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 2023; Changes to the Requirements for the Director of Food and Nutrition Services and Physical Environment Requirements in Long-Term Care Facilities, 47502-47619 [2022-16457]

Agencies

• Centers for Medicare & Medicaid Services
• DEPARTMENT OF HEALTH AND HUMAN SERVICES

[Federal Register Volume 87, Number 148 (Wednesday, August 3, 2022)]
[Rules and Regulations]
[Pages 47502-47619]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2022-16457]



[[Page 47501]]

Vol. 87

Wednesday,

No. 148

August 3, 2022

Part II





Department of Health and Human Services





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Centers for Medicare & Medicaid Services





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42 CFR Parts 413 and 483





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 
2023; Changes to the Requirements for the Director of Food and 
Nutrition Services and Physical Environment Requirements in Long-Term 
Care Facilities; Final Rule

Federal Register / Vol. 87, No. 148 / Wednesday, August 3, 2022 / 
Rules and Regulations

[[Page 47502]]


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DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Medicare & Medicaid Services

42 CFR Parts 413 and 483

[CMS-1765-F and CMS-3347-F]
RIN 0938-AU76 and 0938-AT36


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 2023; Changes to the Requirements for the Director of Food and 
Nutrition Services and Physical Environment Requirements in Long-Term 
Care Facilities

AGENCY: Centers for Medicare & Medicaid Services (CMS), Department of 
Health and Human Services (HHS).

ACTION: Final rule.

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SUMMARY: This final rule updates payment rates; forecast error 
adjustments; diagnosis code mappings; the Patient Driven Payment Model 
(PDPM) parity adjustment; the SNF Quality Reporting Program (QRP); and 
the SNF Value-Based Purchasing (VBP) Program. It also establishes a 
permanent cap policy to smooth the impact of year-to-year changes in 
SNF payments related to changes in the SNF wage index. We also announce 
the application of a risk adjustment for the SNF Readmission Measure 
for COVID-19 beginning in FY 2023. We are finalizing changes to the 
long-term care facility fire safety provisions referencing the National 
Fire Protection Association (NFPA)[supreg] Life Safety Code, and 
Director of Food and Nutrition Services requirements.

DATES: These regulations are effective on October 1, 2022.

FOR FURTHER INFORMATION CONTACT: [email protected] for issues related to 
the SNF PPS.
    Heidi Magladry, (410) 786-6034, for information related to the 
skilled nursing facility quality reporting program.
    Alexandre Laberge, (410) 786-8625, for information related to the 
skilled nursing facility value-based purchasing program.
    Kristin Shifflett, [email protected], and Cameron 
Ingram, [email protected], for information related to the LTC 
requirements for participation.

SUPPLEMENTARY INFORMATION: 

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 final rule can be accessed on 
the SNF PPS Wage Index home page, at https://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. Analysis and Responses to Public Comments on the FY 2023 SNF 
PPS Proposed Rule
    A. General Comments on the FY 2023 SNF PPS Proposed Rule
IV. SNF PPS Rate Setting Methodology and FY 2023 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
V. 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
VI. Other SNF PPS Issues
    A. Permanent Cap on Wage Index Decreases
    B. Technical Updates to PDPM ICD-10 Mappings
    C. Recalibrating the PDPM Parity Adjustment
    D. Request for Information: Infection Isolation
VII. Skilled Nursing Facility Quality Reporting Program (SNF QRP)
    A. Background and Statutory Authority
    B. General Considerations Used for the Selection of Measures for 
the SNF QRP
    C. SNF QRP Quality Measure Beginning With the FY 2025 SNF QRP
    D. SNF QRP Quality Measures Under Consideration for Future 
Years: Request for Information (RFI)
    E. Overarching Principles for Measuring Equity and Healthcare 
Quality Disparities across CMS Quality Programs--Request for 
Information (RFI)
    F. Inclusion of the CoreQ: Short Stay Discharge Measure in a 
Future SNF QRP Program Year--Request for Information (RFI)
    G. Form, Manner, and Timing of Data Submission Under the SNF QRP
    H. Policies Regarding Public Display of Measure Data for the SNF 
QRP
VIII. Skilled Nursing Facility Value-Based Purchasing Program (SNF 
VBP)
    A. Statutory Background
    B. SNF VBP Program Measures
    C. SNF VBP Performance Period and Baseline Period
    D. Performance Standards
    E. SNF VBP Performance Scoring
    F. Adoption of a Validation Process for the SNF VBP Program 
Beginning With the FY 2023 Program Year
    G. SNF Value-Based Incentive Payments for FY 2023
    H. Public Reporting on the Provider Data Catalog website
    I. Requests for Comment Related to Future SNF VBP Program 
Expansion Policies
IX. Changes to the Requirements for the Director of Food and 
Nutrition Services and Physical Environment Requirements in Long-
Term (LTC) Facilities and Summary of Public Comments and Responses 
to the Request for Information on Revising the Requirements for 
Long-Term Care Facilities to Establish Mandatory Minimum Staffing 
Levels
X. Collection of Information Requirements
XI. Economic Analyses
    A. Regulatory Impact Analysis
    B. Regulatory Flexibility Act Analysis
    C. Unfunded Mandates Reform Act Analysis
    D. Federalism Analysis
    E. Regulatory Review Costs

I. Executive Summary

A. Purpose

    This final rule updates the SNF prospective payment rates for 
fiscal year (FY) 2023, 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 final rule) in the Federal Register, 
before the August 1 that precedes the start of each FY. In addition, 
this final rule includes requirements for the Skilled Nursing Facility 
Quality Reporting Program (SNF QRP) and the Skilled Nursing Facility 
Value-Based Purchasing Program (SNF VBP), including adopting new 
quality measures for the SNF VBP Program and finalizing several updates 
to the Program's scoring methodology.

[[Page 47503]]

The SNF QRP adopts one new measure to promote patient safety, begins 
collection of information which will improve the quality of care for 
all SNF patients, and revises associated regulation text. We are 
revising the qualification requirements for the Director of Food and 
Nutrition Services and revising requirements for life safety from fire 
for long-term care facilities that previously used the Fire Safety 
Evaluation System (FSES) to demonstrate compliance with provisions of 
the Life Safety Code (LSC).

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 final rule will reflect an update to the 
rates that we published in the SNF PPS final rule for FY 2022 (86 FR 
42424, August 4, 2021). In addition, the final rule includes a forecast 
error adjustment for FY 2023, updates to the diagnosis code mappings 
used under the Patient Driven Payment Model (PDPM), and includes a 
recalibration of the PDPM parity adjustment. This final rule also 
establishes a permanent cap policy to smooth the impact of year-to-year 
changes in SNF payments related to changes in the SNF wage index.
    This final rule finalizes requirements for the SNF QRP, including 
the adoption of one new measure beginning with the FY 2024 SNF QRP: the 
Influenza Vaccination Coverage among Healthcare Personnel (HCP) (NQF 
#0431) measure. We are also revising the compliance date for the 
Transfer of Health Information measures and certain standardized 
patient assessment data elements. In addition, we are revising 
regulation text that pertains to data submission requirements for the 
SNF QRP.
    We are also finalizing several updates for the SNF VBP Program, 
including a policy to suppress the Skilled Nursing Facility 30-Day All-
Cause Readmission Measure (SNFRM) for the FY 2023 SNF VBP Program Year 
for scoring and payment adjustment purposes. We are also adding two new 
measures to the SNF VBP Program beginning with the FY 2026 SNF VBP 
program year and one new measure beginning with the FY 2027 program 
year. We are also finalizing several updates to the scoring methodology 
beginning with the FY 2026 program year. We are also revising our 
regulation text in accordance with our proposals.
    In addition, we are finalizing LTC facilities LSC changes in Sec.  
483.90(a) to allow older exiting facilities to continue to use the 2001 
FSES mandatory values when determining compliance for containment, 
extinguishment, and people movement requirements as set out in the LSC. 
Older facilities who may not meet the FSES requirements previously used 
the 2000 LSC FSES will be allowed to remain in compliance with the 
older FSES without incurring substantial expenses to change their 
construction types, while maintaining resident and staff safety.
    Additionally, we are finalizing changes to the requirements for the 
Director of Food and Nutrition Services in LTC facilities in Sec.  
483.60. We are revising the required qualifications for a director of 
food and nutrition services to provide that those with several years of 
experience performing as the director of food and nutrition services in 
a facility can continue to do so. Specifically, we have added to the 
current requirements that individuals with 2 or more years of 
experience in the position of a director of food and nutrition services 
and who have also completed a minimum course of study in food safety 
that includes topics integral to managing dietary operations (such as, 
but not limited to: foodborne illness, sanitation procedures, food 
purchasing/receiving, etc.) can continue to qualify as a director of 
food and nutrition services. This will help address concerns related to 
costs associated with training for existing staff and the potential 
need to hire new staff.

C. Summary of Cost and Benefits
[GRAPHIC] [TIFF OMITTED] TR03AU22.001

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 digital 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) to facilitate collaboration with interested parties 
to develop Health Level Seven International[supreg] (HL7) Fast 
Healthcare Interoperability Resource[supreg] (FHIR) standards. These 
standards could support the exchange and reuse of patient assessment 
data derived from the post-acute care (PAC) setting assessment tools, 
such as the minimum data set (MDS), inpatient rehabilitation facility -
patient assessment instrument (IRF-PAI), Long-Term Care Hospital (LTCH) 
continuity assessment record and evaluation (CARE) Data Set (LCDS), 
outcome and assessment information set (OASIS), and other 
sources.1 2 The PACIO Project has focused on HL7 FHIR 
implementation guides for: functional status, cognitive status and new 
use cases on advance directives, re-assessment timepoints, and Speech, 
language, swallowing, cognitive communication and hearing (SPLASCH) 
pathology.\3\ We encourage PAC provider

[[Page 47504]]

and health IT vendor participation as the efforts advance.
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    \1\ HL7 FHIR Release 4. Available at https://www.hl7.org/fhir/.
    \2\ HL7 FHIR. PACIO Functional Status Implementation Guide. 
Available at https://paciowg.github.io/functional-status-ig/.
    \3\ PACIO Project. Available at https://pacioproject.org/about/.
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    The CMS Data Element Library (DEL) continues to be updated and 
serves as a 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).\4\ The DEL furthers CMS' goal of data 
standardization and interoperability. Standards in the DEL can be 
referenced on the CMS website and in the ONC Interoperability Standards 
Advisory (ISA). The 2022 ISA is available at https://www.healthit.gov/isa/sites/isa/files/inline-files/2022-ISA-Reference-Edition.pdf.
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    \4\ Centers for Medicare & Medicaid Services. Newsroom. Fact 
sheet: CMS Data Element Library Fact Sheet. June 21, 2018. Available 
at https://www.cms.gov/newsroom/fact-sheets/cms-data-element-library-fact-sheet.
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    The 21st Century Cures Act (Cures Act) (Pub. L. 114-255, enacted 
December 13, 2016) required HHS and ONC to take steps to promote 
adoption and use of electronic health record (EHR) technology.\5\ 
Specifically, section 4003(b) of the Cures Act required ONC to take 
steps to advance interoperability through the development of a Trusted 
Exchange Framework and Common Agreement aimed at establishing full 
network-to network exchange of health information nationally. On 
January 18, 2022, ONC announced a significant milestone by releasing 
the Trusted Exchange Framework \6\ and Common Agreement Version 1.\7\ 
The Trusted Exchange Framework is a set of non-binding principles for 
health information exchange, and the Common Agreement is a contract 
that advances those principles. The Common Agreement and the Qualified 
Health Information Network Technical Framework Version 1 (incorporated 
by reference into the Common Agreement) establish the technical 
infrastructure model and governing approach for different health 
information networks and their users to securely share clinical 
information with each other, all under commonly agreed to terms. The 
technical and policy architecture of how exchange occurs under the 
Common Agreement follows a network-of-networks structure, which allows 
for connections at different levels and is inclusive of many different 
types of entities at those different levels, such as health information 
networks, healthcare practices, hospitals, public health agencies, and 
Individual Access Services (IAS) Providers.\8\ For more information, we 
refer readers to https://www.healthit.gov/topic/interoperability/trusted-exchange-framework-and-common-agreement.
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    \5\ Sections 4001 through 4008 of Public Law 114-255. Available 
at https://www.govinfo.gov/content/pkg/PLAW-114publ255/html/PLAW-114publ255.htm.
    \6\ The Trusted Exchange Framework (TEF): Principles for Trusted 
Exchange (Jan. 2022). Available at https://www.healthit.gov/sites/default/files/page/2022-01/Trusted_Exchange_Framework_0122.pdf.
    \7\ Common Agreement for Nationwide Health Information 
Interoperability Version 1 (Jan. 2022). Available at https://www.healthit.gov/sites/default/files/page/2022-01/Common_Agreement_for_Nationwide_Health_Information_Interoperability_Version_1.pdf.
    \8\ The Common Agreement defines Individual Access Services 
(IAS) as ``with respect to the Exchange Purposes definition, the 
services provided utilizing the Connectivity Services, to the extent 
consistent with Applicable Law, to an Individual with whom the QHIN, 
Participant, or Subparticipant has a Direct Relationship to satisfy 
that Individual's ability to access, inspect, or obtain a copy of 
that Individual's Required Information that is then maintained by or 
for any QHIN, Participant, or Subparticipant.'' The Common Agreement 
defines ``IAS Provider'' as: ``Each QHIN, Participant, and 
Subparticipant that offers Individual Access Services.'' See Common 
Agreement for Nationwide Health Information Interoperability Version 
1, at 7 (Jan. 2022), https://www.healthit.gov/sites/default/files/page/2022-01/Common_Agreement_for_Nationwide_Health_Information_Interoperability_Version_1.pdf.
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    We invited providers to learn more about these important 
developments and how they are likely to affect SNFs.
    Comment: We received one comment on the information provided in 
this section. The commenter expressed support for efforts across HHS to 
advance health information technology exchange and encouraged use of a 
standard set of data by providers and health IT vendors, including 
efforts through the PACIO project. The commenter also noted a recent 
National Academies report describing technology barriers for PAC 
settings due to not being eligible for previous incentives to purchase 
technology certified under the ONC Health IT Certification Program. The 
commenter supported recommendations in the report for HHS to pursue 
financial incentives for post-acute care settings to adopt certified 
health information technology in order to enable health information 
exchange.
    Response: We will take this comment into consideration as we 
coordinate with Federal partners, including ONC, on interoperability 
initiatives, and to inform future rulemaking.

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) 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. 
Finally, section 111 of the Consolidated Appropriations Act, 2021 (CAA) 
updated section 1888(h) of the Act, authorizing the Secretary to apply 
up to nine additional measures to the VBP program for SNFs.

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

[[Page 47505]]

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 
2022 (86 FR 42424, August 4, 2021).
    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 
final rule provides the required annual updates to the per diem payment 
rates for SNFs for FY 2023.

III. Analysis and Responses to Public Comments on the FY 2023 SNF PPS 
Proposed Rule

    In response to the publication of the FY 2023 SNF PPS proposed 
rule, we received 6,970 public comments from individuals, providers, 
corporations, government agencies, private citizens, trade 
associations, and major organizations. The following are brief 
summaries of each proposed provision, a summary of the public comments 
that we received related to that proposal, and our responses to the 
comments.

A. General Comments on the FY 2023 SNF PPS Proposed Rule

    In addition to the comments we received on specific proposals 
contained within the proposed rule (which we address later in this 
final rule), commenters also submitted the following, more general, 
observations on the SNF PPS and SNF care generally. A discussion of 
these comments, along with our responses, appears below.
    Comment: Commenters submitted comments and recommendations that are 
outside the scope of the proposed rule addressing a number of different 
policies, including the Coronavirus disease 2019 (COVID-19) pandemic. 
This included comments on the flexibilities provided to SNFs during the 
PHE, specifically through the waivers issued under sections 1135 of the 
Act and coverage flexibility provided under section 1812(f) of the Act. 
Commenters also expressed concerns about the substantial additional 
costs due to the PHE that they were concerned would be permanent due to 
changes in patient care, infection control staff and equipment, 
personal protective equipment (PPE), reporting requirements, increased 
wages, increased food prices, and other necessary costs. Some 
commenters who received CARES Act Provider Relief funds indicated that 
those funds were not enough to cover these costs. Additionally, a few 
commenters from rural areas stated that their facilities were heavily 
impacted from the additional costs, particularly the need to raise 
wages, and that this could affect patients' access to care.
    Response: Because these comments are outside the scope of the 
current rulemaking, we are not addressing them in this final rule. We 
may take them under consideration in future rulemaking.
    Comment: We received a number of comments related to monitoring 
Medicare Advantage Organizations (MAOs). These commenters referred to a 
recent OIG report, which discussed how some MAOs have reportedly denied 
or delayed beneficiary access to SNF services. These commenters 
encouraged CMS to review the requirements and policies surrounding the 
payment and practices of MAOs.
    Response: Because these comments are outside the scope of the 
current rulemaking, we are not addressing them in this final rule. We 
may take them under consideration in future rulemaking.
    Comment: One commenter requested that we consider including 
recreational therapy time provided to SNF residents by recreational 
therapists as part of the calculation of the resident's RUG-IV therapy 
classification or as part of determining the number of restorative 
nursing services provided to the resident.
    Response: We appreciate the commenter raising this issue, but we do 
not believe there is sufficient evidence at this time regarding the 
efficacy of recreational therapy interventions or, more notably, data 
which would substantiate a determination of the effect on payment of 
such interventions, as such services were not considered separately, as 
were physical, occupational and speech-language pathology services, 
when RUG-IV was being developed. That is, we note that Medicare Part A 
originally paid for institutional care in various provider settings, 
including SNF, on a reasonable cost basis, but now makes payment using 
PPS methodologies, such as the SNF PPS. To the extent that one of these 
SNFs furnished recreational therapy to its inpatients under the 
previous, reasonable cost methodology, the cost of the services would 
have been included in the base payments when SNF PPS payment rates were 
derived. Under the PPS methodology, Part A makes a comprehensive 
payment for the bundled package of items and services that the facility 
furnishes during the course of a Medicare-covered stay. This package 
encompasses nearly all services that the beneficiary receives during 
the course of the stay--including any medically necessary recreational 
therapy--and payment for such services is included within the 
facility's comprehensive SNF PPS payment for the covered Part A stay 
itself.
    Comment: One commenter encouraged CMS to monitor the use of 
concurrent and group therapy under PDPM and identify any facilities 
that are consistently exceeding the established group and concurrent 
therapy limit. This commenter referred to reports by their members to 
disregard the established limit on these therapy modalities, as well as 
the impact of the PHE on the provision of group and concurrent therapy.
    Response: We continue to monitor all aspects of payment and service 
provision under PDPM. Should we discover any outliers in the provision 
of group and concurrent therapy that consistently exceed the 
established limit on these therapy modalities, we will refer such 
outliers for administrative action.

IV. SNF PPS Rate Setting Methodology and FY 2023 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

[[Page 47506]]

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 the SNF PPS final rule for FY 2022 (86 FR 
42444 through 42463), we rebased and revised the market basket index, 
which included updating the base year from 2014 to 2018.
    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 IV.B.4. of this final rule.
    As outlined in the proposed rule, we proposed a FY 2023 SNF market 
basket percentage of 2.8 percent based on IHS Global Inc.'s (IGI's) 
fourth quarter 2021 forecast of the 2018-based SNF market basket 
(before application of the forecast error adjustment and productivity 
adjustment). We also proposed that if more recent data subsequently 
became available (for example, a more recent estimate of the market 
basket and/or the productivity adjustment), we would use such data, if 
appropriate, to determine the FY 2023 SNF market basket percentage 
change, labor-related share relative importance, forecast error 
adjustment, or productivity adjustment in the SNF PPS final rule.
    Since the proposed rule, we have updated the FY 2023 market basket 
percentage increase based on IGI's second quarter 2022 forecast with 
historical data through the first quarter of 2022. The FY 2023 growth 
rate of the 2018-based SNF market basket is estimated to be 3.9 
percent.
    In section IV.B.5. of this final rule, we discussed 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 outlined in this final rule, we use the percentage 
change in the SNF market basket index to compute the update factor for 
FY 2023. This factor is based on the FY 2023 percentage increase in the 
2018-based SNF market basket index reflecting routine, ancillary, and 
capital-related expenses. As stated previously, in the proposed rule, 
the SNF market basket percentage update was estimated to be 2.8 percent 
for FY 2023 based on IGI's fourth quarter 2021 forecast. For this final 
rule, based on IGI's second quarter 2022 forecast with historical data 
through the first quarter of 2022, the FY 2023 growth rate of the 2018-
based SNF market basket is estimated to be 3.9 percent.
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 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 2021 (the most recently available FY for which there is 
final data), the forecasted or estimated increase in the SNF market 
basket index was 2.2 percent, and the actual increase for FY 2021 is 
3.7 percent, resulting in the actual increase being 1.5 percentage 
point higher 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 2023 market basket percentage 
change of 3.9 percent would be adjusted upward to account for the 
forecast error correction of 1.5 percentage point, resulting in a SNF 
market basket percentage change of 5.1 percent after reducing the 
market basket update by the productivity adjustment of 0.3 percentage 
point, discussed later in this section of the preamble.
    Table 2 shows the forecasted and actual market basket increases for 
FY 2021.

[[Page 47507]]

[GRAPHIC] [TIFF OMITTED] TR03AU22.002

4. 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 productivity 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 productivity adjustment to be equal to the 
10-year moving average of changes in annual economy-wide, private 
nonfarm business multifactor productivity (MFP) (as projected by the 
Secretary for the 10-year period ending with the applicable FY, year, 
cost-reporting period, or other annual period). The U.S. Department of 
Labor's Bureau of Labor Statistics (BLS) publishes the official measure 
of productivity for the U.S. We note that previously the productivity 
measure referenced in section 1886(b)(3)(B)(xi)(II) of the Act was 
published by BLS as private nonfarm business multifactor productivity. 
Beginning with the November 18, 2021 release of productivity data, BLS 
replaced the term multifactor productivity (MFP) with total factor 
productivity (TFP). BLS noted that this is a change in terminology only 
and will not affect the data or methodology. As a result of the BLS 
name change, the productivity measure referenced in section 
1886(b)(3)(B)(xi)(II) of the Act is now published by BLS as private 
nonfarm business total factor productivity. However, as mentioned 
previously in this section, the data and methods are unchanged. We 
refer readers to the BLS website at www.bls.gov for the BLS historical 
published TFP data.
    A complete description of the TFP projection methodology is 
available on our website at https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/MedicareProgramRatesStats/MarketBasketResearch. In addition, in the FY 
2022 SNF final rule (86 FR 42429) we noted that, effective with FY 2022 
and forward, we are changing the name of this adjustment to refer to it 
as the ``productivity adjustment,'' rather than the ``MFP adjustment.''
a. Incorporating the Productivity Adjustment 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. Section 1888(e)(5)(B)(ii) of the Act 
further states that the reduction of the market basket percentage by 
the productivity 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 productivity 
adjustment to the market basket percentage calculated under section 
1888(e)(5)(B)(i) of the Act results in a productivity-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 the FY 2023 SNF PPS proposed rule, 
the proposed productivity adjustment (the 10-year moving average of 
changes in annual economy-wide private nonfarm business TFP for the 
period ending September 30, 2023) was projected to be 0.4 percentage 
point. However, for this final rule, based on IGI's second quarter 2022 
forecast, the estimated 10-year moving average of changes in annual 
economy-wide private nonfarm business TFP for the period ending 
September 30, 2023 is 0.3 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 2023 for the SNF PPS is based on IGI's second quarter 2022 
forecast of the SNF market basket percentage, which is estimated to be 
3.9 percent. This market basket percentage is then increased by 1.5 
percentage point, due to application of the forecast error adjustment 
discussed earlier in this section of the preamble. Finally, as 
discussed earlier in this section of the preamble, we are applying a 
0.3 percentage point productivity adjustment to the FY 2023 SNF market 
basket percentage. The resulting productivity-adjusted FY 2023 SNF 
market basket update is, therefore, equal to 5.1 percent, or 3.9 
percent plus 1.5 percentage point to account for forecast error and 
less 0.3 percentage point to account for the productivity adjustment.
5. Market Basket Update Factor for FY 2023
    Sections 1888(e)(4)(E)(ii)(IV) and (e)(5)(i) of the Act require 
that the update factor used to establish the FY 2023 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, 2021 through September 30, 
2022 to the average market basket level for the period of October 1, 
2022 through September 30, 2023. This process yields a percentage 
change in the 2018-based SNF market basket of 3.9 percent.
    As further explained in section IV.B.3. of this final 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 in absolute terms. Since the 
actual FY 2021 SNF market basket percentage change exceeded the 
forecasted FY 2021 SNF market basket percentage change (FY 2021 is the 
most recently available FY for which there is historical data) by

[[Page 47508]]

more than the 0.5 percentage point threshold, we are adjusting the FY 
2023 market basket percentage change upward by the forecast error 
correction. Applying the 1.5 percentage point forecast error correction 
results in an adjusted FY 2023 SNF market basket percentage change of 
5.4 percent (3.9 percent market basket update plus 1.5 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 productivity adjustment (10-year 
moving average of changes in annual economy-wide private nonfarm 
business TFP for the period ending September 30, 2023) which is 
estimated to be 0.3 percentage point, as described in section IV.B.4. 
of this final rule. Thus, we apply a net SNF market basket update 
factor of 5.1 percent in our determination of the FY 2023 SNF PPS 
unadjusted Federal per diem rates, which reflects a market basket 
increase factor of 3.9 percent, plus the 1.5 percentage point forecast 
error correction and less the 0.3 percentage point productivity 
adjustment.
    As outlined in the proposed rule, we noted that if more recent data 
became available (for example, a more recent estimate of the SNF market 
basket and/or productivity adjustment), we would use such data, if 
appropriate, to determine the FY 2023 SNF market basket percentage 
change, labor-related share relative importance, forecast error 
adjustment, or productivity adjustment in the FY 2023 SNF PPS final 
rule. Since more recent data did become available since the proposed 
rule, as outlined above, we have updated the various adjustment factors 
described through this section accordingly.
    We also noted 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 
productivity 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.
    A discussion of the public comments received on the FY 2023 SNF 
market basket percentage increase to the SNF PPS rates, along with our 
responses, may be found below.
    Comment: One commenter supported and appreciated the proposed 
increase in Medicare rates as a result of the market basket and 
forecast error adjustment. Several commenters supported the increase 
and urged CMS to use the most recent economic data as it becomes 
available in finalizing the payment update to capture the significant 
cost increases and inflation being felt by the long-term care sector 
and across the economy. However, multiple commenters raised concerns 
about whether rising costs, and costs of labor, in particular, are 
being sufficiently accounted for in the SNF market basket. One 
commenter urged CMS to discuss in the final rule how the agency will 
account for these increased costs. One commenter shared that their 
State wage survey of nursing facilities, which is used to inform their 
Medicaid inflation adjustment each year, indicates a 14.8 percent 
increase in nursing compensation (a composite of employee and agency 
staff) from 2022 to 2023, along with non-nursing compensation growth of 
7.3 percent.
    Commenters were concerned that CMS' use of the historical 
Employment Cost Index (ECI) for Wages and Salaries for Private Industry 
Workers in Nursing Care Facilities to measure the price growth of wages 
and salaries may not be accurately capturing employment costs in 
nursing homes, or otherwise not in a timely manner. They stated that 
the quarterly updates of the price proxies do not address changes in 
staffing levels, changes in the occupational mix, increases in the use 
of contract labor or travel nurses, or other drivers of wage rate 
growth such as labor market tightness and consumer inflation.
    One commenter calculated notable differences in Medicare Cost 
Report Direct Care Wage Data and the labor component of market basket 
updates, which they estimated to be about 6 percent between 1998 and 
2021. The commenter suggested spreading an adjustment for this 
difference into the update equally over a 2 to 3-year period. In 
addition, they requested that CMS develop a methodology to account for 
rapidly escalating labor costs in a more timely fashion than the 
current price proxy calculation method captures. The commenter also 
noted faster growth of the BLS Current Employment Statistics (CES) 
average hourly earnings (AHE) series for Production and Non-Supervisory 
Nursing care facility employees (without seasonality adjustment), 
compared to the ECI for Wages and Salaries for Private Industry Workers 
in Nursing Care Facilities.
    One commenter requested that CMS provide a labor-related market 
basket price add-on due to workforce shortages and other challenges not 
addressed by the current market basket methodology.
    Response: We recognize the challenges facing SNFs in operating 
during a high inflationary environment. Due to SNF payments under PPS 
being set prospectively, we rely on a projection of the SNF market 
basket that reflects both recent historical trends, as well as forecast 
expectations over the next roughly 18 months. The forecast error for a 
market basket update is calculated as the actual market basket increase 
for a given year, less the forecasted market basket increase. Due to 
the uncertainty regarding future price trends, forecast errors can be 
both positive and negative. We are confident that the forecast error 
adjustments built into the SNF market basket update factor will account 
for these discrepancies over time.
    In the FY 2023 SNF PPS proposed rule, we proposed a 2018-based SNF 
market basket increase of 2.8 percent based on IGI's fourth quarter 
2021 forecast with historical data through third quarter 2021. For this 
final rule, based on IGI's second quarter 2022 forecast with historical 
data through first quarter 2022 we are finalizing a 2018-based SNF 
market basket increase of 3.9 percent, which is the highest market 
basket update we have implemented in a final rule since the beginning 
of the SNF PPS. The 3.9-percent increase reflects forecasted 
compensation price growth of 4.2 percent (which is approximately 2 
percentage points higher than the 10-year historical average price 
growth for compensation), reflecting increased wage pressures due to 
various economic and industry-specific factors. Additionally, the FY 
2023 productivity-adjusted SNF market basket update of 3.6 percent (3.9 
percent less 0.3 percentage point) will be increased by the FY 2021 
forecast error adjustment of 1.5 percentage point for a total FY 2023 
update of 5.1 percent (3.6 percent plus 1.5 percentage points). A 
forecast error

[[Page 47509]]

for FY 2022 cannot be calculated until historical data through third 
quarter 2022 are available; if there is a FY 2022 forecast error and a 
similar update approach is used for FY 2024, then a forecast error 
adjustment would be applied to the FY 2024 SNF PPS payment update.
    Section 1888(e)(5)(A) of the Act states the Secretary shall 
establish a skilled nursing facility market basket index that reflects 
changes over time in the prices of an appropriate mix of goods and 
services included in covered skilled nursing facility services. The 
2018-based SNF market basket is a fixed-weight, Laspeyres-type price 
index that 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. For the 
compensation cost weight in the 2018-based SNF market basket (which 
includes salaried and contract labor employees), we use the ECI for 
wages and salaries and benefits for nursing care facilities to proxy 
the price increase of SNF labor. The ECI (published by the BLS) 
measures the change in the hourly labor cost to employers, independent 
of the influence of employment shifts among occupations and industry 
categories. Therefore, we believe the ECI for nursing care facilities, 
which only reflects the price change associated with the labor used to 
provide SNF care and appropriately does not reflect other factors that 
might affect labor costs, is an appropriate measure to use in the SNF 
market basket.
    We acknowledge the commenters' concerns regarding the ECI being 
based on 2012 occupational distribution. Our analysis of the 2021 BLS 
Occupational Employment Statistics data, the most recent data available 
(published at https://www.bls.gov/oes/), shows that the salary 
(estimated as the product of employment and average annual salary) 
distribution by occupation for skilled nursing care facilities (NAICS 
6231) is similar to the BLS OES data for 2012. Specifically, we found 
that the healthcare occupational distribution among the major 
occupations--registered nurses (16 percent in 2021), licensed practical 
and vocational nurses (16 percent), nursing assistants (25 percent), 
and therapists (4 percent)--were notably similar between 2012 and 2021. 
Additionally, we found the split between healthcare (70 percent in 
2021) and nonhealthcare (30 percent) salaries by occupation to be 
virtually unchanged.
    We also recognize the commenters' concerns regarding the need for 
increased reliance on the use of contract labor and travel nurses due 
to the overall tightness in the labor market and the more specific 
labor constraints of healthcare staff in particular. The compensation 
cost weight of the SNF market basket includes expenses for wages and 
salaries, employee benefits, and contract labor, with the contract 
labor expenses apportioned to the Wages and Salaries and Employee 
Benefits cost category weights. We analyzed the 2020 Medicare Cost 
Report (MCR) data and found the Compensation cost weight decreased 
slightly from 60.2 percent in 2018 to 59.8 percent in 2020. This was 
due to a decrease in the Contract Labor cost weight from 7.5 percent in 
2018 to 6.8 percent in 2020 offset by a 0.3 percentage point increase 
in employed wages and salaries and benefits combined. Our analysis 
found that while there was an increase in the contract nursing staff 
hours, there was an offsetting decrease in the use of contract therapy 
staff hours. We will continue to analyze the MCR data, including the 
2021 data when available, and assess the appropriateness of rebasing 
and revising the SNF market basket. Any rebasing or revising of the SNF 
market basket, if deemed necessary, would be proposed in future 
rulemaking and subject to public comments.
    Regarding commenters' request that CMS consider other methods and 
data sources to calculate the final rule market basket update by 
exercising administrative authority, we note that we did not propose to 
use other methods or data sources to calculate the final market basket 
update for FY 2023, and therefore, we are not finalizing such an 
approach for this final rule. Further, while the Secretary has the 
discretion under the statute to establish the methodology for 
determining the appropriate mix of goods and services that comprise the 
SNF market basket, the statute requires the SNF PPS payment rates to be 
annually updated by the SNF market basket percentage change. As 
discussed in section IV.B.1. of this final rule, the market basket used 
to update SNF PPS payments has been rebased and revised over the 
history of the SNF PPS to reflect more recent data on SNF cost 
structures, and we believe it continues to appropriately reflect SNF 
cost structures. Consistent with our proposal, we have used more recent 
data to calculate a final SNF market basket update of 5.1 percent for 
FY 2023. Additionally, MedPAC did a full analysis of payment adequacy 
for SNF providers in its March 2022 Report to Congress (https://www.medpac.gov/wp-content/uploads/2022/03/Mar22_MedPAC_ReportToCongress_Ch7_SEC.pdf) and determined that, even 
considering the cost increases that have occurred as a result of the 
PHE associated with the COVID-19 pandemic, payments to SNFs continue to 
be adequate.
    Comment: One commenter recommended that CMS convene a technical 
expert panel to discuss a more long-range approach to collecting and 
imputing appropriate and timely data for market basket labor update 
calculations, in an attempt to encompass factors not captured by 
currently available price proxies.
    Response: We are open to hearing from interested parties about any 
data or analyses available to achieve the shared goal of ensuring that 
the SNF market basket price proxies are technically appropriate. As 
required by statute, any proposed changes to improve and/or update the 
SNF market basket occur through the rulemaking process and interested 
parties have an opportunity to publicly comment and make 
recommendations regarding the appropriateness of proposed changes.
    Comment: One commenter stated that CMS should update the SNF market 
basket more frequently than every 4 to 5 years. The commenter noted 
that the SNF market basket uses a 2018 base year to measure the labor 
vs. non-labor cost inputs of 2018, which was prior to the pandemic and 
related significant labor cost increases.
    Response: We note that while there is no official schedule for 
updating the market baskets, we typically attempt to rebase a market 
basket every 4 to 5 years since we have found that the cost weights are 
relatively stable over time. As the commenter acknowledged, the SNF 
market basket was last rebased in the FY 2022 SNF final rule using 2018 
Medicare cost reports (86 FR 42444 through 42463), the most recent year 
of complete data available at the time of the rebasing. As described in 
that final rule, the primary data source for the major cost weights 
(Wages and Salaries, Employee Benefits, Contract Labor, Pharmaceutical, 
Malpractice, Capital-related, and Home Office) for the 2018-based SNF 
market basket are the MCRs for freestanding SNFs (CMS Form 2540-10, OMB 
NO. 0938-0463). We also indicated in the FY 2022 SNF final rule that we 
planned to review the 2020 MCR data as soon as complete information was 
available, to ensure the market basket relative cost shares are still 
appropriate.

[[Page 47510]]

    Our analysis of the MCR data for 2019 and 2020 showed little change 
in the reported cost weights with the exception of the Pharmaceuticals 
cost weight in 2020. The Pharmaceuticals cost weight (including the 
adjustment for Medicaid dual-eligible drug costs) decreased 
approximately one percentage point from 7.5 percent in 2018 to 6.4 
percent in 2020. The decrease in the Pharmaceuticals cost weight is 
stemming from the estimated Part D drug costs per day for dual-eligible 
Medicare beneficiaries, which decreased in 2020 as a result of an 
increase in the proportion of generic drugs. More detail regarding this 
adjustment is described in the FY 2022 SNF PPS rule (86 FR 42447). The 
2020 Medicare cost report data also indicates that the Compensation 
cost weight is slightly lower at 59.8 percent, compared to the 2018-
based SNF market basket with 60.2 percent. MCR data for 2021 are 
incomplete at this time. Given that the changes to the Compensation 
cost weight for 2020 are minimal and it is unclear whether changes in 
the cost weights are temporary as a result of the PHE, we continue to 
believe it is premature at this time to use more recent MCR data to 
derive a rebased and revised SNF market basket. We will continue to 
monitor these data, and any necessary changes to the SNF market basket 
will be proposed in future rulemaking.
    Comment: One commenter expressed concern about the proposed 0.4 
percent reduction for productivity and asked CMS in the final rule to 
further elaborate on the specific productivity gains that are the basis 
for this proposed market basket offset. The commenter stated that the 
productivity adjustment contradicts their members' PHE experiences of 
actual losses in productivity during the pandemic.
    Response: Section 1888(e)(5)(B)(ii) of the Act requires the 
application of a productivity adjustment to the SNF market basket 
update. As required by statute, the FY 2023 productivity adjustment is 
derived based on the 10-year moving average of changes in annual 
economy-wide private nonfarm business TFP for the period ending FY 
2023, which is currently projected to be 0.3 percent.
    Comment: One commenter stated that they do not support the 
triggering of automatic forecast error adjustments. They expressed 
concern that automatic forecast corrections would, in some years, 
result in making payment increases on top of the statutory increases to 
the payment rates, despite the industry having sizeable average 
Medicare margins. The commenter also noted that eliminating the 
automatic adjustments would result in more stable updates and 
consistency across settings because CMS does not apply automatic 
forecast error adjustments to any other market baskets. They noted that 
although CMS is required by statute to update the payment rates each 
year by the estimated change in the market basket index, it is not 
required to make automatic forecast error corrections.
    Response: When forecast error adjustments for the SNF market basket 
were introduced in the FY 2004 SNF PPS final rule (68 FR 46035), we 
indicated the goal was ``to pay the appropriate amount, to the correct 
provider, for the proper service, at the right time''. We note that 
since implementation, forecast errors have generally been relatively 
small and clustered near zero and that for FY 2008 and subsequent 
years, we increased the threshold at which adjustments are triggered 
from 0.25 to 0.5 percentage point. Our intent in raising the threshold 
was to distinguish typical statistical variances from more major 
unanticipated impacts, such as unforeseen disruptions of the economy 
(such as occurred during the recent PHE) or unexpected inflationary 
patterns (either at lower or higher than anticipated rates).
    Comment: One commenter stated that the market basket update 
reflects the actual cost of delivering services and it should not be 
used to justify the severity of the parity adjustment.
    Response: We are required to update SNF PPS payments annually by 
the market basket update as required under section 
1888(e)(4)(E)(ii)(IV) and (e)(5)(B) of the Act, as amended by section 
53111 of the BBA 2018. We refer readers to section VI.C for a full 
discussion of the need for and the implementation of the parity 
adjustment.
6. Unadjusted Federal Per Diem Rates for FY 2023
    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.1. of that final rule (83 FR 39189), 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 proposed 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 2023 from the average prices for FY 2022. We 
proposed 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 Office of Management and Budget (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.
    Tables 3 and 4 reflect the updated unadjusted Federal rates for FY 
2023, prior to adjustment for case-mix.
[GRAPHIC] [TIFF OMITTED] TR03AU22.003


[[Page 47511]]


[GRAPHIC] [TIFF OMITTED] TR03AU22.004

    Commenters submitted the following comments related to the proposed 
unadjusted federal per diem rates for FY 2021. A discussion of these 
comments, along with our responses, appears below.
    Comment: One commenter stated that the case mix adjusted rates 
shown in Tables 5 and 6 for PT, OT, SLP and nursing rates are higher in 
urban areas than rural areas and noted this may be driving inequities 
and labor shortages between rural and urban nursing homes.
    Response: We disagree with the commenter's statement that the case-
mix adjusted rates for the PT, OT and SLP components are higher in 
urban than rural areas as shown in Tables 5 and 6. Additionally, the 
Federal per diem rates were established separately for urban and rural 
areas using allowable costs from FY 1995 cost reports, and therefore, 
account for and reflect the relative costs differences between urban 
and rural facilities. We note that the SNF PPS payment rates are 
updated annually by an increase factor that reflects changes over time 
in the prices of an appropriate mix of goods and services included in 
the covered SNF services and a portion of these rates are further 
adjusted by a wage index to reflect geographic variations in wages. We 
will continue to monitor our SNF payment policies to ensure they 
reflect as accurately as possible the current costs of care in the SNF 
setting.
    Accordingly, after considering the comments received, for the 
reasons specified in this final rule and in the FY 2023 SNF PPS 
proposed rule, we are finalizing the unadjusted federal per diem rates 
set forth in Tables 3 and 4.

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.
    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 
final 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 instructions and 
the interpretive guidance and policy clarifications posted on the 
appropriate MDS website at https://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 FY 2023 payment rates set forth in this 
proposed rule reflect the use of the PDPM case-mix classification 
system from October 1, 2022, through September 30, 2023. The case-mix 
adjusted PDPM payment rates for FY 2023 are listed separately for urban 
and rural SNFs, in Tables 5 and 6 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 5 and 6 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 5 and 6 reflect the PDPM's structure. Accordingly, Column 1 
of Tables 5 and 6 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

[[Page 47512]]

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 5 and 6 do not reflect adjustments which may be made to the 
SNF PPS rates as a result of the SNF VBP Program, discussed in section 
VII. of this final 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.
    As we noted in the FY 2022 SNF PPS final rule (86 FR 42434), we 
continue to monitor the impact of PDPM implementation on patient 
outcomes and program outlays. Because of this analysis, in section V.C. 
of the proposed rule, we proposed to recalibrate the PDPM parity 
adjustment discussed in the FY 2020 SNF PPS final rule (84 FR 38734). 
Following the methodology of this proposed change, Tables 5 and 6 
incorporate the recalibration of the PDPM parity adjustment.
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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 proposed to continue 
this practice for FY 2023, 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 2023, the updated wage data are for hospital cost reporting 
periods beginning on or after October 1, 2018 and before October 1, 
2019 (FY 2019 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. 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. Therefore, 
as discussed in the proposed rule, in the absence of a SNF-specific 
wage index, we believe the use of the pre-reclassified and pre-floor 
hospital wage data (without the occupational mix adjustment) continue 
to be an appropriate and reasonable proxy for the SNF PPS.

