This results in a total reduction in SNF spending of
$1.2 million. To compare the result of this proposed methodology to
that which would have resulted from using the full FY 2020 SNF
population, we note that if we had used the full FY 2020 SNF
population, the resultant impact would be a reduction in SNF spending
of $1.5 million, which represents 0.004551 percent of total
[[Page 19961]]
payments made under the SNF PPS. Given that these figures are so close
as to result in the same two cent reduction in the FY 2022 SNF PPS
unadjusted per diem rates, and given the reasons for using the subset
population discussed in section V.C. of this proposed rule, we believe
it is appropriate to use this subset population as the basis for the
calculations described throughout this section.
We apply these rate reductions to the NTA and nursing components of
the unadjusted Federal urban and rural per diem rate as shown in Tables
4 and 5.
Table 3 displays the methodology and figures used to calculate
these rate reductions.
Table 3--Estimation of Blood Clotting Factor on Base Rate Reduction
------------------------------------------------------------------------
------------------------------------------------------------------------
Step 1: SNF Utilization Days of Benes with Any BCF
Use:
FY2020 # SNF Benes with Any BCF Use.............. 84
FY2020 Total SNF Util Days for Benes with Any BCF 3,317
Use.............................................
Step 2: Clotting Factor Payment per Day per SNF Bene
with Any BCF Use:
FY2020 Total Part B Clotting Factor Payment for $4,843,551
Benes with Any BCF Use Outside of SNF or
Inpatient Stay..................................
FY2020 Total SNF and Inpatient Util Days for 5,408
Benes with Any BCF Use..........................
FY2020 Total Days Not in SNF or Inpatient Stay 20,142
for Benes with Any BCF Use......................
FY2020 Clotting Factor Payment per Day........... $240
Step 3: % of SNF Payment Associated with Clotting
Factor Use:
FY2020 Estimated Clotting Factor Payment in SNF.. $797,640
FY2020 Total SNF Payment......................... $22,636,345,868
% of SNF Payment Associated with Clotting Factor 0.00352%
Use.............................................
------------------------------------------------------------------------
Tables 4 and 5 reflect the updated unadjusted Federal rates for FY
2022, prior to adjustment for case-mix. The rates in Tables 4 and 5
include the reductions calculated in Table 3 for blood clotting factor
use.
Table 4--FY 2022 Unadjusted Federal Rate Per Diem--Urban
--------------------------------------------------------------------------------------------------------------------------------------------------------
Rate component PT OT SLP Nursing NTA Non-case-mix
--------------------------------------------------------------------------------------------------------------------------------------------------------
Per Diem Amount................................... $62.84 $58.49 $23.46 $109.55 $82.64 $98.10
--------------------------------------------------------------------------------------------------------------------------------------------------------
Table 5--FY 2022 Unadjusted Federal Rate Per Diem--Rural
--------------------------------------------------------------------------------------------------------------------------------------------------------
Rate component PT OT SLP Nursing NTA Non-case-mix
--------------------------------------------------------------------------------------------------------------------------------------------------------
Per Diem Amount................................... $71.63 $65.79 $29.56 $104.66 $78.96 $99.91
--------------------------------------------------------------------------------------------------------------------------------------------------------
C. Case-Mix Adjustment
Under section 1888(e)(4)(G)(i) of the Act, the Federal rate also
incorporates an adjustment to account for facility case-mix, using a
classification system that accounts for the relative resource
utilization of different patient types. The statute specifies that the
adjustment is to reflect both a resident classification system that the
Secretary establishes to account for the relative resource use of
different patient types, as well as resident assessment data and other
data that the Secretary considers appropriate. In the FY 2019 final
rule (83 FR 39162, August 8, 2018), we finalized a new case-mix
classification model, the PDPM, which took effect beginning October 1,
2019. The previous RUG-IV model classified most patients into a therapy
payment group and primarily used the volume of therapy services
provided to the patient as the basis for payment classification, thus
creating an incentive for SNFs to furnish therapy regardless of the
individual patient's unique characteristics, goals, or needs. PDPM
eliminates this incentive and improves the overall accuracy and
appropriateness of SNF payments by classifying patients into payment
groups based on specific, data-driven patient characteristics, while
simultaneously reducing the administrative burden on SNFs.
As we noted in the FY 2021 SNF PPS final rule (85 FR 47600), we
continue to monitor the impact of PDPM implementation on patient
outcomes and program outlays. We hope to release information in the
future that relates to these issues, though we provide some of this
information in section V.C. of this proposed rule. We also continue to
monitor the impact of PDPM implementation as it relates to our
intention to ensure that PDPM is implemented in a budget neutral
manner, as discussed in the FY 2020 SNF PPS final rule (84 FR 38734).
In section V.C. of this proposed rule, we discuss and solicit comments
on a methodology to recalibrate the PDPM parity adjustment as
appropriate to ensure budget neutrality, as we did after the
implementation of RUG-IV in FY 2011.
The PDPM uses clinical data from the MDS to assign case-mix
classifiers to each patient that are then used to calculate a per diem
payment under the SNF PPS, consistent with the provisions of section
1888(e)(4)(G)(i) of the Act. As discussed in section IV.A. of this
proposed rule, the clinical orientation of the case-mix classification
system supports the SNF PPS's use of an administrative presumption that
considers a beneficiary's initial case-mix classification to assist in
making certain SNF level of care determinations. Further, because the
MDS is used as a basis for payment, as well as a clinical assessment,
we have provided extensive training on proper coding and the timeframes
for MDS completion in our Resident Assessment Instrument (RAI) Manual.
As we have stated in prior rules, for an MDS to be considered valid for
use in determining payment, the MDS assessment should be completed in
compliance with the instructions in the RAI Manual in effect at the
time the assessment is completed. For payment and quality monitoring
purposes, the RAI Manual consists of both the Manual
[[Page 19962]]
instructions and the interpretive guidance and policy clarifications
posted on the appropriate MDS website at http://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/MDS30RAIManual.html.
Under section 1888(e)(4)(H) of the Act, each update of the payment
rates must include the case-mix classification methodology applicable
for the upcoming FY. The proposed FY 2022 payment rates set forth in
this proposed rule reflect the use of the PDPM case-mix classification
system from October 1, 2021, through September 30, 2022. We list the
proposed case-mix adjusted PDPM payment rates for FY 2022 separately
for urban and rural SNFs, in Tables 6 and 7 with corresponding case-mix
values.
Given the differences between the previous RUG-IV model and PDPM in
terms of patient classification and billing, it was important that the
format of Tables 6 and 7 reflect these differences. More specifically,
under both RUG-IV and PDPM, providers use a Health Insurance
Prospective Payment System (HIPPS) code on a claim to bill for covered
SNF services. Under RUG-IV, the HIPPS code included the three-character
RUG-IV group into which the patient classified as well as a two-
character assessment indicator code that represented the assessment
used to generate this code. Under PDPM, while providers still use a
HIPPS code, the characters in that code represent different things. For
example, the first character represents the PT and OT group into which
the patient classifies. If the patient is classified into the PT and OT
group ``TA'', then the first character in the patient's HIPPS code
would be an A. Similarly, if the patient is classified into the SLP
group ``SB'', then the second character in the patient's HIPPS code
would be a B. The third character represents the Nursing group into
which the patient classifies. The fourth character represents the NTA
group into which the patient classifies. Finally, the fifth character
represents the assessment used to generate the HIPPS code.
Tables 6 and 7 reflect the PDPM's structure. Accordingly, Column 1
of Tables 6 and 7 represents the character in the HIPPS code associated
with a given PDPM component. Columns 2 and 3 provide the case-mix index
and associated case-mix adjusted component rate, respectively, for the
relevant PT group. Columns 4 and 5 provide the case-mix index and
associated case-mix adjusted component rate, respectively, for the
relevant OT group. Columns 6 and 7 provide the case-mix index and
associated case-mix adjusted component rate, respectively, for the
relevant SLP group. Column 8 provides the nursing case-mix group (CMG)
that is connected with a given PDPM HIPPS character. For example, if
the patient qualified for the nursing group CBC1, then the third
character in the patient's HIPPS code would be a ``P.'' Columns 9 and
10 provide the case-mix index and associated case-mix adjusted
component rate, respectively, for the relevant nursing group. Finally,
columns 11 and 12 provide the case-mix index and associated case-mix
adjusted component rate, respectively, for the relevant NTA group.
Tables 6 and 7 do not reflect adjustments which may be made to the
SNF PPS rates as a result of the SNF VBP program, discussed in section
III.D. of this proposed rule, or other adjustments, such as the
variable per diem adjustment. Further, in the past, we used the revised
OMB delineations adopted in the FY 2015 SNF PPS final rule (79 FR
45632, 45634), with updates as reflected in OMB Bulletin Nos, 15-01 and
17-01, to identify a facility's urban or rural status for the purpose
of determining which set of rate tables would apply to the facility. As
discussed in the FY 2021 SNF PPS final rule (85 FR 47594), we adopted
the revised OMB delineations identified in OMB Bulletin No. 18-04
(available at https://www.whitehouse.gov/wp-content/uploads/2018/09/Bulletin-18-04.pdf) to identify a facility's urban or rural status
effective beginning with FY 2021.
Table 6--PDPM Case-Mix Adjusted Federal Rates and Associated Indexes--URBAN
--------------------------------------------------------------------------------------------------------------------------------------------------------
SLP Nursing Nursing NTA
PDPM group PT CMI PT rate OT CMI OT rate SLP CMI rate Nursing CMG CMI rate NTA CMI rate
--------------------------------------------------------------------------------------------------------------------------------------------------------
A................................. 1.53 $96.15 1.49 $87.15 0.68 $15.95 ES3............. 4.06 $444.77 3.24 $267.75
B................................. 1.70 106.83 1.63 95.34 1.82 42.70 ES2............. 3.07 336.32 2.53 209.08
C................................. 1.88 118.14 1.69 98.85 2.67 62.64 ES1............. 2.93 320.98 1.84 152.06
D................................. 1.92 120.65 1.53 89.49 1.46 34.25 HDE2............ 2.40 262.92 1.33 109.91
E................................. 1.42 89.23 1.41 82.47 2.34 54.90 HDE1............ 1.99 218.00 0.96 79.33
F................................. 1.61 101.17 1.60 93.58 2.98 69.91 HBC2............ 2.24 245.39 0.72 59.50
G................................. 1.67 104.94 1.64 95.92 2.04 47.86 HBC1............ 1.86 203.76 ........ ........
H................................. 1.16 72.89 1.15 67.26 2.86 67.10 LDE2............ 2.08 227.86 ........ ........
I................................. 1.13 71.01 1.18 69.02 3.53 82.81 LDE1............ 1.73 189.52 ........ ........
J................................. 1.42 89.23 1.45 84.81 2.99 70.15 LBC2............ 1.72 188.43 ........ ........
K................................. 1.52 95.52 1.54 90.07 3.7 86.80 LBC1............ 1.43 156.66 ........ ........
L................................. 1.09 68.50 1.11 64.92 4.21 98.77 CDE2............ 1.87 204.86 ........ ........
M................................. 1.27 79.81 1.30 76.04 ........ ........ CDE1............ 1.62 177.47 ........ ........
N................................. 1.48 93.00 1.50 87.74 ........ ........ CBC2............ 1.55 169.80 ........ ........
O................................. 1.55 97.40 1.55 90.66 ........ ........ CA2............. 1.09 119.41 ........ ........
P................................. 1.08 67.87 1.09 63.75 ........ ........ CBC1............ 1.34 146.80 ........ ........
Q................................. ........ ........ ........ ........ ........ ........ CA1............. 0.94 102.98 ........ ........
R................................. ........ ........ ........ ........ ........ ........ BAB2............ 1.04 113.93 ........ ........
S................................. ........ ........ ........ ........ ........ ........ BAB1............ 0.99 108.45 ........ ........
T................................. ........ ........ ........ ........ ........ ........ PDE2............ 1.57 171.99 ........ ........
U................................. ........ ........ ........ ........ ........ ........ PDE1............ 1.47 161.04 ........ ........
V................................. ........ ........ ........ ........ ........ ........ PBC2............ 1.22 133.65 ........ ........
W................................. ........ ........ ........ ........ ........ ........ PA2............. 0.71 77.78 ........ ........
X................................. ........ ........ ........ ........ ........ ........ PBC1............ 1.13 123.79 ........ ........
Y................................. ........ ........ ........ ........ ........ ........ PA1............. 0.66 72.30 ........ ........
--------------------------------------------------------------------------------------------------------------------------------------------------------
[[Page 19963]]
Table 7--PDPM Case-Mix Adjusted Federal Rates and Associated Indexes--RURAL
--------------------------------------------------------------------------------------------------------------------------------------------------------
Nursing Nursing
PDPM Group PT CMI PT rate OT CMI OT rate SLP CMI SLP rate Nursing CMG CMI rate NTA CMI NTA rate
--------------------------------------------------------------------------------------------------------------------------------------------------------
A................................. 1.53 $109.59 1.49 $98.03 0.68 $20.10 ES3............. 4.06 $424.92 3.24 $255.83
B................................. 1.70 121.77 1.63 107.24 1.82 53.80 ES2............. 3.07 321.31 2.53 199.77
C................................. 1.88 134.66 1.69 111.19 2.67 78.93 ES1............. 2.93 306.65 1.84 145.29
D................................. 1.92 137.53 1.53 100.66 1.46 43.16 HDE2............ 2.40 251.18 1.33 105.02
E................................. 1.42 101.71 1.41 92.76 2.34 69.17 HDE1............ 1.99 208.27 0.96 75.80
F................................. 1.61 115.32 1.60 105.26 2.98 88.09 HBC2............ 2.24 234.44 0.72 56.85
G................................. 1.67 119.62 1.64 107.90 2.04 60.30 HBC1............ 1.86 194.67 ........ ........
H................................. 1.16 83.09 1.15 75.66 2.86 84.54 LDE2............ 2.08 217.69 ........ ........
I................................. 1.13 80.94 1.18 77.63 3.53 104.35 LDE1............ 1.73 181.06 ........ ........
J................................. 1.42 101.71 1.45 95.40 2.99 88.38 LBC2............ 1.72 180.02 ........ ........
K................................. 1.52 108.88 1.54 101.32 3.7 109.37 LBC1............ 1.43 149.66 ........ ........
L................................. 1.09 78.08 1.11 73.03 4.21 124.45 CDE2............ 1.87 195.71 ........ ........
M................................. 1.27 90.97 1.30 85.53 ........ ........ CDE1............ 1.62 169.55 ........ ........
N................................. 1.48 106.01 1.50 98.69 ........ ........ CBC2............ 1.55 162.22 ........ ........
O................................. 1.55 111.03 1.55 101.97 ........ ........ CA2............. 1.09 114.08 ........ ........
P................................. 1.08 77.36 1.09 71.71 ........ ........ CBC1............ 1.34 140.24 ........ ........
Q................................. ........ ........ ........ ........ ........ ........ CA1............. 0.94 98.38 ........ ........
R................................. ........ ........ ........ ........ ........ ........ BAB2............ 1.04 108.85 ........ ........
S................................. ........ ........ ........ ........ ........ ........ BAB1............ 0.99 103.61 ........ ........
T................................. ........ ........ ........ ........ ........ ........ PDE2............ 1.57 164.32 ........ ........
U................................. ........ ........ ........ ........ ........ ........ PDE1............ 1.47 153.85 ........ ........
V................................. ........ ........ ........ ........ ........ ........ PBC2............ 1.22 127.69 ........ ........
W................................. ........ ........ ........ ........ ........ ........ PA2............. 0.71 74.31 ........ ........
X................................. ........ ........ ........ ........ ........ ........ PBC1............ 1.13 118.27 ........ ........
Y................................. ........ ........ ........ ........ ........ ........ PA1............. 0.66 69.08 ........ ........
--------------------------------------------------------------------------------------------------------------------------------------------------------
D. Wage Index Adjustment
Section 1888(e)(4)(G)(ii) of the Act requires that we adjust the
Federal rates to account for differences in area wage levels, using a
wage index that the Secretary determines appropriate. Since the
inception of the SNF PPS, we have used hospital inpatient wage data in
developing a wage index to be applied to SNFs. We propose to continue
this practice for FY 2022, as we continue to believe that in the
absence of SNF-specific wage data, using the hospital inpatient wage
index data is appropriate and reasonable for the SNF PPS. As explained
in the update notice for FY 2005 (69 FR 45786), the SNF PPS does not
use the hospital area wage index's occupational mix adjustment, as this
adjustment serves specifically to define the occupational categories
more clearly in a hospital setting; moreover, the collection of the
occupational wage data under the inpatient prospective payment system
(IPPS) also excludes any wage data related to SNFs. Therefore, we
believe that using the updated wage data exclusive of the occupational
mix adjustment continues to be appropriate for SNF payments. As in
previous years, we would continue to use the pre-reclassified IPPS
hospital wage data, without applying the occupational mix, rural floor,
or outmigration adjustment, as the basis for the SNF PPS wage index.
For FY 2022, the updated wage data are for hospital cost reporting
periods beginning on or after October 1, 2017 and before October 1,
2018 (FY 2018 cost report data).
We note that section 315 of the Medicare, Medicaid, and SCHIP
Benefits Improvement and Protection Act of 2000 (BIPA) (Pub. L. 106-
554, enacted December 21, 2000) authorized us to establish a geographic
reclassification procedure that is specific to SNFs, but only after
collecting the data necessary to establish a SNF PPS wage index that is
based on wage data from nursing homes. However, to date, this has
proven to be unfeasible due to the volatility of existing SNF wage data
and the significant amount of resources that would be required to
improve the quality of the data. More specifically, auditing all SNF
cost reports, similar to the process used to audit inpatient hospital
cost reports for purposes of the IPPS wage index, would place a burden
on providers in terms of recordkeeping and completion of the cost
report worksheet. In addition, adopting such an approach would require
a significant commitment of resources by CMS and the Medicare
Administrative Contractors, potentially far in excess of those required
under the IPPS given that there are nearly five times as many SNFs as
there are inpatient hospitals. Therefore, while we continue to believe
that the development of such an audit process could improve SNF cost
reports in such a manner as to permit us to establish a SNF-specific
wage index, we do not believe this undertaking is feasible at this
time.
In addition, we propose to continue to use the same methodology
discussed in the SNF PPS final rule for FY 2008 (72 FR 43423) to
address those geographic areas in which there are no hospitals, and
thus, no hospital wage index data on which to base the calculation of
the FY 2022 SNF PPS wage index. For rural geographic areas that do not
have hospitals and, therefore, lack hospital wage data on which to base
an area wage adjustment, we propose to continue to use the average wage
index from all contiguous Core-Based Statistical Areas (CBSAs) as a
reasonable proxy. For FY 2022, there are no rural geographic areas that
do not have hospitals, and thus, this methodology will not be applied.
For rural Puerto Rico, we propose not to apply this methodology due to
the distinct economic circumstances that exist there (for example, due
to the close proximity to one another of almost all of Puerto Rico's
various urban and non-urban areas, this methodology would produce a
wage index for rural Puerto Rico that is higher than that in half of
its urban areas); instead, we propose that we would continue to use the
most recent wage index previously available for that area. For urban
areas without specific hospital wage index data, we propose that we
would use the average wage indexes of all of the urban areas within the
state to serve as a reasonable proxy for the wage index of that urban
[[Page 19964]]
CBSA. For FY 2022, the only urban area without wage index data
available is CBSA 25980, Hinesville-Fort Stewart, GA.
The wage index applicable to FY 2022 is set forth in Tables A and B
available on the CMS website at http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/WageIndex.html.
In the SNF PPS final rule for FY 2006 (70 FR 45026, August 4,
2005), we adopted the changes discussed in OMB Bulletin No. 03-04 (June
6, 2003), which announced revised definitions for MSAs and the creation
of micropolitan statistical areas and combined statistical areas. In
adopting the CBSA geographic designations, we provided for a 1-year
transition in FY 2006 with a blended wage index for all providers. For
FY 2006, the wage index for each provider consisted of a blend of 50
percent of the FY 2006 MSA-based wage index and 50 percent of the FY
2006 CBSA-based wage index (both using FY 2002 hospital data). We
referred to the blended wage index as the FY 2006 SNF PPS transition
wage index. As discussed in the SNF PPS final rule for FY 2006 (70 FR
45041), after the expiration of this 1-year transition on September 30,
2006, we used the full CBSA-based wage index values.
In the FY 2015 SNF PPS final rule (79 FR 45644 through 45646), we
finalized changes to the SNF PPS wage index based on the newest OMB
delineations, as described in OMB Bulletin No. 13-01, beginning in FY
2015, including a 1-year transition with a blended wage index for FY
2015. 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).
Subsequently, on July 15, 2015, OMB issued OMB Bulletin No. 15-01,
which provided minor updates to and superseded OMB Bulletin No. 13-01
that was issued on February 28, 2013. The attachment to OMB Bulletin
No. 15-01 provided detailed information on the update to statistical
areas since February 28, 2013. The updates provided in OMB Bulletin No.
15-01 were 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 and were
adopted under the SNF PPS in the FY 2017 SNF PPS final rule (81 FR
51983, August 5, 2016). In addition, on August 15, 2017, OMB issued
Bulletin No. 17-01 which announced a new urban CBSA, Twin Falls, Idaho
(CBSA 46300) which was adopted in the SNF PPS final rule for FY 2019
(83 FR 39173, August 8, 2018).
As discussed in the FY 2021 SNF PPS final rule (85 FR 47594), we
adopted the revised OMB delineations identified in OMB Bulletin No. 18-
04 (available at https://www.whitehouse.gov/wp-content/uploads/2018/09/Bulletin-18-04.pdf) beginning October 1, 2020, including a 1-year
transition for FY 2021 under which we applied a 5 percent cap on any
decrease in a hospital's wage index compared to its wage index for the
prior fiscal year (FY 2020). The updated OMB delineations more
accurately reflect the contemporary urban and rural nature of areas
across the country, and the use of such delineations allows us to
determine more accurately the appropriate wage index and rate tables to
apply under the SNF PPS.
As we previously stated in the FY 2008 SNF PPS proposed and final
rules (72 FR 25538 through 25539, and 72 FR 43423), this and all
subsequent SNF PPS rules and notices are considered to incorporate any
updates and revisions set forth in the most recent OMB bulletin that
applies to the hospital wage data used to determine the current SNF PPS
wage index. We note that on March 6, 2020, OMB issued Bulletin No. 20-
01, which provided updates to and superseded OMB Bulletin No. 18-04
that was issued on September 14, 2018. The attachments to OMB Bulletin
No. 20-01 provided detailed information on the updates (available on
the web at https://www.whitehouse.gov/wp-content/uploads/2020/03/Bulletin-20-01.pdf). In the FY 2021 SNF PPS final rule (85 FR 47611),
we stated that we intended to propose any updates from OMB Bulletin No.
20-01 in the FY 2022 SNF PPS proposed rule. After reviewing OMB
Bulletin No. 20-01, we have determined that the changes in OMB Bulletin
20-01 encompassed delineation changes that do not impact the CBSA-based
labor market area delineations adopted in FY 2021. Therefore, while we
are proposing to adopt the updates set forth in OMB Bulletin No. 20-01
consistent with our longstanding policy of adopting OMB delineation
updates, we note that specific wage index updates would not be
necessary for FY 2022 as a result of adopting these OMB updates.
The proposed wage index applicable to FY 2022 is set forth in
Tables A and B and is available on the CMS website at http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/WageIndex.html.
Once calculated, we would apply the wage index adjustment to the
labor-related portion of the Federal rate. Each year, we calculate a
revised labor-related share, based on the relative importance of labor-
related cost categories (that is, those cost categories that are labor-
intensive and vary with the local labor market) in the input price
index. In the SNF PPS final rule for FY 2018 (82 FR 36548 through
36566), we finalized a proposal to revise the labor-related share to
reflect the relative importance of the 2014-based SNF market basket
cost weights for the following cost categories: Wages and Salaries;
Employee Benefits; Professional Fees: Labor-Related; Administrative and
Facilities Support Services; Installation, Maintenance, and Repair
Services; All Other: Labor-Related Services; and a proportion of
Capital-Related expenses. Effective beginning FY 2022, as discussed in
section V.A.4. of this proposed rule, for FY 2022, we are proposing to
rebase and revise the labor-related share to reflect the relative
importance of the proposed 2018-based SNF market basket cost weights
for the following cost categories: Wages and Salaries; Employee
Benefits; Professional fees: Labor-related; Administrative and
Facilities Support services; Installation, Maintenance, and Repair
services; All Other: Labor-Related Services; and a proportion of
Capital-Related expenses. The proposed methodology for calculating the
labor-related portion for FY 2022 is discussed in section V.A. of this
proposed rule.
We calculate the labor-related relative importance from the SNF
market basket, and it approximates the labor-related portion of the
total costs after taking into account historical and projected price
changes between the base year and FY 2022. The price proxies that move
the different cost categories in the market basket do not necessarily
change at the same rate, and the relative importance captures these
changes. Accordingly, the relative importance figure more closely
reflects the cost share weights for FY 2022 than the base year weights
from the SNF market basket. We calculate the labor-related relative
importance for FY 2022 in four steps. First, we compute the FY 2022
price index level for the total market basket and each cost category of
the market basket. Second, we calculate a ratio for each cost category
by dividing the FY 2022 price index level for that cost category by the
total market basket price index level. Third, we determine
[[Page 19965]]
the FY 2022 relative importance for each cost category by multiplying
this ratio by the base year (2018) weight. Finally, we add the FY 2022
relative importance for each of the labor-related cost categories
(Wages and Salaries; Employee Benefits; Professional Fees: Labor-
Related; Administrative and Facilities Support Services; Installation,
Maintenance, and Repair Services; All Other: Labor-related services;
and a portion of Capital-Related expenses) to produce the FY 2022
labor-related relative importance. Table 8 summarizes the proposed
labor-related share for FY 2022, based on IGI's fourth quarter 2020
forecast of the proposed 2018-based SNF market basket with historical
data through third quarter 2020, compared to the labor-related share
that was used for the FY 2021 SNF PPS final rule.
Table 8--Labor-Related Relative Importance, FY 2021 and FY 2022
------------------------------------------------------------------------
Relative Relative
importance, labor- importance, labor-
related share, FY related share, FY
2021 20:2 2022 20:4
forecast \1\ forecast \2\
------------------------------------------------------------------------
Wages and salaries................ 51.1 51.2
Employee benefits................. 9.9 9.5
Professional fees: Labor-related.. 3.7 3.5
Administrative & facilities 0.5 0.6
support services.................
Installation, maintenance & repair 0.6 0.4
services.........................
All other: Labor-related services. 2.6 1.9
Capital-related (.391)............ 2.9 3.0
-------------------------------------
Total......................... 71.3 70.1
------------------------------------------------------------------------
\1\ Published in the Federal Register (85 FR 47605); based on the second
quarter 2020 IHS Global Inc. forecast of the 2014-based SNF market
basket, with historical data through first quarter 2020.
\2\ Based on the fourth quarter 2020 IHS Global Inc. forecast of the
proposed 2018-based SNF market basket.
To calculate the labor portion of the case-mix adjusted per diem
rate, we would multiply the total case-mix adjusted per diem rate,
which is the sum of all five case-mix adjusted components into which a
patient classifies, and the non-case-mix component rate, by the FY 2022
labor-related share percentage provided in Table 8. The remaining
portion of the rate would be the non-labor portion. Under the previous
RUG-IV model, we included tables which provided the case-mix adjusted
RUG-IV rates, by RUG-IV group, broken out by total rate, labor portion
and non-labor portion, such as Table 9 of the FY 2019 SNF PPS final
rule (83 FR 39175). However, as we discussed in the FY 2020 final rule
(84 FR 38738), under PDPM, as the total rate is calculated as a
combination of six different component rates, five of which are case-
mix adjusted, and given the sheer volume of possible combinations of
these five case-mix adjusted components, it is not feasible to provide
tables similar to those that existed in the prior rulemaking.
Therefore, to aid stakeholders in understanding the effect of the
wage index on the calculation of the SNF per diem rate, we have
included a hypothetical rate calculation in Table 9.
Section 1888(e)(4)(G)(ii) of the Act also requires that we apply
this wage index in a manner that does not result in aggregate payments
under the SNF PPS that are greater or less than would otherwise be made
if the wage adjustment had not been made. For FY 2022 (Federal rates
effective October 1, 2021), we would apply an adjustment to fulfill the
budget neutrality requirement. We would meet this requirement by
multiplying each of the components of the unadjusted Federal rates by a
budget neutrality factor, equal to the ratio of the weighted average
wage adjustment factor for FY 2021 to the weighted average wage
adjustment factor for FY 2022. For this calculation, we would use the
same FY 2020 claims utilization data for both the numerator and
denominator of this ratio. We define the wage adjustment factor used in
this calculation as the labor portion of the rate component multiplied
by the wage index plus the non-labor portion of the rate component. The
proposed budget neutrality factor for FY 2022 would be 0.9999.
We note that if more recent data become available (for example,
revised wage data), we would use such data, as appropriate, to
determine the wage index budget neutrality factor in the SNF PPS final
rule.
E. SNF Value-Based Purchasing Program
Beginning with payment for services furnished on October 1, 2018,
section 1888(h) of the Act requires the Secretary to reduce the
adjusted Federal per diem rate determined under section 1888(e)(4)(G)
of the Act otherwise applicable to a SNF for services furnished during
a fiscal year by 2 percent, and to adjust the resulting rate for a SNF
by the value-based incentive payment amount earned by the SNF based on
the SNF's performance score for that fiscal year under the SNF VBP
Program. To implement these requirements, we finalized in the FY 2019
SNF PPS final rule the addition of Sec. 413.337(f) to our regulations
(83 FR 39178).
Please see section VII. of this proposed rule for a further
discussion of our policies for the SNF VBP Program.
F. Adjusted Rate Computation Example
Tables 9, 10, and 11 provide examples generally illustrating
payment calculations during FY 2022 under PDPM for a hypothetical 30-
day SNF stay, involving the hypothetical SNF XYZ, located in Frederick,
MD (Urban CBSA 23244), for a hypothetical patient who is classified
into such groups that the patient's HIPPS code is NHNC1. Table 9 shows
the adjustments made to the Federal per diem rates (prior to
application of any adjustments under the SNF VBP program as discussed
previously) to compute the provider's case-mix adjusted per diem rate
for FY 2022, based on the patient's PDPM classification, as well as how
the variable per diem (VPD) adjustment factor affects calculation of
the per diem rate for a given day of the stay. Table 10 shows the
adjustments made to the case-mix adjusted per diem rate from Table 9 to
account for the provider's wage index. The wage index used in this
example is based on the FY 2022 SNF PPS wage index that appears in
Table A available on the CMS website at http://
[[Page 19966]]
www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/
WageIndex.html. Finally, Table 11 provides the case-mix and wage index
adjusted per-diem rate for this patient for each day of the 30-day
stay, as well as the total payment for this stay. Table 11 also
includes the VPD adjustment factors for each day of the patient's stay,
to clarify why the patient's per diem rate changes for certain days of
the stay. As illustrated in Table 9, SNF XYZ's total PPS payment for
this particular patient's stay would equal $20,571.17.
Table 9--PDPM Case-Mix Adjusted Rate Computation Example
----------------------------------------------------------------------------------------------------------------
Per Diem Rate Calculation
-----------------------------------------------------------------------------------------------------------------
VPD
Component Component Component rate adjustment VPD adj. rate
group factor
----------------------------------------------------------------------------------------------------------------
PT.............................................. N $93.00 1.00 $93.00
OT.............................................. N 87.74 1.00 87.74
SLP............................................. H 67.10 1.00 67.10
Nursing......................................... N 169.80 1.00 169.80
NTA............................................. C 152.06 3.00 456.18
Non-Case-Mix.................................... .............. 98.10 .............. 98.10
-----------------------------------------------
Total PDPM Case-Mix Adj. Per Diem........... .............. .............. .............. $971.92
----------------------------------------------------------------------------------------------------------------
Table 10--Wage Index Adjusted Rate Computation Example
--------------------------------------------------------------------------------------------------------------------------------------------------------
PDPM wage index adjustment calculation
---------------------------------------------------------------------------------------------------------------------------------------------------------
PDPM case-mix Total case mix
HIPPS code adjusted per Labor Wage Wage index Non-labor and wage index
diem portion index adjusted rate portion adj. rate
--------------------------------------------------------------------------------------------------------------------------------------------------------
NHNC1............................................................ $971.92 $681.32 0.9776 $666.06 $290.60 $956.66
--------------------------------------------------------------------------------------------------------------------------------------------------------
Table 11--Adjusted Rate Computation Example
----------------------------------------------------------------------------------------------------------------
Case mix and
NTA VPD PT/OT VPD wage index
Day of stay adjustment adjustment adjusted per
factor factor diem rate
----------------------------------------------------------------------------------------------------------------
1............................................................... 3.0 1.0 $956.66
2............................................................... 3.0 1.0 956.66
3............................................................... 3.0 1.0 956.66
4............................................................... 1.0 1.0 657.31
5............................................................... 1.0 1.0 657.31
6............................................................... 1.0 1.0 657.31
7............................................................... 1.0 1.0 657.31
8............................................................... 1.0 1.0 657.31
9............................................................... 1.0 1.0 657.31
10.............................................................. 1.0 1.0 657.31
11.............................................................. 1.0 1.0 657.31
12.............................................................. 1.0 1.0 657.31
13.............................................................. 1.0 1.0 657.31
14.............................................................. 1.0 1.0 657.31
15.............................................................. 1.0 1.0 657.31
16.............................................................. 1.0 1.0 657.31
17.............................................................. 1.0 1.0 657.31
18.............................................................. 1.0 1.0 657.31
19.............................................................. 1.0 1.0 657.31
20.............................................................. 1.0 1.0 657.31
21.............................................................. 1.0 0.98 653.76
22.............................................................. 1.0 0.98 653.76
23.............................................................. 1.0 0.98 653.76
24.............................................................. 1.0 0.98 653.76
25.............................................................. 1.0 0.98 653.76
26.............................................................. 1.0 0.98 653.76
27.............................................................. 1.0 0.98 653.76
28.............................................................. 1.0 0.96 650.20
29.............................................................. 1.0 0.96 650.20
30.............................................................. 1.0 0.96 650.20
-----------------------------------------------
Total Payment............................................... .............. .............. 20,571.17
----------------------------------------------------------------------------------------------------------------
[[Page 19967]]
IV. Additional Aspects of the SNF PPS
A. SNF Level of Care--Administrative Presumption
The establishment of the SNF PPS did not change Medicare's
fundamental requirements for SNF coverage. However, because the case-
mix classification is based, in part, on the beneficiary's need for
skilled nursing care and therapy, we have attempted, where possible, to
coordinate claims review procedures with the existing resident
assessment process and case-mix classification system discussed in
section III.B.3. of this proposed rule. This approach includes an
administrative presumption that utilizes a beneficiary's correct
assignment, at the outset of the SNF stay, of one of the case-mix
classifiers designated for this purpose to assist in making certain SNF
level of care determinations.
In accordance with Sec. 413.345, we include in each update of the
Federal payment rates in the Federal Register a discussion of the
resident classification system that provides the basis for case-mix
adjustment. We also designate those specific classifiers under the
case-mix classification system that represent the required SNF level of
care, as provided in 42 CFR 409.30. This designation reflects an
administrative presumption that those beneficiaries who are correctly
assigned one of the designated case-mix classifiers on the initial
Medicare assessment are automatically classified as meeting the SNF
level of care definition up to and including the assessment reference
date (ARD) for that assessment.
A beneficiary who does not qualify for the presumption is not
automatically classified as either meeting or not meeting the level of
care definition, but instead receives an individual determination on
this point using the existing administrative criteria. This presumption
recognizes the strong likelihood that those beneficiaries who are
correctly assigned one of the designated case-mix classifiers during
the immediate post-hospital period would require a covered level of
care, which would be less likely for other beneficiaries.
In the July 30, 1999 final rule (64 FR 41670), we indicated that we
would announce any changes to the guidelines for Medicare level of care
determinations related to modifications in the case-mix classification
structure. The FY 2018 final rule (82 FR 36544) further specified that
we would henceforth disseminate the standard description of the
administrative presumption's designated groups via the SNF PPS website
at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/index.html (where such designations appear in the paragraph
entitled ``Case Mix Adjustment''), and would publish such designations
in rulemaking only to the extent that we actually intend to propose
changes in them. Under that approach, the set of case-mix classifiers
designated for this purpose under PDPM was finalized in the FY 2019 SNF
PPS final rule (83 FR 39253) and is posted on the SNF PPS website
(https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/index.html), in the paragraph entitled ``Case Mix Adjustment.''
However, we note that this administrative presumption policy does
not supersede the SNF's responsibility to ensure that its decisions
relating to level of care are appropriate and timely, including a
review to confirm that any services prompting the assignment of one of
the designated case-mix classifiers (which, in turn, serves to trigger
the administrative presumption) are themselves medically necessary. As
we explained in the FY 2000 SNF PPS final rule (64 FR 41667), the
administrative presumption is itself rebuttable in those individual
cases in which the services actually received by the resident do not
meet the basic statutory criterion of being reasonable and necessary to
diagnose or treat a beneficiary's condition (according to section
1862(a)(1) of the Act). Accordingly, the presumption would not apply,
for example, in those situations where the sole classifier that
triggers the presumption is itself assigned through the receipt of
services that are subsequently determined to be not reasonable and
necessary. Moreover, we want to stress the importance of careful
monitoring for changes in each patient's condition to determine the
continuing need for Part A SNF benefits after the ARD of the initial
Medicare assessment.
B. Consolidated Billing
Sections 1842(b)(6)(E) and 1862(a)(18) of the Act (as added by
section 4432(b) of the BBA 1997) require a SNF to submit consolidated
Medicare bills to its Medicare Administrative Contractor (MAC) for
almost all of the services that its residents receive during the course
of a covered Part A stay. In addition, section 1862(a)(18) of the Act
places the responsibility with the SNF for billing Medicare for
physical therapy, occupational therapy, and speech-language pathology
services that the resident receives during a noncovered stay. Section
1888(e)(2)(A) of the Act excludes a small list of services from the
consolidated billing provision (primarily those services furnished by
physicians and certain other types of practitioners), which remain
separately billable under Part B when furnished to a SNF's Part A
resident. These excluded service categories are discussed in greater
detail in section V.B.2. of the May 12, 1998 interim final rule (63 FR
26295 through 26297).
A detailed discussion of the legislative history of the
consolidated billing provision is available on the SNF PPS website at
https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/Downloads/Legislative_History_2018-10-01.pdf. In particular, section
103 of the BBRA 1999 amended section 1888(e)(2)(A)(iii) of the Act by
further excluding a number of individual high-cost, low probability
services, identified by Healthcare Common Procedure Coding System
(HCPCS) codes, within several broader categories (chemotherapy items,
chemotherapy administration services, radioisotope services, and
customized prosthetic devices) that otherwise remained subject to the
provision. We discuss this BBRA 1999 amendment in greater detail in the
SNF PPS proposed and final rules for FY 2001 (65 FR 19231 through
19232, April 10, 2000, and 65 FR 46790 through 46795, July 31, 2000),
as well as in Program Memorandum AB-00-18 (Change Request #1070),
issued March 2000, which is available online at www.cms.gov/transmittals/downloads/ab001860.pdf.
As explained in the FY 2001 proposed rule (65 FR 19232), the
amendments enacted in section 103 of the BBRA 1999 not only identified
for exclusion from this provision a number of particular service codes
within four specified categories (that is, chemotherapy items,
chemotherapy administration services, radioisotope services, and
customized prosthetic devices), but also gave the Secretary the
authority to designate additional, individual services for exclusion
within each of these four specified service categories. In the proposed
rule for FY 2001, we also noted that the BBRA 1999 Conference report
(H.R. Rep. No. 106-479 at 854 (1999) (Conf. Rep.)) characterizes the
individual services that this legislation targets for exclusion as
high-cost, low probability events that could have devastating financial
impacts because their costs far exceed the payment SNFs receive under
the PPS. According to the conferees, section 103(a) of the BBRA 1999 is
an attempt to exclude from the PPS certain services and costly items
that are provided infrequently in SNFs. By contrast, the amendments
enacted in section 103 of
[[Page 19968]]
the BBRA 1999 do not designate for exclusion any of the remaining
services within those four categories (thus, leaving all of those
services subject to SNF consolidated billing), because they are
relatively inexpensive and are furnished routinely in SNFs.
As we further explained in the final rule for FY 2001 (65 FR
46790), and as is consistent with our longstanding policy, any
additional service codes that we might designate for exclusion under
our discretionary authority must meet the same statutory criteria used
in identifying the original codes excluded from consolidated billing
under section 103(a) of the BBRA 1999: They must fall within one of the
four service categories specified in the BBRA 1999; and they also must
meet the same standards of high cost and low probability in the SNF
setting, as discussed in the BBRA 1999 Conference report. Accordingly,
we characterized this statutory authority to identify additional
service codes for exclusion as essentially affording the flexibility to
revise the list of excluded codes in response to changes of major
significance that may occur over time (for example, the development of
new medical technologies or other advances in the state of medical
practice) (65 FR 46791).
Effective with items and services furnished on or after October 1,
2021, section 134 in Division CC of the Consolidated Appropriations
Act, 2021 (Pub. L. 116-260) has established an additional category of
excluded codes in section 1888(e)(2)(A)(iii)(VI) of the Act, for
certain blood clotting factors for the treatment of patients with
hemophilia and other bleeding disorders along with items and services
related to the furnishing of such factors under section 1842(o)(5)(C)
of the Act. The specific factors, and items and services related to the
furnishing of such factors, excluded under this provision are those
identified, as of July 1, 2020, by HCPCS codes J7170, J7175, J7177-
J7183, J7185-J7190, J7192-J7195, J7198-J7203, J7205, and J7207-J7211.
Like the provisions enacted in the BBRA 1999, new section
1888(e)(2)(A)(iii)(VI) of the Act gives the Secretary the authority to
designate additional items and services for exclusion within the
category of items and services described in that section. Section
1888(e)(4)(G)(iii) of the Act further requires that for any services
that are unbundled from consolidated billing under section
1888(e)(2)(A)(iii) of the Act (and, thus, become qualified for separate
payment under Part B), there must also be a corresponding proportional
reduction made in aggregate SNF payments under Part A. Accordingly,
using the methodology described in section III.B.6. of this proposed
rule, we propose to make a proportional reduction of $0.02 in the
unadjusted urban and rural rates to reflect these new exclusions,
effective for items and services furnished on or after October 1, 2021.
In this proposed rule, we specifically invite public comments
identifying HCPCS codes in any of these five service categories
(chemotherapy items, chemotherapy administration services, radioisotope
services, customized prosthetic devices, and blood clotting factors)
representing recent medical advances that might meet our criteria for
exclusion from SNF consolidated billing. We may consider excluding a
particular service if it meets our criteria for exclusion as specified
previously. We request that commenters identify in their comments the
specific HCPCS code that is associated with the service in question, as
well as their rationale for requesting that the identified HCPCS
code(s) be excluded.
We note that the original BBRA amendment and the Consolidated
Appropriations Act, 2021 identified a set of excluded items and
services by means of specifying individual HCPCS codes within the
designated categories that were in effect as of a particular date (in
the case of the BBRA 1999, July 1, 1999, and in the case of the
Consolidated Appropriations Act, 2021, July 1, 2020), as subsequently
modified by the Secretary. In addition, as noted above, the statute
(section 1888(e)(2)(A)(iii)(II)-(VI) of the Act) gives the Secretary
authority to identify additional items and services for exclusion
within the categories of items and services described in the statute,
which are also designated by HCPCS code. Designating the excluded
services in this manner makes it possible for us to utilize program
issuances as the vehicle for accomplishing routine updates to the
excluded codes to reflect any minor revisions that might subsequently
occur in the coding system itself (such as the assignment of a
different code number to a service already designated as excluded, or
the creation of a new code for a type of service that falls within one
of the established exclusion categories and meets our criteria for
exclusion (for example, J7212, ``factor viia (antihemophilic factor,
recombinant)-jncw (sevenfact), 1 microgram'', which became effective on
January 1, 2021 and would fall in the blood clotting factor exclusion
category).