[[Page 47514]]

    In addition, we proposed 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 proposed to continue using the average wage 
index from all contiguous Core-Based Statistical Areas (CBSAs) as a 
reasonable proxy. For FY 2023, there are no rural geographic areas that 
do not have hospitals, and thus, this methodology will not be applied. 
For rural Puerto Rico, we proposed not to apply this methodology due to 
the distinct economic circumstances there (for example, due to the 
close proximity 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 would continue using the most recent wage index previously 
available for that area. For urban areas without specific hospital wage 
index data, we proposed that we would use the average wage indexes of 
all urban areas within the State to serve as a reasonable proxy for the 
wage index of that urban CBSA. For FY 2023, the only urban area without 
wage index data available is CBSA 25980, Hinesville-Fort Stewart, GA.
    The wage index applicable to FY 2023 is set forth in Tables A and B 
available on the CMS website at https://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. For FY 2023 and subsequent years, we proposed 
to apply a permanent 5 percent cap on any decreases to a provider's 
wage index from its wage index in the prior year, regardless of the 
circumstances causing the decline, which was further discussed in 
section V.A. of the proposed rule.
    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 
proposed to adopt the updates set forth in OMB Bulletin No. 20-01 
consistent with our longstanding policy of adopting OMB delineation 
updates, we noted that specific wage index updates would not be 
necessary for FY 2022 as a result of adopting these OMB updates and for 
these reasons we did not make such a proposal for FY 2023.
    The wage index applicable to FY 2023 is set forth in Tables A and B 
available on the CMS website at https://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

[[Page 47515]]

Repair Services; All Other: Labor-Related Services; and a proportion of 
Capital-Related expenses. Effective beginning FY 2022 (86 FR 42437), we 
rebased and revised the labor-related share to reflect the relative 
importance of the 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 methodology for calculating the labor-related portion beginning in 
FY 2022 is discussed in detail in the FY 2022 SNF PPS final rule (86 FR 
42424).
    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 2023. 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 2023 than the base year weights 
from the SNF market basket. We calculate the labor-related relative 
importance for FY 2023 in four steps. First, we compute the FY 2023 
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 2023 price index level for that cost category by the 
total market basket price index level. Third, we determine the FY 2023 
relative importance for each cost category by multiplying this ratio by 
the base year (2018) weight. Finally, we add the FY 2023 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 2023 
labor-related relative importance.
    For the proposed rule, the labor-related share for FY 2023 was 
based on IGI's fourth quarter 2021 forecast of the 2018-based SNF 
market basket with historical data through third quarter 2021. As 
outlined in the proposed rule, we noted that if more recent data became 
available (for example, a more recent estimate of the labor-related 
share relative importance) we would use such data if appropriate for 
the SNF final rule. For this final rule, we base the labor-related 
share for FY 2023 on IGI's second quarter 2022 forecast, with 
historical data through the first quarter 2022. Table 7 summarizes the 
labor-related share for FY 2023, based on IGI's second quarter 2022 
forecast of the 2018-based SNF market basket, compared to the labor-
related share that was used for the FY 2022 SNF PPS final rule.
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    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 2023 
labor-related share percentage provided in Table 7. 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 interested parties 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 2023 (Federal rates 
effective October 1, 2022), we apply an adjustment to fulfill the 
budget neutrality requirement. We meet this requirement by multiplying 
each of the components of the

[[Page 47516]]

unadjusted Federal rates by a budget neutrality factor, equal to the 
ratio of the weighted average wage adjustment factor for FY 2022 to the 
weighted average wage adjustment factor for FY 2023. For this 
calculation, we would use the same FY 2021 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 
2023 set forth in the proposed rule was 1.0011.
    We noted that if more recent data became 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. Since the proposed rule, we have updated the wage adjustment 
factor for FY 2023. Based on this updated information, the budget 
neutrality factor for FY 2023 is 1.0005.
    The following is a summary of the public comments we received on 
the proposed revisions to the Wage Index Adjustment and our responses.
    Comment: Several commenters recommended that CMS develop a SNF-
specific wage index utilizing SNF wage data rather than relying on 
hospital wage data. Most of these commenters recommended CMS utilize 
BLS data, while one commenter recommended CMS focus on Payroll-Based 
Journaling (PBJ) data.
    Response: We appreciate the commenters' suggestion that we develop 
a SNF-specific wage index utilizing SNF wage data instead of hospital 
wage data while considering the use of BLS and PBJ data. We note that, 
consistent with the discussion published most recently in the FY 2021 
SNF PPS final rule (86 FR 42436 through 42439), and in further detail 
in the FY 2019 SNF PPS final rule (83 FR 39172 through 39178) to these 
recurring comments, developing such a wage index would require a 
resource-intensive audit process similar to that used for IPPS hospital 
data, to improve the quality of the SNF cost report data in order for 
it to be used as part of this analysis. We also discussed in the FY 
2019 SNF PPS why utilizing concepts such as BLS data and PBJ are 
unfeasible or not applicable to SNF policy.
    We continue to believe that in the absence of the appropriate SNF-
specific wage data, using the pre-reclassified, pre-rural floor 
hospital inpatient wage data (without the occupational mix adjustment) 
is appropriate and reasonable for the SNF PPS.
    Comment: Several comments suggested that CMS revise the SNF wage 
index to adopt the same geographic reclassification and rural floor 
polices that are used to adjust the IPPS wage index.
    Response: We note that until the development of a SNF-specific wage 
index, the SNF PPS does not account for geographic reclassification 
under section 315 of the Medicare, Medicaid, and SCHIP Benefits 
Improvement and Protection Act of 2000 (BIPA) (Pub. L. 106-554, enacted 
December 21, 2000).
    With regard to implementing a rural floor under the SNF PPS, we do 
not believe it would be prudent at this time to adopt such a policy, 
particularly because MedPAC has repeatedly recommended eliminating the 
rural floor policy from the calculation of the IPPS wage index. For 
example, Chapter 3 of MedPAC's March 2013 Report to Congress on 
Medicare Payment Policy, available at https://www.medpac.gov/docs/default-source/reports/mar13_ch03.pdf, notes on page 65 that, in 2007, 
MedPAC had recommended eliminating these special wage index adjustments 
and adopting a new wage index system to avoid geographic inequities 
that can occur due to current wage index policies (Medicare Payment 
Advisory Commission 2007b)). If we adopted the rural floor policy at 
this time, the SNF PPS wage index could become vulnerable to problems 
similar to those MedPAC identified in its March 2013 Report to 
Congress.
    Furthermore, as we do not have an SNF-specific wage index, we are 
unable to determine the degree, if any, to which a geographic 
reclassification adjustment or a rural floor policy under the SNF PPS 
would be appropriate. The rationale for our current wage index policies 
was most recently published in the FY 2022 SNF PPS final rule (86 FR 
42436) and previously described in the FY 2016 SNF PPS final rule (80 
FR 45401 through 46402).
    After consideration of public comments, we are finalizing our 
proposal to continue to use the updated pre-reclassification and pre-
floor IPPS wage index data to develop the FY 2023 SNF PPS wage index.

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 VIII. of this final rule for further discussion 
of our policies for the SNF VBP Program.

F. Adjusted Rate Computation Example

    Tables 8 through 10 provide examples generally illustrating payment 
calculations during FY 2023 under PDPM for a hypothetical 30-day SNF 
stay, involving the hypothetical SNF XYZ, located in Frederick, MD 
(Urban CBSA 23224), for a hypothetical patient who is classified into 
such groups that the patient's HIPPS code is NHNC1. Table 8 shows the 
adjustments made to the Federal per diem rates (prior to application of 
any adjustments under the SNF VBP Program as discussed previously and 
taking into account the proposed parity adjustment discussed in section 
VI.C. of this final rule) to compute the provider's case-mix adjusted 
per diem rate for FY 2023, 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 9 
shows the adjustments made to the case-mix adjusted per diem rate from 
Table 8 to account for the provider's wage index. The wage index used 
in this example is based on the FY 2023 SNF PPS wage index that appears 
in Table A available on the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/WageIndex.html. Finally, Table 
10 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 10 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 8, 
SNF XYZ's total PPS payment for this particular patient's stay would 
equal $20,821.69.
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V. 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 IV.C. of this final 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/

[[Page 47519]]

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/), 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 Medicare, Medicaid, and SCHIP Balanced Budget Refinement Act 
of 1999 (BBRA 1999) (Pub. L. 106-113, enacted November 29, 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 
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 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 CAA 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. 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.
    In the proposed rule, we specifically solicited 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. In the proposed rule, we noted 
that we may consider excluding a particular service if it meets our 
criteria for exclusion as specified previously. We requested that 
commenters identify in their comments the specific HCPCS code that is 
associated with the service

[[Page 47520]]

in question, as well as their rationale for requesting that the 
identified HCPCS code(s) be excluded.
    In the proposed rule, we noted that the original BBRA amendment and 
the CAA 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 CAA, July 1, 2020), as 
subsequently modified by the Secretary. In addition, as noted in this 
section of the preamble, the statute (sections 1888(e)(2)(A)(iii)(II) 
through (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.
    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, 2022). 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.
    The following is a summary of the public comments we received on 
the proposed revisions to Consolidated Billing and our responses.
    Comment: One commenter stated that consolidated billing exclusions 
remain inadequate and should be revised. The commenter stated that 
there continue to be outlier drug costs that need to be considered for 
exclusion from consolidated billing. The commenter stated that certain 
classes of drugs considered ``Specialty'' drugs are the largest 
exposure items for SNFs and need to be evaluated by CMS. The commenter 
further stated that many pharmaceutical therapies in use today were not 
in existence at the time that consolidated billing PPDs were created. 
Therefore, they cannot be considered ``included'' within the Medicare A 
FFS rate.
    Response: As we noted in the proposed rule, sections 
1888(e)(2)(A)(iii)(II) through (VI) of the Act give the Secretary 
authority to identify additional items and services for exclusion only 
within the categories of items and services described in the statute. 
Accordingly, it is beyond the statutory authority of CMS to exclude 
services that do not fit these categories, or to create additional 
categories of excluded services. Such changes would require 
Congressional action.
    Comment: A commenter requested that CMS to consider agents that 
have evolving indications for use for different malignancies. In 
particular, the commenter requested consideration for both Leuprolide 
Acetate (HCPCS J9217) as well as Denosumab (HCPCS J0897) which 
previously was indicated as an osteoporosis medication but now has 
broader uses. The commenter also requested continued consideration of 
covering expensive antibiotics in Skilled Nursing Facilities as part of 
a Part A covered stay. The commenter stated that use of antibiotics 
such as ceftolozane 50 mg and tazobactam 25 mg (HCPCS J0695) are 
prohibitively expensive for facilities to cover outside of SNF 
consolidated billing and limit beneficiaries' abilities to access these 
skilled rehab services.
    Response: For the reasons discussed previously in prior rulemaking, 
the particular drugs cited in these comments remain subject to 
consolidated billing. In the case of leuprolide acetate, we have 
addressed this when suggested in past rulemaking cycles, most recently 
in the SNF PPS final rules for FY 2019 (83 FR 39162, August 8, 2018) 
and FY 2015 (79 FR 45642, August 5, 2014). In those rules, we explained 
that this drug is unlikely to meet the criterion of ``low probability'' 
specified in the BBRA. With regard to denosumab, it would similarly be 
unlikely to meet the criterion of ``low probability.'' One of the 
indications for treatment is for bone metastases from solid tumors such 
as bone or prostate cancer. This can occur in up to 70 to 90 percent of 
patients with breast or prostate cancer.
    With regard to the suggestion that CMS should exclude antibiotics, 
we note again that it is beyond the statutory authority of CMS to 
exclude services that do not fit the categories for exclusion defined 
by statute, or to create additional categories of excluded services. 
Such changes would require Congressional action.

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 final 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 https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/.

D. Revisions to the Regulation Text

    We proposed to make certain revisions in the regulation text 
itself. Specifically, we proposed to revise Sec.  413.337(b)(4) and add 
new paragraphs (b)(4)(i) through (iii). These proposed revisions 
reflect that the application of the wage index would be made on the 
basis of the location of the facility in an urban or rural area as 
defined in Sec.  413.333, and that starting on October 1, 2022, we 
would apply a cap on decreases to the wage index such that

[[Page 47521]]

the wage index applied to a SNF is not less than 95 percent of the wage 
index applied to that SNF in the prior FY, as discussed in section 
VI.A. of this final rule.
    We did not receive public comments specific to the proposed 
revisions to the regulation text, and therefore, we are finalizing as 
proposed. We discuss comments received on the wage index cap policy 
itself in section VI.A. of this final rule.

VI. Other SNF PPS Issues

A. Permanent Cap on Wage Index Decreases

    As outlined in section III.D. of the proposed rule, we proposed and 
finalized temporary transition policies in the past to mitigate 
significant changes to payments due to changes to the SNF PPS wage 
index. Specifically, for FY 2015 (79 FR 45644 through 45646), we 
implemented a 50/50 blend for all geographic areas consisting of the 
wage index values computed using the then-current OMB area delineations 
and the wage index values computed using new area delineations based on 
OMB Bulletin No. 13-01. In FY 2021 (85 FR 47594, 47617), we implemented 
a 1-year transition to mitigate any negative effects of wage index 
changes by applying a 5 percent cap on any decrease in a SNF's wage 
index from the final wage index from FY 2020. We explained that we 
believed the 5-percent cap would provide greater transparency and would 
be administratively less complex than the prior methodology of applying 
a 50/50 blended wage index. We indicated that no cap would be applied 
to the reduction in the wage index for FY 2022, and we noted that this 
transition approach struck an appropriate balance by providing a 
transition period to mitigate the resulting short-term instability and 
negative impacts on providers and time for them to adjust to their new 
labor market area delineations and wage index values.
    In the FY 2022 final rule (86 FR 42424, 42439), commenters 
recommended that CMS extend the transition period adopted in the FY 
2021 SNF PPS final rule so that SNFs could offset the cuts scheduled 
for FY 2022. Although, we acknowledged that certain changes to wage 
index policy could affect Medicare payment. In addition, we reiterated 
that our policy principles with regard to the wage index include 
generally using the most current data and information available and 
providing that data and information, as well as any approaches to 
addressing any significant effects on Medicare payments resulting from 
these potential scenarios around SNF payment volatility, in notice and 
comment rulemaking. We did not propose to modify the transition policy 
that was finalized in the FY 2021 SNF PPS final rule, and therefore, 
did not extend the transition period for FY 2022. With these policy 
principles in mind for this FY 2023 proposed rule, we considered how 
best to address commenters' concerns discussed in the FY 2022 final 
rule around SNF payment volatility; that is, scenarios in which changes 
to wage index policy may significantly affect Medicare payments.
    In the past, we have established transition policies of limited 
duration to phase in significant changes to labor market. In taking 
this approach in the past, we have sought to strike an appropriate 
balance between maintaining the accuracy of the overall labor market 
area wage index system and mitigating short-term instability and 
negative impacts on providers due to wage index changes. In accordance 
with the requirements of the SNF PPS wage index regulations at Sec.  
413.337(a)(1), we use an appropriate wage index based on the best 
available data, including the best available labor market area 
delineations, to adjust SNF PPS payments for wage differences. We have 
previously stated that, because the wage index is a relative measure of 
the value of labor in prescribed labor market areas, we believe it is 
important to implement new labor market area delineations with as 
minimal a transition as is reasonably possible. However, we recognize 
that changes to the wage index have the potential to create instability 
and significant negative impacts on certain providers even when labor 
market areas do not change. In addition, year-to-year fluctuations in 
an area's wage index can occur due to external factors beyond a 
provider's control, such as the COVID-19 public health emergency (PHE). 
For an individual provider, these fluctuations can be difficult to 
predict. So, we also recognize that predictability in Medicare payments 
is important to enable providers to budget and plan their operations.
    In light of these considerations, we proposed a permanent approach 
to smooth year-to-year changes in providers' wage indexes. We proposed 
a policy that we believe increases the predictability of SNF PPS 
payments for providers, and mitigates instability and significant 
negative impacts to providers resulting from changes to the wage index.
    As previously discussed, we believed applying a 5-percent cap on 
wage index decreases for FY 2021 provided greater transparency and was 
administratively less complex than prior transition methodologies. In 
addition, we believed this methodology mitigated short-term instability 
and fluctuations that can negatively impact providers due to wage index 
changes. Lastly, we have noted that we believed the 5-percent cap we 
applied to all wage index decreases for FY 2021 provided an adequate 
safeguard against significant payment reductions related to the 
adoption of the revised CBSAs. However, we recognize there are 
circumstances that a 1-year mitigation policy, like the one adopted for 
FY 2021, would not effectively address future years where providers 
continue to be negatively affected by significant wage index decreases.
    Typical year-to-year variation in the SNF PPS wage index has 
historically been within 5 percent, and we expect this will continue to 
be the case in future years. For FY 2023, the provider level impact 
analysis indicates that approximately 97 percent of SNFs will 
experience a wage index change within 5 percent. Because providers are 
usually experienced with this level of wage index fluctuation, we 
believe applying a 5-percent cap on all wage index decreases each year, 
regardless of the reason for the decrease, would effectively mitigate 
instability in SNF PPS payments due to any significant wage index 
decreases that may affect providers in any year. We believe this 
approach would address concerns about instability that commenters 
raised in the FY 2022 SNF PPS rule. Additionally, as noted in the 
proposed rule, we believe that applying a 5-percent cap on all wage 
index decreases would support increased predictability about SNF PPS 
payments for providers, enabling them to more effectively budget and 
plan their operations. Lastly, because applying a 5-percent cap on all 
wage index decreases would represent a small overall impact on the 
labor market area wage index system we believe it would ensure the wage 
index is a relative measure of the value of labor in prescribed labor 
market wage areas. As outlined in detail in section XI.A.4. of the 
proposed rule, we estimated that applying a 5-percent cap on all wage 
index decreases will have a very small effect on the wage index budget 
neutrality factor for FY 2023. Because the wage index is a measure of 
the value of labor (wage and wage-related costs) in a prescribed labor 
market area relative to the national average, we anticipate that in the 
absence of proposed policy changes most providers will not experience 
year-to-year wage index

[[Page 47522]]

declines greater than 5 percent in any given year. As noted in the 
proposed rule, we also believe that when the 5-percent cap would be 
applied under this proposal, it is likely that it would be applied 
similarly to all SNFs in the same labor market area, as the hospital 
average hourly wage data in the CBSA (and any relative decreases 
compared to the national average hourly wage) would be similar. While 
this policy may result in SNFs in a CBSA receiving a higher wage index 
than others in the same area (such as situations when delineations 
change), we believe the impact would be temporary. Therefore, we 
anticipate that the impact to the wage index budget neutrality factor 
in future years would continue to be minimal.
    The Secretary has broad authority to establish appropriate payment 
adjustments under the SNF PPS, including the wage index adjustment. As 
discussed earlier in this section, the SNF PPS regulations require us 
to use an appropriate wage index based on the best available data. For 
the reasons discussed earlier in this section, we believe that a 5-
percent cap on wage index decreases would be appropriate for the SNF 
PPS. Therefore, for FY 2023 and subsequent years, we proposed to apply 
a permanent 5-percent cap on any decrease to a provider's wage index 
from its wage index in the prior year, regardless of the circumstances 
causing the decline. That is, we proposed that SNF's wage index for FY 
2023 would not be less than 95 percent of its final wage index for FY 
2022, regardless of whether the SNF is part of an updated CBSA, and 
that for subsequent years, a provider's wage index would not be less 
than 95 percent of its wage index calculated in the prior FY. This 
means, if a SNF's prior FY wage index is calculated with the 
application of the 5-percent cap, then the following year's wage index 
would not be less than 95 percent of the SNF's capped wage index in the 
prior FY. For example, if a SNF's wage index for FY 2023 is calculated 
with the application of the 5-percent cap, then its wage index for FY 
2024 would not be less than 95 percent of its capped wage index in FY 
2023. Lastly, we proposed that a new SNF would be paid the wage index 
for the area in which it is geographically located for its first full 
or partial FY with no cap applied, because a new SNF would not have a 
wage index in the prior FY. As we outlined in the proposed rule, we 
believe this proposed methodology would maintain the SNF PPS wage index 
as a relative measure of the value of labor in prescribed labor market 
areas, increase the predictability of SNF PPS payments for providers, 
and mitigate instability and significant negative impacts to providers 
resulting from significant changes to the wage index. In section XI. of 
the proposed rule, we estimated the impact to payments for providers in 
FY 2023 based on this proposed policy. We also noted that we would 
examine the effects of this policy on an ongoing basis in the future in 
order to assess its continued appropriateness.
    Subject to the aforementioned proposal becoming final, we also 
proposed to revise the regulation text at Sec.  413.337(a)(1) to 
provide that starting October 1, 2022, we would apply a cap on 
decreases to the wage index such that the wage index applied is not 
less than 95 percent of the wage index applied to that SNF in the prior 
year.
    We invited public comments on this proposal. The following is a 
summary of the comments we received on the proposed permanent cap on 
wage index decreases and our responses.
    Comment: MedPAC expressed support for the 5-percent permanent cap 
on wage index decreases policy, but recommended that the 5-percent cap 
limit should apply to both increases and decreases in the wage index 
because they stated that no provider should have its wage index value 
increase or decrease by more than 5 percent.
    Response: We appreciate MedPAC's suggestion that the cap on wage 
index changes of more than 5 percent should also be applied to 
increases in the wage index. However, as we discussed in the FY 2023 
SNF PPS proposed rule (87 FR 22735), one purpose of the proposed policy 
is to help mitigate the significant negative impacts of certain wage 
index changes. Likewise, we explained that we believe that applying a 
5-percent cap on all wage index decreases would support increased 
predictability about SNF PPS payments for providers, enabling them to 
more effectively budget and plan their operations. That is, we proposed 
to cap decreases because we believe that a provider would be able to 
more effectively budget and plan when there is predictability about its 
expected minimum level of SNF PPS payments in the upcoming fiscal year. 
We did not propose to limit wage index increases, because we do not 
believe such a policy would enable SNFs to more effectively budget and 
plan their operations. So, we believe it is appropriate for providers 
that experience an increase in their wage index value to receive the 
full benefit of their increased wage index value.
    Comment: A few commenters requested that CMS retroactively apply 
the 5 percent cap policy to the FY 2022 wage index.
    Response: In the FY 2021 SNF PPS rulemaking cycle, CMS proposed and 
finalized a one-time, 1-year transition policy to mitigate the effects 
of adopting OMB delineations updated in OMB Bulletin 18-04. In the FY 
2023 SNF PPS proposed rule we did not propose to modify the one-time 
transition policy that was finalized in the FY 2021 SNF PPS final rule, 
nor did we propose to extend the transition period for FY 2022. We have 
historically implemented 1-year transitions, as discussed in the FY 
2006 (70 FR 45026) and FY 2015 (79 FR 45644) final rules, to address 
CBSA changes due to substantial updates to OMB delineations. Our policy 
principles, as noted in the FY 2022 final rule (86 FR 42439), with 
regard to the wage index are to use the most updated data and 
information available. Therefore, the FY 2023 wage index policy 
proposal is prospective and is designed to mitigate any significant 
decreases beginning in FY 2023, not retroactively.
    Comment: A number of commenters suggested the 5-percent cap be 
applied in a non-budget neutral manner.
    Response: The statute at section 1888(e)(4)(G)(ii) of the Act 
requires that adjustments for geographic variations in labor costs for 
a FY are made in a budget-neutral. We are required to apply the 
permanent 5-percent cap policy in a budget-neutral manner.
    Comment: A commenter recommended the percentage cap be lower than 
the proposed 5-percent stating they found that most wage indices do not 
swing by 5-percent.
    Response: We appreciate the commenter's suggestion that the 
permanent cap percentage should be lower than 5-percent. However, as we 
discussed in the proposed rule, for FY 2023, the provider level impact 
analysis indicates that approximately 97 percent of SNFs will 
experience a wage index change within 5 percent. Because providers are 
usually experienced with this level of wage index fluctuation, we 
believe applying a 5-percent cap on all wage index decreases each year, 
regardless of the reason for the decrease, would effectively mitigate 
instability in SNF PPS payments due to any significant wage index 
decreases that may affect providers in any year.
    Comment: One commenter was opposed to the implementation of the 
permanent 5-percent cap on wage index decreases at this time, stating 
that the industry struggled prior to the PHE.
    Response: We appreciate the concern with implementing the permanent 
5-percent cap on wage index decreases.

[[Page 47523]]

However, as we discussed in the proposed rule, we believe moving 
forward with the permanent cap on wage index decreases would 
effectively mitigate instability in SNF PPS payments due to any 
significant wage index decreases that may affect providers in any year.
    After consideration of the comments we received, we are finalizing 
the proposed permanent 5-percent cap on wage index decreases for the 
SNF PPS, beginning in FY 2023.

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 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/Medicare-Fee-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 can make changes to the ICD-10 medical code data sets 
effective on April 1 of each year.
    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, 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-Service-Payment/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 proposed several changes to the PDPM ICD-10 code mappings and 
lists. We note that, in the case of any diagnoses that are either 
currently mapped to ``Return to Provider'' or that we proposed to 
classify into this category, this is not intended to reflect any 
judgment on the importance of recognizing and treating these 
conditions, but merely that there are more specific diagnoses than 
those mapped to ``Return to Provider'' or that we do not believe that 
the diagnosis should serve as the primary diagnosis for a Part-A 
covered SNF stay. Our proposed changes were as follows:
    On October 1, 2021, D75.839 ``Thrombocytosis, unspecified,'' took 
effect and was mapped to the clinical category of ``Cardiovascular and 
Coagulations.'' However, there are more specific codes to indicate why 
a patient with thrombocytosis would require SNF care. If the cause is 
unknown, the SNF could use D47.3, ``Essential (hemorrhagic) 
thrombocythemia'' or D75.838, ``other thrombocytosis'' which is a new 
code that took effect on October 1, 2021. Further, elevated platelet 
count without other symptoms is not reason enough for SNF skilled care 
so this would not be used as a primary diagnosis. For this reason, we 
proposed to change the assignment of D75.839 to ``Return to Provider.''
    On October 1, 2021, D89.44, ``Hereditary alpha tryptasemia'' went 
into effect and was mapped to the clinical category, ``Medical 
Management.'' However, this is not a diagnosis that would be treated as 
a primary condition in the SNF, rather it would be treated in the 
outpatient setting. Therefore, we proposed to change the assignment of 
D89.44 to ``Return to Provider.''
    On October 1, 2021, F32.A, ``Depression, unspecified'' went into 
effect and was mapped to ``Medical Management.'' However, there are 
more specific codes that would more adequately capture the diagnosis of 
depression. Further, as we noted in the proposed rule, while we believe 
that SNFs serve an important role in providing services to those 
beneficiaries suffering from mental illness, the SNF setting is not the 
setting that would be most appropriate to treat a patient whose primary 
diagnosis is depression. For this reason, we proposed to change the 
assignment of F32.A to ``Return to Provider.''
    On October 1, 2021, G92.9, ``Unspecified toxic encephalopathy'' 
took effect and was mapped to the clinical category of ``Acute 
Neurologic.'' However, there are more specific codes that should be 
used to describe encephalopathy treated in a SNF. Therefore, we 
proposed to change the assignment of G92.9 to ``Return to Provider.''
    On October 1, 2021, M54.50, ``Low back pain, unspecified'' went 
into effect and was mapped to the clinical category of ``Non-surgical 
Orthopedic/Musculoskeletal.'' However, if low back pain were the 
primary diagnosis, the SNF should have a greater understanding of what 
is causing the pain. There are more specific codes to address this 
condition. Therefore, we proposed to change the assignment of M54.50 to 
``Return to Provider.''
    In the FY 2022 proposed rule (86 FR 19984 through 19985), we 
proposed to reclassify K20.81, ``Other esophagitis with bleeding,'' 
K20.91, ``Esophagitis, unspecified with bleeding,'' and K21.01, 
``Gastro-esophageal reflux disease with esophagitis, with bleeding'' 
from ``Return to Provider'' to ``Medical Management.'' Our rationale 
for the change was a recognition 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. We received one comment 
suggesting additional changes to similar ICD-10 code mappings and 
comorbidity lists that at the time were outside the scope of 
rulemaking. This commenter suggested that we consider remapping the 
following similar diagnosis codes that frequently require SNF skilled 
care, from ``Return to Provider'' to ``Medical Management'': K22.11, 
``Ulcer of esophagus with bleeding;'' K25.0, ``Acute gastric ulcer with 
hemorrhage;'' K25.1, ``Acute gastric ulcer with perforation;'' K25.2, 
``Acute gastric ulcer with both hemorrhage and perforation;'' K26.0, 
``Acute duodenal ulcer with hemorrhage;'' K26.1, ``Acute duodenal ulcer 
with perforation;'' K26.2, ``Acute duodenal ulcer with both hemorrhage 
and perforation;'' K27.0 ``Acute peptic ulcer, site unspecified with 
hemorrhage;'' K27.1, ``Acute peptic ulcer, site unspecified with 
perforation;''

[[Page 47524]]

K27.2, ``Acute peptic ulcer, site unspecified with both hemorrhage and 
perforation;'' K28.0, ``Acute gastrojejunal ulcer with hemorrhage;'' 
K28.1, ``Acute gastrojejunal ulcer with perforation;'' K28.2, ``Acute 
gastrojejunal ulcer with both hemorrhage and perforation;'' and K29.01, 
``Acute gastritis with bleeding.'' Upon review of these codes, we 
recognize that they represent conditions with more specificity than 
originally considered because of the bleeding (or perforation) that is 
part of the conditions and that they would more likely be found in SNF 
patients.'' Therefore, we proposed to remap these ICD-10 codes to 
``Medical Management.''
    We also received a comment requesting we consider remapping M62.81, 
``Muscle weakness (generalized)'' from ``Return to Provider'' to ``Non-
orthopedic Surgery'' with the rationale that there is currently no 
sequela or late-effects ICD-10 code available when patients require 
skilled nursing and therapy due to late effects of resolved infections 
such as pneumonia or urinary tract infections. We considered the 
request and determined that muscle weakness (generalized) is 
nonspecific and if the original condition is resolved, but the 
resulting muscle weakness persists because of the known original 
diagnosis, there are more specific codes that exist that would account 
for why the muscle weakness is on-going, such as muscle wasting or 
atrophy. Therefore, we did not propose this specific remapping. This 
commenter also requested that that we consider remapping R62.7, ``Adult 
failure to thrive'' from ``Return to Provider'' to ``Medical 
Management.'' According to this commenter, physicians often diagnose 
adult failure to thrive when a resident has been unable to have oral 
intake sufficient for survival. Typically, this diagnosis is appended 
when the physician has determined that a feeding tube should be 
considered to provide sufficient intake for survival. According to the 
commenter, it would then appropriately become the primary diagnosis for 
a skilled stay. We considered this request and believe that R6.2 is a 
nonspecific code and SNF primary diagnoses should be coded to the 
highest level of specificity. If the patient has been unable to have 
oral intake, the primary diagnosis (for example, Ulcerative Colitis) 
for admission to a SNF should explain why the patient is unable to have 
oral intake sufficient for survival. Therefore, we did not propose this 
specific remapping.
    We solicited comments on the proposed substantive changes to the 
ICD-10 code mappings discussed previously in this section, as well as 
comments on additional substantive and non-substantive changes that 
commenters believe are necessary. We received public comments on these 
proposals. The following is a summary of the comments we received and 
our responses.
    Comment: Several commenters supported the proposed changes to the 
PDPM ICD-10 mappings. Some commenters expressed concerns with the 
proposed reclassification of certain conditions from a given clinical 
category to a Return to Provider status. For example, some commenters 
stated that, in the case of code F32.A (Depression, unspecified), this 
may be the most appropriate diagnosis, based on the information 
provided in the medical record. These commenters also stated that while 
it may be appropriate to remap code D75.839 to Return to Provider, they 
do not believe the more specific codes discussed in the proposed rule 
for this condition would be appropriate. Similarly, some commenters 
opposed remapping code D89.44 to Return to Provider, as skilled care 
may be necessary to treat the symptoms associated with this condition.
    Response: We appreciate the support for these proposed changes. 
Regarding the comments related to the potential lack of additional 
documentation to support more specific diagnoses, ICD 10 coding 
guidance indicates to code with the highest specificity. The suggestion 
of codes, D47.3 and D75.838, was given to provide examples of more 
specific coding that could potentially be used if appropriate. SNF 
primary diagnoses should be coded to the highest level of specificity. 
By the time a person is in the SNF, the reason for thrombocytosis, 
should be known and since ICD 10 guidelines state that coding should be 
to the highest specificity, the reason for thrombocytosis could be 
listed as the principal diagnosis. Additionally, our goal is to ensure 
that Medicare beneficiaries receive the best care in the appropriate 
place. If a patient requires treatment in a facility for the primary 
reason of depression, Not Otherwise Specified (NOS), then their 
Medicare benefits provide access to treatment in an inpatient 
psychiatric hospital so that the type of depression, as well as 
treatment can be determined by specialists in the field. We remind 
commenters that the ICD-10 mapping reflects diagnoses which may be used 
as the primary diagnosis for a Part-A covered stay, not merely for a 
comorbidity associated with the patient's care. For conditions like 
D89.44 (Hereditary Alpha Tryptasemia), if there are symptoms or 
manifestations of this condition that require skilled care, then those 
symptoms should be provided as the primary diagnosis for the SNF stay, 
rather than the underlying condition which, often times, may be treated 
using oral medications.
    Comment: Some commenters stated that CMS should reconsider mapping 
code M62.81 (Muscle weakness, generalized) and R62.7 (Adult failure to 
thrive) to a clinical category, as these conditions may serve as the 
source of treatment to maintain the patient's existing functional 
status before further decline.
    Response: We considered this request and continue to believe that 
muscle weakness (generalized) is nonspecific and if the original 
condition is resolved, but the resulting muscle weakness persists 
because of the known original diagnosis, there are more specific codes 
that exist that would account for why the muscle weakness is on-going. 
This symptom, without any specification of the etiology or severity, is 
not a reason for daily skilled care in a SNF. Patients with generalized 
weakness should obtain a more specific diagnosis causing the 
generalized weakness. The specific diagnosis should be used to develop 
an appropriate care plan can for the patient. Similarly, in the case of 
a failure to thrive, this diagnosis is nonspecific and does not suggest 
the interventions needed to care for the patient, thus it should not be 
used as a reason for SNF admission. It may indicate that the patient's 
condition has not been thoroughly investigated which would be needed to 
develop an appropriate treatment plan.
    Comment: Several commenters recommended that CMS consider revising 
the PDPM ICD-10 mapping to reclassify certain humeral fracture codes. 
These commenters highlighted that certain select encounter codes for 
humeral fracture are permitted to be coded under the current ICD-10 
mapping, but not other encounter codes. The commenters suggested that 
all the encounter codes associated with these fracture codes be 
included in the appropriate clinical category.
    Response: We appreciate the commenters' suggestion and agree that 
the various encounter codes should be treated in the same manner. We 
will examine the specific codes suggested to determine the most 
efficient manner for addressing this discrepancy.
    Comment: Several commenters raised concerns with areas of 
discordance between the PDPM ICD-10 mapping

[[Page 47525]]

and the Medicare Code Edits (MCE) listing used by Medicare 
Administrative Contractors (MACs) when evaluating the primary diagnosis 
codes listed on claims. These commenters referred to instances when 
claims were denied for including a primary diagnosis code that may be 
found in the PDPM ICD-10 mapping as a valid code but is not accepted by 
the MACs. These commenters recommended that CMS seek to align these two 
code lists.
    Response: We appreciate commenters raising this concern. While 
outside the scope of this rule, we intend to consult with MACs on this 
issue to determine an appropriate path forward.
    After consideration of public comments, we finalize the proposed 
changes to the PDPM ICD-10 mappings, as proposed.

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 implementing 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 
Part A 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 (CMIs) 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) to what we expected total payments would be 
under PDPM based on that same FY 2017 claims and assessment data. In 
the FY 2020 SNF PPS final rule (84 FR 38734 through 38735), we 
finalized an updated standardization multiplier and parity adjustment 
based on FY 2018 claims and assessment data. This analysis resulted in 
an adjustment factor of 1.46, by which all 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 each CMI by 
1.46, the CMIs were inflated by 46 percent to achieve budget 
neutrality.
    We used a similar type of parity adjustment in FY 2011 when we 
transitioned from RUG-III to RUG-IV. 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. 
We then used actual FY 2011 RUG-IV utilization data to recalibrate the 
RUG-IV parity adjustment and decreased 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 SNF 
utilization data to determine if the parity adjustment finalized in the 
FY 2020 SNF PPS final rule (84 FR 38734 through 38735) provided for a 
budget neutral transition between RUG-IV and PDPM as intended. Similar 
to what occurred in FY 2011 with RUG-IV implementation, we observed 
significant differences between the expected SNF PPS payments and case-
mix utilization based on historical data, and the actual SNF PPS 
payments and case-mix utilization under PDPM, based on FY 2020 and FY 
2021 utilization data. As discussed in the FY 2022 SNF PPS final rule 
(86 FR 42466 through 42469), we initially estimated that PDPM may have 
inadvertently triggered a significant increase in overall payment 
levels under the SNF PPS of approximately 5 percent and that 
recalibration of the parity adjustment may be warranted.
    Following the methodology utilized in calculating the initial PDPM 
parity adjustment, we would typically 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 and determine an 
appropriate adjustment factor to achieve parity. However, we 
acknowledged that the typical methodology for recalibrating the parity 
adjustment may not provide an accurate recalibration under PDPM for 
several reasons. First, the ongoing COVID-19 PHE has had impacts on 
nursing home care protocols and many other aspects of SNF operations 
that affected utilization data in FY 2020 and FY 2021. Second, given 
the significant differences in payment incentives and patient 
assessment requirements between RUG-IV and PDPM, using the same 
methodology that we have used in the past to calculate a recalibrated 
PDPM parity adjustment could lead to a potential overcorrection in the 
recalibration.
    In the FY 2022 SNF PPS proposed rule (86 FR 19987 through 19989), 
we solicited comments from interested parties on a potential 
methodology for recalibrating the PDPM parity adjustment to account for 
these potential effects without compromising the accuracy of the 
adjustment. After considering the feedback and recommendations 
received, summarized in the FY 2022 SNF PPS final rule (86 FR 42469 
through 42471), we proposed an updated recalibration methodology and 
presented results from our data monitoring efforts to provide 
transparency on our efforts to parse out the effects of PDPM 
implementation from the effects of the COVID-19 PHE in section V.C.2.d. 
of the proposed rule. We solicited comments on this proposal for 
recalibrating the PDPM parity adjustment to ensure that PDPM is 
implemented in a budget neutral manner, as originally intended. We 
received public comments on these proposals. The following is a summary 
of the comments we received and our responses.
    Comment: Some commenters noted that they understood the need to 
implement PDPM in a budget neutral manner, but requested that CMS 
reconsider the necessity of the parity adjustment. These commenters 
stated that it was unreasonable to expect a budget-neutral transition 
given the ``new normal'' that includes the impacts of COVID-19 and 
questioned the appropriateness of comparing a pre-COVID-19 RUG-IV 
system to a COVID-19 era PDPM system. Other commenters stated that even 
if the COVID-19 PHE had not occurred, it was unreasonable to expect a 
budget-neutral transition given that PDPM encourages providers to put a 
greater emphasis on capturing all patient characteristics. That is, 
while providers have always treated and considered such highly 
individualized characteristics, commenters noted that these were not 
necessarily captured by the MDS under the previous RUG-IV

[[Page 47526]]

payment system and were underrepresented in the data. Therefore, 
commenters disagreed with the notion that an overpayment is occurring 
between the PDPM model and RUG-IV model; rather, they stated the 
increased cost is an appropriate reflection of better capturing of 
patient complexities on the MDS.
    Response: We believe there were significant changes in the coding 
of patient acuity directly following PDPM implementation and before the 
COVID-19 PHE that would have warranted a parity adjustment. In section 
V.C.2.d. of the proposed rule, we described numerous changes observed 
in the data that demonstrate the different impacts of PDPM 
implementation and the COVID-19 PHE on reported patient clinical 
acuity. For example, commenters stated that limitations regarding 
visitation and other infection control protocols due to the PHE led to 
higher levels of mood distress, cognitive decline, functional decline, 
compromised skin integrity, change in appetite, and weight loss 
requiring diet modifications among the non-COVID-19 population. 
However, our data show that many of these metrics had already exhibited 
clear changes concurrent with PDPM implementation and well before the 
start of the COVID-19 PHE. For example, the data showed an average of 4 
percent of stays with depression and 5 percent of stays with a 
swallowing disorder in the fiscal year prior to PDPM implementation (FY 
2019). In the 3 months directly following PDPM implementation and 
before the start of the COVID-19 PHE (October 2019 through December 
2019), these averages increased to 11 percent of stays with depression 
and 17 percent of stays with a swallowing disorder.
    The parity adjustment is meant to correct for the very changes in 
coding intensity of patient characteristics that these commenters 
describe, and similar changes in provider behavior and coding in 
response to payment incentives have occurred in past transitions from 
one payment system to another. As discussed in the FY 2012 SNF PPS 
final rule (76 FR 48492 through 48500), we implemented a similar type 
of parity adjustment in 2011 after observing a large difference between 
expected and actual utilization patterns in the transition from the 
RUG-III to RUG-IV payment system. As with prior system transitions, we 
proposed and finalized implementing PDPM in a budget neutral manner in 
the FY 2019 SNF PPS final rule (83 FR 39256). This meant that the 
transition to PDPM was not intended to result in an increase or 
decrease in the aggregate amount of Medicare Part A payment to SNFs.
    Comment: Some commenters pointed to unintended consequences of 
implementing the parity adjustment on Medicare beneficiaries and other 
residents. Medicare's reimbursement rates for SNF care are higher than 
those of other payers such as Medicaid, and therefore, are a crucial 
support for an otherwise financially challenged SNF industry, 
particularly given the ongoing COVID-19 PHE. Any decrease to those 
rates would be acutely detrimental, especially to smaller, independent 
providers serving low-income populations, possibly resulting in 
facility closures and decreased access to care for beneficiaries.
    Response: We remind commenters that Medicare Part A payments under 
the SNF PPS are solely intended to reflect the costs of providing care 
to beneficiaries covered under Medicare Part A and are not intended to 
augment payments from other payers that may be lower than Medicare Part 
A payment rates.
    After consideration of public comments, we are finalizing our 
proposal to recalibrate the PDPM parity adjustment to ensure that PDPM 
is implemented in a budget neutral manner, as originally intended.
2. Methodology for Recalibrating the PDPM Parity Adjustment
a. Effect of COVID-19 Public Health Emergency
    FY 2020 was a year of significant change under the SNF PPS. In 
addition to implementing PDPM on October 1, 2019, a national COVID-19 
PHE was declared beginning January 27, 2020. With the announcement of 
the COVID-19 PHE, and under authority granted us by section 1812(f) of 
the Act, we issued two temporary modifications to the limitations of 
section 1861(i) of the Act beginning March 1, 2020, that affected SNF 
coverage. The 3-day prior hospitalization modification allows a SNF to 
furnish Medicare Part A services without requiring a 3-day qualifying 
hospital stay, and the benefit period exhaustion modification allows a 
one-time renewal of benefits for an additional 100 days of Part A SNF 
coverage without a 60-day break in a spell of illness. These COVID-19 
PHE-related modifications allow coverage for beneficiaries who would 
not typically be able to access the Part A SNF benefit, such as 
community and long-term care nursing home patients without a prior 
qualifying hospitalization.
    We acknowledged that the COVID-19 PHE had significant impacts on 
nursing home care protocols and many other aspects of SNF operations. 
For months, infection and mortality rates were high among nursing home 
residents. Additionally, facilities were often unable to access testing 
and affordable personal protective equipment (PPE) and were effectively 
closed to visitors and barred from conducting communal events to help 
control infections (March 2021 MedPAC Report to Congress, 204, 
available at https://www.medpac.gov/wp-content/uploads/2021/10/mar21_medpac_report_ch7_sec.pdf). As described in the FY 2022 SNF PPS 
final rule (86 FR 42427), many commenters voiced concerns about 
additional costs due to the COVID-19 PHE that could be permanent due to 
changes in patient care, infection control staff and equipment, 
personal protective equipment, reporting requirements, increased wages, 
increased food prices, and other necessary costs. Some commenters who 
received CARES Act Provider Relief funds indicated that those funds 
were not enough to cover these additional costs. Additionally, a few 
commenters from rural areas stated that their facilities were heavily 
impacted from the additional costs, particularly the need to raise 
wages, and that this could affect patients' access to care.
    However, we noted that the relevant issue for a recalibration of 
the PDPM parity adjustment is whether or not the COVID-19 PHE caused 
changes in the SNF case-mix distribution. In other words, the issue is 
whether patient classification, or the relative percentages of 
beneficiaries in each PDPM group, was different than what it would have 
been if not for the COVID-19 PHE. The parity adjustment addresses only 
to the transition between case-mix classification models (in this case, 
from RUG-IV to PDPM) and is not intended to include other unrelated SNF 
policies such as the market basket increase, which is intended to 
capture the change over time in the input prices for skilled nursing 
facility services described previously. A key aspect of our 
recalibration methodology, described in further detail later in this 
section, involved parsing out the impacts of the COVID-19 PHE and the 
PHE-related modifications from those that occurred solely, or at least 
principally, due to the implementation of PDPM.
b. Effect of PDPM Implementation
    As discussed in the FY 2022 SNF PPS final rule (86 FR 42467), we 
presented evidence that the transition to PDPM impacted certain aspects 
of SNF patient classification and care provision prior to the beginning 
of the COVID-19 PHE.