Accordingly, in the event that we identify through the current
rulemaking cycle any new services that would actually represent a
substantive change in the scope of the exclusions from SNF consolidated
billing, we would identify these additional excluded services by means
of the HCPCS codes that are in effect as of a specific date (in this
case, October 1, 2021). By making any new exclusions in this manner, we
could similarly accomplish routine future updates of these additional
codes through the issuance of program instructions. The latest list of
excluded codes can be found on the SNF Consolidated Billing website at
https://www.cms.gov/Medicare/Billing/SNFConsolidatedBilling.
C. Payment for SNF-Level Swing-Bed Services
Section 1883 of the Act permits certain small, rural hospitals to
enter into a Medicare swing-bed agreement, under which the hospital can
use its beds to provide either acute- or SNF-level care, as needed. For
critical access hospitals (CAHs), Part A pays on a reasonable cost
basis for SNF-level services furnished under a swing-bed agreement.
However, in accordance with section 1888(e)(7) of the Act, SNF-level
services furnished by non-CAH rural hospitals are paid under the SNF
PPS, effective with cost reporting periods beginning on or after July
1, 2002. As explained in the FY 2002 final rule (66 FR 39562), this
effective date is consistent with the statutory provision to integrate
swing-bed rural hospitals into the SNF PPS by the end of the transition
period, June 30, 2002.
Accordingly, all non-CAH swing-bed rural hospitals have now come
under the SNF PPS. Therefore, all rates and wage indexes outlined in
earlier sections of this proposed rule for the SNF PPS also apply to
all non-CAH swing-bed rural hospitals. As finalized in the FY 2010 SNF
PPS final rule (74 FR 40356 through 40357), effective October 1, 2010,
non-CAH swing-bed rural hospitals are required to complete an MDS 3.0
swing-bed assessment which is limited to the required demographic,
payment, and quality items. As discussed in the FY 2019 SNF PPS final
rule (83 FR 39235), revisions were made to the swing bed assessment to
support implementation of PDPM, effective October 1, 2019. A discussion
of the assessment schedule and the MDS effective beginning FY 2020
appears in the FY 2019 SNF PPS final rule (83 FR 39229 through 39237).
The latest changes in the MDS for swing-bed rural hospitals appear on
the SNF PPS website at http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/index.html.
[[Page 19969]]
D. Revisions to the Regulation Text
We propose to make certain revisions in the regulation text itself.
Specifically, we propose to redesignate current 42 CFR
411.15(p)(2)(xvii) and 489.20(s)(17) to Sec. 411.15(p)(2)(xviii) and
489.20(s)(18), and update the regulation text at Sec. Sec.
411.15(p)(2)(xvii) and 489.20(s)(17) to reflect the recently-enacted
exclusion from SNF consolidated billing at section
1888(e)(2)(A)(iii)(VI) of the Act effective for items and services
furnished on or after October 1, 2021. Specifically, proposed revised
Sec. Sec. 411.15(p)(2)(xvii) and 489.20(s)(17) would reflect the
exclusion of certain blood clotting factors for the treatment of
patients with hemophilia and other bleeding disorders (identified by
designated HCPCS codes in effect as of July 1, 2020, as subsequently
modified by CMS), and items and services related to the furnishing of
such factors, and would allow for the exclusion of any additional blood
clotting factors identified by CMS and items and services related to
the furnishing of such factors. In addition, we are proposing to make
conforming changes to the regulation text at Sec. Sec.
411.15(p)(2)(xiii) through (xvi) and 489.20(s)(13) through (16) to
reflect the authority that has always existed for CMS to make updates
to the list of excluded codes as provided in section
1888(e)(2)(A)(iii)(II) through (V) of the Act, and as discussed in
section IV. C. of this proposed rule.
V. Other SNF PPS Issues
A. Rebasing and Revising the SNF Market Basket
Section 1888(e)(5)(A) of the Act requires the Secretary to
establish a market basket index that reflects the changes over time in
the prices of an appropriate mix of goods and services included in
covered SNF services. Accordingly, we have developed a SNF market
basket index that encompasses the most commonly used cost categories
for SNF routine services, ancillary services, and capital-related
expenses. We use the SNF market basket index, adjusted in the manner
described in section III.B. of this proposed rule, to update the SNF
PPS per diem rates and to determine the labor-related share on an
annual basis.
The SNF 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 (the proposed base period is 2018) and total base
period expenditures are estimated for a set of mutually exclusive and
exhaustive spending categories and the proportion of total costs that
each category represents is calculated. These proportions are called
cost or expenditure weights. Second, each expenditure category is
matched to an appropriate price or wage variable, referred to as a
price proxy. In nearly every instance, these price proxies are derived
from publicly available statistical series that are published on a
consistent schedule (preferably at least on a quarterly basis).
Finally, the expenditure weight for each cost category is multiplied by
the level of its respective price proxy. The sum of these products
(that is, the expenditure weights multiplied by their price levels) for
all cost categories yields the composite index level of the market
basket in a given period. Repeating this step for other periods
produces a series of market basket levels over time. Dividing an index
level for a given period by an index level for an earlier period
produces a rate of growth in the input price index over that timeframe.
Effective for cost reporting periods beginning on or after July 1,
1998, we revised and rebased our 1977 routine costs input price index
and adopted a total expenses SNF input price index using FY 1992 as the
base year. In the FY 2002 SNF PPS final rule (66 FR 39582), we rebased
and revised the market basket to a base year of FY 1997. In the FY 2008
SNF PPS final rule (72 FR 43425), we rebased and revised the market
basket to a base year of FY 2004. In the FY 2014 SNF PPS final rule (78
FR 47939), we revised and rebased the SNF market basket, which included
updating the base year from FY 2004 to FY 2010. Lastly, in the FY 2018
SNF PPS final rule (82 FR 36548), we revised and rebased the SNF market
basket, which included updating the base year from FY 2010 to FY 2014.
For FY 2022 and subsequent fiscal years, we are proposing to rebase the
market basket to reflect 2018 Medicare-allowable total cost data
(routine, ancillary, and capital-related) from freestanding SNFs and to
revise applicable cost categories and price proxies used to determine
the market basket. Medicare-allowable costs are those costs that are
eligible to be paid under the SNF PPS. For example, the SNF market
basket excludes home health agency (HHA) costs as these costs would be
paid under the HHA PPS and, therefore, these costs are not SNF PPS
Medicare-allowable costs. We propose to maintain our policy of using
data from freestanding SNFs, which represent 93 percent of the total
SNFs shown in Table 12. We believe using freestanding Medicare cost
report (MCR) data, as opposed to the hospital-based SNF MCR data, for
the proposed cost weight calculation is most appropriate because of the
complexity of hospital-based data and the representativeness of the
freestanding data. Because hospital-based SNF expenses are embedded in
the hospital cost report, any attempt to incorporate data from
hospital-based facilities requires more complex calculations and
assumptions regarding the ancillary costs related to the hospital-based
SNF unit. We believe the use of freestanding SNF cost report data is
technically appropriate for reflecting the cost structures of SNFs
serving Medicare beneficiaries.
We are proposing to use 2018 as the base year as we believe that
the 2018 MCRs represent the most recent, complete set of MCR data
available to develop cost weights for SNFs at the time of rulemaking.
We believe it is important to regularly rebase and revise the SNF
market to reflect more recent data. Historically, the cost weights
change minimally from year to year as they represent percent of total
costs rather than cost levels; however, given the PHE for COVID-19, we
will continue to monitor the upcoming MCR data to see if a more
frequent rebasing schedule is necessary than our recent historical
precedent of about every 4 years. The 2018 Medicare cost reports are
for cost reporting periods beginning on and after October 1, 2017 and
before October 1, 2018. While these dates appear to reflect fiscal year
data, we note that a Medicare cost report that begins in this timeframe
is generally classified as a ``2018 cost report''. For example, we
found that of the available 2018 Medicare cost reports for SNFs,
approximately 7 percent had an October 1, 2017 begin date,
approximately 70 percent of the reports had a January 1, 2018 begin
date, and approximately 12 percent had a July 1, 2018 begin date. For
this reason, we are defining the base year of the market basket as
``2018-based'' instead of ``FY 2018-based''.
Specifically, we are proposing to develop cost category weights for
the 2018-based SNF market basket in two stages. First, we are proposing
to derive eight major expenditures or cost weights from the 2018 MCR
data (CMS Form 2540-10, OMB NO. 0938-0463) for freestanding SNFs: Wages
and Salaries; Employee Benefits; Contract Labor; Pharmaceuticals;
Professional Liability
[[Page 19970]]
Insurance; Home Office/Related Organization Contract Labor; Capital-
related; and a residual ``All Other''. These are the same cost
categories calculated using the 2014 MCR data for the 2014-based SNF
market basket. The residual ``All Other'' category would reflect all
remaining costs that are not captured in the other seven cost
categories. Second, we are proposing to divide the residual ``All
Other'' cost category into more detailed subcategories, using U.S.
Department of Commerce Bureau of Economic Analysis' (BEA) 2012
Benchmark Input-Output (I-O) ``use table before redefinitions,
purchaser's value'' for the Nursing and Community Care Facilities
industry (NAICS 623A00) aged to 2018 using applicable price proxy
growth for each category of costs. Furthermore, we are proposing to
continue to use the same overall methodology as was used for the 2014-
based SNF market basket to develop the capital related cost weights of
the proposed 2018-based SNF market basket.
1. Development of Cost Categories and Weights
a. Use of Medicare Cost Report Data To Develop Major Cost Weights
In order to create a market basket that is representative of
freestanding SNF providers serving Medicare patients and to help ensure
accurate major cost weights (which is the percent of total Medicare-
allowable costs, as defined below), we propose to apply edits to remove
reporting errors and outliers. Specifically, the SNF MCRs used to
calculate the market basket cost weights exclude any providers that
reported costs less than or equal to zero for the following categories:
Total facility costs (Worksheet B, part 1, column 18, line 100); total
operating costs (Worksheet B, part 1, column 18, line 100 less
Worksheet B, part 2, column 18, line 100); Medicare general inpatient
routine service costs (Worksheet D, part 1, column 1, line 1); and
Medicare PPS payments (Worksheet E, part 3, column 1, line 1). We also
limited our sample to providers that had a MCR reporting period that
was between 10 and 14 months. The final sample used included roughly
13,500 MCRs (about 90 percent of the universe of SNF MCRs for 2018).
The sample of providers is representative of the national universe of
providers by region, by ownership-type (proprietary, nonprofit, and
government), and by urban/rural status.
Additionally, for all of the major cost weights, except Home
Office/Related Organization Contract Labor costs, the data are trimmed
to remove outliers (a standard statistical process) by: (1) Requiring
that major expenses (such as Wages and Salaries costs) and total
Medicare-allowable costs are greater than zero; and (2) excluding the
top and bottom five percent of the major cost weight (for example,
Wages and Salaries costs as a percent of total Medicare-allowable
costs). We note that missing values are assumed to be zero, consistent
with the methodology for how missing values are treated in the 2014-
based market basket methodology.
For the Home Office/Related Organization Contract Labor cost
weight, we propose to first exclude providers whose Home Office/Related
Organization Contract Labor costs are greater than Medicare-allowable
total costs and then apply a trim that excludes those reporters with a
Home Office/Related Organization Contract Labor cost weight above the
99th percentile. This allows providers with no Home Office/Related
Organization Contract Labor costs to be included in the Home Office/
Related Organization Contract Labor cost weight calculation . If we
were to trim the top and bottom Home Office/Related Organization
Contract Labor cost weight, we would exclude providers with a zero cost
weight and the MCR data (Worksheet S-2 line 45) indicate that not all
SNF providers have a home office. Providers without a home office would
report administrative costs that might typically be associated with a
home office in the Wages and Salaries and Employee Benefits cost
weights, or in the residual ``All-Other'' cost weight if they purchased
these types of services from external contractors. We believe the
trimming methodology that excludes those who report Home Office costs
above the 99th percentile is appropriate as it removes extreme outliers
while also allowing providers with zero Home Office/Related
Organization Contract Labor costs to be included in the Home Office/
Related Organization Contract Labor cost weight calculation.
The trimming process is done individually for each cost category so
that providers excluded from one cost weight calculation are not
automatically excluded from another cost weight calculation. We note
that these proposed trimming methods are the same types of edits
performed for the 2014-based SNF market basket, as well as other PPS
market baskets (including but not limited to the IPPS market basket and
HHA market basket). We believe this trimming process improves the
accuracy of the data used to compute the major cost weights by removing
possible data misreporting.
The final weights of the proposed 2018-based SNF market basket are
based on weighted means. For example, the aggregate Wages and Salaries
cost weight, after trimming, is equal to the sum of total Medicare-
allowable wages and salaries of all providers divided by the sum of
total Medicare-allowable costs for all providers in the sample. This
methodology is consistent with the methodology used to calculate the
2014-based SNF market basket cost weights and other PPS market basket
cost weights. We note that for each of the cost weights, we evaluated
the distribution of providers and costs by region, by ownership-type,
and by urban/rural status. For all of the cost weights, with the
exception of the PLI (which is discussed in more detail later), the
trimmed sample was nationally representative.
For all of the cost weights, we use Medicare-allowable total costs
as the denominator (for example, Wages and Salaries cost weight = Wages
and Salaries costs divided by Medicare-allowable total costs).
Medicare-allowable total costs were equal to total costs (after
overhead allocation) from Worksheet B part I, column 18, for lines 30,
40 through 49, 51, 52, and 71 plus estimated Medicaid drug costs, as
defined below. We included estimated Medicaid drug costs in the
pharmacy cost weight, as well as the denominator for total Medicare-
allowable costs. This is the same methodology used for the 2014-based
SNF market basket. The inclusion of Medicaid drug costs was finalized
in the FY 2008 SNF PPS final rule (72 FR 43425 through 43430), and for
the same reasons set forth in that final rule, we are proposing to
continue to use this methodology in the proposed 2018-based SNF market
basket.
We describe the detailed methodology for obtaining costs for each
of the eight cost categories determined from the Medicare Cost Report
below. The methodology used in the 2014-based SNF market basket can be
found in the FY 2018 SNF PPS final rule (82 FR 36548 through 36555).
(1) Wages and Salaries: To derive Wages and Salaries costs for the
Medicare-allowable cost centers, we are proposing first to calculate
total facility wages and salaries costs as reported on Worksheet S-3,
part II, column 3, line 1. We are then proposing to remove the wages
and salaries attributable to non-Medicare-allowable cost centers (that
is, excluded areas), as well as a portion of overhead wages and
salaries attributable to these excluded areas. Excluded area wages and
salaries are equal to wages and salaries as reported on Worksheet S-3,
part II, column 3, lines 3, 4, and 7
[[Page 19971]]
through 11 plus nursing facility and non-reimbursable salaries from
Worksheet A, column 1, lines 31, 32, 50, and 60 through 63.
Overhead wages and salaries are attributable to the entire SNF
facility; therefore, we are proposing to include only the proportion
attributable to the Medicare-allowable cost centers. We are proposing
to estimate the proportion of overhead wages and salaries attributable
to the non-Medicare-allowable costs centers in two steps. First, we
propose to estimate the ratio of excluded area wages and salaries (as
defined above) to non-overhead total facility wages and salaries (total
facility wages and salaries (Worksheet S-3, part II, column 3, line 1)
less total overhead wages and salaries (Worksheet S-3, Part III, column
3, line 14)). Next, we propose to multiply total overhead wages and
salaries by the ratio computed in step 1. We excluded providers whose
excluded areas wages and salaries were greater than total facility
wages and salaries and/or their excluded area overhead wages and
salaries were greater than total facility wages and salaries (about 50
providers). This is similar to the methodology used to derive Wages and
Salaries costs in the 2014-based SNF market basket. For the 2014-based
SNF market basket, we estimated the proportion of overhead wages and
salaries that is attributable to the non-Medicare allowable costs
centers (that is, excluded areas) by multiplying the ratio of excluded
area wages and salaries (as defined above) to total wages and salaries
as reported on Worksheet S-3, Part II, column 3, line 1 by total
overhead wages and salaries as reported on Worksheet S-3, Part III,
column 3, line 14.
(2) Employee Benefits: Medicare-allowable employee benefits are
equal to total facility benefits as reported on Worksheet S-3, part II,
column 3, lines 17 through 19 minus non-Medicare-allowable (that is,
excluded area) employee benefits and minus a portion of overhead
benefits attributable to these excluded areas. Excluded area employee
benefits are derived by multiplying total excluded area wages and
salaries (as defined above in the `Wages and Salaries' section) times
the ratio of total facility benefits to total facility wages and
salaries. This ratio of benefits to wages and salaries is defined as
total facility benefit costs to total facility wages and salary costs
(as reported on Worksheet S-3, part II, column 3, line 1). Likewise,
the portion of overhead benefits attributable to the excluded areas is
derived by multiplying overhead wages and salaries attributable to the
excluded areas (as defined in the `Wages and Salaries' section) times
the ratio of total facility benefit costs to total facility wages and
salary costs (as defined above). Similar to the Wages and Salaries cost
weight, we excluded providers whose excluded areas benefits were
greater than total facility benefits and/or their excluded area
overhead benefits were greater than total facility benefits (zero
providers were excluded because of this edit). This is similar to the
methodology used to derive Employee Benefits costs in the 2014-based
SNF market basket.
(3) Contract Labor: We are proposing to derive Medicare-allowable
contract labor costs from Worksheet S-3, part II, column 3, line 14,
which reflects costs for contracted direct patient care services (that
is, nursing, therapeutic, rehabilitative, or diagnostic services
furnished under contract rather than by employees and management
contract services). This is the same methodology used to derive the
Contract Labor costs in the 2014-based SNF market basket.
(4) Pharmaceuticals: We are proposing to calculate pharmaceuticals
costs using the non-salary costs from the Pharmacy cost center
(Worksheet B, part I, column 0, line 11 less Worksheet A, column 1,
line 11) and the Drugs Charged to Patients' cost center (Worksheet B,
part I, column 0, line 49 less Worksheet A, column 1, line 49). Since
these drug costs were attributable to the entire SNF and not limited to
Medicare-allowable services, we propose to adjust the drug costs by the
ratio of Medicare-allowable pharmacy total costs (Worksheet B, part I,
column 11, for lines 30, 40 through 49, 51, 52, and 71) to total
pharmacy costs from Worksheet B, part I, column 11, line 11. Worksheet
B, part I allocates the general service cost centers, which are often
referred to as ``overhead costs'' (in which pharmacy costs are
included) to the Medicare-allowable and non-Medicare-allowable cost
centers. This adjustment was made for those providers who reported
Pharmacy cost center expenses. Otherwise, we assumed the non-salary
Drugs Charged to Patients costs were Medicare-allowable. Since drug
costs for Medicare patients are included in the SNF PPS per diem rate,
a provider with Medicare days should have also reported costs in the
Drugs Charged to Patient cost center. We found a small number of
providers (roughly 60) did not report Drugs Charged to Patients' costs
despite reporting Medicare days (an average of about 2,600 Medicare
days per provider) and, therefore, these providers were excluded from
the Pharmaceuticals cost weight calculations. This is similar to the
methodology used for the 2014-based SNF market basket.
Second, as was done for the 2014-based SNF market basket, we
propose to continue to adjust the drug expenses reported on the MCR to
include an estimate of total Medicaid drug costs, which are not
represented in the Medicare-allowable drug cost weight. As stated
previously in this section, the proposed 2018-based SNF market basket
reflects total Medicare-allowable costs (that is, total costs for all
payers for those services reimbursable under the SNF PPS). For the FY
2006-based SNF market basket (72 FR 43426), commenters noted that the
total pharmaceutical costs reported on the MCR did not include
pharmaceutical costs for dual-eligible Medicaid patients as these were
directly reimbursed by Medicaid. Since all of the other cost category
weights reflect expenses associated with treating Medicaid patients
(including the compensation costs for dispensing these drugs), we made
an adjustment to include these Medicaid drug expenses so the market
basket cost weights would be calculated consistently.
Similar to the 2014-based SNF market basket, we propose to estimate
Medicaid drug costs based on data representing dual-eligible Medicaid
beneficiaries. Medicaid drug costs are estimated by multiplying
Medicaid dual-eligible drug costs per day times the number of Medicaid
days as reported in the Medicare-allowable skilled nursing cost center
(Worksheet S-3, part I, column 5, line 1) in the SNF MCR. Medicaid
dual-eligible drug costs per day (where the day represents an
unduplicated drug supply day) were estimated using 2018 Part D claims
for those dual-eligible beneficiaries who had a Medicare SNF stay
during the year. The total drug costs per unduplicated day for 2018 of
$24.48 represented all drug costs (including the drug ingredient cost,
the dispensing fee, vaccine administration fee and sales tax) incurred
during the 2018 calendar year for those dual-eligible beneficiaries who
had a SNF Medicare stay during that 2018 calendar year. Therefore, they
include drug costs incurred during a Medicaid SNF stay occurring in the
2018 calendar year. By comparison, the 2014-based SNF market basket
also relied on data from the Part D claims, which yielded a dual-
eligible Medicaid drug cost per day of $19.62 for 2014.
We continue to believe that Medicaid dual-eligible beneficiaries
are a reasonable proxy for the estimated drug costs per day incurred by
Medicaid patients staying in a skilled nursing unit under a Medicaid
stay. The skilled nursing unit is the Medicare-allowable unit in a SNF,
which encompasses more
[[Page 19972]]
skilled nursing and rehabilitative care compared to a nursing facility
or long-term care unit. We believe that Medicaid patients receiving
this skilled nursing care would on average have similar drug costs per
day to dual-eligible Medicare beneficiaries who have received Medicare
skilled nursing care in the skilled nursing care unit during the year.
We note that our previous analysis of the Part D claims data showed
that Medicare beneficiaries with a SNF stay during the year have higher
drug costs than Medicare patients without a SNF stay during the year.
Also, in 2018, dual-eligible beneficiaries with a SNF stay during the
year had drug costs per day of $24.48, which were approximately two
times higher than the drug costs per day of $13.19 for nondual-eligible
beneficiaries with a SNF Part A stay during the year.
The Pharmaceuticals cost weight using only 2018 MCR data (without
the inclusion of the Medicaid dual-eligible drug costs) is 2.6 percent,
compared to the proposed Pharmaceuticals cost weight (including the
adjustment for Medicaid dual-eligible drug costs) of 7.5 percent. The
2014-based SNF market basket had a Pharmaceuticals cost weight using
only 2014 MCR data without the inclusion of the Medicaid dual-eligible
drug costs of 2.9 percent and a total Pharmaceuticals cost weight of
7.3 percent. Therefore, the 0.2 percentage point increase in the
Pharmaceuticals cost weight is a result of a 0.5-percentage point
increase in the Medicaid dual-eligible drug cost weight (reflecting the
25 percent increase in the Medicaid dual-eligible drug costs per day
between 2014 and 2018) and a 0.3-percentage point decrease in the MCR
drug cost weight. The decrease in the MCR drug cost weight was
consistent, in aggregate, across urban and rural status SNFs as well as
across for-profit, government, and nonprofit ownership type SNFs.
(5) Professional Liability Insurance: We are proposing to calculate
the professional liability insurance costs from Worksheet S-2 of the
MCRs as the sum of premiums; paid losses; and self-insurance (Worksheet
S-2, Part I, columns 1 through 3, line 41). This was the same
methodology used to derive the Professional Liability costs for the
2014-based SNF market basket.
About 60 percent of SNFs (about 8,000) reported professional
liability costs. After trimming, about 7,200 (reflecting about 850,000
Skilled Nursing unit beds) were included in the calculation of the
Professional Liability Insurance (PLI) cost weight for the proposed
2018-based SNF market basket. These providers treated roughly 870,000
Medicare beneficiaries and had a Medicare length of stay (LOS) of 33
days, a skilled nursing unit occupancy rate of 80 percent, and an
average skilled nursing unit bed size of 125 beds, which are all
consistent with the national averages. We also verified that this
sample of providers are representative of the national distribution of
providers by ownership-type and urban/rural status. We note that the
sample of providers is less consistent with the national distribution
of providers by region; however, we performed a sensitivity analysis
where the PLI cost weight was reweighted based on the national regional
distribution and the impacts were less than a 0.1 percentage point on
the cost weight.
We note that based on prior comments during the rebasing of the
2014-based SNF market basket, we reviewed in detail the AON 2018
Professional and General Liability Benchmark for Long Term Care
Providers \2\ that examines professional liability and general
liability claim costs for long term care providers (including SNF beds,
as well as independent living, assisted living, home health care, and
rehabilitation facilities, representing about 186,000 long term care
beds). This study, although informative, was not appropriate for
calculating a PLI cost weight as it represents more than just SNFs
serving Medicare patients and captures claim losses as opposed to PLI
costs (premiums, paid losses, and self-insurance) incurred during a
cost reporting year. We note that only 13 percent of providers reported
PLI paid losses or PLI self-insurance costs on the MCR while over 90
percent of providers reported PLI premiums indicating that the majority
of losses incurred by Medicare participating SNFs will be covered by
insurance premiums paid over time. Our comparison of the MCR data to
the AON study for those select states' data provided did show
consistencies between the average state PLI costs per bed relative to
the national average (as measured by the MCR) and AON's loss per
occupied bed relative to national values indicating that states with
higher losses per occupied bed have higher PLI costs per total bed.
---------------------------------------------------------------------------
\2\ https://www.aon.com/risk-services/thought-leadership/report-2018-long-term-care.jsp.
---------------------------------------------------------------------------
We believe the MCR data continues to be the most appropriate data
source to calculate the PLI cost weight for the proposed 2018-based SNF
market basket as it is representative of SNFs serving Medicare
beneficiaries and reflects PLI costs (premiums, paid losses, and self-
insurance) incurred during the provider's cost reporting year.
(6) Capital-Related: We are proposing to derive the Medicare-
allowable capital-related costs from Worksheet B, part II, column 18
for lines 30, 40 through 49, 51, 52, and 71. This is the same
methodology to derive capital-related costs used in the 2014-based SNF
market basket.
(7) Home Office/Related Organization Contract Labor Costs: We are
proposing to calculate Medicare-allowable Home Office/Related
Organization Contract Labor costs to be equal to data reported on
Worksheet S-3, part II, column 3, line 16. We note that for the 2014-
based SNF market basket we also used Worksheet S-3, part II, column 3,
line 16 (Home office salaries & wage related costs) to determine these
expenses; however, we referred to this category as Home Office Contract
Labor Costs. The instructions for this data state ``enter the salaries
and wage related costs (as defined on lines 17 and 18 below) paid to
personnel who are affiliated with a home office and/or related
organization, who provide services to the SNF and/or NF, and whose
salaries are not included on Worksheet A, column 1,'' and therefore, we
are referring to this cost category as Home Office/Related Organization
Contract Labor costs. Furthermore, for this rebasing we are no longer
adjusting these expenses by the ratio of Medicare allowable operating
costs to total facility operating costs as done for the 2014-based SNF
market basket as the instructions indicate these expenses are for the
SNF and NF units.
About 7,000 providers (about 53 percent) in 2018 reported having a
home office (as reported on Worksheet S-2, part I, line 45); a lower
share of providers than those in the 2014-based SNF market basket. As
discussed in section VI.A.1. of this proposed rule, providers without a
home office can incur these expenses directly by having their own
staff, for which the costs would be included in the Wages and Salaries
and Employee Benefits cost weights. Alternatively, providers without a
home office could also purchase related services from external
contractors for which these expenses would be captured in the residual
``All-Other'' cost weight. For this reason, unlike the other major cost
weights described previously, we did not exclude providers that did not
report Home Office/Related Organization Contract Labor costs. We note
that this is similar to the methodology that was used for other PPS
market baskets such as the 2017-based LTCH market basket (85 FR 58911).
[[Page 19973]]
(8) All Other (residual): The ``All Other'' cost weight is a
residual, calculated by subtracting the major cost weights (Wages and
Salaries, Employee Benefits, Contract Labor, Pharmaceuticals,
Professional Liability Insurance, Capital-Related, and Home Office/
Related Organization Contract Labor) from 100.
Table 12 shows the proposed major cost categories and their
respective cost weights as derived from the 2018 Medicare cost reports.
Table 12--Major Cost Categories Derived From the SNF Medicare Cost
Reports *
------------------------------------------------------------------------
Proposed 2018-
Major cost categories based 2014-based
------------------------------------------------------------------------
Wages and Salaries...................... 44.1 44.3
Employee Benefits....................... 8.6 9.3
Contract Labor.......................... 7.5 6.8
Pharmaceuticals......................... 7.5 7.3
Professional Liability Insurance........ 1.1 1.1
Capital-related......................... 8.2 7.9
Home Office/Related Organization 0.7 0.7
Contract Labor.........................
All other (residual).................... 22.3 22.6
------------------------------------------------------------------------
* Total may not sum to 100 due to rounding.
Compared to the 2014-based SNF market basket, the Wages and
Salaries cost weight and the Employee Benefits cost weight as
calculated directly from the Medicare cost reports decreased by 0.2
percentage point and 0.7 percentage point, respectively. The Contract
Labor cost weight increased 0.7 percentage point and so in aggregate,
the Compensation cost weight decreased 0.2 percentage point.
As we did for the 2014-based SNF market basket (82 FR 36555), we
are proposing to allocate contract labor costs 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. Using
the 2018 Medicare cost report data, this percentage is 84 percent (1
percentage point higher than the percent in the 2014-based SNF market
basket); therefore, we are proposing to allocate approximately 84
percent of the Contract Labor cost weight to the Wages and Salaries
cost weight and 16 percent to the Employee Benefits cost weight.
Table 13 shows the Wages and Salaries and Employee Benefits cost
weights after contract labor allocation for the proposed 2018-based SNF
market basket and the 2014-based SNF market basket.
Table 13--Wages and Salaries and Employee Benefits Cost Weights After
Contract Labor Allocation
------------------------------------------------------------------------
Proposed 2018-
Major cost categories based market 2014-based
basket market basket
------------------------------------------------------------------------
Wages and Salaries...................... 50.4 50.0
Employee Benefits....................... 9.9 10.5
------------------------------------------------------------------------
b. Derivation of the Detailed Operating Cost Weights
To further divide the ``All Other'' residual cost weight estimated
from the 2018 Medicare cost report data into more detailed cost
categories, we are proposing to use the 2012 Benchmark I-O ``Use
Tables/Before Redefinitions/Purchaser Value'' for Nursing and Community
Care Facilities industry (NAICS 623A00), published by the Census
Bureau's, Bureau of Economic Analysis (BEA). These data are publicly
available at the following website at http://www.bea.gov/industry/io_annual.htm. The BEA Benchmark I-O data are generally scheduled for
publication every 5 years with 2012 being the most recent year for
which data is available. The 2012 Benchmark I-O data are derived from
the 2012 Economic Census and are the building blocks for BEA's economic
accounts; therefore, they represent the most comprehensive and complete
set of data on the economic processes or mechanisms by which output is
produced and distributed.\3\ BEA also produces Annual I-O estimates.
However, while based on a similar methodology, these estimates are less
comprehensive and provide less detail than benchmark data.
Additionally, the annual I-O data are subject to revision once
benchmark data become available. For these reasons, we propose to
inflate the 2012 Benchmark I-O data aged forward to 2018 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. Next, the relative shares of the cost shares
that each cost category represents to the total residual I-O costs are
calculated. These resulting 2018 cost shares of the I-O data are
applied to the ``All Other'' residual cost weight to obtain detailed
cost weights for the residual costs for the proposed 2018-based SNF
market basket. For example, the cost for Food: Direct Purchases
represents 11.3 percent of the sum of the ``All Other'' 2012 Benchmark
I-O Expenditures inflated to 2018. Therefore, the Food: Direct
Purchases cost weight is 2.5 percent of the proposed 2018-based SNF
market basket (11.3 percent x 22.3 percent = 2.5 percent). For the
2014-based SNF market basket (82 FR 36553), we used a similar
methodology utilizing the 2007 Benchmark I-O data (aged to 2014).
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\3\ http://www.bea.gov/papers/pdf/IOmanual_092906.pdf.
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Using this methodology, we are proposing to derive 19 detailed SNF
market basket cost category weights from the proposed 2018-based SNF
market basket ``All Other'' residual cost
[[Page 19974]]
weight (22.3 percent). These categories are: (1) Fuel: Oil and Gas; (2)
Electricity and Other Non-Fuel Utilities; (3) Food: Direct Purchases;
(4) Food: Contract Services; (5) Chemicals; (6) Medical Instruments and
Supplies; (7) Rubber and Plastics; (8) Paper and Printing Products; (9)
Apparel; (10) Machinery and Equipment; (11) Miscellaneous Products;
(12) Professional Fees: Labor-Related; (13) Administrative and
Facilities Support Services; (14) Installation, Maintenance, and Repair
Services; (15) All Other: Labor-Related Services; (16) Professional
Fees: Nonlabor-Related; (17) Financial Services; (18) Telephone
Services; and (19) All Other: Nonlabor-Related Services. The 2014-based
SNF market basket had separate cost categories for Postage services and
Water and Sewerage. Due to the small weights (less than 0.1 percentage
point), we are proposing that Postage costs be included in the All
Other: Non-labor-Related Services and Water and Sewerage costs be
included in the Electricity and Other Non-Fuel Utilities category.
We note that the machinery and equipment expenses are for equipment
that is paid for in a given year and not depreciated over the asset's
useful life. Depreciation expenses for moveable equipment are accounted
for in the capital component of the proposed 2018-based SNF market
basket (described in section IV.A.1.c. of this proposed rule).
c. Derivation of the Detailed Capital Cost Weights
Similar to the 2014-based SNF market basket, we further divided the
Capital-related cost weight into: Depreciation, Interest, Lease and
Other Capital-related cost weights.
We calculated the depreciation cost weight (that is, depreciation
costs excluding leasing costs) using depreciation costs from Worksheet
S-2, column 1, lines 20 and 21. Since the depreciation costs reflect
the entire SNF facility (Medicare and non-Medicare-allowable units), we
used total facility capital costs (Worksheet B, Part I, Column 18, line
100) as the denominator. This methodology assumes that the depreciation
of an asset is the same regardless of whether the asset was used for
Medicare or non-Medicare patients. This methodology yielded
depreciation costs as a percent of capital costs of 25.3 percent for
2018. We then apply this percentage to the proposed 2018-based SNF
market basket Medicare-allowable Capital-related cost weight of 8.2
percent, yielding a Medicare-allowable depreciation cost weight
(excluding leasing expenses, which is described in more detail below)
of 2.1 percent. To further disaggregate the Medicare-allowable
depreciation cost weight into fixed and moveable depreciation, we are
proposing to use the 2018 SNF MCR data for end-of-the-year capital
asset balances as reported on Worksheet A-7. The 2018 SNF MCR data
showed a fixed/moveable split of 86/14. The 2014-based SNF market
basket, which utilized the same data from the 2014 MCRs, had a fixed/
moveable split of 83/17.
We also derived the interest expense share of capital-related
expenses from 2018 SNF MCR data, specifically from Worksheet A, column
2, line 81. Similar to the depreciation cost weight, we calculated the
interest cost weight using total facility capital costs. This
methodology yielded interest costs as a percent of capital costs of
22.8 percent for 2018. We then apply this percentage to the proposed
2018-based SNF market basket Medicare-allowable Capital-related cost
weight of 8.2 percent, yielding a Medicare-allowable interest cost
weight (excluding leasing expenses) of 1.9 percent. As done with the
last rebasing (82 FR 36556), we are proposing to determine the split of
interest expense between for-profit and not-for-profit facilities based
on the distribution of long-term debt outstanding by type of SNF (for-
profit or not-for-profit/government) from the 2018 SNF MCR data. We
estimated the split between for-profit and not-for-profit interest
expense to be 25/75 percent compared to the 2014-based SNF market
basket with 27/73 percent.
Because the detailed data were not available in the MCRs, we used
the most recent 2017 Census Bureau Service Annual Survey (SAS) data to
derive the capital-related expenses attributable to leasing and other
capital-related expenses. The 2014-based SNF market basket used the
2014 SAS data. We note that we are proposing to use the 2017 SAS data
because the Census Bureau no longer publishes these detailed capital-
related expenses effective for 2018.
Based on the 2017 SAS data, we determined that leasing expenses are
62 percent of total leasing and capital-related expenses costs. In the
2014-based SNF market basket, leasing costs represent 63 percent of
total leasing and capital-related expenses costs. We then apply this
percentage to the proposed 2018-based SNF market basket residual
Medicare-allowable capital costs of 4.2 percent derived from
subtracting the Medicare-allowable depreciation cost weight and
Medicare-allowable interest cost weight from the 2018-based SNF market
basket of total Medicare-allowable capital cost weight (8.2 percent-2.1
percent-1.9 percent = 4.2 percent). This produces the proposed 2018-
based SNF Medicare-allowable leasing cost weight of 2.6 percent and
all-other capital-related cost weight of 1.6 percent.
Lease expenses are not broken out as a separate cost category in
the SNF market basket, but are distributed among the cost categories of
depreciation, interest, and other capital-related expenses, reflecting
the assumption that the underlying cost structure and price movement of
leasing expenses is similar to capital costs in general. As was done
with past SNF market baskets and other PPS market baskets, we assumed
10 percent of lease expenses are overhead and assigned them to the
other capital-related expenses cost category. This is based on the
assumption that leasing expenses include not only depreciation,
interest, and other capital-related costs but also additional costs
paid to the lessor. We distributed the remaining lease expenses to the
three cost categories based on the proportion of depreciation,
interest, and other capital-related expenses to total capital costs,
excluding lease expenses.
Table 14 shows the capital-related expense distribution (including
expenses from leases) in the proposed 2018-based SNF market basket and
the 2014-based SNF market basket.
Table 14--Comparison of the Capital-Related Expense Distribution of the
Proposed 2018-Based SNF Market Basket and the 2014-Based SNF Market
Basket
------------------------------------------------------------------------
Proposed 2018-
Cost category based SNF 2014-based SNF
market basket market basket
------------------------------------------------------------------------
Capital-related Expenses................ 8.2 7.9
Total Depreciation.................. 3.0 2.9
[[Page 19975]]
Total Interest...................... 2.7 3.0
Other Capital-related Expenses...... 2.6 2.0
------------------------------------------------------------------------
Note: The cost weights are calculated using three decimal places. For
presentational purposes, we are displaying one decimal and, therefore,
the detail capital cost weights may not add to the total capital-
related expenses cost weight due to rounding.
Table 15 presents the proposed 2018-based SNF market basket and the
2014-based SNF market basket.
Table 15--Proposed 2018-Based SNF Market Basket and 2014-Based SNF
Market Basket
------------------------------------------------------------------------
Proposed 2018-
Cost category based SNF 2014-Based SNF
market basket market basket
------------------------------------------------------------------------
Total................................... 100.0 100.0
Compensation............................ 60.2 60.4
Wages and Salaries \1\.............. 50.4 50.0
Employee Benefits \1\............... 9.9 10.5
Utilities............................... 1.5 2.6
Electricity and Other Non-Fuel 1.0 1.4
Utilities..........................
Fuel: Oil and Gas................... 0.4 1.3
Professional Liability Insurance........ 1.1 1.1
All Other............................... 29.0 27.9
Other Products...................... 17.6 14.3
Pharmaceuticals................. 7.5 7.3
Food: Direct Purchase........... 2.5 3.1
Food: Contract Purchase......... 4.3 0.7
Chemicals....................... 0.2 0.2
Medical Instruments and Supplies 0.6 0.6
Rubber and Plastics............. 0.7 0.8
Paper and Printing Products..... 0.5 0.8
Apparel......................... 0.5 0.3
Machinery and Equipment......... 0.5 0.3
Miscellaneous Products.......... 0.3 0.3
All Other Services...................... 11.5 13.6
Labor-Related Services.............. 6.4 7.4
Professional Fees: Labor-related 3.5 3.8
Installation, Maintenance, and 0.6 0.6
Repair Services................
Administrative and Facilities 0.4 0.5
Support........................
All Other: Labor-Related 1.9 2.5
Services.......................
Non Labor-Related Services.......... 5.1 6.2
Professional Fees: Nonlabor- 2.0 1.8
Related........................
Financial Services.............. 1.3 2.0
Telephone Services.............. 0.3 0.5
All Other: Nonlabor-Related 1.5 2.0
Services \3\...................
Capital-Related Expenses................ 8.2 7.9
Total Depreciation.................. 3.0 2.9
Building and Fixed Equipment.... 2.5 2.5
Movable Equipment............... 0.4 0.4
Total Interest...................... 2.7 3.0
For-Profit SNFs................. 0.7 0.8
Government and Nonprofit SNFs... 2.0 2.1
Other Capital-Related Expenses...... 2.6 2.0
------------------------------------------------------------------------
Note: The cost weights are calculated using three decimal places. For
presentational purposes, we are displaying one decimal and, therefore,
the detailed cost weights may not add to the aggregate cost weights or
to 100.0 due to rounding.
\1\ Contract labor is distributed to wages and salaries and employee
benefits based on the share of total compensation that each category
represents.
\2\ Water and Sewerage costs are included in the Electricity and Other
Non-Fuel Utilities cost category in the proposed 2018-based SNF market
basket.
\3\ Postage costs are included in the All Other Non-labor-related cost
category in the proposed 2018-based SNF market basket.
[[Page 19976]]
2. Price Proxies Used To Measure Operating Cost Category Growth
After developing the 27 cost weights for the proposed 2018-based
SNF market basket, we selected the most appropriate wage and price
proxies currently available to represent the rate of change for each
expenditure category. With four exceptions (three for the capital-
related expenses cost categories and one for Professional Liability
Insurance (PLI)), we base the wage and price proxies on 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 2012 NAICS and the
occupational ECIs are based on the 2000 and 2010 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 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 evaluated the price proxies using the criteria of reliability,
timeliness, availability, and relevance. 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 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 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.
Finally, 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 to propose in this
regulation 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 20 lists all price proxies for the proposed 2018-based SNF
market basket. Below is a detailed explanation of the price proxies
used for each operating cost category.
Wages and Salaries: We are proposing to use the ECI for
Wages and Salaries for Private Industry Workers in Nursing Care
Facilities (NAICS 6231; BLS series code CIU2026231000000I) to measure
price growth of this category. NAICS 623 includes facilities that
provide a mix of health and social services, with many of the health
services being largely some level of nursing services. Within NAICS 623
is NAICS 6231, which includes nursing care facilities primarily engaged
in providing inpatient nursing and rehabilitative services. These
facilities, which are most comparable to Medicare-certified SNFs,
provide skilled nursing and continuous personal care services for an
extended period of time, and, therefore, have a permanent core staff of
registered or licensed practical nurses. This is the same index used in
the 2014-based SNF market basket.
Employee Benefits: We are proposing to use the ECI for
Benefits for Nursing Care Facilities (NAICS 6231) to measure price
growth of this category. The ECI for Benefits for Nursing Care
Facilities is calculated using BLS's total compensation (BLS series ID
CIU2016231000000I) for nursing care facilities series and the relative
importance of wages and salaries within total compensation. We believe
this constructed ECI series is technically appropriate for the reason
stated above in the Wages and Salaries price proxy section. This is the
same index used in the 2014-based SNF market basket.
Electricity and Other Non-Fuel Utilities: We are proposing
to use the PPI Commodity for Commercial Electric Power (BLS series code
WPU0542) to measure the price growth of this cost category as
Electricity costs account for 93 percent of these expenses. This is the
same index used for the Electricity cost category in the 2014-based SNF
market basket. As previously noted, we are proposing to include Water
and Sewerage costs within the Electricity and Other Non-Fuel Utilities
cost category, and to no longer use the CPI All Urban for Water and
Sewerage Maintenance as we did for the 2014-based SNF market basket,
due to the small size of this estimated cost weight (less than 0.1
percent).