[[Page 47527]]

For example, our data showed that SNF patients received an average of 
approximately 93 therapy minutes per utilization day in FY 2019. 
Between October 2019 and December 2019, the 3 months after PDPM 
implementation and before the onset of the COVID-19 PHE, the average 
number of therapy minutes SNF patients received per day dropped to 
approximately 68 minutes per utilization day, a decrease of 
approximately 27 percent. Given this reduction in therapy provision 
since PDPM implementation, we found that using patient assessment data 
collected under PDPM would lead to a significant underestimation of 
what RUG-IV case-mix and payments would have been (for example, the 
Ultra-High and Very-High Rehabilitation assignments are not nearly as 
prevalent using PDPM-reported data), which would in turn lead to an 
overcorrection in the parity adjustment. Additionally, there were 
significant changes in the patient assessment schedule such as the 
removal of the Change of Therapy Other Medicare Required Assessment 
(COT-OMRA). Without having an interim assessment between the 5-day 
assessment and the patient's discharge from the facility, we were 
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 the initial RUG-IV classification.
    Therefore, given the significant differences in payment incentives 
and patient assessment requirements between RUG-IV and PDPM, using the 
same methodology that we have used in the past to calculate a 
recalibrated PDPM parity adjustment could lead to a potential 
overcorrection in the recalibration. In the FY 2022 SNF PPS proposed 
rule (86 FR 19988), we described an alternative recalibration 
methodology that used FY 2019 RUG-IV case-mix distribution as a proxy 
for what total RUG-IV payments would have been absent PDPM 
implementation. We believed that this methodology provided a more 
accurate representation of what RUG-IV payments would have been, were 
it not for the changes precipitated by PDPM implementation, than using 
data reported under PDPM to reclassify these patients under RUG-IV. We 
solicited comments from interested parties on this aspect of our 
potential methodology for recalibrating the PDPM parity adjustment and 
they were generally receptive to this approach, as described in the FY 
2022 SNF PPS final rule (86 FR 42468 through 42470).
c. FY 2022 SNF PPS Proposed Rule Potential Parity Adjustment 
Methodology and Comments
    In the FY 2022 SNF PPS proposed rule (86 FR 19986 through 19987), 
we presented a potential methodology that attempted to account for the 
effects of the COVID-19 PHE by removing those stays with a COVID-19 
diagnosis and those stays using a PHE-related modification from our 
data set, and we solicited comment on how interested parties believed 
the COVID-19 PHE affected the distribution of patient case-mix in ways 
that were not sufficiently captured by our subset population 
methodology. According to our data analysis, 10 percent of SNF stays in 
FY 2020 and 17 percent of SNF stays in FY 2021 included a COVID-19 ICD-
10 diagnosis code either as a primary or secondary diagnosis, while 17 
percent of SNF stays in FY 2020 and 27 percent of SNF stays in FY 2021 
utilized a PHE-related modification (with the majority of these cases 
using the prior hospitalization modification), as identified by the 
presence of a ``Disaster Relief (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 86 
percent and 81 percent of SNF beneficiaries had a qualifying prior 
hospitalization in FY 2020 and FY 2021, respectively. These general 
statistics are important, as they highlight that while the PHE for 
COVID-19 certainly impacted many aspects of nursing home operations, 
the large majority of SNF beneficiaries entered into Part A SNF stays 
in FY 2020 and FY 2021 as they would have in any other year; that is, 
without using a PHE-related modification, with a prior hospitalization, 
and without a COVID-19 diagnosis.
    Moreover, as discussed FY 2022 SNF PPS proposed rule (86 FR 19988), 
we found that even after removing those using a PHE-related 
modification and those with a COVID-19 diagnosis from our data set, the 
observed inadvertent increase in SNF payments since PDPM was 
implemented was approximately the same. To calculate expected total 
payments under RUG-IV, 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. The total payments under RUG-IV also accounted for the 
human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/
AIDS) add-on of a 128 percent increase in the PPS per diem payment 
under RUG-IV, and a provider's FY 2020 urban or rural status. To 
calculate the actual total payments under PDPM, 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 the variable per diem 
adjustment, the presence of an HIV diagnosis on the claim to account 
for the PDPM AIDS add-on of 18 percent to the nursing component and the 
highest point value (8 points) to the NTA component, and a provider's 
urban or rural status. Using this approach, and as described in the FY 
2022 SNF PPS final rule (86 FR 42468 through 42469), we initially 
estimated 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 percent increase in 
aggregate spending under PDPM for FY 2020 when considering the subset 
population. This finding suggested that a large portion of the changes 
observed in SNF utilization are due to PDPM and not the PHE for COVID-
19, as the ``new'' population of SNF beneficiaries (that is, COVID-19 
patients and those using a PHE-related modification) did not appear to 
be the main cause of the increase in SNF payments after implementation 
of PDPM. Although these results are similar, we believed it would be 
more appropriate to pursue a potential recalibration using the subset 
population.
    As described in the FY 2022 SNF PPS final rule (86 FR 42469 through 
42471), some commenters agreed with our approach, stating that our 
subset population was a reasonable method to account for the effect of 
the COVID-19 PHE, and made a few suggestions for improvements. They 
stated that our analysis may have undercounted COVID-19 patients 
because there was no COVID-19 specific diagnosis code available before 
April 2020 and a shortage of tests at the beginning of the PHE led to 
SNFs being unable to report COVID-19 cases. To address these issues, 
commenters suggested that CMS consider using non-specific respiratory 
diagnoses or depression as proxies for COVID-19 cases. While we 
considered this option, we believed that such a change would 
overestimate the population to be excluded due to the

[[Page 47528]]

non-specific nature of those diagnoses. Additionally, because we did 
not provide our COVID-19 population definition in the FY 2022 SNF PPS 
proposed or final rules, commenters were concerned that our methodology 
did not include COVID-19 diagnoses from the Minimum Data Set (MDS) 
patient assessments in addition to SNF claims. Commenters were also 
concerned that we did not exclude transitional stays resulting from 
CMS' instruction to assess all patients anew in October 2019 using the 
PDPM MDS assessment, even though some patients were in the middle or 
end of their Medicare Part A coverage. We addressed these concerns by 
sharing a revised COVID-19 population definition in section V.C.2.d. of 
the proposed rule.
    However, many commenters expressed concern that our subset 
population methodology would not accurately represent what the SNF 
patient case-mix would look like outside of the COVID-19 PHE 
environment, stating that data collected during the PHE was entirely 
too laden with COVID-19 related effects on the entire SNF population to 
be utilized and pointing to multiple reasons for greater clinical 
acuity even among our subset population. For example, because elective 
surgeries were halted, those admitted were the most compromised who 
could not be cared for at home. Additionally, limitations regarding 
visitation and other infection control protocols led to higher levels 
of mood distress, cognitive decline, functional decline, compromised 
skin integrity, change in appetite, and weight loss requiring diet 
modifications. In response to these comments, we conducted 
comprehensive data analysis and monitoring to identify changes in 
provider behavior and payments since implementing PDPM and presented a 
revised parity adjustment methodology in section V.C.2.d. of the 
proposed rule that we believed more accurately accounted for these 
changes while excluding the effect of the COVID-19 PHE on the SNF 
population.
d. FY 2023 SNF PPS Proposed Parity Adjustment Methodology
    As outlined in section V.C.2.d. of the proposed rule, we proposed a 
revised methodology for the calculating the parity adjustment that 
considers the comments received in response to the potential 
methodology described in the FY 2022 SNF PPS proposed rule (86 FR 19986 
through 19987). In response to the comments received about the subset 
population methodology, we modified our definition of COVID-19, which 
we derived from the Centers for Disease Control and Prevention (CDC) 
coding guidelines, to align with the definition used by publicly 
available datasets from CMS's Office of Enterprise Data and Analytics 
(OEDA) and found no significant impact on our calculations. For the FY 
2022 SNF proposed rule, we defined the COVID-19 population to include 
stays that have either the interim COVID-19 code B97.29 recorded as a 
primary or secondary diagnosis in addition to one of the symptom codes 
J12.89, J20.8, J22, or J80, or the new COVID-19 code U07.1 recorded as 
a primary or secondary diagnosis on their SNF claims or MDS 5-day 
admission assessments. For the FY 2023 SNF proposed rule, we defined 
the COVID-19 population to include stays that have the interim COVID-19 
code B97.29 from January 1, 2020 to March 31, 2020 or the new COVID-19 
code U07.1 from April 1, 2020 onward recorded as a primary or secondary 
diagnosis on their SNF claims, MDS 5-day admission assessments, or MDS 
interim payment assessments. Both FY 2022 and FY 2023 definitions of 
the COVID-19 population excluded transitional stays. We noted that we 
found no significant impact on our calculations, as the COVID-19 
population definition change only increased the stay count of our 
subset population by less than 1 percent.
    In response to the comments described previously and based on 
additional data collection through FY 2021, we identified a 
recalibration methodology that we believed better accounted for COVID-
19 related effects. We proposed to use the same type of subset 
population discussed in the FY 2022 SNF PPS proposed rule (86 FR 
19960), which excluded stays that either used a section 1812(f) of the 
Act modification or that included a COVID-19 diagnosis, with a 1-year 
``control period'' derived from both FY 2020 and FY 2021 data. 
Specifically, we used 6 months of FY 2020 data from October 2019 
through March 2020 and 6 months of FY 2021 data from April 2021 through 
September 2021 (which our data suggests were periods with relatively 
low COVID-19 prevalence) to create a full 1-year period with no 
repeated months to account for seasonality effects. As shown in Table 
11, we believed this combined approach provided the most accurate 
representation of what the SNF case-mix distribution would look like 
under PDPM outside of a COVID-19 PHE environment. While using the 
subset population method alone for FY 2020 and FY 2021 data results in 
differences of 0.31 percent and 0.40 percent between the full and 
subset populations, respectively, introducing the control period closed 
the gap between the full and subset population adjustment factors to 
0.02 percent, suggesting that the control period captures additional 
COVID-19 related effects on patient acuity that the subset population 
method alone does not. Accordingly, the combined methodology of using 
the subset population with data from the control period resulted in the 
lowest parity adjustment factor. Table 12 shows that while using the 
subset population method would lead to a 4.9 percent adjustment factor 
($1.6 billion) using FY 2020 data and a 5.3 percent adjustment factor 
($1.8 billion) using FY 2021 data, introducing the control period 
reduced the adjustment factor to 4.6 percent ($1.5 billion). We note 
that these estimates are revised from those provided in the FY 2023 SNF 
PPS proposed rule, based on a more recent SNF baseline budget estimate 
provided by the CMS Office of the Actuary. The robustness of the 
control period approach was further demonstrated by the fact that using 
data from the control period, with either the full or subset 
population, would lead to approximately the same parity adjustment 
factor of 4.58 percent as compared to 4.6 percent.

[[Page 47529]]

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[GRAPHIC] [TIFF OMITTED] TR03AU22.012

    Our data analysis and monitoring efforts provided further support 
for the accuracy and appropriateness of a 4.6 percent parity adjustment 
factor, as we have identified numerous changes that demonstrate the 
different impacts of PDPM implementation and the COVID-19 PHE on 
reported patient clinical acuity. As described earlier, commenters 
stated that limitations regarding visitation and other infection 
control protocols due to the PHE led to higher levels of mood distress, 
cognitive decline, functional decline, compromised skin integrity, 
change in appetite, and weight loss requiring diet modifications among 
the non-COVID-19 population. However, our data showed that most of 
these metrics, with the exception of functional decline and compromised 
skin integrity, had already exhibited clear changes concurrent with 
PDPM implementation and well before the start of the COVID-19 PHE. For 
example, in regard to higher levels of mood distress and cognitive 
decline, we observed an average of 4 percent of stays with depression 
and 40 percent of stays with cognitive impairment, with an average mood 
score of 1.9, in the fiscal year prior to PDPM implementation (FY 
2019). In the 3 months directly following PDPM implementation and 
before the start of the COVID-19 PHE (October 2019 to December 2019), 
these averages increased to 11 percent of stays with depression and 44 
percent of stays with cognitive impairment, with an average mood scale 
of 2.9. As for change in appetite and weight loss requiring diet 
modifications, we observed an average of 15 percent of stays with any 
SLP comorbidity, 5 percent of stays with a swallowing disorder, and 22 
percent of stays with a mechanically altered diet in FY 2019. In the 3 
months directly following PDPM implementation, these averages increased 
to 19 percent of stays with any SLP comorbidity, 17 percent of stays 
with a swallowing disorder, and 25 percent of stays with a mechanically 
altered diet. Notably, we also observed that the percentage of stays 
with a swallowing disorder that did not also receive a mechanically 
altered diet increased from 1 percent in FY 2019 to 5 percent in the 3 
months directly following PDPM implementation. While many of these 
metrics increased further after the start of the COVID-19 PHE, they 
remained elevated at around their post-PDPM implementation levels even 
during periods of low COVID-19 prevalence. As a result, our parity 
adjustment calculations remained much the same even during months when 
rates of COVID-19 cases were quite low, suggesting that patient case 
mix classification has stabilized independent of the ongoing COVID-19 
PHE.
    Another reason that commenters cited to explain the greater 
clinical acuity among the subset population is that, because elective 
surgeries were halted, patients who were admitted were more severely 
ill and could not be treated at home. We acknowledged that the subset 
population methodology, or any method predicated on data from the 
COVID-19 PHE period, may not accurately represent what SNF patient 
case-mix would look like outside of the COVID-19 PHE environment 
because while we could remove data that we believed were due to COVID-
19 impacts, it was more difficult to add data back in that was missing 
due to the COVID-19 PHE.
    However, we believed that the addition of the control period to the 
subset population methodology helped to resolve this issue. For 
example, there likely would have been more joint replacements were it 
not for the COVID-19 PHE. Our data showed that the rate of major joint 
replacement or spinal surgery decreased from 7.6 percent of stays in FY 
2019, to 5.5 percent of stays in FY 2021, to 5.2 percent of stays in FY 
2022. Similarly, rates of orthopedic surgery decreased from 9.1 percent 
of stays in FY 2019, to 9.0 percent of stays in FY 2021, to 8.8 percent 
of stays in FY 2022. Using the control period, which excluded the 
periods of highest COVID-19 prevalence and lowest rates of elective 
surgeries, we arrived at rates of 6.4 percent of stays with major joint 
replacement or spinal surgery, and 9.5 percent of stays with orthopedic 
surgery. Therefore, as we noted in section V.C.2.d. the proposed rule, 
we believed that using the control period would be a closer 
representation of SNF patient case-mix outside of a COVID-19 PHE 
environment than using either FY 2021 or FY 2022 data alone.
    Given the results of our data analyses, we proposed adopting the 
methodology based upon the subset population during the control period 
and lowering the PDPM parity adjustment factor from 46 percent to 38 
percent for each of the PDPM case-mix adjusted components if we were to 
implement the 4.6 percent parity adjustment factor in FY 2023. We noted 
that the parity adjustment would be calculated and applied at a 
systemic level to all facilities paid under the SNF PPS, and there may 
be variation between facilities based on their unique patient 
population, share of non-case-

[[Page 47530]]

mix component payment, and urban or rural status. We invited comments 
on the methodology outlined in section V.C.2.d. of the proposed rule 
for recalibrating the PDPM parity adjustment, as well as the findings 
of our analysis described throughout section V.C.2. of the proposed 
rule.
    To assist commenters in providing comments on this issue, we also 
posted a file on the CMS website at https://www.cms.gov/medicare/medicare-fee-for-service-payment/snfpps, which provided 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.
    We invited comments on our proposed combined methodology of using 
the subset population and data from the control period for the purposes 
of calculating the recalibrated parity adjustment factor. The following 
is a summary of the comments we received and our responses.
    Comment: A few commenters provided comments in relation to the 
proposed methodology for calculating the parity adjustment. Some 
commenters noted our proposed methodology to be a reasonable and much 
improved approach compared to the approach proposed in FY 2022 SNF PPS 
proposed rule, as our revised methodology addresses many of the key 
issues raised by interested parties (86 FR 42469 through 42471).
    However, one commenter suggested removing August and September 2021 
due to the Delta variant. Another commenter suggested a modified 
control period to eliminate April and May 2021 as patients and 
healthcare personnel were still in the process of receiving the initial 
dose of the COVID-19 vaccine, and August and September 2021 due to 
early phase of the Delta variant surge. The commenter also provided 
analysis regarding COVID-19 spillover effects, which they defined as 
effects that occur in non-COVID-19 patient CMIs when MDS patient 
assessment patterns change from what would have occurred if not for the 
pandemic, using the percentage change over time in various patient 
clinical and zip-code level demographic characteristics, the latter 
used as proxies for the demographics of the SNF population in a 
particular zip code. The commenter stated that some metrics, such as 
HCC risk scores, English proficiency, educational level, and poverty 
level returned to or dropped below pre-COVID-19 PHE baseline levels, 
suggesting that the revised parity adjustment factor is adequate to 
account for COVID-19 spillover effects. However, the commenter also 
stated that other metrics, such as PDPM component CMI trends; MDS items 
for respiratory failure, pressure ulcers, and depression; and claim 
items for age, race, dual, and disability status did not return to pre-
COVID-19 PHE baseline levels, suggesting that the revised parity 
adjustment factor may not be adequate to account for COVID-19 spillover 
effects. Based on these findings, the commenters stated that they 
believed that there are COVID-19 spillover effects that remain despite 
CMS's improved parity adjustment approach, and they recommended that 
CMS further evaluate the data to exclude the months of April, May, 
August, and September 2021 from the parity adjustment calculations, as 
discussed above. The commenter also stated that modifying the control 
period in this way would mitigate most of the remaining spillover 
effects and would result in an additional 0.1 to 0.2 percent reduction 
below the proposed 4.6 percent parity adjustment amount.
    Response: We note that many of the differences shown in the data 
the commenter provided are quite small (some less than a small fraction 
of 1 percent) and could be attributed to the continuation of the impact 
of PDPM implementation or regular year-to-year variations in the 
composition of the SNF population (or zip-code level population more 
generally), rather than true COVID-19 spillover effects. We also note 
that the commenter did not consider data from before PDPM 
implementation to support what they believe should be a more 
appropriate parity adjustment factor, as they used data from October 
2019 to February 2020 to define their ``pre-pandemic'' study 
population.
    In contrast, the data analyses we presented earlier in the preamble 
show significant changes in the coding of patient case mix concurrent 
with PDPM implementation. For example, in the year prior to PDPM 
implementation (FY 2019), we observed an average of 4 percent of stays 
coded with depression and 5 percent of stays coded with a swallowing 
disorder. In the 3 months directly following PDPM implementation and 
before the start of the COVID-19 PHE (October 2019 to December 2019), 
these averages increased to 11 percent of stays coded with depression 
and 17 percent of stays coded with a swallowing disorder. While these 
and other clinical metrics increased in acuity after the start of the 
COVID 19 PHE in January 2020, they remained elevated at around their 
immediate post-PDPM implementation levels even during periods of low 
COVID-19 prevalence. As a result, our parity adjustment calculations 
remained much the same even during months when rates of COVID-19 cases 
were quite low, suggesting that the 4.6 percent parity adjustment 
factor captures the effect of PDPM implementation and excludes the 
effects of the COVID-19 PHE.
    Moreover, we believe that it is important to have an adequate and 
representative amount of time in both 2020 and 2021 upon which to 
calculate a parity adjustment factor, rather than choosing specific 
months that would result in the lowest possible parity adjustment 
factor. Our analysis of Medicare Part A data from SNFs in April, May, 
August, and September 2021 show that these were months of low COVID-19 
prevalence in SNFs compared to other months in FY 2020 and FY 2021. We 
intentionally chose 6 months of FY 2020 data from October 2019 through 
March 2020 and 6 months of FY 2021 data from April 2021 through 
September 2021, which our Medicare Part A monitoring data showed were 
periods with the lowest COVID-19 prevalence in SNFs, to create a full 
1-year period with no repeated months to account for seasonality 
effects. While we used less than a year of data in calculating the 
recalibration of the RUG-IV parity adjustment when transitioning 
between RUG-III and RUG-IV in FY 2012 (76 FR 48493), that change was 
between two payment models that were, in several ways, very similar 
(for example, the relationship between therapy intensity and payment 
classification). This time, in light of the significant differences 
between the PDPM and the RUG-IV payment models, in addition to the 
impact of the COVID-19 PHE, we believe it is necessary to use a full 
year of data.
    After consideration of these public comments, we are finalizing a 
parity adjustment factor of 4.6 percent using the combined subset 
population and control period methodology, as proposed. As discussed 
later in section VI.C.4. of this final rule, we are finalizing the 
implementation of the parity adjustment with a 2-year phase-in period, 
which means that, for each of the PDPM case-mix adjusted components, we 
would lower the PDPM parity adjustment factor from 46 percent to 42 
percent in FY 2023 and we would further lower the PDPM parity 
adjustment factor from 42 percent to 38 percent in FY 2024.

[[Page 47531]]

3. Methodology for Applying the Recalibrated PDPM Parity Adjustment
    As discussed in the FY 2022 SNF PPS proposed rule (86 FR 19988), we 
believed it would be appropriate to apply the recalibrated parity 
adjustment across all PDPM CMIs in equal measure, as the initial 
increase to the PDPM CMIs to achieve budget neutrality was applied 
equally, and therefore, this method would properly implement and 
maintain the integrity of the PDPM classification methodology as it was 
originally designed. Tables 5 and 6 in section III.C. of the proposed 
rule set forth what the PDPM CMIs and case-mix adjusted rates would be 
if we apply the recalibration methodology in equal measure in FY 2023.
    We acknowledged that we received several comments in response to 
last year's rule objecting to this approach given that our data 
analysis, presented in Table 23 of the FY 2022 SNF PPS proposed rule 
(86 FR 19987), showed 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. As described in the FY 2022 SNF PPS final rule (86 
FR 42471), some commenters disagreed with adjusting the CMIs across all 
case-mix adjusted components in equal measure, suggesting that this 
approach would harm patient care by further reducing PT and OT therapy 
minutes. Instead, the commenters recommended a targeted approach that 
focuses the parity adjustment on the SLP, Nursing, and NTA components 
in proportion to how they are driving the unintended increase observed 
under PDPM.
    We considered these comments, but believe that it would be most 
appropriate to propose applying the parity adjustment across all 
components equally. First, as described earlier, the initial increase 
to the PDPM CMIs to achieve budget neutrality was applied across all 
components, and therefore, it would be appropriate to implement a 
revision to the CMIs in the same way. Second, the reason we did not 
observe the same magnitude of change in the PT and OT components is 
that, in designing the PDPM payment system, the data used to help 
determine what payment groups SNF patients would classify into under 
PDPM was collected under the prior payment model (RUG-IV), which 
included incentives that encouraged significant amounts of PT and OT. 
Given that PT and OT were furnished in such high amounts under RUG-IV, 
we had already assumed that a significant portion of patients would be 
classified into the higher paying PT and OT groups corresponding to 
having a Section GG function score of 10 to 23. Therefore, this left 
little room for additional increases in PT and OT classification after 
PDPM implementation. In other words, the PT and OT components results 
were as expected according to the original design of PDPM, while the 
SLP, Nursing, and NTA results were not.
    However, to fully explore the alternative targeted approach that 
commenters suggested, we updated our analysis of the average CMI by 
PDPM component from Table 23 of the FY 2022 SNF PPS proposed rule (86 
FR 19987) and found that a similar pattern still holds when comparing 
the expected average CMIs for FY 2019 and the expected actual CMIs for 
the subset population during the control period. Table 13 shows 
significant increases in average case-mix of 18.6 percent for the SLP 
component and the 10.8 percent for the Nursing component, a moderate 
increase of 3.0 percent for the NTA component, and a slight increase of 
0.4 percent for the PT and OT components, respectively. We also 
provided Table 14 to show the potential impact of applying the 4.6 
percent PDPM parity adjustment factor to the PDPM CMIs in a targeted 
manner in FY 2023, instead of an equal approach as presented in Tables 
5 and 6 in section III.C. of the proposed rule. We invited comments on 
whether interested parties believe a targeted approach is preferable to 
our proposed equal approach.
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[[Page 47532]]


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BILLING CODE 4120-01-C
    We received public comments on these proposals. The following is a 
summary of the comments we received and our responses.
    Comment: A few commenters supported our proposal to apply the 
parity adjustment evenly over all CMIs for all case-mix groups, the 
same approach that was taken when the original adjustment was 
implemented. One commenter stated that the targeted approach, which 
results in a larger reduction for some CMIs than others, may have 
unintended adverse effects on some facilities and that an equally 
distributed percentage reduction would have a more equitable impact on 
all facilities. Another commenter believed an equal approach would be 
the least disruptive policy implementation, rather than set a precedent 
for potential future changes to the individual CMI components. The 
commenter also added that regardless of which CMIs are reduced, 
facilities are still receiving a single per-diem payment. A third 
commenter agreed that, in the absence of re-designing the PDPM payment 
model from the ground-up based on observed PDPM CMIs, the adoption of 
an even distribution for the parity adjustment would best maintain the 
stability of the PDPM payment model. A fourth commenter strongly 
opposed a targeted approach to all categories, believing that SLP 
services were undervalued in the RUG-IV system and utilization of SLP 
services appropriately meets beneficiary needs under PDPM, but were not 
previously reported since there were no financial incentives for SNFs 
to report SLP services under RUG-IV.
    Two commenters supported a targeted approach and expressed concern 
about a reduction in payment for the PT and OT components, given that 
the majority of increased spending is not attributed to these 
components, leading to a reduction in PT and OT services. The 
commenters urged CMS to use the data to adjust PDPM in an accurate and 
precise manner, rather than simply reducing every CMI.
    Response: We agree that applying the parity adjustment equally 
across all PDPM CMIs would be the most equitable and least disruptive 
policy implementation, rather than set a precedent for potential future 
changes to the individual CMI components. We also agree that regardless 
of which CMIs are reduced, facilities are still receiving a single per-
diem payment and a reduction in the PT and OT CMIs should not impact 
the provision of these services, as the main driver for determining the 
appropriate provision of these services should the unique 
characteristics, goals, or needs, of each SNF patient. As we stated in 
the FY 2020 SNF PPS final rule (84 FR 38748), 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.
    After consideration of public comments, we are finalizing the 
application of the parity adjustment equally across all components, as 
proposed.

[[Page 47533]]

4. Delayed and Phased Implementation
    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, we acknowledged that applying a reduction in payments without 
time to prepare could create a financial burden for providers, 
particularly considering the ongoing COVID-19 PHE. Therefore, in the FY 
2022 SNF PPS proposed rule (86 FR 19988 through 19990), we solicited 
comments on two potential mitigation strategies to ease the transition 
to prospective budget neutrality: delayed implementation and phased 
implementation. We noted that for either of these options, the 
adjustment would be applied prospectively, and the CMIs would not be 
adjusted to account for deviations from budget neutrality in years 
before the payment adjustments are implemented.
    A delayed implementation strategy would mean that we would 
implement the reduction in payment in a later year than the year the 
reduction is finalized. For example, considering the 4.6 percent 
reduction discussed previously in this preamble, if this reduction is 
finalized in FY 2023 with a 1-year delayed implementation, this would 
mean that the full 4.6 percent reduction will be applied prospectively 
to the PDPM CMIs in FY 2024. By comparison, a phased implementation 
strategy would mean that the amount of the reduction would be spread 
out over some number of years. For example, if we were to implement a 
2-year phase-in period to the 4.6 percent reduction discussed 
previously in the proposed rule with no delayed implementation, this 
would mean that the PDPM CMIs would be reduced by 2.3 percent in the 
first year of implementation in FY 2023 and then reduced by the 
remaining 2.3 percent in the second and final year of implementation in 
FY 2024. We could also use a combination of both mitigation strategies, 
such as a 1-year delayed implementation with a 2-year phase-in period, 
would mean that the PDPM CMIs would be reduced by 2.3 percent in the 
first year of implementation in FY 2024 and then reduced by the 
remaining 2.3 percent in the second and final year of implementation in 
FY 2025.
    In the FY 2022 SNF PPS proposed rule (86 FR 19988 through 19990), 
we solicited comments on the possibility of combining the delayed and 
phased implementation approaches and what interested parties believed 
would be appropriate to appropriately mitigate the impact of the 
reduction in SNF PPS payments. As described in the FY 2022 SNF PPS 
final rule (86 FR 42470 through 42471), most commenters supported 
combining both mitigation strategies of delayed implementation of 2 
years and a gradual phase-in of no more than 1 percent per year. MedPAC 
supported delayed implementation, but did not believe a phased-in 
approach was warranted given the high level of aggregate payment to 
SNFs. Further, MedPAC's March 2022 Report to Congress (available at 
https://www.medpac.gov/wp-content/uploads/2022/03/Mar22_MedPAC_ReportToCongress_Ch7_SEC.pdf) has found that since 2000, 
the aggregate Medicare margin for freestanding SNFs has consistently 
been above 10 percent each year. In 2020, the aggregate Medicare margin 
was 16.5 percent, a sizable increase from 11.9 percent in 2019. 
Additionally, the aggregate Medicare margin in 2020 increased to an 
estimated 19.2 percent when including Federal relief funds for the 
COVID-19 PHE (March 2022 MedPAC Report to Congress, 251-252). Given 
these high Medicare margins, we did not believe that a delayed 
implementation or a phase-in approach was needed. Rather, these 
mitigation strategies would continue to pay facilities at levels that 
exceed intended SNF payments, had PDPM been implemented in a budget 
neutral manner as finalized by CMS in the FY 2019 SNF PPS final rule 
(83 FR 39256), which we cannot recoup.
    It is also important to note that the parity adjustment 
recalibration would serve to remove an unintended increase in payments 
from moving to a new case mix classification system, rather than 
decreasing an otherwise appropriate payment amount. Thus, as we noted 
in section V.C.4. of the proposed rule, we did not believe that the 
recalibration should negatively affect facilities, beneficiaries, and 
quality of care, or create an undue hardship on providers.
    Therefore, we proposed to recalibrate the parity adjustment in FY 
2023 with no delayed implementation or phase-in period in order to 
allow for the most rapid establishment of payments at the appropriate 
level, ensuring that PDPM will be budget-neutral as intended and 
preventing the continued accumulation of excess SNF payments. We noted 
that while this proposal would lead to a prospective reduction in 
Medicare Part A SNF payments of approximately 4.6 percent in FY 2023, 
the reduction would be substantially mitigated by the proposed FY 2023 
net SNF market basket update factor of 3.9 percent discussed in section 
III.B of the proposed rule. Taken together, we had stated that the 
preliminary net budget impact in FY 2023 would be an estimated decrease 
of $320 million in aggregate payment to SNFs if the parity adjustment 
is implemented in 1 year.
    However, we continue to believe that in implementing PDPM, it is 
essential that we stabilize the baseline as quickly as possible without 
creating a significant adverse effect on the industry or to 
beneficiaries. Therefore, we solicited comments on our proposal to 
recalibrate the parity adjustment by 4.6 percent in FY 2023, and 
whether interested parties believe delayed implementation or a phase-in 
period are warranted, in light of the data analysis and policy 
considerations presented previously. We received public comments on 
these proposals. The following is a summary of the comments we received 
and our responses.
    Comment: We received a few comments in support of the proposed 
parity adjustment with no phase-in period. The commenters indicated 
that the SNF industry has been on notice for a year that an additional 
reduction to the payment rates would be necessary to maintain budget 
neutrality and noted that the parity adjustment of 4.6 percent proposed 
for FY 2023 was smaller than the SNF industry might have expected, 
given CMS's initial estimate of 5 percent in the FY 2022 SNF PPS 
proposed rule (86 FR 19988). The commenters also stated that no phase-
in period is warranted in FY 2023 as, based on CMS' final calculations, 
it has overpaid the industry about 4.6 percent per year since the PDPM 
was implemented in FY 2020, or approximately $5 billion over FY 2020, 
FY 2021, and FY 2022.
    Response: We appreciate these comments and agree that the SNF 
industry was made aware of the potential for CMS to implement parity 
adjustment in prior rulemaking.
    Comment: The majority of commenters strongly objected to 
implementing the 4.6 percent adjustment all in 1 year, instead 
requesting that CMS implement a mitigation strategy of phasing the 
parity adjustment in over a number of years, with the majority 
requesting a 3-year phase-in period and a significant number requesting 
a 2- to 3-year phase-in period. Some commenters requested a 1-year 
delay combined with a 4- to 5-year phase-in period of no more than 1 
percent of the parity adjustment implemented per year.
    The commenters stated that a phased-in approach would assure some 
predictability and stability to the SNF industry by making a negative 
net annual update less likely to occur each

[[Page 47534]]

year of the phase-in. The commenters pointed to several reasons why the 
SNF industry could not withstand a negative payment adjustment at this 
time. Many commenters stated that their facilities are still facing 
financial difficulties due to the ongoing COVID-19 PHE, with decreased 
census numbers, the continued need to purchase PPE, and the 
discontinuation of CARES Act Provider Relief funds. Many commenters 
also pointed to the unfavorable current economic climate with inflation 
at above 8 percent and historically high fuel prices, which they did 
not believe were adequately accounted for in the market basket. 
Finally, the majority of commenters pointed to the high cost of labor, 
resulting in staffing shortages as healthcare workers opt for other 
healthcare or non-healthcare settings offering higher pay.
    Response: We appreciate the comments raised on the potential impact 
on providers of finalizing this adjustment with no delay or phase-in 
period. We acknowledge the concerns raised about financial difficulties 
due to the ongoing COVID-19 PHE and due to the current economic 
climate. The parity adjustment addresses the transition between case-
mix classification models (in this case, from RUG-IV to PDPM) and is 
not intended to include other unrelated SNF policies such as the market 
basket increase, which is intended to capture the change over time in 
the prices of skilled nursing facility services.
    As stated in section V.C.4. of the proposed rule, we believe that 
it is essential to stabilize the baseline budget without creating a 
significant adverse effect on SNFs. While we understand the comments 
raised on the potential financial impact on providers of finalizing 
this adjustment with less than a 3-year phase-in period, we believe 
that it would be best to implement this adjustment as soon as possible 
in order to maintain budget neutrality in the SNF payment system. We 
remind commenters that, in the FY 2022 SNF PPS final rule, we stated it 
would be imperative to act in a well-considered but expedient manner 
once excess payments are identified (86 FR 42471).
    However, we also recognize that the ongoing COVID-19 PHE provides a 
basis for taking a more cautious approach in order to mitigate the 
potential negative impacts on providers, such as the potential for 
facility closures or disproportionate impacts on rural and small 
facilities. Given this, we believe that it would be appropriate to 
implement a phased-in approach to recalibrating the PDPM parity 
adjustment. Therefore, after considering these comments, and in order 
to balance mitigating the financial impact on providers of 
recalibrating the PDPM parity adjustment with ensuring accurate 
Medicare Part A SNF payments, we are finalizing the proposed 
recalibration of the PDPM parity adjustment with a 2-year phase-in 
period, resulting in a 2.3 percent reduction in FY 2023 ($780 million) 
and a 2.3 percent reduction in FY 2024.

D. Request for Information: Infection Isolation

    Under the SNF PPS, various patient characteristics are used to 
classify patients in Medicare-covered SNF stays into payment groups. 
One of these characteristics is isolation due to an active infection. 
In order for a patient to qualify to be coded as being isolated for an 
active infectious disease, the patient must meet all of the following 
criteria:
    1. The patient has active infection with highly transmissible or 
epidemiologically significant pathogens that have been acquired by 
physical contact or airborne or droplet transmission.
    2. Precautions are over and above standard precautions. That is, 
transmission-based precautions (contact, droplet, and/or airborne) must 
be in effect.
    3. The patient is in a room alone because of active infection and 
cannot have a roommate. This means that the resident must be in the 
room alone and not cohorted with a roommate regardless of whether the 
roommate has a similar active infection that requires isolation.
    4. The patient must remain in his or her room. This requires that 
all services be brought to the resident (for example, rehabilitation, 
activities, dining, etc.).
    Being coded for infection isolation can have a significant impact 
on the Medicare payment rate for a patient's SNF stay. The increase in 
a SNF patient's payment rate as a result of being coded under infection 
isolation is driven by the increase in the relative costliness of 
treating a patient who must be isolated due to an infection. More 
specifically, in 2005, we initiated a national nursing home staff time 
measurement (STM) study, the Staff Time and Resource Intensity 
Verification (STRIVE) Project. The STRIVE project was the first 
nationwide time study for nursing homes in the United States to be 
conducted since 1997, and the data collected were used to establish 
payment systems for Medicare skilled nursing facilities (SNFs) as well 
as Medicaid nursing facilities (NFs).
    In the STRIVE project final report, titled ``Staff Time and 
Resource Intensity Verification Project Phase II'' section 4.8 
(available at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/TimeStudy), we discussed how infection isolation was 
categorized into the Extensive Services RUG-III category based on the 
high resource intensity that was required for treating patients for 
whom facilities would code this category on the MDS. The significant 
increase in payment associated with this item is intended to account 
for the increase in relative resource utilization and costs associated 
with treating a patient isolated due to an active infection, as well as 
the PPE and additional protocols which must be followed treating such a 
patient, which are significantly greater than treating patients outside 
of such an environment.
    During the COVID-19 PHE, a number of interested parties raised 
concerns with the definition of ``infection isolation'', as it relates 
to the treatment of SNF patients being cohorted due to either the 
diagnosis or suspected diagnosis of COVID-19. Specifically, interested 
parties took issue with criterion 1, which requires that the patient 
have an active infection, rather than suspicion of an active infection, 
and criterion 3, which requires that the patient be in the room alone, 
rather than being cohorted with other patients. To this point, we have 
maintained that the definition of ``infection isolation'' is 
appropriate and should not be changed in response to the circumstances 
of the COVID-19 PHE. Due to the ubiquitous nature of the PHE and 
precautions that are being taken throughout SNFs with regard to PPE and 
other COVID-19 related needs, we understand that the general costs for 
treating all SNF patients may have increased. However, as the case-mix 
classification model is intended to adjust payments based on relative 
differences in the cost of treating different SNF patients, we are 
unclear on if the relative increase in resource intensity for each 
patient being treated within a cohorted environment is the same 
relative increase as it would be for treating a single patient isolated 
due to an active infection.
    We invited the public to submit their comments about isolation due 
to active infection and how the PHE has affected the relative staff 
time resources necessary for treating these patients. Specifically, we 
invited comments on whether or not the relative increase in resource 
utilization for each of the patients within a cohorted room, all with 
an active infection, is the same or

[[Page 47535]]

comparable to that of the relative increase in resource utilization 
associated with a patient that is isolated due to an active infection. 
We received public comments on this request for information. The 
following is a summary of the comments we received and our responses.
    Comment: We received several comments on this request for 
information. Commenters suggested that criterion 1 and criterion 3 
above should be revised. More specifically, commenters recommended that 
criterion 1 be revised to allow for ``suspected,'' rather than only 
active, cases of infection. Additionally, commenters recommended that 
criterion 3 be revised to allow providers to code infection isolation 
in cases where patients are cohorted due to an active infection. These 
commenters provided evidence to suggest that the costs of caring for 
cohorted patients are similar to those of a patient that is isolated 
due to active infection. Some commenters further suggested that CMS 
consider adding items to the MDS that would allow coding for cohorted 
patients, with the possibility of a lower CMI adjustment for such 
patients, as compared to those in full isolation. Some commenters also 
recommended revisions to the MDS manual and coding guidance to ensure 
that coding for infection isolation is consistent with CDC guidance. 
Finally, some commenters suggested that CMS consider a new time study 
to evaluate the cost of treating cohorted patients isolated with an 
active infection.
    Response: We appreciate the comments that we received on this 
request for information and will consider these comments as we plan for 
future rulemaking on this issue.

VII. Skilled Nursing Facility Quality Reporting Program (SNF 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-critical access hospital (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 
productivity 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 2023 SNF QRP
    The SNF QRP currently has 15 measures for the FY 2023 SNF QRP, 
which are outlined in Table 15. For a discussion of the factors used to 
evaluate whether a measure should be removed from the SNF QRP, we refer 
readers to Sec.  413.360(b)(3).
BILLING CODE 4120-01-P

[[Page 47536]]

[GRAPHIC] [TIFF OMITTED] TR03AU22.015

BILLING CODE 4120-01-C

C. SNF QRP Quality Measures Beginning With the FY 2025 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) of the Act) 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 proposed to adopt one new measure for the SNF QRP beginning with 
the FY 2025 SNF QRP: the Influenza Vaccination Coverage among 
Healthcare Personnel (HCP) (NQF #0431) measure as an ``other measure'' 
under section 1899B(d)(1) of the Act. In accordance with section 
1899B(a)(1)(B) of the Act, the data used to calculate this measure are 
standardized and interoperable. As proposed, the measure supports the 
``Preventive Care'' Meaningful Measure area and the ``Promote Effective 
Prevention and Treatment of Chronic Disease'' healthcare priority.\9\ 
The Influenza Vaccination Coverage among HCP measure (the HCP Influenza 
Vaccine measure) is a process measure, developed by the Centers for 
Disease Control and Prevention (CDC), and reports on the percentage of 
HCP who receive the influenza vaccination. This measure is currently 
used in other post-acute care (PAC) Quality Reporting Programs (QRPs), 
including the Inpatient Rehabilitation Facility (IRF) QRP and the Long-
Term Care Hospital (LTCH) QRP. The measure is described in more detail 
in section VII.C.1. of this final rule.
---------------------------------------------------------------------------

    \9\ CMS Measures Inventory Tool. (2022). Influenza Vaccination 
Coverage among Healthcare Personnel. Retrieved from https://cmit.cms.gov/CMIT_public/ReportMeasure?measureId=854.
---------------------------------------------------------------------------

    In addition, we proposed to revise the compliance date for the 
collection of the Transfer of Health (TOH) Information to the Provider-
PAC measure, the TOH Information to the Patient-PAC measure, and 
certain standardized patient assessment data elements from October 1st 
of the year that is at least 2 full fiscal

[[Page 47537]]

years after the end of the COVID-19 PHE to October 1, 2023. We believe 
the COVID-19 PHE revealed why the TOH Information measures and 
standardized patient assessment data elements are important to the SNF 
QRP. The new data elements will facilitate communication and 
coordination across care settings as well as provide information to 
support our mission of analyzing the impact of the COVID-19 PHE on 
patients to improve the quality of care in SNFs. We described the 
proposal in more detail in section VI.C.2. of the proposed rule.
    We also proposed to make certain revisions to regulation text at 
Sec.  413.360 to include a new paragraph to reflect all the data 
completion thresholds required for SNFs to meet the compliance 
threshold for the annual payment update (APU), as well as certain 
conforming revisions. We described the proposal in more detail in 
section VI.C.3. of the proposed rule.
1. Influenza Vaccination Coverage Among Healthcare Personnel (NQF 
#0431) Measure Beginning With the FY 2025 SNF QRP
a. Background
    The CDC Advisory Committee on Immunization Practices (ACIP) 
recommends that all persons 6 months of age and older, including HCP 
and persons training for professions in healthcare, should be 
vaccinated annually against influenza.\10\ The basis of this 
recommendation stems from the spells of illness, hospitalizations, and 
mortality associated with the influenza virus. Between 2010 and 2020, 
the influenza virus resulted in 12,000 to 52,000 deaths in the United 
States each year, depending on the severity of the 
strain.11 12 Preliminary estimates from the CDC revealed 35 
million cases, 380,000 hospitalizations, and 20,000 deaths linked to 
influenza in the United States during the 2019 to 2020 influenza 
season.\13\ Persons aged 65 years and older are at higher risk for 
experiencing burdens related to severe influenza due to the changes in 
immune defenses that come with increasing age.14 15 The CDC 
estimates that 70 to 85 percent of seasonal influenza-related deaths 
occur among people aged 65 years and older, and 50 to 70 percent of 
influenza-related hospitalizations occur among this age group.\16\ 
Residents of long-term care facilities, who are often of older age, 
have greater susceptibility for acquiring influenza due to general 
frailty and comorbidities, close contact with other residents, 
interactions with visitors, and exposure to staff who rotate between 
multiple facilities.17 18 19 Therefore, monitoring and 
reporting influenza vaccination rates among HCP is important as HCP are 
at risk for acquiring influenza from residents and exposing influenza 
to residents.\20\ For example, one early report of HCP influenza 
infections during the 2009 H1N1 influenza pandemic estimated 50 percent 
of HCP had contracted the influenza virus from patients or coworkers 
within the healthcare setting.\21\
---------------------------------------------------------------------------

    \10\ Grohskopf, L.A., Alyanak, E., Broder, K.R., Walter, E.B., 
Fry, A.M., & Jernigan, D.B. (2019). Prevention and Control of 
Seasonal Influenza with Vaccines: Recommendations of the Advisory 
Committee on Immunization Practices -- United States, 2019-20 
Influenza Season. MMWR Recommendations and Reports, 68(No. RR-3), 1-
21. https://www.cdc.gov/mmwr/volumes/68/rr/rr6803a1.htm?s_cid=rr6803a1_w.
    \11\ Centers for Disease Control and Prevention (CDC). (2021). 
Disease Burden of Flu. Retrieved from https://www.cdc.gov/flu/about/burden/?CDC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Fflu%2Fabout%2Fdisease%2Fus_flu-related_deaths.htm.
    \12\ Frentzel, E., Jump, R., Archbald-Pannone, L., Nace, D.A., 
Schweon, S.J., Gaur, S., Naqvi, F., Pandya, N., Mercer, W., & 
Infection Advisory Subcommittee of AMDA, The Society for Post-Acute 
and Long-Term Care Medicine (2020). Recommendations for Mandatory 
Influenza Vaccinations for Health Care Personnel From AMDA's 
Infection Advisory Subcommittee. Journal of the American Medical 
Directors Association, 21(1), 25-28.e2. https://doi.org/10.1016/j.jamda.2019.11.008.
    \13\ Centers for Disease Control and Prevention (CDC). (2021). 
Estimated Flu-Related Illnesses, Medical Visits, Hospitalizations, 
and Deaths in the United States--2019-2020 Flu Season. Retrieved 
from https://www.cdc.gov/flu/about/burden/2019-2020.html.
    \14\ Centers for Disease Control and Prevention (CDC). (2021). 
Retrieved from Flu & People 65 Years and Older: https://www.cdc.gov/flu/highrisk/65over.htm?CDC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Fflu%2Fabout%2Fdi
sease%2F65over.htm.
    \15\ Frentzel, E., Jump, R., Archbald-Pannone, L., Nace, D.A., 
Schweon, S.J., Gaur, S., Naqvi, F., Pandya, N., Mercer, W., & 
Infection Advisory Subcommittee of AMDA, The Society for Post-Acute 
and Long-Term Care Medicine (2020). Recommendations for Mandatory 
Influenza Vaccinations for Health Care Personnel From AMDA's 
Infection Advisory Subcommittee. Journal of the American Medical 
Directors Association, 21(1), 25-28.e2. https://doi.org/10.1016/j.jamda.2019.11.008.
    \16\ Centers for Disease Control and Prevention (CDC). (2021). 
Retrieved from Flu & People 65 Years and Older: https://www.cdc.gov/flu/highrisk/65over.htm?CDC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Fflu%2Fabout%2Fdi
sease%2F65over.htm.
    \17\ Lansbury, L.E., Brown, C.S., & 
Nguyen[hyphen]Van[hyphen]Tam, J.S. (2017). Influenza in 
long[hyphen]term care facilities. Influenza Other Respir Viruses, 
11(5), 356-366. https://dx.doi.org/10.1111%2Firv.12464.
    \18\ Pop-Vicas, A., & Gravenstein, S. (2011). Influenza in the 
elderly: a mini-review. Gerontology, 57(5), 397-404. https://doi.org/10.1159/000319033.
    \19\ Strausbaugh, L.J., Sukumar, S.R., & Joseph, C.L. (2003). 
Infectious disease outbreaks in nursing homes: an unappreciated 
hazard for frail elderly persons. Clinical Infectious Diseases, 
36(7), 870-876. https://doi.org/10.1086/368197.
    \20\ Wilde, J.A., McMillan, J.A., Serwint, J., Butta, J., 
O'Riordan, M.A., & Steinhoff, M.C. (1999). Effectiveness of 
influenza vaccine in health care professionals: a randomized trial. 
JAMA, 281(10), 908-913. https://doi.org/10.1001/jama.281.10.908.
    \21\ Harriman, K., Rosenberg, J., Robinson, S., et al. (2009). 
Novel influenza A (H1N1) virus infections among health-care 
personnel--United States, April-May 2009. MMWR Morbidity and 
Mortality Weekly Report, 58(23), 641-645. Retrieved from https://www.cdc.gov/mmwr/preview/mmwrhtml/mm5823a2.htm.
---------------------------------------------------------------------------

    Despite the fact that influenza commonly spreads between HCP and 
SNF residents, vaccine hesitancy and organizational barriers often 
prevent influenza vaccination. For example, although the CDC emphasizes 
the importance for HCP to receive the influenza vaccine, the 2017 to 
2018 influenza season shows higher influenza vaccination coverage among 
HCP working in hospitals (approximately 92 percent) and lower coverage 
among those working in long-term care facilities (approximately 68 
percent).22 23 HCP working in long-term care facilities, 
including SNFs, have expressed concerns about the influenza vaccine's 
effectiveness and safety, fearing potential side effects and adverse 
reactions.\24\ Other HCP believe healthy individuals are not 
susceptible to infection and therefore find vaccination 
unnecessary.\25\ In addition, many HCP do not prioritize influenza 
vaccination, expressing a lack of time to get vaccinated.\26\ Lower HCP 
influenza vaccination in long-term care facilities also stems from 
organizational barriers,

[[Page 47538]]

such as inadequate vaccine recordkeeping, frequent staff turnover, an 
absence of influenza vaccine mandates, a lack of communication about 
vaccination rates, and a lack of incentives encouraging HCP flu 
vaccination.\27\ Given the fact that influenza vaccination coverage 
among HCP is typically lower in long-term care settings, such as SNFs, 
when compared to other care settings, we noted in the proposed rule 
that we believe the measure as proposed has the potential to increase 
influenza vaccination coverage in SNFs, promote patient safety, and 
increase the transparency of quality of care in the SNF setting.
---------------------------------------------------------------------------