Fuel: Oil and Gas: We are proposing to change the proxy
used for the Fuel: Oil and Gas cost category. Our analysis of the
Bureau of Economic Analysis' 2012 Benchmark I-O data for Nursing and
Community Care Facilities shows approximately 96 percent of SNF Fuel:
Oil and Gas expenses are for Petroleum Refineries (NAICS 324110),
Natural gas (NAICS 221200), and Other Petroleum and Coal Products
Manufacturing (NAICS 324190). We are proposing to create a blended
index based on those three NAICS chemical expenses listed above that
account for 96 percent of SNF chemical expenses. We are proposing to
create this blend based on each NAICS' expenses as a share of their
sum. Therefore, we are proposing a blended proxy of 61 percent of the
PPI Industry for Petroleum Refineries (BLS series code PCU32411-32411),
7 percent of the PPI Commodity for Natural Gas (BLS series code
WPU0531), and 32 percent of the PPI for Other Petroleum and Coal
Products manufacturing (BLS series code PCU32419-32419).
The 2014-based SNF market basket also used a blended chemical proxy
that was based on 2007 Benchmark I-O data. We believe our proposed
Fuel: Oil and Gas blended index for the 2018-based SNF market basket is
technically appropriate as it reflects more recent data on SNFs
purchasing patterns. Table 16 provides the weights for the proposed
2018-based blended chemical index and the 2014-based blended chemical
index.
[[Page 19977]]
Table 16--Proposed Fuel: Oil and Gas Blended Index Weights
------------------------------------------------------------------------
Proposed 2018-
NAICS Price proxy based index 2014-based
(%) index (%)
------------------------------------------------------------------------
221200............ PPI Commodity for 7 35
Natural Gas.
324110............ PPI Industry for 61 65
Petroleum
Refineries.
324190............ PPI for Other 32 n/a
Petroleum and Coal
Products
manufacturing.
-------------------------------
Total......... .................... 100 100
------------------------------------------------------------------------
Professional Liability Insurance: We are proposing to use
the CMS Hospital Professional Liability Insurance Index to measure
price growth of this category. We were unable to find a reliable data
source that collects SNF-specific PLI data. Therefore, we are proposing
to use the CMS Hospital Professional Liability Index, which tracks
price changes for commercial insurance premiums for a fixed level of
coverage, holding non-price factors constant (such as a change in the
level of coverage). This is the same index used in the 2014-based SNF
market basket. We believe this is an appropriate proxy to measure the
price growth associated of SNF professional liability insurance as it
captures the price inflation associated with other medical institutions
that serve Medicare patients.
Pharmaceuticals: We are proposing to use the PPI Commodity
for Pharmaceuticals for Human Use, Prescription (BLS series code
WPUSI07003) to measure the price growth of this cost category. This is
the same index used in the 2014-based SNF market basket.
Food: Wholesale Purchases: We are proposing to use the PPI
Commodity for Processed Foods and Feeds (BLS series code WPU02) to
measure the price growth of this cost category. This is the same index
used in the 2014-based SNF market basket.
Food: Retail Purchase: We are proposing to use the CPI All
Urban for Food Away From Home (All Urban Consumers) (BLS series code
CUUR0000SEFV) to measure the price growth of this cost category. This
is the same index used in the 2014-based SNF market basket.
Chemicals: For measuring price change in the Chemicals
cost category, we are proposing to use a blended PPI composed of the
Industry PPIs for Other Basic Organic Chemical Manufacturing (NAICS
325190) (BLS series code PCU32519-32519), Soap and Cleaning Compound
Manufacturing (NAICS 325610) (BLS series code PCU32561-32561), and
Other Miscellaneous Chemical Product Manufacturing (NAICS 325998) (BLS
series code PCU325998325998).
Using the 2012 Benchmark I-O data, we found that these three NAICS
industries accounted for approximately 96 percent of SNF chemical
expenses. The remaining four percent of SNF chemical expenses are for
three other incidental NAICS chemicals industries such as Paint and
Coating Manufacturing. We are proposing to create a blended index based
on those three NAICS chemical expenses listed above that account for 96
percent of SNF chemical expenses. We are proposing to create this blend
based on each NAICS' expenses as a share of their sum. These expenses
as a share of their sum are listed in Table 17.
The 2014-based SNF market basket also used a blended chemical proxy
that was based on 2007 Benchmark I-O data. We believe our proposed
chemical blended index for the 2018-based SNF market basket is
technically appropriate as it reflects more recent data on SNFs
purchasing patterns. Table 17 provides the weights for the proposed
2018-based blended chemical index and the 2014-based blended chemical
index.
Table 17--Proposed Chemical Blended Index Weights
------------------------------------------------------------------------
Proposed 2018-
NAICS Price proxy based index 2014-based
(%) index (%)
------------------------------------------------------------------------
325190............ PPI for Other Basic 34 22
Organic Chemical
Manufacturing.
325610............ PPI for Soap and 21 37
Cleaning Compound
Manufacturing.
325998............ PPI for Other 45 41
Miscellaneous
Chemical Product
Manufacturing.
-------------------------------
Total......... .................... 100 100
------------------------------------------------------------------------
Medical Instruments and Supplies: We are proposing to
change the proxy used for the Medical Instruments and Supplies cost
weight. The 2012 Benchmark I-O data shows 46 percent of medical
instruments and supply costs are for Surgical and medical instrument
manufacturing costs (NAICS 339112) and 54 percent are for Surgical
appliance and supplies manufacturing costs (NAICS 339113). To proxy the
price changes associated with NAICS 339112, we propose using the PPI--
Commodity--Surgical and medical instruments (BLS series code WPU1562).
This the same price proxy we used in the 2014-based SNF market basket.
To proxy the price changes associated with NAICS 339113, we are
proposing to use 50 percent for the PPI--Commodity--Medical and
surgical appliances and supplies (BLS series code WPU1563) and 50
percent for the PPI Commodity data for Miscellaneous products-Personal
safety equipment and clothing (BLS series code WPU1571). The latter
price proxy would reflect personal protective equipment including but
not limited to face shields and protective clothing. The 2012 Benchmark
I-O data does not provide specific expenses for personal protective
equipment (which would be reflected in the NAICS 339113 expenses);
however, we recognize that this category reflects costs faced by SNFs.
In absence of any specific cost data on personal protective equipment,
we are proposing to include the PPI Commodity data for
[[Page 19978]]
Miscellaneous products-Personal safety equipment and clothing (BLS
series code WPU1571) in the blended proxy for Medical Instruments and
Supplies cost category with a weight of 27 percent (that is, 50 percent
of the NAICS 339113 expenses as a percent of the sum of NAICS 339113
and NAICS 339112 expenses from the I-O).
The 2014-based SNF market basket used a blend composed of 60
percent of the PPI Commodity for Medical and Surgical Appliances and
Supplies (BLS series code WPU1563) and 40 percent of the PPI Commodity
for Surgical and Medical Instruments (BLS series code WPU1562). Table
18 provides the proposed Medical Instruments and Supplies cost weight
blended price proxy.
Table 18--Proposed Medical Instruments and Supplies Blended Index
Weights
------------------------------------------------------------------------
Proposed 2018-
NAICS Price proxy based index 2014-based
(%) index (%)
------------------------------------------------------------------------
339112............ PPI--Commodity--Surg 46 40
ical and medical
instruments
(WUI1562).
339113............ PPI--Commodity--Medi 27 60
cal and surgical
appliances and
supplies (WPU1563).
PPI Commodity data 27 n/a
for Miscellaneous
products--Personal
safety equipment
and clothing
(WPU1571).
-------------------------------
Total......... .................... 100 100
------------------------------------------------------------------------
Rubber and Plastics: We are proposing to use the PPI
Commodity for Rubber and Plastic Products (BLS series code WPU07) to
measure price growth of this cost category. This is the same index used
in the 2014-based SNF market basket.
Paper and Printing Products: We are proposing to use the
PPI Commodity for Converted Paper and Paperboard Products (BLS series
code WPU0915) to measure the price growth of this cost category. This
is the same index used in the 2014-based SNF market basket.
Apparel: We are proposing to use the PPI Commodity for
Apparel (BLS series code WPU0381) to measure the price growth of this
cost category. This is the same index used in the 2014-based SNF market
basket.
Machinery and Equipment: We are proposing to use the PPI
Commodity for Machinery and Equipment (BLS series code WPU11) to
measure the price growth of this cost category. This is the same index
used in the 2014-based SNF market basket.
Miscellaneous Products: For measuring price change in the
Miscellaneous Products cost category, we are proposing to use the PPI
Commodity for Finished Goods less Food and Energy (BLS series code
WPUFD4131). Both food and energy are already adequately represented in
separate cost categories and should not also be reflected in this cost
category. This is the same index used in the 2014-based SNF market
basket.
Professional Fees: Labor-Related: We are proposing 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 index used in the
2014-based SNF market basket.
Administrative and Facilities Support Services: We are
proposing 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 index used in the 2014-based SNF market basket.
Installation, Maintenance and Repair Services: We are
proposing to use the ECI for Total Compensation for All Civilian
Workers in Installation, Maintenance, and Repair (BLS series code
CIU1010000430000I) to measure the price growth of this new cost
category. This is the same index used in the 2014-based SNF market
basket.
All Other: Labor-Related Services: We are proposing 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 index used in the 2014-
based SNF market basket.
Professional Fees: NonLabor-Related: We are
proposing 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 index
used in the 2014-based SNF market basket.
Financial Services: We are proposing 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 index used in the 2014-
based SNF market basket.
Telephone Services: We are proposing to use the
CPI All Urban for Telephone Services (BLS series code CUUR0000SEED) to
measure the price growth of this cost category. This is the same index
used in the 2014-based SNF market basket.
All Other: NonLabor-Related Services: We are proposing to
use the CPI All Urban for All Items Less Food and Energy (BLS series
code CUUR0000SA0L1E) to measure the price growth of this cost category.
This is the same index used in the 2014-based SNF market basket. As
previously noted, we are proposing to include Postage costs within the
All Other: NonLabor-Related Services cost category, and to no longer
use the CPI All Urban for Postage as we did for the 2014-based SNF
market basket, due to the small size of this estimated cost weight
(less than 0.1 percent).
3. Price Proxies Used To Measure Capital Cost Category Growth
We are proposing to apply the same capital price proxies as were
used in the 2014-based SNF market basket, with the exception of the
For-profit interest cost category, and below is a detailed explanation
of the price proxies used for each capital cost category. We also are
proposing 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 the same method that was used
for the 2014-based SNF market basket and is described below.
Depreciation--Building and Fixed Equipment: We are
proposing to use the BEA Chained Price Index for Private Fixed
Investment in Structures, Nonresidential, Hospitals and Special Care
(BEA Table 5.4.4. Price Indexes for Private Fixed Investment in
Structures by Type). This BEA index is intended to capture prices for
construction of facilities such as hospitals, nursing
[[Page 19979]]
homes, hospices, and rehabilitation centers. This is the same index
used in the 2014-based SNF market basket.
Depreciation--Movable Equipment: We are proposing to use
the PPI Commodity for Machinery and Equipment (BLS series code WPU11).
This price index reflects price inflation associated with a variety of
machinery and equipment that would be utilized by SNFs including but
not limited to medical equipment, communication equipment, and
computers. This is the same index used in the 2014-based SNF market
basket.
Nonprofit Interest: We are proposing to use the average
yield on Municipal Bonds (Bond Buyer 20-bond index). This is the same
index used in the 2014-based SNF market basket.
For-Profit Interest: For the For-Profit Interest cost
category, we are proposing to use the iBoxx AAA Corporate Bond Yield
index instead of the Moody's AAA Corporate Bond Yield index that was
used for the 2014-based SNF market basket. Effective for December 2020,
the Moody's AAA Corporate Bond series is no longer available for use
under license to IGI, the nationally-recognized economic and financial
forecasting firm with whom we contract to forecast the components of
the market baskets and MFP. Therefore, we are proposing to replace the
price proxy for the For-Profit interest cost category. We compared the
iBoxx AAA Corporate Bond Yield index with the Moody's AAA Corporate
Bond Yield index and found that the average growth rates in the two
series were similar. Over the historical time period of FY 2000 to FY
2020, the 4-quarter percent change moving average growth in the iBoxx
series was approximately 0.1 percentage point higher, on average, than
the Moody's AAA corporate Bond Yield index.
Other Capital: Since this category includes fees for
insurances, taxes, and other capital-related costs, we are proposing to
use the CPI for Rent of Primary Residence (BLS series code
CUUS0000SEHA), which would reflect the price growth of these costs.
This is the same index used in the 2014-based SNF market basket.
We believe that these price proxies are the most appropriate
proxies for SNF capital costs that meet our selection criteria of
relevance, timeliness, availability, and reliability.
As stated above, we are proposing to continue to vintage weight the
capital price proxies for Depreciation and Interest to capture the
long-term consumption of capital. To capture the long-term nature, the
price proxies are vintage-weighted; and the vintage weights are
calculated using a two-step process. First, we determine the expected
useful life of capital and debt instruments held by SNFs. Second, we
identify the proportion of expenditures within a cost category that is
attributable to each individual year over the useful life of the
relevant capital assets, or the vintage weights.
We rely on Bureau of Economic Analysis (BEA) fixed asset data to
derive the useful lives of both fixed and movable capital, which is the
same data source used to derive the useful lives for the 2014-based SNF
market basket. The specifics of the data sources used are explained
below.
a. Calculating Useful Lives for Moveable and Fixed Assets
Estimates of useful lives for movable and fixed assets for the
proposed 2018-based SNF market basket are 9 and 26 years, respectively.
These estimates are based on three data sources from the BEA: (1)
Current-cost average age; (2) historical-cost average age; and (3)
industry-specific current cost net stocks of assets.
BEA current-cost and historical-cost average age data by asset type
are not available by industry but are published at the aggregate level
for all industries. The BEA does publish current-cost net capital
stocks at the detailed asset level for specific industries. There are
64 detailed movable assets (including intellectual property) and there
are 32 detailed fixed assets in the BEA estimates. Since we seek
aggregate useful life estimates applicable to SNFs, we developed a
methodology to approximate movable and fixed asset ages for nursing and
residential care services (NAICS 623) using the published BEA data. For
the proposed 2018 SNF market basket, we use the current-cost average
age for each asset type from the BEA fixed assets Table 2.9 for all
assets and weight them using current-cost net stock levels for each of
these asset types in the nursing and residential care services
industry, NAICS 6230. (For example, nonelectro medical equipment
current-cost net stock (accounting for about 35 percent of total
moveable equipment current-cost net stock in 2018) is multiplied by an
average age of 4.7 years. Current-cost net stock levels are available
for download from the BEA website at https://apps.bea.gov/iTable/index_FA.cfm. We then aggregate the ``weighted'' current-cost net stock
levels (average age multiplied by current-cost net stock) into moveable
and fixed assets for NAICS 6230. We then adjust the average ages for
moveable and fixed assets by the ratio of historical-cost average age
(Table 2.10) to current-cost average age (Table 2.9).
This produces historical cost average age data for movable
(equipment and intellectual property) and fixed (structures) assets
specific to NAICS 6230 of 4.7 and 13.1 years for 2018, respectively.
The average age reflects the average age of an asset at a given point
in time, whereas we want to estimate a useful life of the asset, which
would reflect the average over all periods an asset is used. To do
this, we multiply each of the average age estimates by two to convert
to average useful lives with the assumption that the average age is
normally distributed (about half of the assets are below the average at
a given point in time, and half above the average at a given point in
time). This produces estimates of likely useful lives of 9.49 and 26.19
years for movable and fixed assets, which we round to 9 and 26 years,
respectively. We are proposing an interest vintage weight time span of
24 years, obtained by weighting the fixed and movable vintage weights
(26 years and 9 years, respectively) by the fixed and movable split (86
percent and 14 percent, respectively). This is the same methodology
used for the 2014-based SNF market basket, which had useful lives of 23
years and 10 years for fixed and moveable assets, respectively. We
estimate that the impact of revising the useful lives had a minor
impact on the average historical growth rate of the proposed 2018-based
SNF market basket total aggregate capital cost price proxy. Over the FY
2016 to FY 2020 time period, the percent change moving average in the
total aggregate capital cost price proxy was about 0.06 percentage
point higher, on average, based on the proposed 2018-based SNF market
basket compared to the 2014-based SNF market basket.
b. Constructing Vintage Weights
Given the expected useful life of capital (fixed and moveable
assets) and debt instruments, we must determine the proportion of
capital expenditures attributable to each year of the expected useful
life for each of the three asset types: Building and fixed equipment,
moveable equipment, and interest. These proportions represent the
vintage weights. We were not able to find a historical time series of
capital expenditures by SNFs. Therefore, we approximated the capital
expenditure patterns of SNFs over time, using alternative SNF data
sources. For building and fixed equipment, we used the stock of beds in
nursing homes from the National Nursing Home Survey (NNHS) conducted by
the National
[[Page 19980]]
Center for Health Statistics (NCHS) for 1962 through 1999. For 2000
through 2010, we extrapolated the 1999 bed data forward using a 5-year
moving average of growth in the number of beds from the SNF MCR data.
For 2011 to 2014, we extrapolate the 2010 bed data forward using the
average growth in the number of beds over the 2011 to 2014 time period.
For 2015 to 2018, we propose to extrapolate the 2014 bed data forward
using the average growth in the number of beds over the 2015 to 2018
time period. We then used the change in the stock of beds each year to
approximate building and fixed equipment purchases for that year. This
procedure assumes that bed growth reflects the growth in capital-
related costs in SNFs for building and fixed equipment. We believe that
this assumption is reasonable because the number of beds reflects the
size of a SNF, and as a SNF adds beds, it also likely adds fixed
capital.
As was done for the 2014-based SNF market basket (as well as prior
market baskets), we are proposing to estimate moveable equipment
purchases based on the ratio of ancillary costs to routine costs. The
time series of the ratio of ancillary costs to routine costs for SNFs
measures changes in intensity in SNF services, which are assumed to be
associated with movable equipment purchase patterns. The assumption
here is that as ancillary costs increase compared to routine costs, the
SNF caseload becomes more complex and would require more movable
equipment. The lack of movable equipment purchase data for SNFs over
time required us to use alternative SNF data sources. A more detailed
discussion of this methodology was published in the FY 2008 SNF final
rule (72 FR 43428). We believe the resulting two time series,
determined from beds and the ratio of ancillary to routine costs,
reflect real capital purchases of building and fixed equipment and
movable equipment over time.
To obtain nominal purchases, which are used to determine the
vintage weights for interest, we converted the two real capital
purchase series from 1963 through 2018 determined above to nominal
capital purchase series using their respective price proxies (the BEA
Chained Price Index for Nonresidential Construction for Hospitals &
Special Care Facilities and the PPI for Machinery and Equipment). We
then combined the two nominal series into one nominal capital purchase
series for 1963 through 2018. Nominal capital purchases are needed for
interest vintage weights to capture the value of debt instruments.
Once we created these capital purchase time series for 1963 through
2018, we averaged different periods to obtain an average capital
purchase pattern over time: (1) For building and fixed equipment, we
averaged 31, 26-year periods; (2) for movable equipment, we averaged
48, 9-year periods; and (3) for interest, we averaged 33, 24-year
periods. We calculate the vintage weight for a given year by dividing
the capital purchase amount in any given year by the total amount of
purchases during the expected useful life of the equipment or debt
instrument. To provide greater transparency, we posted on the CMS
market basket website at http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/MedicareProgramRatesStats/MarketBasketResearch.html, an illustrative spreadsheet that contains an
example of how the vintage-weighted price indexes are calculated.
The vintage weights for the proposed 2018-based SNF market basket
and the 2014-based SNF market basket are presented in Table 19.
Table 19--Proposed 2018-Based Vintage Weights and 2014-Based Vintage Weights
--------------------------------------------------------------------------------------------------------------------------------------------------------
Building and fixed equipment Movable equipment Interest
-----------------------------------------------------------------------------------------------
Year \1\ Proposed 2018- 2014-Based 23 Proposed 2018- 2014-Based 10 Proposed 2018- 2014-Based 21
based 26 years years based 9 years years based 24 years years
--------------------------------------------------------------------------------------------------------------------------------------------------------
1....................................................... 0.049 0.056 0.135 0.085 0.027 0.032
2....................................................... 0.050 0.055 0.140 0.087 0.028 0.033
3....................................................... 0.049 0.054 0.128 0.091 0.029 0.034
4....................................................... 0.047 0.052 0.112 0.097 0.031 0.036
5....................................................... 0.045 0.049 0.119 0.099 0.032 0.037
6....................................................... 0.043 0.046 0.111 0.102 0.034 0.039
7....................................................... 0.041 0.044 0.084 0.108 0.036 0.041
8....................................................... 0.040 0.043 0.080 0.109 0.037 0.043
9....................................................... 0.037 0.040 0.091 0.110 0.038 0.044
10...................................................... 0.035 0.038 .............. 0.112 0.040 0.045
11...................................................... 0.036 0.038 .............. .............. 0.043 0.048
12...................................................... 0.036 0.039 .............. .............. 0.047 0.052
13...................................................... 0.036 0.039 .............. .............. 0.049 0.056
14...................................................... 0.036 0.039 .............. .............. 0.051 0.058
15...................................................... 0.035 0.039 .............. .............. 0.050 0.060
16...................................................... 0.036 0.039 .............. .............. 0.048 0.059
17...................................................... 0.036 0.040 .............. .............. 0.048 0.057
18...................................................... 0.038 0.041 .............. .............. 0.048 0.057
19...................................................... 0.037 0.043 .............. .............. 0.048 0.056
20...................................................... 0.036 0.042 .............. .............. 0.048 0.056
21...................................................... 0.035 0.042 .............. .............. 0.047 0.057
22...................................................... 0.035 0.042 .............. .............. 0.047 ..............
23...................................................... 0.035 0.042 .............. .............. 0.047 ..............
24...................................................... 0.033 .............. .............. .............. 0.049 ..............
25...................................................... 0.032 .............. .............. .............. .............. ..............
26...................................................... 0.032 .............. .............. .............. .............. ..............
-----------------------------------------------------------------------------------------------
[[Page 19981]]
Total............................................... 1.000 1.000 1.000 1.000 1.000 1.000
--------------------------------------------------------------------------------------------------------------------------------------------------------
Note: The vintage weights are calculated using thirteen decimals. For presentation purposes, we are displaying three decimals and therefore, the detail
vintage weights may not add to 1.000 due to rounding.
\1\ Year 1 represents the vintage weight applied to the farthest year while the vintage weight for year 26, for example, would apply to the most recent
year.
Table 20 shows all the price proxies for the proposed 2018-based
SNF market basket.
Table 20--Proposed Price Proxies for the Proposed 2018-Based SNF Market
Basket
------------------------------------------------------------------------
Cost category Weight Proposed price proxy
------------------------------------------------------------------------
Total.......................... 100.0
Compensation................... 60.2
Wages and Salaries \1\..... 50.4 ECI for Wages and
Salaries for Private
Industry Workers in
Nursing Care
Facilities.
Employee Benefits \1\...... 9.9 ECI for Total Benefits
for Private Industry
Workers in Nursing
Care Facilities.
Utilities...................... 1.5
Electricity and Other Non- 1.0 PPI Commodity for
Fuel Utilities. Commercial Electric
Power.
Fuel: Oil and Gas.......... 0.4 Blend of Fuel PPIs.
Professional Liability 1.1 CMS Professional
Insurance. Liability Insurance
Premium Index.
All Other...................... 29.0
Other Products............. 17.6
Pharmaceuticals........ 7.5 PPI Commodity for
Pharmaceuticals for
Human Use,
Prescription.
Food: Direct Purchase.. 2.5 PPI Commodity for
Processed Foods and
Feeds.
Food: Contract Purchase 4.3 CPI for Food Away From
Home (All Urban
Consumers).
Chemicals.............. 0.2 Blend of Chemical PPIs.
Medical Instruments and 0.6 Blend of Medical
Supplies. Instruments and
Supplies PPIs.
Rubber and Plastics.... 0.7 PPI Commodity for
Rubber and Plastic
Products.
Paper and Printing 0.5 PPI Commodity for
Products. Converted Paper and
Paperboard Products.
Apparel................ 0.5 PPI Commodity for
Apparel.
Machinery and Equipment 0.5 PPI Commodity for
Machinery and
Equipment.
Miscellaneous Products. 0.3 PPI Commodity for
Finished Goods Less
Food and Energy.
All Other Services............. 11.5
Labor-Related Services..... 6.4
Professional Fees: 3.5 ECI for Total
Labor-related. Compensation for
Private Industry
Workers in
Professional and
Related.
Installation, 0.6 ECI for Total
Maintenance, and Compensation for All
Repair Services. Civilian workers in
Installation,
Maintenance, and
Repair.
Administrative and 0.4 ECI for Total
Facilities Support. Compensation for
Private Industry
Workers in Office and
Administrative
Support.
All Other: Labor- 1.9 ECI for Total
Related Services. Compensation for
Private Industry
Workers in Service
Occupations.
Non Labor-Related Services. 5.1
Professional Fees: 2.0 ECI for Total
Nonlabor-Related. Compensation for
Private Industry
Workers in
Professional and
Related.
Financial Services..... 1.3 ECI for Total
Compensation for
Private Industry
Workers in Financial
Activities.
Telephone Services..... 0.3 CPI for Telephone
Services.
All Other: Nonlabor- 1.5 CPI for All Items Less
Related Services. Food and Energy.
Capital-Related Expenses... 8.2
Total Depreciation......... 3.0
Building and Fixed 2.5 BEA's Chained Price
Equipment. Index for Private
Fixed Investment in
Structures,
Nonresidential,
Hospitals and Special
Care--vintage weighted
26 years.
Movable Equipment...... 0.4 PPI Commodity for
Machinery and
Equipment--vintage
weighted 9 years.
Total Interest............. 2.7
[[Page 19982]]
For-Profit SNFs........ 0.7 iBoxx--Average yield on
Aaa bond--vintage
weighted 24 years.
Government and 2.0 Bond Buyer--Average
Nonprofit SNFs. yield on Domestic
Municipal Bonds--
vintage weighted 24
years.
Other Capital-Related 2.6 CPI for Rent of Primary
Expenses. Residence.
------------------------------------------------------------------------
Note: The cost weights are calculated using three decimal places. For
presentation purposes, we are displaying one decimal and, therefore,
the detailed cost weights may not add to the aggregate cost weights or
to 100.0 due to rounding.
\1\ Contract labor is distributed to wages and salaries and employee
benefits based on the share of total compensation that each category
represents.
4. Labor-Related Share
We define the labor-related share (LRS) as those expenses that are
labor-intensive and vary with, or are influenced by, the local labor
market. Each year, we calculate a revised labor-related share based on
the relative importance of labor-related cost categories in the input
price index. Effective for FY 2022, we are proposing to revise and
update the labor-related share to reflect the relative importance of
the proposed 2018-based SNF market basket cost categories that we
believe are labor-intensive and vary with, or are influenced by, the
local labor market. For the proposed 2018-based SNF market basket these
are: (1) Wages and Salaries (including allocated contract labor costs
as described above); (2) Employee Benefits (including allocated
contract labor costs as described above); (3) Professional fees: Labor-
related; (4) Administrative and Facilities Support Services; (5)
Installation, Maintenance, and Repair Services; (6) All Other: Labor-
Related Services; and (7) a proportion of capital-related expenses. We
propose to continue to include a proportion of capital-related expenses
because a portion of these expenses are deemed to be labor-intensive
and vary with, or are influenced by, the local labor market. For
example, a proportion of construction costs for a medical building
would be attributable to local construction workers' compensation
expenses.
Consistent with previous SNF market basket revisions and rebasings,
the All Other: Labor-related services cost category is mostly comprised
of building maintenance and security services (including, but not
limited to, landscaping services, janitorial services, waste management
services services) and dry cleaning and laundry services. Because these
services tend to be labor-intensive and are mostly performed at the SNF
facility or in the local area (and therefore, unlikely to be purchased
in the national market), we believe that they meet our definition of
labor-related services.
These are the same cost categories we have included in the LRS for
the 2014-based SNF market basket rebasing (82 FR 36563) as well as the
same categories included in the LRS for the 2016-based IRF market
basket (84 FR 39087), 2016-based IPF market basket (84 FR 38445), and
2017-based LTCH market basket (85 FR 58910).
As discussed in the FY 2018 SNF PPS proposed rule (82 FR 21040), in
an effort to determine more accurately the share of nonmedical
professional fees (included in the proposed 2018-based SNF market
basket Professional Fees cost categories) that should be included in
the labor-related share, we surveyed SNFs regarding the proportion of
those fees that are attributable to local firms and the proportion that
are purchased from national firms. Based on these weighted results, we
determined that SNFs purchase, on average, the following portions of
contracted professional services inside their local labor market:
78 percent of legal services.
86 percent of accounting and auditing services.
89 percent of architectural, engineering services.
87 percent of management consulting services.
Together, these four categories represent 3.5 percentage points of
the total costs for the proposed 2018-based SNF market basket. We
applied the percentages from this special survey to their respective
SNF market basket weights to separate them into labor-related and
nonlabor-related costs. As a result, we are designating 2.9 of the 3.5
percentage points total to the labor-related share, with the remaining
0.6 percentage point categorized as nonlabor-related.
In addition to the professional services as previously listed, for
the 2018-based SNF market basket, we propose to allocate a proportion
of the Home Office/Related Organization Contract Labor cost weight,
calculated using the Medicare cost reports as previously stated, into
the Professional Fees: Labor-related and Professional Fees: Nonlabor-
related cost categories. We propose 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.
Similar to the 2014-based SNF market basket, we propose for the
2018-based SNF market basket to use the Medicare cost reports for SNFs
to determine the home office labor-related percentages. The Medicare
cost report requires a SNF to report information regarding their home
office provider. Using information on the Medicare cost report, we
compared the location of the SNF with the location of the SNF's home
office. We propose to classify a SNF with a home office located in
their respective labor market if the SNF and its home office are
located in the same Metropolitan Statistical Area (MSA). Then we
determine the proportion of the Home Office/Related Organization
Contract Labor cost weight that should be allocated to the labor-
related share based on the percent of total Home Office/Related
Organization Contract Labor costs for those SNFs that had home offices
located in their respective local labor markets of total Home Office/
Related Organization Contract Labor costs for SNFs with a home office.
We determined a SNF's and its home office's MSA using their zip code
information from the Medicare cost report. Using this methodology, we
determined that 21 percent of SNFs' Home Office/Related Organization
Contract Labor costs were for home offices located in their respective
local labor markets. Therefore, we propose to allocate 21 percent of
the Home Office/Related Organization Contract Labor cost weight (0.14
percentage point = 0.69 percent x 21 percent) to the Professional Fees:
Labor-related cost weight and 79 percent of the Home Office/Related
Organization Contract Labor cost weight to the Professional Fees:
Nonlabor-related cost weight (0.55
[[Page 19983]]
percentage point = 0.69 percent x 79 percent). The 2014-based SNF
market basket used a similar methodology for allocating the Home
Office/Related Organization Contract Labor cost weight to the labor-
related share.
In summary, based on the two allocations mentioned earlier, we
propose to apportion 3.0 percentage points of the Professional Fees
(2.9 percentage points) and Home Office/Related Organization Contract
Labor (0.1 percentage point) cost weights into the Professional Fees:
Labor-Related cost category. This amount was added to the portion of
professional fees that we already identified as labor-related using the
I-O data such as contracted advertising and marketing costs
(approximately 0.45 percentage point of total costs) resulting in a
Professional Fees: Labor-Related cost weight of 3.5 percent.
Table 21 compares the FY 2022 labor-related share based on the
proposed 2018-based SNF market basket relative importance and the FY
2021 labor-related share based on the 2014-based SNF market basket
relative importance as finalized in the FY 2021 SNF final rule (85 FR
47605).
Table 21--FY 2021 and Proposed FY 2022 SNF Labor-Related Share
------------------------------------------------------------------------
Relative Proposed relative
importance, labor- importance, labor-
related share, FY related share, FY
2021 20:2 forecast 2022 20:4 forecast
\1\ \2\
------------------------------------------------------------------------
Wages and salaries \3\.......... 51.1 51.2
Employee benefits\*\............ 9.9 9.5
Professional fees: Labor-related 3.7 3.5
Administrative & facilities 0.5 0.6
support services...............
Installation, maintenance & 0.6 0.4
repair services................
All other: Labor-related 2.6 1.9
services.......................
Capital-related (.391).......... 2.9 3.0
---------------------------------------
Total....................... 71.3 70.1
------------------------------------------------------------------------
\1\ Published in the Federal Register (85 FR 47605); based on the second
quarter 2020 IHS Global Inc. forecast of the 2014-based SNF market
basket, with historical data through first quarter 2020.
\2\ Based on the fourth quarter 2020 IHS Global Inc. forecast of the
proposed 2018-based SNF market basket.
\3\ The Wages and Salaries and Employee Benefits cost weight reflect
contract labor costs as described above.
The proposed FY 2022 SNF labor-related share is 1.2 percentage
points lower than the FY 2021 SNF labor-related share (based on the
2014-based SNF market basket). The major reason for the lower labor-
related share is due to the incorporation of the 2012 Benchmark I-O
data, primarily stemming from a decrease in the All Other: Labor-
related services and Professional Fees: Labor-related services cost
weights, and a decrease in the Compensation cost weight as a result of
incorporating the 2018 MCR data.
5. Proposed Market Basket Estimate for the FY 2022 SNF PPS Update
As discussed previously in this proposed rule, beginning with the
FY 2022 SNF PPS update, we are proposing to adopt the 2018-based SNF
market basket as the appropriate market basket of goods and services
for the SNF PPS. Consistent with historical practice, we estimate the
market basket update for the SNF PPS based on IHS Global Inc.'s (IGI)
forecast. IGI is a nationally recognized economic and financial
forecasting firm that contracts with CMS to forecast the components of
the market baskets and multifactor productivity (MFP). Based on IGI's
fourth quarter 2020 forecast with historical data through the third
quarter of 2020, the most recent estimate of the proposed 2018-based
SNF market basket update for FY 2022 is 2.3 percent-0.1 percentage
point lower (after rounding) than the FY 2022 percent change of the
2014-based SNF market basket. We are also proposing that if more recent
data subsequently become available (for example, a more recent estimate
of the market basket and/or the MFP), we would use such data, if
appropriate, to determine the FY 2022 SNF market basket percentage
change, labor-related share relative importance, forecast error
adjustment, or MFP adjustment in the SNF PPS final rule.
Table 22 compares the proposed 2018-based SNF market basket and the
2014-based SNF market basket percent changes. For the historical period
between FY 2017 and FY 2020, there is no difference in the average
growth rates between the two market baskets. For the forecasted period
between FY 2021 and FY 2023, the average difference between the two
market baskets is -0.1 percentage point.
Table 22--Proposed 2018-Based SNF Market Basket and 2014-Based SNF
Market Basket, Percent Changes: 2017-2023
------------------------------------------------------------------------
Proposed 2018-
Fiscal year (FY) Based SNF market 2014-Based SNF
basket market basket
------------------------------------------------------------------------
Historical data:
FY 2017..................... 2.5 2.7
FY 2018..................... 2.6 2.6
FY 2019..................... 2.4 2.3
FY 2020..................... 2.1 2.0
Average FY 2017-2020........ 2.4 2.4
Forecast:
FY 2021..................... 2.4 2.4
FY 2022..................... 2.3 2.4
[[Page 19984]]
FY 2023..................... 2.6 2.7
Average FY 2021-2023........ 2.4 2.5
------------------------------------------------------------------------
Source: IHS Global, Inc. 4th quarter 2020 forecast with historical data
through 3rd quarter 2020.
B. Technical Updates to PDPM ICD-10 Mappings
In the FY 2019 SNF PPS final rule (83 FR 39162), we finalized the
implementation of the Patient Driven Payment Model (PDPM), effective
October 1, 2019. The PDPM utilizes International Classification of
Diseases, Version 10 (ICD-10) codes in several ways, including to
assign patients to clinical categories used for categorization under
several PDPM components, specifically the PT, OT, SLP and NTA
components. The ICD-10 code mappings and lists used under PDPM are
available on the PDPM website at https://www.cms.gov/Medicare/MedicareFee-for-Service-Payment/SNFPPS/PDPM.
Each year, the ICD-10 Coordination and Maintenance Committee, a
Federal interdepartmental committee that is chaired by representatives
from the National Center for Health Statistics (NCHS) and by
representatives from CMS, meets biannually and publishes updates to the
ICD-10 medical code data sets in June of each year. These changes
become effective October 1 of the year in which these updates are
issued by the committee. The ICD-10 Coordination and Maintenance
Committee also has the ability to make changes to the ICD-10 medical
code data sets effective on April 1.
In the FY 2020 SNF PPS final rule (84 FR 38750), we outlined the
process by which we maintain and update the ICD-10 code mappings and
lists associated with the PDPM, as well as the SNF GROUPER software and
other such products related to patient classification and billing, so
as to ensure that they reflect the most up to date codes possible.
Beginning with the updates for FY 2020, we apply nonsubstantive changes
to the ICD-10 codes included on the PDPM code mappings and lists
through a subregulatory process consisting of posting updated code
mappings and lists on the PDPM website at https://www.cms.gov/Medicare/Medicare-Fee-for-ServicePayment/SNFPPS/PDPM. Such nonsubstantive
changes are limited to those specific changes that are necessary to
maintain consistency with the most current ICD-10 medical code data
set. On the other hand, substantive changes, or those that go beyond
the intention of maintaining consistency with the most current ICD-10
medical code data set, will be proposed through notice and comment
rulemaking. For instance, changes to the assignment of a code to a
comorbidity list or other changes that amount to changes in policy are
considered substantive changes for which we would undergo notice and
comment rulemaking.
We are proposing several changes to the PDPM ICD-10 code mappings
and lists. Our proposed changes are as follows:
On October 1, 2020 two ICD-10 codes representing types of sickle-
cell disease; D57.42 ``Sickle-cell thalassemia beta zero without
crisis'' and D57.44 ``Sickle-cell thalassemia beta plus without
crisis'' took effect and were clinically mapped to the category of
``Medical Management''. However, there are more specific codes to
indicate why a patient with sickle-cell disease would require SNF care,
and if the patient is not in crisis, this most likely indicates that
SNF care is not required. For this reason, we propose to change the
assignment of D57.42 and D57.44 to ``Return to Provider''.
On October 1, 2020, three new ICD-10 codes representing types of
esophageal conditions; K20.81 ``Other esophagitis with bleeding'',
K20.91, ``Esophagitis, unspecified with bleeding, and K21.01 ``Gastro-
esophageal reflux disease with esophagitis, with bleeding'' took effect
and were clinically mapped to ``Return to Provider''. Upon review of
these codes, we recognize that these codes represent these esophageal
conditions with more specificity than originally considered because of
the bleeding that is part of the conditions and that they would more
likely be found in SNF patients. Therefore, we propose to change the
assignment of K20.81, K20.91, and K21.01 to ``Medical Management'' in
order to promote more accurate clinical category assignment.
In December 2020, the CDC announced several additions to the ICD-10
Classification related to COVID-19 that became effective on January 1,
2021. One such code, M35.81 ``Multisystem inflammatory syndrome'', was
assigned to ``Non-Surgical Orthopedic/Musculoskeletal''. However,
Multisystem inflammatory syndrome can involve more than the
musculoskeletal system. It can also involve the gastrointestinal tract,
heart, central nervous system, and kidneys. For this reason, we propose
to change the assignment of M35.81 to ``Medical Management'' in order
to promote more accurate clinical category assignment.
On October 1, 2020, three new ICD-10 codes representing types of
neonatal cerebral infarction were classified as ``Return to Provider.''
These codes were P91.821 ``Neonatal cerebral infarction, right side of
brain,'' P91.822, ``Neonatal cerebral infarction, left side of brain,''
and P91.823, ``Neonatal cerebral infarction, bilateral.'' While a
neonate is unlikely to be a Medicare beneficiary, this diagnosis could
continue to be used later in life hence placing those with this
condition in the acute neurologic category. Therefore, we propose to
change the assignment of P91.821, P91.822, and P91.823 to ``Acute
Neurologic'' in order to promote more accurate clinical category
assignment.
On April 1, 2020, U07.0, ``Vaping-related disorder,'' took effect
and was classified as a ``Return to Provider'' code because at the
time, ``Vaping-related disorder'' was not considered a code that would
be a primary diagnosis during a SNF stay. However, upon further review,
we believe that many patients who exhibit this diagnosis require
steroids, empiric antibiotics and oxygen for care which could carry
over to the post-acute setting. For this reason, we propose to change
the assignment of U07.0 to ``Pulmonary'' classification in order to
promote more accurate clinical category assignment.
In the FY 2021 proposed rule (85 FR 20939), we sought comments on
additional substantive and nonsubstantive changes that commenters
believed were necessary. We received three comments suggesting several
changes to the ICD-10 to clinical category mappings. One of those
changes was substantive, requiring notice and comment rulemaking. The
[[Page 19985]]
commenter suggested that the FY 2020 ICD-10 to clinical category
mapping of G93.1 ``Anoxic brain damage, not elsewhere classified'' be
changed to ``Acute Neurologic'' from ``Return to Provider,'' which we
would consider a substantive change. Codes that result in ``Return to
Provider'' are codes that cannot be used in I0020B of the MDS because
item I0020B is used to establish the primary medical condition that a
patient presents with during a SNF stay. Although some codes are
considered ``Return to Provider'' for payment purposes, they are still
used to support the care and services used for secondary and co-
morbidity diagnoses. The ICD-10 code, G93.1 was initially clinically
mapped to ``Return to provider'' because ``Anoxic brain damage, not
elsewhere classified'' was non-specific and did not fully describe a
patient's deficits and may not have been an acute condition. However,
upon further review, our clinicians determined that although this may
not be an acute condition, ``Anoxic brain damage, not elsewhere
classified'' would still likely result in a need for SNF care and is
similar to conditions such as ``Compression of the brain'', ``Cerebral
edema'', and ``encephalopathy'', which are mapped into the ``Acute
Neurologic'' category. Therefore, we propose to change the assignment
of G93.1 ``Anoxic brain damage, not elsewhere classified'' to ``Acute
Neurologic''.
We invite comments on the proposed substantive changes to the ICD-
10 code mappings discussed previously, as well as comments on
additional substantive and non-substantive changes that commenters
believe are necessary.
C. Recalibrating the PDPM Parity Adjustment
1. Background
On October 1, 2019, we implemented the Patient Driven Payment Model
(PDPM) under the SNF PPS, a new case-mix classification model that
replaced the prior case-mix classification model, the Resource
Utilization Groups, Version IV (RUG-IV). As discussed in the FY 2019
SNF PPS final rule (83 FR 39256), as with prior system transitions, we
proposed and finalized to implement PDPM in a budget neutral manner.
This means that the transition to PDPM, along with the related policies
finalized in the FY 2019 SNF PPS final rule, were not intended to
result in an increase or decrease in the aggregate amount of Medicare
payment to SNFs. We believe ensuring parity is integral to the process
of providing ``for an appropriate adjustment to account for case mix''
that is based on appropriate data in accordance with section
1888(e)(4)(G)(i) of the Act. Section V.I. of the FY 2019 SNF PPS final
rule (83 FR 39255 through 39256) discusses the methodology that we used
to implement PDPM in a budget neutral manner. Specifically, we
multiplied each of the PDPM case-mix indexes (CMI) by an adjustment
factor that was calculated by comparing total payments under RUG-IV,
using FY 2017 claims and assessment data (the most recent final claims
data available at the time), and what we expected total payments would
be under the then proposed PDPM based on that same FY 2017 claims and
assessment data. In the FY 2020 SNF PPS final rule (84 FR 38734-38735),
CMS finalized an updated standardization multiplier and parity
adjustment based on FY 2018 claims and assessment data. Through this
comparison, and as discussed in the FY 2020 SNF PPS final rule, this
analysis resulted in an adjustment factor of 1.46, by which the PDPM
CMIs were multiplied so that total estimated payments under PDPM would
be equal to total actual payments under RUG-IV, assuming no changes in
the population, provider behavior, and coding. By multiplying the CMIs
by 1.46, the CMIs were inflated by 46 percent in order to achieve
budget neutrality.
A similar type of adjustment was used when we transitioned from
RUG-III to RUG-IV in FY 2011. However, as discussed in the FY 2012 SNF
PPS final rule (76 FR 48492 through 48500), we observed that, once
actual RUG-IV utilization data became available, the actual RUG-IV
utilization patterns differed significantly from those we had projected
using the historical data that grounded the RUG-IV parity adjustment.
As a result, in the FY 2012 SNF PPS final rule, we used actual FY 2011
RUG-IV utilization data to recalibrate the RUG-IV parity adjustment.
Based on the use of FY 2011 RUG-IV utilization data, we decreased the
RUG-IV parity adjustment applied to the nursing CMIs for all RUG-IV
therapy groups from an adjustment factor of 61 percent to an adjustment
factor of 19.84 percent (while maintaining the original 61 percent
total nursing CMI increase for all non-therapy RUG-IV groups). As a
result of this recalibration, FY 2012 SNF PPS rates were reduced by
12.5 percent, or $4.47 billion, in order to achieve budget neutrality
under RUG-IV prospectively.
Since PDPM implementation, we have closely monitored PDPM
utilization data to ascertain, among other things, if the PDPM parity
adjustment provided for a budget neutral transition to this new case-
mix classification model. Similar to what occurred in FY 2011 with RUG-
IV implementation, we have observed significant differences between
expected SNF PPS payments and case-mix utilization, based on historical
data, and the actual SNF PPS payments and case-mix utilization under
the PDPM, based on FY 2020 data. As a result, it would appear that
rather than simply achieving parity, the FY 2020 parity adjustment may
have inadvertently triggered a significant increase in overall payment
levels under the SNF PPS. We believe that, based on the data from this
initial phase of PDPM, a recalibration of the PDPM parity adjustment is
warranted to ensure that the adjustment serves its intended purpose to
make the transition between RUG-IV and PDPM budget neutral.
However, we also acknowledge that the pandemic-related PHE for
COVID-19, which began during the first year of PDPM and has continued
into at least part of FY 2021, has had a likely impact on SNF PPS
utilization data. Further, following the methodology utilized in
calculating the initial parity adjustment, we typically would use
claims and assessment data for a given year to classify patients under
both the current system and the prior system to compare aggregate
payments between the prior system and new system and determine an
appropriate adjustment factor to achieve parity. When we performed a
similar recalibration of the RUG-IV parity adjustment, for example, we
used data from FY 2011, the first year of RUG-IV implementation, as the
basis for recalibrating the RUG-IV parity adjustment. However, in
addition to the aforementioned potential issues with the FY 2020 SNF
utilization data arising from the PHE for COVID-19, we are concerned
that given the significant differences in both patient assessment
requirements and payment incentives between RUG-IV and PDPM, using the
same methodology we have used in the past to calculate a recalibrated
PDPM parity adjustment could lead to a potentially inaccurate
recalibration.
Therefore, given these issues, and for the reasons below, we are
taking this opportunity to present some of the results of our PDPM data
monitoring efforts and a potential recalibration methodology intended
to address the issues presented above. First, it is important to
provide transparency on the observed impacts of PDPM implementation, as
we do believe there have been significant changes observed in SNF
utilization that are tied strictly to PDPM and not the PHE for COVID-
19. Second, we wish to make clear why we believe that the typical
methodology for recalibrating the parity adjustment
[[Page 19986]]
may not provide an accurate recalibration under PDPM. Finally, we view
this as an opportunity to seek comment on a path forward for
recalibrating the PDPM parity adjustment to ensure that PDPM is
implemented in a budget neutral manner, as intended.
2. FY 2020 Changes in SNF Case-Mix Utilization
FY 2020 was a year of significant change under the SNF PPS. In
addition to implementing PDPM, a national PHE for COVID-19 was
declared. With the announcement of the PHE for COVID-19, we also
announced a number of waivers which impacted SNF operations and the
population of Medicare beneficiaries who were able to access the Part A
SNF benefit. Most notably, under authority granted us by section
1812(f) of the Act, we issued a waiver of section 1861(i) of the Act,
specifically the requirement that in order for a SNF stay to be covered
by Medicare, a beneficiary must have a prior inpatient hospital stay of
not less than 3 consecutive days before being admitted to the Part A
SNF stay. Additionally, this waiver also allowed certain beneficiaries
renewed SNF coverage without first having to start a new benefit
period. The section 1812(f) waiver, particularly the component which
permits beneficiaries to access the Part A SNF benefit without a prior
hospitalization, allowed beneficiaries who would not typically be able
to access the Part A SNF benefit to receive a Part A covered SNF stay
(for example, long term care nursing home patients without any prior
hospitalization). A key aspect of our methodology for recalibrating the
PDPM parity adjustment involves parsing out the impact of these waivers
and the different population of beneficiaries that had access to the
SNF benefit as result of these waivers from the population of
beneficiaries that would have been admitted to SNFs subsequent to PDPM
implementation without these waivers, as well as differences in the
type of care these patients received. We would note that while the PHE
for COVID-19 clearly had impacts on nursing home care protocols and
many other aspects of SNF operations, the relevant issue for pursuing a
recalibration of the PDPM parity adjustment is whether or not these
changes caused the SNF case-mix distribution to be distinctly different
from what it would have been were it not for the PHE for COVID-19. In
other words, while different people were able to access the Part A SNF
benefit than would typically be able to do so, the issue is whether or
not the relative percentage of beneficiaries in each PDPM group is
different than what those percentages would have been were it not for
the PHE for COVID-19 and related waivers. We solicit comments on
whether stakeholders believe that the PHE for COVID-19 impacted on the
distribution of patient case-mix.
To understand the potential impact of the PHE for COVID-19 on SNF
utilization data, we can begin by understanding the overall utilization
of the waivers and the overall frequency of COVID-19 diagnoses among
the SNF population. In FY 2020, only approximately 9.8 percent of SNF
stays included a COVID-19 ICD-10 diagnosis code (either as a primary or
secondary diagnosis), while 15.6 percent of SNF stays utilized a
section 1812(f) waiver (with the majority of these cases using the
prior hospitalization waiver), as identified by the presence of a
``DR'' condition code on the SNF claim. As compared to prior years,
when approximately 98 percent of SNF beneficiaries had a qualifying
prior hospital stay, approximately 87 percent of SNF beneficiaries had
a qualifying prior hospitalization in FY 2020. These general statistics
are important, as they highlight that while the PHE for COVID-19
certainly impacted many aspects of nursing home operations, the
overwhelming majority of SNF beneficiaries entered into Part A SNF
stays in FY 2020 as they would have in any other year; that is, without
using a PHE-related waiver, with a prior hospitalization, and without a
COVID-19 diagnosis. In fact, as we discuss further below, even when
removing those using a PHE-related waiver and those with a COVID-19
diagnosis from our dataset, the observed inadvertent increase in SNF
payments since PDPM was implemented is approximately the same. This
would seem to imply that this ``new'' population of SNF beneficiaries
(that is, COVID-19 patients and those using a section 1812(f) waiver)
does not appear to be the cause of the increase in SNF payments after
implementation of PDPM, since we would expect a much greater impact on
the calculation of the necessary recalibration from removing this
population from our analysis if that were the case.
Moreover, we do believe that there is clear evidence that PDPM
alone is impacting certain aspects of SNF patient classification and
care provision. For example, through FY 2019, the average number of
therapy minutes SNF patients received per day was approximately 91
minutes. Beginning almost immediately with PDPM implementation (and
well before the onset of the pandemic), the average number of therapy
minutes SNF patients received per day dropped to approximately 62, a
decrease of over 30 percent. Given both the immediacy and ubiquity of
this change in the SNF data, without any concurrent change in the SNF
population, it is clear that this overall decrease in the amount of
therapy services provided to SNF patients is a result of PDPM
implementation and not other factors. A number of media articles
further corroborated this finding, which identified significant changes
in therapy staffing and care directives at the outset of PDPM.
Similarly, we also observed an increase in non-individualized modes of
therapy provision beginning with PDPM implementation. Specifically,
while the percentage of SNF stays which included concurrent or group
therapy was approximately 1 percent for each of these therapy modes
prior to FY 2020, these numbers rose to approximately 32 percent and 29
percent, respectively, beginning in the first month of PDPM
implementation. Coincidentally, these numbers then dropped to 8 percent
and 4 percent, respectively, beginning in April 2020, close to when the
PHE for COVID-19 was declared (highlighting at least one impact of the
PHE for COVID-19 on SNF care provision and utilization). We also note
that while these findings (increases in concurrent and group therapy
utilization) were anticipated prior to PDPM implementation based on
comments on the FY 2019 and FY 2020 SNF PPS proposed rules, we maintain
the belief that the unique characteristics and goals of each SNF
patient should drive patient care decisions. As we stated in the FY
2020 SNF PPS final rule (84 FR 38748), we believe that financial
motives should not override the clinical judgment of a therapist or
therapy assistant or pressure a therapist or therapy assistant to
provide less than appropriate therapy. We would also note that, despite
these changes in therapy provision, we did not identify any significant
changes in health outcomes for SNF patients. For example, we observed
no changes in the percentage of stays with falls with major injury, the
percentage of stays ending with Stage 2-4 or unstageable pressure
ulcers or deep tissue injury, the percentage of stays readmitted to an
inpatient hospital setting within 30 days of SNF discharge, or other
similar metrics. We will continue to monitor these and other metrics to
identify any adverse trends that may have been caused by changes in
care patterns that
[[Page 19987]]
accompanied the implementation of PDPM.
These changes in therapy provision highlight the reasons we believe
that the typical methodology for recalibrating a parity adjustment
would not be appropriate in the context of PDPM. As discussed
previously in this proposed rule and in the FY 2012 SNF PPS final rule
(76 FR 26371), we would typically utilize claims and assessment data
from a given period under the new payment system, classify patients
under both the current and prior payment model using this same set of
data, compare aggregate payments under each payment model, and
calculate an appropriate adjustment factor to achieve budget
neutrality. However, given the significant changes in therapy provision
since PDPM implementation, we found that using patient assessment data
collected under PDPM (for example, FY 2020 data) would lead to a
drastic underestimation of RUG-IV case mix for purposes of determining
what aggregate payments would have been under RUG-IV for the same
period. In other words, given the significant reduction in the overall
amount of therapy provided to SNF patients since PDPM implementation,
as well as changes in the way that the therapy is provided (for
example, increases in group and concurrent therapy), classifying SNF
patients into RUG-IV payment groups using data collected under PDPM
would lead to a RUG-IV case-mix distribution that contrasts
significantly with historical trends under RUG-IV. This finding is
precisely why we do not believe that the typical methodology for
recalibrating the PDPM parity adjustment would result in an accurate
calculation of the revised parity adjustment factor and may lead to an
overcorrection. We invite comments on the information presented above,
as well as on the potential impact of using the reported FY 2020
patient assessment data from the MDS to reclassify SNF beneficiaries
under RUG-IV, consistent with the same type of recalibration
methodology we have used after prior system transitions. Below, we
discuss the methodology we are considering for recalibrating the PDPM
parity adjustment, which we believe accounts for this change in therapy
provision.
3. Methodology for Recalibrating the PDPM Parity Adjustment
As discussed above, we have identified an inadvertent increase in
SNF spending since implementing PDPM. As in the past, identifying the
scope and magnitude of this type of inadvertent increase begins with
looking at the type of case-mix distribution that was expected under
the new case-mix system and the actual case-mix distribution that
occurs under the new case-mix system. In the FY 2012 SNF PPS proposed
rule (76 FR 26371), we were able to provide a table which listed each
of the RUG-IV payment groups with the projected and actual percentage
of SNF days of service associated with each group. Due to the number of
possible payment groups under PDPM, this type of table is not possible.
However, Table 23 provides the average PDPM case-mix index expected for
each of the PDPM rate components based on data from FY 2019. This
average is calculated for each component by summing the expected PDPM
case-mix index for each day of service and then dividing this number by
the total number of FY 2019 days of service. Table 23 also provides the
actual average PDPM case-mix index for each of these components in two
different ways. First, we used FY 2020 data for the full SNF population
and, following the same methodology described above to determine the
expected average PDPM case-mix index, we summed the case-mix index for
each day of service in FY 2020 and then divided this by the total
number of FY 2020 days of service. Second, we used FY 2020 data for the
SNF population excluding those SNF stays where either the patient was
diagnosed with COVID-19 or the stay utilized a PHE for COVID-19 related
waiver (for example, the waiver issued under authority granted by
section 1812(f) of the Act to allow Part A coverage of a SNF stay
without a qualifying prior hospital stay), as identified by the
presence of a ``DR'' condition code on the associated SNF claim. We
evaluated the average CMI using this subset of the SNF population as we
believe it would provide a way to identify the effect of the PHE for
COVID-19 on FY 2020 case mix and the recalibration calculation if we
were to use FY 2020 data collected during the PHE for COVID-19. The
results of this analysis are provided in Table 23.
Table 23--Average Case-Mix Index, Expected and Actual, by Component
----------------------------------------------------------------------------------------------------------------
Expected CMI Actual CMI Actual CMI
(FY 2019 (FY 2020) (FY 2020
Estimate) ---------------- without DR or
Component ---------------- COVID)
Average CMI ---------------
Average CMI Average CMI
----------------------------------------------------------------------------------------------------------------
PT.............................................................. 1.53 1.50 1.52
OT.............................................................. 1.52 1.51 1.52
SLP............................................................. 1.39 1.71 1.67
Nursing......................................................... 1.43 1.67 1.62
NTA............................................................. 1.14 1.20 1.21
----------------------------------------------------------------------------------------------------------------
According to this analysis, while we observed slight decreases in
the average CMI for the PT and OT rate components for both the full and
subset FY 2020 populations as compared to what was expected, we
observed significant increases in the average CMI for the SLP, Nursing,
and NTA components for both the full and subset FY 2020 populations as
compared to what was expected, with increases of 22.6 percent, 16.8
percent, and 5.6 percent, respectively, for the full FY 2020 SNF
population. We believe these significant increases in the average case-
mix for these components is primarily responsible for the inadvertent
increase in spending under PDPM. Further, given that we observe similar
increases in the average CMI for these components even when using the
subset of the FY 2020 SNF population that excludes those patients
diagnosed with COVID-19 or who used a PHE-related waiver, we believe
that these increases in average case-mix for these components are the
result of PDPM and not the PHE for COVID-19. We invite comments on this
approach and the extent to which commenters believe that the PHE for
COVID-19 may have impacted on the PDPM case-mix distribution in ways
not captured in Table 23 or in the discussion provided here.
Our basic methodology for recalibrating the parity adjustment has
[[Page 19988]]
been to compare total payments under the new case-mix model with what
total payments would have been under the prior case-mix model, were the
new model not implemented. In the context of the PDPM, this means
comparing total FY 2020 payments under PDPM to what FY 2020 payments
would have been under RUG-IV if PDPM were not implemented. In order to
calculate the actual total payments under PDPM for this proposed rule,
we used data reported on FY 2020 claims. Specifically, we used the
Health Insurance Prospective Payment System (HIPPS) code on the SNF
claim to identify the patient's case-mix assignment and associated
CMIs, utilization days on the claim to calculate stay payments and to
compute the variable per diem adjustment, the presence of an HIV
diagnosis on the claim to account for the PDPM AIDS add-on, and
finally, we accounted for the provider's urban or rural status. As with
the analysis that led to Table 23, we calculated total payments both
for the full SNF population in FY 2020, as well as the subset of that
population removing those with a COVID-19 diagnosis and those using a
PHE-related waiver.
In order to calculate expected total payments under RUG-IV, in
light of the discussion above (which describes why we believe it would
not be appropriate simply to reclassify SNF patients under RUG-IV using
the information reported in FY 2020), we used the percentage of stays
in each RUG-IV group in FY 2019 and multiplied these percentages by the
total number of FY 2020 days of service. We then multiplied the number
of days for each RUG-IV group by the RUG-IV per diem rate, which we
obtained by inflating the FY 2019 SNF PPS RUG-IV rates by the FY 2020
market basket update factor, as we would have were it not for the
implementation of PDPM. The total payments under RUG-IV also account
for the difference in how the AIDS add-on is calculated under RUG-IV,
as compared to PDPM, and similarly accounts for a provider's FY 2020
urban or rural status.
We believe that this methodology provides a more accurate
representation of what RUG-IV payments would have been in FY 2020 were
it not for the change in payment incentives and care provision
precipitated by PDPM implementation, than using data reported under
PDPM to reclassify these patients under RUG-IV. In particular, given
the reduction in therapy utilization under PDPM, as compared to RUG-IV,
using the therapy utilization data reported under PDPM to reclassify
SNF patients back into RUG-IV groups would produce a case-mix
distribution that would be significantly different from the RUG-IV
case-mix distribution we would have expected were it not for PDPM
implementation. Since the reduction in therapy would lead to a
reduction in the RUG-IV case-mix assignments (for example, Ultra-High
and Very-High Rehabilitation assignments are not nearly as prevalent
using PDPM-reported data as they are using data that existed prior to
PDPM), this would lead to an underestimation of what RUG-IV payments
would have been in FY 2020. This, in turn, would lead to an
overcorrection in recalibrating the parity adjustment due to the low
estimated total RUG-IV payments. Additionally, given the significant
changes in the patient assessment schedule, specifically the removal of
the Change of Therapy Other Medicare Required Assessment, we cannot
know if the patient would continue to remain classified in the RUG-IV
group into which the patient classified on the 5-day assessment beyond
that assessment window. In other words, without having an interim
assessment between the 5-day assessment and the patient's discharge
from the facility, we would be unable to determine if the RUG-IV group
into which the patient classified on the 5-day assessment changed
during the stay or if the patient continued to receive an amount of
therapy services consistent with this initial RUG-IV classification. As
a result, using reported data under PDPM could lead to a
reclassification of patients under RUG-IV that is not consistent with
how patients would have been classified under RUG-IV if PDPM had not
been implemented. As such, we believe that using the FY 2019 RUG-IV
case-mix distribution as a proxy for what the RUG-IV case-mix
distribution would have been in FY 2020 were it not for PDPM
implementation, provides a more accurate calculation of what total RUG-
IV payments would have been during FY 2020 absent PDPM implementation.
The result of these analyses was that we identified a 5.3 percent
increase in aggregate spending under PDPM as compared to expected total
payments under RUG-IV for FY 2020 when considering the full SNF
population, and a 5.0 percent increase in aggregate spending under PDPM
for FY 2020 when considering the subset population. Although these
results are similar, in light of the potential differences in the PDPM
case-mix distribution which may have been precipitated by the admission
of patients diagnosed with COVID-19 and patients whose stays utilized a
PHE-related waiver, we believe it would be more appropriate to pursue a
recalibration using the subset population. We invite comments on our
methodology, particularly on the use of the FY 2019 RUG-IV case-mix
distribution to calculate expected FY 2020 SNF payments if PDPM were
not implemented and on using the subset FY 2020 SNF population which
excludes patients diagnosed with COVID-19 and those using a PHE-related
waiver in our recalibration calculation rather than the full FY 2020
SNF population.
Based on the above discussion and analysis, we have described above
a potential path towards a recalibration of the PDPM parity adjustment
using a subset of the full FY 2020 SNF data set. Since the initial
increase applied to the PDPM CMIs to achieve budget neutrality applied
equally across all case-mix adjusted components, we believe it would be
appropriate, in the event an adjustment is made, to adjust the CMIs
across all such components in equal measure. Using the methodology
described above, the resultant PDPM parity adjustment factor would be
lowered from 46 percent to 37 percent for each of the PDPM case-mix
adjusted components. If this were applied for FY 2022, we estimate that
this methodology would result in a reduction in SNF spending of 5.0
percent, or approximately $1.7 billion.
Tables 24 and 25 set forth what the FY 2022 PDPM CMIs and case-mix
adjusted rates would be if we applied the recalibration methodology
described above in FY 2022.
Table 24--Recalibrated PDPM Case-Mix Adjusted Federal Rates and Associated Indexes--URBAN
--------------------------------------------------------------------------------------------------------------------------------------------------------
Nursing Nursing
PDPM group PT CMI PT rate OT CMI OT rate SLP CMI SLP rate Nursing CMG CMI rate NTA CMI NTA rate
--------------------------------------------------------------------------------------------------------------------------------------------------------
A............................ 1.44 $90.49 1.40 $81.89 0.64 $15.01 ES3........ 3.82 $418.48 3.05 $252.05
B............................ 1.60 100.54 1.53 89.49 1.71 40.12 ES2........ 2.89 316.60 2.38 196.68
C............................ 1.77 111.23 1.59 93.00 2.51 58.88 ES1........ 2.76 302.36 1.73 142.97
D............................ 1.81 113.74 1.44 84.23 1.37 32.14 HDE2....... 2.26 247.58 1.25 103.30
[[Page 19989]]
E............................ 1.34 84.21 1.33 77.79 2.2 51.61 HDE1....... 1.87 204.86 0.9 74.38
F............................ 1.52 95.52 1.51 88.32 2.80 65.69 HBC2....... 2.11 231.15 0.68 56.20
G............................ 1.57 98.66 1.54 90.07 1.92 45.04 HBC1....... 1.75 191.71 ......... .........
H............................ 1.09 68.50 1.08 63.17 2.69 63.11 LDE2....... 1.96 214.72 ......... .........
I............................ 1.06 66.61 1.11 64.92 3.32 77.89 LDE1....... 1.63 178.57 ......... .........
J............................ 1.34 84.21 1.36 79.55 2.81 65.92 LBC2....... 1.62 177.47 ......... .........
K............................ 1.43 89.86 1.45 84.81 3.48 81.64 LBC1....... 1.35 147.89 ......... .........
L............................ 1.03 64.73 1.04 60.83 3.96 92.90 CDE2....... 1.76 192.81 ......... .........
M............................ 1.20 75.41 1.22 71.36 ......... ......... CDE1....... 1.52 166.52 ......... .........
N............................ 1.39 87.35 1.41 82.47 ......... ......... CBC2....... 1.46 159.94 ......... .........
O............................ 1.46 91.75 1.46 85.40 ......... ......... CA2........ 1.03 112.84 ......... .........
P............................ 1.02 64.10 1.03 60.24 ......... ......... CBC1....... 1.26 138.03 ......... .........
Q............................ ......... ......... ......... ......... ......... ......... CA1........ 0.88 96.40 ......... .........
R............................ ......... ......... ......... ......... ......... ......... BAB2....... 0.98 107.36 ......... .........
S............................ ......... ......... ......... ......... ......... ......... BAB1....... 0.93 101.88 ......... .........
T............................ ......... ......... ......... ......... ......... ......... PDE2....... 1.48 162.13 ......... .........
U............................ ......... ......... ......... ......... ......... ......... PDE1....... 1.38 151.18 ......... .........
V............................ ......... ......... ......... ......... ......... ......... PBC2....... 1.15 125.98 ......... .........
W............................ ......... ......... ......... ......... ......... ......... PA2........ 0.67 73.40 ......... .........
X............................ ......... ......... ......... ......... ......... ......... PBC1....... 1.06 116.12 ......... .........
Y............................ ......... ......... ......... ......... ......... ......... PA1........ 0.62 67.92 ......... .........
--------------------------------------------------------------------------------------------------------------------------------------------------------
Table 25: Recalibrated PDPM Case-Mix Adjusted Federal Rates and Associated Indexes--RURAL
--------------------------------------------------------------------------------------------------------------------------------------------------------
Nursing Nursing
PDPM group PT CMI PT rate OT CMI OT rate SLP CMI SLP rate Nursing CMG CMI rate NTA CMI NTA rate
--------------------------------------------------------------------------------------------------------------------------------------------------------
A............................ 1.44 $103.15 1.40 $92.11 0.64 $18.92 ES3........ 3.82 $399.80 3.05 $240.83
B............................ 1.60 114.61 1.53 100.66 1.71 50.55 ES2........ 2.89 302.47 2.38 187.92
C............................ 1.77 126.79 1.59 104.61 2.51 74.20 ES1........ 2.76 288.86 1.73 136.60
D............................ 1.81 129.65 1.44 94.74 1.37 40.50 HDE2....... 2.26 236.53 1.25 98.70
E............................ 1.34 95.98 1.33 87.50 2.2 65.03 HDE1....... 1.87 195.71 0.9 71.06
F............................ 1.52 108.88 1.51 99.34 2.8 82.77 HBC2....... 2.11 220.83 0.68 53.69
G............................ 1.57 112.46 1.54 101.32 1.92 56.76 HBC1....... 1.75 183.16 ......... .........
H............................ 1.09 78.08 1.08 71.05 2.69 79.52 LDE2....... 1.96 205.13 ......... .........
I............................ 1.06 75.93 1.11 73.03 3.32 98.14 LDE1....... 1.63 170.60 ......... .........
J............................ 1.34 95.98 1.36 89.47 2.81 83.06 LBC2....... 1.62 169.55 ......... .........
K............................ 1.43 102.43 1.45 95.40 3.48 102.87 LBC1....... 1.35 141.29 ......... .........
L............................ 1.03 73.78 1.04 68.42 3.96 117.06 CDE2....... 1.76 184.20 ......... .........
M............................ 1.20 85.96 1.22 80.26 ......... ......... CDE1....... 1.52 159.08 ......... .........
N............................ 1.39 99.57 1.41 92.76 ......... ......... CBC2....... 1.46 152.80 ......... .........
O............................ 1.46 104.58 1.46 96.05 ......... ......... CA2........ 1.03 107.80 ......... .........
P............................ 1.02 73.06 1.03 67.76 ......... ......... CBC1....... 1.26 131.87 ......... .........
Q............................ ......... ......... ......... ......... ......... ......... CA1........ 0.88 92.10 ......... .........
R............................ ......... ......... ......... ......... ......... ......... BAB2....... 0.98 102.57 ......... .........
S............................ ......... ......... ......... ......... ......... ......... BAB1....... 0.93 97.33 ......... .........
T............................ ......... ......... ......... ......... ......... ......... PDE2....... 1.48 154.90 ......... .........
U............................ ......... ......... ......... ......... ......... ......... PDE1....... 1.38 144.43 ......... .........
V............................ ......... ......... ......... ......... ......... ......... PBC2....... 1.15 120.36 ......... .........
W............................ ......... ......... ......... ......... ......... ......... PA2........ 0.67 70.12 ......... .........
X............................ ......... ......... ......... ......... ......... ......... PBC1....... 1.06 110.94 ......... .........
Y............................ ......... ......... ......... ......... ......... ......... PA1........ 0.62 64.89 ......... .........
--------------------------------------------------------------------------------------------------------------------------------------------------------
We invite comments on the methodology described in this section of
the proposed rule for recalibrating the PDPM parity adjustment, as well
as the findings of our analysis described throughout this section. To
assist commenters in providing comments on this issue, we have also
posted a file on the CMS website, at https://www.cms.gov/snfpps, which
provides the FY 2019 RUG-IV case-mix distribution and calculation of
total payments under RUG-IV, as well as PDPM case-mix utilization data
at the case-mix group and component level to demonstrate the
calculation of total payments under PDPM. As we noted in the FY 2012
SNF PPS final rule (76 FR 48493), we believe it is imperative that we
act in a well-considered but expedient manner once excess payments are
identified, as we did in FY 2012.
However, in the event we confirm the finding that the current
implementation of PDPM is not budget neutral and that a recalibration
is appropriate, despite the importance of ensuring that PDPM is budget
neutral going forward, we acknowledge the possibility that applying
such a significant reduction in payments in a single year and without
time to prepare for the reduction in revenue could create a financial
burden for providers. In light of this possibility, we are also
considering a number of potential mitigation strategies that would help
to ease the transition to prospective budget neutrality in the event an
adjustment is finalized. These strategies fall into two broad
categories: Delayed implementation; and phased implementation.
With regard to a delayed implementation strategy, this would mean
that we would implement the reduction in payment, or some portion of
the reduction in payment if combined with a phased implementation
approach described below, in a later year than the year in which the
reduction is finalized. For example, considering the 5 percent
reduction discussed above, if this reduction was finalized in FY 2022
with
[[Page 19990]]
a 1 year delayed implementation, this would mean that the full 5
percent reduction would be prospectively applied to the PDPM CMIs in FY
2023. If the reduction was finalized in FY 2022 with a 2 year delayed
implementation, then the reduction in the PDPM CMIs would be applied
prospectively beginning in FY 2024. This type of strategy, on its own,
does not serve to mitigate the overall amount of the reduction in a
single year, but rather serves to provide facilities with time to
prepare for the impending reduction in payments. We solicit comments on
whether stakeholders believe that, in the event we finalize the parity
adjustment recalibration, we should finalize this recalibration with a
delayed implementation. Additionally, to the extent that stakeholders
believe that a delayed implementation would be warranted, we solicit
comments on the appropriate length of the delay.
With regard to a phased implementation strategy, this would mean
that the amount of the reduction would be spread out over some number
of years. Such an approach helps to mitigate the impact of the
reduction in payments by applying only a portion of the reduction in a
given year. For example, if we were to use a 2-year phased
implementation approach to the 5 percent reduction discussed above,
this would mean that the PDPM CMIs would be reduced by 2.5 percent in
the first year of implementation and then reduced by the remaining 2.5
percent in the second and final year of implementation. So, for
example, if this adjustment was finalized for FY 2022, then the PDPM
CMIs would be reduced by 2.5 percent in FY 2022 and then reduced by an
additional 2.5 percent in FY 2023. We note that the number of years for
a phased implementation approach could be as little as 2 years but as
long as necessary to appropriately mitigate the yearly impact of the
reduction. For example, we could implement a 5-year phased approach for
this reduction, which would apply a one percent reduction to the PDPM
CMIs each year for 5 years. We solicit comments on the need for a
phased implementation approach to recalibrating the PDPM parity
adjustment, as well as on the appropriate length of such an approach.
We would, finally, note that these mitigation strategies may be
used in combination with each other. For example, we could finalize a 2
year phased approach with a 1 year delayed implementation. Using FY
2022 as the hypothetical year in which such an approach could be
finalized, this would mean that there would be no reduction to the PDPM
CMIs in FY 2022, a 2.5 percent reduction to the PDPM CMIs in FY 2023
and then a 2.5 percent reduction in the PDPM CMIs in FY 2024. We
solicit comments on the possibility of combining these approaches and
what stakeholders believe would be appropriate, using these approaches,
to appropriately mitigate the impact of the reduction in SNF PPS
payments.
We note that in any of these options, the adjustment would be
applied prospectively, and the case mix indexes would not be adjusted
to account for deviations from budget neutrality in years before the
payment adjustments were implemented.
We are considering these approaches as they may be warranted to
mitigate potential negative impacts on providers resulting from
implementation of such a reduction in the SNF PPS rates entirely within
a single year in the event we determine that recalibrating the parity
adjustment is necessary to achieve budget neutrality. However, we
believe that these alternatives would continue to reimburse in amounts
that significantly exceed our intended policy in excess of the rates
that would have been paid had we maintained the prior payment
classification system rather than in a budget neutral manner as
intended, and as we stated above, we believe it is imperative that we
act in a well-considered but appropriately expedient manner once excess
payments are identified. In addition, as we move forward with programs
designed to enhance and restructure our post-acute care payment
systems, we believe that payments under the SNF PPS should be
established at their intended and most appropriate levels as quickly as
possible. Moreover, stabilizing the baseline is a necessary first step
toward properly implementing and maintaining the integrity of the PDPM
classification methodology and the SNF PPS as a whole as discussed
above. We invite comments on the mitigation strategies described above
for mitigating the impact of recalibrating the PDPM parity adjustment
in the event we finalize a recalibration.
VI. Skilled Nursing Facility (SNF) Quality Reporting Program (QRP)
A. Background and Statutory Authority
The Skilled Nursing Facility Quality Reporting Program (SNF QRP) is
authorized by section 1888(e)(6) of the Act, and it applies to
freestanding SNFs, SNFs affiliated with acute care facilities, and all
non-CAH swing-bed rural hospitals. Section 1888(e)(6)(A)(i) of the Act
requires the Secretary to reduce by 2 percentage points the annual
market basket percentage update described in section 1888(e)(5)(B)(i)
of the Act applicable to a SNF for a fiscal year, after application of
section 1888(e)(5)(B)(ii) of the Act (the multifactor productivity
(MFP) adjustment) and section 1888(e)(5)(B)(iii) of the Act, in the
case of a SNF that does not submit data in accordance with sections
1888(e)(6)(B)(i)(II) and (III) of the Act for that fiscal year. For
more information on the requirements we have adopted for the SNF QRP,
we refer readers to the FY 2016 SNF PPS final rule (80 FR 46427 through
46429), FY 2017 SNF PPS final rule (81 FR 52009 through 52010), FY 2018
SNF PPS final rule (82 FR 36566 through 36605), FY 2019 SNF PPS final
rule (83 FR 39162 through 39272), and FY 2020 SNF PPS final rule (84 FR
38728 through 38820).
B. General Considerations Used for the Selection of Measures for the
SNF QRP
For a detailed discussion of the considerations we use for the
selection of SNF QRP quality, resource use, or other measures, we refer
readers to the FY 2016 SNF PPS final rule (80 FR 46429 through 46431).
1. Quality Measures Currently Adopted for the FY 2022 SNF QRP
The SNF QRP currently has 13 measures for the FY 2022 SNF QRP,
which are outlined in Table 26. For a discussion of the factors used to
evaluate whether a measure should be removed from the SNF QRP, we refer
readers to 42 CFR 413.360(b)(3).
Table 26--Quality Measures Currently Adopted for the FY 2022 SNF QRP
------------------------------------------------------------------------
Short name Measure name & data source
------------------------------------------------------------------------
Resident Assessment Instrument Minimum Data Set (Assessment-Based)
------------------------------------------------------------------------
Pressure Ulcer/Injury............. Changes in Skin Integrity Post-Acute
Care: Pressure Ulcer/Injury.
[[Page 19991]]
Application of Falls.............. Application of Percent of Residents
Experiencing One or More Falls with
Major Injury (Long Stay) (NQF
#0674).
Application of Functional Application of Percent of Long-Term
Assessment/Care Plan. Care Hospital (LTCH) Patients with
an Admission and Discharge
Functional Assessment and a Care
Plan That Addresses Function (NQF
#2631).
Change in Mobility Score.......... Application of IRF Functional
Outcome Measure: Change in Mobility
Score for Medical Rehabilitation
Patients (NQF #2634).
Discharge Mobility Score.......... Application of IRF Functional
Outcome Measure: Discharge Mobility
Score for Medical Rehabilitation
Patients (NQF #2636).
Change in Self-Care Score......... Application of the IRF Functional
Outcome Measure: Change in Self-
Care Score for Medical
Rehabilitation Patients (NQF
#2633).
Discharge Self-Care Score......... Application of IRF Functional
Outcome Measure: Discharge Self-
Care Score for Medical
Rehabilitation Patients (NQF
#2635).
DRR............................... Drug Regimen Review Conducted With
Follow-Up for Identified Issues--
Post Acute Care (PAC) Skilled
Nursing Facility (SNF) Quality
Reporting Program (QRP).
TOH-Provider *.................... Transfer of Health Information to
the Provider Post-Acute Care (PAC).
TOH-Patient *..................... Transfer of Health Information to
the Patient Post-Acute Care (PAC).
------------------------------------------------------------------------
Claims-Based
------------------------------------------------------------------------
MSPB SNF.......................... Medicare Spending Per Beneficiary
(MSPB)-Post Acute Care (PAC)
Skilled Nursing Facility (SNF)
Quality Reporting Program (QRP).
DTC............................... Discharge to Community (DTC)-Post
Acute Care (PAC) Skilled Nursing
Facility (SNF) Quality Reporting
Program (QRP) (NQF #3481).
PPR............................... Potentially Preventable 30-Day Post-
Discharge Readmission Measure for
Skilled Nursing Facility (SNF)
Quality Reporting Program (QRP).
------------------------------------------------------------------------
* In response to the public health emergency (PHE) for the Coronavirus
Disease 2019 (COVID-19), CMS released an Interim Final Rule (85 FR
27595 through 27597) which delayed the compliance date for collection
and reporting of the Transfer of Health Information measures for at
least two full fiscal years after the end of the PHE.
C. SNF QRP Quality Measure Proposals Beginning With the FY 2023 SNF QRP
Section 1899B(h)(1) of the Act permits the Secretary to remove,
suspend, or add quality measures or resource use or other measures
described in sections 1899B(c)(1) and (d)(1) of the Act, respectively,
so long as the Secretary publishes in the Federal Register (with a
notice and comment period) a justification for such removal, suspension
or addition. Section 1899B(a)(1)(B) of the Act requires that all of the
data that must be reported in accordance with section 1899B(a)(1)(A) of
the Act (including resource use or other measure data under section
1899B(d)(1)) be standardized and interoperable to allow for the
exchange of the information among post-acute care (PAC) providers and
other providers and the use by such providers of such data to enable
access to longitudinal information and to facilitate coordinated care.
We propose to adopt two new measures for the SNF QRP beginning with
the FY 2023 SNF QRP: The SNF Healthcare-Associated Infections Requiring
Hospitalization measure (SNF HAI) and the COVID-19 Vaccination Coverage
among Healthcare Personnel (HCP) \4\ measure as an ``other measure''
under section 1899B(d)(1) of the Act. The SNF HAI measure is an outcome
measure. The data used to report the SNF HAI measure are standardized
and interoperable and would allow providers to exchange this data and
compare outcomes across the care continuum and PAC settings. Clinical
data captured in every clinical setting informs a resident's current
medical care plan, facilitates coordinated care, and improves Medicare
beneficiary outcomes. We plan to develop HAI measures in other PAC
settings, such as the Inpatient Rehabilitation Facility (IRF) Quality
Reporting Program and the Long-Term Care Hospital (LTCH) Quality
Reporting Program. The proposed measure supports the CMS Meaningful
Measures Initiative through the Making Care Safer by Reducing Harm
Caused in the Delivery of Care domain. We have previously solicited
feedback on the SNF HAI measure as a future measure for the SNF QRP and
received several comments of support as well as a few comments
recommending suggestions (84 FR 38765). The measure is described in
more detail below.
---------------------------------------------------------------------------
\4\ The measure steward changed the name of the measure from
SARS-CoV-2 Vaccination Coverage among Healthcare Personnel to COVID-
19 Vaccination Coverage among Healthcare Personnel. There were no
changes to the measure itself, other than the name change.
---------------------------------------------------------------------------
We are proposing the COVID-19 Vaccination Coverage among HCP
measure as an ``other'' measure under section 1899B(d)(1) of the Act
beginning with the FY 2023 SNF QRP. In accordance with section
1899B(a)(1)(B) of the Act, the data used to calculate this measure are
standardized and interoperable. The proposed measure supports the
Meaningful Measures domain of Promote Effective Prevention and
Treatment of Chronic Disease. We identified the measure concept as a
priority in response to the current public health crisis. This process
measure was developed with the Centers for Disease Control and
Prevention (CDC) to track COVID-19 vaccination coverage among HCP in
the SNF setting. This measure is described in more detail below.
In addition, we propose to update the denominator for one measure,
the Transfer of Health (TOH) Information to the Patient--Post-Acute
Care (PAC) measure to exclude residents discharged home under the care
of an organized home health service or hospice.
1. Proposed Skilled Nursing Facility (SNF) Healthcare-Associated
Infections (HAI) Requiring Hospitalization Quality Measure Beginning
With the FY 2023 SNF QRP
a. Background
Monitoring the occurrence of HAIs among SNF residents can provide
valuable information about a SNF's quality of care. Although HAIs are
not considered ``never events'', or serious adverse errors in the
provision of health care services that should never occur,
[[Page 19992]]
most are preventable as they are often the result of poor processes and
structures of care.\5\ Evidence suggests there is a wide variation in
HAI rates among SNF providers. An analysis of FY 2018 SNF claims
indicates a performance gap in HAI rates across SNFs. Among the 14,347
SNFs included in the sample for the analysis, risk-adjusted measure
scores ranged from a minimum of 2.19 percent to a maximum of 19.83
percent. Further, a 2014 report from the Office of the Inspector
General (OIG) estimated that one in four adverse events among SNF
residents are due to HAIs, and more than half of all HAIs are
potentially preventable.\6\ Typically, HAIs result from inadequate
patient management following a medical intervention, such as surgery or
device implementation, or poor adherence to protocol and antibiotic
stewardship guidelines.7 8 9 Several provider
characteristics are also related to HAIs including staffing levels (for
example, high turnover, low staff-to-resident ratios, etc.), facility
structure characteristics (for example, national chain membership, high
occupancy rates, etc.), and adoption or lack thereof of infection
surveillance and prevention policies.10 11 12 13 14 15
Inadequate prevention and treatment of HAIs is likely to result in poor
health care outcomes for residents and wasteful resource use. For
example, HAIs are associated with longer lengths of stay, use of
higher-intensity care (for example, critical care services and hospital
readmissions), increased mortality, and high health care
costs.\16,17,18,19\ Monitoring SNF HAI rates would provide information
about each facility's adeptness in infection prevention and management.
---------------------------------------------------------------------------
\5\ CMS. (2006). Eliminating Serious Preventable, and Costly
Medical Errors--Never Events. Retrieved from https://www.cms.gov/newsroom/fact-sheets/eliminating-serious-preventable-and-costly-medical-errors-never-events.
\6\ Office of Inspector General. (2014). Adverse events in
skilled nursing facilities: National incidence among Medicare
beneficiaries. Retrieved from https://oig.hhs.gov/oei/reports/oei-06-11-00370.pdf.
\7\ Beganovic, M., & Laplante, K. (2018). Communicating with
Facility Leadership; Metrics for Successful Antimicrobial
Stewardship Programs (Asp) in Acute Care and Long-Term Care
Facilities. Rhode Island medical journal (2013), 101(5) (2018), 45-
49.
\8\ Cooper, D., McFarland, M., Petrilli, F., & Shells, C.
(2019). Reducing inappropriate antibiotics for urinary tract
infections in long-term care: A replication study. Journal of
Nursing Care Quality, 34(1), 16-21. http://dx.doi.org/10.1097/NCQ.0000000000000343.
\9\ Feldstein, D., Sloane, P.D., & Feltner, C. (2018).
Antibiotic stewardship programs in nursing homes: A systematic
review. Journal of the American Medical Directors Association,
19(2), 110-116. http://dx.doi.org/10.1016/j.jamda.2017.06.019.
\10\ Castle, N., Engberg, J.B., Wagner, L.M., & Handler, S.
(2017). Resident and facility factors associated with the incidence
of urinary tract infections identified in the Nursing Home Minimum
Data Set. Journal of Applied Gerontology, 36(2), 173-194. http://dx.doi.org/10.1177/0733464815584666.
\11\ Crnich, C.J., Jump, R., Trautner, B., Sloane, P.D., & Mody,
L. (2015). Optimizing antibiotic stewardship in nursing homes: A
narrative review and recommendations for improvement. Drugs & Aging,
32(9), 699-716. http://dx.doi.org/10.1007/s40266-015-0292-7.
\12\ Dick, A.W., Bell, J.M., Stone, N.D., Chastain, A.M.,
Sorbero, M., & Stone, P.W. (2019). Nursing home adoption of the
National Healthcare Safety Network Long-term Care Facility
Component. American Journal of Infection Control, 47(1), 59-64.
http://dx.doi.org/10.1016/j.ajic.2018.06.018.
\13\ Cooper, D., McFarland, M., Petrilli, F., & Shells, C.
(2019). Reducing inappropriate antibiotics for urinary tract
infections in long-term care: A replication study. Journal of
Nursing Care Quality, 34(1), 16-21. http://dx.doi.org/10.1097/NCQ.0000000000000343.
\14\ Gucwa, A.L., Dolar, V., Ye, C., & Epstein, S. (2016).
Correlations between quality ratings of skilled nursing facilities
and multidrug-resistant urinary tract infections. American Journal
of Infection Control, 44(11), 1256-1260. http://dx.doi.org/10.1016/j.ajic.2016.03.015.
\15\ Travers, J.L., Stone, P.W., Bjarnadottir, R.I.,
Pogorzelska-Maziarz, M., Castle, N.G., & Herzig, C.T. (2016).