    \22\ Black, C.L., Yue, X., Ball, S.W., Fink, R.V., de Perio, 
M.A., Laney, A.S., Williams, W.W., Graitcer, S.B., Fiebelkorn, A.P., 
Lu, P.J., & Devlin, R. (2018). Influenza Vaccination Coverage Among 
Health Care Personnel--United States, 2017-18 Influenza Season. MMWR 
Morbidity and Mortality Weekly Report, 67(38), 1050-1054. https://doi.org/10.15585/mmwr.mm6738a2.
    \23\ Jaklevic, M.C. (2020). Flu Vaccination Urged During COVID-
19 Pandemic. JAMA. 324(10), 926-927. https://doi.org/10.1001/jama.2020.15444.
    \24\ Frentzel, E., Jump, R., Archbald-Pannone, L., Nace, D.A., 
Schweon, S.J., Gaur, S., Naqvi, F., Pandya, N., Mercer, W., & 
Infection Advisory Subcommittee of AMDA, The Society for Post-Acute 
and Long-Term Care Medicine (2020). Recommendations for Mandatory 
Influenza Vaccinations for Health Care Personnel From AMDA's 
Infection Advisory Subcommittee. Journal of the American Medical 
Directors Association, 21(1), 25-28.e2. https://doi.org/10.1016/j.jamda.2019.11.008.
    \25\ Kenny, E., McNamara, [Aacute]., Noone, C., & Byrne, M. 
(2020). Barriers to seasonal influenza vaccine uptake among health 
care workers in long-term care facilities: A cross-sectional 
analysis. British Journal of Health Psychology, 25(3), 519-539. 
https://doi.org/10.1111/bjhp.12419.
    \26\ Kose, S., Mandiracioglu, A., Sahin, S., Kaynar, T., Karbus, 
O., & Ozbel, Y. (2020). Vaccine hesitancy of the COVID-19 by health 
care personnel. International Journal of Clinical Practice, 75(5), 
e13917. https://doi.org/10.1111/ijcp.13917.
    \27\ Ofstead, C.L., Amelang, M.R., Wetzler, H.P., & Tan, L. 
(2017). Moving the needle on nursing staff influenza vaccination in 
long-term care: Results of an evidence-based intervention. Vaccine, 
35(18), 2390-2395. https://doi.org/10.1016/j.vaccine.2017.03.041.
---------------------------------------------------------------------------

    Although concerns about vaccine effectiveness often prevent some 
HCP from getting the influenza vaccine, the CDC notes that higher 
influenza vaccination rates reduce the risk of influenza-related 
illness between 40 to 60 percent among the overall population during 
seasons when the circulating influenza virus is well-matched to viruses 
used to make influenza vaccines.\28\ During the 2019 to 2020 influenza 
season, vaccinations prevented 7.5 million influenza-related illnesses, 
105,000 influenza-related hospitalizations, and 6,300 deaths.\29\ 
Additionally, among adults with influenza-associated hospitalization, 
influenza vaccination is also associated with a 26 percent lower risk 
of intensive care unit admission, and a 31 percent lower risk of 
influenza-related deaths compared to individuals who were unvaccinated 
against influenza.\30\ Several cluster-randomized trials comparing HCP 
influenza vaccination groups to control groups demonstrate reductions 
in long-term care resident mortality rates as related to HCP influenza 
vaccination.31 32 33 34 To reduce vaccine hesitancy and 
organizational barriers to influenza vaccination, several strategies 
can be used to increase influenza vaccination among HCP. These include 
availability of on-site influenza vaccinations and educational 
campaigns about influenza risks and vaccination 
benefits.35 36 37
---------------------------------------------------------------------------

    \28\ Centers for Disease Control and Prevention (CDC). (2021). 
Retrieved from Vaccine Effectiveness: How Well Do Flu Vaccines 
Work?: https://www.cdc.gov/flu/vaccines-work/vaccineeffect.htm.
    \29\ Centers for Disease Control and Prevention (CDC). (2021). 
Retrieved from Vaccine Effectiveness: How Well Do Flu Vaccines 
Work?: https://www.cdc.gov/flu/vaccines-work/vaccineeffect.htm.
    \30\ Ferdinands, J.M., Thompson, M.G., Blanton, L., Spencer, S., 
Grant, L., & Fry, A.M. (2021). Does influenza vaccination attenuate 
the severity of breakthrough infections? A narrative review and 
recommendations for further research. Vaccine, 39(28), 3678-3695. 
https://doi.org/10.1016/j.vaccine.2021.05.011.
    \31\ Carman, W.F., Elder, A.G., Wallace, L.A., McAulay, K., 
Walker, A., Murray, G.D., & Stott, D.J. (2000). Effects of influenza 
vaccination of health-care workers on mortality of elderly people in 
long-term care: a randomised controlled trial. Lancet (London, 
England), 355(9198), 93-97. https://doi.org/10.1016/S0140-6736(99)05190-9.
    \32\ Hayward, A.C., Harling, R., Wetten, S., Johnson, A.M., 
Munro, S., Smedley, J., Murad, S., & Watson, J.M. (2006). 
Effectiveness of an influenza vaccine programme for care home staff 
to prevent death, morbidity, and health service use among residents: 
cluster randomised controlled trial. BMJ (Clinical Research ed.), 
333(7581), 1241. https://doi.org/10.1136/bmj.39010.581354.55.
    \33\ Lemaitre, M., Meret, T., Rothan-Tondeur, M., Belmin, J., 
Lejonc, J.L., Luquel, L., Piette, F., Salom, M., Verny, M., Vetel, 
J.M., Veyssier, P., & Carrat, F. (2009). Effect of influenza 
vaccination of nursing home staff on mortality of residents: a 
cluster-randomized trial. Journal of the American Geriatrics 
Society, 57(9), 1580-1586. https://doi.org/10.1111/j.1532-5415.2009.02402.x.
    \34\ Potter, J., Stott, D.J., Roberts, M.A., Elder, A.G., 
O'Donnell, B., Knight, P.V., & Carman, W.F. (1997). Influenza 
vaccination of health care workers in long-term-care hospitals 
reduces the mortality of elderly patients. Journal of Infectious 
Diseases, 175(1), 1-6. https://doi.org/10.1093/infdis/175.1.1.
    \35\ Bechini, A., Lorini, C., Zanobini, P., Mand[ograve] 
Tacconi, F., Boccalini, S., Grazzini, M., Bonanni, P., & Bonaccorsi, 
G. (2020). Utility of Healthcare System-Based Interventions in 
Improving the Uptake of Influenza Vaccination in Healthcare Workers 
at Long-Term Care Facilities: A Systematic Review. Vaccines (Basel), 
8(2), 165. https://doi.org/10.3390/vaccines8020165.
    \36\ Ofstead, C.L., Amelang, M.R., Wetzler, H.P., & Tan, L. 
(2017). Moving the needle on nursing staff influenza vaccination in 
long-term care: Results of an evidence-based intervention. Vaccine, 
35(18), 2390-2395. https://doi.org/10.1016/j.vaccine.2017.03.041.
    \37\ Yue, X., Black, C., Ball, S., Donahue, S., de Perio, M.A., 
Laney, A.S., & Greby, S. (2019). Workplace Interventions and 
Vaccination-Related Attitudes Associated With Influenza Vaccination 
Coverage Among Healthcare Personnel Working in Long-Term Care 
Facilities, 2015-2016 Influenza Season. Journal of the American 
Medical Directors Association, 20(6), 718-724. https://doi.org/10.1016/j.jamda.2018.11.029.
---------------------------------------------------------------------------

    Addressing HCP influenza vaccination in SNFs is particularly 
important as vulnerable populations often reside in SNFs. Vulnerable 
populations are less likely to receive the influenza vaccine, and thus, 
are susceptible to contracting the virus. For example, not only are 
Black residents more likely to receive care from facilities with lower 
overall influenza vaccination rates, but Black residents are also less 
likely to be offered and receive influenza vaccinations in comparison 
to White residents.38 39 40 41 Racial and ethnic disparities 
in influenza vaccination, specifically among Black and Hispanic 
populations, are also higher among short-stay residents receiving care 
for less than 100 days in the nursing home.\42\ Additionally, Medicare 
fee-for-service beneficiaries of Black, Hispanic, rural, and lower-
income populations are less likely to receive inactivated influenza 
vaccines, and non-White beneficiaries are generally less likely to 
receive high-dose influenza vaccines in comparison to White 
beneficiaries.43 44 45 Therefore, the measure as proposed 
has the potential to increase influenza vaccination coverage of HCP in 
SNFs, as well as prevent the spread of the influenza virus to 
vulnerable populations who are less likely to receive influenza 
vaccinations.
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    \38\ Cai, S., Feng, Z., Fennell, M.L., & Mor, V. (2011). Despite 
small improvement, black nursing home residents remain less likely 
than whites to receive flu vaccine. Health Affairs (Project Hope), 
30(10), 1939-1946. https://doi.org/10.1377/hlthaff.2011.0029.
    \39\ Luo, H., Zhang, X., Cook, B., Wu, B., & Wilson, M.R. 
(2014). Racial/Ethnic Disparities in Preventive Care Practice Among 
U.S. Nursing Home Residents. Journal of Aging and Health, 26(4), 
519-539. https://doi.org/10.1177/0898264314524436.
    \40\ Mauldin, R.L., Sledge, S.L., Kinney, E.K., Herrera, S., & 
Lee, K. (2021). Addressing Systemic Factors Related to Racial and 
Ethnic Disparities among Older Adults in Long-Term Care Facilities. 
IntechOpen.
    \41\ Travers, J.L., Dick, A.W., & Stone, P.W. (2018). Racial/
Ethnic Differences in Receipt of Influenza and Pneumococcal 
Vaccination among Long-Stay Nursing Home Residents. Health Services 
Research, 53(4), 2203-2226. https://doi.org/10.1111/1475-6773.12759.
    \42\ Riester, M.R., Bosco, E., Bardenheier, B.H., Moyo, P., 
Baier, R.R., Eliot, M., Silva, J.B., Gravenstein, S., van Aalst, R., 
Chit, A., Loiacono, M.M., & Zullo, A.R. (2021). Decomposing Racial 
and Ethnic Disparities in Nursing Home Influenza Vaccination. 
Journal of the American Medical Directors Association, 22(6), 1271-
1278.e3. https://doi.org/10.1016/j.jamda.2021.03.003.
    \43\ Hall, L.L., Xu, L., Mahmud, S.M., Puckrein, G.A., Thommes, 
E.W., & Chit, A. (2020). A Map of Racial and Ethnic Disparities in 
Influenza Vaccine Uptake in the Medicare Fee-for-Service Program. 
Advances in Therapy, 37(5), 2224-2235. https://doi.org/10.1007/s12325-020-01324-y.
    \44\ Inactivated vaccines use the killed version of the germ 
that causes a disease. Inactivated vaccines usually don't provide 
immunity (protection) that is as strong as the live vaccines. For 
more information regarding inactivated vaccines we refer readers to 
the following web page: https://hhs.gov/immunization/basics/types/.
    \45\ High-dose flu vaccines contain four times the amount of 
antigen (the inactivated virus that promotes a protective immune 
response) as a regular flu shot. They are associated with a stronger 
immune response following vaccination. For more information 
regarding high-dose flu vaccines, we refer readers to the following 
web page: https://www.cdc.gov/flu/highrisk/65over.htm.
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    The COVID-19 pandemic has exposed the importance of implementing 
infection prevention strategies, including the promotion of HCP 
influenza vaccination. Activity of the influenza virus has been lower 
during the COVID-19 pandemic as several strategies to reduce the spread 
of COVID-19 have also reduced the spread of influenza, including mask 
mandates, social distancing, and increased hand

[[Page 47539]]

hygiene.\46\ However, even though more people are receiving COVID-19 
vaccines, it is still important to encourage annual HCP influenza 
vaccination to prevent healthcare systems from getting overwhelmed by 
the co-circulation of COVID-19 and influenza viruses. A 2020 literature 
search revealed several studies in which those with severe cases of 
COVID-19, requiring hospitalization, were less likely to be vaccinated 
against influenza.\47\ HCP vaccinations against influenza may prevent 
the spread of illness between HCP and residents, thus reducing resident 
morbidities associated with influenza and pressure on already stressed 
healthcare systems. In fact, several thousand nursing homes voluntarily 
reported weekly influenza vaccination coverage through a National 
Healthcare Safety Network (NHSN) module based on the NQF #0431 measure 
during the overlapping 2020 to 2021 influenza season and COVID-19 
pandemic. Even after the COVID-19 pandemic ends, promoting HCP 
influenza vaccination is important in preventing morbidity and 
mortality associated with influenza.
---------------------------------------------------------------------------

    \46\ Wang, X., Kulkarni, D., Dozier, M., Hartnup, K., Paget, J., 
Campbell, H., Nair, H., & Usher Network for COVID-19 Evidence 
Reviews (UNCOVER) group (2020). Influenza vaccination strategies for 
2020-21 in the context of COVID-19. Journal of Global Health, 10(2), 
021102. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7719353/.
    \47\ Del Riccio, M., Lorini, C., Bonaccorsi, G., Paget, J., & 
Caini, S. (2020). The Association between Influenza Vaccination and 
the Risk of SARS-CoV-2 Infection, Severe Illness, and Death: A 
Systematic Review of the Literature. International Journal of 
Environmental Research and Public Health, 17(21), 7870. https://doi.org/10.3390/ijerph17217870.
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    As discussed in the proposed rule, variation in influenza 
vaccination coverage rates indicate the proposed measure's usability 
and use. A CDC analysis during the 2020 to 2021 influenza season 
revealed that among 16,535 active, CMS-certified nursing homes, 17.3 
percent voluntarily submitted data for the proposed measure through the 
NHSN. Average staff influenza vaccination coverage was approximately 64 
percent, ranging from 0.3 percent to 100 percent with an interquartile 
range of 40 to 93.9 percent. Variation in influenza vaccination 
coverage rates by facility demonstrates the utility of the measure for 
resident choice of facility. Variation in influenza vaccination rates 
by type of HCP demonstrates the utility of the proposed measure for 
targeted quality improvement efforts.
    For these reasons, we proposed to adopt the CDC-developed Influenza 
Vaccination Coverage among Healthcare Personnel (NQF #0431) measure for 
the SNF QRP, as collected through the CDC's NHSN, to report the 
percentage of HCP who receive the influenza vaccine. We explained in 
the proposed rule that we believe this measure will encourage HCP to 
receive the influenza vaccine, resulting in fewer cases, less 
hospitalizations, and lower mortality associated with the virus.
b. Stakeholder Input and Pilot Testing
    In the development and specification of this 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 in 
accordance with section 1890A of the Act. To meet this requirement, 
opportunities were provided for stakeholder input by a Delphi panel and 
Steering Committee through the measure's pilot testing. The measure's 
pilot testing assessed reliability and validity among 234 facilities 
and five facility types (that is, long-term care facilities, acute care 
hospitals, ambulatory surgery centers, physician practices, and 
dialysis centers) across four jurisdictions (that is, California, New 
Mexico, New York City, and western Pennsylvania) between 2010 and 
2011.48 49
---------------------------------------------------------------------------

    \48\ Libby, T.E., Lindley, M.C., Lorick, S.A., MacCannell, T., 
Lee, S.J., Smith, C., Geevarughese, A., Makvandi, M., Nace, D.A., & 
Ahmed, F. (2013). Reliability and validity of a standardized measure 
of influenza vaccination coverage among healthcare personnel. 
Infection Control and Hospital Epidemiology, 34(4), 335-345. https://doi.org/10.1086/669859.
    \49\ The Libby et al. (2013) article (preceding footnote) is 
referenced throughout the entirety of section VI.C.1.b. of this 
rule.
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    Two methods were used to conduct reliability testing, including 
interrater reliability testing and the use of case studies. Interrater 
reliability was assessed among 96 facilities, including 19 long-term 
care facilities, by comparing agreement between two raters: facility 
staff and project staff. Project staff reviewed individual-level 
records from randomly selected facilities to assess agreement with how 
facility staff classified HCP into numerator and denominator 
categories. For more information regarding numerator and denominator 
definitions, refer to section VI.C.1.e. of the proposed rule. 
Interrater reliability results demonstrated high adjusted agreement 
between facility and project staff for numerator data (91 percent) and 
denominator data (96 percent). Most numerator disagreements resulted 
from healthcare facilities reporting verbal declinations in the 
``declined vaccination'' numerator rather than categorizing verbal 
declinations as ``missing/unknown'' as there was no written 
documentation of the declination. There was also numerator disagreement 
related to contraindications as HCP did not properly cite true medical 
contraindications. Adhering to true medical contraindications and 
tracking declinations of the influenza vaccine among HCP should 
additionally improve reliability.
    Case studies were also used to assess reliability. Facilities 
received a series of 23 vignettes, in which they were instructed to 
select appropriate numerator and denominator categories for the 
hypothetical cases described in each vignette. Most numerator and 
denominator elements were categorized correctly. For example, 95.6 
percent of facility staff correctly categorized employees that were 
vaccinated at the facility, 88.6 percent correctly categorized 
employees vaccinated elsewhere, etc.\50\ However, problematic 
denominator elements included poor facility understanding of how to 
classify physician-owners of healthcare facilities who work part-time 
and physicians who were credentialed by a facility but had not admitted 
patients in the past 12 months. Problematic numerator elements were 
related to confusion about reporting persistent deferrals of 
vaccination and verbal vaccine declinations for non-medical reasons.
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    \50\ For a full list of case study categorization results, 
please refer to the following study: Libby, T.E., Lindley, M.C., 
Lorick, S.A., MacCannell, T., Lee, S.J., Smith, C., Geevarughese, 
A., Makvandi, M., Nace, D.A., & Ahmed, F. (2013). Reliability and 
validity of a standardized measure of influenza vaccination coverage 
among healthcare personnel. Infection Control and Hospital 
Epidemiology, 34(4), 335-345. https://doi.org/10.1086/669859.
---------------------------------------------------------------------------

    Two methods were also used for validity testing: convergent 
validity assessments and face validity assessment. Convergent validity 
examined the association between the number of evidence-based 
strategies used by a healthcare facility to promote influenza 
vaccination and the facility's reported vaccination rate among each HCP 
denominator group. The association between employee vaccination rates 
and the number of strategies used was borderline significant. The 
association between credentialed non-employee vaccination rates and the 
number of strategies used was significant, and the association between 
other non-employee vaccination rates and the number of strategies used 
was also significant, demonstrating convergent validity.
    Face validity was assessed through a Delphi panel, which convened 
in June 2011 and provided stakeholder input on the proposed measure. 
The Delphi

[[Page 47540]]

panel, comprised of nine experts in influenza vaccination measurement 
and quality improvement from several public and private organizations, 
rated elements of the proposed measure using a Likert scale. The Delphi 
panel discussed pilot testing results from the first round of ratings 
during a one-hour moderated telephone conference. After the conference 
concluded, panelists individually rated a revised set of elements. 
Ultimately, the Delphi panel reached a consensus that the majority of 
the proposed measure's numerator definitions had strong face validity. 
However, the panel raised concerns regarding the accuracy of self-
reported data and deemed validity lowest for denominator categories of 
credentialed and other nonemployees of the facility.
    After the conclusion of measure testing, the proposed measure's 
specifications were revised in alignment with the Delphi panel's 
ratings and with guidance from a Steering Committee. The CDC-convened 
Steering Committee was comprised of representatives from several 
institutions, including CMS, the Joint Commission, the Federation of 
American Hospitals, the American Osteopathic Association, the American 
Medical Association, and others. To address concerns raised through 
pilot testing and to reduce institutional barriers to reporting, 
denominator specifications were revised to include a more limited 
number of HCP among whom vaccination could be measured with greater 
reliability and accuracy: employees; licensed independent 
practitioners; and adult students/trainees and volunteers. The measure 
was also revised to require vaccinations received outside of the 
facility to be documented, but allow for self-report of declinations 
and medical contraindications. Verbal declinations were assigned to the 
``declined'' numerator category, and an ``unknown'' category was added 
to give facilities actionable data on unvaccinated HCP who may not have 
purposefully declined. For more information regarding pilot testing 
results and measure input from the Delphi panel and Steering Committee, 
refer to the article published in the Infection Control & Hospital 
Epidemiology journal by the measure developer.\51\
---------------------------------------------------------------------------

    \51\ Libby, T.E., Lindley, M.C., Lorick, S.A., MacCannell, T., 
Lee, S.J., Smith, C., Geevarughese, A., Makvandi, M., Nace, D.A., & 
Ahmed, F. (2013). Reliability and validity of a standardized measure 
of influenza vaccination coverage among healthcare personnel. 
Infection Control and Hospital Epidemiology, 34(4), 335-345. https://doi.org/10.1086/669859.
---------------------------------------------------------------------------

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 in the list.
    We included the Influenza Vaccination Coverage among HCP measure 
under the SNF QRP Program in the publicly available ``List of Measures 
Under Consideration for December 1, 2021'' (MUC List).\52\ Shortly 
after, several National Quality Forum (NQF)-convened Measure 
Applications Partnership (MAP) workgroups met virtually to provide 
input on the proposed measure. The MAP Rural Health workgroup convened 
on December 8, 2021. Members generally agreed that the proposed measure 
would be suitable for use by rural providers within the SNF QRP 
program, noting the measure's rural relevance. Likewise, the MAP Health 
Equity workgroup met on December 9, 2021, in which the majority of 
voting members agreed that the proposed measure has potential for 
decreasing health disparities. The MAP Post-Acute Care/Long-Term Care 
(PAC/LTC) workgroup met on December 16, 2021, in which the majority of 
voting workgroup members supported rulemaking of the proposed measure. 
Finally, the MAP Coordinating Committee convened on January 19, 2022, 
in which the committee agreed with the MAP's preliminary measure 
recommendation of support for rulemaking.
---------------------------------------------------------------------------

    \52\ Centers for Medicare & Medicaid Services. (2021). List of 
Measures Under Consideration for December 1, 2021. CMS.gov. https://www.cms.gov/files/document/measures-under-consideration-list-2020-report.pdf.
---------------------------------------------------------------------------

    In addition to receiving feedback from MAP workgroup and committee 
members, NQF received four comments by industry stakeholders during the 
proposed measure's MAP pre-rulemaking process. Commenters were 
generally supportive of the measure as SNF QRP adoption would promote 
measure interoperability, encourage vaccination, and likely decrease 
the spread of infection. One commenter was not supportive of the 
measure due to burdens of NHSN data submission.
    Overall, the MAP offered support for rulemaking, noting that the 
measure aligns with the IRF and LTCH PAC QRPs and adds value to the 
current SNF QRP measure set since influenza vaccination among HCP is 
not currently addressed within the SNF QRP program. The MAP noted the 
importance of vaccination coverage among HCP as an actionable strategy 
that can decrease viral transmission, morbidity, and mortality within 
SNFs. The final MAP report is available at https://www.qualityforum.org/Publications/2022/03/MAP_2021-2022_Considerations_for_Implementing_Measures_Final_Report_-_Clinicians,_Hospitals,_and_PAC-LTC.aspx.
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 under section 1899B of the Act be endorsed by the entity with 
a contract under section 1890(a) of the Act, currently the 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 Influenza Vaccination Coverage among HCP measure 
initially received NQF endorsement in 2008 as NQF #0431. Measure 
endorsement was renewed in 2017, and the measure is due for maintenance 
in the spring 2022 cycle. The measure was originally tested in nursing 
homes and has been endorsed by NQF for use in nursing home settings 
since the measure was first endorsed. No additional modifications were 
made to the proposed measure for the spring 2022 measure maintenance 
cycle, but as noted in section VI.C.1.a. of the proposed rule, several 
thousand nursing homes voluntarily reported weekly influenza 
vaccination coverage through an NHSN module based on the NQF #0431 
measure during the overlapping 2020 to 2021 influenza season and COVID-
19 pandemic. The measure is currently used in several of our programs, 
including the Hospital Inpatient and Prospective Payment System (PPS)-
Exempt Cancer Hospital QRPs. Among PAC programs, the proposed measure 
is also reported in the IRF and LTCH QRPs as adopted in the FY 2014 IRF 
PPS final rule (78 FR 47905 through 47906) and the FY 2013 Inpatient 
Prospective Payment System (IPPS)/LTCH PPS final rule (77 FR 53630 
through 53631), respectively.

[[Page 47541]]

    After review of the NQF's consensus-endorsed measures, we were 
unable to identify any NQF-endorsed measures for SNFs focused on 
capturing influenza vaccinations among HCP. For example, although the 
Percent of Residents or Patients Who Were Assessed and Appropriately 
Given the Seasonal Influenza Vaccine (Short Stay) (NQF #0680) and the 
Percent of Residents Assessed and Appropriately Given the Seasonal 
Influenza Vaccine (Long Stay) (NQF #0681) measures are both NQF-
endorsed and assess rates of influenza vaccination, they assess 
vaccination rates among residents in the nursing home rather than HCP 
in the SNF. Additionally, the Percent of Programs of All-Inclusive Care 
for the Elderly (PACE) Healthcare Personnel with Influenza Immunization 
measure resembles the proposed measure since it assesses influenza 
vaccination among HCP; however, it is not NQF-endorsed and is not 
specific to the SNF setting.
    Therefore, after consideration of other available measures, we 
found the NQF-endorsed Influenza Vaccination Coverage among HCP measure 
appropriate for the SNF QRP, and we proposed the measure beginning with 
the FY 2025 SNF QRP. Application of the Influenza Vaccination Coverage 
among HCP measure within the SNF QRP promotes measure harmonization 
across quality reporting programs that also report this measure. 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 Influenza Vaccination Coverage among HCP measure is a process 
measure developed by the CDC to track influenza vaccination coverage 
among HCP in facilities such as SNFs. The measure reports on the 
percentage of HCP who receive influenza vaccination. The term 
``healthcare personnel'' refers to all paid and unpaid persons working 
in a healthcare setting, contractual staff not employed by the 
healthcare facility, and persons not directly involved in patient care 
but potentially exposed to infectious agents that can be transmitted to 
and from HCP. As explained in the proposed rule, since the proposed 
measure is a process measure, rather than an outcome measure, it does 
not require risk-adjustment.
    The proposed measure's denominator is the number of HCP who are 
physically present in the healthcare facility for at least 1 working 
day between October 1st and March 31st of the following year, 
regardless of clinical responsibility or patient contact. The proposed 
measure's reporting period is October 1st through March 31st; this 
reporting period refers to the proposed measure's denominator only. The 
denominator would be calculated separately for three required 
categories: Employees, meaning all persons who receive a direct 
paycheck from the reporting facility (that is, on the SNF's payroll); 
Licensed independent practitioners,\53\ such as physicians, advanced 
practice nurses, and physician assistants who are affiliated with the 
reporting facility, who do not receive a direct paycheck from the 
reporting facility; and Adult students/trainees and volunteers who do 
not receive a direct paycheck from the reporting facility. A 
denominator can be calculated for an optional category as well: Other 
contract personnel are defined as persons providing care, treatment, or 
services at the facility through a contract who do not fall into any of 
the three required denominator categories.
---------------------------------------------------------------------------

    \53\ Refer to the proposed measure's specifications in The 
National Healthcare Safety Network (NSHN) Manual Healthcare 
Personnel Safety Component Protocol--Healthcare Personnel 
Vaccination Module: Influenza Vaccination Summary linked at https://www.cdc.gov/nhsn/pdfs/hps-manual/vaccination/hps-flu-vaccine-protocol.pdf for an exhaustive list of those included in the 
licensed independent practitioners' definition.
---------------------------------------------------------------------------

    The proposed measure's numerator consists of all HCP included in 
the denominator population who received an influenza vaccine any time 
from when it first became available (such as August or September) 
through March 31st of the following year and who fall into one of the 
following categories: (a) received an influenza vaccination 
administered at the healthcare facility; (b) reported in writing (paper 
or electronic) or provided documentation that an influenza vaccination 
was received elsewhere; (c) were determined to have a medical 
contraindication/condition of severe allergic reaction to eggs or other 
component(s) of the vaccine, or a history of Guillain-Barr[eacute] 
syndrome (GBS) within 6 weeks after a previous influenza vaccination; 
(d) were offered but declined the influenza vaccination; or (e) had an 
unknown vaccination status or did not meet any of the definitions of 
the other numerator categories (a through d). As described in the FY 
2014 IRF PPS final rule, measure numerator data are required based on 
data collected from October 1st or whenever the vaccine becomes 
available.\54\ Therefore, if the vaccine is available prior to October 
1st, any vaccine given before October 1st is credited toward 
vaccination coverage. Likewise, if the vaccine becomes available after 
October 1st, the vaccination counts are to begin as soon as possible 
after October 1st.
---------------------------------------------------------------------------

    \54\ FY 2014 IRF PPS final rule. 78 FR 47906.
---------------------------------------------------------------------------

    We proposed that SNFs submit data for the measure through the CDC/
NHSN data collection and submission framework.\55\ In alignment with 
the data submission frameworks utilized for this measure in the IRF and 
LTCH QRPs, SNFs would use the HCP influenza data reporting module in 
the NHSN Healthcare Personnel Safety (HPS) Component and complete two 
forms. SNFs would complete the first form (CDC 57.203) to indicate the 
type of data they plan on reporting to the NHSN by selecting the 
``Influenza Vaccination Summary'' option under ``Healthcare Personnel 
Vaccination Module'' to create a reporting plan. SNFs would then 
complete a second form (CDC 57.214) to report the number of HCP who 
have worked at the healthcare facility for at least 1 day between 
October 1st and March 31st (denominator) and the number of HCP who fall 
into each numerator category. To meet the minimum data submission 
requirements, SNFs would enter a single influenza vaccination summary 
report at the conclusion of the measure reporting period. If SNFs 
submit data more frequently, such as on a monthly basis, the 
information would be used to calculate one summary score for the 
proposed measure which would be publicly reported on Care Compare. See 
sections VI.G.2. and VI.H.2. of the proposed rule for more information 
regarding data submission requirements for this measure and its public 
reporting plan. Details related to the use of NHSN for data submission 
can be found at the CDC's NHSN HPS Component web page at https://www.cdc.gov/nhsn/hps/vaccination/?CDC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Fnhsn%2Finpatient-
rehab%2Fvaccination%2Findex.html.
---------------------------------------------------------------------------

    \55\ Centers for Disease Control and Prevention (CDC). (2021). 
https://www.cdc.gov/nhsn/hps/weekly-covid-vac/. Healthcare 
Personnel Safety Component (HPS). CDC.gov.
---------------------------------------------------------------------------

    We solicited public comment on our proposal to add a new measure, 
Influenza Vaccination Coverage among Healthcare Personnel (NQF #0431), 
to the SNF QRP beginning with the FY 2025 SNF QRP. The following is a 
summary of the comments we received and our responses.
    Comment: We received several supportive comments for our proposal 
to adopt the Influenza Vaccination Coverage among Healthcare Personnel 
(HCP) (NQF #0431) measure for the SNF QRP. Several commenters agreed 
that regular reporting of influenza

[[Page 47542]]

vaccination rates among SNF HCP would reduce the risk of infection 
transmission from HCP to SNF patients. Another commenter supported the 
measure, noting that (1) influenza causes significant healthcare costs 
and mortality of elderly patients and (2) the measure provides an 
opportunity for nursing leaders to educate their staff and use 
evidence-based strategies, such as motivational interviewing, to 
encourage staff to adopt a behavior change that is beneficial for 
public health. Two facilities supported the proposal, noting that they 
already require employees to receive annual influenza vaccinations 
unless there is an appropriate medical or religious exemption. Multiple 
commenters supported the reporting of HCP influenza vaccination rates 
as it may encourage SNFs to take responsibility for supporting HCP 
access to recommended immunizations, incentivize facilities to adopt 
programs encouraging workers to receive influenza vaccines, provide 
additional information about a SNF's infection response and readiness 
efforts, and increase the transparency of quality of care among SNFs. 
Other commenters supported the measure for other reasons, such as the 
fact that it is consistent with CDC guidelines for long-term care 
workers, promotes alignment and consistency across PAC QRPs, and is 
NQF-endorsed.
    Response: We believe the proposed measure will promote the health 
and well-being of SNF patients and HCP, and that reporting this measure 
will contribute to overall infection control within SNFs.
    Comment: One commenter supported the measure, but expressed concern 
that it could create an administrative burden for community and long-
term care pharmacies or consultant pharmacists within long-term care 
settings. The commenter pointed out staffing issues experienced by 
long-term care pharmacies when pharmacists leave the pharmacy to 
perform on-site vaccinations at the SNF.
    Response: We note that the measure neither requires the influenza 
vaccine to be administered to HCP at SNFs, nor does it require the 
vaccine to be administered by a pharmacist or a long-term care pharmacy 
in order for HCP to be captured in the measure's numerator.\56\ The 
influenza vaccination may either be received at the SNF or an HCP may 
provide written or electronic documentation that the vaccine was 
received elsewhere. We provide a full description of the measure 
numerator earlier in this section (VII.C.1.e.) of this final rule.
---------------------------------------------------------------------------

    \56\ Centers for Disease Control and Prevention (CDC). (2021). 
Measure Specification: NHSN COVID-19 Vaccination Coverage Updated 
August 2021. Retrieved from https://www.cdc.gov/nhsn/pdfs/nqf/covid-vax-hcpcoverage-508.pdf.
---------------------------------------------------------------------------

    Comment: One commenter noted concern over payment reductions if a 
specified percentage of HCP are not vaccinated against influenza, and 
noted that SNFs are already struggling financially to overcome pandemic 
costs.
    Response: The SNF QRP is a pay-for-reporting program, which means 
that SNFs are only financially penalized if they fail to comply with 
the QRP's data submission standards. For the HCP Influenza Vaccine 
measure, the data submission standard consists of one data submission 
per year at the conclusion of the measure reporting period. SNFs would 
not have to reach a particular threshold of HCP influenza vaccination 
among HCP to comply with measure data submission standards. 
Additionally, the HCP Influenza Vaccine measure would be submitted 
through the CDC's NHSN collection and submission framework, which is 
free to SNF providers. While we acknowledge the challenges the PHE has 
presented, we refer SNFs to section XI.A.5. of this final rule, where 
we estimate the measure will only require an annual cost of $9.38 per 
SNF for annual data submission. Because of the minimal cost associated 
with annual data submission and the fact that data submission 
requirements are not associated with vaccination thresholds, we believe 
that SNFs will be able to successfully meet the data submission 
requirements for the HCP Influenza Vaccine measure at a minimal cost.
    Comment: One commenter supported CMS's increased focus on infection 
control but is concerned about whether the measure aligns with the 
Improving Medicare Post-Acute Care Transformation (IMPACT) Act. The 
commenter noted that the IMPACT Act requires the reporting of 
standardized patient assessment data, while the Influenza Vaccination 
Coverage among HCP measure collects HCP data rather than patient data, 
and therefore may not be useful to consumers.
    Response: The IMPACT Act added section 1899B to the Act and 
requires the reporting of standardized patient assessment data with 
regard to quality measures and standardized patient assessment data 
elements.\57\ The IMPACT Act does not state that quality reporting 
programs can only report patient-level data. The Act also requires the 
submission of data pertaining to quality measures, resource use, and 
other domains. The Influenza Vaccination Coverage among HCP measure is 
proposed for adoption as an ``other'' measure under section 1899B(d)(1) 
of the Act. In accordance with section 1899B(a)(1)(B) of the Act, the 
data used to calculate this measure are standardized and interoperable. 
A similar NHSN-based measure, COVID-19 Vaccination Coverage among HCP, 
was added to the SNF QRP under the same statutory authority in the FY 
2022 SNF PPS final rule.\58\ The statute intends for standardized PAC 
data to improve Medicare beneficiary outcomes through shared-decision 
making, care coordination, and enhanced discharge planning. As the 
Influenza Vaccination Coverage among HCP measure's purpose is to report 
HCP vaccination rates and encourage infection prevention and control 
within a facility, we disagree with the commenter and find the measure 
useful to consumers' shared decision-making processes.
---------------------------------------------------------------------------

    \57\ Centers for Medicare & Medicaid Services (CMS). (2021). 
IMPACT Act of 2014 Data Standardization & Cross Setting Measures. 
Retrieved from https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-of-2014-Data-Standardization-and-Cross-Setting-Measures.
    \58\ 86 FR 42424.
---------------------------------------------------------------------------

    Comment: Several commenters did not support the proposal to adopt 
the Influenza Vaccination Coverage among HCP (NQF #0431) measure due to 
staffing concerns. Some of these commenters noted that mandated HCP 
vaccination may hamper efforts to increase facility staffing levels, 
and one commenter questioned whether CMS intends to mandate influenza 
vaccination as a condition of employment at a later time. One commenter 
expressed concern that collecting vaccination information would invade 
staff's personal lives and intensify staff shortages.
    Response: We disagree with the commenter that the HCP Influenza 
Vaccine measure may hamper efforts to increase facility staffing levels 
because CMS is not mandating SNF employees receive an influenza vaccine 
as a condition of employment. The SNF QRP is a pay-for-reporting 
program and the actual number of SNF HCP who have been vaccinated does 
not impact SNFs' ability to successfully report the measure. 
Additionally, hospitals, IRFs, and LTCHs have been collecting HCP 
influenza vaccination data for almost 10 years and have not reported to 
CMS that it hampers their hiring ability. In regards to privacy 
concerns, the NHSN HPS Component used to report HCP influenza data 
collects summary

[[Page 47543]]

information and does not require SNFs to enter staff personal 
identifiable information.
    Comment: Some commenters stated that the proposal to add the HCP 
Influenza Vaccine measure to the SNF QRP is an unfunded mandate. A few 
commenters were concerned about the amount of unfunded mandated 
reporting that has occurred over the course of the COVID-19 PHE, and 
another commenter urged CMS not to finalize new data reporting 
requirements during the COVID-19 PHE, because SNFs do not have the 
resources to manage another unfunded mandate.
    Response: We acknowledge the commenters' concerns. However, we have 
examined the impacts of this proposed measure 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), and section 202 of the Unfunded Mandates Reform Act 
of 1995 (UMRA, March 22, 1995; Pub. L. 104-4). 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.
    As required, we have considered the benefits and costs of the 
proposed measure. This measure would facilitate patient care and care 
coordination during the discharge planning process. A discharging 
hospital or facility, in collaboration with the patient and family, 
could use this measure to coordinate care and ensure patient 
preferences are considered in the discharge plan. Patients at high risk 
for negative outcomes due to influenza (perhaps due to underlying 
conditions) can use healthcare provider vaccination rates when they are 
selecting a SNF for next-level care. Additionally, the data submission 
method is free to SNFs, and we estimate the annual data submission will 
require a cost $9.38 per SNF annually. We believe we have selected an 
approach that maximizes net benefits.
    Comment: One commenter requested that CMS consider hybrid care 
delivery models where staff, including, but not limited to, respiratory 
therapists, physical therapists, or dieticians/dietary aides, may cross 
between different quality reporting programs on the same campus. The 
commenters requested that inclusion and exclusion criteria must be 
clearly stated for valid comparisons.
    Response: We thank the commenter for their suggestion, and will 
take it under consideration. Further we note that the criteria for HCP 
included and excluded from the HCP Influenza Vaccine measure can be 
found in the NHSN Healthcare Personnel Safety Component Protocol at 
https://www.cdc.gov/nhsn/pdfs/hps-manual/vaccination/hps-flu-vaccine-protocol.pdf.
    Comment: Some commenters noted the importance of how the measure's 
denominator is defined. Specifically, two commenters suggested the 
measure's denominator should be modified to exclude non-employed staff, 
such as agency and contracted staff, and/or be limited to direct care 
staff in the SNF. One of these commenters noted that such modifications 
to the measure's denominator will better assess a SNF's ability to 
engage with and vaccinate its staff while not necessarily rewarding or 
penalizing SNFs based on vaccination coverage that may occur outside of 
the facility's control. Other commenters stated how CMS will define 
``employee'' in reference to the measure's denominator will be 
significant.
    Response: As described in section VII.G.2. of this final rule, the 
proposed measure does not require SNFs to report all facility contract 
personnel. The proposed measure requires vaccination information to be 
reported for three required categories of HCP who are physically 
present in the healthcare facility for at least 1 working day within 
the measure's data collection period. Healthcare personnel captured in 
the measure's denominator include: (1) employees of the SNF (or those 
who receive a direct paycheck from the reporting facility), (2) 
licensed independent practitioners (including MD, DO, advanced practice 
nurses, physician assistants, and post-residency fellows affiliated 
with the reporting facility, but who are not directly employed by the 
facility), and (3) adult students/trainees and volunteers regardless of 
clinical responsibility or patient contact. SNFs are not required (but 
have the option) to report influenza vaccination status on other 
contract personnel. Since the SNF QRP is a pay-for-reporting program, 
SNFs are not rewarded or penalized based on the rate of HCP 
vaccination. While CMS acknowledges that SNFs do not have direct 
control over an HCP's choice to receive a vaccine, the SNF does have 
direct control over reporting the data required for the HCP Influenza 
Vaccine measure, which is the only requirement to comply with the SNF 
QRP.
    SNFs should use the specifications and data collection tools for 
the HCP Influenza Vaccine measure as required by CDC as of the time 
that the data are submitted. For more information about HCP included in 
the measure's denominator, please refer to the NHSN Manual Healthcare 
Personnel Safety Component Protocol Healthcare Personnel Vaccination 
Module: Influenza Vaccination Summary web page at https://www.cdc.gov/nhsn/pdfs/hps-manual/vaccination/hps-flu-vaccine-protocol.pdf.
    Comment: One commenter expressed concern about adopting infection-
specific regulations for particular viruses as these actions could set 
a precedent for future regulations that potentially burden both CMS as 
well as SNFs.
    Response: We strive to promote high quality and efficiency in the 
delivery of healthcare to the beneficiaries we serve. Valid, reliable, 
and relevant quality measures are fundamental to the effectiveness of 
our QRPs. We are aware of potential provider burdens and only implement 
quality initiatives that have the potential to assure quality 
healthcare for Medicare beneficiaries through accountability and public 
disclosure. The Influenza Vaccination Coverage among HCP measure is 
consistent with CMS's Meaningful Measures 2.0, which includes safety as 
a key component of achieving value-based care and promoting health 
equity. The COVID-19 PHE has exposed the threat that emerging 
infectious diseases pose, and the importance of implementing infection 
prevention strategies, including the promotion of HCP influenza 
vaccination. We believe the proposed measure has the potential to 
generate actionable data on vaccination rates that can be used to 
target quality improvement among SNF providers.
    Comment: One commenter expressed concerns about the HCP Influenza 
Vaccine measure due to the commenter's belief that SNFs are already 
required to report vaccine status to CMS on a weekly basis and are 
financially penalized for a failure to report. The commenter was also 
concerned that SNFs would receive a double penalty if the proposal were 
finalized.
    Response: It is unclear what the commenter means by the term 
``double penalty,'' but we interpret the commenter to be concerned 
about being penalized twice: once for a failure to report COVID-19 
vaccine data to CMS on a weekly basis and a second time for failure to 
report HCP influenza vaccine data. The LTC facility requirements of 
participation (requirements) at Sec.  483.80(g) and the SNF QRP are two 
separate requirements. The LTC facility requirements require nursing 
homes to

[[Page 47544]]

report weekly on the COVID-19 vaccination status of all residents and 
staff as well as COVID-19 therapeutic treatment administered to 
residents. As discussed in section VII.C.1.e. of this final rule, we 
proposed that SNFs would report the number of HCP who receive influenza 
vaccination. The reporting requirement for the HCP Influenza Vaccine 
measure is different from the COVID-19 vaccination information 
reporting requirement in the May 2021 IFC.\59\ Each system has its own 
methods of validation and carries separate penalties.
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    \59\ Medicare and Medicaid Programs; COVID-19 Vaccine 
Requirements for Long-Term Care (LTC) Facilities and Intermediate 
Care Facilities for Individuals with Intellectual Disabilities (CFs-
IID) Residents, Clients, and Staff. 86 FR 26306. May 13, 2021.
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    Comment: One commenter stated that evidence continues to support 
that the best measures to prevent transmission from person to person 
are consistent infection control measures by the healthcare providers 
and encouraged CMS to review literature evidence more critically, and 
be able to discern between conflicting evidence in a more effective 
manner. Another commenter noted that although vaccines are beneficial, 
other infection control practices, such as mask wearing, can prevent 
influenza outbreaks within the SNF.
    Response: We appreciate the comment and agree with the commenter 
that evidence continues to support the use of consistent infection 
control measures. Evidence also points to the importance of vaccination 
as a part of a multi-pronged approach within SNF infection prevention 
and control programs, especially to prevent the transmission of highly 
contagious conditions, such as influenza. We will continue to 
critically review evidence in our measure development processes.
    Comment: Commenters suggested CMS delay implementation of the 
measure due to the PHE and staffing crisis. One commenter stated the 
data may be misleading to consumers due to changes in staffing from one 
influenza season to the next, the effectiveness of the vaccine, and the 
fact that the measure includes all HCP regardless of possible contact 
with the Medicare beneficiary.
    Response: The PHE further emphasizes the need for CMS to prioritize 
infection prevention and control initiatives, such as HCP influenza 
vaccination. HCP vaccinations against influenza may prevent the spread 
of illness between HCP and residents, thus reducing resident 
morbidities associated with influenza and pressure on already stressed 
healthcare systems. The HCP Influenza Vaccine measure has been 
successfully reported in the IRF QRP since 2014 and the LTCH QRP since 
2013, and CMS has had no questions or complaints from consumers about 
the value of the information when selecting a PAC provider. We disagree 
with the commenter that including all HCP in the measure, regardless of 
possible contact with the Medicare beneficiary, could result in 
misleading measure data because it is possible for any and all HCP to 
come into contact with Medicare beneficiaries. We do not require SNFs 
to differentiate between HCP who come into contact with Medicare 
beneficiaries versus those who do not as this would place additional 
reporting burdens on SNFs. Therefore, as described in section VII.G.2. 
of this final rule, we proposed the Influenza Vaccination Coverage 
among HCP measure to include HCP (as defined by the measure's 
denominator) who are physically present in the healthcare facility for 
at least 1 working day within the measure's data collection period 
since all types of HCP may come into contact with SNF residents.
    Comment: One commenter urged CMS to add the HCP Influenza Vaccine 
measure to the SNF QRP as soon as possible because influenza season is 
anticipated as an annual occurrence nationally. In addition, the 
commenter stated that because the data used to calculate the measure 
are standardized and interoperable, CMS should be able to support an 
earlier implementation than the FY 2025 QRP.
    Response: We agree with the commenter that we should adopt the 
measure sooner than the FY 2025 SNF QRP because it has the potential to 
increase influenza vaccination coverage in SNFs, promote patient 
safety, and increase the transparency of quality of care in the SNF 
setting as described in section VII.C.1.a. of this final rule. 
Therefore, we are finalizing this measure beginning with the FY 2024 
SNF QRP. We are also finalizing our proposal to require SNFs to begin 
reporting data on this measure for the period October 1, 2022 through 
March 31, 2023, with a reporting deadline of May 15, 2023. This initial 
data reporting deadline gives us sufficient time to calculate the first 
year of measure results for the FY 2024 SNF QRP. Accordingly, we are 
finalizing our adoption of the measure beginning with the FY 2024 SNF 
QRP rather than the FY 2025 SNF QRP as proposed.
    Comment: We received several comments that were not related to our 
SNF QRP proposals. One commenter responded to several proposals from 
the FY 2022 SNF PPS proposed rule,\60\ while another commenter 
encouraged CMS to ensure immunizations are affordable and accessible. 
One commenter noted the number of measures currently reported on Care 
Compare and emphasized the importance of risk-adjusting measures due to 
COVID-19. Another commenter stated it is critical that changes to the 
QRP are accompanied with appropriate financial incentives so SNFs may 
invest in technologies that improve patient safety and compliance with 
data submission thresholds. Another commenter recommended the COVID-19 
Vaccination Coverage among HCP numerator be aligned with the Influenza 
Vaccination Coverage among HCP measure. Finally, two commenters 
suggested CMS explore inclusion of Medicare Advantage patients in 
quality measure calculations.
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    \60\ 86 FR 19990 through 20005.
---------------------------------------------------------------------------