Factors associated with resident influenza vaccination in a national
sample of nursing homes. American Journal of Infection Control,
44(9), 1055-1057. http://dx.doi.org/10.1016/j.ajic.2016.01.019.
\16\ CMS. (2006). Eliminating Serious Preventable, and Costly
Medical Errors--Never Events. Retrieved from https://www.cms.gov/newsroom/fact-sheets/eliminating-serious-preventable-and-costly-medical-errors-never-events.
\17\ Centers for Disease Control and Prevention (2009). The
Direct Medical Costs of Healthcare-Associated Infections in U.S.
Hospitals and the Benefits of Prevention. Retrieved from https://www.cdc.gov/hai/pdfs/hai/scott_costpaper.pdf.
\18\ Ouslander, J.G., Diaz, S., Hain, D., & Tappen, R. (2011).
Frequency and diagnoses associated with 7- and 30-day readmission of
skilled nursing facility patients to a nonteaching community
hospital. Journal of the American Medical Directors Association,
12(3), 195-203. http://dx.doi.org/10.1016/j.jamda.2010.02.015.
\19\ Zimlichman, E., Henderson, D., Tamir, O., Franz, C., Song,
P., Yamin, C.K., . . . Bates, D.W. (2013). Health care-associated
infections: A meta-analysis of costs and financial impact on the US
health care system. JAMA Internal Medicine, 173(22), 2039-2046.
Retrieved from https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/1733452.
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Addressing HAIs in SNFs is particularly important as several
factors place SNF residents at high risk for infection, including
increased age, cognitive and functional decline, use of indwelling
devices, frequent care transitions, and close contact with other
resident and healthcare workers.20 21 Furthermore, in SNFs,
COVID-19 has a disproportionate impact on racial and ethnic minorities
as well as people living with disabilities.22 23 Emerging
COVID-19 studies reveal higher patient spread due to poor infection
control, staff rotations between multiple SNFs, and poor patient COVID-
19 screenings.24 25 An analysis comparing SNF HAI rates
using FY 2019 data with the currently reported rates of COVID-19 in
SNFs found that nursing homes with higher HAI rates in FY 2019 also
have a higher number of COVID-19 cases.\26\ This analysis was presented
to the PAC-LTC MAP Workgroup at the January 11th meeting (http://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=94559, slide 134). We believe this
finding supports a relationship not only between this measure and
overall HAI prevention and control in SNFs, but also in predicting
those SNFs more likely to have higher rates of infection in future
pandemics. Several interventions may reduce HAI rates among SNFs, thus
improving quality of care. These interventions include the adoption of
infection surveillance and prevention policies, safety procedures,
antibiotic stewardship, and staff education and training
[[Page 19993]]
programs.27 28 29 30 31 32 33 Additionally, infection
prevention and control programs with core components in education,
monitoring, and feedback on infection rates from surveillance programs
or feedback on infection control practices from audits have been found
to be successful interventions for reducing HAIs.\34\ The effectiveness
of these interventions suggests improvement of HAI rates among SNF
residents is possible through modifying provider-led processes and
interventions.
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\20\ Montoya, A., & Mody, L. (2011). Common infections in
nursing homes: A review of current issues and challenges. Aging
Health, 7(6), 889-899. http://dx.doi.org/10.2217/ahe.11.80.
\21\ Office of Disease Prevention and Health Promotion. (2013).
Long-term care facilities. In U.S. Department of Health and Human
Services, National action plan to prevent health care-associated
infections: Road map to elimination (pp. 194-239). Retrieved from
https://health.gov/our-work/health-care-quality/health-care-associated-infections/national-hai-action-plan.
\22\ Chidambaram, P., Neuman T., Garfield R. (2020). Racial and
Ethnic Disparities in COVID-19 Cases and Deaths in Nursing Homes.
Retrieved from https://www.kff.org/coronavirus-covid-19/issue-brief/racial-and-ethnic-disparities-in-covid-19-cases-and-deaths-in-nursing-homes/.
\23\ Li Y., Cen X., Temkin-Greener R. (2020). Racial and Ethnic
Disparities in COVID-19 Infections and Deaths Across U.S. Nursing
Homes. Journal of the American Geriatrics Society, 68(11), 2454-
2461. https://pubmed.ncbi.nlm.nih.gov/32955105/.
\24\ Kimball, A., Hatfield, K.M., Arons, M., James, A., Taylor,
J., Spicer, K., Bardossy, A.C., Oakley, L.P., Tanwar, S., Chisty,
Z., Bell, J.M., Methner, M., Harney, J., Jacobs, J.R., Carlson,
C.M., McLaughlin, H.P., Stone, N., Clark, S., Brostrom-Smith, C.,
Page, L.C., . . . CDC COVID-19 Investigation Team (2020).
Asymptomatic and Presymptomatic SARS-CoV-2 Infections in Residents
of a Long-Term Care Skilled Nursing Facility--King County,
Washington, March 2020. MMWR. Morbidity and mortality weekly report,
69(13), 377-381. https://doi.org/10.15585/mmwr.mm6913e1.
\25\ McMichael, T.M., Clark, S., Pogosjans, S., Kay, M., Lewis,
J., Baer, A., Kawakami, V., Lukoff, M.D., Ferro, J., Brostrom-Smith,
C., Riedo, F.X., Russell, D., Hiatt, B., Montgomery, P., Rao, A.K.,
Currie, D.W., Chow, E.J., Tobolowsky, F., Bardossy, A.C., Oakley,
L.P., . . . Public Health--Seattle & King County, EvergreenHealth,
and CDC COVID-19 Investigation Team (2020). COVID-19 in a Long-Term
Care Facility--King County, Washington, February 27-March 9, 2020.
MMWR. Morbidity and mortality weekly report, 69(12), 339-342.
https://doi.org/10.15585/mmwr.mm6912e1.
\26\ The CMS COVID-19 Nursing Home Dataset used in this analysis
was not limited to just the SNF, but applied to the entire nursing
home. The study population of the analysis includes Medicare-
certified nursing homes providing SNF care.
\27\ Office of Inspector General. (2014). Adverse events in
skilled nursing facilities: National incidence among Medicare
beneficiaries. Retrieved from https://oig.hhs.gov/oei/reports/oei-06-11-00370.pdf.
\28\ Beganovic, M., & Laplante, K. (2018). Communicating with
Facility Leadership; Metrics for Successful Antimicrobial
Stewardship Programs (Asp) in Acute Care and Long-Term Care
Facilities. Rhode Island medical journal (2013), 101(5) (2018), 45-
49.
\29\ Crnich, C.J., Jump, R., Trautner, B., Sloane, P.D., & Mody,
L. (2015). Optimizing antibiotic stewardship in nursing homes: A
narrative review and recommendations for improvement. Drugs & Aging,
32(9), 699-716. http://dx.doi.org/10.1007/s40266-015-0292-7.
\30\ Freeman-Jobson, J.H., Rogers, J.L., & Ward-Smith, P.
(2016). Effect of an education presentation on the knowledge and
awareness of urinary tract infection among non-licensed and licensed
health care workers in long-term care facilities. Urologic Nursing,
36(2), 67-71. http://dx.doi.org/10.7257/1053-816X.2016.36.2.67
Crnich, C.J., Jump, R., Trautner, B., Sloane, P.D., & Mody, L.
(2015). Optimizing antibiotic stewardship in nursing homes: A
narrative review and recommendations for improvement. Drugs & Aging,
32(9), 699-716. http://dx.doi.org/10.1007/s40266-015-0292-7.
\31\ Hutton, D.W., Krein, S.L., Saint, S., Graves, N., Kolli,
A., Lynem, R., & Mody, L. (2018). Economic evaluation of a catheter-
associated urinary tract infection prevention program in nursing
homes. Journal of the American Geriatrics Society, 66(4), 742-747.
http://dx.doi.org/10.1111/jgs.15316.
\32\ Nguyen, H.Q., Tunney, M.M., & Hughes, C.M. (2019).
Interventions to Improve Antimicrobial Stewardship for Older People
in Care Homes: A Systematic Review. Drugs & aging, 36(4), 355-369.
https://doi.org/10.1007/s40266-019-00637-0.
\33\ Sloane, P.D., Zimmerman, S., Ward, K., Kistler, C.E.,
Paone, D., Weber, D.J., Wretman, C.J., & Preisser, J.S. (2020). A 2-
Year Pragmatic Trial of Antibiotic Stewardship in 27 Community
Nursing Homes. Journal of the American Geriatrics Society, 68(1),
46-54. https://doi.org/10.1111/jgs.16059.
\34\ Lee, M.H., Lee GA, Lee SH, Park YH (2019). Effectiveness
and core components of infection prevention and control programmes
in long-term care facilities: A systematic review. Retrieved from
https://pubmed.ncbi.nlm.nih.gov/30794854/.
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The proposed SNF HAI measure uses Medicare fee-for-service (FFS)
claims data to estimate the risk-standardized rate of HAIs that are
acquired during SNF care and result in hospitalization. Unlike other
HAI measures that target specific infections, this measure would target
all HAIs serious enough to require admission to an acute care hospital.
Given the current COVID-19 public health emergency, we believe this
measure would promote patient safety and increase the transparency of
quality of care in the SNF setting. This measure also compares SNFs to
their peers to statistically separate those that perform better than or
worse than each other in infection prevention and management. We
believe peer comparison would encourage SNFs to improve the quality of
care they deliver.
b. Stakeholder and Technical Expert Panel (TEP) Input
In our development and specification of this measure, we employed a
transparent process in which we sought input from stakeholders and
national experts and engaged in a process that allowed for pre-
rulemaking input, in accordance with section 1890A of the Act.
To meet this requirement, we provided the following opportunities
for stakeholder input. Our measure development contractor for the SNF
HAI measure convened a Technical Expert Panel (TEP) on May 9, 2019 to
obtain expert input on the development of an HAI measure for use in the
SNF QRP. The TEP consisted of stakeholders with a diverse range of
expertise, including SNF and PAC subject matter knowledge, clinical and
infectious disease expertise, patient and family perspectives, and
measure development experience. The TEP supported the proposed measure
concept and provided substantive input regarding the measure's
specifications. Recommendations provided by the TEP included refining
the measure's operational definition, exclusion criteria, and HAI ICD-
10 diagnosis code list, among other considerations. All recommendations
from the TEP were taken into consideration and applied appropriately
where feasible. A summary of the TEP proceedings titled SNF HAI Final
TEP Report is available on the SNF QRP Measures and Technical
Information page at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/Skilled-Nursing-Facility-Quality-Reporting-Program/SNF-Quality-Reporting-Program-Measures-and-Technical-Information.
Following the TEP, our measure development contractor released
draft quality measure specifications for public comment on the SNF HAI
measure. Stakeholder feedback was solicited on the proposed measure by
requesting comment on the CMS Measures Management System Blueprint
site. The comment submission period was from September 14, 2020 to
October 14, 2020. Comments on the measure varied. Many commenters
supported the idea of adopting an HAI measure to improve prevention
efforts; however, commenters also offered criticisms about the
measure's specifications and implementation. The summary report of the
September 14 to October 14, 2020 public comment period titled SNF HAI
Public Comment Summary Report is available on the SNF QRP Measures and
Technical Information page at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/Skilled-Nursing-Facility-Quality-Reporting-Program/SNF-Quality-Reporting-Program-Measures-and-Technical-Information.
c. Measure Applications Partnership (MAP) Review
Our pre-rulemaking process includes making publicly available a
list of quality and efficiency measures, called the Measures under
Consideration (MUC) List, that the Secretary is considering adopting
through the Federal rulemaking process for use in Medicare programs.
This allows multi-stakeholder groups to provide recommendations to the
Secretary on the measures included on the list.
We included the SNF HAI measure under the SNF QRP Program in the
publicly available ``List of Measures under Consideration for December
21, 2020'' (MUC List).\35\ The National Quality Forum (NQF)-convened
Measure Applications Partnership (MAP) Post-Acute Care/Long-Term Care
(PAC-LTC) workgroup met virtually on January 11, 2021 and provided
input on the proposed measure. The MAP offered conditional support of
the SNF HAI measure for rulemaking contingent upon NQF endorsement,
noting that the measure adds value to the SNF QRP by presenting one
overall measurement of all HAIs acquired during SNF care that result in
hospitalizations, information that is not currently available. The MAP
recognized that the proposed measure is intended to reflect global
infection control for a facility, and may encourage SNFs to access
processes and perform interventions to reduce adverse events among SNF
residents that are due to HAIs. The MAP Rural Health
[[Page 19994]]
Workgroup also agreed that the SNF HAI measure is suitable for use with
rural providers in the SNF QRP. The final MAP report is available at
http://www.qualityforum.org/Publications/2021/03/MAP_2020-2021_Considerations_for_Implementing_Measures_Final_Report_-_Clinicians,_Hospitals,_and_PAC-LTC.aspx.
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\35\ National Quality Forum. List of Measures Under
Consideration for December 21, 2020. Accessed at https://www.cms.gov/files/document/measures-under-consideration-list-2020-report.pdf on January 12, 2021.
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Additionally, measure testing was conducted on the SNF HAI measure.
Split-half testing revealed the proposed measure's moderate
reliability. Validity testing of the measure showed good model
discrimination as the HAI model can accurately predict HAI cases while
controlling for differences in resident case-mix. The SNF HAI TEP also
showed strong support for the face validity of the proposed measure.
For measure testing details, refer to the document titled, Skilled
Nursing Facility Healthcare-Associated Infections Requiring
Hospitalization for the Skilled Nursing Facility Quality Reporting
Program Technical Report available on the SNF QRP Measures and
Technical Information page at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/Skilled-Nursing-Facility-Quality-Reporting-Program/SNF-Quality-Reporting-Program-Measures-and-Technical-Information. This proposed
measure is not currently NQF endorsed, but CMS plans to submit the
measure for NQF endorsement in the future.
d. Competing and Related Measures
Section 1899B(e)(2)(A) of the Act requires that, absent an
exception under section 1899B(e)(2)(B) of the Act, measures specified
under section 1899B of the Act be endorsed by the entity with a
contract under section 1890(a), currently the National Quality Forum
(NQF). In the case of a specified area or medical topic determined
appropriate by the Secretary for which a feasible and practical measure
has not been endorsed, section 1899B(e)(2)(B) permits the Secretary to
specify a measure that is not so endorsed, as long as due consideration
is given to measures that have been endorsed or adopted by a consensus
organization identified by the Secretary.
The proposed SNF HAI measure is not NQF endorsed, so we considered
whether there are other available measures that assess HAIs in SNFs.
After review of the NQF's consensus-endorsed measures, we were unable
to identify any NQF endorsed measures for SNFs focused on capturing
several types of severe infections attributable to the SNF setting in
one composite score. For example, although the measures Percent of
Residents with a Urinary Tract Infection (Long-Stay) (NQF #0684),
National Healthcare Safety Network (NHSN) Catheter-Associated Urinary
Tract Infections (NQF #0138), NHSN Central Line-Associated Bloodstream
Infections (NQF #0139), and NHSN Facility-Wide Inpatient Hospital-onset
Clostridium Difficile Infection (NQF #1717) are NQF endorsed and all
report on specific types of infections, they do not provide an overall
HAI rate and are not specific to the SNF setting. Additionally,
although the Skilled Nursing Facility 30-Day All-Cause Readmission
measure (NQF #2510), the Potentially Preventable 30-Day Post-Discharge
Readmission measure for SNF QRP, and the Skilled Nursing Facility 30-
Day Potentially Preventable Readmission after Hospital Discharge
measure (SNFPPR) are all specific to the SNF setting, they are not
solely focused on infections. We intend to submit this proposed measure
to the NQF for consideration of endorsement when feasible.
Therefore, after consideration of other available measures, we find
that the exception under section 1899B(e)(2)(B) of the Act applies and
are proposing the measure, Skilled Nursing Facility (SNF) Healthcare-
Associated Infections (HAI) Requiring Hospitalization measure beginning
with the FY 2023 SNF QRP.
e. Quality Measure Calculation
The proposed measure estimates the risk-standardized rate of HAIs
that are acquired during SNF care and result in hospitalization using 1
year of Medicare FFS claims data.
Both the proposed measure numerator and denominator are risk-
adjusted. The measure's adjusted numerator is the estimated number of
SNF stays predicted to have an HAI that results in hospitalization. The
estimate starts with the observed count of the measure outcome, which
is then risk-adjusted for resident characteristics and a statistical
estimate of the SNF effect beyond resident case mix. The term ``SNF
effect'' represents provider-specific behaviors that result in
facilities' HAI rates. These behaviors may include adherence to
evidence-based infection control policies and procedures. The adjusted
denominator is the expected number of SNF stays with the measure
outcome. The adjusted denominator is calculated by risk-adjusting the
total eligible SNF stays for resident characteristics excluding the SNF
effect.
The proposed measure is calculated using a standardized risk ratio
(SRR) in which the predicted number of HAIs for SNF stays per provider
is divided by the expected number of HAIs. For each SNF, a risk-
adjusted rate of HAIs that are acquired during SNF care and result in
hospitalization is calculated by multiplying the SRR by the overall
national observed rate of HAIs for all SNF stays. The measure is risk-
adjusted for age and gender characteristics, original reason for
Medicare Entitlement, principal diagnosis during the prior proximal
inpatient (IP) stay, types of surgery or procedure from the prior
proximal IP stay, length of stay and ICU/CCU utilization from the prior
proximal IP stay, dialysis treatment from the prior proximal IP stay,
and HCC comorbidities and number of prior IP stays within 1 year
preceding the SNF stay. For technical information about this proposed
measure, including information about the measure calculation, risk
adjustment, and exclusions, refer to the document titled, Skilled
Nursing Facility Healthcare-Associated Infections Requiring
Hospitalization for the Skilled Nursing Facility Quality Reporting
Program Technical Report available on the SNF QRP Measures and
Technical Information page at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/Skilled-Nursing-Facility-Quality-Reporting-Program/SNF-Quality-Reporting-Program-Measures-and-Technical-Information. If this measure
is finalized, we intend to publicly report this measure using four
quarters of claims data. We refer readers to section VI.H.2. of this
proposed rule for information regarding public reporting.
We invite public comment on our proposal to adopt the quality
measure, the Skilled Nursing Facility (SNF) Healthcare-Associated
Infections (HAIs) Requiring Hospitalization, beginning with the FY 2023
SNF QRP.
2. Proposed COVID-19 Vaccination Coverage Among Healthcare Personnel
(HCP) Measure Beginning With the FY 2023 SNF QRP
a. Background
On January 31, 2020, the Secretary of the U.S. Department of Health
and Human Services (HHS) declared a public health emergency (PHE) for
the United States in response to the global outbreak of SARS-CoV-2, a
novel (new) coronavirus that causes a disease named ``coronavirus
disease 2019'' (COVID-19).\36\ COVID-19 is a contagious
[[Page 19995]]
respiratory infection \37\ that can cause serious illness and death.
Older individuals, racial and ethnic minorities, and those with
underlying medical conditions are considered to be at higher risk for
more serious complications from COVID-19.38 39 As of April
4, 2021 the U.S. reported over 30 million cases of COVID-19 and over
553,000 COVID-19 deaths.\40\ Hospitals and health systems saw
significant surges of COVID-19 patients as community infection levels
increased.\41\ In December 2020 and January 2021, media outlets
reported that more than 100,000 Americans were in the hospital with
COVID-19.\42\
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\36\ U.S. Dept. of Health and Human Services, Office of the
Assistant Secretary for Preparedness and Response. (2020).
Determination that a Public Health Emergency Exists. Available at
https://www.phe.gov/emergency/news/healthactions/phe/Pages/2019-nCoV.aspx.
\37\ Centers for Disease Control and Prevention. (2020). Your
Health: Symptoms of Coronavirus. Available at https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html.
\38\ Centers for Disease Control and Prevention (2021). Health
Equity Considerations and Racial and Ethnic Minority Groups.
Available at https://www.cdc.gov/coronavirus/2019-ncov/community/health-equity/race-ethnicity.html.
\39\ Centers for Disease Control and Prevention. (2020). Your
Health: Symptoms of Coronavirus. Available at https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html.
\40\ Centers for Disease Control and Prevention. (2020). CDC
COVID Data Tracker. Available at https://covid.cdc.gov/covid-data-tracker/#cases_casesper100klast7days.
\41\ Associated Press. Tired to the Bone. Hospitals Overwhelmed
with Virus Cases. November 18, 2020. Accessed on December 16, 2020,
at https://apnews.com/article/hospitals-overwhelmed-coronavirus-cases-74a1f0dc3634917a5dc13408455cd895. Also see: New York Times.
Just how full are U.S. intensive care units? New data paints an
alarming picture. November 18, 2020. Accessed on December 16, 2020,
at https://www.nytimes.com/2020/12/09/world/just-how-full-are-us-intensive-care-units-new-data-paints-an-alarming-picture.html.
\42\ NPR. U.S. Hits 100,000 COVID-19 Hospitalizations, Breaks
Daily Death Record. Dec. 2, 2020. Accessed on December 17, 2020 at
https://www.npr.org/sections/coronavirus-live-updates/2020/12/02/941902471/u-s-hits-100-000-covid-19-hospitalizations-breaks-daily-death-record; The Wall Street Journal. Coronavirus Live Updates:
U.S. Hospitalizations, Newly Reported Cases, Deaths Edge Downward.
Accessed on January 11 at https://www.wsj.com/livecoverage/covid-2021-01-11.
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Evidence indicates that COVID-19 primarily spreads when individuals
are in close contact with one another.\43\ The virus is typically
transmitted through respiratory droplets or small particles created
when someone who is infected with the virus coughs, sneezes, sings,
talks or breathes.\44\ Experts believe that COVID-19 spreads less
commonly through contact with a contaminated surface \45\ (although
that is not thought to be a common way that COVID-19 spreads), and that
in certain circumstances, infection can occur through airborne
transmission.\46\ According to the CDC, those at greatest risk of
infection are persons who have had prolonged, unprotected close contact
(that is, within 6 feet for 15 minutes or longer) with an individual
with confirmed SARS-CoV-2 infection, regardless of whether the
individual has symptoms.\47\ Although personal protective equipment
(PPE) and other infection-control precautions can reduce the likelihood
of transmission in health care settings, COVID-19 can spread between
healthcare personnel (HCP) and patients given the close contact that
may occur during the provision of care.\48\ The CDC has emphasized that
health care settings, including long-term care settings, can be high-
risk places for COVID-19 exposure and transmission.\49\
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\43\ Centers for Disease Control and Prevention. (2021). COVID-
19. Your Health. Frequently Asked Questions. Accessed on January 11,
2021 at https://www.cdc.gov/coronavirus/2019-ncov/faq.html.
\44\ Centers for Disease Control and Prevention (2021). COVID-
19. Your Health. Frequently Asked Questions. Accessed on January 11,
2021 at https://www.cdc.gov/coronavirus/2019-ncov/faq.html.
\45\ Centers for Disease Control and Prevention (2021). COVID-
19. Your Health. Frequently Asked Questions. Accessed on January 11,
2021 at https://www.cdc.gov/coronavirus/2019-ncov/faq.html.
\46\ Centers for Disease Control and Prevention. (2020). Centers
for Disease Control Scientific Brief: SARS-CoV-2 and Potential
Airborne Transmission. Available at https://www.cdc.gov/coronavirus/2019-ncov/more/scientific-brief-sars-cov-2.html.
\47\ Centers for Disease Control and Prevention. (2020).
Clinical Questions about COVID-19: Questions and Answers. Accessed
on December 2, 2020 at https://www.cdc.gov/coronavirus/2019-ncov/hcp/faq.html.
\48\ Centers for Disease Control and Prevention. (2020). Interim
U.S. Guidance for Risk Assessment and Work Restrictions for
Healthcare Personnel with Potential Exposure to COVID-19. Accessed
on December 2 at https://www.cdc.gov/coronavirus/2019-ncov/hcp/guidance-risk-assesment-hcp.html.
\49\ Dooling, K, McClung, M, et al. ``The Advisory Committee on
Immunization Practices' Interim Recommendations for Allocating
Initial Supplies of COVID-19 Vaccine--United States, 2020.'' Morb
Mortal Wkly Rep. 2020; 69(49): 1857-1859.
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Vaccination is a critical part of the nation's strategy to
effectively counter the spread of COVID-19 and ultimately help restore
societal functioning.\50\
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\50\ Centers for Disease Control and Prevention. (2020). COVID-
19 Vaccination Program Interim Playbook for Jurisdiction Operations.
Accessed on December 18 at https://www.cdc.gov/vaccines/imz-managers/downloads/COVID-19-Vaccination-Program-Interim_Playbook.pdf.
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On December 11, 2020, the Food and Drug Administration (FDA) issued
the first Emergency Use Authorization (EUA) for a COVID-19 vaccine in
the U.S.\51\ Subsequently, the FDA issued EUAs for additional COVID-19
vaccines. In issuing these EUAs, the FDA determined that it was
reasonable to conclude that the known and potential benefits of each
vaccine, when used as authorized to prevent COVID-19, outweighed its
known and potential risks.52 53 54
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\51\ U.S. Food and Drug Administration. (2020). Pfizer-BioNTech
COVID-19 Vaccine EUA Letter of Authorization. Available at https://www.fda.gov/media/144412/download.
\52\ Ibid.
\53\ U.S. Food and Drug Administration. (2021). ModernaTX, Inc.
COVID-19 Vaccine EUA Letter of Authorization. Available at https://www.fda.gov/media/144636/download.
\54\ U.S. Food and Drug Administration (2020). Janssen Biotech,
Inc. COVID-19 Vaccine EUA Letter of Authorization. Available at
https://www.fda.gov/media/146303/download.
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As part of its national strategy to address COVID-19, the current
administration stated that it would work with states and the private
sector to execute an aggressive vaccination strategy and has outlined a
goal of administering 200 million shots in 100 days.\55\ Although the
goal of the U.S. government is to ensure that every American who wants
to receive a COVID-19 vaccine can receive one, Federal agencies
recommended that early vaccination efforts focus on those critical to
the PHE response, including healthcare personnel (HCP), and individuals
at highest risk for developing severe illness from COVID-19.\56\ For
example, the CDC's Advisory Committee on Immunization Practices (ACIP)
recommended that HCP should be among those individuals prioritized to
receive the initial, limited supply of the COVID-19 vaccination, given
the potential for transmission in health care settings and the need to
preserve health care system capacity.\57\ Research suggests most states
followed this recommendation,\58\ and HCP began
[[Page 19996]]
receiving the vaccine in mid-December of 2020.\59\
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\55\ The White House. Remarks by President Biden on the COVID-19
Response and the State of Vaccinations. March 29, 2021. Accessed at
https://www.whitehouse.gov/briefing-room/speeches-remarks/2021/03/29/remarks-by-president-biden-on-the-covid-19-response-and-the-state-of-vaccinations/.
\56\ Health and Human Services, Department of Defense. (2020)
From the Factory to the Frontlines: The Operation Warp Speed
Strategy for Distributing a COVID-19 Vaccine. Accessed December 18
at https://www.hhs.gov/sites/default/files/strategy-for-distributing-covid-19-vaccine.pdf; Centers for Disease Control
(2020). COVID-19 Vaccination Program Interim Playbook for
Jurisdiction Operations. Accessed December 18 at https://www.cdc.gov/vaccines/imz-managers/downloads/COVID-19-Vaccination-Program-Interim_Playbook.pdf.
\57\ Dooling, K, McClung, M, et al. ``The Advisory Committee on
Immunization Practices' Interim Recommendations for Allocating
Initial Supplies of COVID-19 Vaccine--United States, 2020.'' Morb.
Mortal Wkly Rep. 2020; 69(49): 1857-1859. ACIP also recommended that
long-term care residents be prioritized to receive the vaccine,
given their age, high levels of underlying medical conditions, and
congregate living situations make them high risk for severe illness
from COVID-19.
\58\ Kates, J, Michaud, J, Tolbert, J. ``How Are States
Prioritizing Who Will Get the COVID-19 Vaccine First?'' Kaiser
Family Foundation. December 14, 2020. Accessed on December 16 at
https://www.kff.org/policy-watch/how-are-states-prioritizing-who-will-get-the-covid-19-vaccine-first/.
\59\ Associated Press. `Healing is Coming:' US Health Workers
Start Getting Vaccine. December 15, 2020. Accessed on December 16 at
https://apnews.com/article/us-health-workers-coronavirus-vaccine-56df745388a9fc12ae93c6f9a0d0e81f.
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HCP are at risk of carrying COVID-19 infection to patients,
experiencing illness or death as a result of COVID-19 themselves, and
transmitting it to their families, friends, and the general public. We
believe it is important to require that SNFs report HCP vaccination in
order to assess whether they are taking steps to limit the spread of
COVID-19 among their HCP, reduce the risk of transmission of COVID-19
within their facilities, and to help sustain the ability of SNFs to
continue serving their communities throughout the PHE and beyond.
Currently, as required under the May 8, 2020 Interim final rule with
comment period (85 FR 27601-27602), SNFs are required to submit COVID-
19 data through the CDC's NHSN Long-term Care Facility COVID-19 Module
of the NHSN. Examples of data reported in the module include: suspected
and confirmed COVID-19 infections among residents and staff, including
residents previously treated for COVID-19; total deaths and COVID-19
deaths among residents and staff; personal protective equipment and
hand hygiene supplies in the facility; ventilator capacity and supplies
available in the facility; resident beds and census; access to COVID-19
testing while the resident is in the facility; and staffing shortages.
Although HCP and resident COVID-19 vaccination data reporting modules
are currently available through the NHSN, the reporting of this data is
voluntary. \60\
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\60\ Centers for Disease Control and Prevention. Weekly COVID-19
Vaccination Data Reporting. Accessed at https://www.cdc.gov/nhsn/ltc/weekly-covid-vac/index.html.
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We also believe that publishing facility-level COVID-19 HCP
vaccination rates on Care Compare would be helpful to many patients,
including those who are at high-risk for developing serious
complications from COVID-19, as they choose facilities from which to
seek treatment. Under CMS' Meaningful Measures Framework, the COVID-19
Vaccination Coverage among Healthcare Personnel measure addresses the
quality priority of ``Promote Effective Prevention & Treatment of
Chronic Disease'' through the Meaningful Measures Area of ``Preventive
Care.''
Therefore, we are proposing a new measure, COVID-19 Vaccination
Coverage among HCP to assess the proportion of a SNF's healthcare
workforce that has been vaccinated against COVID-19.
b. Stakeholder Input
In the development and specification of the measure, a transparent
process was employed to seek input from stakeholders and national
experts and engage in a process that allows for pre-rulemaking input on
each measure, under section 1890A of the Act.\61\ To meet this
requirement, the following opportunity was provided for stakeholder
input.
---------------------------------------------------------------------------
\61\ Centers for Medicare & Medicaid Services. Pre-rulemaking.
Accessed at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityMeasures/Pre-Rulemaking.
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The pre-rulemaking process includes making publicly available a
list of quality and efficiency measures, called the Measures Under
Consideration (MUC) List that the Secretary is considering adopting,
through Federal rulemaking process, for use in Medicare program(s).
This allows multi-stakeholder groups to provide recommendations to the
Secretary on the measures included on the list. The COVID-19
Vaccination Coverage among Healthcare Personnel measure was included on
the publicly available ``List of Measures under Consideration for
December 21, 2020'' (MUC List).\62\ Five comments were received from
industry stakeholders during the pre-rulemaking process on the COVID-19
Vaccination Coverage among HCP measure, and support was mixed.
Commenters generally supported the concept of the measure. However,
there was concern about the availability of the vaccine and measure
definition for HCP, and some commenters encouraged CMS to continue to
update the measure as new evidence comes in.
---------------------------------------------------------------------------
\62\ National Quality Forum. List of Measures Under
Consideration for December 21, 2020. Accessed at https://www.cms.gov/files/document/measures-under-consideration-list-2020-report.pdf on January 12, 2021.
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c. Measure Applications Partnership (MAP) Review
When the Measure Applications Partnership (MAP) PAC-LTC Workgroup
convened on January 11, 2021, it reviewed the MUC List and the COVID-19
Vaccination Coverage among HCP measure. The MAP recognized that the
proposed measure represents a promising effort to advance measurement
for an evolving national pandemic and that it would bring value to the
SNF QRP measure set by providing transparency about an important COVID-
19 intervention to help limit COVID-19 infections.\63\ The MAP also
stated that collecting information on COVID-19 vaccination coverage
among healthcare personnel and providing feedback to facilities would
allow facilities to benchmark coverage rates and improve coverage in
their facility, and that reducing rates of COVID-19 in healthcare
personnel may reduce transmission among patients and reduce instances
of staff shortages due to illness.\64\
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\63\ Measure Applications Partnership. MAP Preliminary
Recommendations 2020-2021. Accessed on February 3, 2021 at https://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=94650.
\64\ Ibid.
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In its preliminary recommendations, the MAP PAC-LTC Workgroup did
not support this measure for rulemaking, subject to potential for
mitigation.\65\ To mitigate its concerns, the MAP believed that the
measure needed well-documented evidence, finalized specifications,
testing, and NQF endorsement prior to implementation.\66\ Subsequently,
the MAP Coordinating Committee met on January 25, 2021, and reviewed
the COVID-19 Vaccination Coverage among Healthcare Personnel measure.
In the 2020-2021 MAP Final Recommendations, the MAP offered conditional
support for rulemaking contingent on CMS bringing the measure back to
the MAP once the specifications are further clarified. The final MAP
report is available at http://www.qualityforum.org/Publications/2021/03/MAP_2020-2021_Considerations_for_Implementing_Measures_Final_Report_-_Clinicians,_Hospitals,_and_PAC-LTC.aspx.
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\65\ Ibid.
\66\ Ibid.
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In response to the MAP request for CMS to bring the measure back
once the specifications were further clarified, CMS met with the MAP
Coordinating Committee on March 15, 2021. First, CMS and CDC clarified
the alignment of the COVID-19 Vaccination Coverage among HCP with the
Influenza Vaccination Coverage among HCP (NQF #0431), an NQF-endorsed
measure since 2012. The COVID-19 Vaccination Coverage among HCP measure
is calculated using the same approach as the Influenza Vaccination
Coverage among HCP measure.\67\ The approach to identifying HCPs
eligible for the COVID-19 vaccination is analogous to those used in the
NQF endorsed flu measure which underwent rigorous review from technical
experts about the validity of that approach and for which
[[Page 19997]]
ultimately received NQF endorsement. More recently, prospective cohorts
of health care personnel, first responders, and other essential and
frontline workers over 13 weeks in eight U.S. locations confirmed that
authorized COVID-19 vaccines are highly effective in real-world
conditions. Vaccine effectiveness of full immunization with two doses
of vaccines was 90 percent.\68\
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\67\ The Influenza Vaccination Coverage among Healthcare
Personnel (NQF #0431) measure which is NQF endorsed and was adopted
in the IRF QRP in the FY 2014 IRF PPS Final Rule (78 FR 47905
through 47906), and in the LTCH QRP in the FY 2013 IPPS/LTCH PPS
Final Rule (77 FR 53630 through 53631).
\68\ Centers for Disease Control and Preventions. Morbidity and
Mortality Weekly Report. March 29, 2021. Available at https://www.cdc.gov/mmwr/volumes/70/wr/mm7013e3.htm?s_cid=mm7013e3_w.
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Additionally, to support the measure's data element validity, CDC
conducted testing of the COVID-19 vaccination numerator using data
collected through the NHSN and independently reported through the
Federal Pharmacy Partnership for Long-term Care Program for delivering
vaccines to long-term care facilities. These are two completely
independent data collection systems. In initial analyses of the first
month of vaccination, the number of HCP vaccinated in approximately
1,200 facilities which had data from both systems, the number of HCP
vaccinated was highly correlated between these two systems with a
correlation coefficient of nearly 90 percent in the second two weeks of
reporting. Of note, assessment of data element reliability may not be
required by NQF if data element validity is demonstrated.\69\ To assess
the validity of new performance measure score (in this case, percentage
of COVID-19 vaccination coverage), NQF allows assessment by face
validity (that is, subjective determination by experts that the measure
appears to reflect quality of care, done through a systematic and
transparent process),\70\ and the MAP concurred with the face validity
of the COVID-19 Vaccination Coverage among HCP measure. Materials from
the March 15, 2021 MAP Coordinating Committee meeting are on the NQF
website at https://www.qualityforum.org/ProjectMaterials.aspx?projectID=75367.
---------------------------------------------------------------------------
\69\ National Quality Form. Key Points for Evaluating Scientific
Acceptability. Revised January 3, 2020. https://
www.qualityforum.org/Measuring_Performance/Scientific_Methods_Panel/
Docs/
Evaluation_Guidance.aspx#:~:text=NQF%20is%20not%20prescriptive%20abou
t,reliability%20or%20validity%20testing%20results.&text=Reliability%2
0and%20validity%20must%20be,source%20and%20level%20of%20analysis).
\70\ Ibid.
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This measure is not NQF endorsed, but the CDC, in collaboration
with CMS, plans to submit the measure for NQF endorsement in the
future.
d. Competing and Related Measures
Section 1899B(e)(2)(A) of the Act requires that absent an exception
under section 1899B(e)(2)(B) of the Act, each measure specified by the
Secretary be endorsed by the entity with a contract under section
1890(a) of the Act, currently the National Quality Forum (NQF). In the
case of a specified area or medical topic determined appropriate by the
Secretary for which a feasible and practical measure has not been
endorsed, section 1899B(e)(2)(B) of the Act permits the Secretary to
specify a measure that is not so 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. The proposed COVID-
19 Vaccination Coverage among HCP measure is not currently NQF endorsed
and has not been submitted to the NQF for consideration, so we
considered whether there are other available measures that assess
COVID-19 vaccinations among HCP. After review of the NQF's consensus-
endorsed measures, we were unable to identify any NQF endorsed measures
for SNFs focused on capturing COVID-19 vaccination coverage of HCP, and
we found no other feasible and practical measure on the topic of COVID-
19 vaccination coverage among HCP. The only other vaccination coverage
of HCP measure found was the Influenza Vaccination Coverage among
Healthcare Personnel (NQF #0431) measure which is NQF endorsed and was
adopted in the IRF QRP in the FY 2014 IRF PPS Final Rule (78 FR 47905
through 47906), and in the LTCH QRP in the FY 2013 IPPS/LTCH PPS Final
Rule (77 FR 53630 through 53631).
Given the novel nature of the SARS-CoV-2 virus, and the significant
and immediate risk it poses in SNFs, we believe it is necessary to
propose the measure as soon as possible. Therefore, after consideration
of other available measures that assess COVID-19 vaccination rates
among HCP, we believe the exception under section 1899B(e)(2)(B) of the
Act applies. This proposed measure has the potential to generate
actionable data on vaccination rates that can be used to target quality
improvement among SNF providers.
e. Quality Measure Calculation
The COVID-19 Vaccination Coverage among Healthcare Personnel (HCP)
measure is a process measure developed by the CDC to track COVID-19
vaccination coverage among HCP in facilities such as SNFs. Since this
proposed measure is a process measure, rather than an outcome measure,
it does not require risk-adjustment.
The denominator would be the number of HCP eligible to work in the
facility for at least one day during the reporting period, excluding
persons with contraindications to COVID-19 vaccination that are
described by the CDC.\71\ While the SNF QRP applies to freestanding
SNFs, SNFs affiliated with acute care facilities, and all non-CAH
swing-bed rural hospitals, we believe it is necessary to include all
HCP within the facility in the measure denominator because all HCP
would have access to and may interact with SNF residents.
---------------------------------------------------------------------------
\71\ Centers for Disease Control and Prevention. Interim
Clinical Considerations for Use of COVID-19 Vaccines Currently
Authorized in the United Sates. Contraindications found in Appendix
B: Triage of people presenting for the vaccination. Accessed at
https://www.cdc.gov/vaccines/covid-19/info-by-product/clinical-considerations.html.
---------------------------------------------------------------------------
The numerator would be the cumulative number of HCP eligible to
work in the facility for at least one day during the reporting period
and who received a complete vaccination course against SARS-CoV-2. A
complete vaccination course may require one or more doses depending on
the specific vaccine used. The finalized measure specifications are on
the CDC website at https://www.cdc.gov/nhsn/nqf/index.html.
We propose that SNFs would submit data for the measure through the
CDC/NHSN data collection and submission framework.\72\ SNFs would use
the COVID-19 vaccination data reporting module in the NHSN Healthcare
Personnel Safety (HPS) Component to report the number of HCP eligible
who have worked at the facility that week (denominator) and the number
of those HCP who have received a completed COVID-19 vaccination course
(numerator). SNFs would submit COVID-19 vaccination data for at least 1
week each month. If SNFs submit more than 1 week of data in a month,
the most recent week's data would be used for measure calculation
purposes. Each quarter, the CDC would calculate a summary measure of
COVID-19 vaccination coverage from the 3 monthly modules of data
reported for the quarter. This quarterly rate would be publicly
reported on the Care Compare website. Subsequent to the first refresh,
one additional quarter of data would be added to the measure
calculation during each advancing refresh, until the point four full
quarters of data is reached. Thereafter, the measure would be reported
using four rolling quarters of data on Care Compare.
---------------------------------------------------------------------------
\72\ Centers for Disease Control and Prevention. Surveillance
for Weekly HCP COVID-19 Vaccination. Accessed at https://www.cdc.gov/nhsn/hps/weekly-covid-vac/index.html on February 10,
2021.
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For purposes of submitting data to CMS for the FY 2023 SNF QRP,
SNFs
[[Page 19998]]
would be required to submit data for the period October 1, 2021 through
December 31, 2021. Following the initial data submission quarter for
the FY 2023 SNF QRP, subsequent compliance for the SNF QRP would be
based on four quarters of such data submission. For more information on
the measure's proposed public reporting period, we refer readers to
section VI.H.3. of this proposed rule.
We invite public comment on our proposal to add a new measure,
COVID-19 Vaccination Coverage among Healthcare Personnel, to the SNF
QRP beginning with the FY 2023 SNF QRP.
3. Proposed Update to the Transfer of Health (TOH) Information to the
Patient--Post-Acute Care (PAC) Measure Beginning With the FY 2023 SNF
QRP
We are proposing to update the Transfer of Health Information to
the Patient--Post-Acute Care (PAC) measure denominator to exclude
residents discharged home under the care of an organized home health
service or hospice. This measure assesses for and reports on the timely
transfer of health information, specifically transfer of a medication
list. We adopted this measure in the FY 2020 SNF PPS final rule (84 FR
38761 through 38764) beginning with the FY 2022 SNF QRP. It is a
process-based measure that evaluates for the transfer of information
when a resident is discharged from his or her current PAC setting to a
private home/apartment, board and care home, assisted living, group
home, transitional living, or home under the care of an organized home
health service organization or hospice.
This measure, adopted under section 1899B(c)(1)(E) of the Act, was
developed to be a standardized measure for the IRF QRP, LTCH QRP, SNF
QRP, and Home Health (HH) QRP. The measure is calculated by one
standardized data element that asks, ``At the time of discharge, did
the facility provide the resident's current reconciled medication list
to the resident, family, and/or caregiver?'' The discharge location is
captured by items on the Minimum Data Set (MDS).
Specifically, we are proposing to update the measure denominator.
Currently, the measure denominators for both the TOH-Patient and the
TOH-Provider measure assess the number of residents discharged home
under the care of an organized home health service organization or
hospice. In order to align the measure with the IRF QRP, LTCH QRP and
HH QRP and avoid counting the resident in both TOH measures in the SNF
QRP, we are proposing to remove this location from the definition of
the denominator for the TOH-Patient measure. Therefore, we are
proposing to update the denominator for the TOH-Patient measure to only
discharges to a private home/apartment, board and care home, assisted
living, group home, or transitional living. For additional technical
information regarding the TOH-Patient measure, we refer readers to the
document titled ``Final Specifications for SNF QRP Quality Measures and
Standardized Patient Assessment Data Elements (SPADEs)'' available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/Downloads/Final-Specifications-for-SNF-QRP-Quality-Measures-and-SPADEs.pdf.
We invite public comment on our proposal to update the denominator
of the Transfer of Health (TOH) Information to the Patient--Post-Acute
Care (PAC) measure beginning with the FY 2023 SNF QRP.
D. SNF QRP Quality Measures Under Consideration for Future Years:
Request for Information (RFI)
We are seeking input on the importance, relevance, appropriateness,
and applicability of each of the measures and concepts under
consideration listed in Table 27 for future years in the SNF QRP.
Table 27--Future Measures and Measure Concepts Under Consideration for
the SNF QRP
------------------------------------------------------------------------
Assessment-based quality measures and measure concepts
-------------------------------------------------------------------------
Frailty.
Patient reported outcomes.
Shared decision making process.
Appropriate pain assessment and pain management processes.
Health equity.
------------------------------------------------------------------------
While we will not be responding to specific comments submitted in
response to this Request for Information (RFI) in the FY 2022 SNF PPS
final rule, we intend to use this input to inform our future measure
development efforts.
E. Fast Healthcare Interoperability Resources (FHIR) in Support of
Digital Quality Measurement in Quality Programs--Request for
Information (RFI)
1. Background
The SNF QRP is authorized by section 1888(e)(6) of the Act and
furthers our mission to improve the quality of health care for
beneficiaries through measurement, transparency, and public reporting
of data. The SNF QRP and CMS's other quality programs are foundational
for contributing to improvements in health care, enhancing patient
outcomes, and informing consumer choice. In October 2017, we launched
the Meaningful Measures Framework. This framework captures our vision
to address health care quality priorities and gaps, including
emphasizing digital quality measurement (dQM), reducing measurement
burden, and promoting patient perspectives, while also focusing on
modernization and innovation. The scope of the Meaningful Measures
Framework has evolved to accommodate the changes in the health care
environment, initially focusing on measure and burden reduction to
include the promotion of innovation and modernization of all aspects of
quality.\73\ There is a need to streamline our approach to data
collection, calculation, and reporting to fully leverage clinical and
patient-centered information for measurement, improvement, and
learning.
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\73\ Meaningful Measures 2.0: Moving from Measure Reduction to
Modernization. Available at https://www.cms.gov/meaningful-measures-20-moving-measure-reduction-modernization.
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In alignment with Meaningful Measures 2.0, we are seeking feedback
on our future plans to define digital quality measures (dQMs) for the
SNF QRP. We also are seeking feedback on the potential use of Fast
Healthcare Interoperable Resources (FHIR) for dQMs within the SNF QRP
aligning where possible with other quality programs. FHIR is a free and
open source standards framework (in both commercial and government
settings) created by Health Level Seven International (HL7[supreg])
that establishes a common language and process for all health
information technology.
2. Definition of Digital Quality Measures
We are considering adopting a standardized definition of Digital
Quality Measures (dQMs) in alignment across quality programs, including
the SNF QRP. We are considering in the future to propose the adoption
within the SNF QRP the following definition: Digital Quality Measures
(dQMs) are quality measures that use one or more sources of health
information that are captured and can be transmitted electronically via
interoperable systems.\74\ A dQM includes a calculation that processes
digital data to produce a measure score or measure scores. Data sources
for dQMs may
[[Page 19999]]
include administrative systems, electronically submitted clinical
assessment data, case management systems, EHRs, instruments (for
example, medical devices and wearable devices), patient portals or
applications (for example, for collection of patient-generated health
data), health information exchanges (HIEs) or registries, and other
sources. As an example, the quality measures calculated from patient
assessment data submitted electronically to CMS would be considered
digital quality measures.
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\74\ Definition taken from the CMS Quality Conference 2021.
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3. Use of FHIR for Future dQMs in the SNF QRP
One of the first areas CMS has identified relative to improving our
digital strategy is through the use of Fast Healthcare Interoperability
Resources (FHIR)-based standards to exchange clinical information
through application programming interfaces (APIs), aligning with other
programs where possible, to allow clinicians to digitally submit
quality information one time that can then be used in many ways. We
believe that in the future proposing such a standard within the SNF QRP
could potentially enable collaboration and information sharing, which
is essential for delivering high-quality care and better outcomes at a
lower cost.
We are currently evaluating the use of FHIR based APIs to access
assessment data collected and maintained through the Quality
Improvement and Evaluation System (QIES) and internet QIES (iQIES)
health information systems and are working with healthcare standards
organizations to assure that their evolving standards fully support our
assessment instrument content. Further, as more SNFs are adopting EHRs,
we are evaluating using the FHIR interfaces for accessing patient data
(including standard assessments) directly from SNF EHRs. Accessing data
in this manner could also enable the exchange of data for purposes
beyond data reporting to CMS, such as care coordination further
increasing the value of EHR investments across the healthcare
continuum. Once providers map their EHR data to a FHIR API in standard
FHIR formats it could be possible to send and receive the data needed
for measures and other uses from their EHRs through FHIR APIs.
4. Future Alignment of Measures Across Reporting Programs, Federal and
State Agencies, and the Private Sector
We are committed to using policy levers and working with
stakeholders to achieve interoperable data exchange and to transition
to full digital quality measurement in our quality programs. We are
considering the future potential development and staged implementation
of a cohesive portfolio of dQMs across our quality programs (including
the SNF QRP), agencies, and private payers. This cohesive portfolio
would require, where possible, alignment of: (1) Measure concepts and
specifications including narrative statements, measure logic, and value
sets; and (2) the individual data elements used to build these measure
specifications and calculate the measures. Further, the required data
elements would be limited to standardized, interoperable elements to
the fullest extent possible; hence, part of the alignment strategy will
be the consideration and advancement of data standards and
implementation guides for key data elements. We would coordinate
closely with quality measure developers, Federal and state agencies,
and private payers to develop and to maintain a cohesive dQM portfolio
that meets our programmatic requirements and that fully aligns across
Federal and state agencies and payers to the extent possible.
We intend this coordination to be ongoing and allow for continuous
refinement to ensure quality measures remain aligned with evolving
healthcare practices and priorities (for example, patient reported
outcomes (PROs), disparities, care coordination), and track with the
transformation of data collection. This includes conformance with
standards and health IT module updates, future adoption of technologies
incorporated within the ONC Health IT Certification Program and may
also include standards adopted by ONC (for example, to enable
standards-based APIs). The coordination would build on the principles
outlined in HHS' Nation Health Quality Roadmap.\75\ It would focus on
the quality domains of safety, timeliness, efficiency, effectiveness,
equitability, and patient-centeredness. It would leverage several
existing Federal and public-private efforts including our Meaningful
Measures 2.0 Framework; the Federal Electronic Health Record
Modernization (DoD/VA); the Core Quality Measure Collaborative, which
convenes stakeholders from America's Health Insurance Plans (AHIP),
CMS, NQF, provider organizations, private payers, and consumers and
develops consensus on quality measures for provider specialties; and
the NQF-convened Measure Applications Partnership (MAP), which
recommends measures for use in public payment and reporting programs.
We would coordinate with HL7's ongoing work to advance FHIR resources
in critical areas to support patient care and measurement such as
social determinants of health. Through this coordination, we would
identify which existing measures could be used or evolved to be used as
dQMs, in recognition of current healthcare practice and priorities.
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\75\ Department of Health and Human Services. National Health
Quality Roadmap. May 15, 2020. Available at https://www.hhs.gov/sites/default/files/national-health-quality-roadmap.pdf.
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This multi-stakeholder, joint Federal, state, and industry effort,
made possible and enabled by the pending advances towards true
interoperability, would yield a significantly improved quality
measurement enterprise. The success of the dQM portfolio would be
enhanced by the degree to which the measures achieve our programmatic
requirements as well as the requirements of other agencies and payers.
5. Solicitation of Comments
We seek input on the following steps that would enable
transformation of CMS' quality measurement enterprise to be fully
digital:
What EHR/IT systems do you use and do you participate in a
health information exchange (HIE)?
How do you currently share information with other
providers?
In what ways could we incentivize or reward innovative
uses of health information technology (IT) that could reduce burden for
post-acute care settings, including but not limited to SNFs?
What additional resources or tools would post-acute care
settings, including but not limited to SNFs, and health IT vendors find
helpful to support the testing, implementation, collection, and
reporting of all measures using FHIR standards via secure APIs to
reinforce the sharing of patient health information between care
settings?
Would vendors, including those that service post-acute
care settings, such as SNFs, be interested in or willing to participate
in pilots or models of alternative approaches to quality measurement
that would align standards for quality measure data collection across
care settings to improve care coordination, such as sharing patient
data via secure FHIR API as the basis for calculating and reporting
digital measures?
We plan to continue working with other agencies and stakeholders to
coordinate and to inform our transformation to dQMs leveraging health
IT standards. While we will not be responding to specific comments
submitted in response to this RFI in the FY 2022 SNF PPS final rule, we
will
[[Page 20000]]
actively consider all input as we develop future regulatory proposals
or future subregulatory policy guidance. Any updates to specific
program requirements related to quality measurement and reporting
provisions would be addressed through separate and future notice-and-
comment rulemaking, as necessary.
F. Closing the Health Equity Gap in Post-Acute Care Quality Reporting
Programs--Request for Information (RFI)
1. Background
Significant and persistent inequities in health outcomes exist in
the United States. In recognition of persistent health disparities and
the importance of closing the health equity gap, we request information
on revising several CMS programs to make reporting of health
disparities based on social risk factors and race and ethnicity more
comprehensive and actionable for providers and patients. Belonging to a
racial or ethnic minority group; living with a disability; being a
member of the lesbian, gay, bisexual, transgender, and queer (LGBTQ+)
community; or being near or below the poverty level is often associated
with worse health outcomes.76 77 78 79 80 81 82 83 Such
disparities in health outcomes are the result of a number of factors,
but importantly for CMS programs, although not the sole determinant,
poor access and provision of lower quality health care contribute to
health disparities. For instance, numerous studies have shown that
among Medicare beneficiaries, racial and ethnic minority individuals
often receive lower quality of care, report lower experiences of care,
and experience more frequent hospital readmissions and operative
complications.84 85 86 87 88 89
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\76\ Joynt KE, Orav E, Jha AK. Thirty-Day Readmission Rates for
Medicare Beneficiaries by Race and Site of Care. JAMA. 2011;
305(7):675-681.
\77\ Lindenauer PK, Lagu T, Rothberg MB, et al. Income
Inequality and 30 Day Outcomes After Acute Myocardial Infarction,
Heart Failure, and Pneumonia: Retrospective Cohort Study. British
Medical Journal. 2013; 346.
\78\ Trivedi AN, Nsa W, Hausmann LRM, et al. Quality and Equity
of Care in U.S. Hospitals. New England Journal of Medicine. 2014;
371(24):2298-2308.
\79\ Polyakova, M., et al. Racial Disparities In Excess All-
Cause Mortality During The Early COVID-19 Pandemic Varied
Substantially Across States. Health Affairs. 2021; 40(2): 307-316.
\80\ Rural Health Research Gateway. Rural Communities: Age,
Income, and Health Status. Rural Health Research Recap. November
2018.
\81\ https://www.minorityhealth.hhs.gov/assets/PDF/Update_HHS_Disparities_Dept-FY2020.pdf.
\82\ www.cdc.gov/mmwr/volumes/70/wr/mm7005a1.htm.
\83\ Poteat TC, Reisner SL, Miller M, Wirtz AL. COVID-19
Vulnerability of Transgender Women With and Without HIV Infection in
the Eastern and Southern U.S. Preprint. medRxiv.
2020;2020.07.21.20159327. Published 2020 Jul 24. doi:10.1101/
2020.07.21.20159327.
\84\ Martino, SC, Elliott, MN, Dembosky, JW, Hambarsoomian, K,
Burkhart, Q, Klein, DJ, Gildner, J, and Haviland, AM. Racial,
Ethnic, and Gender Disparities in Health Care in Medicare Advantage.
Baltimore, MD: CMS Office of Minority Health. 2020.
\85\ Guide to Reducing Disparities in Readmissions. CMS Office
of Minority Health. Revised August 2018. Available at https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/OMH_Readmissions_Guide.pdf.
\86\ Singh JA, Lu X, Rosenthal GE, Ibrahim S, Cram P. Racial
disparities in knee and hip total joint arthroplasty: an 18-year
analysis of national Medicare data. Ann Rheum Dis. 2014
Dec;73(12):2107-15.
\87\ Rivera-Hernandez M, Rahman M, Mor V, Trivedi AN. Racial
Disparities in Readmission Rates among Patients Discharged to
Skilled Nursing Facilities. J Am Geriatr Soc. 2019 Aug;67(8):1672-
1679.
\88\ Joynt KE, Orav E, Jha AK. Thirty-Day Readmission Rates for
Medicare Beneficiaries by Race and Site of Care. JAMA.
2011;305(7):675-681.
\89\ Tsai TC, Orav EJ, Joynt KE. Disparities in surgical 30-day
readmission rates for Medicare beneficiaries by race and site of
care. Ann Surg. Jun 2014;259(6):1086-1090.
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Readmission rates for common conditions in the Hospital
Readmissions Reduction Program are higher for black Medicare
beneficiaries and higher for Hispanic Medicare beneficiaries with
Congestive Heart Failure and Acute Myocardial
Infarction.90 91 92 93 94 Studies have also shown that
African Americans are significantly more likely than white Americans to
die prematurely from heart disease and stroke.\95\ The COVID-19
pandemic has further illustrated many of these longstanding health
inequities with higher rates of infection, hospitalization, and
mortality among black, Latino, and Indigenous and Native American
persons relative to white persons.96 97 As noted by the
Centers for Disease Control ``long-standing systemic health and social
inequities have put many people from racial and ethnic minority groups
at increased risk of getting sick and dying from COVID-19''.\98\ One
important strategy for addressing these important inequities is by
improving data collection to allow for better measurement and reporting
on equity across post-acute care programs and policies.
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\90\ Rodriguez F, Joynt KE, Lopez L, Saldana F, Jha AK.
Readmission rates for Hispanic Medicare beneficiaries with heart
failure and acute myocardial infarction. Am Heart J. Aug
2011;162(2):254-261 e253.
\91\ Centers for Medicare and Medicaid Services. Medicare
Hospital Quality Chartbook: Performance Report on Outcome Measures;
2014.
\92\ Guide to Reducing Disparities in Readmissions. CMS Office
of Minority Health. Revised August 2018. Available at https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/OMH_Readmissions_Guide.pdf.
\93\ Prieto-Centurion V, Gussin HA, Rolle AJ, Krishnan JA.
Chronic obstructive pulmonary disease readmissions at minority-
serving institutions. Ann Am Thorac Soc. Dec 2013;10(6):680-684.
\94\ Joynt KE, Orav E, Jha AK. Thirty-Day Readmission Rates for
Medicare Beneficiaries by Race and Site of Care. JAMA.
2011;305(7):675-681.
\95\ HHS. Heart disease and African Americans. (March 29, 2021).
https://www.minorityhealth.hhs.gov/omh/browse.aspx?lvl=4&lvlid=19.
\96\ https://www.cms.gov/files/document/medicare-covid-19-data-snapshot-fact-sheet.pdf.
\97\ Ochieng N, Cubanski J, Neuman T, Artiga S, and Damico A.
Racial and Ethnic Health Inequities and Medicare. Kaiser Family
Foundation. February 2021. Available at https://www.kff.org/medicare/report/racial-and-ethnic-health-inequities-and-medicare/.
\98\ https://www.cdc.gov/coronavirus/2019-ncov/community/health-equity/race-ethnicity.html.
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We are also committed to achieving equity in health care outcomes
for our beneficiaries by supporting providers in quality improvement
activities to reduce health inequities, enabling them to make more
informed decisions, and promoting provider accountability for health
care disparities.99 100 For the purposes of this rule, we
are using a definition of equity established in Executive Order 13985,
as ``the consistent and systematic fair, just, and impartial treatment
of all individuals, including individuals who belong to underserved
communities that have been denied such treatment, such as Black,
Latino, and Indigenous and Native American persons, Asian Americans and
Pacific Islanders and other persons of color; members of religious
minorities; lesbian, gay, bisexual, transgender, and queer (LGBTQ+)
persons; persons with disabilities; persons who live in rural areas;
and persons otherwise adversely affected by persistent poverty or
inequality.'' \101\ We note that this definition was recently
established by the current administration, and provides a useful,
common definition for equity across different areas of government,
although numerous other definitions of equity exist.
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\99\ https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/Downloads/CMS-Quality-Strategy.pdf.
\100\ Report to Congress: Improving Medicare Post-Acute Care
Transformation (IMPACT) Act of 2014 Strategic Plan for Accessing
Race and Ethnicity Data. January 5, 2017. Available at https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/Research-Reports-2017-Report-to-Congress-IMPACT-ACT-of-2014.pdf.
\101\ https://www.federalregister.gov/documents/2021/01/25/2021-01753/advancing-racial-equity-and-support-for-underserved-communities-through-the-Federal-government.
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Our ongoing commitment to closing the equity gap in CMS quality
programs is demonstrated by a portfolio of programs aimed at making
information
[[Page 20001]]
on the quality of health care providers and services, including
disparities, more transparent to consumers and providers. The CMS
Equity Plan for Improving Quality in Medicare outlines a path to equity
which aims to support Quality Improvement Networks and Quality
Improvement Organizations (QIN-QIOs); Federal, state, local, and tribal
organizations; providers; researchers; policymakers; beneficiaries and
their families; and other stakeholders in activities to achieve health
equity. The CMS Equity Plan includes three core elements: (1)
Increasing understanding and awareness of disparities; (2) developing
and disseminating solutions to achieve health equity; and (3)
implementing sustainable actions to achieve health equity.\102\ The CMS
Quality Strategy and Meaningful Measures Framework \103\ include
elimination of racial and ethnic disparities as a central principle.
Our ongoing commitment to closing the health equity gap in the SNF QRP
is demonstrated by the adoption of standardized patient assessment data
elements (SPADEs) which include several social determinants of health
(SDOH) that were finalized in the FY 2020 SNF PPS final rule for the
SNF QRP (84 FR 38805 through 38817).
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\102\ Centers for Medicare & Medicaid Services Office of
Minority Health. The CMS Equity Plan for Improving Quality in
Medicare. https://www.cms.gov/About-CMS/Agency-Information/OMH/OMH_Dwnld-CMS_EquityPlanforMedicare_090615.pdf.
\103\ https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/MMF/General-info-Sub-Page.
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We continue to work with Federal and private partners to better
leverage data on social risk to improve our understanding of how these
factors can be better measured in order to close the health equity gap.
Among other things, we have developed an Inventory of Resources for
Standardized Demographic and Language Data Collection \104\ and
supported collection of specialized International Classification of
Disease, 10th Edition, Clinical Modification (ICD-10-CM) codes for
describing the socioeconomic, cultural, and environmental determinants
of health. We continue to work to improve our understanding of this
important issue and to identify policy solutions that achieve the goals
of attaining health equity for all patients.
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\104\ Centers for Medicare and Medicaid Services. Building an
Organizational Response to Health Disparities Inventory of Resources
for Standardized Demographic and Language Data Collection. 2020.
https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/Data-Collection-Resources.pdf.
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2. Solicitation of Public Comment
Under authority of the IMPACT Act and section 1888(e)(6) of the
Act, we are seeking comment on the possibility of revising measure
development, and the collection of other SPADEs that address gaps in
health equity in the SNF QRP. Any potential health equity data
collection or measure reporting within a CMS program that might result
from public comments received in response to this solicitation would be
addressed through a separate notice-and-comment rulemaking in the
future.
Specifically, we are inviting public comment on the following:
Recommendations for quality measures, or measurement
domains that address health equity, for use in the SNF QRP.
As finalized in the FY 2020 SNF PPS final rule (84 FR
38805 through 38817), SNFs must report certain standardized patient
assessment data elements (SPADEs) on SDOH, including race, ethnicity,
preferred language, interpreter services, health literacy,
transportation and social isolation.\105\ CMS is seeking guidance on
any additional items, including SPADEs that could be used to assess
health equity in the care of SNF residents, for use in the SNF QRP.
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\105\ In response to the COVID-19 PHE, CMS released an Interim
Final Rule (85 FR 27595 through 27597) which delayed the compliance
date for the collection and reporting of the SDOH for at least two
full fiscal years after the end of the PHE.
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Recommendations for how CMS can promote health equity in
outcomes among SNF residents. For example, we are interested in
feedback regarding whether including facility-level quality measure
results stratified by social risk factors and social determinants of
health (for example, dual eligibility for Medicare and Medicaid, race)
in confidential feedback reports could allow facilities to identify
gaps in the quality of care they provide. (For example, methods similar
or analogous to the CMS Disparity Methods \106\ which provide hospital-
level confidential results stratified by dual eligibility for
condition-specific readmission measures, which are currently included
in the Hospital Readmission Reduction Program (see 84 FR 42496 through
42500)).
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\106\ https://qualitynet.cms.gov/inpatient/measures/disparity-methods/methodology.
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Methods that commenters or their organizations use in
employing data to reduce disparities and improve patient outcomes,
including the source(s) of data used, as appropriate.
Given the importance of structured data and health IT
standards for the capture, use, and exchange of relevant health data
for improving health equity, the existing challenges providers'
encounter for effective capture, use, and exchange of health
information, including data on race, ethnicity, and other social
determinants of health, to support care delivery and decision making.
While we will not be responding to specific comments submitted in
response to this RFI in the FY 2022 SNF PPS final rule, we intend to
use this input to inform future policy development. We look forward to
receiving feedback on these topics, and note for readers that responses
to the RFI should focus on how they could be applied to the quality
reporting program requirements. Please note that any responses provided
will not impact payment decisions.
G. Form, Manner, and Timing of Data Submission Under the SNF QRP
1. Background
We refer readers to the regulatory text at 42 CFR 413.360(b) for
information regarding the current policies for reporting SNF QRP data.
2. Proposed Schedule for Data Submission of the SNF HAI Measure
Beginning With the FY 2023 QRP
The SNF HAI measure, which we propose in section VI.C.1. of this
proposed rule, is a Medicare FFS claims-based measure. Because claims-
based measures can be calculated based on data that have already been
submitted to the Medicare program for payment purposes, no additional
information collection would be required from SNFs. We are proposing to
use 1 year of FY 2019 claims data (October 1, 2018 through September
30, 2019) for the FY 2023 SNF QRP. We are proposing to use FY 2019 data
to calculate this measure because it is the most recent fiscal year of
data that has not been exempted due to the PHE. Beginning with the FY
2024 SNF QRP, compliance with APU reporting requirements would use FY
2021 claims data (October 1, 2020 through September 30, 2021) and
advance by one FY with each annual refresh. Due to the fact that Q1 and
Q2 2020 data were excepted by CMS related to the COVID-19 PHE, these
quarters of data would not be used for purposes of the QRP. For
information on public reporting of the SNF HAI measure, we refer you to
Table 31 in section VI.H.4.c. of this proposed rule.
We invite public comment on this proposal.
[[Page 20002]]
3. Proposed Method of Data Submission for COVID-19 Vaccination Coverage
Among Healthcare Personnel Measure
As discussed in section VI.C.2 of this proposed rule, we propose to
require that SNFs submit data on the COVID-19 Vaccination Coverage
among Healthcare Personnel Measure through the Centers for Disease
Control and Prevention (CDC)/National Healthcare Safety Network (NHSN).
The NHSN is a secure, internet-based surveillance system maintained by
the CDC that can be utilized by all types of healthcare facilities in
the United States, including acute care hospitals, long term acute care
hospitals, psychiatric hospitals, rehabilitation hospitals, outpatient
dialysis centers, ambulatory surgery centers, and SNFs. The NHSN
enables healthcare facilities to collect and use vaccination data, and
information on other adverse events. NHSN collects data via a Web-based
tool hosted by the CDC (http://www.cdc.gov/). The NHSN is provided free
of charge. We propose for SNFs to submit the data needed to calculate
the COVID-19 Vaccination Coverage among Healthcare Personnel measure
using the NHSN's standard data submission requirements. CDC/NHSN
requirements include adherence to training requirements, use of CDC
measure specifications, data element definitions, data submission
requirements and instructions, data submission timeframes, as well as
NHSN participation forms and indications to CDC allowing CMS to access
data for this measure for the SNF quality reporting program purposes.
Detailed requirements for NHSN participation, measure specifications,
and data collection can be found at http://www.cdc.gov/nhsn/. We
propose to require SNFs to use the specifications and data collection
tools for the proposed COVID-19 Vaccination Coverage among Healthcare
Personnel measure as required by CDC as of the time that the data are
submitted.
We invite public comment on this proposal.
4. Proposed Schedule for Data Submission of the COVID-19 Vaccination
Coverage Among Healthcare Personnel Measure Beginning With the FY 2023
SNF QRP
As discussed in section VI.C.2. of this proposed rule, we are
proposing to adopt the COVID-19 Vaccination Coverage among HCP quality
measure beginning with the FY 2023 SNF QRP. Given the time-sensitive
nature of this measure in light of the PHE, we propose an initial data
submission period from October 1, 2021 through December 31, 2021.
Starting in CY 2022, SNFs would be required to submit data for the
entire calendar year beginning with the FY 2024 SNF QRP.
SNFs would submit data for the measure through the CDC/NHSN web-
based surveillance system. SNFs would use the COVID-19 vaccination data
collection module in the NHSN Long-term Care (LTC) Component to report
the cumulative number of HCP eligible to work in the healthcare
facility for at least 1 day during the reporting period, excluding
persons with contraindications to COVID-19 vaccination (denominator)
and the cumulative number of HCP eligible to work in the SNF for at
least 1 day during the reporting period and who received a complete
vaccination course against COVID-19 (numerator). SNFs would submit
COVID-19 vaccination data through the NHSN for at least 1 week each
month and the CDC would report to CMS quarterly.
We invite public comment on this proposal.
5. Consolidated Appropriations Act and the SNF QRP
On December 27, 2020, Congress enacted the Consolidated
Appropriations Act, 2021 (CAA) (Pub. L. 116-260). Section 111(a)(3) of
Division CC of the CAA amends section 1888 of the Act by adding a new
paragraph (h)(12), which requires the Secretary to apply a process to
validate the measures submitted under the SNF VBP and the measures and
data submitted under the SNF QRP as appropriate, which may be similar
to the process specified under the Hospital Inpatient Quality Reporting
(IQR) Program for validating inpatient hospital measures. We plan to
develop a process for validating the SNF QRP measures and data and
implement this policy as soon as technically feasible. We will provide
more details and seek public comment in future rulemaking. For more
information on the SNF VBP please refer to section VII. of this rule.
H. Proposed Policies Regarding Public Display of Measure Data for the
SNF QRP
1. Background
Section 1899B(g) of the Act requires the Secretary to establish
procedures for making the SNF QRP data available to the public,
including the performance of individual SNFs, after ensuring that SNFs
have the opportunity to review their data prior to public display. SNF
QRP measure data are currently displayed on the Nursing homes including
rehab services website within Care Compare and the Provider Data
Catalog. Both Care Compare and the Provider Data Catalog replaced
Nursing Home Compare and Data.Medicare.gov, which were retired in
December 2020. For a more detailed discussion about our policies
regarding public display of SNF QRP measure data and procedures for the
opportunity to review and correct data and information, we refer
readers to the FY 2017 SNF PPS final rule (81 FR 52045 through 52048).
2. Proposal to Publicly Report the Skilled Nursing Facility Healthcare-
Associated Infections Requiring Hospitalization Measure Beginning With
the FY 2023 SNF QRP
We propose public reporting for the SNF HAI measure beginning with
the April 2022 Care Compare refresh or as soon as technically feasible
using data collected from discharges in FY 2019 beginning October 1,
2018 through September 30, 2019. Provider preview reports would be
distributed in January 2022. A SNF's HAI rates would be displayed based
on 1 fiscal year of data. Since we cannot publicly report data from Q1
and Q2 of 2020 due to the PHE, we are proposing to use data collected
from discharges in FY 2021 (October 1, 2020 through September 30, 2021)
for public reporting of the SNF HAI measure in the October 2022 Care
Compare refresh. Thereafter, the SNF HAI measure would be calculated
using four quarters of FY data for the annual refresh on Care Compare.
Claims-based measures are only refreshed on Care Compare annually. To
ensure statistical reliability of the data, we propose assigning SNFs
with fewer than 25 eligible stays during a performance period to a
separate category: ``The number of resident stays is too small to
report.'' Eligible stays meet the measure's denominator inclusion
criteria, and we refer readers to the Skilled Nursing Facility
Healthcare-Associated Infections Requiring Hospitalization for the
Skilled Nursing Facility Quality Reporting Program Technical Report
available at https://www.cms.gov/files/document/snf-hai-technical-report.pdf/ for more details. If a SNF had fewer than 25 eligible
stays, the SNF's performance would not be publicly reported for the
measure for that performance period. We refer readers to CMS's SNF QRP
Public Reporting web page for more information available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/Skilled-Nursing-Facility-Quality-Reporting-Program/SNF-Quality-Reporting-Program-Public-Reporting.
[[Page 20003]]
We invite public comment on this proposal for the public display of
the SNF HAI measure on Care Compare.
3. Proposal to Publicly Report the COVID-19 Vaccination Coverage Among
Healthcare Personnel (HCP) Measure Beginning With the FY 2023 SNF QRP
We propose to publicly report the COVID-19 Vaccination Coverage
among Healthcare Personnel measure beginning with the October 2022 Care
Compare refresh or as soon as technically feasible using data collected
for Q4 2021 (October 1, 2021 through December 31, 2021). If finalized
as proposed, a SNF's HCP COVID-19 vaccination coverage rate would be
displayed based on one quarter of data. Provider preview reports would
be distributed in July 2022. Thereafter, HCP COVID-19 vaccination
coverage rates would be displayed based on one quarter of data updated
quarterly. Subsequent to this, one additional quarter of data would be
added to the measure calculation during each advancing refresh, until
the point four full quarters of data is reached. Thereafter, the
measure would be reported using four rolling quarters of data.
We invite public comment on this proposal for the public display of
the COVID-19 Vaccination Coverage among HCP measure on Care Compare.
4. Proposals for Public Reporting of Quality Measures in the SNF QRP
With Fewer Quarters Due to COVID-19 Public Health Emergency (PHE)
Exemptions
a. COVID-19 Public Health Emergency Temporary Exemptions
Under the authority of section 319 of the Public Health Service
Act, the Secretary of Health and Human Services declared a public
health emergency (PHE) effective as of January 27, 2020. On March 13,
2020, subsequent to a presidential declaration of national emergency
under the Stafford Act, the Secretary invoked section 1135(b) of the
Act (42 U.S.C. 1320b-5) to waive or modify the requirements of titles
XVIII, XIX, and XXI of the Act and regulations related to the PHE for
COVID-19, effective as of March 1, 2020.\107\ On March 27, 2020, we
sent a guidance memorandum under the subject title, ``Exceptions and
Extensions for Quality Reporting Requirements for Acute Care Hospitals,
PPS-Exempt Cancer Hospitals, Inpatient Psychiatric Facilities, Skilled
Nursing Facilities, Home Health Agencies, Hospices, Inpatient
Rehabilitation Facilities, Long-Term Care Hospitals, Ambulatory
Surgical Centers, Renal Dialysis Facilities, and MIPS Eligible
Clinicians Affected by COVID-19'' to the Medicare Learning Network
(MLN) Connects Newsletter and Other Program-Specific Listserv
Recipients,\108\ hereafter referred to as the March 27, 2020 CMS
Guidance Memo. In that memo we granted an exception to the SNF QRP
reporting requirements from Q4 2019 (October 1, 2019-December 31,
2019), Q1 2020 (January 1, 2020-March 31, 2020), and Q2 2020 (April 1,
2020-June 30, 2020). We also stated that we would not publicly report
any SNF QRP data that might be greatly impacted by the exceptions from
Q1 and Q2 of 2020. This exception impacted the schedule for public
reporting that would have included those two quarters of data.
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\107\ https://www.phe.gov/emergency/news/healthactions/section1135/Pages/covid19-13March20.aspx.
\108\ https://www.cms.gov/files/document/guidance-memo-exceptions-and-extensions-quality-reporting-and-value-based-purchasing-programs.pdf.
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SNF quality measures are publicly reported on Care Compare. Care
Compare uses four quarters of data for MDS assessment-based measures
and eight quarters for claims-based measures. Table 28 displays the
original schedule for public reporting of SNF QRP measures.\109\
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\109\ More information about the SNF QRP Public Reporting
schedule can be found on the SNF QRP Public Reporting website at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/Skilled-Nursing-Facility-Quality-Reporting-Program/SNF-Quality-Reporting-Program-Public-Reporting.
Table 28--SNF Quarters in Care Compare Original Schedule for Refreshes Affected by COVID-19 PEH Exemptions--Assessment and Claims Based Measures
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Quarter refresh SNF quarters in original schedule for care compare
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January 2021............................. MDS: Q2 2019--Q1 2020 (4 quarters). Claims: Q4 2017--Q3 2019 (8 quarters).
April 2021............................... MDS: Q3 2019--Q2 2020 (4 quarters). Claims: Q4 2017--Q3 2019 (8 quarters).
July 2021................................ MDS: Q4 2019--Q3 2020 (4 quarters). Claims: Q4 2017--Q3 2019 (8 quarters).
October 2021............................. MDS: Q1 2020--Q4 2020 (4 quarters). Claims: Q4 2018--Q3 2020 (8 quarters).
January 2022............................. MDS: Q2 2020--Q1 2021 (4 quarters). Claims: Q4 2018--Q3 2020 (8 quarters).
April 2022............................... MDS: Q3 2020--Q2 2021 (4 quarters). Claims: Q4 2018--Q3 2020 (8 quarters).
July 2022................................ MDS: Q4 2020--Q3 2021 (4 quarters). Claims: Q4 2018--Q3 2020 (8 quarters).
October 2022............................. MDS: Q1 2021--Q4 2021 (4 quarters). Claims: Q4 2019--Q3 2021 (8 quarters).
January 2023............................. MDS: Q2 2021--Q1 2022 (4 quarters). Claims: Q4 2019--Q3 2021 (8 quarters).
Apri1 2023............................... MDS: Q3 2021--Q2 2022 (4 quarters). Claims: Q4 2019--Q3 2021 (8 quarters).
July 2023................................ MDS: Q4 2021--Q3 2022 (4 quarters). Claims: Q4 2019--Q3 2021 (8 quarters).
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During 2020, we conducted testing to inform decisions about
publicly reporting data for those refreshes which include partially
and/or fully exempt data (discussed below). The testing helped us
develop a plan for posting data that are as up-to-date as possible and
that also meet acceptable standards for public reporting. We believe
that the plan allows us to provide consumers with helpful information
on the quality of SNF care, while also making the necessary adjustments
to accommodate the exemption provided SNFs. The following sections
provide the results of our testing, and explain how we used the results
to develop plans for accommodating exempt and partially-exempt data in
public reporting.
b. Exempted Quarters
In the March 27, 2020 Medicare Learning Network (MLN) Newsletter on
Exceptions and Extensions for Quality Reporting Program (QRP)
Requirements, we stated that we would not report any PAC quality data
that might be greatly impacted by the exemptions granted for Quarter 1
and Quarter 2 of 2020. Given the timing of the PHE onset, we determined
that we would not use SNF MDS assessments or SNF claims from Quarter 1
and Quarter 2 of 2020 for public reporting, but that we would
[[Page 20004]]
assess the COVID-19 PHE impact on data from Quarter 4 2019. Before
proceeding with the October 2020 refresh, we conducted testing to
ensure that, despite the voluntary nature of reporting for that
quarter, public reporting would still meet our public reporting
standards. We found the level of reporting, measured in the number of
eligible stays and providers, and the reported outcomes, to be in line
with levels and trends observed in FY 2018 and FY 2019. We note that
Quarter 4 2019 ended before the onset of the COVID-19 pandemic in the
United States. Thus, we proceeded with including these data in SNF QRP
measure calculations for the October 2020 refresh.
c. Update on Data Freeze and Proposal for January 2022 Public Reporting
Methodology for SNF Claims-Based and MDS Assessment-Based Measures
In addition to the January 2021 refresh, there are several other
forthcoming refreshes for which the original public reporting schedules
included exempted quarters of SNF QRP data. The impacted refreshes for
MDS assessment and claims based measures are outlined in (Table 28). We
determined that freezing the data displayed on the website with the
October 2020 refresh values--that is, hold data constant after the
October 2020 refresh data on the website without subsequent update--
would be the most straightforward, efficient, and equitable approach
for SNFs. Thus, we decided that, for as many refreshes as necessary, we
would hold data constant on the website with the October 2020 data, and
communicate this decision to the public.
Because October 2020 refresh data will become increasingly out-of-
date and thus less useful for consumers, we analyzed whether it would
be possible to use fewer quarters of data for one or more refreshes and
thus reduce the number of refreshes that continue to display October
2020 data. Using fewer quarters of more up-to-date data requires that
(1) a sufficient percentage of SNFs would still likely have enough
assessment data to report quality measures (reportability); and (2)
fewer quarters would likely produce similar measure scores for
providers, with similar reliability, and thus not unfairly represent
the quality of care SNFs provide during the period reported in a given
refresh (reliability).
To assess these criteria, we conducted reportability and
reliability analysis using 3 quarters of data in a refresh, instead of
the standard 4 quarters of data for reporting assessment-based measures
and using 6 quarters instead of 8 for claims-based measures.
Specifically, we used historical data to calculate MDS assessment based
and SNF claims based quality measures under two scenarios:
1. Standard Public Reporting (SPR) Base Scenario: We used four
quarters of CY 2019 data as a proxy alternative for the exempted
quarters in CY 2020 in order to compare results. For assessment-based
measures, the quarters used in this scenario are Q1 through Q4 2019.
For claims-based measures, the quarters used in this scenario are Q1
2018 through Q4 2019.
2. COVID-19 Affected Reporting (CAR) Scenario: We calculated SNF
QRP measures using 3 quarters (Q2 2019 through Q4 2019) of SNF QRP data
for assessment-based measures, and 6 quarters (Q1 2018 through Q4 2018
and Q3 2019 through Q4 2019) for claims-based measures. The CAR
scenario uses the most recently available data to simulate the public
health emergency reality where quarters 1 and 2 of a calendar year must
be excluded from calculation. Quarterly trends in MDS assessment-based
and claims based measures indicate that these measures do not exhibit
substantial seasonal variation.
To assess performance in these scenarios, we calculated the
reportability as the percent of SNFs meeting the case minimum for
public reporting (the public reporting threshold). To test the
reliability of restricting the SNFs included in the SPR Base Scenario
to those included in the CAR Scenario, we performed three tests on the
set of SNFs included in both scenarios. First, we evaluated measure
correlation using the Pearson and Spearman correlation coefficients,
which assess the alignment of SNFs' provider scores. Second, for each
scenario, we conducted a split-half reliability analysis and estimated
intraclass correlation (ICC) scores, where higher scores imply better
internal reliability. Modest differences in ICC scores between both
scenarios would suggest that using fewer quarters of data does not
impact the internal reliability of the results. Third, we estimated
reliability scores where a higher value indicates that measure scores
are relatively consistent for patients admitted to the same SNF and
variation in the measure reflects true differences across providers. To
calculate the reliability results, we restricted the SNFs included in
the SPR scenario to those included in the CAR scenario.
Our testing indicated that the expected impact of using fewer
quarters of data on reportability and reliability of MDS assessment-
based and claims based measures is acceptable.
We are proposing to use the CAR scenario as the approach for the
following affected refreshes for MDS assessment-based measures, the
affected refresh is the January 2022 refresh; for claims-based
measures, the affected refreshes occur from January 2022 through July
2023. For the earlier four affected refreshes (January, April, July,
and October 2021), we decided to hold constant the Care Compare website
with October 2020 data. We communicated this decision in a Public
Reporting Tip Sheet, which is located at https://www.cms.gov/files/document/snfqrp-covid19prtipsheet-october2020.pdf.
Our proposal of the CAR approach for the affected refreshes would
allow us to begin displaying more recent data in January 2022, rather
than continue displaying October 2020 data (Q1 2019 through Q4 2019 for
assessment-based measures, Q4 2017 through Q3 2019 for claims-based
measures). We believe that resuming public reporting starting in
January 2022 with fewer quarters of data can assist consumers by
providing more recent quality data as well as more actionable data for
SNF providers. Our testing results indicate we can achieve these
positive impacts with acceptable changes in reportability and
reliability. Table 29 summarizes the revised schedule (that is, frozen
data) and the proposed schedule (that is, using fewer quarters in the
affected refreshes) for assessment-based measures. Tables 30 and 31
summarize the revised schedule (that is, frozen data) and the proposed
schedule (that is, using fewer quarters in the affected refreshes) for
claims-based measures.
We invite public comment on the proposal to use the CAR scenario to
publicly report SNF measures for the January 2022-July 2023 refreshes.
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VII. Skilled Nursing Facility Value-Based Purchasing (SNF VBP) Program
A. Background
Section 215(b) of the Protecting Access to Medicare Act of 2014
(PAMA) (Pub. L. 113-93) authorized the SNF VBP Program (the
``Program'') by adding section 1888(h) to the Act. As a prerequisite to
implementing the SNF VBP Program, in the FY 2016 SNF PPS final rule (80
FR 46409 through 46426), we adopted an all-cause, all-condition
hospital readmission measure, as required by section 1888(g)(1) of the
Act, and discussed other policies to implement the Program such as
performance standards, the performance period and baseline period, and
scoring. SNF VBP Program policies have been codified in our regulations
at Sec. 413.338. For additional background information on the SNF VBP
Program, including an overview of the SNF VBP Report to Congress and a
summary of the Program's statutory requirements, we refer readers to
the following prior final rules:
In the FY 2017 SNF PPS final rule (81 FR 51986 through
52009), we adopted an all-condition, risk-adjusted potentially
preventable hospital readmission measure for SNFs, as required by
section 1888(g)(2) of the Act, adopted policies on performance
standards, performance scoring, and sought comment on an exchange
function methodology to translate SNF performance scores into value-
based incentive payments, among other topics.
In the FY 2018 SNF PPS final rule (82 FR 36608 through
36623), we adopted additional policies for the Program, including an
exchange function methodology for disbursing value-based incentive
payments.
In the FY 2019 SNF PPS final rule (83 FR 39272 through
39282), we adopted more policies for the Program, including a scoring
adjustment for low-volume facilities.
In the FY 2020 SNF PPS final rule (84 FR 38820 through
38825), we adopted additional policies for the Program, including a
change to our public reporting policy and an update to the deadline for
the Phase One Review and Correction process. We also adopted a data
suppression policy for low-volume SNFs.
In the FY 2021 SNF PPS final rule (85 FR 47624 through
47627), we amended regulatory text definitions at Sec. 413.338(a)(9)
and (11) to reflect the definition of Performance Standards and the
updated Skilled Nursing Facility Potentially Preventable Readmissions
after Hospital Discharge measure name, respectively. We also updated
the Phase One Review and Correction deadline and codified that update
at Sec. 413.338(e)(1). Additionally, we codified the data suppression
policy for low-volume SNFs at Sec. 413.338(e)(3)(i), (ii), and (iii)
and amended Sec. 413.338(e)(3) to reflect that SNF performance
information will be publicly reported on the Nursing Home Compare
website and/or successor website (84 FR 38823 through 38824) which
since December 2020 is the Provider Data Catalogue website (https://data.cms.gov/provider-data/).
The SNF VBP Program applies to freestanding SNFs, SNFs affiliated
with acute care facilities, and all non-CAH swing-bed rural hospitals.
Section 1888(h)(1)(B) of the Act requires that the SNF VBP Program
apply to payments for services furnished on or after October 1, 2018.
We believe the implementation of the SNF VBP Program is an important
step towards transforming how payment is made for care, moving
increasingly towards rewarding better value, outcomes, and innovations
instead of merely rewarding volume.