    Response: These comments fall outside the scope of the FY 2023 SNF 
PPS proposed rule.
    After consideration of public comments, we are finalizing our 
proposal to adopt the Influenza Vaccination Coverage among Healthcare 
Personnel (NQF #0431) measure beginning with the FY 2024 SNF QRP, since 
this measure influences patient safety and should be implemented within 
the SNF QRP as soon as possible.
2. Revised Compliance Date for Certain Skilled Nursing Facility Quality 
Reporting Program Requirements Beginning With the FY 2024 SNF QRP
a. Background
    Section 1888(d)(6)(B)(i)(III) of the Act requires that, for FY 2019 
and each subsequent year, SNFs must report standardized patient 
assessment data required under section 1899B(b)(1) of the Act. Section 
1899B(a)(1)(C) of the Act requires, in part, the Secretary to modify 
the PAC assessment instruments in order for PAC providers, including 
SNFs, to submit standardized patient assessment data under the Medicare 
program. In the FY 2020 SNF PPS final rule (84 FR 38755 through 38817), 
we adopted two TOH Information quality measures as well as standardized 
patient assessment data that would satisfy five categories defined by 
section 1899B(c)(1). The TOH Information to the Provider--Post-Acute 
Care (PAC) measure and the TOH Information to the Patient--PAC measure 
are process-based measures that assess whether or not a current 
reconciled medication list is given to the subsequent provider when a 
patient is discharged or

[[Page 47545]]

transferred from his or her current PAC setting or is given to the 
patient, family, or caregiver when the patient is discharged from a PAC 
setting to a private home/apartment, a board and care home, assisted 
living, a group home, or transitional living. Section 1899B(b)(1)(B) of 
the Act defines standardized patient assessment data as data required 
for at least the quality measures described in section 1899B(c)(1) of 
the Act and that is with respect to the following categories: (1) 
functional status; (2) cognitive function; (3) special services, 
treatments, and interventions; (4) medical conditions and 
comorbidities; (5) impairments; and (6) other categories deemed 
necessary and appropriate by the Secretary.
    The interim final rule with comment period that appeared in the May 
8, 2020 Federal Register (85 FR 27550) (hereafter referred to as the 
``May 8th COVID-19 IFC''), delayed the compliance date for certain 
reporting requirements under the SNF QRP (85 FR 27596 through 27597). 
Specifically, we delayed the requirement for SNFs to begin reporting 
the TOH Information to the Provider-PAC and the TOH Information to the 
Patient-PAC measures and the requirement for SNFs to begin reporting 
certain standardized patient assessment data elements from October 1, 
2020, to October 1st of the year that is at least 2 full fiscal years 
after the end of the COVID-19 PHE. We also delayed the adoption of the 
updated version of the Minimum Data Set (MDS) 3.0 v1.18.1 \61\ which 
SNFs would have used to report the TOH Information measures and certain 
standardized patient assessment data elements.
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    \61\ The MDS version referred to in IFC-2 was MDS 3.0 v1.18.1. 
This version number, MDS 3.0 v1.18.11, reflects the version that 
would be implemented if the proposal is finalized.
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    Currently, SNFs must use the MDS 3.0 v1.18.11 to begin collecting 
data on the two TOH Information measures beginning with discharges on 
October 1st of the year that is at least 2 full fiscal years after the 
end of the COVID-19 PHE. SNFs must also begin collecting data on 
certain standardized patient assessment data elements on the MDS 3.0 
v1.18.11, beginning with admissions and discharges (except for the 
preferred language, need for interpreter services, hearing, vision, 
race, and ethnicity standardized patient assessment data elements, 
which would be collected at admission only) on October 1st of the year 
that is at least 2 full fiscal years after the end of the COVID-19 PHE. 
The delay to begin collecting data for these measures was intended to 
provide relief to SNFs from the added burden of implementing an updated 
instrument during the COVID-19 PHE. As discussed in the proposed rule, 
we wanted to provide maximum flexibilities for SNFs to respond to the 
public health threats posed by the COVID-19 PHE, and to reduce the 
burden in administrative efforts associated with attending trainings, 
training their staff, and working with their vendors to incorporate the 
updated assessment instruments into their operations.
    At the time the May 8th COVID-19 IFC was published, we believed 
this delay would not have a significant impact on the SNF QRP. However, 
we were in the initial months of the COVID-19 PHE, and very little was 
known about the SARS-CoV-2 virus. Additionally, we believed the delay 
in the collection of the TOH Information measures and standardized 
patient assessment data elements were necessary to allow SNFs to focus 
on patient care and staff safety. However, the COVID-19 PHE has 
illustrated the important need for these TOH Information measures and 
standardized patient assessment data elements under the SNF QRP. The 
PHE's disproportionate impact among non-Hispanic Black, and Hispanic 
and Latino persons 62 63 64 65 66 67 68 demonstrates the 
importance of analyzing this impact in order to improve quality of care 
within SNFs especially during a crisis. 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. The information will inform our 
Meaningful Measures framework.
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    \62\ Bhumbra, S., Malin, S., Kirkpatrick, L., et al. (2020). 
Clinical Features of Critical Coronavirus Disease 2019 in Children. 
Pediatric Critical Care Medicine, 02, 02. https://doi.org/10.1097/PCC.0000000000002511.
    \63\ Ebinger, J.E., Achamallah, N., Ji, H., Claggett, B.L., Sun, 
N., Botting, P., et al. (2020). Pre-existing Traits Associated with 
Covid-19 Illness Severity. PLoS ONE, 15(7), e0236240. https://doi.org/10.1101/2020.04.29.20084533.
    \64\ Gold, J.A.W., Wong, K.K., Szablewski, C.M., Patel, P.R., 
Rossow, J., da Silva, J., et al. (2020). Characteristics and 
Clinical Outcomes of Adult Patients Hospitalized with COVID-19--
Georgia, March 2020. MMWR Morbidity and Mortality Weekly Report, 
69(18), 545-550. https://dx.doi.org/10.15585/mmwr.mm6918e1.
    \65\ Hsu, H.E., Ashe, E.M., Silverstein, M., Hofman, M., Lange, 
S.J., Razzaghi, H., et al. (2020). Race/Ethnicity, Underlying 
Medical Conditions, Homelessness, and Hospitalization Status of 
Adult Patients with COVID-19 at an Urban Safety-Net Medical Center--
Boston, Massachusetts, 2020. MMWR Morbidity and Mortality Weekly 
Report, 69(27), 864-869. https://dx.doi.org/10.15585/mmwr.mm6927a3.
    \66\ Kim, L., Whitaker, M., O'Hallaran, A., et al. (2020). 
Hospitalization Rates and Characteristics of Children Aged <18 Years 
Hospitalized with Laboratory-confirmed COVID-19--COVID-NET, 14 
states, March 1-July 25, 2020. MMWR Morbidity and Mortality Weekly 
Report, 69(32), 1081-1088. https://dx.doi.org/10.15585/mmwr.mm6932e3.
    \67\ Killerby, M.E., Link-Gelles, R., Haight, S.C., Schrodt, 
C.A., England, L., Gomes, D.J., et al. (2020). Characteristics 
Associated with Hospitalization Among Patients with COVID-19--
Metropolitan Atlanta, Georgia, March-April 2020. MMWR Morbidity and 
Mortality Weekly Report, 69(25), 790-794. https://dx.doi.org/10.15585/mmwr.mm6925e1.
    \68\ Price-Haywood, E.G., Burton, J., Fort, D., & Seoane, L. 
(2020). Hospitalization and Mortality among Black Patients and White 
Patients with Covid-19. New England Journal of Medicine, 382(26), 
2534-2543. https://doi.org/10.1056/NEJMsa2011686.
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b. Current Assessment of SNFs' Capabilities
    To accommodate the COVID-19 PHE, we provided additional guidance 
and flexibilities, and as a result SNFs have had the opportunity to 
adopt new processes and modify existing processes to accommodate the 
significant health crisis presented by the COVID-19 PHE. For example, 
we held regular ``Office Hours'' conference calls to provide SNFs 
regular updates on the availability of supplies, as well as answer 
questions about delivery of care, reporting, and billing. We also 
supported PAC providers, including SNFs, by providing flexibilities in 
the delivery of care in response to the PHE,\69\ such as waiving the 
requirements at Sec.  483.30 for physician and non-physician 
practitioners to perform in-person visits, allowing them to use 
telehealth methods where deemed appropriate. We also waived the nurse 
aide training and certification requirements Sec.  483.35(d) (with the 
exception of Sec.  483.35(d)(1)(i)), allowing SNFs to employ nurse 
aides for longer than 4 months even when they have yet not met the 
standard training and certification requirements, and we waived the 
requirement at Sec.  483.95(g)(1) for nursing aides to receive at least 
12 hours of in-service training annually. To reduce provider burden, we 
waived the Pre-Admission Screening and Annual Resident Review (PASARR) 
at Sec.  483.20(k), allowing SNFs more flexibility in scheduling Level 
1 assessments. We narrowed the scope of requirements for a SNF's 
Quality Assurance and Performance Improvement (QAPI) program to the 
aspects of care most associated with COVID-19 (Sec.  483.75), that is 
infection control and adverse events. Additionally, we waived timeframe

[[Page 47546]]

requirements on MDS assessments and transmission at Sec.  483.20, along 
with waiving requirements for submitting staffing data through the 
Payroll-Based Journal (PBJ) system at Sec.  483.70(q), to grant SNFs 
the greater flexibility needed to adapt to the rapidly evolving burdens 
of the PHE. While the MDS and PBJ requirements have since been 
terminated, many of these waivers for SNFs are still in effect today.
---------------------------------------------------------------------------

    \69\ Centers for Medicare & Medicaid Services (CMS). COVID-19 
Emergency Declaration Blanket waivers for Health Care Providers. 
Retrieved from https://www.cms.gov/files/document/covid-19-emergency-declaration-waivers.pdf. Accessed 11/23/2021.
---------------------------------------------------------------------------

    In addition, as of March 1, 2022, 86.2 percent of the population 
aged 12 and older (81.3 percent of those 5 and older) had received at 
least one COVID-19 vaccination.\70\ Further, although there was a 
recent increase in COVID-19 cases, vaccinated individuals aged 18 years 
and older through March 4, 2022 were 3.2 times less likely to test 
positive, over 9 times less likely to be hospitalized, and experienced 
41 times lower risk of death, compared to unvaccinated individuals.\71\ 
We also believe that SNFs have more information and interventions to 
deploy to effectively prevent and treat COVID-19 than they had at the 
time the May 8th COVID-19 IFC was finalized,72 73 74 75 
including three vaccines that are either approved or authorized in the 
United States to prevent COVID-19, and antiviral drugs that are 
approved or authorized to treat COVID-19.76 77 78 79 80 
Also, recent reports suggest that the rollout of COVID-19 vaccines has 
alleviated some of the burden on SNFs imposed by the 
PHE.81 82
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    \70\ CDC COVID Data Tracker. Retrieved from https://covid.cdc.gov/covid-data-tracker/#vaccinations_vacc-people-onedose-pop-5yr. Accessed 3/4/2022.
    \71\ CDC COVID Data Tracker. Accessed 3/4/2022. Retrieved from 
https://covid.cdc.gov/covid-data-tracker/#rates-by-vaccine-status.
    \72\ COVID research: a year of scientific milestones. Nature. 
May 5, 2021. Retrieved from https://www.nature.com/articles/d41586-020-00502-w.
    \73\ CDC COVID Data Tracker. Accessed 2/10/2022. Retrieved from 
https://covid.cdc.gov/covid-data-tracker/#datatracker-home.
    \74\ Clinical trial of therapeutics for severely ill 
hospitalized COVID-19 patients begins. National Institutes of Health 
News Releases. April 22, 2021. Retrieved from https://www.nih.gov/news-events/news-releases/clinical-trialtherapeutics-severely-ill-hospitalized-covid-19-patients-begins.
    \75\ COVID-19 Treatment Guidelines. National Institutes of 
Health. Updated October 27, 2021. Retrieved from https://www.covid19treatmentguidelines.nih.gov/whats-new/.
    \76\ Here's Exactly Where We are with Vaccine and Treatments for 
COVID-19. Healthline. November 9, 2021. Retrieved from https://www.healthline.com/health-news/heres-exactly-where-were-at-with-vaccines-and-treatments-forcovid-19.
    \77\ U.S. Food and Drug Administration (2021). Janssen Biotech, 
Inc. COVID-19 Vaccine EUA Letter of Authorization. Available at 
https://www.fda.gov/media/146303/download. Accessed 7/8/2022.
    \78\ On January 31, 2021, FDA approved a second COVID-19 
vaccine. Available at https://www.fda.gov/news-events/press-announcements/coronavirus-covid-19-update-fda-takes-key-action-approving-second-covid-19-vaccine. Accessed 7/8/22. The Moderna 
COVID-19 Vaccine also continues to be available under EUA. U.S. Food 
and Drug Administration (2022). Spikevax and Moderna COVID-19 
Vaccine. https://www.fda.gov/emergency-preparedness-and-response/coronavirus-disease-2019-covid-19/spikevax-and-moderna-covid-19-vaccine. Accessed 7/8/22.
    \79\ FDA Approves First COVID-19 Vaccine. Available at https://www.fda.gov/news-events/press-announcements/fda-approves-first-covid-19-vaccine. Accessed 7/8/22. The Pfizer-BioNTech vaccine also 
continues to be available under EUA. U.S. Food and Drug 
Administration (2021). Comirnaty and Pfizer-BioNTech COVID-19 
Vaccine. Available at https://www.fda.gov/emergency-preparedness-and-response/coronavirus-disease-2019-covid-19/comirnaty-and-pfizer-biontech-covid-19-vaccine. Accessed 7/8/2022.
    \80\ FDA Approves First Treatment for COVID-19. October 22, 
2020. Available at https://www.fda.gov/newsevents/press-announcements/fda-approves-first-treatment-covid-19. Accessed 9/9/
2021. Emergency Use Authorization, https://www.fda.gov/emergency-preparedness-and-response/mcm-legal-regulatory-and-policy-framework/emergency-use-authorization. Accessed7/8 2022.
    \81\ Domi, M., Leitson, M., Gifford, D., Nicolaou, A., 
Sreenivas, K., & Bishnoi, C. (2021). The BNT162b2 vaccine is 
associated with lower new COVID-19 cases in nursing home residents 
and staff. Journal of the American Geriatrics Society, 69(8), 2079-
2089. https://doi.org/10.1111/jgs.17224.
    \82\ American Health Care Association and National Center for 
Assisted Living. COVID-19 Vaccines Helping Long Term Care Facilities 
Rebound From The Pandemic. May 25, 2021. Retrieved from https://www.ahcancal.org/News-and-Communications/Press-Releases/Pages/COVID-19-Vaccines-Helping-Long-Term-Care-Facilities-Rebound-From-The-Pandemic.aspx.
---------------------------------------------------------------------------

    Despite the COVID-19 PHE, we must maintain our commitment to the 
quality of care for all patients, and we continue to believe that the 
collection of the standardized patient assessment data elements and TOH 
Information measures will contribute to this effort. That includes an 
ongoing commitment to achieving health equity by improving data 
collection to better measure and analyze disparities across programs 
and policies.83 84 85 86 87 88 89 90 We also note that in 
response to the ``Request for Information to Close the Health Equity 
Gap'' in the FY 2022 SNF PPS proposed rule (86 FR 20000), we heard from 
stakeholders that it is important to gather additional information 
about race, ethnicity, gender, language, and other social determinants 
of health (SDOH). Some SNFs noted they had already begun to collect 
some of this information for use in their operations. Our commitment to 
the quality of care for all patients also includes improving the 
quality of care in SNFs through a reduction in preventable adverse 
events. Health information, such as medication information, that is 
incomplete or missing increases the likelihood of a patient or resident 
safety risk, and is often life-threatening.91 92 93 94 95 96 
Poor communication and coordination across healthcare settings 
contributes to patient complications, hospital readmissions, emergency 
department visits, and medication

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errors.97 98 99 100 101 102 103 104 105 106 Further delaying 
the data collection has the potential to further exacerbate these 
issues. We believe the benefit of having this information available in 
a standardized format outweighs the potential burden of collecting 
these data, as data availability is a necessary step in addressing 
health disparities in SNFs.
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    \83\ COVID-19 Health Equity Interactive Dashboard. Emory 
University. Accessed January 12, 2022. Retrieved from https://covid19.emory.edu/.
    \84\ COVID-19 is affecting Black, Indigenous, Latinx, and other 
people of color the most. The COVID Tracking Project. March 7, 2021. 
Accessed January 12, 2022. Retrieved from https://covidtracking.com/race.
    \85\ Centers for Medicare & Medicaid Services (CMS). CMS Quality 
Strategy. 2016. Available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/Downloads/CMS-Quality-Strategy.pdf.
    \86\ 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.
    \87\ Rural Health Research Gateway. Rural Communities: Age, 
Income, and Health Status. Rural Health Research Recap. November 
2018.
    \88\ https://www.minorityhealth.hhs.gov/assets/PDF/Update_HHS_Disparities_Dept-FY2020.pdf.
    \89\ www.cdc.gov/mmwr/volumes/70/wr/mm7005a1.htm.
    \90\ Poteat, T.C ., Reisner, S.L., Miller, M., Wirtz, A.L. 
(2020). COVID-19 Vulnerability of Transgender Women With and Without 
HIV Infection in the Eastern and Southern U.S. Preprint. medRxiv, 
2020.07.21.20159327. https://doi.org/10.1101/2020.07.21.20159327.
    \91\ Kwan, J.L., Lo, L., Sampson, M., & Shojania, K.G. (2013). 
Medication reconciliation during transitions of care as a patient 
safety strategy: a systematic review. Annals of Internal Medicine, 
158(5), 397-403.
    \92\ Boockvar, K.S., Blum, S., Kugler, A., Livote, E., 
Mergenhagen, K.A., Nebeker, J.R., & Yeh, J. (2011). Effect of 
admission medication reconciliation on adverse drug events from 
admission medication changes. Archives of Internal Medicine, 171(9), 
860-861.
    \93\ Bell, C.M., Brener, S.S., Gunraj, N., Huo, C., Bierman, 
A.S., Scales, D.C., & Urbach, D.R. (2011). Association of ICU or 
hospital admission with unintentional discontinuation of medications 
for chronic diseases. JAMA, 306(8), 840-847.
    \94\ Basey, A.J., Krska, J., Kennedy, T.D., & Mackridge, A.J. 
(2014). Prescribing errors on admission to hospital and their 
potential impact: a mixed-methods study. BMJ Quality & Safety, 
23(1), 17-25.
    \95\ Desai, R., Williams, C.E., Greene, S.B., Pierson, S., & 
Hansen, R.A. (2011). Medication errors during patient transitions 
into nursing homes: characteristics and association with patient 
harm. American Journal of Geriatric Pharmacotherapy, 9(6), 413-422.
    \96\ Boling, P.A. (2009). Care transitions and home health care. 
Clinical Geriatric Medicine, 25(1), 135-148.
    \97\ Barnsteiner, J.H. (2005). Medication Reconciliation: 
Transfer of medication information across settings--keeping it free 
from error. American Journal of Nursing, 105(3 Suppl), 31-36.
    \98\ Arbaje, A.I., Kansagara, D.L., Salanitro, A.H., Englander, 
H.L., Kripalani, S., Jencks, S.F., & Lindquist, L.A. (2014). 
Regardless of age: incorporating principles from geriatric medicine 
to improve care transitions for patients with complex needs. Journal 
of General Internal Medicine, 29(6), 932-939.
    \99\ Jencks, S.F., Williams, M.V., & Coleman, E.A. (2009). 
Rehospitalizations among patients in the Medicare fee-for-service 
program. New England Journal of Medicine, 360(14), 1418-1428.
    \100\ Institute of Medicine. (2007). Preventing medication 
errors: quality chasm series. Washington, DC: The National Academies 
Press. Available at https://www.nap.edu/read/11623/chapter/1.
    \101\ Kitson, N.A., Price, M., Lau, F.Y., & Showler, G. (2013). 
Developing a medication communication framework across continuums of 
care using the Circle of Care Modeling approach. BMC Health Services 
Research, 13(1), 1-10.
    \102\ Mor, V., Intrator, O., Feng, Z., & Grabowski, D.C. (2010). 
The revolving door of rehospitalization from skilled nursing 
facilities. Health Affairs, 29(1), 57-64.
    \103\ Institute of Medicine. (2007). Preventing medication 
errors: quality chasm series. Washington, DC: The National Academies 
Press. Available at https://www.nap.edu/read/11623/chapter/1.
    \104\ Kitson, N.A., Price, M., Lau, F.Y., & Showler, G. (2013). 
Developing a medication communication framework across continuums of 
care using the Circle of Care Modeling approach. BMC Health Services 
Research, 13(1), 1-10.
    \105\ Forster, A.J., Murff, H.J., Peterson, J.F., Gandhi, T.K., 
& Bates, D.W. (2003). The incidence and severity of adverse events 
affecting patients after discharge from the hospital. Annals of 
Internal Medicine, 138(3), 161-167.
    \106\ King, B.J., Gilmore-Bykovsky, A.L., Roiland, R.A., 
Polnaszek, B.E., Bowers, B.J., & Kind, A.J. (2013). The consequences 
of poor communication during transitions from hospital to skilled 
nursing facility: a qualitative study. Journal of the American 
Geriatrics Society, 61(7), 1095-1102.
---------------------------------------------------------------------------

    Given the flexibilities described earlier in this section, SNFs' 
increased knowledge and interventions to deploy to effectively prevent 
and treat COVID-19, and the trending data on COVID-19, we believe that 
SNFs are in a better position to accommodate the reporting of the TOH 
Information measures and certain standardized patient assessment data 
elements. Specifically, we believe SNFs have learned how to adapt and 
now have the administrative capacity to attend training, train their 
staff, and work with their vendors to incorporate the updated 
assessment instruments into their operations. Moreover, these 
standardized patient assessment data elements are reflective of patient 
characteristics that providers may already be recording for their own 
purposes, such as preferred language, race, ethnicity, hearing, vision, 
health literacy, and cognitive function. It is also important to align 
the collection of these data with the IRFs and LTCHs that will begin 
collecting this information on October 1, 2022, and home health 
agencies (HHAs) that will begin collecting this information on January 
1, 2023.\107\
---------------------------------------------------------------------------

    \107\ Calendar Year 2020 Home Health final rule (86 FR 62385 
through 62390).
---------------------------------------------------------------------------

c. Collection of the Transfer of Health (TOH) Information to the 
Provider-PAC Measure, the Transfer of Health (TOH) Information to the 
Patient-PAC Measure and Certain Standardized Patient Assessment Data 
Elements Beginning October 1, 2023
    We proposed to revise the compliance date specified in the May 8th 
COVID-19 IFC from October 1st of the year that is at least 2 full FYs 
after the end of the COVID-19 PHE to October 1, 2023. This revised date 
would begin the collection of data on the TOH Information to the 
Provider-PAC measure and TOH Information to the Patient-PAC measure, 
and certain standardized patient assessment data elements on the 
updated version of the MDS assessment instrument referred to as MDS 3.0 
v1.18.11. We believe this revised date of October 1, 2023, which is a 
3-year delay from the original compliance date finalized in the FY 2020 
SNF PPS final rule (84 FR 38755 through 38764), balances the support 
that SNFs have needed during much of the COVID-19 PHE, the 
flexibilities we provided to support SNFs, and the time necessary to 
develop preventive and treatment options along with the need to collect 
these important data. We believe this date is sufficiently far in 
advance for SNFs to make the necessary preparations to begin reporting 
these data elements and the TOH Information measures. As described in 
section VI.C.2 of the proposed rule, the need for the standardized 
patient assessment data elements and TOH Information measures has been 
shown to be even more pressing with issues of health inequities, 
exacerbated by the COVID-19 PHE. These data, which include information 
on SDOH, provides information that is expected to improve quality of 
care for all, and is not already found in assessment or claims data 
currently available. Consequently, we proposed to revise the compliance 
date to reflect this balance and assure that data collection begins on 
October 1, 2023.
    As stated in the FY 2020 SNF PPS final rule (84 FR 38774), we will 
provide the training and education for SNFs to be prepared for this 
implementation date. In addition, if we adopt an October 1, 2023 
compliance date, we would release a draft of the updated version of the 
MDS 3.0 v1.18.11 in early 2023 with sufficient lead time to prepare for 
the October 1, 2023 start date.
    Based upon our evaluation, we proposed that SNFs collect the TOH 
Information to the Provider-PAC measure, the TOH Information to the 
Patient-PAC measure, and certain standardized patient assessment data 
elements beginning October 1, 2023. We also proposed that SNFs begin 
collecting data on the two TOH Information measures beginning with 
discharges on October 1, 2023. We proposed that SNFs begin collecting 
data on the six categories of standardized patient assessment data 
elements on the MDS 3.0 v1.18.11, beginning with admissions and 
discharges (except for the preferred language, need for interpreter 
services, hearing, vision, race, and ethnicity standardized patient 
assessment data elements, which would be collected at admission only) 
on October 1, 2023. We solicited public comment on this proposal. The 
following is a summary of the comments we received and our responses.
    Comment: Several commenters supported our proposal to revise the 
compliance date for the TOH Information measures and certain 
standardized patient assessment data elements beginning with the FY 
2024 QRP. One commenter acknowledged that CMS must maintain its 
commitment to quality of care for all patients and they support the 
collection of certain standardized patient assessment data as an 
important part of improving patient care. Two commenters stated that 
they recognize the importance of collecting these data to advance 
health equity and improve quality of care for all beneficiaries. These 
commenters also noted that the date was further into the future than 
the IRF and LTCH QRPs, and therefore they appreciated CMS's 
acknowledgement of the unique support needs of SNFs during the COVID-19 
public health emergency. Other commenters noted that despite the 
ongoing challenges of the pandemic, they believe SNFs will be able to 
report this information. Another commenter supported the prompt 
initiation of the data collection to enhance holistic care, call 
attention to impairments to be mitigated or resolved, and to facilitate 
clear communication between residents and providers. Further, the 
commenters noted that such data collection could allow for examination 
of SNF performance stratified for factors associated with healthcare 
disparities, such as race and ethnicity.
    Response: We agree that the data will advance quality of care for 
all patients.

[[Page 47548]]

We believe that as the healthcare community continues to learn about 
the enormous impact that social determinants of health (SDOH) and 
social risk factors (SRFs) have on patient health and health 
outcomes,\108\ it becomes more critical to collect this information to 
better understand the impact of the PHE on our healthcare system, as 
well as how to address the inequities that the PHE has made so visible. 
We believe it will help SNFs, physicians, and other practitioners 
caring for patients in SNFs better prepare for the complex and 
resource-intensive care needs of patients with new and emerging 
viruses.
---------------------------------------------------------------------------

    \108\ Hood, C.M., Gennuso, K.P., Swain, G.R., & Catlin, B.B. 
(2016). County Health Rankings: Relationships Between Determinant 
Factors and Health Outcomes. American Journal of Preventive 
Medicine, 50(2), 129-135. Available at https://pubmed.ncbi.nlm.nih.gov/26526164/. Accessed 9/1/21.
---------------------------------------------------------------------------

    We also agree with the commenter that despite the COVID-19 PHE, 
SNFs will be able to successfully report the standardized patient 
assessment data and TOH Information measures. As of July 6, 2022, 89.86 
percent of the population aged 12 and older (83.3 percent of those 5 
and older) had received at least one COVID-19 vaccination, indicating 
an increase of 3.5 percent and 2 percent, respectively in the last 4 
months.\109\ Further strengthening our conclusion that SNFs are able to 
meet the revised compliance date is that there are even more treatments 
available to treat COVID-19.\110\ As of May 31, 2022, there are two 
treatments currently approved by the FDA for use in COVID-19 and 13 
COVID-19 treatments authorized for Emergency Use.\111\
---------------------------------------------------------------------------

    \109\ CDC COVID Data Tracker. Accessed 3/4/2022. Retrieved from 
https://covid.cdc.gov/covid-data-tracker/#vaccinations_vacc-people-onedose-pop-5yr.
    \110\ Coronavirus Treatment Acceleration Program (CTAP). 
Available at https://www.fda.gov/drugs/coronavirus-covid-19-drugs/coronavirus-treatment-acceleration-program-ctap. Accessed 7/8/22.
    \111\ Please see the Emergency Use Authorization web page for 
more details. This number includes 1 EUA authorizing both medical 
devices and a drug for emergency use.
---------------------------------------------------------------------------

    Comment: Several commenters supported the proposal to revise the 
compliance date for the TOH Information measures and certain 
standardized patient assessment data elements beginning with the FY 
2024 QRP, but at the same time reminding CMS that concerns exist around 
the timing for the release of the newer version of the MDS 3.0, which 
contains new data elements. The commenters specifically raised 
questions about the ability of providers and health IT developers to 
develop, test, and implement software for the new MDS and its 
associated reporting requirements. One commenter requested adequate 
time to develop, test, and deploy new software, noting that in the 
past, health IT developers have indicated they need 18 months for this 
process. Two commenters also urged CMS to provide adequate lead time 
for training staff on the changes required by the new assessment items.
    Response: We understand providers' concerns with developing 
software for the new MDS and the need to train staff. However, SNFs 
have known since July 30, 2019 \112\ that CMS would be implementing an 
updated version of the MDS to collect the TOH Information measures and 
certain standardized patient assessment data elements. As described in 
section VII.C.2.a., the May 8th COVID-19 IFC only delayed the 
compliance date for these reporting requirements.
---------------------------------------------------------------------------

    \112\ 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 2020. 84 FR 38728.
---------------------------------------------------------------------------

    On July 31, 2019, we posted the specifications for the TOH 
Information measures and standardized patient assessment data elements 
on the IMPACT Act Downloads and Videos web page which SNFs could use to 
begin developing their software and train their staff. Specifically, 
the Final Specifications for SNF QRP Quality Measures and SPADEs 
document,\113\ provides information on each of the TOH Information 
measures, including the items' description, measure numerator and 
denominator, as well as the assessment items and responses. 
Additionally, each of the new standardized patient assessment data 
elements is described and accompanied by the assessment item and 
response(s). We also suggest SNF information technology (IT) vendors 
look at the Inpatient Rehabilitation Facility Patient Assessment 
Instrument (IRF-PAI) Version 4.0 and the Long-Term Care Hospital (LTCH) 
Continuity Assessment Record and Evaluation (CARE) Data Set (LCDS) 
Version 5.0 to see how these assessment items are embedded into those 
assessment tools. As we discussed in section VI.2.b. of the SNF PPS 
proposed rule, the new items that will be collected are standardized 
and interoperable data elements. As such, the items that would be 
collected by the MDS are the same items that will be collected by IRFs 
and LTCHs on October 1, 2022, and home health agencies (HHAs) on 
January 1, 2023.\114\ Since the Final Specifications for SNF QRP 
Quality Measures and SPADEs document has been available to SNFs since 
July 31, 2019, we believe IT vendors will have enough time to update 
their software prior to October 1, 2023. We also note that since IT 
vendors for IRFs, LTCHs and HH agencies will have already updated their 
systems, IT vendors in SNFs may benefit from their experience.
---------------------------------------------------------------------------

    \113\ https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/Downloads/Final-Specifications-for-SNF-QRP-Quality-Measures-and-SPADEs.pdf.
    \114\ Calendar Year 2020 Home Health final rule (86 FR 62385 
through 62390).
---------------------------------------------------------------------------

    In response to the comment that health IT vendors need 18 months to 
develop, test, and deploy new software, we note that historically we 
have tried to provide vendors with the information they need to make 
adjustments to their software well ahead of the implementation date. 
This was especially important in the early years of assessment data 
submission to CMS, but we have found in recent years, vendors are very 
mature in the software development process for MDS and do not require 
such extensive lead times. The time, form, and manner in which the MDS 
will be submitted is not changing; rather it is a variation in the data 
elements being collected. Therefore, the implementation of this 
proposal should not require health IT vendors to completely rewrite 
their software.
    In response to the commenters' concerns for training staff, we plan 
to provide multiple training resources and opportunities for SNFs to 
take advantage of, reducing the burden to SNFs in creating their own 
training resources. These training resources may include online 
learning modules, tip sheets, questions and answers documents, and/or 
recorded webinars and videos, and would be available to providers in 
early 2023, allowing SNFs several months to ensure their staff take 
advantage of the learning opportunities. Having the materials online 
and on-demand would give staff the flexibility of learning about the 
new items at times that minimize disruption to patient care schedules. 
The SNF QRP Helpdesk would also be available for providers to submit 
their follow-up questions by email, further enhancing the educational 
resources.
    We received several comments urging us not to revise the compliance 
date for the TOH Information measures and certain standardized patient 
assessment data elements beginning with the FY 2024 QRP. We will 
address each of these comments here.

[[Page 47549]]

    Comment: Many commenters raised concerns with revising the 
compliance date from October 1st of the year that is at least 2 full 
fiscal years after the end of the PHE to October 1, 2023 given the fact 
that the PHE is still in effect as of the date of our proposal, while 
another suggested no new quality metrics should be implemented within 1 
calendar year from the date the COVID-19 PHE officially ends. One 
commenter stated that the delay was intended to provide relief to SNFs, 
and it would be inappropriate to move up the date while the PHE is 
still in effect. Another commenter supported the implementation of the 
TOH Information measures since it reflects a process already being 
completed in SNFs, but stated the proposed implementation of the MDS 
3.0 with the new standardized patient assessment data elements would be 
overwhelming to facilities at this time given the impact on quality 
measures, care area triggers, and care plans. One commenter disagreed 
with CMS's assertion that the flexibilities and assistance granted by 
the agency during the PHE, as well as the promising trends in COVID-19 
vaccination and death rates, have left providers in a better position 
to collect the standardized patient assessment data. Another commenter 
pointed to the uncertainty around current therapeutics' and vaccines' 
effectiveness against new variants, which they believe leave the SNF 
population potentially susceptible to an ever-changing COVID-19 
ecosystem, and stated that further stressing SNFs with additional 
reporting at a time when the COVID-19 PHE may still be burdening SNFs 
and their residents may lead to unforeseen consequences like inaccurate 
and inconsistent data lessening the value of this reporting. Other 
commenters acknowledged that the acute impacts of COVID-19 have 
lessened but are concerned that COVID-19's rippling effects continue to 
impact SNF operations.
    Response: As stated in section VI.C.2 of the FY 2023 SNF PPS 
proposed rule (87 FR 22750 through 22754), we have provided SNFs a 
number of flexibilities to accommodate the COVID-19 PHE, including 
delaying the adoption of the updated version of the MDS 3.0 v1.18.0 
with which SNFs would have used to report the TOH Information measures 
and standardized patient assessment data elements (85 FR 27595 through 
27596). Despite the COVID-19 PHE, we must maintain our commitment to 
quality of care for all patients, and we continue to believe that the 
collection of the standardized patient assessment data elements and TOH 
Information measures will contribute to this effort. That includes 
staying committed to achieving health equity by improving data 
collection to better measure and analyze disparities across programs 
and policies 115 116 117 118 119 120 and improving the 
quality of care in SNFs through a reduction in preventable adverse 
events. Health information, such as medication information, that is 
incomplete or missing increases the likelihood of a patient or resident 
safety risk, and is often life-
threatening.121 122 123 124 125 126 Poor communication and 
coordination across healthcare settings contribute to patient 
complications, hospital readmissions, emergency department visits, and 
medication errors.127 128 129 130 131 132 133 134 135 136 
While we understand that there are concerns related to the timeline 
proposed, we believe specifying an earlier date for the data collection 
is necessary to maintain our commitment to quality of care for all 
patients. Furthermore, it is important to align the collection of these 
data with the IRFs and LTCHs that will begin collecting this 
information on October 1, 2022, and HHAs that will begin collecting 
this information on January 1, 2023.\137\ We have strived to balance 
the scope and level of detail of the data elements against the 
potential burden placed on SNFs.
---------------------------------------------------------------------------

    \115\ Centers for Medicare & Medicaid Services (CMS). CMS 
Quality Strategy. 2016. Available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/Downloads/CMS-Quality-Strategy.pdf.
    \116\ 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.
    \117\ Rural Health Research Gateway. Rural Communities: Age, 
Income, and Health Status. Rural Health Research Recap. November 
2018.
    \118\ https://www.minorityhealth.hhs.gov/assets/PDF/Update_HHS_Disparities_Dept-FY2020.pdf.
    \119\ www.cdc.gov/mmwr/volumes/70/wr/mm7005a1.htm.
    \120\ Poteat, T.C., Reisner, S.L., Miller, M., & Wirtz, A.L. 
(2020). COVID-19 Vulnerability of Transgender Women With and Without 
HIV Infection in the Eastern and Southern U.S. Preprint. medRxiv, 
2020.07.21.20159327. https://doi.org/10.1101/2020.07.21.20159327.
    \121\ Kwan, J.L., Lo, L., Sampson, M., & Shojania, K. G. (2013). 
Medication reconciliation during transitions of care as a patient 
safety strategy: a systematic review. Annals of Internal Medicine, 
158(5), 397-403.
    \122\ Boockvar, K.S., Blum, S., Kugler, A., Livote, E., 
Mergenhagen, K.A., Nebeker, J.R., & Yeh, J. (2011). Effect of 
admission medication reconciliation on adverse drug events from 
admission medication changes. Archives of Internal Medicine, 171(9), 
860-861.
    \123\ Bell, C.M., Brener, S.S., Gunraj, N., Huo, C., Bierman, 
A.S., Scales, D.C., & Urbach, D.R. (2011). Association of ICU or 
hospital admission with unintentional discontinuation of medications 
for chronic diseases. JAMA, 306(8), 840-847.
    \124\ Basey, A.J., Krska, J., Kennedy, T.D., & Mackridge, A.J. 
(2014). Prescribing errors on admission to hospital and their 
potential impact: a mixed-methods study. BMJ Quality & Safety, 
23(1), 17-25.
    \125\ Desai, R., Williams, C.E., Greene, S.B., Pierson, S., & 
Hansen, R.A. (2011). Medication errors during patient transitions 
into nursing homes: characteristics and association with patient 
harm. American Journal of Geriatric Pharmacotherapy, 9(6), 413-422.
    \126\ Boling, P.A. (2009). Care transitions and home health 
care. Clinical Geriatric Medicine, 25(1), 135-148.
    \127\ Barnsteiner, J.H. (2005). Medication Reconciliation: 
Transfer of medication information across settings--keeping it free 
from error. American Journal of Nursing, 105(3 Suppl), 31-36.
    \128\ Arbaje, A.I., Kansagara, D.L., Salanitro, A.H., Englander, 
H.L., Kripalani, S., Jencks, S.F., & Lindquist, L.A. (2014). 
Regardless of age: incorporating principles from geriatric medicine 
to imp rove care transitions for patients with complex needs. 
Journal of General Internal Medicine, 29(6), 932-939.
    \129\ Jencks, S.F., Williams, M.V., & Coleman, E.A. (2009). 
Rehospitalizations among patients in the Medicare fee-for-service 
program. New England Journal of Medicine, 360(14), 1418-1428.
    \130\ Institute of Medicine. Preventing medication errors: 
quality chasm series. Washington, DC: The National Academies Press 
2007. Available at https://www.nap.edu/read/11623/chapter/1.
    \131\ Kitson, N. A., Price, M., Lau, F.Y., & Showler, G. (2013). 
Developing a medication communication framework across continuums of 
care using the Circle of Care Modeling approach. BMC Health Services 
Research, 13(1), 1-10.
    \132\ Mor, V., Intrator, O., Feng, Z., & Grabowski, D.C. (2010). 
The revolving door of rehospitalization from skilled nursing 
facilities. Health Affairs, 29(1), 57-64.
    \133\ Institute of Medicine. Preventing medication errors: 
quality chasm series. Washington, DC: The National Academies Press 
2007. Available at https://www.nap.edu/read/11623/chapter/1.
    \134\ Kitson, N.A., Price, M., Lau, F.Y., & Showler, G. (2013). 
Developing a medication communication framework across continuums of 
care using the Circle of Care Modeling approach. BMC Health Services 
Research, 13(1), 1-10.
    \135\ Forster, A.J., Murff, H.J., Peterson, J.F., Gandhi, T.K., 
& Bates, D.W. (2003). The incidence and severity of adverse events 
affecting patients after discharge from the hospital. Annals of 
Internal Medicine, 138(3), 161-167.
    \136\ King, B.J., Gilmore[hyphen] Bykovsky, A.L., Roiland, R.A., 
Polnaszek, B.E., Bowers, B.J., & Kind, A.J. (2013). The consequences 
of poor communication during transitions from hospital to skilled 
nursing facility: a qualitative study. Journal of the American 
Geriatrics Society, 61(7), 1095-1102.
    \137\ Calendar Year 2020 Home Health final rule (86 FR 62385 
through 62390).
---------------------------------------------------------------------------

    Comment: Several commenters stated that implementing the MDS 3.0 
v1.18.11 would require additional staffing, specifically nursing staff, 
at a time when there is a national staffing crisis. Two commenters 
noted that the workforce shortages have been compounded by burnout 
among SNF workers resulting in experienced professionals leaving the 
workforce earlier than expected, with one stating it would take years 
to replace them. Another commenter cited a Kaiser Family Foundation 
study reporting more than a quarter of nursing

[[Page 47550]]

homes have reported staffing shortages as recently as March of this 
year.
    Response: The impacts of the COVID-19 PHE on the healthcare system, 
including staffing shortages, make it especially important now to 
monitor quality of care.\138\ Still, we are mindful of burden that may 
occur from the collection and reporting of our measures. We emphasize, 
however, that the TOH Information Provider-PAC and TOH Information 
Patient-PAC measures consist of one item each, and further, the 
activities associated with the measures align with the existing 
Requirements of Participation for SNFs related to transferring 
information at the time of discharge to safeguard patients.\139\ As a 
result, the information gathered will reflect a process that SNFs 
should already be conducting, and will demonstrate the quality of care 
provided by SNFs.
---------------------------------------------------------------------------

    \138\ Nursing and Patient Safety. Agency for Healthcare Research 
and Quality. April 21, 2021. Available at https://psnet.ahrq.gov/primer/nursing-and-patient-safety. Accessed 10/4/2021.
    \139\ Requirements for Long-Term Care Facilities. Part 483-
Requriements for States and Long-Term Care Facilities; Subpart B--
Requirements for Long Term Care Facilities; 42 CFR 483.15--
Admission, transfer and discharge rights.
---------------------------------------------------------------------------

    We do not believe that shortages in staffing will affect 
implementation of the new MDS because many of the data elements adopted 
as standardized patient assessment data elements in the FY 2020 SNF PPS 
final rule are already collected on the MDS 1.17.2 using current SNF 
staffing levels. For example, the hearing, vision, preferred language, 
Brief Interview for Mental Status (BIMS), Confusion Assessment Method 
(CAM(copyright)), and the Patient Health Questionnaire (PHQ) are items 
that were finalized as standardized patient assessment data elements in 
the FY 2020 SNF PPS final rule and are already being collected by SNFs 
on the MDS 1.17.2. However, those items have not historically been 
collected in the IRF and LTCH settings, and therefore will be ``new'' 
items to collect beginning October 1, 2022. Therefore, MDS 1.18.11 
results in fewer ``new'' standardized patient assessment data elements 
for SNFs, as compared to other PAC settings.
    Examples of the ``new'' standardized patient assessment data 
elements that will be collected on the MDS 1.18.11 include ethnicity, 
access to transportation, health literacy, social isolation, and pain 
interference.\140\ We note that in response to the ``Request for 
Information to Close the Health Equity Gap'' in the FY 2022 SNF PPS 
proposed rule (86 FR 20000), we heard from SNFs that they had already 
started collecting additional information about race, ethnicity, 
gender, language, and other SDOH. Given the fact that some SNFs are 
able to collect this information at current staffing levels and many of 
the items categorized as standardized patient assessment data elements 
will not be new items for SNFs, we do not believe that staff shortages 
will interfere with implementing the MDS 3.0 v1.18.11.
---------------------------------------------------------------------------

    \140\ Although there are new pain interference items, the 
current assessment item for Pain Effect on Function will be removed.
---------------------------------------------------------------------------