B. Measures
For background on the measures we have adopted for the SNF VBP
Program, we refer readers to the FY 2016 SNF PPS final rule (80 FR
46419), where we finalized the Skilled Nursing Facility 30-Day All-
Cause Readmission Measure (SNFRM) (NQF #2510) that we are currently
using for the SNF VBP Program. We also refer readers to the FY 2017 SNF
PPS final rule (81 FR 51987 through 51995), where we finalized the
Skilled Nursing Facility 30-Day Potentially Preventable Readmission
Measure (SNFPPR) that we will use for the SNF VBP Program instead of
the SNFRM as soon as practicable, as required by statute. The SNFPPR
measure's name is now ``Skilled Nursing Facility Potentially
Preventable Readmissions after Hospital Discharge measure'' (Sec.
413.338(a)(11)). We intend to submit the SNFPPR measure for NQF
endorsement review during the Fall 2021 cycle, and to assess transition
timing of the SNFPPR measure to the SNF VBP Program after NQF
endorsement review is complete.
1. Proposed Flexibilities for the SNF VBP Program in Response to the
Public Health Emergency Due to COVID-19
In previous rules, we have identified the need for flexibility in
our quality programs to account for the impact of changing conditions
that are beyond participating facilities' or practitioners' control. We
identified this need because we would like to ensure that participants
in our programs are not affected negatively when their quality
performance suffers not due to the care provided, but due to external
factors.
A significant example of the type of external factor that may
affect quality measurement is the COVID-19 public health emergency
(PHE), which has had, and continues to have, significant and ongoing
effects on the provision of medical care in the country and around the
world. The COVID-19 pandemic and associated PHE has impeded effective
quality measurement in many ways. Changes to clinical practices to
incorporate safety protocols for medical personnel and patients, as
well as unpredicted changes in the number of stays and facility-level
case mixes, have affected the data that SNFs report under the SNF VBP
Program and the resulting measure calculations. CMS is currently
considering whether the SNF readmission measure specifications should
be updated to account for changes in SNF admission and/or hospital
readmission patterns that we have observed during the PHE.
Additionally, because COVID-19 prevalence is not identical across the
country, facilities located in different areas have been affected
differently at different times throughout the pandemic. Under those
circumstances, we remain concerned that the SNF readmission measure
scores are distorted, which would result in skewed payment incentives
and inequitable payments, particularly for SNFs that have treated more
COVID-19 patients than others.
It is not our intention to penalize SNFs based on measure scores
that we believe are distorted by the COVID-19 pandemic, and are thus
not reflective of the quality of care that the measure in the SNF VBP
Program was designed to assess. As discussed above, the COVID-19
pandemic has had, and continues to have, significant and enduring
effects on health care systems around the world, and affects care
decisions, including readmissions to the hospital as measured by the
SNF VBP Program. As a result of the PHE, SNFs could provide care to
their patients that meets the underlying clinical standard but results
in worse measured performance, and by extension, lower incentive
payments in the SNF VBP Program. Additionally, because COVID-19
prevalence has not been identical across the country, SNFs located in
different regions have been affected differently during the PHE. As a
result, we are concerned that regional differences in COVID-19
prevalence during the revised performance period for the FY 2022 SNF
VBP Program, which includes one quarter of data
[[Page 20007]]
during the pandemic (July 1, 2020 through September 30, 2020), have
directly affected SNF readmission measure scores for the FY 2022 SNF
VBP program year. Although these regional differences in COVID-19
prevalence rates do not reflect differences in the quality of care
furnished by SNFs, they directly affect the value-based incentive
payments that these SNFs are eligible to receive and could result in an
unfair and inequitable distribution of those incentives. These
inequities could be especially pronounced for SNFs that have treated a
large number of COVID-19 patients.
Therefore, we are proposing to adopt a policy for the duration of
the PHE for COVID-19 that would enable us to suppress the use of SNF
readmission measure data for purposes of scoring and payment
adjustments in the SNF VBP Program if we determine that circumstances
caused by the PHE for COVID-19 have affected the measure and the
resulting performance scores significantly. Under this proposed policy,
if we determine that the suppression of the SNF readmission measure is
warranted for a SNF VBP program year, we would propose to calculate the
SNF readmission measure rates for that program year but then suppress
the use of those rates to generate performance scores, rank SNFs, and
generate value-based incentive payment percentages based on those
performance scores. We would instead assign each eligible SNF's
performance score of zero for the program year to mitigate the effect
that the distorted measure results would otherwise have on SNF's
performance scores and incentive payment multipliers. We would also
reduce each eligible SNF's adjusted Federal per diem rate by the
applicable percent (2 percent) and then further adjust the resulting
amounts by a value-based incentive payment amount equal to 60 percent
of the total reduction. Those SNFs subject to the Low-Volume Adjustment
policy would receive 100 percent of their 2 percent withhold per the
policy previously finalized in the FY 2020 SNF PPS final rule (84 FR
38823 through 38824). We would also provide each SNF with its SNF
readmission measure rate in confidential feedback reports so that the
SNF is aware of the observed changes to its measure rates. We would
also publicly report the FY 2022 SNF readmission measure rates with
appropriate caveats noting the limitations of the data due to the PHE
for COVID-19.
In developing this proposed policy, we considered what
circumstances caused by the PHE for COVID-19 would affect a quality
measure significantly enough to warrant its suppression in a value-
based purchasing program. We believe that a significant deviation in
measured performance that can be reasonably attributed to the PHE for
COVID-19 is a significant indicator of changes in clinical conditions
that affect quality measurement. Similarly, we believe that a measure
may be focused on a clinical topic or subject that is proximal to the
disease, pathogen, or other health impacts of the PHE. As has been the
case during the COVID-19 PHE, we believe that rapid or unprecedented
changes in clinical guidelines and care delivery, potentially including
appropriate treatments, drugs, or other protocols, may affect quality
measurement significantly and should not be attributed to the
participating facility positively or negatively. We also note that
scientific understanding of a particular disease or pathogen may evolve
quickly during an emergency, especially in cases of new disease or
conditions. Finally, we believe that, as evidenced during the COVID-19
PHE, national or regional shortages or changes in health care
personnel, medical supplies, equipment, diagnostic tools, and patient
case volumes or facility-level case mix may result in significant
distortions to quality measurement.
Based on these considerations, we developed a number of Measure
Suppression Factors that we believe should guide our determination of
whether to propose to suppress the SNF readmission measure for one or
more program years that overlap with the PHE for COVID-19. We are
proposing to adopt these Measure Suppression Factors for use in the SNF
VBP and, for consistency, the following other value-based purchasing
programs: Hospital Value-Based Purchasing Program, Hospital
Readmissions Reduction Program, HAC Reduction Program, and End-Stage
Renal Disease Quality Incentive Program. We believe that these Measure
Suppression Factors will help us evaluate the SNF readmission measure
in the SNF VBP program and that their adoption in the other value-based
purchasing programs noted above will help ensure consistency in our
measure evaluations across programs. The proposed Measure Suppression
Factors are:
(1) Significant deviation in national performance on the measure
during the PHE for COVID-19, which could be significantly better or
significantly worse compared to historical performance during the
immediately preceding program years.
(2) Clinical proximity of the measure's focus to the relevant
disease, pathogen, or health impacts of the PHE for COVID-19.
(3) Rapid or unprecedented changes in:
Clinical guidelines, care delivery or practice,
treatments, drugs, or related protocols, or equipment or diagnostic
tools or materials; or
The generally accepted scientific understanding of the
nature or biological pathway of the disease or pathogen, particularly
for a novel disease or pathogen of unknown origin.
(4) Significant national shortages or rapid or unprecedented
changes in:
Healthcare personnel;
Medical supplies, equipment, or diagnostic tools or
materials; or
Patient case volumes or facility-level case mix.
We also considered alternatives to this proposed policy that could
also fulfill our objective to not hold facilities accountable for
measure results that are distorted due to the PHE for COVID-19. As
noted above, the country continues to grapple with the effects of the
COVID-19 PHE, and in March 2020, we issued a nationwide, blanket
Extraordinary Circumstances Exception (ECE) for all hospitals and other
facilities participating in our quality reporting and value-based
purchasing programs in response to the PHE for COVID-19. This blanket
ECE excepted all data reporting requirements for Q1 and Q2 2020 data.
For claims-based measures, we also stated that we would exclude all
qualifying Q1 and Q2 2020 claims from our measure calculations. We
considered extending the blanket ECE that we issued for Q1 and Q2 2020
to also include Q3 2020 data. However, this option would result in less
than 12 months of data being used to calculate the single readmissions
measure in the Program for multiple Program years, which we do not
believe would provide an accurate assessment of the quality of care
provided in SNFs. This option would also leave no comprehensive data
available for us to provide confidential performance feedback to
providers nor for monitoring and to inform decision-making for
potential future programmatic changes, particularly as the PHE is
extended.
We view this measure suppression proposal as a necessity to ensure
that the SNF VBP program does not reward or penalize facilities based
on factors that the SNF readmission measure was not designed to
accommodate. We intend for this proposed policy to provide short-term
relief to SNFs when we have determined that one or more of the Measure
Suppression Factors
[[Page 20008]]
warrants the suppression of the SNF readmission measure.
We invite public comments on this proposal for the adoption of a
measure suppression policy for the SNF VBP Program for the duration of
the PHE for COVID-19, and also on the proposed Measure Suppression
Factors that we developed for purposes of this proposed policy.
We are also inviting comment on whether we should consider adopting
a measure suppression policy that would apply in a future national PHE,
and if so, whether under such a policy, we should have the flexibility
to suppress quality measures without specifically proposing to do so in
rulemaking. We also request comment on whether we should in future
years consider adopting any form of regional adjustment for the
proposed measure suppression policy that could take into account any
disparate effects of circumstances affecting hospitals around the
country that would prompt us to suppress a measure. For example, COVID-
19 affected different regions of the country at different rates
depending on factors like time of year, geographic density, state and
local policies, and health care system capacity. In future years and
for future PHEs, should they arise, we also request commenters'
feedback on whether we should, rather than suppress a measure
completely, consider a suppression policy with more granular effects
based on our assessment of the geographic effects of the circumstances,
and if so, how region-based measure suppression could be accounted for
within the program's scoring methodology.
2. Proposal To Suppress the SNFRM for the FY 2022 SNF VBP Program Year
In this proposed rule, we are proposing to suppress the SNFRM for
the FY 2022 SNF VBP Program Year under proposed Measure Suppression
Factor: (4) Significant national shortages or rapid or unprecedented
changes in: (iii) Patient case volumes or facility-level case mix.
In response to the PHE for COVID-19, we granted an extraordinary
circumstance exemption (ECE) for SNFs participating in the SNF VBP
Program. Under the ECE, SNF qualifying claims for the period January 1,
2020-June 30, 2020 are excepted from the calculation of the SNFRM.
Because this ECE excepted data for 6 months of the performance period
that we had previously finalized for the FY 2022 SNF VBP program year
(84 FR 38822), we updated the performance period for that program year
in the ``Medicare and Medicaid Programs, Clinical Laboratory
Improvement Amendments, and Patient Protection and Affordable Care Act:
Additional Policy and Regulatory Revisions in Response to the COVID-19
Public Health Emergency'' interim final rule with comment (``the
September 2nd IFC'') (85 FR 54820). Specifically, we finalized that the
new performance period for the FY 2022 SNF VBP Program year would be
April 1, 2019-December 31, 2019 and July 1, 2020-September 30, 2020
because we believed that this period, which combined 9 months of data
prior to the start of the PHE for COVID-19 and 3 months of data after
the end of the ECE, would provide sufficiently reliable data for
evaluating SNFs for the FY 2022 SNF VBP Program. However, analyses
conducted by our contractor since the publication of the September 2nd
IFC have found that when July-September 2020 SNF data are compared with
July-September 2019 SNF data, the July-September 2020 SNF data showed
25 percent fewer SNF admissions and 26 percent fewer readmissions from
a SNF to a hospital. These impacts have affected the reliability of the
SNFRM. Generally speaking, the SNFRM's reliability decreases as the
sample size and measured outcome (that is, readmissions) decrease. A
drop of 25 percent in SNF admissions and 26 percent in readmissions to
the hospital from July-September 2020 has significantly reduced the
sample size needed to calculate both the measure cohort and outcome for
the FY 2022 SNF VBP, thus jeopardizing the measure reliability. Our
contractor's analysis using FY 2019 data showed that such changes may
lead to a 15 percent decrease in the measure reliability, assessed by
the intra[hyphen]class correlation coefficient (ICC). In addition, the
current risk-adjustment model does not factor in COVID-19 or the fact
that SNFs are treating different types of patients as a result of the
COVID-19 PHE. Nearly 10 percent of SNF residents in July-September 2020
had a current or prior diagnosis of COVID-19, with uneven regional
impacts. The SNFRM does not adjust for COVID-19 in the risk adjustment
methodology, as the measure was developed before the pandemic. As a
result, risk-adjusted rates, which compare SNFs to each other
nationally, are likely to reflect variation in COVID-19 prevalence
rather than variation in quality of care. We do not believe that
assessing SNFs on a quality measure affected significantly by the
varied regional response to the COVID-19 PHE presents a clear picture
of the quality of care provided by an individual SNF. The data also
demonstrated other important changes in SNF patient case-mix during the
PHE for COVID-19, including an 18 percent increase in dual-eligible
residents and a 9 percent increase in African-American SNF residents at
the facility level. They have been disproportionately impacted by
COVID, both in terms of morbidity and mortality. We are currently
conducting analyses to determine whether and how the SNFRM
specifications may need to be updated to account for SNF residents with
a primary or secondary diagnosis of COVID-19 for future program years.
We also plan to conduct such analysis for the SNFPPR measure.
We considered whether we could propose to remove the July 1, 2020-
September 30, 2020 data from the updated performance period for the FY
2022 SNF VBP program year and calculate the SNFRM using a 9-month
performance period (April 1, 2019-December 31, 2019). To determine
whether the measure would be reliable using data during this period,
which would be closer to 8 months once we remove all SNF stays whose
30-day readmission risk-window extended to or after January 1, 2020, we
performed reliability analyses using a formula that relates the
reliability of a measure to its intraclass correlation (ICC), and found
that an estimate of reliability using all 12 combinations of potential
8-month data periods from FY 2019 (that is, October through May,
November through June, and so on) \110\ produces an average reliability
estimate of 0.367, which is lower than our generally accepted minimum
reliability threshold of 0.40.
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\110\ We assessed multiple 8-month data periods and averaged the
reliability results to obtain a complete understanding of
reliability across FY 2019, the most recent full year of production
data available for analysis, and avoid potential issues caused by
seasonality.
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We also considered substituting the July 1, 2020-September 30, 2020
period with an alternate data period; however, we are limited
operationally in terms of which data may be used. Using data from
further in the future would cause a delay in the calculation and
dissemination of results for the FY 2022 Program. Such a delay could
require us to make adjustments to the otherwise applicable Federal per
diem rate paid to SNFs in FY 2022 on a delayed basis, which would be an
extremely burdensome process for the MACs and a potentially confusing
process for SNFs. While using older data is feasible, and we recognize
that we adopted a performance period in the September 2nd IFC that
duplicated the use of data from a previous performance period, our
[[Page 20009]]
preference is to use as much new data as possible to assess SNF
performance each year and to avoid, where feasible, using the same data
as a performance period in multiple program years. Further revising the
FY 2022 Program performance period to include older data would create a
substantial overlap with the FY 2021 Program's performance period. Such
a significant overlap would result in SNFs receiving payments in FY
2022 based largely on the same performance used to assess SNFs for the
FY 2021 program year. Using over 80 percent of the same data twice as a
performance period could result in some SNFs being penalized (or
receiving a bonus) twice for nearly the same performance.
Therefore, due to concerns about the validity of the measure when
calculated as currently specified on data during the PHE given the
significant changes in SNF patient case volume and facility-level case
mix described above, and lacking any viable alternatives, we are
proposing to suppress the use of SNF readmission measure data for
purposes of scoring and payment adjustments in the FY 2022 program
year, under the proposed Measure Suppression Factor (4) Significant
national or regional shortages or rapid or unprecedented changes in:
(iii) Patient case volumes or facility-level case mix.
Under this proposed suppression policy, for all SNFs participating
in the FY 2022 SNF VBP program, we will use the previously finalized
performance period and baseline period to calculate each SNF's RSRR for
the SNFRM. Then, we would suppress the use of SNF readmission measure
data for purposes of scoring and payment adjustments. Specifically, we
are proposing to change the scoring methodology to assign all SNFs a
performance score of zero in the FY 2022 Program year. This would
result in all participating SNFs receiving an identical performance
score, as well as an identical incentive payment multiplier. We would
then apply the Low-Volume Adjustment policy as previously finalized in
the FY 2020 SNF PPS final rule (84 FR 38823 through 38824). That is, if
a SNF has fewer than 25 eligible stays during the performance period
for a program year we will assign that SNF a performance score
resulting in a net-neutral payment incentive multiplier. SNFs will not
be ranked for the FY 2022 SNF VBP program.
Under this proposal we would reduce each participating SNF's
adjusted Federal per diem rate for FY 2022 by 2 percentage points and
award each participating SNF 60 percent of that 2 percent withhold,
resulting in a 1.2 percent payback for the FY 2022 program year. We
believe this continued application of the 2 percent withhold is
required under section 1888(h)(5)(C)(ii)(III) of the Act and that a
payback percentage that is spread evenly across all qualifying SNFs is
the most equitable way to reduce the impact of the withhold in light of
our proposal to award a performance score of zero to all SNFs. Those
SNFs subject to the Low-Volume Adjustment policy would receive 100
percent of their 2 percent withhold per the previously finalized policy
increasing the overall payback percentage to an estimated 62.9 percent.
Further, we propose to provide quarterly confidential feedback
reports to SNFs and publicly report the SNFRM rates for the FY 2022 SNF
VBP Program year. However, we will make clear in the public
presentation of those data that the measure has been suppressed for
purposes of scoring and payment adjustments because of the effects of
the PHE for COVID-19 on the data used to calculate the measure. We
propose to codify this policy at Sec. 413.338(g).
We invite public comment on this proposal.
3. Proposed Revision to the SNFRM Risk Adjustment Look-Back Period for
the FY 2023 SNF VBP Program
In the FY 2021 SNF PPS final rule (85 FR 47624), we finalized the
FY 2023 Program performance period as FY 2021 (October 1, 2020-
September 30, 2021). In the FY 2016 SNF PPS final rule (80 FR 46418),
we finalized that the risk adjustment model would account for certain
risk-factors within 365 days prior to the discharge from the hospital
to the SNF (a 365-day lookback period). Under the COVID-19 ECE, SNF
qualifying claims for the period January 1, 2020-June 30, 2020 are
excepted from the calculation of the SNFRM; using FY 2021 data this
results in at least 3 months of lookback being available for all SNF
stays included in the measure without extending into or beyond June 30,
2020. Here, we propose instead a 90-day lookback period for risk
adjustment in the FY 2023 performance period (FY 2021) only. Using a
90-day risk-adjustment period will allow us to use the most recent
claims available for risk-adjustment, and an identical risk-adjustment
lookback period for all stays included in the measure. It also allows
us to avoid combining data from both prior to and during the COVID-19
PHE in the risk-adjustment lookback period, which would be necessary if
we attempted to maintain a 12-month look-back period due to the COVID-
19 ECE. Using a 90-day lookback period for risk adjustment will allow
us to look back 90 days prior to the discharge from the hospital to the
SNF for each SNF stay. Analyses conducted on FY 2019 performance data
found that when compared to the 365-day lookback period traditionally
used, a 90-day lookback period resulted in similar model performance
(that is, the C-statistic was nearly identical). We are also
considering similarly reducing the risk-adjustment lookback period for
the applicable FY 2023 program baseline year which would align the
risk-adjustment lookback period for the baseline and performance years
in the FY 2023 program; we invite comments on this consideration.
We invite public comment on the proposed updates to the risk
adjustment look-back period for the FY 2023 Performance Period.
4. Request for Comments on Potential Future Measures for the SNF VBP
Program
On December 27, 2020, Congress enacted the Consolidated
Appropriations Act, 2021 (CAA) (Pub. L. 116-260). Section 111(a)(1) of
Division CC of the CAA amends section 1888(h)(1) of the Act to, with
respect to payments for services furnished on or after October 1, 2022,
preclude the SNF VBP from applying to a SNF for which there are not a
minimum number (as determined by the Secretary) of cases for the
measures that apply to the facility for the performance period for the
applicable fiscal year, or measures that apply to the facility for the
performance period for the applicable fiscal year. Section 111(a)(2) of
the CAA amended section 1888(h)(2)(A) of the Act to, with respect to
payments for services furnished on or after October 1, 2023, require
the Secretary to apply the readmission measure specified under section
1888(g)(1) of the Act, and allow the Secretary to apply up to 9
additional measures determined appropriate, which may include measures
of functional status, patient safety, care coordination, or patient
experience. To the extent that the Secretary decides to apply
additional measures, section 1888(h)(2)(A)(ii) of the Act, as amended
by section 111(a)(2)(C) of the CAA, requires the Secretary to consider
and apply, as appropriate, quality measures specified under section
1899B(c)(1) of the Act. Finally, section 111(a)(3) of the CAA amended
section 1888(h) of the Act by adding a new paragraph (12), which
requires that the Secretary apply a process to validate the measures
and data submitted under the SNF VBP and the SNF QRP, as appropriate,
which may be similar to the process specified under the Hospital
Inpatient Quality Reporting (IQR) Program for validating
[[Page 20010]]
inpatient hospital measures. In this proposed rule, we are seeking
input from stakeholders regarding which measures we should consider
adding to the SNF VBP Program. We intend to use future rulemaking to
address these new statutory requirements.
Currently, the SNF VBP Program includes only a single quality
measure, the SNFRM, which we intend to transition to the SNFPPR measure
as soon as practicable. Both the SNFRM and SNFPPR assess the risk-
adjusted rate of readmissions to hospitals, for SNF residents within 30
days of discharge from a prior hospital stay. Consistent with amended
section 1888(h)(2)(A)(ii) of the Act, in considering which measures
might be appropriate to add to the SNF VBP Program, we are considering
additional clinical topics such as measures of functional status,
patient safety, care coordination, and patient experience, as well as
measures on those topics that are utilized in the SNF Quality Reporting
Program (QRP). For more information about the SNF QRP measures, please
visit: https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/Skilled-Nursing-Facility-Quality-Reporting-Program/SNF-Quality-Reporting-Program-Measures-and-Technical-Information.
We are also considering measures on clinical topics that are not
included in the SNF QRP's measure set because we believe that other
clinical topics would be helpful to our efforts to robustly assess the
quality of care furnished by SNFs.
In expanding the SNF VBP measure set, we are also considering
measures that we already require for Long-Term Care Facilities (LTCFs),
which include both SNFs and nursing facilities (NFs), to collect and
report under other initiatives. Approximately 94 percent of LTCFs are
dually certified as both a SNF and NF (Provider Data Catalog Nursing
Homes and Rehab Services Provider Information File January 2021)
(https://data.cms.gov/provider-data/dataset/4pq5-n9py). The vast
majority of LTCF residents are also Medicare beneficiaries, regardless
of whether they are in a Medicare Part A SNF stay, because they are
enrolled in Medicare Part B and receive Medicare coverage of certain
services provided by the LTCF even if they are a long-term care
resident. Therefore, we believe that expanding the SNF VBP measure set
to assess the quality of care that SNFs provide to all residents of the
facility, regardless of payer, would best represent the quality of care
provided to all Medicare beneficiaries in the facility. We are
requesting public comment on whether the measures in an expanded SNF
VBP measure set should require SNFs to collect data on all residents in
the facility, regardless of payer.
We have identified the measures listed in Table 31 as measures we
could add to the SNF VBP Program measure set, and we seek comment on
those measures, including which of those measures would be best suited
for the program. We also seek public comment on any measures or measure
concepts that are not listed in Table 31 that stakeholders believe we
should consider for the SNF VBP Program. In considering an initial set
of measures with which SNFs should largely be familiar (through the SNF
QRP, 5-Star Rating Program and/or the Nursing Home Quality Initiative
(NHQI)), we believe we can implement a measure set that would impose
minimal additional burden on SNFs.
Table 31--Quality Measures Under Consideration for an Expanded Skilled Nursing Facility Value-Based Purchasing
Program
----------------------------------------------------------------------------------------------------------------
Meaningful measure area NQF Quality measure
----------------------------------------------------------------------------------------------------------------
Minimum Data Set
----------------------------------------------------------------------------------------------------------------
Functional Outcomes.................. A2635...................... Application of IRF Functional Outcome
Measure: Discharge Self-Care Score for
Medical Rehabilitation Patients.*
Functional Outcomes.................. A2636...................... Application of IRF Functional Outcome
Measure: Discharge Mobility Score for
Medical Rehabilitation Patients.*
Preventable Healthcare Harm.......... 0674....................... Percent of Residents Experiencing One or
More Falls with Major Injury (Long Stay).**
Preventable Healthcare Harm.......... 0679....................... Percent of High Risk Residents with Pressure
Ulcers (Long Stay).**
Functional Outcomes.................. N/A........................ Percent of Residents Whose Ability to Move
Independently Worsened (Long Stay).**
Functional Outcomes.................. N/A........................ Percent of Residents Whose Need for Help
with Activities of Daily Living Has
Increased (Long Stay).**
Transfer of Health Information and N/A........................ Transfer of Health Information to the
Interoperability. Provider-Post Acute Care.*
Medication Management................ N/A........................ Percentage of Long-Stay Residents who got an
Antipsychotic Medication.**
----------------------------------------------------------------------------------------------------------------
Medicare Fee-For-Service Claims Based Measures
----------------------------------------------------------------------------------------------------------------
Community Engagement................. 3481....................... Discharge to Community Measure-Post Acute
Care Skilled Nursing Facility Quality
Reporting Program.*
Patient-focused Episode of Care...... N/A........................ Medicare Spending per Beneficiary (MSPB)-
Post Acute Care Skilled Nursing Facility
Quality Reporting Program.*
Healthcare-Associated Infections..... N/A........................ Skilled Nursing Facility Healthcare-
Associated Infections Requiring
Hospitalization Measure.~
Admissions and Readmissions to N/A........................ Number of hospitalizations per 1,000 long-
Hospitals. stay resident days (Long Stay).**
----------------------------------------------------------------------------------------------------------------
Patient-Reported Outcome-Based Performance Measure
----------------------------------------------------------------------------------------------------------------
Functional Outcomes.................. N/A........................ Patient-Reported Outcomes Measurement
Information System [PROMIS]- PROMIS Global
Health, Physical.
----------------------------------------------------------------------------------------------------------------
[[Page 20011]]
Survey Questionnaire (similar to Consumer Assessment of Healthcare Providers and Systems (CAHPS))
----------------------------------------------------------------------------------------------------------------
Patient's Experience of Care......... 2614....................... CoreQ: Short Stay Discharge Measure.
----------------------------------------------------------------------------------------------------------------
Payroll Based Journal
----------------------------------------------------------------------------------------------------------------
N/A.................................. N/A........................ Nurse staffing hours per resident day:
Registered Nurse (RN) hours per resident
per day; Total nurse staffing (including
RN, licensed practical nurse (LPN), and
nurse aide) hours per resident per day.**
----------------------------------------------------------------------------------------------------------------
* Measures adopted in the SNF Quality Reporting Program (QRP).
** ** These measures are reported on the Nursing Home Care Compare website (https://www.medicare.gov/care-compare/ compare/).
~ Measure proposed in section VII.C.1 of this proposed rule for adoption in the SNF QRP.
In addition to the staffing measures listed in Table 31 that focus
on nurse staffing hours per resident day and that are currently
reported on the Nursing Home Care Compare website, we are also
interested in measures that focus on staff turnover. We have been
developing measures of staff turnover, as required by section 1128I(g)
of the Act, with the goal of making the information publicly available.
Through our implementation of the Payroll-Based Journal (PBJ) staffing
data collection program, we have indicated that we will be reporting
rates of turnover in the future (for more information on this program,
see CMS memorandum QSO-18-17-NH \111\). As we plan to report staff
turnover information in the near future, we are also seeking comment on
inclusion of these measures in the SNF VBP Program.
---------------------------------------------------------------------------
\111\ https://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/SurveyCertificationGenInfo/Downloads/QSO18-17-NH.pdf.
---------------------------------------------------------------------------
We are also considering two patient-reported measures, as listed in
Table 31, to assess residents' views of their healthcare.
The CoreQ: Short Stay Discharge Measure calculates the percentage
of individuals discharged in a 6-month time period from a SNF, within
100 days of admission, who are satisfied with their SNF stay. This
patient reported outcome measure is based on the CoreQ: Short Stay
Discharge questionnaire that utilizes four items: (1) In recommending
this facility to your friends and family, how would you rate it
overall; (2) Overall, how would you rate the staff; (3) How would you
rate the care you receive; (4) How would you rate how well your
discharge needs were met. For additional information about the CoreQ:
Short Stay Discharge Measure, please visit https://cmit.cms.gov/CMIT_public/ViewMeasure?MeasureId=3436.
We welcome public comment on future measures for the SNF VBP
Program, and on whether the measures in an expanded SNF VBP measure set
should require SNFs to collect data on all residents in the facility,
regardless of payer.
C. SNF VBP Performance Period and Baseline Period
1. Background
We refer readers to the FY 2016 SNF PPS final rule (80 FR 46422)
for a discussion of our considerations for determining performance
periods under the SNF VBP Program. In the FY 2019 SNF PPS final rule
(83 FR 39277 through 39278), we adopted a policy whereby we will
automatically adopt the performance period and baseline period for a
SNF VBP program year by advancing the performance period and baseline
period by 1 year from the previous program year.
2. Revised Performance Period for the FY 2022 SNF VBP Program
In the September 2nd IFC, we updated the performance period for the
FY 2022 SNF VBP Program to April 1, 2019 through December 31, 2019 and
July 1, 2020 through September 30, 2020. We also noted that the
baseline period of the FY 2022 Program had not been impacted by the PHE
for COVID-19 and will remain as FY 2018 (October 1, 2017 through
September 30, 2018), and the FY 2022 Program performance standards
included in the FY 2020 final rule (84 FR 38822 through 38823) will
remain as finalized.
However, as noted in section VII.B.3. of this proposed rule, there
are concerns about the validity of the measure when calculated as
currently specified on data during the PHE (specifically, July 1, 2020
through September 30, 2020) given the significant changes in SNF
patient case volume and facility-level case mix described above.
Therefore, we are proposing to suppress the SNFRM for the FY 2022
program year. We will calculate each SNF's RSRR for the SNFRM. Then, we
would change the scoring methodology to assign all SNFs a performance
score of zero. This would result in all participating SNFs receiving an
identical performance score, as well as an identical incentive payment
multiplier. We would then apply the Low-Volume Adjustment policy as
previously finalized in the FY 2020 SNF PPS final rule (84 FR 38823
through 38824). That is, if a SNF has fewer than 25 eligible stays
during the performance period for a program year we will assign that
SNF a performance score resulting in a net-neutral payment incentive
multiplier. We will continue to provide quarterly confidential feedback
reports to facilities and publicly report based on the usable data from
the previously finalized performance period (April 1, 2019 through
December 31, 2019) and the previously finalized baseline period (FY
2018).
3. Performance Period for the FY 2023 SNF VBP Program
In the FY 2021 SNF PPS final rule (85 FR 47624), we finalized that
the Performance Period for the FY 2023 program year would be October 1,
2020-September 30, 2021 (FY 2021) and the baseline would be FY 2019
(October 1, 2018-September 30, 2019). We are not proposing any updates
to the performance period and baseline period previously finalized for
FY 2023.
We also considered alternatives to the previously finalized
performance period for FY 2023. We considered modifying the performance
period for the FY 2023 program year to Calendar Year 2021 (January 1,
2021-December 31, 2021). However, CY 2021 data are available later than
FY 2021 data, and would likely result in a delay calculating SNFRM
scores for SNFs and a subsequent delay in the application of
[[Page 20012]]
payment incentives for the FY 2023 program year.
We acknowledge that the COVID-19 PHE extends into both performance
period options. We believe that following the completion of testing,
SNF readmission measure specifications may account for changes in SNF
admission and/or hospital readmission patterns that we have observed
during the PHE as noted above.
We invite public comment on this alternative to the previously
finalized Performance Period for the FY 2023 SNF VBP program.
4. Performance Period and Baseline Period for the FY 2024 SNF VBP
Program
Under the policy finalized in the FY 2019 SNF PPS final rule (83 FR
39277 through 39278), for the FY 2024 program year, the performance
period would be FY 2022 and the baseline period would be FY 2020.
However, under the ECE, SNF qualifying claims for a 6-month period in
FY 2020 (January 1, 2020-June 30, 2020) are excepted from the
calculation of the SNFRM, which means that we will not have a full year
of data to calculate the SNFRM for the FY 2020 baseline period.
Moreover, as described in more detail in section VII.B.3 above, we are
proposing to suppress the SNFRM for the FY 2022 program year, in part
because there are concerns about the validity of the measure when
calculated as currently specified on data during the PHE (specifically,
July 1, 2020 through September 30, 2020) given the significant changes
in SNF patient case volume and facility-level case mix described above.
As the SNF VBP Program uses only a single measure calculated on 1 year
of data and uses each year of data first as a performance period and
then later on as a baseline period in the Program, the removal of 9
months of data in light of the COVID-19 PHE as described above will
necessarily result in data being used more than once in the Program.
Therefore, to ensure enough data are available to reliably calculate
the SNFRM, we are proposing FY 2019 data be used for the baseline
period for the FY 2024 program year. We also considered using FY 2021,
which will be the baseline period for the FY 2025 program year under
our current policy. However, it is operationally infeasible for us to
calculate the baseline for the FY 2024 program year using FY 2021 data
in time to establish the performance standards for that program year at
least 60 days prior to the start of the performance period, as required
under section 1888(h)(3)(C) of the Act.
We invite public comment on this proposal.
D. Performance Standards
1. Background
We refer readers to the FY 2017 SNF PPS final rule (81 FR 51995
through 51998) for a summary of the statutory provisions governing
performance standards under the SNF VBP Program and our finalized
performance standards policy. We adopted the final numerical values for
the FY 2022 performance standards in the FY 2020 SNF PPS final rule (84
FR 38822), and adopted the final numerical values for the FY 2023
performance standards in the FY 2021 SNF PPS final rule (85 FR 47625).
We also adopted a policy allowing us to correct the numerical values of
the performance standards in the FY 2019 SNF PPS final rule (83 FR
39276 through 39277).
We are not proposing any changes to these performance standard
policies in this proposed rule.
2. SNF VBP Performance Standards Correction Policy
In the FY 2019 SNF PPS final rule (83 FR 39276 through 39277), we
finalized a policy to correct numerical values of performance standards
for a program year in cases of errors. We also finalized that we will
only update the numerical values for a program year one time, even if
we identify a second error, because we believe that a one-time
correction will allow us to incorporate new information into the
calculations without subjecting SNFs to multiple updates. We stated
that any update we make to the numerical values based on a calculation
error will be announced via the CMS website, listservs, and other
available channels to ensure that SNFs are made fully aware of the
update. In the FY 2021 SNF PPS final rule (85 FR 47625), we amended the
definition of ``Performance standards'' at Sec. 413.338(a)(9),
consistent with these policies finalized in the FY 2019 SNF PPS final
rule, to reflect our ability to update the numerical values of
performance standards if we determine there is an error that affects
the achievement threshold or benchmark. We are not proposing any
changes to the performance standards correction policy in this proposed
rule.
3. Performance Standards for the FY 2024 Program Year
In section VII.C.1, we propose to use FY 2019 data for the baseline
period for the FY 2024 program year. Based on this baseline period, we
estimate that the performance standards would have the numerical values
noted in Table 32. We note that these values represent estimates based
on the most recently available data, and that we will update the
numerical values in the FY 2022 SNF PPS final rule.
Table 32--Estimated FY 2024 SNF VBP Program Performance Standards
----------------------------------------------------------------------------------------------------------------
Achievement
Measure ID Measure description threshold Benchmark
----------------------------------------------------------------------------------------------------------------
SNFRM................................. SNF 30-Day All-Cause Readmission 0.79270 0.83028
Measure (NQF #2510).
----------------------------------------------------------------------------------------------------------------
E. SNF VBP Performance Scoring
We refer readers to the FY 2017 SNF PPS final rule (81 FR 52000
through 52005) for a detailed discussion of the scoring methodology
that we have finalized for the Program. We also refer readers to the FY
2018 SNF PPS final rule (82 FR 36614 through 36616) for discussion of
the rounding policy we adopted. We also refer readers to the FY 2019
SNF PPS final rule (83 FR 39278 through 39281), where we adopted: (1) A
scoring policy for SNFs without sufficient baseline period data, (2) a
scoring adjustment for low-volume SNFs, and (3) an extraordinary
circumstances exception policy.
In section VII.B.3. of this proposed rule, we are proposing to
suppress the SNFRM for the FY 2022 program year. If finalized, for all
SNFs participating in the FY 2022 SNF VBP program, we will use the
previously finalized performance period and baseline period to
calculate each SNF's RSRR for the SNFRM. Then, we would assign all SNFs
a performance score of zero. This would result in all participating
SNFs receiving an identical performance score, as well as an identical
incentive payment multiplier. We would then apply the Low-Volume
Adjustment policy as previously finalized. That is, if a SNF has fewer
than 25 eligible stays during the performance period for a
[[Page 20013]]
program year we will assign that SNF a performance score resulting in a
net-neutral payment incentive multiplier. SNFs will not be ranked for
the FY 2022 SNF VBP program.
F. SNF Value-Based Incentive Payments
We refer readers to the FY 2018 SNF PPS final rule (82 FR 36616
through 36621) for discussion of the exchange function methodology that
we have adopted for the Program, as well as the specific form of the
exchange function (logistic, or S-shaped curve) that we finalized, and
the payback percentage of 60 percent. We adopted these policies for FY
2019 and subsequent fiscal years.
We also discussed the process that we undertake for reducing SNFs'
adjusted Federal per diem rates under the Medicare SNF PPS and awarding
value-based incentive payments in the FY 2019 SNF PPS final rule (83 FR
39281 through 39282).
In section VII.B.3. of this proposed rule, we are proposing to
suppress the SNFRM for the FY 2022 program year. If finalized, for all
SNFs participating in the FY 2022 SNF VBP program, we will use the
previously finalized performance period and baseline period to
calculate each SNF's RSRR for the SNFRM. Then, we would assign all SNFs
a performance score of zero. This would result in all participating
SNFs receiving an identical performance score, as well as an identical
incentive payment multiplier. SNFs will not be ranked for the FY 2022
SNF VBP program. We would then apply the Low-Volume Adjustment policy
as previously finalized. That is, if a SNF has fewer than 25 eligible
stays during the performance period for a program year we will assign
that SNF a performance score resulting in a net-neutral payment
incentive multiplier.
We are also proposing to reduce each participating SNF's adjusted
Federal per diem rate for FY 2022 by 2 percentage points and to award
each participating SNF 60 percent of that 2 percent withhold, resulting
in a 1.2 percent payback for the FY 2022 program year. We believe this
continued application of the 2 percent withhold is required under
section 1888(h)(5)(C)(ii)(III) of the Act and that a payback percentage
that is spread evenly across all SNFs is the most equitable way to
reduce the impact of the withhold in light of our proposal to award a
performance score of zero to all SNFs. Those SNFs subject to the Low-
Volume Adjustment policy which would receive 100 percent of their 2
percent withhold per the previously finalized policy, increasing the
overall payback percentage to an estimated 62.9 percent. We propose to
codify this policy at Sec. 413.338(g).
We invite public comment on this proposed change to the SNF VBP
payment policy for the FY 2022 program year.
G. Public Reporting on the Nursing Home Compare Website or a Successor
Website
1. Background
Section 1888(g)(6) of the Act requires the Secretary to establish
procedures to make SNFs' performance information on SNF VBP Program
measures available to the public on the Nursing Home Compare website or
a successor website, and to provide SNFs an opportunity to review and
submit corrections to that information prior to its publication. We
began publishing SNFs' performance information on the SNFRM in
accordance with this directive and the statutory deadline of October 1,
2017. In December 2020, we retired the Nursing Home Compare website and
are now using the Provider Data Catalogue website (https://data.cms.gov/provider-data/) to make quality data available to the
public, including SNF VBP performance information.
Additionally, section 1888(h)(9)(A) of the Act requires the
Secretary to make available to the public certain information on SNFs'
performance under the SNF VBP Program, including SNF performance scores
and their ranking. Section 1888(h)(9)(B) of the Act requires the
Secretary to post aggregate information on the Program, including the
range of SNF performance scores and the number of SNFs receiving value-
based incentive payments, and the range and total amount of those
payments.
In the FY 2017 SNF PPS final rule (81 FR 52009), we discussed the
statutory requirements governing public reporting of SNFs' performance
information under the SNF VBP Program. In the FY 2018 SNF PPS final
rule (82 FR 36622 through 36623), we finalized our policy to publish
SNF VBP Program performance information on the Nursing Home Compare or
successor website after SNFs have had an opportunity to review and
submit corrections to that information under the two-phase Review and
Correction process that we adopted in the FY 2017 SNF PPS final rule
(81 FR 52007 through 52009) and for which we adopted additional
requirements in the FY 2018 SNF PPS final rule. In the FY 2018 SNF PPS
final rule, we also adopted requirements to rank SNFs and adopted data
elements that we will include in the ranking to provide consumers and
stakeholders with the necessary information to evaluate SNFs'
performance under the Program (82 FR 36623).
In section VII.B.3. of this proposed rule, we are proposing to
suppress the SNFRM for the FY 2022 program year. Under this proposal,
for all SNFs participating in the FY 2022 SNF VBP program, we will use
the previously finalized performance period and baseline period to
calculate each SNF's RSRR for the SNFRM. Then, we would assign all SNFs
a performance score of zero. This would result in all participating
SNFs receiving an identical performance score, as well as an identical
incentive payment multiplier. We would then apply the Low-Volume
Adjustment policy as previously finalized. That is, if a SNF has fewer
than 25 eligible stays during the performance period for a program year
we will assign that SNF a performance score resulting in a net-neutral
payment incentive multiplier.
While we will publicly report the SNFRM rates for the FY 2022
program year, we will make clear in the public presentation of those
data that we are suppressing the use of those data for purposes of
scoring and payment adjustments in the FY 2022 SNF VBP given the
significant changes in SNF patient case volume and facility-level case
mix described above. SNFs will not be ranked for the FY 2022 SNF VBP
program.
2. Data Suppression Policy for Low-Volume SNFs
In the FY 2020 SNF PPS final rule (84 FR 38823 through 38824), we
adopted a data suppression policy for low-volume SNF performance
information. Specifically, we finalized that we will suppress the SNF
performance information available to display as follows: (1) If a SNF
has fewer than 25 eligible stays during the baseline period for a
program year, we will not display the baseline risk-standardized
readmission rate (RSRR) or improvement score, although we will still
display the performance period RSRR, achievement score, and total
performance score if the SNF had sufficient data during the performance
period; (2) if a SNF has fewer than 25 eligible stays during the
performance period for a program year and receives an assigned SNF
performance score as a result, we will report the assigned SNF
performance score and we will not display the performance period RSRR,
the achievement score, or improvement score; and (3) if a SNF has zero
eligible cases during the performance period for a program year, we
will not display any information for that SNF. We codified
[[Page 20014]]
this policy in the FY 2021 SNF PPS final rule (85 FR 47626) at Sec.
413.338(e)(3)(i), (ii), and (iii).
In section VII.B.3. of this proposed rule, we are proposing to
suppress the SNFRM for the FY 2022 program year. Under this proposal,
for all SNFs participating in the FY 2022 SNF VBP program, we will use
the previously finalized performance period and baseline period to
calculate each SNF's RSRR for the SNFRM. Then, we would assign all SNFs
a performance score of zero. This would result in all participating
SNFs receiving an identical performance score, as well as an identical
incentive payment multiplier. We would then apply the Low-Volume
Adjustment policy as previously finalized. That is, if a SNF has fewer
than 25 eligible stays during the performance period for a program year
we will assign that SNF a performance score resulting in a net-neutral
payment incentive multiplier. SNFs will not be ranked for the FY 2022
SNF VBP program.