    Comment: Two commenters noted that the length of the revised MDS 
assessment instrument is expected to increase from 51 pages to 
approximately 61 pages, a change they believe will require significant 
investments in staff education and training, which would divert these 
resources from direct patient care.
    Response: As stated earlier in this final rule, many of the data 
elements that would be adopted as standardized patient assessment data 
elements are already collected by SNFs. The increase in the number of 
pages is the result of providing additional response options for 
several of the existing data elements and does not necessarily 
translate to additional time and burden. Additionally, the new version 
of the MDS 3.0 is expected to be 58 pages, rather than 61 pages.
    We plan to provide multiple training resources and opportunities 
for SNFs on the revised MDS assessment tool, which may include online 
learning modules, tip sheets, questions and answers documents, and/or 
recorded webinars and videos. We plan to make these training resources 
available to SNFs in early 2023, allowing SNFs several months to ensure 
their staff take advantage of the learning opportunities, and to allow 
SNFs to spread the cost of training out over several quarters.
    Comment: One commenter supported collecting, analyzing, and using 
data on social risk factors. This commenter noted, however, that it 
would create confusion and unnecessary administrative burden for CMS to 
quickly add data elements to the MDS because they happen to be 
available now, only to replace them with other data elements developed 
with the feedback from CMS's Requests for Information (RFIs) and its 
ongoing work with its Disparity Methods.\141\
---------------------------------------------------------------------------

    \141\ The Disparity Methods Confidential Reporting refers to 
CMS's confidential reporting to educate hospitals about two 
disparity methods and allow hospitals to review their results and 
data related to readmission rates for patients with social risk 
factors. Available at https://qualitynet.cms.gov/inpatient/measures/disparity-methods. Accessed 7/8/22.
---------------------------------------------------------------------------

    Response: To clarify, the standardized patient assessment data 
elements that would be collected in the MDS 3.0 v1.18.11 were finalized 
in the FY 2020 SNF PPS final rule (84 FR 38755 through 38817). The RFI 
published in section VI.E. of the FY 2023 SNF PPS proposed rule (87 FR 
22754 through 22761) requested public comment on Overarching Principles 
for Measuring Equity and Healthcare Quality Disparities across CMS 
Quality Programs and on Approaches to Assessing Drivers of Healthcare 
Quality Disparities and Developing Measures of Healthcare Equity in the 
SNF QRP, which may or may not include using standardized patient 
assessment data elements. Any new data elements that may come out of 
the RFI would have to go through the public notice and comment period 
before being implemented. Therefore, we do not anticipate confusion or 
unnecessary administrative burden as a result of the feedback received 
to the FY 2023 SNF RFI.
    Comment: Two commenters urged CMS to delay the implementation of 
the MDS 3.0 v1.18.11 until it has received the first full year of data 
collection on the TOH Information measures and standardized patient 
assessment data elements in the IRF and LTCH settings in order to 
better inform provider education and technical assistance for SNF 
providers.
    Response: The revised date of October 1, 2023, is a 3-year delay 
from the original compliance date finalized in the FY 2020 SNF PPS 
final rule (84 FR 38755 through 38764), and balances the support that 
SNFs have needed during the COVID-19 PHE with the need to collect this 
important data. We believe the revised date is sufficiently far in 
advance for SNFs to make the necessary preparations to begin reporting 
these data elements and the TOH Information measures. As stated 
earlier, the IRF and LTCH will begin collecting the TOH Information 
measures and the standardized patient assessment data elements on 
October 1, 2022. CMS began answering questions from providers in 
November 2021, after the proposal was finalized.\142\ CMS released 
virtual trainings programs for IRF and LTCH providers in April 2022 
that reviewed the updated guidance for their respective updated 
assessment tools, and hosted two live Question and Answer sessions on 
June 15 and June 16, 2022. A major focus of the trainings was on the 
cross-setting implementation of the standardized patient assessment

[[Page 47551]]

data elements they begin collecting October 1, 2022. Therefore, CMS 
would have over a year to inform provider education and technical 
assistance for SNF providers prior to implementation.
---------------------------------------------------------------------------

    \142\ Calendar Year 2020 Home Health final rule (86 FR 62385 
through 62390).
---------------------------------------------------------------------------

    We also note that in response to the ``Request for Information to 
Close the Health Equity Gap'' in the FY 2022 SNF PPS proposed rule (86 
FR 20000), interested parties stressed the importance of gathering 
additional information about race, ethnicity, gender, language, and 
other SDOH. Some SNFs noted they had already begun to collect some of 
this information for use in their operations. We do not believe further 
delaying the data collection would provide any additional information 
to better inform provider education and technical assistance for SNF 
providers.
    Comment: We received comments regarding states' and other payer 
programs use of section G data elements, the impact of changes to SNF 
regulations and requirements on the demands of these other payment 
systems, and the need for CMS to provide more infrastructure support to 
adopt certified electronic technology to facilitate meaningful data 
exchange.
    Response: These comments fall outside the scope of the FY 2023 SNF 
PPS proposed rule.
    Comment: One commenter stated their support for CMS' proposed 
update to the denominator of the TOH Information to the Patient-PAC 
measure.
    Response: We believe this comment was directed at the proposals in 
the FY 2022 SNF proposed rule (86 FR 19998), and we thank the commenter 
for their support. In the FY 2022 SNF PPS Final Rule (86 FR 42490), we 
finalized the proposal to remove the location of home under the care of 
an organized home health service organization or hospice from the 
denominator of the TOH Information to the Patient-PAC measure.
    After consideration of the comments received, we are finalizing our 
proposal that SNFs begin collecting the TOH Information to the 
Provider-PAC measure, the TOH Information to the Patient-PAC measure, 
and the six categories of standardized patient assessment data elements 
on the MDS v1.18.11 for admissions and discharges (except for the 
hearing, vision, race, and ethnicity standardized patient assessment 
data elements, which would be collected at admission only) on or after 
October 1, 2023.
3. Revisions to the Regulation Text (Sec.  413.360)
    The FY 2022 SNF PPS final rule (86 FR 42480 through 42489) added 
the COVID-19 Vaccination Coverage among Healthcare Personnel (HCP 
COVID-19 Vaccine) measure to the SNF QRP beginning with the FY 2024 
QRP. The data submission method for the HCP COVID-19 Vaccine measure is 
the NHSN. The NHSN is a system maintained by the CDC, whose mission it 
is to protect the health security of the nation. The NHSN is used to 
collect and report on healthcare-acquired infections, such as catheter-
associated urinary tract infections and central-line-associated 
bloodstream infections. The NHSN also collects vaccination information 
since vaccines play a major role in preventing the spread of harmful 
infections. Healthcare-acquired infections are a threat to 
beneficiaries, SNFs, and the public. Given the significance of the 
information collected through the NHSN, and the fact that infection 
prevention affects all beneficiaries, 100 percent of the information 
required to calculate the HCP COVID-19 Vaccine measure must be 
submitted to the NHSN. The HCP COVID-19 Vaccine measure is an important 
part of the nation's response to the COVID-19 PHE, and therefore 100 
percent of the information is necessary to monitor the health and 
safety of beneficiaries.
    For consistency in our regulations, we proposed conforming 
revisions to the Requirements under the SNF QRP at Sec.  413.360. 
Specifically, we proposed to redesignate Sec.  413.360(b)(2) to Sec.  
413.360(f)(2) and add a new paragraph (f) for the SNF QRP data 
completeness thresholds. The new paragraph would reflect all data 
completion thresholds required for SNFs to meet or exceed in order to 
avoid receiving a 2-percentage-point reduction to their APU for a given 
fiscal year.
    At Sec.  413.360(b), Data submission requirement, we proposed to 
remove paragraph (b)(2) and redesignate paragraph (b)(3) as paragraph 
(b)(2). At Sec.  413.360, we proposed to add a new paragraph (f), Data 
completion thresholds.
    At Sec.  413.360(f)(1), we proposed to add new language to state 
that SNFs must meet or exceed two separate data completeness 
thresholds: One threshold set at 80 percent for completion of required 
quality measures data and standardized patient assessment data 
collected using the MDS submitted through the CMS-designated data 
submission system, beginning with FY 2018 and for all subsequent 
payment updates; and a second threshold set at 100 percent for measures 
data collected and submitted using the CDC NHSN, beginning with FY 2023 
and for all subsequent payment updates.
    At Sec.  413.360(f)(2), we proposed to add new language to state 
that these thresholds (80 percent for completion of required quality 
measures data and standardized patient assessment data on the MDS; 100 
percent for CDC NHSN data) will apply to all measures and standardized 
patient assessment data requirements adopted into the SNF QRP.
    At Sec.  413.360(f)(3), we proposed to add new language to state 
that a SNF must meet or exceed both thresholds to avoid receiving a 2-
percentage-point reduction to their APU for a given fiscal year.
    We solicited public comment on this proposal. The following is a 
summary of the comments we received and our responses.
    Comment: One commenter urged CMS not to establish a 100 percent 
compliance threshold for measures submitted to the QRP using the NHSN. 
The commenter stated that SNFs need more experience with submitting 
data through the NHSN and that NHSN reporting requirements should be 
simplified in order to make a 100 percent compliance threshold more 
reasonable.
    Response: We disagree that SNFs need more experience with 
submitting data through the NHSN before we finalize the proposal. Since 
May 21, 2021, SNFs have been submitting the COVID-19 vaccination status 
of all residents and staff through the NHSN on a weekly basis.\143\ 
Similarly, SNFs would submit the HCP Influenza Vaccine measure through 
the NHSN at the conclusion of the measure reporting period.
---------------------------------------------------------------------------

    \143\ Medicare and Medicaid Programs; COVID-19 Vaccine 
Requirements for Long-Term Care (LTC) Facilities and Intermediate 
Care Facilities for Individuals with Intellectual Disabilities 
(ICFs-IID) Residents, Clients, and Staff (86 FR 26315-26316). May 8, 
2021.
---------------------------------------------------------------------------

    If SNFs experience data submission issues, the NHSN has a Helpdesk 
to which providers can submit questions about data submission. If a 
facility continues to have questions or experience additional issues 
after a ticket has closed, the CDC encourages providers to submit a new 
email with a detailed subject line to ensure an expeditious Helpdesk 
reply with input from a subject matter expert team.
    Comment: Several commenters requested that CMS clarify what 100 
percent reporting means for purposes of meeting the compliance 
threshold.
    Response: To meet the minimum data submission requirements for 
measure data collected and submitted using the CDC NHSN, SNFs must 
submit 100 percent of the data to the NHSN in order to calculate the 
measure. For example,

[[Page 47552]]

NHSN is the data submission method for the HCP COVID-19 Vaccine measure 
for the SNF QRP. Therefore, SNFs must submit to the NHSN 100 percent of 
the information required to calculate the HCP COVID-19 Vaccine measure 
in order to meet the compliance threshold.
    Similarly, for the HCP Influenza Vaccine measure, SNFs must submit 
to the NHSN 100 percent of the information required to calculate the 
measure. To meet the minimum data submission requirements for the HCP 
Influenza Vaccine measure, SNFs must enter a single influenza 
vaccination summary report at the conclusion of the measure reporting 
period. If SNFs submit data more frequently, such as on a monthly 
basis, the information would be used to calculate one summary score for 
the proposed measure which would be publicly reported on Care Compare 
and used to determine compliance with the SNF QRP.
    Comment: One commenter requested clarification on the proposed 
conforming language to the regulatory text at Sec.  413.360. 
Specifically, the commenter requested that CMS clarify the procedural 
steps SNFs must take to meet or exceed the two separate data 
completeness thresholds.\144\ The commenter inquired how many files a 
SNF must submit and how often in order to meet the 100 percent 
completion threshold.
---------------------------------------------------------------------------

    \144\ One threshold set at 80 percent for completion of required 
quality measures data and standardized patient assessment data 
collected using the MDS submitted through the CMS-designated data 
submission system, beginning with FY 2018 and for all subsequent 
payment updates; and a second threshold set at 100 percent for 
measures data collected and submitted using the CDC NHSN, beginning 
with FY 2023 and for all subsequent payment updates.
---------------------------------------------------------------------------

    Response: The proposed revisions to the regulatory text at Sec.  
413.360 would add language to state that SNFs must meet or exceed two 
separate data completeness thresholds depending on the data submission 
method: (1) an 80 percent threshold for completion of required data 
elements collected using the MDS submitted through the CMS designated 
data submission system; and (2) a 100 percent threshold for measures 
collected and submitted using the NHSN.
    With the addition of the HCP Influenza Vaccine measure adopted in 
this final rule, the SNF QRP would have two measures submitted via the 
NHSN: (1) the HCP COVID-19 Vaccine measure and (2) the HCP Influenza 
Vaccine measure. SNFs must follow separate data submission guidelines 
for each measure to meet the 100 percent completion threshold. For the 
HCP COVID-19 Vaccine measure, SNFs use the COVID-19 vaccination data 
collection module in the NHSN Long-term Care Component to report the 
number of HCP eligible to work at the facility for at least 1 day 
during the reporting period excluding persons with contraindications to 
COVID-19 vaccination that are described by the CDC \145\ (denominator) 
and the number of those people who have received a completed COVID-19 
vaccination course (numerator). To meet the minimum data submission 
requirements for the HCP COVID-19 Vaccine measure, SNFs submit COVID-19 
vaccination data through the NHSN for at least 1 week each month. For 
example, if a SNF only submitted COVID-19 vaccination data for 1 week 
each month from January through September of a given calendar year, but 
failed to submit information for October, November, and December of 
that same calendar year, that SNF would not meet the 100 percent 
completion threshold for this measure and would face a 2-percentage-
point reduction to its APU.
---------------------------------------------------------------------------

    \145\ Use of COVID-19 Vaccines in the United Stated. Interim 
Clinical Considerations. Available at https://www.cdc.gov/vaccines/covid-19/clinical-considerations/covid-19-vaccines-us.html. Accessed 
7/7/2022.
---------------------------------------------------------------------------

    Similarly, for the HCP Influenza Vaccine measure, SNFs would use 
the HCP influenza data reporting module in the NHSN HPS Component and 
complete two forms. The first form (CDC 57.203) would indicate the type 
of data SNFs plan on reporting to the NHSN by selecting the ``Influenza 
Vaccination Summary'' option under ``Healthcare Personnel Vaccination 
Module'' to create a reporting plan. The second form (CDC 57.214) would 
report the number of HCP who have worked at the healthcare facility for 
at least 1 day between October 1st and March 31st (denominator) and the 
number of HCP who fall into each numerator category. To meet the 
minimum data submission requirements for the HCP Influenza Vaccine 
measure, SNFs would enter a single influenza vaccination summary report 
at the conclusion of the measure reporting period. If SNFs submit data 
more frequently, such as on a monthly basis, the information would be 
used to calculate one summary score for the proposed measure which 
would be publicly reported on Care Compare and used to determine 
compliance with the SNF QRP.
    To meet the 100 percent compliance threshold for the HCP Influenza 
Vaccine measure, a SNF must submit a single influenza vaccination 
summary report at the conclusion of the reporting period. A SNF that 
submits an influenza vaccination summary report for October through 
December of an influenza season, but not for the remainder of the 
influenza season, would not meet the 100 percent completion threshold 
for this measure.
    To meet the 80 percent compliance threshold for purposes of 
calculating the SNF's APU, a SNF would need to submit a minimum of 80 
percent of its MDS with 100 percent of the required data elements 
collected during the reporting period to the CMS Quality Improvement 
and Evaluation System (QIES) Assessment Submission and Processing 
(ASAP) system or a successor system. The SNF QRP Table for Reporting 
Assessment-Based Measures for each FY SNF QRP APU is available for 
download on the SNF Quality Reporting Measures and Technical 
Information web page in the Downloads section.\146\
---------------------------------------------------------------------------

    \146\ SNF Quality Reporting Measures and Technical Information 
web page. 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.
---------------------------------------------------------------------------

    Comment: One commenter questioned whether a SNF would be compliant 
if it meets the 80 percent requirements but fails to meet the 100 
percent requirements.
    Response: We interpret the comment to be referring to the 80 
percent compliance threshold for the required data elements submitted 
using the MDS 3.0 and the 100 percent compliance threshold proposed for 
measures submitted using the NHSN data submission framework. In 
accordance with section 1888(e)(6)(A)(i) of the Act, the Secretary must 
reduce by 2 percentage points the APU 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. Consistent with the measures we are finalizing, 
SNF providers must meet both the 80 percent and 100 percent compliance 
thresholds for that applicable fiscal year to comply with the 
requirements of the SNF QRP beginning with FY 2023 QRP and for all 
subsequent payment updates.
    After consideration of the comments received, we are finalizing our 
proposal to make conforming revisions to the requirements under the SNF 
QRP at Sec.  413.360. Specifically, we are redesignating Sec.  
413.360(b)(2) to Sec.  413.360(f)(2) and adding a new paragraph (f) for 
the SNF QRP data completeness thresholds.

[[Page 47553]]

D. SNF QRP Quality Measures Under Consideration for Future Years: 
Request for Information (RFI)

    We solicited input on the importance, relevance, and applicability 
of the concepts under consideration listed in Table 16 in the SNF QRP. 
More specifically, we solicited input on a cross-setting functional 
measure that would incorporate the domains of self-care and mobility. 
Our measure development contractor for the cross-setting functional 
outcome measure convened a Technical Expert Panel (TEP) on June 15 and 
June 16, 2021 to obtain expert input on the development of a functional 
outcome measure for PAC. During this meeting, the possibility of 
creating one measure to capture both self-care and mobility was 
discussed. We also solicited input on measures of health equity, such 
as structural measures that assess an organization's leadership in 
advancing equity goals or assess progress toward achieving equity 
priorities. Finally, we solicited input on the value of a COVID-19 
Vaccination Coverage measure that would assess whether SNF patients 
were up to date on their COVID-19 vaccine.
[GRAPHIC] [TIFF OMITTED] TR03AU22.016

    Comment: Most commenters supported the concept of a cross-setting 
functional outcome measure that is inclusive of both self-care and 
mobility items. Commenters provided information relative to potential 
risk adjustment methodologies as well as other tests and measures that 
could be used to capture functional outcomes. Commenters were mixed on 
whether they supported the measure concept of a PAC-COVID-19 
vaccination coverage among patients. Two commenters noted the measure 
should account for other variables, such as whether the vaccine was 
offered, as well as patients with medical contraindications to the 
vaccine. Comments were generally supportive of the concept of measuring 
health equity in the SNF QRP. In addition, several commenters suggested 
other measures and measure concepts CMS should consider.
    Response: As discussed in the proposed rule, we are not responding 
to specific comments submitted in response to this RFI in this final 
rule, but we intend to use this input to inform our future measure 
development efforts.

E. Overarching Principles for Measuring Equity and Healthcare Quality 
Disparities Across CMS Quality Programs--Request for Information (RFI)

1. Solicitation of Public Comments
    The goal of the request for information was to describe some key 
principles and approaches that we would consider when advancing the use 
of quality measure development and stratification to address healthcare 
disparities and advance health equity across our programs.
    We invited general comments on the principles and approaches 
described previously in this section of the rule, as well as additional 
thoughts about disparity measurement guidelines suitable for 
overarching consideration across CMS's QRP programs. Specifically, we 
invited comments on:
     Identification of Goals and Approaches for Measuring 
Healthcare Disparities and Using Measure Stratification Across CMS 
Quality Reporting Programs:
    ++ The use of the within- and between-provider disparity methods in 
SNFs to present stratified measure results.
    ++ The use of decomposition approaches to explain possible causes 
of measure performance disparities.
    ++ Alternative methods to identify disparities and the drivers of 
disparities.
     Guiding Principles for Selecting and Prioritizing Measures 
for Disparity Reporting:
    ++ Principles to consider for prioritization of health equity 
measures and measures for disparity reporting, including prioritizing 
stratification for validated clinical quality measures, those measures 
with established disparities in care, measures that have adequate 
sample size and representation among healthcare providers and outcomes, 
and measures of appropriate access and care.
     Principles for SRF and Demographic Data Selection and Use:
    ++ Principles to be considered for the selection of SRFs and 
demographic data for use in collecting disparity data including the 
importance of expanding variables used in measure stratification to 
consider a wide range of SRFs, demographic variables, and other markers 
of historic disadvantage. In the absence of patient-reported data we 
will consider use of administrative data, area-based indicators, and 
imputed variables as appropriate.
     Identification of Meaningful Performance Differences:
    ++ Ways that meaningful difference in disparity results should be 
considered.
     Guiding Principles for Reporting Disparity Measures:
    ++ Guiding principles for the use and application of the results of 
disparity measurement.
     Measures Related to Health Equity:
    ++ The usefulness of a Health Equity Summary Score (HESS) for SNFs, 
both in terms of provider actionability to improve health equity, and 
in terms of whether this information would support Care Compare website 
users in making informed healthcare decisions.
    ++ The potential for a structural measure assessing a SNF's 
commitment to health equity, the specific domains that should be 
captured, and options for reporting these data in a manner that would 
minimize burden.
    ++ Options to collect facility-level information that could be used 
to support the calculation of a structural measure of health equity.
    ++ Other options for measures that address health equity.
    We received several comments on the RFI for Overarching Principles 
for Measuring Equity and Healthcare Quality Disparities Across CMS 
Quality Programs. While we will not be responding to specific comments 
submitted in response to this RFI, the following is a summary of some 
comments received:
    Comment: Several commenters provided feedback on the use of the 
within-provider and between-provider disparity methods to present 
stratified measure results. Overall, comments were generally supportive 
of

[[Page 47554]]

implementing both methods in order to provide a more complete picture 
of the quality of care provided to beneficiaries with SRFs. In terms of 
specific feedback related to the implementation of these stratification 
approaches, one commenter noted that when making between-facility 
comparisons, CMS should appropriately account for the share of patients 
within a facility with various risk factors. Another commenter noted 
that in the hospital setting, some stratification metrics moved widely 
across deciles when only a few patients improved performance, 
suggesting the importance of evaluating the statistical reliability of 
stratification methodologies implemented in the SNF setting.
    One commenter expressed support for the measure performance 
disparity decomposition approach because it will likely provide 
valuable data while placing minimal burden on SNFs. Several commenters 
emphasized that providing stratified results alone to providers does 
not provide sufficient information to identify underlying factors that 
contribute to health inequities. While these commenters did not 
explicitly point to the disparity decomposition approach as a solution, 
the decomposition approach described could be a promising method to 
identify specific drivers of performance disparities, which would 
increase the actionability of stratified measure information while 
providing no additional burden to providers.
    A handful of commenters responded to CMS's request for information 
about measures CMS could develop to assess and encourage health equity, 
including comments regarding the usefulness and actionability of a HESS 
and the potential for a structural measure to assess SNFs' commitment 
to health equity. We first summarize the comments regarding the HESS, 
then summarize comments related to a structural measure to assess 
commitment to equity.
    Three commenters specifically addressed the HESS. One commenter 
encouraged CMS to clarify that the HESS would assess individual SNFs as 
opposed to the individual clinicians within each SNF. The two remaining 
commenters either supported or appreciated the HESS in concept, but 
raised several concerns pertaining to technical barriers, ambiguity in 
the methodology, and usability of the measure. In terms of technical 
concerns, one commenter noted that a standardized set of demographic 
data elements must be available for each patient, and stated that 
demographic data elements are not yet standardized across healthcare 
settings and organizations. Regarding methodological concerns, one 
commenter questioned how one could combine within-facility disparities 
and disparities across facilities into a single summary score in a 
manner that would accurately reflect the individual factors that may 
lead to these different types of disparities, without masking other 
factors. Other commenters raised similar concerns about the usability 
of the HESS, primarily stemming from the extent to which disparities 
across multiple measures and SRFs are aggregated into a single score. 
Specifically, one commenter noted that one SRF included in the HESS 
could mask the effects of other SRFs, which could potentially lead to 
misinterpretation of the overall score. Similarly, another commenter 
noted that performance on the composite HESS might obscure measure-
level and SRF-specific disparities.
    Two commenters addressed the potential for a structural measure to 
assess health equity. One commenter noted that the development of a 
structural measure to assess engagement and commitment of leadership 
toward advancing health equity should be included as one of several 
guiding principles to address health disparities and achieve health 
equity. Another commenter cautioned against the development of 
structural measures, suggesting that such measures would only 
demonstrate whether an organization is ``good at checking the box'' for 
the purpose of meeting the requirements of a measure.
    Several commenters addressed the selection of SRFs and demographic 
data in collecting disparity data. One commenter supported the Center 
for Outcomes Research and Evaluation's (CORE's) efforts to categorize 
SDOH. Several commenters supported collecting data through current 
standardized resident assessment processes using variables with robust, 
established data sources. They believe revisions to an item already 
used across settings would capitalize on existing workflows and be 
easily updated within electronic health record (EHR) systems, resulting 
in minimal staff burden. One commenter recommended using existing items 
such as A1000 in Section A of the MDS assessment that addresses Race 
and Ethnicity, and revising gender identification options in MDS item 
A0800--Gender, which currently only includes binary Male/Female 
options. Another commenter recommended CMS consider how to best capture 
sexual orientation and gender identity among Medicare and Medicaid 
beneficiaries.
    Several commenters preferred using self-reported social, economic, 
and demographic tools over imputed data sources, but also recognized 
the challenges with collecting self-reported data, and so they stated 
that in the absence of self-reported data, they would support the use 
of certain proxies, such as the Area Deprivation Index (ADI) or other 
area-based indicators of social risk. One commenter also suggested 
utilizing indexes from the Agency for Healthcare Research and Quality, 
CDC, and the Health Resources and Services Administration to 
incorporate data about area-based indicators of social risk would 
reduce burden on organizations or clinicians.
    One commenter noted that using both methods of capturing data might 
be the best option: (1) a self-report demographic like the social 
determinants of health reported through the standardized patient 
assessment data elements that gives a picture of the unique resident's 
perspective, while (2) the area-based indices provide objective data on 
the risk factors present in the resident's usual environment.
    Two commenters did not support selecting race and ethnicity for 
collecting disparity data. One commenter stated that ``race'' and 
``ethnicity'' are social constructs that have no reliable biological 
basis in the clinical context, and are so overly broad, vague, and ill-
defined that, even in combination with other indicators, they are 
unlikely to provide useful information and may even obscure individual 
experience to the detriment of individualized patient care. Another 
commenter also had significant reservations about using race and 
ethnicity data as the basis for stratifying measures and explaining 
differences in health and outcomes due to concerns about the variation 
in the manner in which race and ethnicity are defined and the 
categories collected by institutions.
    Commenters suggested collecting other SRFs, including dual 
eligibility for Medicare and Medicaid, and detailed standardized 
demographic and language data. The Medicare Payment Advisory Commission 
(MedPAC) commented on its recent work to expand its definition of 
``low-income'' as a proxy for beneficiary social risk. It defined 
``low-income'' beneficiaries as those who are eligible for full or 
partial Medicaid benefits or receive the Part D low-income subsidy 
(LIS). This expanded definition includes beneficiaries who do not 
qualify for Medicaid benefits in their states but who do qualify for 
the LIS based on having limited assets and an income below 150 percent 
of the

[[Page 47555]]

federal poverty level. MedPAC found that compared to the non-LIS 
Medicare population, LIS beneficiaries have relatively low incomes and 
differ in other regards, including being twice as likely to be Black or 
Hispanic and three times as likely to be disabled.
    Commenters spoke to the importance of considering how SRF data 
could be interoperable and constructed in a way to facilitate exchange. 
One commenter suggested that CMS consider recommendations from The 
Gravity Project. Another requested that CMS make a concerted effort to 
advance standards for the collection of socio-demographic information, 
using existing tools such as the United States Core Data for 
Interoperability (USCDI), Z-codes, HL7, and Fast Healthcare 
Interoperability Resources (FHIR) standards.
    We received several comments on the topic of confidential reporting 
of stratified and unstratified measure results. Most commenters 
supported the concept of selecting and prioritizing measures for 
disparity reporting. One commenter stated they want meaningful, 
actionable data, while another commenter recommended that, in addition 
to providing confidential feedback to nursing homes on stratified 
measure results, CMS should also provide information to make this 
feedback meaningful to nursing homes, such as how to interpret the 
information and what can be done to address identified disparities. 
This commenter suggested using the cumulative data to identify 
disparities at a regional or national level on which targeted training 
and resources could be provided, either by CMS or by the Quality 
Improvement Organizations (QIOs). Another commenter urged CMS to use 
ease of data access as an additional guiding principle when making 
disparity reporting decisions.
    As for public reporting of stratified and unstratified results, 
many commenters urged CMS to carefully evaluate performance using the 
confidential reporting of data prior to applying the same methodologies 
to public reporting of stratified measure results. Another commenter 
recommended CMS outline a clear plan for transitioning to public 
reporting as it plans for the initial private reporting. MedPAC, 
however, supported it because MedPAC believes it should enable 
comparisons of individual providers with State and national averages to 
give consumers meaningful reference points.
    Response: We appreciate all of the comments and interest in this 
important topic. Public input is very valuable in the continuing 
development of our health equity quality measurement efforts and 
broader commitment to health equity, a key pillar of our strategic 
vision as well as a core agency function. Thus, we will continue to 
take all concerns, comments, and suggestions into account for future 
development and expansion of policies to advance health equity across 
the SNF QRP, including by supporting SNFs in their efforts to ensure 
equity for all of their patients, and to identify opportunities for 
improvements in health outcomes. 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. Inclusion of the CoreQ: Short Stay Discharge Measure in a Future SNF 
QRP Program Year-Request for Information (RFI)

1. Solicitation of Public Comment
    In the proposed rule, we requested stakeholder feedback on future 
adoption and implementation of the CoreQ: Short Stay Discharge Measure 
(CoreQ) into the SNF QRP.
    Specifically, we sought comment on the following:
     Would you support utilizing the CoreQ to collect patient-
reported outcomes (PROs)?
     Do SNFs believe the questions asked in the CoreQ would add 
value to their patient engagement and quality-of-care goals?
     Should CMS establish a minimum number of surveys to be 
collected per reporting period or a waiver for small providers?
     How long would facilities and customer satisfaction 
vendors need to accommodate data collection and reporting for all 
participating SNFs?
     What specific challenges do SNFs anticipate for collecting 
the CoreQ measure? What are potential solutions for those challenges?
    Comment: We received a few comments on this RFI that were generally 
supportive of the addition of a PRO measure or patient experience 
measure to the SNF QRP. However, support for the CoreQ measure 
specifically was mixed among commenters. One commenter stated that 
since the CoreQ has a limited number of questions, it may not fully 
reflect patient experience at a given facility. Another commenter would 
not support the CoreQ since it excludes residents who leave a facility 
against medical advice and residents with guardians, and this commenter 
stated it would be important to hear from both of these resident 
populations. Two commenters cautioned CMS to consider the burden 
associated with contracting with vendors to administer such a measure.
    Response: We are not responding to specific comments submitted in 
response to this RFI in this final rule, but we intend to use this 
input to inform our future measure development efforts.

G. Form, Manner, and Timing of Data Submission Under the SNF QRP

1. Background
    We refer readers to the current regulatory text at Sec.  413.360(b) 
for information regarding the policies for reporting SNF QRP data.
2. Proposed Schedule for Data Submission of the Influenza Vaccination 
Coverage Among Healthcare Personnel (NQF #0431) Measure Beginning With 
the FY 2024 SNF QRP
    As discussed in section VI.C.1. of the proposed rule, we proposed 
to adopt the Influenza Vaccination Coverage among HCP quality measure 
beginning with the FY 2025 SNF QRP. However, after consideration of 
public comments, we are finalizing our proposal to adopt the Influenza 
Vaccination Coverage among Healthcare Personnel (NQF #0431) measure 
beginning with the FY 2024 SNF QRP. The CDC has determined that the 
influenza vaccination season begins on October 1st (or when the vaccine 
becomes available) and ends on March 31st of the following year. 
Therefore, we proposed an initial data submission period from October 
1, 2022 through March 31, 2023. We also noted that in subsequent years, 
data collection for this measure will be from October 1st through March 
31st of the following year.
    This measure requires that the provider submit a minimum of one 
report to the NHSN by the data submission deadline of May 15th for each 
influenza season following the close of the data collection period each 
year to meet our requirements. Although facilities may edit their data 
after May 15th, the revised data will not be shared with us.\147\ SNFs 
would submit data for the measure through the CDC/NHSN web-based 
surveillance system. SNFs would use the Influenza Vaccination Summary 
option under the NHSN HPS Component to report the number of HCP

[[Page 47556]]

who receive the influenza vaccination (numerator) among the total 
number of HCP in the facility for at least 1 working day between 
October 1st and March 31st of the following year, regardless of 
clinical responsibility or patient contact (denominator).
---------------------------------------------------------------------------

    \147\ Centers for Disease Control and Prevention (CDC). (2021). 
HCP Influenza Vaccination Summary Reporting FAQs. Retrieved from 
https://www.cdc.gov/nhsn/faqs/vaccination/faq-influenza-vaccination-
summary-
reporting.html#:~:text=To%20meet%20CMS%20reporting%20requirements,not
%20be%20shared%20with%20CMS.
---------------------------------------------------------------------------

    We sought public comment on this proposal. The following is a 
summary of the comments we received and our responses.
    Comment: Several commenters urged CMS to be cautious in executing 
reporting for this measure since HCP influenza vaccination data are not 
currently reported by nursing homes and new processes will need to be 
implemented for measure data collection. Commenters recommended that 
(1) CMS provide ample notification to providers to ensure timely 
reporting of the measure, (2) reporting requirements of the measure 
should align with what is outlined in the proposed rule, and (3) CMS 
should only require reporting of the measure once per influenza season. 
Commenters also cautioned CMS that enforcement of any requirement must 
follow a traditional citation route without automatic financial 
penalties, given that SNFs that fail to report measure data will be 
penalized through the QRP framework itself.
    One commenter expressed concerns that SNFs would be required to 
verify the influenza vaccination status of every employee, especially 
those who are immunized by an outside provider, and that the increase 
in administrative burden may take away resources to care for residents. 
Another commenter sought clarification about the measure's data 
collection process, noting that CMS must be clear and allow for ongoing 
flexibility in data collection and potential dispute.
    Response: The HCP Influenza Vaccine measure reporting requirements 
would align with those outlined in the proposed rule. Specifically, the 
data collection period is October 1st to March 31st of the following 
year, with an annual data submission deadline due no later than May 
15th. Additionally, we provide an updated SNF QRP Deadlines for Data 
Collection and Final Submission document on an annual basis. These 
deadlines provide sufficient notification to providers to ensure timely 
reporting of the measure. Providers may refer to this document on the 
SNF QRP Data Submission Deadlines web page at https://www.cms.gov/
Medicare/Quality-Initiatives-Patient-Assessment-Instruments/
NursingHomeQualityInits/Skilled-Nursing-Facility-Quality-Reporting-
Program/SNF-Quality-Reporting-Program-Data-Submission-
Deadlines#:~:text=When%20does%20SNF%20quality%20data,day%20of%20the%20su
bmission%20deadline. We also send out reminders of the data submission 
deadlines via CMS listserv announcements. Providers can subscribe to 
the listserv to receive these email updates and for the latest SNF 
quality reporting program information on the CMS Email Updates web page 
at https://public.govdelivery.com/accounts/USCMS/subscriber/new?pop=t&topic_id=USCMS_7819.
    To report HCP influenza vaccination summary data to the NHSN, all 
facilities must complete two required forms: (1) HCP Safety Monthly 
Reporting Plan Form (57.203), and (2) HCP Influenza Vaccination Summary 
Form (57.214). Facilities reporting annual HCP influenza vaccination 
data would report through the NHSN's Healthcare Personnel Safety (HPS) 
Component; therefore, providers should use form 57.203 and select the 
``Influenza Vaccination Summary'' option under the ``Healthcare 
Personnel Vaccination Module'' to create a reporting plan. For more 
data collection and submission details, we refer providers to the HCP 
Influenza Vaccination Summary Reporting FAQs on the CDC NHSN web page 
at https://www.cdc.gov/nhsn/faqs/vaccination/faq-influenza-vaccination-summary-reporting.html. We also provide additional information 
regarding provider trainings later in this section.
    Although the measure may require that SNFs spend additional time 
obtaining verification of HCP influenza vaccination, the importance of 
preventing infection among susceptible residents warrants collection of 
HCP influenza vaccination rates. We note that SNFs already have a 
process in place for tracking employee vaccinations, since they have 
been reporting HCP COVID-19 vaccination since October 1, 2021. We 
emphasize that tracking influenza vaccination rates among HCP is less 
burdensome than tracking COVID-19 vaccination rates, since SNFs are 
only required to track and submit data for one annual vaccination per 
HCP instead of potentially multiple vaccinations and boosters for the 
COVID-19 vaccination.
    Comment: Several commenters requested CMS not to finalize the 
Influenza Vaccination Coverage among HCP measure due to the burden 
associated with reporting it. Commenters expressed concern that 
additional NHSN reporting will place burden on facilities on top of the 
existing NHSN reporting requirement of COVID-19 data. One commenter 
noted provider confusion with NHSN data submission requirements as some 
have unintentionally submitted data for certain modules that were not 
required. This commenter also highlighted the burdens associated with 
obtaining Secure Access Management Services (SAMS) Level 3 access in 
accordance with the CDC's reporting requirements for SNFs. A final 
commenter recommended using National Immunization Records as a data 
source for the measure, rather than spending additional time to report 
HCP vaccination status to the NHSN.
    Response: We emphasize that the Influenza Vaccination Coverage 
among HCP measure only requires providers to submit a minimum of one 
report to the NHSN for each influenza season. We also clarify a 
statement in section VI.C.1.a. of the FY 2023 SNF PPS proposed rule 
that a CDC analysis of the 2020 through 2021 influenza season revealed 
that among 16,535 active, CMS-certified nursing homes, 17.3 percent 
voluntarily submitted at least 1 weekly influenza vaccination 
measurement through the NHSN. We believe such voluntary reporting 
supports the feasibility of annual measure data collection and 
reporting by nursing homes. We also believe that the burden of 
submitting data should be reduced since providers will have some 
familiarity with the NHSN through their experience of reporting of the 
COVID-19 Vaccination Coverage among HCP measure.\148\
---------------------------------------------------------------------------

    \148\ 86 FR 42424.
---------------------------------------------------------------------------

    In response to provider confusion with NHSN data submission 
requirements, facilities may refer to the Healthcare Personnel Safety 
Component--Healthcare Personnel Vaccination Module Influenza 
Vaccination Summary Comprehensive Training Slides at https://www.cdc.gov/nhsn/pdfs/training/hcp/hcp-flu-vaccination-summary-reporting-general-training.pdf, to learn how to report required data. 
To view provider trainings that are specific to long-term care 
facilities, providers may refer to the Healthcare Personnel Safety 
Component--Healthcare Personnel Vaccination Module Influenza 
Vaccination Summary Long-Term Care Facilities training slides at the 
following CDC web page at https://www.cdc.gov/nhsn/pdfs/training/vaccination/hcp-flu-vax-summary-reporting-ltc.pdf. The CDC also plans 
to offer additional training in the fall of 2022 to review annual 
influenza vaccination reporting and answer provider questions in real 
time via a webinar chat feature.

[[Page 47557]]

    In regard to concerns about provider requirements to obtain SAMS 
Level 3 access, we would like to highlight that 14,849 long-term care 
facilities (98 percent) with a CMS Certification Number (CCN) already 
have at least one SAMS Level 3 user. We additionally note that 12,133 
long-term care facilities (80 percent) have two or more SAMS level 3 
users. Therefore, many facilities will not need to spend additional 
time requesting SAMS Level 3 access to meet the data submission 
requirements of the Influenza Vaccination Coverage among HCP measure. 
Additionally, SAMS has expedited the timeline for gaining Level 3 
access by allowing users to submit identity verification documents to 
the CDC using Experian. More information for gaining SAMS Level 3 
access can be retrieved at the About SAMS CDC web page at https://www.cdc.gov/nhsn/sams/about-sams.html.
    Lastly, regarding commenter suggestions to retrieve HCP influenza 
vaccination from national immunization records, there is no such 
national organization.\149\ While some vaccine providers participate in 
immunization registries such as the Immunization Information Systems 
(IIS), the HCP Influenza Vaccine measure would not require SNFs to 
participate in such registries,\150\ making the NHSN the comprehensive 
method for tracking HCP influenza vaccination rates for purposes of the 
SNF QRP.
---------------------------------------------------------------------------

    \149\ Centers for Disease Control and Prevention (CDC). (2016). 
Keeping your Vaccine Records Up to Date. Retrieved from https://www.cdc.gov/vahccines/adults/vaccination-records.html.
    \150\ Centers for Disease Control and Prevention (CDC). (2019). 
About Immunization Information systems. Retrieved from https://www.cdc.gov/vaccines/programs/iis/about.html.
---------------------------------------------------------------------------

    Comment: One commenter noted technical issues encountered with the 
NHSN reporting system since SNFs began using it in May 2021, suggesting 
that CMS should implement provider protections to mitigate NHSN data 
submission issues that may be beyond providers' control. Another 
commenter opposed the measure proposal due to technical issues with the 
NHSN reporting system that are beyond providers' control. One commenter 
outlined several NHSN technical issues experienced by providers, such 
as (1) frequent changing of NHSN module tables and required content, 
(2) NHSN acceptance of incomplete data resulting in SNF non-compliance, 
(3) mislabeling SNF CMS Certification Numbers (CCNs) by the NSHN, (4) 
errors with comma-separated items on group NHSN uploads, (5) auto-
populated NHSN error messages that do not identify which portion of the 
submission may have an error, (6) delays in NHSN Helpdesk response and/
or closing a ticket without ensuring the issue has been resolved, (7) 
provider software incompatibility and ransomware attacks which have 
prevented transmission of files, and (8) unavailability of 
telecommunication due to weather-related interruptions.
    Response: We discussed providers' concerns regarding technical 
difficulties that may arise in submitting data to the NHSN. The CDC has 
provided responses to each concern as outlined throughout the remainder 
of this response. First, the CDC highlights that the NHSN conducted 
surveillance of annual influenza vaccination beginning with the 2012 
through 2013 influenza season. Results of the surveillance reveal that 
multiple facility types (for example, acute care facilities, inpatient 
rehabilitation facilities, long-term acute care facilities, etc.) have 
successfully reported these data over several years. Surveillance to 
track influenza vaccination has not required frequent changes to NHSN 
module tables and required content because annual influenza vaccination 
recommendations for healthcare workers have not changed for several 
years, unlike COVID-19 vaccination data reporting where guidance is 
still evolving and changing.
    Regarding concerns about NHSN acceptance of incomplete data 
submission leading to provider non-compliance, the CDC notes that 
fields are set as required in the current NHSN annual influenza module, 
which prevents incomplete data submission for this reporting metric. 
Resources and training materials for annual influenza surveillance are 
available on the NHSN Healthcare Personnel (HCP) Flu Vaccination CDC 
web page at https://www.cdc.gov/nhsn/hps/vaccination/.
    In response to concerns about mislabeled CMS CCNs, the CDC 
emphasizes that providers are responsible for correctly entering their 
CCNs into the NHSN application. If a SNF has correctly entered its CCN 
and influenza surveillance data appropriately, data will automatically 
be sent to CMS to meet SNF QRP data submission requirements. The NHSN 
continues to provide support and education to SNFs when they reach out 
about correcting their CCN in the NHSN application. SNFs may view 
checklists to ensure their annual influenza vaccination data are 
reported accurately on the NHSN Healthcare Personnel (HCP) Flu 
Vaccination CDC web page at https://www.cdc.gov/nhsn/hps/vaccination/, under the ``Annual Flu Summary'' heading. In addition, 
providers can view information regarding data verification on the 
following CDC web page: Submission of Healthcare Personnel (HCP) 
Influenza Vaccination Summary Data in NHSN at https://www.cdc.gov/nhsn/pdfs/hps-manual/vaccination/verification-hcp-flu-data.pdf. If a 
provider seeks to change the CCN listed for a SNF in the NHSN, the 
provider may refer to the following CDC NHSN guidance document: Long-
Term Care Facility (LTCF) How to Add and Edit Facility CMS 
Certification Number (CCN) within NHSN at the following web page at 
https://www.cdc.gov/nhsn/pdfs/ltc/ccn-guidance-508.pdf. Lastly, 
providers may view additional NHSN resources at the CDC NHSN CMS 
Quality Reporting Program Frequently Asked Questions web page at 
https://www.cdc.gov/nhsn/faqs/cms/faq_cms_hai.html.
    Regarding concerns with comma-separated items on group uploads, the 
CDC notes that uploading data via a comma-separated values (CSV) file 
is not an option for annual influenza vaccination data reporting. 
However, the CDC anticipates having this option available in the 
upcoming 2022 through 2023 influenza season. The CDC acknowledged that 
as COVID-19 surveillance needs evolved, data fields changed 
accordingly, and at times it led to unexpected issues with CSV upload 
and short delays in reporting. The CDC prioritizes resolving such 
issues quickly and communicating with users and partners. The NHSN 
continues to offer support to facilitate data uploading.
    Moreover, in response to concerns about auto-populated error 
messages, the NHSN continues to work to make error messages detailed 
and clear for users. For example, common errors are covered during user 
trainings (i.e., webinars, email blasts, etc.). The CDC continues to 
revise error messages based on user feedback, encouraging plain 
language detailed messages. If there are specific alerts causing 
confusion for annual influenza vaccination data, providers are 
encouraged to contact [email protected].
    Regarding NHSN Helpdesk concerns, if a SNF continues to have 
questions or experience additional issues after a ticket has closed, 
the CDC encourages providers to submit a new email with a detailed 
subject line to ensure an expeditious Helpdesk reply with input from a 
subject matter expert team. When submitting annual influenza 
vaccination data, SNFs have been instructed to include ``HPS Flu 
Summary'' along with their facility type in the subject line of the 
email for a more immediate response.