3. Public Reporting of SNF VBP Performance Information on Nursing Home
Compare or a Successor Website
Section 1888(h)(9)(A) of the Act requires that the Secretary make
available to the public on the Nursing Home Compare website or a
successor website information regarding the performance of individual
SNFs for a fiscal year, including the performance score for each SNF
for the fiscal year and each SNF's ranking, as determined under section
1888(h)(4)(B) of the Act. Additionally, section 1888(h)(9)(B) of the
Act requires that the Secretary periodically post aggregate information
on the SNF VBP Program on the Nursing Home Compare website or a
successor website, including the range of SNF performance scores, and
the number of SNFs receiving value-based incentive payments and the
range and total amount of those payments. In the FY 2018 SNF PPS final
rule (82 FR 36622 through 36623), we finalized our policy to publish
SNF measure performance information under the SNF VBP Program on
Nursing Home Compare.
In the FY 2021 SNF PPS final rule (85 FR 47626), we finalized an
amendment to Sec. 413.338(e)(3) to reflect that we will publicly
report SNF performance information on the Nursing Home Compare website
or a successor website located at https://www.medicare.gov/care-compare/. We are not proposing any changes to the public reporting
policies in this proposed rule.
H. Proposal To Update and Codify the Phase One Review and Correction
Claims ``Snapshot'' Policy
In the FY 2017 SNF PPS final rule (81 FR 52007 through 52009), we
adopted a two-phase review and corrections process for SNFs' quality
measure data that will be made public under section 1888(g)(6) of the
Act and SNF performance information that will be made public under
section 1888(h)(9) of the Act. We detailed the process for requesting
Phase One corrections and finalized a policy whereby we would accept
Phase One corrections to a quarterly report provided during a calendar
year until the following March 31.
In the FY 2020 SNF PPS final rule (84 FR 38824 through 38835), we
updated this policy to reflect a 30-day Phase One Review and Correction
deadline rather than through March 31st following receipt of the
performance period quality measure quarterly report.
In the FY 2021 SNF PPS final rule (85 FR 47626 through 47627), we
updated the 30-day deadline for Phase One Review and Correction and
codified the policy at Sec. 413.338(e)(1). Under the updated policy,
beginning with the baseline period quality report issued on or after
October 1, 2020 that contains the baseline period measure rate and
underlying claim information used to calculate the measure rate for the
applicable program year, SNFs have 30 days following the date that CMS
provides those reports to review and submit corrections to the data
contained in those reports. We also stated that if the issuance dates
of these reports are significantly delayed or need to be shifted for
any reason, we would notify SNFs through routine communication channels
including, but not limited to memos, emails, and notices on the CMS SNF
VBP website at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/SNF-VBP/SNF-VBP-Page.
We are now proposing to include a Phase One Review and Correction
claims ``snapshot'' policy beginning with the baseline period and
performance period quality measure quarterly reports issued on or after
October 1, 2021. This proposed policy would limit the Phase One Review
and Correction to errors made by CMS or its contractors when
calculating a SNF's readmission measure rate and will not allow
corrections to the underlying administrative claims data used to
calculate those rates. Under this proposed policy, the administrative
claims data we use to calculate a SNF's readmission measure rate for
purposes of a baseline period or performance period for a given SNF VBP
program year would be held constant (that is, frozen in a ``snapshot'')
from the time we extract it for that purpose. This proposal would align
the review and correction policy for the SNF VBP Program with the
review and correction policy we have adopted for other value-based
purchasing programs, including the Hospital Readmissions Reduction
Program (HRRP), Hospital-Acquired Condition (HAC) Reduction Program,
and Hospital Value-Based Purchasing (VBP) Program.
For purposes of this program, we propose to calculate the SNF
readmission measure rates using a static ``snapshot'' of claims updated
as of 3 months following the last index SNF admission in the applicable
baseline period or performance period. The source of the administrative
claims data we use to calculate the SNF readmission measure is the
Medicare Provider Analysis and Review (MedPAR). For example, if the
last index SNF admission date for the applicable baseline period or
performance period is September 30th, 2019, we would extract the
administrative claims data from the MedPAR file as that data exists on
December 31st, 2019. SNFs would then receive their SNF readmission
measure rate and accompanying stay-level information in their
confidential quality measure quarterly reports, and they would have an
opportunity to review and submit corrections to our calculations as
part of the Phase One corrections process. SNFs, however, would not be
able to correct any of the underlying administrative claims data (for
example, a SNF discharge destination code) we use to generate the
measure rate.
The use of a data ``snapshot'' enables us to provide as timely
quality data as possible, both to SNFs for the purpose of quality
improvement and to the public for the purpose of transparency. After
the claims ``snapshot'' is taken through our extraction of the data
from MedPAR, it takes several months to incorporate other data needed
for the SNF readmission measure calculations, generate and check the
calculations, as well as program, populate, and deliver the
confidential quarterly reports and accompanying data to SNFs. Because
several months lead time is necessary after acquiring the input data to
generate these calculations, if we were to delay our data extraction
point beyond the date that is 3 months after the last SNF index
admission attributable to a baseline period or performance period, we
believe this would create an
[[Page 20015]]
unacceptably long delay both for SNFs to receive timely data for
quality improvement and transparency, and, incentive payments for
purposes of this program. Therefore, we believe that a 3-month claims
``run-out'' period between the date of the last SNF index admission and
the date of the data extraction is a reasonable period that allows SNFs
time to correct their administrative claims or add any missing claims
before those claims are used for measure calculation purposes while
enabling us to timely calculate the measure. If unforeseen
circumstances require the use of additional months of claims ``run-
out'', that is, more than 3 months, we would notify SNFs through
routine communication channels including, but not limited to, memos,
emails, quarterly reports and notices on the CMS SNF VBP website at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/SNF-VBP/SNF-VBP-Page.
We believe this proposed policy would address both fairness and
operational concerns associated with calculating measure rates and
would provide consistency across value-based purchasing programs.
We are also proposing to codify this policy in our regulations by
revising Sec. 413.338(e)(1) to remove the policies that would no
longer be applicable beginning October 1, 2021 and state the newly
proposed policy that would be effective, if finalized, on October 1,
2021.
We invite public comment on this proposal to update the Phase One
Review and Correction policy.
I. Proposal To Update the Instructions for Requesting an ECE in Sec.
413.338(d)(4)(ii) of the SNF VBP Regulations
We are proposing to update the instructions for a SNF to request an
extraordinary circumstances exception (ECE). Specifically, we are
proposing to update the email address that a SNF must use to send the
request, as well as the URL for our QualityNet website from
QualityNet.org to QualityNet.cms.gov. We are also proposing to remove
the separate reference to newspapers because newspapers are already
included in the broader term ``media articles.'' We are proposing to
update Sec. 413.338(d)(4)(ii) of our regulations to reflect these
changes.
We invite public comment on this proposal.
VIII. Collection of Information Requirements
This proposed rule would not impose any new or revised ``collection
of information'' requirements or burden as it pertains to CMS. For the
purpose of this section of the preamble, collection of information is
defined under 5 CFR 1320.3(c) of the Paperwork Reduction Act of 1995's
(PRA) (44 U.S.C. 3501 et seq.) implementing regulations. Consequently,
this rule is not subject to the requirements of the PRA.
We propose in section VI.C.1. of this proposed rule, the SNF HAIs
Requiring Hospitalization measure beginning with the FY 2023 SNF QRP.
All claims-based measures are calculated using data that are already
reported to the Medicare program for payment purposes. Since the data
source for this quality measure is Medicare fee-for-service claims,
there is no additional burden for providers.
In section VI.C.2. of this proposed rule, we propose that SNFs
submit data on the COVID-19 Vaccination Coverage among Healthcare
Personnel (HCP) measure beginning with the FY 2023 SNF QRP. We note
that the CDC would account for the burden associated with the COVID-19
Vaccination Coverage among HCP measure collection under OMB control
number 0920-1317 (expiration January 31, 2024). However, the CDC
currently has a PRA waiver for the collection and reporting of
vaccination data under section 321 of the National Childhood Vaccine
Injury Act of 1986 (Pub. L. 99-660, enacted on November 14, 1986)
(NCVIA).\112\ We refer readers to section X.A.5. of this proposed rule,
where CMS has provided an estimate of the burden and cost to SNFs, and
note that the CDC will include it in a revised information collection
request for 0920-1317.
---------------------------------------------------------------------------
\112\ Section 321 of the NCVIA provides the PRA waiver for
activities that come under the NCVIA, including those in the NCVIA
at section 2102 of the Public Health Service Act (42 U.S.C. 300aa-
2). Section 321 is not codified in the U.S. Code, but can be found
in a note at 42 U.S.C. 300aa-1.
---------------------------------------------------------------------------
In section VI.C.3. of this proposed rule, we are proposing to
update the Transfer of Health (TOH) Information to the Patient--Post
Acute Care (PAC) measure to exclude residents discharged home under the
care of an organized home health service or hospice. This measure was
adopted in the FY 2020 SNF PPS final rule (84 FR 38728) and the
associated burden was accounted for in OMB 0938-1140 (expiration
November 30, 2022). The proposed update would not affect the
information collection burden already established.
In section VI.G.3. of this proposed rule, we are proposing that
SNFs submit data on the COVID-19 Vaccination among HCP measure through
the CDC/National Healthcare Safety Network (NHSN). The NHSN is a
secure, internet-based surveillance system maintained by the CDC and
provided free of charge to healthcare facilities including SNFs.
While the NHSN is currently not utilized by SNFs for purposes of
meeting the SNF QRP requirements, nursing homes were enrolled in the
NHSN in 2020 and are currently submitting mandatory COVID-19 data
through the Long-term Care Facility COVID-19 module (https://www.cdc.gov/nhsn/ltc/covid19/index.html). As such, there is no
additional information collection burden related to the onboarding and
training of SNF providers to utilize this system. In section VII.B.3.
of this proposed rule, we are proposing to suppress the Skilled Nursing
Facility 30-Day All-Cause Readmission Measure (SNFRM) for the FY 2022
SNF VBP Program Year. Because the data source for this quality measure
is Medicare fee-for-service claims, there is no additional burden for
SNFs. All claims-based measures can be calculated based on data that
are already reported to the Medicare program for payment purposes.
IX. Response to Comments
Because of the large number of public comments we normally receive
on Federal Register documents, we are not able to acknowledge or
respond to them individually. We will consider all comments we receive
by the date and time specified in the DATES section of this preamble,
and, when we proceed with a subsequent document, we will respond to the
comments in the preamble to that document.
X. Economic Analyses
A. Regulatory Impact Analysis
1. Statement of Need
This proposed rule updates the FY 2022 SNF prospective payment
rates as required under section 1888(e)(4)(E) of the Act. It also
responds to section 1888(e)(4)(H) of the Act, which requires the
Secretary to provide for publication in the Federal Register before the
August 1 that precedes the start of each FY, the unadjusted Federal per
diem rates, the case-mix classification system, and the factors to be
applied in making the area wage adjustment. As these statutory
provisions prescribe a detailed methodology for calculating and
disseminating payment rates under the SNF PPS, we do not have the
discretion
[[Page 20016]]
to adopt an alternative approach on these issues.
2. Introduction
We have examined the impacts of this proposed 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 (UMRA, March
22, 1995; Pub. L. 104-4), Executive Order 13132 on Federalism (August
4, 1999), and the Congressional Review Act (5 U.S.C. 804(2)).
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). Executive
Order 13563 emphasizes the importance of quantifying both costs and
benefits, of reducing costs, of harmonizing rules, and of promoting
flexibility. This rule has been designated an economically significant
rule, under section 3(f)(1) of Executive Order 12866. Accordingly, we
have prepared a regulatory impact analysis (RIA) as further discussed
below. Also, the rule has been reviewed by OMB.
3. Overall Impacts
This rule would update the SNF PPS rates contained in the SNF PPS
final rule for FY 2021 (85 FR 47594). We estimate that the aggregate
impact would be an increase of approximately $444 million in Part A
payments to SNFs in FY 2022. This reflects a $445 million increase from
the update to the payment rates and a $1.2 million decrease due to the
proposed reduction to the SNF PPS rates to account for the recently
excluded blood-clotting factors (and items and services related to the
furnishing of such factors) in section 1888(e)(2)(A)(iii)(VI) of the
Act. We note that these impact numbers do not incorporate the SNF VBP
reductions that we estimate would total $191.64 million in FY 2022. We
would note that events may occur to limit the scope or accuracy of our
impact analysis, as this analysis is future-oriented, and thus, very
susceptible to forecasting errors due to events that may occur within
the assessed impact time period.
In accordance with sections 1888(e)(4)(E) and (e)(5) of the Act and
implementing regulations at Sec. 413.337(d), we would update the FY
2021 payment rates by a factor equal to the market basket index
percentage change reduced by the forecast error adjustment and the MFP
adjustment to determine the payment rates for FY 2022. The impact to
Medicare is included in the total column of Table 33. In proposing the
SNF PPS rates for FY 2022, we are proposing a number of standard annual
revisions and clarifications mentioned elsewhere in this proposed rule
(for example, the proposed update to the wage and market basket indexes
used for adjusting the Federal rates).
The annual update proposed in this rule would apply to SNF PPS
payments in FY 2022. Accordingly, the analysis of the impact of the
annual update that follows only describes the impact of this single
year. Furthermore, in accordance with the requirements of the Act, we
will publish a rule or notice for each subsequent FY that will provide
for an update to the payment rates and include an associated impact
analysis.
4. Detailed Economic Analysis
The FY 2022 SNF PPS payment impacts appear in Table 33. Using the
most recently available data, in this case FY 2020, we apply the
current FY 2021 CMIs, wage index and labor-related share value to the
number of payment days to simulate FY 2021 payments. Then, using the
same FY 2020 data, we apply the proposed FY 2022 CMIs, wage index and
labor-related share value to simulate FY 2022 payments. We would note
that, given that this same data is being used for both parts of this
calculation, as compared to other analyses discussed in this proposed
rule which compare data from FY 2020 to data from other fiscal years,
any issues discussed throughout this proposed rule with regard to data
collected in FY 2020 would not cause any difference in this economic
analysis. We tabulate the resulting payments according to the
classifications in Table 33 (for example, facility type, geographic
region, facility ownership), and compare the simulated FY 2021 payments
to the simulated FY 2022 payments to determine the overall impact. The
breakdown of the various categories of data in Table 33 follows:
The first column shows the breakdown of all SNFs by urban
or rural status, hospital-based or freestanding status, census region,
and ownership.
The first row of figures describes the estimated effects
of the various proposed changes on all facilities. The next six rows
show the effects on facilities split by hospital-based, freestanding,
urban, and rural categories. The next nineteen rows show the effects on
facilities by urban versus rural status by census region. The last
three rows show the effects on facilities by ownership (that is,
government, profit, and non-profit status).
The second column shows the number of facilities in the
impact database.
The third column shows the effect of the proposed annual
update to the wage index. This represents the effect of using the most
recent wage data available. The total impact of this change is 0.0
percent; however, there are distributional effects of the proposed
change.
The fourth column shows the effect of all of the changes
on the FY 2022 payments. The proposed update of 1.3 percent is constant
for all providers and, though not shown individually, is included in
the total column. It is projected that aggregate payments would
increase by 1.3 percent, assuming facilities do not change their care
delivery and billing practices in response.
As illustrated in Table 33, the combined effects of all of the
changes vary by specific types of providers and by location. For
example, due to changes in this proposed rule, rural providers would
experience a 1.8 percent increase in FY 2022 total payments. Finally,
we note that we did not include in Table 33 the distributional impacts
associated with the blood-clotting factor exclusion because the
reduction is so minor that it does not have any visible effect on the
distributional impacts included in the Table 33.
Table 33--Impact to the SNF PPS for FY 2022
----------------------------------------------------------------------------------------------------------------
Number Update wage Total change
Provider characteristics providers data (%) (%)
----------------------------------------------------------------------------------------------------------------
Group:
Total....................................................... 15,440 0.0 1.3
[[Page 20017]]
Urban....................................................... 10,887 -0.1 1.2
Rural....................................................... 4,553 0.4 1.8
Hospital-based urban........................................ 385 -0.2 1.1
Freestanding urban.......................................... 10,502 -0.1 1.2
Hospital-based rural........................................ 451 0.3 1.6
Freestanding rural.......................................... 4,102 0.4 1.7
Urban by region:
New England................................................. 742 -0.7 0.6
Middle Atlantic............................................. 1,447 -0.5 0.8
South Atlantic.............................................. 1,820 0.4 1.7
East North Central.......................................... 2,145 -0.2 1.1
East South Central.......................................... 539 -0.4 0.9
West North Central.......................................... 919 0.4 1.7
West South Central.......................................... 1,342 -0.3 1.0
Mountain.................................................... 536 0.1 1.4
Pacific..................................................... 1,391 0.2 1.5
Outlying.................................................... 6 0.4 1.7
Rural by region:
New England................................................. 129 -0.9 0.4
Middle Atlantic............................................. 245 0.5 1.8
South Atlantic.............................................. 597 1.2 2.5
East North Central.......................................... 909 0.5 1.8
East South Central.......................................... 526 -0.1 1.2
West North Central.......................................... 1,058 -0.3 1.0
West South Central.......................................... 756 0.4 1.7
Mountain.................................................... 222 0.5 1.8
Pacific..................................................... 111 0.3 1.6
Ownership:
For profit.................................................. 10,809 0.0 1.3
Non-profit.................................................. 3,637 0.0 1.3
Government.................................................. 994 0.2 1.5
----------------------------------------------------------------------------------------------------------------
Note: The Total column includes the proposed FY 2022 1.3 percent market basket increase factor. Additionally, we
found no SNFs in rural outlying areas.
5. Impacts for the SNF QRP for FY 2022
Estimated impacts for the SNF QRP are based on analysis discussed
in section VIII.B. of this proposed rule. The proposed SNF QRP
requirements add no additional burden to the active collection under
OMB control number #0938-1140 (CMS-10387; expiration November 30,
2022).
In accordance with section 1888(e)(6)(A)(i) of the Act, the
Secretary must reduce by 2 percentage points the annual payment update
applicable to a SNF for a fiscal year if the SNF does not comply with
the requirements of the SNF QRP for that fiscal year. In section VI.A.
of this proposed rule, we discuss the method for applying the 2
percentage point reduction to SNFs that fail to meet the SNF QRP
requirements. As discussed in section VI.C. of this proposed rule, we
are proposing to add two new measures to the SNF QRP beginning with the
FY 2023 SNF QRP: SNF Healthcare-Associated Infections Requiring
Hospitalization Measure (SNF-HAI) and the COVID-19 Vaccination Coverage
among Healthcare Personnel measure. The SNF-HAI measure is a claims-
based measure, and therefore, would impose no additional burden to the
SNFs.
We believe that the burden associated with the SNF QRP is the time
and effort associated with complying with the non-claims-based measures
requirements of the SNF QRP. Although the burden associated with the
COVID-19 Vaccination Coverage among HCP measure is not accounted for
under the CDC PRA package currently approved under OMB control number
0920-1317 due to the NCVIA waiver the cost and burden is discussed here
and will be included in a revised information collection request for
0920-1317.
Consistent with the CDC's experience of collecting data using the
NHSN, we estimate that it would take each SNF an average of 1 hour per
month to collect data for the COVID-19 Vaccination Coverage among HCP
measure and enter it into NHSN. We have estimated the time to complete
this entire activity, since it could vary based on provider systems and
staff availability. We believe it would take an administrative
assistant from 45 minutes up to 1 hour and 15 minutes to enter this
data into NHSN. For the purposes of calculating the costs associated
with the collection of information requirements, we obtained mean
hourly wages from the U.S. Bureau of Labor Statistics' May 2019
National Occupational Employment and Wage Estimates.\113\ To account
for overhead and fringe benefits, we have doubled the hourly wage.
These amounts are detailed in Table 34.
---------------------------------------------------------------------------
\113\ https://www.bls.gov/oes/current/oes_nat.htm. Accessed on
March 30, 2021.
[[Page 20018]]
Table 34--U.S. Bureau of Labor and Statistics' May 2019 National Occupational Employment and Wage Estimates
----------------------------------------------------------------------------------------------------------------
Overhead and
Occupation title Occupation code Mean hourly fringe benefit Adjusted hourly
wage ($/hr) ($/hr) wage ($/hr)
----------------------------------------------------------------------------------------------------------------
Administrative Assistant.................... 43-6013 $18.31 $18.31 $36.62
----------------------------------------------------------------------------------------------------------------
Based on this time range, it would cost each SNF between $27.47 and
$45.78 each month or an average cost of $36.62 each month, and between
$329.64 and $549.36 each year, or an average cost of $439.44 each year.
We believe the data submission for the COVID-19 Vaccination Coverage
among HCP measure would cause SNFs to incur additional average burden
of 12 hours per year for each SNF and a total annual burden of 180,936
hours for all SNFs. The estimated annual cost across all 15,078 SNFs in
the U.S. for the submission of the COVID-19 Vaccination Coverage among
HCP measure would be between $4,970,312 and $8,283,250.08, and an
average of $6,625,872.
We recognize that many SNFs may also be reporting other COVID-19
data to HHS. However, we believe the benefits of reporting data on the
COVID-19 Vaccination Coverage among HCP measure to assess whether SNFs
are taking steps to limit the spread of COVID-19 among their HCP,
reduce the risk of transmission of COVID-19 within their facilities,
and to help sustain the ability of SNFs to continue serving their
communities throughout the PHE and beyond outweigh the costs of
reporting. We welcome comments on the estimated time to collect data
and enter it into NHSN.
6. Impacts for the SNF VBP Program
The estimated impacts of the FY 2022 SNF VBP Program are based on
historical data and appear in Table 35. We modeled SNF performance in
the Program using SNFRM data from FY 2018 as the baseline period and an
8-month period from February 1, 2019 through September 30, 2019 as the
performance period. Additionally, we modeled a logistic exchange
function with a payback percentage of 60 percent, as we finalized in
the FY 2018 SNF PPS final rule (82 FR 36619 through 36621), though we
note that the 60 percent payback percentage for FY 2022 will be
adjusted to account for the low-volume scoring adjustment that we
adopted in the FY 2019 SNF PPS final rule (83 FR 39278 through 39280).
However, in section VII.B.3. of this proposed rule, we are proposing to
suppress the SNFRM for the FY 2022 program year. If finalized, we will
award each participating SNF 60 percent of their 2 percent withhold,
except those SNFs subject to the low-volume scoring adjustment, which
would receive 100 percent of their 2 percent withhold. We estimated
that the low-volume scoring adjustment would increase the 60 percent
payback percentage for FY 2022 by approximately 2.9 percentage points
(or $16.4 million), resulting in a payback percentage for FY 2022 that
is 62.9 percent of the estimated $516.2 million in withheld funds for
that fiscal year. Based on the 60 percent payback percentage (as
modified by the low-volume scoring adjustment), we estimated that we
will redistribute approximately $324.5 million in value-based incentive
payments to SNFs in FY 2022, which means that the SNF VBP Program is
estimated to result in approximately $191.6 million in savings to the
Medicare Program in FY 2022.
Our detailed analysis of the estimated impacts of the FY 2022 SNF
VBP Program follows in Table 35.
Table 35--SNF VBP Program Estimated Impacts for FY 2022
----------------------------------------------------------------------------------------------------------------
Mean Risk-
Standardized Mean Mean Percent of total
Characteristic Number of Readmission performance incentive payment after
facilities Rate (SNFRM) score multiplier applying
(%) incentives
----------------------------------------------------------------------------------------------------------------
Group:
Total..................... 15,026 19.90 1.4545 0.99426 100
Urban..................... 10,845 19.94 1.1528 0.99379 85.29
Rural..................... 4,181 19.81 2.2371 0.99547 14.71
Hospital-based urban *.... 284 19.68 1.1794 0.99383 1.79
Freestanding urban *...... 10,520 19.95 1.1423 0.99377 83.47
Hospital-based rural *.... 182 19.55 2.6050 0.99604 0.43
Freestanding rural *...... 3,803 19.81 2.1749 0.99538 14.12
Urban by region:
New England............... 744 20.10 0.8104 0.99326 5.38
Middle Atlantic........... 1,462 19.78 0.7155 0.99311 16.57
South Atlantic............ 1,874 20.00 0.6407 0.99299 17.01
East North Central........ 2,065 20.08 1.3950 0.99417 13.32
East South Central........ 555 20.08 0.9471 0.99347 3.53
West North Central........ 923 19.92 2.1104 0.99528 4.23
West South Central........ 1,312 20.11 1.6811 0.99461 7.48
Mountain.................. 523 19.56 1.4090 0.99419 3.72
Pacific................... 1,381 19.67 0.9702 0.99351 14.05
Outlying.................. 6 20.96 2.5766 0.9960 0.00
Rural by region:
New England............... 122 19.30 1.6896 0.99462 0.64
Middle Atlantic........... 210 19.53 1.1779 0.99383 0.90
South Atlantic............ 473 19.91 1.5144 0.99435 2.11
East North Central........ 895 19.69 1.8310 0.99484 3.35
[[Page 20019]]
East South Central........ 495 20.06 1.1139 0.99373 2.26
West North Central........ 1,006 19.77 3.5653 0.99753 1.99
West South Central........ 689 20.13 2.5430 0.99595 2.18
Mountain.................. 199 19.43 2.5378 0.99594 0.66
Pacific................... 91 19.22 1.5856 0.99446 0.60
Outlying.................. 1 19.37 5.1533 1.0000 0.00
Ownership:
Government................ 877 19.77 2.5149 0.9959 3.28
Profit.................... 10,583 19.95 1.3693 0.9941 74.38
Non-Profit................ 3,566 19.81 1.4466 0.9943 22.33
----------------------------------------------------------------------------------------------------------------
* The group category which includes hospital-based/freestanding by urban/rural excludes 237 swing-bed SNFs.
7. Alternatives Considered
As described in this section, we estimated that the aggregate
impact for FY 2022 under the SNF PPS would be an increase of
approximately $444 million in Part A payments to SNFs. This reflects a
$445 million increase from the update to the payment rates, and a $1.2
million decrease due to the proposed reduction to the SNF PPS rates to
account for the recently excluded blood-clotting factors (and items and
services related to the furnishing of such factors) in section
1888(e)(2)(A)(iii)(VI) of the Act.
Section 1888(e) of the Act establishes the SNF PPS for the payment
of Medicare SNF services for cost reporting periods beginning on or
after July 1, 1998. This section of the statute prescribes a detailed
formula for calculating base payment rates under the SNF PPS, and does
not provide for the use of any alternative methodology. It specifies
that the base year cost data to be used for computing the SNF PPS
payment rates must be from FY 1995 (October 1, 1994, through September
30, 1995). In accordance with the statute, we also incorporated a
number of elements into the SNF PPS (for example, case-mix
classification methodology, a market basket index, a wage index, and
the urban and rural distinction used in the development or adjustment
of the Federal rates). Further, section 1888(e)(4)(H) of the Act
specifically requires us to disseminate the payment rates for each new
FY through the Federal Register, and to do so before the August 1 that
precedes the start of the new FY; accordingly, we are not pursuing
alternatives for this process.
With regard to the alternatives considered related to the other
provisions contained in this proposed rule, such as the proposed
methodology for calculating the proportional reduction to the rates to
account for the exclusion of blood clotting factors from SNF
consolidated billing, we discuss any alternatives considered within
those sections.
With regard to the proposed SNF VBP measure suppression policy, we
discuss any alternatives considered within those sections.
8. Accounting Statement
As required by OMB Circular A-4 (available online at https://obamawhitehouse.archives.gov/omb/circulars_a004_a-4/), in Tables 36, 37
and 38, we have prepared an accounting statement showing the
classification of the expenditures associated with the provisions of
this proposed rule for FY 2022. Tables 33 and 36 provide our best
estimate of the possible changes in Medicare payments under the SNF PPS
as a result of the policies in this proposed rule, based on the data
for 15,440 SNFs in our database. Tables 35 and 37 provide our best
estimate of the possible changes in Medicare payments under the SNF VBP
as a result of the policies we have proposed for this program. Tables
34 and 38 provide our best estimate of the additional cost to SNFs to
submit the data for the SNF QRP as a result of the policies in this
proposed rule.
Table 36--Accounting Statement: Classification of Estimated
Expenditures, From the 2021 SNF PPS Fiscal Year to the 2022 SNF PPS
Fiscal Year
------------------------------------------------------------------------
Category Transfers
------------------------------------------------------------------------
Annualized Monetized Transfers......... $444 million.*
From Whom To Whom?..................... Federal Government to SNF
Medicare Providers.
------------------------------------------------------------------------
* The net increase of $444 million in transfer payments is a result of
the $445 million increase due to the proposed market basket increase
of 1.3 percent, reduced by $1.2 million due to the proposed
proportional reduction associated with excluding blood clotting
factors from SNF consolidated billing.
Table 37--Accounting Statement: Classification of Estimated Expenditures
for the FY 2022 SNF VBP Program
------------------------------------------------------------------------
Category Transfers
------------------------------------------------------------------------
Annualized Monetized Transfers......... $324.5 million.*
From Whom To Whom?..................... Federal Government to SNF
Medicare Providers.
------------------------------------------------------------------------
* This estimate does not include the two percent reduction to SNFs'
Medicare payments (estimated to be $516.15 million) required by
statute.
[[Page 20020]]
Table 38--Accounting Statement: Classification of Estimated Expenditures
for the FY 2022 SNF QRP Program
------------------------------------------------------------------------
Category Transfers/Costs
------------------------------------------------------------------------
Costs for SNFs to Submit Data for QRP.. $6.6 million.*
------------------------------------------------------------------------
* Costs associated with the submission of data for the COVID-19
Vaccination Coverage among HCP will occur in FY 2022 and is likely to
continue in future years.
9. Conclusion
This rule updates the SNF PPS rates contained in the SNF PPS final
rule for FY 2021 (85 FR 47594). Based on the above, we estimate that
the overall payments for SNFs under the SNF PPS in FY 2022 are
projected to increase by approximately $444 million, or 1.3 percent,
compared with those in FY 2021. We estimate that in FY 2022, SNFs in
urban and rural areas would experience, on average, a 1.2 percent
increase and 1.8 percent increase, respectively, in estimated payments
compared with FY 2021. Providers in the rural South Atlantic region
would experience the largest estimated increase in payments of
approximately 2.5 percent. Providers in the rural New England region
would experience the smallest estimated increase in payments of 0.4
percent.
B. Regulatory Flexibility Act Analysis
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, non-profit organizations, and small
governmental jurisdictions. Most SNFs and most other providers and
suppliers are small entities, either by reason of their non-profit
status or by having revenues of $30 million or less in any 1 year. We
utilized the revenues of individual SNF providers (from recent Medicare
Cost Reports) to classify a small business, and not the revenue of a
larger firm with which they may be affiliated. As a result, for the
purposes of the RFA, we estimate that almost all SNFs are small
entities as that term is used in the RFA, according to the Small
Business Administration's latest size standards (NAICS 623110), with
total revenues of $30 million or less in any 1 year. (For details, see
the Small Business Administration's website at http://www.sba.gov/category/navigation-structure/contracting/contracting-officials/eligibility-size-standards). In addition, approximately 20 percent of
SNFs classified as small entities are non-profit organizations.
Finally, individuals and states are not included in the definition of a
small entity.
This rule would update the SNF PPS rates contained in the SNF PPS
final rule for FY 2021 (85 FR 47594). Based on the above, we estimate
that the aggregate impact for FY 2022 would be an increase of $444
million in payments to SNFs, resulting from the SNF market basket
update to the payment rates, reduced by the impact of excluding blood
clotting factors (and items and services related to the furnishing of
such factors) from SNF consolidated billing under section
1888(e)(2)(A)(iii)(VI) and (e)(4)(G)(iii) of the Act. While it is
projected in Table 33 that all providers would experience a net
increase in payments, we note that some individual providers within the
same region or group may experience different impacts on payments than
others due to the distributional impact of the FY 2022 wage indexes and
the degree of Medicare utilization.
Guidance issued by the Department of Health and Human Services on
the proper assessment of the impact on small entities in rulemakings,
utilizes a cost or revenue impact of 3 to 5 percent as a significance
threshold under the RFA. In their March 2021 Report to Congress
(available at http://www.medpac.gov/docs/default-source/reports/mar21_medpac_ch7_sec.pdf), MedPAC states that Medicare covers
approximately 9 percent of total patient days in freestanding
facilities and 16 percent of facility revenue (March 2020 MedPAC Report
to Congress, 224). As indicated in Table 33, the effect on facilities
is projected to be an aggregate positive impact of 1.3 percent for FY
2022. As the overall impact on the industry as a whole, and thus on
small entities specifically, is less than the 3 to 5 percent threshold
discussed previously, the Secretary has determined that this proposed
rule would not have a significant impact on a substantial number of
small entities for FY 2022.
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 603 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 an MSA and has fewer
than 100 beds. This proposed rule would affect small rural hospitals
that: (1) Furnish SNF services under a swing-bed agreement or (2) have
a hospital-based SNF. We anticipate that the impact on small rural
hospitals would be a positive impact. Moreover, as noted in previous
SNF PPS final rules (most recently, the one for FY 2021 (85 FR 47594)),
the category of small rural hospitals is included within the analysis
of the impact of this proposed rule on small entities in general. As
indicated in Table 33, the effect on facilities for FY 2022 is
projected to be an aggregate positive impact of 1.3 percent. As the
overall impact on the industry as a whole is less than the 3 to 5
percent threshold discussed above, the Secretary has determined that
this proposed rule would not have a significant impact on a substantial
number of small rural hospitals for FY 2022.
C. Unfunded Mandates Reform Act Analysis
Section 202 of the Unfunded Mandates Reform Act of 1995 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 2021, that
threshold is approximately $158 million. This proposed rule would
impose no mandates on state, local, or tribal governments or on the
private sector.
D. Federalism Analysis
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. This proposed rule would have no substantial direct
effect on state and local governments, preempt state law, or otherwise
have federalism implications.
[[Page 20021]]
E. Congressional Review Act
This proposed regulation is subject to the Congressional Review Act
provisions of the Small Business Regulatory Enforcement Fairness Act of
1996 (5 U.S.C. 801 et seq.) and has been transmitted to the Congress
and the Comptroller General for review.
F. Regulatory Review Costs
If regulations impose administrative costs on private entities,
such as the time needed to read and interpret this proposed 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 last year's proposed rule would be the number of
reviewers of this year's proposed rule. We acknowledge that this
assumption may understate or overstate the costs of reviewing this
rule. It is possible that not all commenters reviewed last year's
proposed rule in detail, and it is also possible that some reviewers
chose not to comment on that proposed rule. For these reasons, we
believe that the number of commenters on last year's proposed rule is a
fair estimate of the number of reviewers of this proposed rule.
We also recognize that different types of entities are in many
cases affected by mutually exclusive sections of the proposed rule, and
therefore, for the purposes of our estimate we assume that each
reviewer reads approximately 50 percent of the rule.
Using the national mean hourly wage data from the May 2019 BLS
Occupational Employment Statistics (OES) for medical and health service
managers (SOC 11-9111), we estimate that the cost of reviewing this
rule is $110.74 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 4 hours for
the staff to review half of the proposed rule. For each SNF that
reviews the rule, the estimated cost is $442.96 (4 hours x $110.74).
Therefore, we estimate that the total cost of reviewing this regulation
is $20,819.12 ($442.96 x 47 reviewers).
In accordance with the provisions of Executive Order 12866, this
proposed rule was reviewed by the Office of Management and Budget.
List of Subjects
42 CFR Part 411
Diseases, Medicare, Reporting and recordkeeping requirements.
42 CFR Part 413
Principles of reasonable cost reimbursement; payment for end-stage
renal disease services; optional prospectively determined payment rates
for skilled nursing facilities; payment for acute kidney injury
dialysis.
42 CFR Part 489
Health facilities, Medicare, Reporting and recordkeeping
requirements.
For the reasons set forth in the preamble, the Centers for Medicare
& Medicaid Services proposes to amend 42 CFR chapter IV as set forth
below:
PART 411--EXCLUSIONS FROM MEDICARE AND LIMITATIONS ON MEDICARE
PAYMENT
0
1. The authority citation for part 411 continues to read as follows:
Authority: 42 U.S.C. 1302, 1395w-101 through 1395w-152, 1395hh,
and 1395nn.
0
2. Amend Sec. 411.15 by--
0
a. Revising paragraphs (p)(2)(xiii) through (xvi);
0
b. Redesignating paragraph (p)(2)(xvii) as (p)(2)(xviii); and
0
c. Adding new paragraph (p)(2)(xvii).
The revisions and addition read as follows:
Sec. 411.15 Particular services excluded from coverage.
* * * * *
(p) * * *
(2) * * *
(xiii) Those chemotherapy items identified, as of July 1, 1999, by
HCPCS codes J9000-J9020, J9040-J9151, J9170-J9185, J9200-J9201, J9206-
J9208, J9211, J9230-J9245, and J9265-J9600, and as of January 1, 2004,
by HCPCS codes A9522, A9523, A9533, and A9534 (as subsequently modified
by CMS), and any additional chemotherapy items identified by CMS.
(xiv) Those chemotherapy administration services identified, as of
July 1, 1999, by HCPCS codes 36260-36262, 36489, 36530-36535, 36640,
36823, and 96405-96542 (as subsequently modified by CMS), and any
additional chemotherapy administration services identified by CMS.
(xv) Those radioisotope services identified, as of July 1, 1999, by
HCPCS codes 79030-79440 (as subsequently modified by CMS), and any
additional radioisotope services identified by CMS.
(xvi) Those customized prosthetic devices (including artificial
limbs and their components) identified, as of July 1, 1999, by HCPCS
codes L5050-L5340, L5500-L5611, L5613-L5986, L5988, L6050-L6370, L6400-
6880, L6920-L7274, and L7362-L7366 (as subsequently modified by CMS)
and any additional customized prosthetic devices identified by CMS,
which are delivered for a resident's use during a stay in the SNF and
intended to be used by the resident after discharge from the SNF.
(xvii) Those blood clotting factors indicated for the treatment of
patients with hemophilia and other bleeding disorders identified, as of
July 1, 2020, by HCPCS codes J7170, J7175, J7177-J7183, J7185-J7190,
J7192-J7195, J7198-J7203, J7205, and J7207-J7211 (as subsequently
modified by CMS) and items and services related to the furnishing of
such factors, and any additional blood clotting factors identified by
CMS and items and services related to the furnishing of such factors.
* * * * *
PART 413--PRINCIPLES OF REASONABLE COST REIMBURSEMENT; PAYMENT FOR
END-STAGE RENAL DISEASE SERVICES; PROSPECTIVELY DETERMINED PAYMENT
RATES FOR SKILLED NURSING FACILITIES; PAYMENT FOR ACUTE KIDNEY
INJURY DIALYSIS
0
3. The authority citation for part 413 continues to read as follows:
Authority: 42 U.S.C. 1302, 1395d(d), 1395f(b), 1395g, 1395l(a),
(i), and (n), 1395x(v), 1395hh, 1395rr, 1395tt, and 1395ww.
0
4. Amend Sec. 413.338 by revising paragraphs (d)(4)(ii) and (e)(1) and
adding paragraph (g) to read as follows:
Sec. 413.338 Skilled nursing facility value-based purchasing program.
* * * * *
(d) * * *
(4) * * *
(ii) A SNF may request an exception within 90 days of the date that
the extraordinary circumstances occurred by sending an email to the
designated email address for SNF VBP ECE requests, which is
[email protected]. The email must include a completed Extraordinary
Circumstances Request form (available on https://qualitynet.cms.gov/)
and any available evidence of the impact of the extraordinary
circumstances on the care that the SNF furnished to patients including,
but not limited to, photographs and media articles.
* * * * *
(e) * * *
(1) CMS will provide quarterly confidential feedback reports to
SNFs on their performance on the SNF
[[Page 20022]]
readmission measure. Beginning with the baseline period and performance
period quality measure quarterly reports issued on or after October 1,
2021, which contain the baseline period and performance period measure
rates, respectively, SNFs will have 30 days following the date CMS
provides each of these reports to review and submit corrections to the
SNF readmission measure rates contained in that report. The
administrative claims data used to calculate a SNF's readmission
measure rates are not subject to review and correction under this
paragraph (e)(1). All correction requests must be accompanied by
appropriate evidence showing the basis for the correction to the SNF
readmission measure rates.
* * * * *
(g) Special rules for the FY 2022 SNF VBP Program. (1) CMS will
calculate a SNF readmission measure rate for each SNF based on its
performance on the SNF readmission measure during the performance
period specified by CMS for fiscal year 2022, but CMS will not
calculate a performance score for any SNF using the methodology
described in paragraphs (d)(1) and (2) of this section. CMS will
instead assign a performance score of zero to each SNF, with the
exception of those SNFs qualifying for the low-volume scoring
adjustment described in paragraph (d)(3) of this section.
(2) CMS will calculate the value-based incentive payment adjustment
factor for each SNF using a performance score of zero and will then
calculate the value-based incentive payment amount for each SNF using
the methodology described in paragraph (c)(2)(ii) of this section. CMS
will then apply low-volume scoring adjustment described in paragraph
(d)(3) of this section.
(3) CMS will provide confidential feedback reports to SNFs on their
performance on the SNF readmission measure in accordance with
paragraphs (e)(1) and (2) of this section.
(4) CMS will publicly report SNF performance on the SNF readmission
measure in accordance with paragraph (e)(3) of this section.
PART 489--PROVIDER AGREEMENTS AND SUPPLIER APPROVAL
0
5. The authority citation for part 489 continues to read as follows:
Authority: 42 U.S.C. 1302, 1395i-3, 1395x, 1395aa(m), 1395cc,
1395ff, and 1395(hh).
0
6. Amend Sec. 489.20 by--
0
a. Revising paragraphs (s)(13) through (16);
0
b. Redesignating paragraph (s)(17) as paragraph (s)(18); and
0
c. Adding new paragraph (s)(17) to read as follows:
Sec. 489.20 Basis commitments.
* * * * *
(s) * * *
(13) Those chemotherapy items identified, as of July 1, 1999, by
HCPCS codes J9000-J9020, J9040-J9151, J9170-J9185, J9200-J9201, J9206-
J9208, J9211, J9230-J9245, and J9265-J9600, and as of January 1, 2004,
by HCPCS codes A9522, A9523, A9533, and A9534 (as subsequently modified
by CMS), and any additional chemotherapy items identified by CMS.
(14) Those chemotherapy administration services identified, as of
July 1, 1999, by HCPCS codes 36260-36262, 36489, 36530-36535, 36640,
36823, and 96405-96542 (as subsequently modified by CMS), and any
additional chemotherapy administration services identified by CMS.
(15) Those radioisotope services identified, as of July 1, 1999, by
HCPCS codes 79030-79440 (as subsequently modified by CMS), and any
additional radioisotope services identified by CMS.
(16) Those customized prosthetic devices (including artificial
limbs and their components) identified, as of July 1, 1999, by HCPCS
codes L5050-L5340, L5500-L5611, L5613-L5986, L5988, L6050-L6370, L6400-
6880, L6920-L7274, and L7362-L7366 (as subsequently modified by CMS)
and any additional customized prosthetic devices identified by CMS,
which are delivered for a resident's use during a stay in the SNF and
intended to be used by the resident after discharge from the SNF.
(17) Those blood clotting factors indicated for the treatment of
patients with hemophilia and other bleeding disorders identified, as of
July 1, 2020, by HCPCS codes J7170, J7175, J7177-J7183, J7185-J7190,
J7192-J7195, J7198-J7203, J7205, and J7207-J7211 (as subsequently
modified by CMS) and items and services related to the furnishing of
such factors, and any additional blood clotting factors identified by
CMS and items and services related to the furnishing of such factors.
* * * * *
Dated: March 29, 2021.
Elizabeth Richter,
Acting Administrator, Centers for Medicare & Medicaid Services.
Dated: April 8, 2021.
Xavier Becerra,
Secretary, Department of Health and Human Services.
[FR Doc. 2021-07556 Filed 4-8-21; 4:15 pm]
BILLING CODE 4120-01-P