[[Page 47558]]

    In regard to general submission concerns such as software 
incompatibility and ransomware attacks that have prevented the 
transmission of data files, the NHSN provides CSV templates and CSV 
template example files if SNFs prefer to upload data directly to the 
platform. CSV templates will be made available to SNFs reporting annual 
influenza vaccination data for the 2022 through 2023 influenza season. 
Once available, CSV templates will appear similarly to how the COVID-19 
Vaccination Coverage among HCP resources appear on the Weekly HCP & 
Resident COVID-19 Vaccination CDC NHSN web page https://www.cdc.gov/nhsn/ltc/weekly-covid-vac/, under a CSV Data Import header.
    Lastly, in response to concerns about technical data submission 
issues that may arise beyond providers' control, such as 
telecommunication issues resulting from weather-related interruptions, 
the CMS reconsideration and exception and extension process is 
available to SNFs if they are found to be non-compliant with the SNF 
QRP data submission requirements and believe they have a valid reason 
for an exception. For information about the reconsideration and 
exception and extension request process, please visit the SNF QRP 
Reconsideration and Exception & Extension CMS web page at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/Skilled-Nursing-Facility-Quality-Reporting-Program/SNF-QR-Reconsideration-and-Exception-and-Extension.
    Comment: Two commenters expressed concern over the quality of 
provider-submitted data to the NHSN, noting the importance of data 
validation efforts, and oppose the adoption of the measure until there 
are data validation and provider Review and Correct Reports comparable 
to other provider-submitted SNF QRP data. The commenters noted that 
since SNFs receive their provider preview reports in July, SNFs do not 
have an opportunity to correct any discrepancies that could be found if 
given more time to review their data. Another commenter supported the 
measure concept but would like clarification regarding Review and 
Correct Reports.
    Response: The Influenza Vaccination Coverage among HCP measure is 
stewarded by the CDC NHSN. To date, we have never added any of the CDC 
NHSN measures to the Review and Correct Report, as the data for these 
measures are at the CDC. In lieu of this, the CDC makes accessible to 
PAC providers, including SNFs, reports that are similar to the Review 
and Correct Reports that allow for real-time review of data submissions 
for all CDC NHSN measures adopted for use in the CMS PAC QRPs, 
including the SNF QRP. These reports are referred to as ``CMS Reports'' 
within the ``Analysis Reports'' page in the NHSN Application. Such a 
report exists for each CDC NHSN measure within each of the PAC 
programs, and each report is intended to mimic the data that will be 
sent to CMS on their behalf. This report will exist to serve the same 
``review and correct'' purposes for the Influenza Vaccination Coverage 
among HCP measure. The CDC publishes reference guides for each facility 
type (including SNFs) and each NHSN measure, which explain how to run 
and interpret the reports.
    Additionally, we will make available to SNFs a preview of SNF 
performance on the Influenza Vaccination Coverage among HCP measure on 
the SNF Provider Preview Report, which will be issued approximately 3 
months prior to displaying the measure on Care Compare. As always, SNFs 
will have a full 30 days to preview their data. Should SNFs disagree 
with their measure results, they can request a formal review of their 
data by us. Instructions for submitting such a request are available on 
the CMS SNF Quality Reporting Program Public Reporting web page at 
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/Skilled-Nursing-Facility-Quality-Reporting-Program/SNF-Quality-Reporting-Program-Public-Reporting.
    After consideration of public comments, we are finalizing the 
schedule of data submission for the Influenza Vaccination Coverage 
among HCP Measure (NQF #0431) as proposed.

H. 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. Public Reporting of the Influenza Vaccination Coverage Among 
Healthcare Personnel (NQF #0431) Measure Beginning With the FY 2024 SNF 
QRP
    We proposed to publicly report the Influenza Vaccination Coverage 
among HCP (NQF #0431) measure beginning with the October 2023 Care 
Compare refresh or as soon as technically feasible using data collected 
from October 1, 2022 through March 31, 2023. If finalized as proposed, 
a SNF's Influenza Vaccination Coverage among HCP rate would be 
displayed based on 6 months of data. Provider preview reports would be 
distributed in July 2023. Thereafter, Influenza Vaccination Coverage 
among HCP rates would be displayed based on 6 months of data, 
reflecting the reporting period of October 1st through March 31st, 
updated annually. We invited public comment on this proposal for the 
public display of the Influenza Vaccination Coverage among Healthcare 
Personnel (NQF #0431) measure on Care Compare.
    The following is a summary of the comments we received and our 
responses.
    Comment: One commenter noted that public reporting of this measure 
would provide the previous influenza season's data to consumers and 
would not reflect the vaccination rates of the current influenza year.
    Response: The measure's public reporting schedule is in alignment 
with those of the IRF and LTCH QRPs, supporting the standardized and 
interoperable requirement of the IMPACT Act, and the ability to compare 
data for the same time period across PAC providers when using Care 
Compare. Additionally, the public display of HCP influenza vaccine data 
in October 2023 allows for a 6-month data collection period (October 1, 
2022 through March 31, 2023), a period of 6 weeks for providers to 
submit their data to the NHSN, our analysis of the data, and a period 
of time for SNFs to review their Provider Preview Report and alert us 
if they believe there are errors in the data. We believe this reporting 
schedule, outlined in section VI.G.2. of the proposed rule, is 
reasonable, and expediting this schedule may establish undue burden on 
providers and jeopardize the integrity of the data.
    After consideration of public comments, we are finalizing the

[[Page 47559]]

proposal to publicly report the Influenza Vaccination Coverage among 
Healthcare Personnel (NQF #0413) measure beginning with the October 
2023 refresh or as soon as technically feasible, as proposed.

VIII. Skilled Nursing Facility Value-Based Purchasing (SNF VBP) Program

A. Statutory Background

    Section 215(b) of the Protecting Access to Medicare Act of 2014 
(Pub. L. 113-93) authorized the SNF VBP Program (the ``Program'') by 
adding section 1888(h) to the Act. Additionally, section 111 of the 
Consolidated Appropriations Act, 2021 authorized the Secretary to apply 
additional measures to the SNF VBP Program for payments for services 
furnished on or after October 1, 2023. The SNF VBP Program applies to 
freestanding SNFs, SNFs affiliated with acute care facilities, and all 
non-CAH swing bed rural hospitals. We believe the SNF VBP Program has 
helped to transform how payment is made for care, moving increasingly 
towards rewarding better value, outcomes, and innovations instead of 
merely rewarding volume.
    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 42 CFR 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) through (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 Catalog website (https://data.cms.gov/provider-data/).
     In the September 2nd interim final rule with comment (IFC) 
(85 FR 54837), we revised the performance period for the FY 2022 SNF 
VBP Program to be April 1, 2019 through December 31, 2019 and July 1, 
2020 through September 30, 2020, in response to the COVID-19 Public 
Health Emergency (PHE).
     In the FY 2022 SNF PPS final rule (86 FR 42502 through 
42517), we adopted additional policies for the Program, including a 
measure suppression policy to offer flexibility in response to the 
COVID-19 PHE. We adopted policies to suppress the SNFRM for scoring and 
payment purposes for the FY 2022 SNF VBP program year, to revise the 
SNFRM risk-adjustment lookback period for the FY 2023 SNF VBP program 
year, and to use FY 2019 data for the baseline period for the FY 2024 
SNF VBP program year. We also updated the Phase One Review and 
Correction process and updated the instructions for requesting an 
Extraordinary Circumstances Exception (ECE). Finally, we finalized a 
special scoring policy assigning all SNFs a performance score of zero, 
effectively ranking all SNFs equally in the FY 2022 SNF VBP program 
year. This policy was codified at Sec.  413.338(g) of our regulations.
    To improve the clarity of our regulations, we proposed to update 
and renumber the ``Definitions'' used in Sec.  413.338 by revising 
paragraphs (a)(1) and (4) through (17). We invited public comment on 
these proposed updates.
    We did not receive any public comments on our proposal to update 
and renumber the ``Definitions'' used in Sec.  413.338 by revising 
paragraphs (a)(1) and (4) through (17) and therefore, we are finalizing 
the updates as proposed.

B. SNF VBP Program 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 as soon as practicable, and to assess transition 
timing of the SNFPPR measure to the SNF VBP Program after NQF 
endorsement review is complete.
1. Suppression of the SNFRM for the FY 2023 Program Year
a. Background
    As discussed in the FY 2023 SNF proposed rule, we remain concerned 
about the effects of the PHE for COVID-19 on our ability to assess 
performance on the SNFRM in the SNF VBP Program. As of mid-December 
2021, more than 50 million COVID-19 cases and 800,000 COVID-19 deaths 
have been reported in the United States (U.S.).\151\ COVID-19 has 
overtaken the 1918 influenza pandemic as the deadliest disease in 
American history.\152\ Moreover, the individual and public health 
ramifications of COVID-19 extend beyond the direct effects of COVID-19 
infections. Several studies have

[[Page 47560]]

demonstrated significant mortality increases in 2020, beyond those 
attributable to COVID-19 deaths. One paper quantifies the net impact 
(direct and indirect effects) of the pandemic on the U.S. population 
during 2020 using three metrics: excess deaths, life expectancy, and 
total years of life lost. The findings indicate there were 375,235 
excess deaths, with 83 percent attributable to direct effects, and 17 
percent attributable to indirect effects, of COVID-19. The decrease in 
life expectancy was 1.67 years, translating to a reversion of 14 years 
in historical life expectancy gains. Total years of life lost in 2020 
was 7,362,555 across the U.S. (73 percent directly attributable, 27 
percent indirectly attributable to COVID-19), with considerable 
heterogeneity at the individual State level.\153\
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    \151\ https://covid.cdc.gov/covid-data-tracker/#datatracker-home.
    \152\ https://www.statnews.com/2021/09/20/covid-19-set-to-overtake-1918-spanish-flu-as-deadliest-disease-in-american-history/.
    \153\ Chan, E.Y.S., Cheng, D., & Martin, J. (2021). Impact of 
COVID-19 on excess mortality, life expectancy, and years of life 
lost in the United States. PloS one, 16(9), e0256835. https://pubmed.ncbi.nlm.nih.gov/34469474/.
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b. Suppression of the SNFRM for the FY 2023 SNF VBP Program Year
    In the FY 2022 SNF PPS final rule (86 FR 42503 through 42505), we 
adopted a quality measure suppression policy for the duration of the 
PHE for COVID-19 that enables us to suppress the use of the SNFRM 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.
    We also adopted a series of Measure Suppression Factors to 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. The Measure Suppression Factors that we adopted are:
     Measure Suppression Factor 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.
     Measure Suppression Factor 2: Clinical proximity of the 
measure's focus to the relevant disease, pathogen, or health impacts of 
the PHE for COVID-19.
     Measure Suppression Factor 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.
     Measure Suppression Factor 4: Significant national 
shortages or rapid or unprecedented changes in:
    ++ Healthcare personnel.
    ++ Medical supplies, equipment, or diagnostic tools or materials.
    ++ Patient case volumes or facility-level case-mix.
    We refer readers to the FY 2022 SNF PPS final rule (86 FR 42503 
through 42505) for additional details on this policy, including 
summaries of the public comments that we received and our responses.
    Additionally, in the FY 2022 SNF PPS final rule (86 FR 42505 
through 42507), we suppressed the SNFRM for the FY 2022 SNF VBP program 
year under Measure Suppression Factor (4): Significant national 
shortages or rapid or unprecedented changes in: (iii) Patient case 
volumes or facility-level case mix. We refer readers to that final rule 
for additional discussion of the analyses we conducted of SNFRM 
performance during the PHE for COVID-19, how the measure's reliability 
changed, how its current risk-adjustment model does not factor in 
COVID-19, and how the PHE affected different regions of the country at 
different times, as well as summaries of the public comments that we 
received on that proposal and our responses.
    The PHE for COVID-19 has had direct, significant, and continuing 
effects on our ability to measure SNFs' performance on the SNFRM. SNFs 
are experiencing a significant downward trend in admissions compared 
with their pre-COVID-19 admission rates. For the FY 2021 program year, 
a total of 1,566,540 SNF admissions were eligible for inclusion in the 
SNFRM (based on FY 2019 data). We have estimated that approximately 
1,069,789 admissions would be eligible for inclusion for the FY 2023 
program year (based on currently available data, which ranged from July 
1, 2020 through June 30, 2021), representing a volume decrease of 
approximately 32 percent. Based on this lower number of eligible SNF 
admissions, we have estimated that only 75.2 percent of SNFs would be 
eligible to be scored on the SNFRM for FY 2021, compared with 82.4 
percent that were eligible to be scored for FY 2019. As discussed in 
the FY 2023 SNF PPS proposed rule, given the significant decrease in 
SNF admissions during FY 2021, we remain concerned that using FY 2021 
data to calculate SNFRM rates for the FY 2023 program year will have 
significant negative impacts on the measure's 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-class correlation coefficient (ICC).
    As discussed in the FY 2023 SNF PPS proposed rule, we also remain 
concerned that the pandemic's disparate effects on different regions of 
the country throughout the PHE have presented challenges to our 
assessments of performance on the SNFRM. According to CDC data,\154\ 
for example, new COVID-19 cases at the beginning of FY 2021 (October 1, 
2020) were highest in Texas (3,534 cases), California (3,062 cases), 
and Wisconsin (3,000 cases). By April 1, 2021, however, new cases were 
highest in Michigan (6,669 cases), Florida (6,377 cases), and New 
Jersey (5,606 cases). This variation in COVID-19 case rates throughout 
the PHE has also been demonstrated in several studies. For example, 
studies have found widespread geographic variation in county-level 
COVID-19 cases across the U.S.155 156 157 Specifically, one 
study found that, across U.S. census regions, counties in the Midwest 
had the greatest cumulative rate of COVID-19 cases.\158\ Another study 
found that U.S. counties with more immigrant residents, as well as more 
Central American or Black residents, have more COVID-19 cases.\159\ 
These geographic variations in COVID-19 case rates are often linked to 
a wide range of county-level

[[Page 47561]]

characteristics, including sociodemographic and health-related 
factors.\160\ In addition, these studies have found evidence of 
temporal variation in county-level COVID-19 cases. For example, one 
study found that while many county-level factors show persistent 
effects on COVID-19 severity over time, some factors have varying 
effects on COVID-19 severity over time.\161\ The significant variation 
in COVID-19 case rates across the U.S. can affect the validity of 
performance data. Therefore, we do not believe it would be fair or 
equitable to assess SNFs' performance on the measure using FY 2021 
data, which has been affected by these variations in COVID-19 case 
rates.
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    \154\ ``United States COVID-19 Cases and Deaths by State,'' 
Centers for Disease Control. Retrieved from https://data.cdc.gov/Case-Surveillance/United-States-COVID-19-Cases-and-Deaths-by-State-o/9mfq-cb36/data on March 22, 2022.
    \155\ Desmet, K., & Wacziarg, R. (2022). JUE Insight: 
Understanding spatial variation in COVID-19 across the United 
States. Journal of Urban Economics, 127, 103332. https://doi.org/10.1016/j.jue.2021.103332.
    \156\ Messner, W., & Payson, SE (2020). Variation in COVID-19 
outbreaks at the US State and county levels. Public Health, 187, 15-
18. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7396895/pdf/main.pdf.
    \157\ Khan, S.S., Krefman, A.E., McCabe, M.E., Petito, L.C., 
Yang, X., Kershaw, K.N., Pool, L.R., & Allen, N.B. (2022). 
Association between county-level risk groups and COVID-19 outcomes 
in the United States: a socioecological study. BMC Public Health, 
22, 81. https://doi.org/10.1186/s12889-021-12469-y.
    \158\ Khan, S.S., Krefman, A.E., McCabe, M.E., Petito, L.C., 
Yang, X., Kershaw, K.N., Pool, L.R., & Allen, N.B. (2022). 
Association between county-level risk groups and COVID-19 outcomes 
in the United States: a socioecological study. BMC Public Health, 
22, 81. https://doi.org/10.1186/s12889-021-12469-y.
    \159\ Strully, K., Yang, T-C., & Lui, H. (2021). Regional 
variation in COVID-19 disparities: connections with immigrant and 
Latinx communities in U.S. counties. Annals of Epidemiology, 53, 56-
62. https://doi.org/10.1016/j.annepidem.2020.08.016.
    \160\ CDC COVID-19 Response Team. (2020). Geographic Differences 
in COVID-19 Cases, Deaths, and Incidence--United States, February 
12--April 7, 2020. MMWR Morbidity and Mortality Weekly Report, 
69(15), 465-471. https://dx.doi.org/10.15585/mmwr.mm6915e4.
    \161\ Desmet, K., & Wacziarg, R. (2022). JUE Insight: 
Understanding spatial variation in COVID-19 across the United 
States. Journal of Urban Economics, 127, 103332. https://doi.org/10.1016/j.jue.2021.103332.
---------------------------------------------------------------------------

    Increases in the number of COVID-19 cases are typically followed by 
an increase in the number of COVID-19 related hospitalizations, 
especially among the unvaccinated. Although COVID-19 vaccines began to 
come available in December of 2020, it was only readily available in 
early summer 2021 resulting in less than half of eligible Americans 
being fully vaccinated by the beginning of the fourth quarter of FY 
2021. In addition, the vaccination rates were not evenly distributed 
across the country. Regions with significantly lower vaccination rates 
experienced higher hospitalization and ICU rates making them more prone 
to capacity challenges. Hospital capacity challenges have the potential 
to influence decisions that impact their downstream post-acute 
partners. As a result, for the first 3 quarters of FY 2021 performance 
year, low vaccinated regions' SNFs could have faced care coordination 
challenges with their partnering hospitals that regions with high 
vaccination rates did not experience. The continuation of the pandemic 
into 2021 did not necessarily impact all measures in the post-acute 
space, but measures related to hospital care may be impacted because of 
how closely the surge in COVID-19 cases was related to the surge in 
COVID-19 related hospital cases. Unlike other value-based purchasing 
programs that have multiple measures, the SNF VBP Program's single-
measure requirement, currently the SNFRM, means that suppression of the 
measure will directly impact the payment adjustment.
    The combination of fewer admissions to SNFs, regional differences 
in the prevalence of COVID-19 throughout the PHE and changes in 
hospitalization patterns in FY 2021 has impacted our ability to use the 
SNFRM to calculate payments for the FY 2023 program year.
    Based on the significant and continued decrease in the number of 
patients admitted to SNFs, which likely reflects shifts in utilization 
patterns due to the risk of spreading COVID-19 in SNFs, we proposed to 
suppress the SNFRM for the FY 2023 SNF VBP program year under Measure 
Suppression Factor (4): Significant national shortages or rapid or 
unprecedented changes in: (iii) Patient case volumes or facility-level 
case-mix.
    As with the suppression policy that we adopted for the FY 2022 SNF 
VBP Program, we proposed for the FY 2023 SNF VBP Program that we will 
use the previously finalized performance period (FY 2021) and baseline 
period (FY 2019) to calculate each SNF's RSRR for the SNFRM. We also 
proposed to suppress the use of SNF readmission measure data for 
purposes of scoring and payment adjustments. We further proposed to 
assign all participating SNFs a performance score of zero in the FY 
2023 SNF VBP Program Year. We stated that this assignment would result 
in all participating SNFs receiving an identical performance score, as 
well as an identical incentive payment multiplier.
    We proposed to reduce each participating SNF's adjusted Federal per 
diem rate for FY 2023 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 2023 SNF VBP Program Year. We continue 
to believe that 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 participating 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.
    However, as discussed in the proposed rule, we further proposed to 
remove the low-volume adjustment policy from the SNF VBP Program 
beginning with the FY 2023 program year, and instead, implement case 
and measure minimums that SNFs must meet in order to be eligible to 
participate in the SNF VBP Program for a program year.
    We proposed that SNFs that do not report a minimum of 25 eligible 
stays for the SNFRM for the FY 2023 program year will not be included 
in the SNF VBP Program for that program year. As a result, the payback 
percentage for FY 2023 will remain at 60.00 percent.
    For the FY 2023 program year, we also proposed to provide quarterly 
confidential feedback reports to SNFs and to publicly report the SNFRM 
rates for the FY 2023 SNF VBP Program Year. However, in the proposed 
rule, we stated that 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 (87 FR 22765). We stated in 
the proposed rule that the public presentation will be limited to SNFs 
that reported the minimum number of eligible stays. Finally, we 
proposed to codify these policies for the FY 2023 SNF VBP in our 
regulation text at Sec.  413.338(i).
    As stated in the proposed rule, we continue to be concerned about 
effects of the COVID-19 PHE but are encouraged by the rollout of COVID-
19 vaccinations and treatment for those diagnosed with COVID-19 and 
believe that SNFs are better prepared to adapt to this virus. Our 
measure suppression policy focuses on a short-term, equitable approach 
during this unprecedented PHE, and it was not intended for indefinite 
application. Additionally, we emphasized the importance of value-based 
care and incentivizing quality care tied to payment. The SNF VBP 
Program is an example of our effort to link payments to healthcare 
quality in the SNF setting. We stated our understanding that the COVID-
19 PHE is ongoing and unpredictable in nature; however, we also stated 
our belief that 2022 presents a more promising outlook in the fight 
against COVID-19. Over the course of the pandemic, providers have 
gained experience managing the disease, surges of COVID-19 infection, 
and supply chain fluctuations.\162\ While COVID-19 cases among nursing 
home staff reached a recent peak in January of 2022, those case counts 
dropped significantly by the week ending February 6, 2022, to 
22,206.\163\ COVID-19 vaccinations and boosters have also been taken up 
by a significant majority of nursing home residents, and according to 
CDC, by February 6, 2022, more than 68 percent of completely

[[Page 47562]]

vaccinated nursing home residents had received boosters.\164\ Finally, 
the Biden-Harris Administration has mobilized efforts to distribute 
home test kits,\165\ N-95 masks,\166\ and increase COVID-19 testing in 
schools.\167\ In light of this more promising outlook, we stated in the 
proposed rule that we intend to resume the use of the SNFRM for scoring 
and payment adjustment purposes beginning with the FY 2024 program 
year. That is, for FY 2024, for each SNF, we will calculate measure 
scores in the SNF VBP Program. We will then calculate a SNF performance 
score for each SNF and convert the SNF performance scores to value-
based incentive payments.
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    \162\ McKinsey and Company. (2021). How COVID-19 is Reshaping 
Supply Chains. Available at https://www.mckinsey.com/business-functions/operations/our-insights/how-covid-19-is-reshaping-supply-chains.
    \163\ ``Nursing Home Covid-19 Data Dashboard.'' Centers for 
Disease Control, retrieved from https://www.cdc.gov/nhsn/covid19/ltc-report-overview.html on February 14, 2022.
    \164\ ``Nursing Home Covid-19 Data Dashboard.'' Centers for 
Disease Control, retrieved from https://www.cdc.gov/nhsn/covid19/ltc-report-overview.html on February 14, 2022.
    \165\ The White House. (2022). Fact Sheet: The Biden 
Administration to Begin Distributing At-Home, Rapid COVID-19 Tests 
to Americans for Free. Available at https://www.whitehouse.gov/briefing-room/statements-releases/2022/01/14/fact-sheet-the-biden-administration-to-begin-distributing-at-home-rapid-covid-19-tests-to-americans-for-free/.
    \166\ Miller, Z. 2021. The Washington Post. Biden to give away 
400 million N95 masks starting next week. Available at https://www.washingtonpost.com/politics/biden-to-give-away-400-million-n95-masks-starting-next-week/2022/01/19/5095c050-7915-11ec-9dce-7313579de434_story.html.
    \167\ The White House. (2022). FACT SHEET: Biden-Harris 
Administration Increases COVID-19 Testing in Schools to Keep 
Students Safe and Schools Open. Available at https://www.whitehouse.gov/briefing-room/statements-releases/2022/01/12/fact-sheet-biden-harris-administration-increases-covid-19-testing-in-schools-to-keep-students-safe-and-schools-open/.
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    We invited public comment on our proposal to suppress the SNFRM for 
the FY 2023 program year and to codify our scoring and payment 
proposals for FY 2023 in our regulation text. We received the following 
comments and provide our responses:
    Comment: Many commenters supported our proposal to suppress the 
SNFRM for FY 2023 and our plans to resume use of the SNFRM beginning 
with FY 2024 noting the impacts of COVID-19 on readmission rates. One 
commenter suggested that we consider alternative quality measures in 
the long term that would encourage providers to use SNFs as a short-
term care venue for patients likely to be readmitted. Another commenter 
recommended that we provide confidential feedback reports to providers 
rather than publicly reporting SNFRM rates until we end our measure 
suppression policy and that we delay calculating SNF performance scores 
in FY 2024 until the end of the PHE.
    Response: We appreciate the support for our proposal to suppress 
the SNFRM for FY 2023 and our plans to resume use of the SNFRM 
beginning with FY 2024 noting the impacts of COVID-19 on readmission 
rates. We disagree with the commenter's suggestion to provide only 
confidential feedback reports to SNFs until we end the suppression 
policy. We continue to believe that stakeholders benefit immensely from 
access to quality data, and as we stated in the proposed rule, we will 
include appropriate caveats on the suppressed measure data when 
published. We will consider additional quality measurement topics for 
the Program in future rulemaking.
    Comment: Many commenters recommended that we increase the Program's 
payback percentage to 70 percent while we suppress the SNFRM for FY 
2023. One commenter suggested that we return the full 2 percent 
withheld from SNFs' Medicare payments, while another suggested that we 
extend suppression through the end of any future PHE.
    Response: We did not propose to change the previously finalized 
payback percentage for the SNF VBP Program in the proposed rule, and we 
view comments requesting that we change that policy to be beyond the 
scope of the proposed rule. 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, which we are finalizing in this 
final rule, to award a performance score of zero to all SNFs. We also 
do not believe it would be appropriate to preemptively extend the 
quality measure suppression policy through the end of any future PHE, 
as the suppression policy focuses on identifying how quality 
measurement has been affected by a specific PHE.
    After considering the public comments, we are finalizing our 
proposal to suppress the SNFRM for the FY 2023 SNF VBP Program as 
proposed and codifying it, as well as finalizing the special scoring 
and payment policies for FY 2023, at Sec.  413.338(i) of our 
regulations.
2. Technical Updates to the SNFRM To Risk-Adjust for COVID-19 Patients 
Beginning With the FY 2023 Program Year
    The emergence of the COVID-19 PHE, along with the high prevalence 
of COVID-19 in patients admitted to SNFs, has prompted us to examine 
whether we should develop an adjustment to the SNFRM that would 
properly account for COVID-19 patients. As detailed in the proposed 
rule, we considered four options that such an adjustment could take. 
After careful examination of each of the four options, we are updating 
the technical specifications of the SNFRM such that COVID-19 patients 
(diagnosed at any time within 12 months prior to or during the prior 
proximal hospitalization [PPH]) will remain in the measure's cohort, 
but we will add a variable to the risk-adjustment model that accounts 
for the clinical differences in outcomes for these patients. We stated 
that we believe this change is technical in nature and does not 
substantively change the SNFRM.
    In order to determine whether and how to update the SNFRM, we first 
sought to understand the frequency of COVID-19 diagnoses in patients 
admitted to a SNF between July 1, 2020 and June 30, 2021. Of the 
1,069,789 SNF stays included in the year of data, 134,674 (13 percent) 
had a primary or secondary diagnosis of COVID-19. Of those patients 
with COVID-19, 108,859 (81 percent) had a primary or secondary COVID-19 
diagnosis during the PPH and 25,815 (19 percent) had a COVID-19 
diagnosis in their history only (within 12 months of the SNF 
admission).
    We then compared clinical and demographic characteristics between 
patients with and without COVID-19 between July 1, 2020, and June 30, 
2021. When compared to the 30-day readmission rate for patients without 
COVID-19 (20.2 percent), the observed 30-day readmission rate was 
noticeably higher for patients with COVID-19 during the PPH (23.4 
percent) and patients with a history of COVID-19 (26.9 percent). Both 
groups also experienced higher 30-day mortality rates compared to 
patients without COVID-19 (14.9 percent versus 8.8 percent and 10.7 
percent versus 8.8 percent, respectively). Admissions for patients with 
COVID-19 during the PPH or a history of COVID-19 were also much more 
likely to be for patients who were dual-eligible (40.3 percent versus 
28.9 percent and 45.2 percent versus 28.9 percent, respectively) and 
for patients who were non-white (21.1 percent versus 15.2 percent and 
24.4 percent versus 15.2 percent, respectively).
    Next, we compared readmission odds ratios for patients with COVID-
19 during the PPH and for patients with a history of COVID-19. Patients 
with COVID-19 during the PPH had significantly higher odds of 
readmission (1.18), while patients with a history of COVID-19 but no 
COVID-19 during the PPH had significantly lower odds of readmission 
(0.84), after adjusting for all

[[Page 47563]]

other variables in the SNFRM risk-adjustment model.
    Although patients with only a history of COVID-19 had higher 
observed readmission rates than patients with COVID-19 during the PPH 
(26.9 percent versus 23.4 percent), they experienced lower readmission 
odds ratios (0.84 versus 1.18). This is because patients with a history 
of COVID-19 during the 12 months prior to the SNF admission are 
generally much sicker and have a substantially higher number of average 
comorbidities (15) compared to patients with COVID-19 during the PPH 
(10). We expect unadjusted readmission rates for patients with a 
history of COVID-19 to be higher because they are suffering from many 
more comorbidities, making it more likely they will be readmitted to 
the hospital. After adjusting for all their other comorbidities, we 
concluded that COVID-19 is not a significant reason for why they return 
to the hospital. Instead, their other comorbidities are a more 
significant cause of their readmission; that is, patients with a 
history of COVID-19 but no COVID-19 during the PPH have lower odds of 
being readmitted to a hospital once they've been admitted to the SNF. 
However, we stated in the proposed rule that we believed it was 
important to keep the history of COVID-19 variable in the model for two 
reasons: (1) to address any potential concerns with the face validity 
of the measure if it did not adjust for history of COVID-19; and (2) to 
account for long COVID-19 and other possible long-term effects of the 
virus. On the other hand, patients with a COVID-19 diagnosis during the 
PPH remain at higher odds of readmission even after accounting for 
their other comorbidities. Even when all other comorbidities are taken 
into account in the current risk-adjustment model, a COVID-19 diagnosis 
during the PPH still raises a patient's odds of being readmitted 
compared to patients who did not have any COVID-19 diagnosis during the 
PPH.
    After having examined the prevalence of COVID-19 in SNF patients 
and the differences between patients with and without COVID-19, we then 
evaluated several options for how to account for COVID-19 in the 
measure. We evaluated four options.
     Under Option 1, we considered and tested whether to add a 
binary risk-adjustment variable for patients who had a primary or 
secondary diagnosis of COVID-19 during the PPH.
     Under Option 2, we considered and tested whether to add a 
binary risk-adjustment variable for patients who had a history of 
COVID-19 in the 12 months prior to the PPH.
     Under Option 3, we combined the first 2 options into a 
categorical risk-adjustment variable. The reference category is 
patients without a history of COVID-19 and no COVID-19 diagnosis during 
the PPH. The first comparison category is patients who had a history of 
COVID-19 in the 12 months prior to the PPH and no COVID-19 diagnosis 
during the PPH. The second comparison category is patients who had a 
primary or secondary diagnosis of COVID-19 during the PPH. If a patient 
had both a history of COVID-19 and a COVID-19 diagnosis during the PPH, 
they would be included in the second comparison category.
     Under Option 4, we considered and tested removing patients 
with a COVID-19 diagnosis during the PPH from the measure cohort.
    We compared how well the model predicted whether patients were 
readmitted or not (model fit and performance) for these four options to 
a reference period (FY 2019) that predated COVID-19. Ideally, whichever 
option we chose would perform as similarly as possible to the reference 
period, providing us with confidence that the emergence of COVID-19 has 
not caused the model to perform worse.
    The percentage of SNFs that would receive a measure score (75 
percent), measure reliability (0.45), and C-statistic (0.66) was 
identical for the first 3 risk-adjustment options. The percentage of 
SNFs with a measure score, measure reliability score, and C-statistic 
values was 71 percent, 0.41, and 0.67 for Option 4 (excluding COVID-19 
patients), respectively. The percentage of SNFs with a measure score 
was lower for the first 3 options than the baseline period (75 percent 
versus 82 percent), but the measure reliability was nearly identical 
(0.45 versus 0.46), as was the C-statistic (0.66 versus 0.68).
    We also considered removing readmissions from the outcome for 
patients with a primary or secondary diagnosis of COVID-19 during the 
readmission hospital stay but decided it would not be appropriate for 
this measure. Community spread of COVID-19 in SNFs is a possible marker 
of poor infection control and patients who are admitted to a SNF 
without any COVID-19 diagnoses but then potentially acquire COVID-19 in 
a SNF should not be excluded from the readmission outcome.
    After careful examination, we selected Option 3 and are modifying 
the SNFRM beginning with the FY 2023 SNF VBP program year by adding a 
risk-adjustment variable for both COVID-19 during the PPH and patients 
with a history of COVID-19. As we stated, this option both maintains 
the integrity of the model (as demonstrated by nearly identical measure 
reliability and C-statistic values) and allows the measure to 
appropriately adjust for SNF patients with COVID-19. In the proposed 
rule, we stated our belief that this approach will continue to maintain 
the validity and reliability of the SNFRM. This approach will retain 
COVID-19 patients in the measure cohort and prevent a further decrease 
in the sample size, which would harm the measure's reliability.
    As discussed in the proposed rule and in section VIII.B.2.c. of 
this final rule, though we believe risk-adjusting the SNFRM for COVID-
19 is an important step in maintaining the validity and reliability of 
the SNFRM, this risk-adjustment alone is not sufficient for ensuring a 
reliable SNF performance score in light of the overall decrease in SNF 
admissions in FY 2021. That is, the risk-adjustment is designed to 
maintain the scientific reliability of the measure, but it does not 
mitigate the effects of the PHE on patient case volumes and the 
resulting impact on the validity of the SNFRM.
    We received several public comments on our technical update to the 
SNFRM to risk-adjust for COVID-19 patients beginning with the FY 2023 
program year.
    Comment: Some commenters supported our proposal to update the SNFRM 
to risk-adjust for COVID-19 patients. One commenter agreed with our 
approach but noted that removing COVID-19 patients from the measure may 
reduce the sample sizes and result in excluding more facilities from 
the Program, which may mean missing important indicators of quality 
performance. Another commenter stated that our proposed risk-adjustment 
best allows the measure's calculation by removing beneficiaries that 
were affected directly by a COVID-19 infection. One commenter also 
recommended that we continue to review COVID-19 data and refine our 
risk-adjustment policies as we learn more about the impacts and 
prevalence of ``long'' COVID-19.
    Response: We clarify that we selected Option 3, which retains 
COVID-19 patients in the measure cohort and prevents a decrease in the 
sample size, while also adjusting for patients with a COVID-19 
diagnosis. Furthermore, we decided to risk-adjust for patients with a 
history of COVID-19 because of the evolving evidence on the impact of 
``long'' COVID-19 and the recognition that we still have much to learn 
about the long-term effects of COVID-19. We will continue to review the 
impacts of

[[Page 47564]]

COVID-19 on the measure's data and will make technical updates to the 
risk-adjustment methodology for the SNFRM as appropriate.
3. Adoption of Quality Measures for the SNF VBP Expansion Beginning 
With the FY 2026 Program Year
a. Background
    Section 1888(h)(2)(A)(ii) of the Act (as amended by section 
111(a)(2)(C) of the Consolidated Appropriations Act, 2021 (Pub. L. 116-
120)) allows the Secretary to add up to nine new measures to the SNF 
VBP Program with respect to payments for services furnished on or after 
October 1, 2023. These measures may include measures of functional 
status, patient safety, care coordination, or patient experience. 
Section 1888(h)(2)(A)(ii) of the Act also requires that the Secretary 
consider and apply, as appropriate, quality measures specified under 
section 1899B(c)(1) of the Act.
    Currently, the SNF VBP Program includes only a single quality 
measure, the SNFRM, which we intend to transition to the SNFPPR as soon 
as practicable. Both the SNFRM and the SNFPPR assess the rate of 
hospital readmissions. In considering which measures might be 
appropriate to add to the SNF VBP Program, we requested public comment 
on potential future measures to include in the expanded SNF VBP Program 
in the FY 2022 SNF PPS proposed rule (86 FR 20009 through 20011). We 
refer readers to summaries of input from interested parties in the FY 
2022 SNF PPS final rule (86 FR 42507 through 42511). As stated in the 
proposed rule, we considered this input as we developed our quality 
measure proposals for this year's proposed rule.
    In the FY 2023 SNF PPS proposed rule (87 FR 22767 through 22777), 
we proposed to adopt three new quality measures for the SNF VBP 
Program. Specifically, we proposed to adopt two new quality measures 
for the SNF VBP Program beginning with the FY 2026 program year: (1) 
Skilled Nursing Facility (SNF) Healthcare Associated Infections (HAI) 
Requiring Hospitalization (SNF HAI) measure; and (2) Total Nursing 
Hours per Resident Day Staffing (Total Nurse Staffing) measure. We also 
proposed to adopt an additional quality measure for the SNF VBP Program 
beginning with the FY 2027 program year: Discharge to Community (DTC)--
Post-Acute Care (PAC) Measure for Skilled Nursing Facilities (NQF 
#3481). We are finalizing the adoption of these measures, and we 
discuss each in more detail in the following sections.
    We stated in the proposed rule that although none of these quality 
measures have been specified under section 1899B(c)(1) of the Act, we 
determined after consideration of those measures that none are 
appropriate for adoption into the SNF VBP Program until, at a minimum, 
we have had sufficient time to review their specifications and conduct 
further analyses to ensure that they are suited for meeting the 
objectives of the SNF VBP Program. We stated that we are currently 
reviewing measures of patient falls and functional status, which are 
both specified under section 1899B(c)(1) of the Act, to determine 
whether any of them would be appropriate for the SNF VBP Program. We 
also stated our belief that it is important to cover the full range of 
SNF services in the SNF VBP Program, which includes measure topics 
beyond those specified under section 1899B(c)(1) of the Act. Since we 
have determined that the measures specified under section 1899B(c)(1) 
of the Act are not yet appropriate for the SNF VBP Program, we proposed 
to begin the Program expansion with measures that address other 
important indicators of SNF care quality, including measures that align 
with the topics listed under section 1888(h)(2)(A)(ii) of the Act and 
align with HHS priorities.
    As proposed, the SNF HAI measure is a patient safety measure, and 
the DTC PAC SNF measure is a care coordination measure. Regarding the 
proposed Total Nurse Staffing measure, we stated in the proposed rule 
that many studies have found that the level of nurse staffing is 
associated with patient safety,\168\ patient functional 
status,169 170 and patient experience.171 172 
Nursing home staffing, including SNF staffing, is also a high priority 
for the Department of Health and Human Services (HHS) and the Biden-
Harris Administration because of its central role in the quality of 
care for Medicare beneficiaries.\173\
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    \168\ Horn S.D., Buerhaus P., Bergstrom N., et al. RN staffing 
time and outcomes of long-stay nursing home residents: Pressure 
ulcers and other adverse outcomes are less likely as RNs spend more 
time on direct patient care. Am J Nurs 2005 6:50-53. https://pubmed.ncbi.nlm.nih.gov/16264305/.
    \169\ Centers for Medicare and Medicaid Services. 2001 Report to 
Congress: Appropriateness of Minimum Nurse Staffing Ratios in 
Nursing Homes, Phase II. Baltimore, MD: Centers for Medicare and 
Medicaid Services. https://phinational.org/wp-content/uploads/legacy/clearinghouse/PhaseIIVolumeIofIII.pdf.
    \170\ Bostick J.E., Rantz M.J., Flesner M.K., Riggs C.J. 
Systematic review of studies of staffing and quality in nursing 
homes. J Am Med Dir Assoc. 2006;7:366-376. https://pubmed.ncbi.nlm.nih.gov/16843237/.
    \171\ https://www.wolterskluwer.com/en/expert-insights/study-patient-satisfaction-grows-with-nurse-staffing.
    \172\ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8522577/.
    \173\ https://www.whitehouse.gov/briefing-room/statements-releases/2022/02/28/fact-sheet-protecting-seniors-and-people-with-disabilities-by-improving-safety-and-quality-of-care-in-the-nations-nursing-homes/.
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    We stated in the proposed rule that we believe adopting these 
measures to begin affecting SNF payments in the FY 2026 program year 
would provide SNFs with sufficient time to prepare and become familiar 
with the quality measures, as well as with the numerous other 
programmatic changes that we proposed would take effect in the FY 2023 
program year.
    As we discussed in the FY 2023 SNF PPS proposed rule (87 FR 22786 
through 22787), we also considered and requested public comment on 
additional quality measures for potential adoption in the SNF VBP 
Program through future rulemaking.
    We received a general comment on the SNF VBP Program's measures.
    Comment: One commenter supported the concept of adding new measures 
to the Program but expressed concern about the increase in estimated 
savings to Medicare via reduced payments to SNFs. The commenter stated 
that adding new measures effectively reduces provider reimbursement 
rates because they must absorb the burden and costs of reporting new 
measures.
    Response: We carefully consider the reporting burden for all 
quality measures that we propose to adopt in the SNF VBP Program. 
Specifically, we weigh a measure's reporting burden against the 
benefits of adopting that measure in the Program. Our goal is to 
minimize the reporting burdens that we impose on SNFs under the SNF VBP 
Program and we will continue considering this topic as we explore 
proposing additional measures for the Program. We also note that the 
SNF HAI and DTC PAC SNF measures that we are finalizing in this final 
rule are calculated using Medicare claims data and do not impose any 
new reporting burdens on SNFs. In addition, the Total Nurse Staffing 
measure that we are finalizing in this final rule is calculated using 
information that SNFs already submit to us for the Nursing Home Five-
Star Quality Rating System, and therefore, this measure will not impose 
any new reporting burdens on SNFs.
    We proposed to update our regulations at Sec.  413.338(d)(5) to 
note that, for a given fiscal year, we will specify the measures for 
the SNF VBP Program. We did not receive any public comments on our 
proposal to update Sec.  413.338(d)(5) of our regulations, and

[[Page 47565]]

therefore, we are finalizing our proposal as proposed.
b. Adoption of the Skilled Nursing Facility Healthcare-Associated 
Infections (HAI) Requiring Hospitalization Measure Beginning With the 
FY 2026 SNF VBP Program Year
    As part of the SNF VBP Program expansion authorized under the CAA, 
we proposed to adopt the SNF HAI measure for the FY 2026 SNF VBP 
Program and subsequent years. The SNF HAI measure is an outcome measure 
that estimates the risk-standardized rate of HAIs that are acquired 
during SNF care and result in hospitalization using 1 year of Medicare 
fee-for-service (FFS) claims data. As proposed, the SNF HAI measure 
assesses SNF performance on infection prevention and management, which 
will align the Program with the Patient Safety domain of CMS's 
Meaningful Measures 2.0 Framework. In addition, the SNF HAI measure is 
currently part of the SNF QRP measure set. For more information on this 
measure in the SNF QRP, 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 also refer readers to the SNF HAI Measure Technical 
Report, available at https://www.cms.gov/files/document/snf-hai-technical-report.pdf, for the measure specifications, which we proposed 
to adopt as the SNF HAI measure specifications for the SNF VBP Program.
(1) Background
    Healthcare-associated infections (HAIs) are defined as infections 
acquired while receiving care at a health care facility that were not 
present or incubating at the time of admission.\174\ As stated in the 
proposed rule, HAIs are a particular concern in the SNF setting, and 
thus, monitoring the occurrence of HAIs among SNF residents can provide 
valuable information about a SNF's quality of care. A 2014 report from 
the Office of the Inspector General (OIG) estimated that one in four 
adverse events among SNF residents is due to HAIs, and approximately 
half of all HAIs are potentially preventable.\175\ In addition, 
analyses from FY 2019 found a wide variation in facility-level HAI 
rates among SNF providers with 25 or more stays, which indicates a 
performance gap. Specifically, among the 14,102 SNFs included in the 
sample, the FY 2019 facility-level, risk-adjusted rate of SNF HAIs 
requiring hospitalization ranged from 2.36 percent to 17.62 
percent.\176\
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    \174\ World Health Organization. (2010). The burden of health 
care-associated infections worldwide. Retrieved from https://www.who.int/news-room/feature-stories/detail/the-burden-of-health-care-associated-infection-worldwide.
    \175\ 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.
    \176\ https://www.cms.gov/files/document/snf-hai-technical-report.pdf.
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    While HAIs are not considered ``never events,'' or serious adverse 
errors in the provision of health care services that should never 
occur, most are preventable.\177\ HAIs are most often the result of 
poor processes and structures of care. Specifically, evidence suggests 
that inadequate patient management following a medical intervention, 
such as surgery or device implantation, and poor adherence to infection 
control protocols and antibiotic stewardship guidelines contribute to 
the occurrence of HAIs.178 179 180 In addition, several 
provider characteristics relate to the occurrence of HAIs, including 
staffing levels (for example, low staff-to-resident ratios), facility 
structure characteristics (for example, high occupancy rates), and 
adoption, or lack thereof, of infection surveillance and prevention 
policies.181 182 183 184 185 186
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    \177\ 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.
    \178\ Beganovic, M. and 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, 101(5), 45-49. https://www.rimed.org/rimedicaljournal/2018/06/2018-06-45-antimicrobial-beganovic.pdf.
    \179\ Cooper, D., McFarland, M., Petrilli, F., & Shells, C. 
(2019). Reducing Inappropriate Antibiotics for Urinary Tract 
Infections in Long-term Care: A Replication Stud-y. Journal of 
Nursing Care Quality, 34(1), 1621. https://doi.org/10.1097/NCQ.0000000000000343.
    \180\ 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. https://dx.doi.org/10.1016/j.jamda.2017.06.019.
    \181\ 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. https://dx.doi.org/10.1177/0733464815584666.
    \182\ 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. https://dx.doi.org/10.1007/s40266-015-0292-7.
    \183\ 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. 
https://dx.doi.org/10.1016/j.ajic.2018.06.018.
    \184\ 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. https://dx.doi.org/10.1097/NCQ.0000000000000343.
    \185\ 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. https://dx.doi.org/10.1016/j.ajic.2016.03.015.
    \186\ 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. https://dx.doi.org/10.1016/j.ajic.2016.01.019.
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    Inadequate prevention and treatment of HAIs is likely to result in 
poor health care outcomes for SNF residents, as well as wasteful 
resource use. Specifically, studies find that HAIs are associated with 
longer lengths of stay, use of higher-intensity care (for example, 
critical care services and hospital readmissions), increased mortality, 
and higher health care costs.187 188 189 190 Addressing HAIs 
in SNFs is particularly important as several factors place SNF 
residents at increased risk for infections, including increased age, 
cognitive and functional decline, use of indwelling devices, frequent 
care transitions, and close contact with other residents and healthcare 
workers.191 192 Further, infection prevention and control

[[Page 47566]]

deficiencies are consistently among the most frequently cited 
deficiencies in surveys conducted to assess SNF compliance with Federal 
quality standards.\193\ Infection prevention and control deficiencies 
can include practices directly related to the occurrence and risks of 
HAIs, such as inconsistent use of hand hygiene practices or improper 
use of protective equipment or procedures during an infectious disease 
outbreak, which further underscores the importance of efforts to 
improve practices to reduce the prevalence of HAIs.
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    \187\ 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.
    \188\ 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.
    \189\ 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. https://dx.doi.org/10.1016/j.jamda.2010.02.015.
    \190\ Zimlichman, E., Henderson, D., Tamir, O., Franz, C., Song, 
P., Yamin, C.K., Keohane, C., Denham, C.R., & 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. https://doi.org/10.1001/jamainternmed.2013.9763.
    \191\ Montoya, A., & Mody, L. (2011). Common infections in 
nursing homes: A review of current issues and challenges. Aging 
Health, 7(6), 889-899. https://dx.doi.org/10.2217/ahe.11.80.
    \192\ U.S. Department of Health and Human Services, Office of 
Disease Prevention and Health Promotion. (2013). Chapter 8: Long-
Term Care Facilities (p. 194-239) in National Action Plan to Prevent 
Health Care-Associated Infections: Road Map to Elimination. 
Retrieved from https://health.gov/sites/default/files/2019-09/hai-action-plan-ltcf.pdf.
    \193\ Infection Control Deficiencies Were Widespread and 
Persistent in Nursing Homes Prior to COVID-19 Pandemic (GAO-20-
576R), May, 2020. https://www.gao.gov/products/gao-20-576r.
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    Given the effects of HAIs, preventing and reducing their occurrence 
in SNFs is critical to delivering safe and high-quality care. As 
discussed in the proposed rule, we continue to believe the SNF HAI 
measure, as proposed, aligns with this goal by monitoring the 
occurrence of HAIs and assessing SNFs on their performance on infection 
prevention and control efforts. In doing so, we continue to believe the 
measure may promote patient safety and increase the transparency of 
care quality in the SNF setting, which aligns the SNF VBP Program with 
the Patient Safety domain of CMS's Meaningful Measures 2.0 Framework. 
Prevention and reduction of HAIs has also been a priority at Federal, 
State, and local levels. For example, the HHS Office of Disease 
Prevention and Health Promotion has created a National Action Plan to 
Prevent HAIs, with specific attention to HAIs in LTC facilities. We 
refer readers to additional information on the National Action Plan 
available at https://www.hhs.gov/oidp/topics/health-care-associated-infections/hai-action-plan/.
    Evidence suggests there are several interventions that SNFs may 
utilize to effectively reduce HAI rates among their residents and thus, 
improve quality of care. These interventions include adoption of 
infection surveillance and prevention policies, safety procedures, 
antibiotic stewardship, and staff education and training 
programs.194 195 196 197 198 199 200 In addition, infection 
prevention and control programs with core components in education, 
monitoring, and feedback have been found to be successful in reducing 
HAI rates.\201\ The effectiveness of these interventions suggest 
improvement of HAI rates among SNF residents is possible through 
modification of provider-led processes and interventions, which 
supports the overall goal of the SNF VBP Program.
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    \194\ 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.
    \195\ Beganovic, M. and 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, 101(5), 45-49. https://www.rimed.org/rimedicaljournal/2018/06/2018-06-45-antimicrobial-beganovic.pdf.
    \196\ 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. https://dx.doi.org/10.1007/s40266-015-0292-7.
    \197\ 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. Retrieved from https://pubmed.ncbi.nlm.nih.gov/27281862/.
    \198\ 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. 
https://dx.doi.org/10.1111/jgs.15316.
    \199\ 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.
    \200\ 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.
    \201\ Lee, M.H., Lee GA, Lee S.H., & Park Y.H. (2019). 
Effectiveness and core components of infection prevention and 
control programs in long-term care facilities: a systematic review. 
https://www.journalofhospitalinfection.com/action/showPdf?pii=S0195-6701%2819%2930091-X.
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(2) Overview of Measure
    The SNF HAI measure, which was finalized for adoption in the SNF 
QRP in the FY 2022 SNF PPS final rule (86 FR 42473 through 42480), is 
an outcome measure that 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. A HAI is defined, for the purposes of 
this measure, as an infection that is likely to be acquired during SNF 
care and severe enough to require hospitalization, or an infection 
related to invasive (not implanted) medical devices (for example, 
catheters, insulin pumps, and central lines). Several types of 
infections are excluded from the measure, which we discuss in section 
VIII.B.2.b.(4). of this final rule. In addition, all SNF stays with an 
admission date during the 1-year period are included in the measure 
cohort, except those meeting the exclusion criteria, which we also 
discuss in section VIII.B.2.b.(4). of this final rule.
    Unlike other HAI measures that target specific infections, this 
measure targets all HAIs serious enough to require admission to an 
acute care hospital.
    The goal of this measure is to identify SNFs that have notably 
higher rates of HAIs acquired during SNF care, when compared to their 
peers and to the national average HAI rate.
    Validity and reliability testing has been conducted for this 
measure. For example, split-half testing on the SNF HAI measure 
indicated moderate reliability. In addition, validity testing showed 
good model discrimination as the HAI model can accurately predict HAI 
cases while controlling for differences in resident case-mix. We refer 
readers to the SNF HAI Measure Technical Report for further details on 
the measure testing results available at https://www.cms.gov/files/document/snf-hai-technical-report.pdf.
(a) Measure Applications Partnership (MAP) Review
    The SNF HAI measure was included as a SNF VBP measure under 
consideration in the publicly available ``List of Measures Under 
Consideration for December 1, 2021.'' \202\
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    \202\ https://www.cms.gov/files/document/measures-under-consideration-list-2021-report.pdf.
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    The MAP offered conditional support of the SNF HAI measure for 
rulemaking, contingent upon NQF endorsement, noting that the measure 
would add value to the Program due to the addition of an overall 
measurement of all HAIs acquired within SNFs requiring hospitalization. 
We refer readers to the final 2021-2022 MAP report available at https://www.qualityforum.org/Publications/2022/03/MAP_2021-2022_Considerations_for_Implementing_Measures_Final_Report_-_Clinicians,_Hospitals,_and_PAC-LTC.aspx. We are preparing to submit 
the SNF HAI measure for NQF endorsement, consistent with the MAP 
recommendation.
(3) Data Sources
    As proposed, the SNF HAI measure uses Medicare FFS claims data to 
estimate the risk-adjusted rate of HAIs that are acquired during SNF 
care and result in hospitalization. Specifically, this measure uses 
data from the Medicare Enrollment Database (EDB), as well as Medicare 
SNF and inpatient hospital claims from the CMS Common Working File 
(CWF). HAIs are identified using the principal diagnosis code and the 
Present on Admission (POA) indicators on the Medicare inpatient 
rehospitalization claim within a specified incubation window. We refer 
readers to the SNF HAI Measure Technical Report for further details on

[[Page 47567]]

how these data components are utilized in calculating the SNF HAI 
measure available at https://www.cms.gov/files/document/snf-hai-technical-report.pdf. We note that the proposed SNF HAI measure is 
calculated entirely using administrative data and therefore, it will 
not impose any additional data collection or submission burden for 
SNFs.
(4) Inclusion and Exclusion Criteria
    The measure's cohort includes all Part A FFS Medicare SNF residents 
18 years and older who have a SNF admission date during the 1-year 
measure period and who do not meet any of the exclusion criteria, which 
we describe next. Additionally, the hospital admission must occur 
during the time period which begins on day 4 after SNF admission and 
ends 3 days after SNF discharge. We note that residents who died during 
the SNF stay or during the post-discharge window (3 days after SNF 
discharge), and residents with a missing discharge date (or have 
``active'' SNF stays) are included in the measure's cohort.
    There are several scenarios in which a SNF stay is excluded from 
the measure cohort and thus, excluded from the measure denominator. 
Specifically, any SNF stay that meets one or more of the following 
criteria is excluded from the cohort and measure denominator:
     Resident is less than 18 years old at SNF admission.
     The SNF length of stay was shorter than 4 days.
     Residents who were not continuously enrolled in Part A FFS 
Medicare during the SNF stay, 12 months prior to the measure period, 
and 3 days after the end of the SNF stay.
     Residents who did not have a Part A short-term acute care 
hospital stay within 30 days prior to the SNF admission date. The 
short-term stay must have positive payment and positive length of stay.
     Residents who were transferred to a Federal hospital from 
a SNF as determined by the discharge status code on the SNF claim.
     Residents who received care from a provider located 
outside the U.S., Puerto Rico, or another U.S. territory as determined 
from the first 2 characters of the SNF CMS Certification Number.
     SNF stays in which data were missing on any variable used 
in the measure calculation or risk-adjustment. This also included stays 
where Medicare did not pay for the stay, which is identified by non-
positive payment on the SNF claim.
    The measure numerator includes several HAI conditions. We refer 
readers to Appendix A of the SNF HAI Measure Technical Report, 
available at https://www.cms.gov/files/document/snf-hai-technical-report.pdf, for a complete list of the ICD-10 codes that correspond to 
the HAI conditions included in the measure numerator. There are also 
several types of HAIs that are excluded from the proposed measure 
numerator. For example, HAIs reported during emergency department 
visits and observations stays are excluded from the numerator. In 
addition, the HAI definition excludes infections that meet any of the 
following criteria:
     Chronic infections (for example, chronic viral hepatitis 
B).
     Infections that typically require a long period of time to 
present (for example, typhoid arthritis).
     Infections that are likely related to the prior hospital 
stay (for example, postprocedural retroperitoneal abscess).
     Sequela (a condition which is the consequence of a 
previous disease or injury) and subsequent encounter codes.
     Codes that include ``cause disease classified elsewhere.''
     Codes likely to represent secondary infection, where the 
primary infection would likely already be coded (for example, 
pericarditis, myocarditis, or cardiomyopathy).
     Infections likely to be community acquired.
     Infections common in other countries and/or acquired 
through animal contact.
     Preexisting infections that fall within the CDC's National 
Healthcare Safety Network (NHSN) Repeat Infection Timeframe (RIT) of 14 
days. We refer readers to the SNF HAI Measure Technical Report for 
additional information on the repeat infection timeframe (RIT) and 
conditions that are considered preexisting (https://www.cms.gov/files/document/snf-hai-technical-report.pdf).
(5) Risk-Adjustment
    Risk-adjustment is a statistical process used to account for risk 
factor differences across SNF residents. By controlling for these 
differences in resident case-mix, we can better isolate the proposed 
measure's outcome and its relationship to the quality of care delivered 
by SNFs. As proposed, the SNF HAI measure's numerator and denominator 
are both risk-adjusted. Specifically, the denominator is risk-adjusted 
for resident characteristics excluding the SNF effect. The numerator is 
risk-adjusted for resident characteristics, as well as a statistical 
estimate of the SNF effect beyond resident case-mix. The SNF effect, or 
the provider-specific behaviors that influence a SNF's HAI rates, 
accounts for clustering of patients within the same SNF and captures 
variation in the measure outcome across SNFs, which helps isolate 
differences in measure performance. The risk-adjustment model for this 
measure includes the following resident characteristic variables:
     Age and sex category.
     Original reason for Medicare entitlement.
     Surgery or procedure category from the prior proximal 
inpatient (IP) stay.
     Dialysis treatment, but not end-stage renal disease (ESRD) 
on the prior proximal IP claim.
     Principal diagnosis on the prior proximal IP hospital 
claim.
     Hierarchical Condition Categories (HCC) comorbidities.
     Length of stay of the prior proximal IP stay.
     Prior intensive care or coronary care utilization during 
the prior proximal IP stay.
     The number of prior IP stays within a 1-year lookback 
period from SNF admission.
(6) Measure Calculation
(a) Numerator
    The risk-adjusted numerator is the estimated number of SNF stays 
predicted to have a HAI that is acquired during SNF care and results in 
hospitalization. This estimate begins with the unadjusted, observed 
count of the measure outcome, or the raw number of stays with a HAI 
acquired during SNF care and resulting in hospitalization. The 
unadjusted, observed count of the measure outcome is then risk-adjusted 
for resident characteristics and a statistical estimate of the SNF 
effect beyond resident case-mix, which we discussed in section 
VIII.B.3.b.(5). of this final rule.
(b) Denominator
    The risk-adjusted denominator is the expected number of SNF stays 
with the measure outcome, which represents the predicted number of SNF 
stays with the measure outcome if the same SNF residents were treated 
at an ``average'' SNF. The calculation of the risk-adjusted denominator 
begins with the total eligible Medicare Part A FFS SNF stays during the 
measurement period and then applying risk-adjustment for resident 
characteristics, excluding the SNF effect, as we discussed in section 
VIII.B.3.b.(5). of this final rule.
    The SNF HAI measure rate, which is reported at the facility-level, 
is the risk-standardized rate of HAIs that are acquired during SNF care 
and result in

[[Page 47568]]

hospitalization. This risk-adjusted HAI rate is calculated by 
multiplying the standardized risk ratio (SRR) for a given SNF by the 
national average observed rate of HAIs for all SNFs. The SRR is a ratio 
that measures excess HAIs and is the predicted number of HAIs (adjusted 
numerator) divided by the expected number of HAIs (adjusted 
denominator). A lower measure score for the SNF HAI measure indicates 
better performance in prevention and management of HAIs. For technical 
information on the proposed measure's calculation, we refer readers to 
the SNF HAI Measure Technical Report available at https://www.cms.gov/files/document/snf-hai-technical-report.pdf.
    Because a ``lower is better'' rate could cause confusion among SNFs 
and the public, we proposed to invert SNF HAI measure rates, similar to 
the approach used for the SNFRM, for scoring. Specifically, we proposed 
to invert SNF HAI measure rates using the following calculation:

SNF HAI Inverted Rate = 1 - Facility's SNF HAI rate

    This calculation will invert SNFs' HAI measure rates such that 
higher SNF HAI measure rates will reflect better performance. In the 
proposed rule, we stated our belief that this inversion is important to 
incentivize improvement in a clear and understandable manner, so that 
``higher is better'' for all measure rates included in the Program.
(7) Confidential Feedback Reports and Public Reporting
    We refer readers to the FY 2017 SNF PPS final rule (81 FR 52006 
through 52007) for discussion of our policy to provide quarterly 
confidential feedback reports to SNFs on their measure performance. We 
also refer readers to the FY 2022 SNF PPS final rule (86 FR 42516 
through 42517) for a summary of our two-phase review and corrections 
policy for SNFs' quality measure data. Furthermore, we refer readers to 
the FY 2018 SNF PPS final rule (82 FR 36622 through 36623) and the FY 
2021 SNF PPS final rule (85 FR 47626) where we finalized our policy to 
publicly report SNF measure performance information under the SNF VBP 
Program on the Provider Data Catalog website currently hosted by HHS 
and available at https://data.cms.gov/provider-data/. We proposed to 
update and redesignate the confidential feedback report and public 
reporting policies, which are currently codified at Sec.  413.338(e)(1) 
through (3), to Sec.  413.338(f), to include the SNF HAI measure.
    We invited public comment on our proposal to adopt the SNF HAI 
measure beginning with the FY 2026 SNF VBP program year. We received 
the following comments and provide our responses:
    Comment: Many commenters supported our proposal to adopt the SNF 
HAI measure beginning with the FY 2026 SNF VBP program year. Commenters 
noted that the SNF HAI measure is an important quality indicator, that 
the measure imposes a low reporting burden on SNFs, and that SNFs are 
already familiar with the measure because it is currently adopted in 
the SNF QRP.
    Response: We agree that the SNF HAI measure is an important quality 
indicator. Monitoring SNF HAI rates provides valuable information on a 
SNF's infection prevention and management practices, and the overall 
quality of care. We also agree that SNFs are already familiar with the 
SNF HAI measure and that because the measure is calculated using 
Medicare FFS claims data, the adoption of the measure for the SNF VBP 
Program would impose no new reporting burden on SNFs.
    Comment: Several commenters offered qualified support for our 
proposal to adopt the SNF HAI measure and offered recommendations for 
improving the measure. Several commenters noted that the SNF HAI 
measure has not been endorsed by NQF and a few commenters suggested 
that we delay finalizing the measure until it has received NQF 
endorsement. A few commenters also recommended that we update the 
measure's specifications to exclude hospital- and community-acquired 
infections, as well as to exclude or risk-adjust for hospitalizations 
due to COVID-19 infection. One commenter recommended that we collect 
SNF HAI measure data but not publicly report those data until the PHE 
for COVID-19 has expired. Another commenter suggested that we develop a 
better reporting system in CASPER for the measure. Lastly, one 
commenter recommended that we link SNF HAI measure data to race and 
ethnicity information to assess care disparities.
    Response: We thank the commenters for their recommendations. As 
part of our routine measure monitoring work, we intend to consider 
whether any of these recommendations would warrant further analysis or 
potential updates to the measure's specifications.
    We intend to submit the SNF HAI measure to the NQF for 
consideration of endorsement. However, we also believe that the SNF HAI 
measure provides valuable quality of care information. For example, the 
HHS Office of Inspector General estimated that one in four adverse 
events among SNF residents is due to HAIs with approximately half of 
all HAIs being potentially preventable.\203\ The identification of HAIs 
by SNFs provides actionable information that SNFs can use to improve 
their quality of care and prevent their residents from having to be 
hospitalized. For these reasons, we continue to believe that it is 
important to include this measure in the SNF VBP Program.
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    \203\ 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.
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    Comment: Several commenters opposed the use of Medicare FFS claims 
data for calculating the SNF HAI measure and expressed concerns about 
the validity and accuracy of those claims data. Some commenters 
recommended that we adopt NHSN-based measures instead of claims-based 
measures. Another commenter recommended that the measure undergo 
additional testing before its inclusion in the Program.
    Response: As we discussed in the proposed rule (87 FR 22769), 
validity and reliability testing results showed that the SNF HAI 
measure has acceptable reliability and validity when calculated from 
Medicare FFS claims data. In addition, during development of this 
measure, the TEP considered the appropriateness of using alternative 
data sources, including NHSN data. The TEP ultimately recommended 
against using those sources because they would increase the reporting 
burden on SNFs. We refer commenters to the SNF HAI Final TEP Summary 
Report, available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/Downloads/SNF-HAI-Final-TEP-Report-7-15-19_508C.pdf for more information.
    Comment: One commenter expressed concern that SNFs must rely on 
hospitals accurately capturing HAIs because the measure is calculated 
using hospital claims data. Another commenter noted that performance 
scores may be inaccurate because there is variation in hospital 
documentation of HAIs.
    Response: We use inpatient hospital claims to calculate the SNF HAI 
measure because the measure's main outcome is HAIs that require 
hospitalization. In addition, we commissioned a medical record review 
for the purpose of analyzing the accuracy of hospital coding of 
Hospital Acquired Conditions (HACs), which include HAIs, and Present on 
Admission (POA) conditions. This study did not find patterns of

[[Page 47569]]

widespread underreporting of HACs or overreporting of POA status.\204\ 
The study found that only 3 percent of HAC cases were underreported and 
91 percent of all cases coded POA were accurate. Another medical record 
review we conducted assessed the accuracy of the principal diagnosis 
coded on a Medicare claim to identify whether a patient was admitted 
for a diagnosis included in our list of potentially preventable 
readmission (PPR) diagnoses.\205\ The study analyzed inpatient 
discharges from October 2015 through September 2017 and found high 
agreement between principal diagnoses in Medicare claims and 
corresponding medical records. Specifically, the agreement rate between 
principal diagnoses in Medicare claims and information in the 
corresponding medical records ranged from 83 percent to 94 percent by 
study hospital. Additionally, 91 percent to 97 percent of principal 
diagnoses from the corresponding medical records were included in our 
list of PPR diagnoses. Therefore, we disagree with commenters' concerns 
about the accuracy of hospital inpatient claims data.
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    \204\ Cafardi, S.G., Snow, C.L., Holtzman, L., Waters, H., 
McCall, N.T., Halpern, M., Newman, L., Langer, J., Eng, T., & 
Guzman, C.R. (2012). Accuracy of Coding in the Hospital-Acquired 
Conditions Present on Admission Program Final Report. Retrieved from 
https://www.cms.gov/medicare/medicare-fee-for-service-payment/hospitalacqcond/downloads/accuracy-of-coding-final-report.pdf.
    \205\ He, F., Daras, L.C., Renaud, J., Ingber, M., Evans, R., & 
Levitt, A. (2019, June 3). Reviewing Medical Records to Assess the 
Reliability of Using Diagnosis Codes in Medicare Claims to Identify 
Potentially Preventable Readmissions. Retrieved from https://academyhealth.confex.com/academyhealth/2019arm/meetingapp.cgi/Paper/ 
31496.
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    Comment: Several commenters opposed our proposal to adopt the SNF 
HAI measure, stating that SNFs will experience a significant time lag 
between claims submission and when data derived from those claims are 
used to measure quality performance. One commenter stated that while 
measuring HAIs in the SNF setting is ``vital,'' the topic is so 
important and complex that CMS should develop a measure that delivers 
more timely, accurate and actionable information. Another commenter was 
concerned that SNFs have not had time to review their performance data 
on this measure, thus making improvement plans difficult to implement. 
One commenter questioned whether providers would be able to use data 
from this measure to improve the quality of their care.
    Response: We understand commenters' concerns regarding the time 
gap. As we discuss in section VIII.C.3. of this final rule, we are 
finalizing our proposal to adopt FY 2022 as the baseline period and FY 
2024 as the performance period for the SNF HAI measure for the FY 2026 
SNF VBP Program. Under section 1888(h)(3)(C) of the Act, we are 
required to calculate and announce performance standards no later than 
60 days prior to the start of the performance period. To meet this 
statutory requirement, we need sufficient time between the end of the 
baseline period and the start of the performance period to calculate 
and announce performance standards, which are derived from baseline 
period data. Therefore, we continue to believe that a baseline period 
that occurs 2 fiscal years prior to the start of the performance period 
is most appropriate for this measure. In addition, under section 
1888(h)(7) of the Act, we are required to announce the net results of 
the Program's adjustments to a SNF's Medicare payment no later than 60 
days prior to the fiscal year involved. To meet this statutory 
requirement, we need sufficient time between the end of the performance 
period and the applicable fiscal program year to calculate and announce 
the net results of the Program's adjustments to a SNF's Medicare 
payment. Therefore, we continue to believe that a performance period 
that occurs two fiscal years prior to the applicable fiscal program 
year is most appropriate for this measure We refer readers to section 
VIII.C.3. of this final rule for further details on the baseline and 
performance periods for the SNF HAI measure. Given these statutory 
requirements, and the time needed to calculate valid and reliable 
measure rates, we have narrowed the time gap to the extent feasible at 
this time.
    We continue to believe that the data provided by the SNF HAI 
measure will be valuable to SNFs and their efforts to improve care 
quality. Specifically, a SNF's HAI rate provides information on the 
effectiveness of its current infection prevention and management 
practices, as well as provides information regarding opportunities for 
improvement. As we discussed in the FY 2023 SNF PPS proposed rule (87 
FR 22769), evidence suggests that there are several interventions that 
SNFs may utilize to effectively reduce HAI rates among their residents 
to improve quality of care, including infection surveillance and 
prevention policies, safety procedures, antibiotic stewardship, and 
staff education and training programs. The effectiveness of these 
interventions suggest that improvement of HAI rates among SNF residents 
is possible through modification of provider-led processes, which 
further demonstrates the value in measuring HAI rates among SNF 
residents.
    Comment: One commenter opposed our proposal to adopt the SNF HAI 
measure because of their belief that the SNF HAI measure only captures 
HAIs that result in hospitalization and does not prioritize other HAIs 
and their underlying causes.
    Response: We agree with the commenter that detecting all HAIs in 
the measure definition would provide additional data to SNFs and 
empower additional quality improvement. However, we decided to include 
only those HAIs requiring hospitalization in the SNF HAI measure to 
avoid the risk of overloading SNFs with information on every possible 
HAI in their SNF HAI measure rate.\206\ This decision was consistent 
with the recommendation of our TEP, which concluded that a concentrated 
list of severe infections would be more valuable to SNFs and would make 
the measure more actionable.
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    \206\ Levitt, A.T., Freeman, C., Schwartz, C.R., McMullen, T., 
Felder, S., Harper, R., Van, C.D., Li, Q., Chong, N., Hughes, K., 
Daras, L.C., Ingber, M., Smith, L., & Erim, D. (2019). Final 
Technical Expert Panel Summary Report: Development of a Healthcare-
Associated Infections Quality Measure for the Skilled Nursing 
Facility Quality Reporting Program. Retrieved from https://www.cms.gov/Medicare/Quality-Initiatives-Patient-AssessmentInstruments/NursingHomeQualityInits/Downloads/SNF-HAI-Final-TEP-Report-7-15-19_508C.pdf.
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    Comment: A few commenters expressed concern that the SNF HAI 
measure does not account for other resident characteristics, including 
social risk factors, or provider characteristics, such as facility 
size, location, and teaching status, that influence HAI rates.
    Response: We understand commenters' concerns regarding the risk-
adjustment model for the SNF HAI measure. As part of our routine 
measure monitoring work, we intend to continue assessing the 
appropriateness of the risk-adjustment model. In addition, as described 
in our RFI in the proposed rule (87 FR 22789), we are considering 
whether it would be appropriate to incorporate adjustments in the SNF 
VBP Program, beyond an individual measure's risk-adjustment model, to 
account for social risk factors as part of our efforts to measure and 
improve health equity. Further, we note that the risk-adjustment model 
for the SNF HAI accounts for the following resident characteristic 
variables: age and sex category; original reason for Medicare 
entitlement; surgery or procedure category from the prior proximal

[[Page 47570]]

inpatient (IP) stay; dialysis treatment, but not end-stage renal 
disease (ESRD) on the prior proximal IP claim; principal diagnosis on 
the prior proximal IP hospital claim; hierarchical condition categories 
(HCC) comorbidities; length of stay of the prior proximal IP stay; 
prior intensive care or coronary care utilization during the prior 
proximal IP stay; and the number of prior IP stays within a 1-year 
lookback period from SNF admission. We refer the commenters to section 
VIII.B.3.b.(5). of this final rule for further discussion of the risk-
adjustment model.
    Comment: Some commenters opposed our proposal to adopt the SNF HAI 
measure due to various concerns with the measure specifications. Some 
commenters expressed validity concerns, stating that the measure's list 
of exclusion criteria is incomplete. One commenter stated that the 
inability to define the magnitude of the clinical problem addressed by 
the SNF HAI measure makes it difficult for SNFs to identify benchmarks 
and goals. Another commenter suggested that the proposed time window 
for excluding infections prior to SNF admission is not long enough.
    Response: We disagree with commenters' concerns regarding the 
validity of the measure. As we discussed in the FY 2023 SNF PPS 
proposed rule (87 FR 22769), the validity testing for this measure 
showed that the HAI model can accurately predict HAI cases while 
controlling for differences in resident case-mix.
    Our measure contractor developed the exclusion criteria with input 
from subject matter experts with clinical expertise specific to 
infectious diseases and the SNF population. We continue to believe the 
set of exclusion criteria helps ensure that we only capture HAIs 
requiring hospitalization that can be directly attributed to care 
during a SNF stay. We also agree with the members of the SNF HAI 
measure TEP, which found that the exclusion criteria were realistic and 
comprehensive.\207\ With regard to identifying benchmarks and goals for 
the SNF HAI measure, we note that our analysis of FY 2019 data 
demonstrated that there is a performance gap in HAI rates across SNFs. 
Specifically, among the 14,102 SNFs included in the sample, risk-
adjusted SNF HAI measure rates ranged from a minimum of 2.36 percent to 
a maximum of 17.62 percent.\208\ In addition, we calculate specific 
performance standards, based on data gathered from all participating 
SNFs, that we use as benchmarks and achievement thresholds. We continue 
to believe each SNF can use this information to set goals for quality 
improvement that meet the needs of their facility. As we discuss in 
detail in the next comment response, we have made several resources 
available to assist SNFs with reducing HAIs and improving their quality 
of care.
---------------------------------------------------------------------------

    \207\ https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/Downloads/SNF-HAI-Final-TEP-Report-7-15-19_508C.pdf.
    \208\ Acumen LLC & CMS. (2021). Skilled Nursing Facility 
Healthcare-Associated Infections Requiring Hospitalization for the 
Skilled Nursing Facility Quality Reporting Program: Technical 
Report. Retrieved from https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/Skilled-Nursing-FacilityQuality-Reporting-Program/SNF-Quality-ReportingProgram-Measures-and-Technical-Information.
---------------------------------------------------------------------------

    Comment: A few commenters expressed concerns about a lack of 
resources in SNFs currently. One commenter noted that no new measures 
should be adopted because of current staffing burdens. Another 
commenter stated that SNFs may not have the resources for quality 
improvement efforts and recommended that CMS offer quality improvement 
support to reduce HAIs.
    Response: We note that the SNF HAI measure, as well as the DTC PAC 
SNF and Total Nurse Staffing measures, will not impose any new 
reporting burdens on SNFs. In addition, as finalized, the SNF HAI and 
Total Nurse Staffing measures will not begin affecting SNF payments 
until the FY 2026 program year, and the DTC PAC SNF measure will not 
begin affecting SNF payments until the FY 2027 program year. We 
continue to believe that this provides SNFs with sufficient time to 
prepare for implementation of these measures.
    We also note that we have made several resources available to 
assist SNFs with reducing HAIs and improving quality of care. These 
include training in partnership with the CDC and Quality Improvement 
Organizations (QIOs), many of which are available at https://www.cdc.gov/longtermcare/prevention/ and https://www.cdc.gov/longtermcare/prevention/. Additionally, the CMS Office of 
Minority Health (OMH) offers a Disparity Impact Statement, which is a 
tool that all health care stakeholders can use to identify and address 
health disparities: https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/Disparities-Impact-Statement-508-rev102018.pdf.
    After considering the public comments, we are finalizing our 
proposal to adopt the SNF HAI Requiring Hospitalization Measure 
beginning with the FY 2026 SNF VBP program year as proposed.
c. Adoption of the Total Nursing Hours per Resident Day Staffing 
Measure Beginning With the FY 2026 SNF VBP Program Year
    We proposed to adopt the Total Nursing Hours per Resident Day 
Staffing (Total Nurse Staffing) measure for the FY 2026 program year 
and subsequent years. The Total Nurse Staffing measure is a structural 
measure that uses auditable electronic data reported to CMS's Payroll 
Based Journal (PBJ) system to calculate total nursing hours per 
resident day. Given the well-documented impact of nurse staffing on 
patient outcomes and quality of care, this measure, as proposed, will 
align the Program with the Person-Centered Care domain of CMS's 
Meaningful Measures 2.0 Framework. In addition, the Total Nurse 
Staffing measure is currently included in the Five-Star Quality Rating 
System. For more information on the Five-Star Quality Rating System, 
see https://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/CertificationandComplianc/FSQRS.
(1) Background
    Staffing is a crucial component of quality care for nursing home 
residents. Numerous studies have explored the relationship between 
nursing home staffing levels and quality of care. The findings and 
methods of these studies have varied, but most have found a strong, 
positive relationship between staffing and quality 
outcomes.209 210 211 212 213 Specifically, studies have 
shown an association between nurse staffing levels and 
hospitalizations,214 215 pressure

[[Page 47571]]

ulcers,216 217 218 weight loss,219 220 functional 
status,221 222 and survey deficiencies,223 224 
among other quality and clinical outcomes. The strongest relationships 
have been identified for registered nurse (RN) staffing; several 
studies have found that higher RN staffing is associated with better 
care quality.225 226 We recognize that the relationship 
between nurse staffing and quality of care is multi-faceted, with 
elements such as staff turnover playing a critical role.\227\ We refer 
readers to additional discussion of staffing turnover in section 
VIII.I.1.a. of this final rule.
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    \209\ Bostick J.E., Rantz M.J., Flesner M.K., Riggs C.J. 
Systematic review of studies of staffing and quality in nursing 
homes. J Am Med Dir Assoc. 2006;7:366-376. https://pubmed.ncbi.nlm.nih.gov/16843237/.
    \210\ Backhaus R., Verbeek H., van Rossum E., Capezuti E., Hamer 
J.P.H. Nursing staffing impact on quality of care in nursing homes: 
a systemic review of longitudinal studies. J Am Med Dir Assoc. 
2014;15(6):383- 393. https://pubmed.ncbi.nlm.nih.gov/24529872/.
    \211\ Spilsbury K., Hewitt C., Stirk L., Bowman C. The 
relationship between nurse staffing and quality of care in nursing 
homes: a systematic review. Int J Nurs Stud. 2011; 48(6):732-750. 
https://pubmed.ncbi.nlm.nih.gov/21397229/https://pubmed.ncbi.nlm.nih.gov/21397229/.
    \212\ Castle N. Nursing home caregiver staffing levels and 
quality of care: a literature review. J Appl Gerontol. 2008;27:375-
405. https://doi.org/10.1177%2F0733464808321596.
    \213\ Spilsbury et al.
    \214\ Centers for Medicare and Medicaid Services. 2001 Report to 
Congress: Appropriateness of Minimum Nurse Staffing Ratios in 
Nursing Homes, Phase II. Baltimore, MD: Centers for Medicare and 
Medicaid Services. https://phinational.org/wp-content/uploads/legacy/clearinghouse/PhaseIIVolumeIofIII.pdf.
    \215\ Dorr D.A., Horn S.D., Smout R.J. Cost analysis of nursing 
home registered nurse staffing times. J Am Geriatr Soc. 2005 
May;53(5):840-5. doi: 10.1111/j.1532-5415.2005.53267.x. PMID: 
15877561. https://pubmed.ncbi.nlm.nih.gov/15877561/https://pubmed.ncbi.nlm.nih.gov/15877561/.
    \216\ Alexander, G.L. An analysis of nursing home quality 
measures and staffing. Qual Manag Health Care. 2008;17:242-251. 
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3006165/.
    \217\ Horn S.D., Buerhaus P., Bergstrom N., et al. RN staffing 
time and outcomes of long-stay nursing home residents: Pressure 
ulcers and other adverse outcomes are less likely as RNs spend more 
time on direct patient care. Am J Nurs 2005 6:50-53. https://pubmed.ncbi.nlm.nih.gov/16264305/.
    \218\ Bostick et al.
    \219\ Centers for Medicare and Medicaid Services. 2001 Report to 
Congress: Appropriateness of Minimum Nurse Staffing Ratios in 
Nursing Homes, Phase II. Baltimore, MD: Centers for Medicare and 
Medicaid Services. https://phinational.org/wp-content/uploads/legacy/clearinghouse/PhaseIIVolumeIofIII.pdf.
    \220\ Bostick et al.
    \221\ Centers for Medicare and Medicaid Services. 2001 Report to 
Congress: Appropriateness of Minimum Nurse Staffing Ratios in 
Nursing Homes, Phase II. Baltimore, MD: Centers for Medicare and 
Medicaid Services. https://phinational.org/wp-content/uploads/legacy/clearinghouse/PhaseIIVolumeIofIII.pdf.
    \222\ Bostick et al.
    \223\ Castle N.G., Wagner L.M., Ferguson-Rome J.C., Men A., 
Handler S.M. Nursing home deficiency citations for infection 
control. Am J Infect Control. 2011 May;39(4):263-9. doi: 10.1016/
j.ajic.2010.12.010. PMID: 21531271.
    \224\ Castle N., Wagner L., Ferguson J., Handler S. Hand hygiene 
deficiency citations in nursing homes. J Appl Gerontol. 2014 
Feb;33(1):24-50. doi: 10.1177/0733464812449903. Epub 2012 Aug 1. 
PMID: 24652942. https://pubmed.ncbi.nlm.nih.gov/24652942/.
    \225\ Backhaus R., Verbeek H., van Rossum E., Capezuti E., Hamer 
J.P.H. Nursing staffing impact on quality of care in nursing homes: 
a systemic review of longitudinal studies. J Am Med Dir Assoc. 
2014;15(6):383-393. https://pubmed.ncbi.nlm.nih.gov/24529872/.
    \226\ Dellefield M.E., Castle N.G., McGilton K.S., Spilsbury K. 
The relationship between registered nurses and nursing home quality: 
an integrative review (2008-2014). Nurs Econ. 2015;33(2):95-108, 
116. https://pubmed.ncbi.nlm.nih.gov/26281280/.
    \227\ Bostick et al.
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    The PHE due to COVID-19 has further underscored the critical 
importance of sufficient staffing to quality and clinical outcomes. 
Several recent studies have found that higher staffing is associated 
with lower COVID-19 incidence and fewer deaths.228 229 230
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    \228\ R. Tamara Konetzka, Elizabeth M. White, Alexander Pralea, 
David C. Grabowski, Vincent Mor, A systematic review of long-term 
care facility characteristics associated with COVID--19 outcomes, 
Journal of the American Geriatrics Society, 10.1111/jgs.17434, 69, 
10, (2766-2777), (2021). https://agsjournals.onlinelibrary.wiley.com/doi/10.1111/jgs.17434.
    \229\ Williams, C.S., Zheng Q., White A., Bengtsson A., Shulman 
E.T., Herzer K.R., Fleisher L.A. The association of nursing home 
quality ratings and spread of COVID-19. Journal of the American 
Geriatrics Society, 10.1111/jgs. 17309, 69, 8, (2070-2078), 2021. 
https://doi.org/10.1111/jgs.17309.
    \230\ Gorges, R.J. and Konetzka, R.T. Staffing Levels and COVID-
19 Cases and Outbreaks in U.S. Nursing Homes. Journal of the 
American Geriatrics Society, 10.1111/jgs. 16787, 68, 11, (2462-
2466), 2020. https://agsjournals.onlinelibrary.wiley.com/doi/full/10.1111/jgs.16787.
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    Multiple Institute of Medicine (IOM) reports have examined the 
complex array of factors that influence care quality in nursing homes, 
including staffing variables such as staffing levels and 
turnover.231 232 In the 2004 report, ``Keeping Patients 
Safe: Transforming the Work Environment of Nurses,'' the IOM's 
Committee on the Work Environment for Nurses and Patient Safety 
highlighted the positive relationships between higher nursing staffing 
levels, particularly RN levels, and better patient outcomes, and 
recognized the need for minimum staffing standards to support 
appropriate levels of nursing staff in nursing homes.\233\
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    \231\ Institute of Medicine. 1996. Nursing Staff in Hospitals 
and Nursing Homes: Is It Adequate? Washington, DC: The National 
Academies Press. https://doi.org/10.17226/5151.
    \232\ Institute of Medicine 2004. Keeping Patients Safe: 
Transforming the Work Environment of Nurses. Washington, DC: The 
National Academies Press. https://doi.org/10.17226/10851.
    \233\ IOM, 2004.
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    Previously published Phase I and Phase II ``Reports to Congress on 
the Appropriateness of Minimum Staffing Ratios in Nursing Homes'' 
further studied the relationship between quality and nurse staffing 
levels and provided compelling evidence of the relationship between 
staffing ratios and quality of care.234 235 The Phase II 
report, completed in 2001, identified staffing thresholds that 
maximized quality outcomes, demonstrating a pattern of incremental 
benefits of increased nurse staffing until a threshold was reached. 
Specifically, the Phase II study used Medicaid Cost Report data from a 
representative sample of 10 states, including over 5,000 facilities, to 
identify staffing thresholds below which quality of care was 
compromised and above which there was no further benefit of additional 
staffing with respect to quality. The study found evidence of a 
relationship between higher staffing and better outcomes for total 
nurse staffing levels up to 4.08 hours per resident day and RN staffing 
levels up to 0.75 RN hours per resident day. In the 2001 study, minimum 
staffing levels at any level up to these thresholds were associated 
with incremental quality improvements, and no significant quality 
improvements were observed for staffing levels above these thresholds. 
The findings were also supported by case studies of individual 
facilities, units, and residents.
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    \234\ Centers for Medicare and Medicaid Services. Report to 
Congress: Appropriateness of Minimum Nurse Staffing Ratios in 
Nursing Homes, Phase I (2000). Baltimore, MD: Centers for Medicare 
and Medicaid Services. https://phinational.org/wp-content/uploads/legacy/clearinghouse/Phase_I_VOL_I.pdf.
    \235\ Centers for Medicare and Medicaid Services. 2001 Report to 
Congress: Appropriateness of Minimum Nurse Staffing Ratios in 
Nursing Homes, Phase II. Baltimore, MD: Centers for Medicare and 
Medicaid Services. https://phinational.org/wp-content/uploads/legacy/clearinghouse/PhaseIIVolumeIofIII.pdf.
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    We have long identified staffing as one of the vital components of 
a nursing home's ability to provide quality care and used staffing data 
to gauge its impact on quality of care in nursing homes more accurately 
and effectively. In 2003, the National Quality Forum Nursing Home 
Steering Committee recommended that a nurse staffing quality measure be 
included in the set of nursing home quality measures that are publicly 
reported by us. The Total Nurse Staffing measure is currently used in 
the Nursing Home Five-Star Quality Rating System, as one of two 
measures that comprise the staffing domain. For more information on the 
Five-Star Quality Rating System, we refer readers to https://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/CertificationandComplianc/FSQRS.
    Current Federal requirements for nurse staffing are outlined in the 
LTC facility requirements for participation (requirements).\236\ The 
regulations at 42 CFR 483.35 specify, in part, that every facility must 
have sufficient nursing staff with the appropriate competencies and 
skill sets to provide nursing and related services to assure resident 
safety and attain or maintain the highest practicable physical, mental, 
and psychosocial well-being of each resident, as determined by resident 
assessments and individual plans of care and considering the number, 
acuity and diagnoses of the facility's resident

[[Page 47572]]

population in accordance with the facility assessment required at Sec.  
483.70(e). We adopted this competency-based approach to sufficient 
staffing to ensure every nursing home provides the staffing levels 
needed to meet the specific needs of their resident population, 
including their person-centered care goals. We also note that current 
regulations require (unless these requirements are waived) facilities 
to have an RN onsite at least 8 consecutive hours a day, 7 days a week 
and around-the-clock services from licensed nursing staff under 
sections 1819(b)(4)(C) and 1919(b)(4)(C) of the Act, and Sec.  
483.35(a) and (b).
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    \236\ FY 2017 Consolidated Medicare and Medicaid Requirements 
for Participation for Long-Term Care Facilities Final Rule (81 FR 
68688 through 68872). https://www.govinfo.gov/content/pkg/FR-2016-10-04/pdf/2016-23503.pdf.
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    Section 1128I(g) of the Act requires facilities to electronically 
submit direct care staffing information (including agency and contract 
staff) based on payroll and other auditable data. In August 2015, we 
amended the requirements for LTC facilities at Sec.  483.70(q) to 
require the electronic submission of payroll-based staffing data, which 
includes RNs, licensed practical nurses (LPNs) or vocational nurses, 
certified nursing assistants, and other types of medical personnel as 
specified by us, along with census data, data on agency and contract 
staff, and information on turnover, tenure and hours of care provided 
by each category of staff per resident day.\237\ We developed the PBJ 
system to enable facilities to submit the required staffing information 
in a format that is auditable to ensure accuracy. Development of the 
PBJ system built on several earlier studies that included extensive 
testing of payroll-based staffing measures. The first mandatory PBJ 
reporting period began July 1, 2016.
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    \237\ 80 FR 46390, Aug. 4, 2015 (https://www.govinfo.gov/content/pkg/FR-2015-08-04/pdf/2015-18950.pdf).
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    We post staffing information publicly to help consumers understand 
staffing levels and how they differ across nursing homes. See sections 
1819(i)(1)(A)(i) and 1919(i)(1)(A)(i) of the Act. However, there are 
currently no staffing measures in the SNF VBP Program.
    Given the strong evidence regarding the relationship between 
sufficient staffing levels and improved care for residents, inclusion 
of this measure in the SNF VBP Program adds an important new dimension 
to provide a more comprehensive assessment of and accountability for 
the quality of care provided to residents and serves to drive 
improvements in staffing that are likely to translate into better 
resident care. PBJ data show that there is variability across SNFs in 
performance on this measure, and that there is an opportunity and 
potential for many SNFs to improve their staffing levels. For Q4 CY 
2020, average total nurse staffing was 4.09 hours per resident day for 
the case-mix adjusted Total Nurse Staffing measure, with considerable 
variability across facilities ranging from 2.81 hours per resident day 
to 5.93 hours per resident day. Staffing levels increased after April 
2018, when we first reported PBJ-based staffing measures on Nursing 
Home Compare and using them in the Five-Star Quality Rating System. 
Average nursing staffing hours per resident day increased from 3.85 in 
Q4 CY 2017 (publicly reported in April 2018) to 4.08 for Q4 CY 2020 
(publicly reported in April 2021).
    Inclusion of this measure in the SNF VBP Program also aligns with 
our current priorities and focus areas for the Program and optimizing 
the use of measures that SNFs are already reporting to us. Because the 
measure is currently used in the Nursing Home Five-Star Quality Rating 
System, inclusion of this measure in the Program does not add reporting 
or administrative burden to SNFs. Recognizing the importance of 
staffing to supporting and advancing person-centered care needs, this 
measure will align the Program with the Person-Centered Care domain of 
CMS's Meaningful Measures 2.0 Framework.
(2) Overview of Measure
    The Total Nurse Staffing measure is a structural measure that uses 
auditable electronic data reported to CMS's PBJ system to calculate 
total nursing hours, which includes RNs, LPNs, and certified nurse 
aides (CNA), per resident day. The measure uses a count of daily 
resident census derived from Minimum Data Set (MDS) resident 
assessments and is case-mix adjusted based on the distribution of MDS 
resident assessments by Resource Utilization Groups, version IV (RUG-IV 
groups). The measure was specified and originally tested at the 
facility level with SNFs as the care setting. The measure is not 
currently NQF endorsed; however, we plan to submit it for endorsement 
in the next 1 to 2 years.
    Data on the measure have been publicly reported on the Provider 
Data Catalog website currently hosted by HHS, available at https://data.cms.gov/provider-data/, for many years and have been used in the 
Nursing Home Five-Star Quality Rating System since its inception in 
2008. The data source for the measure changed in 2018, when we started 
collecting payroll-based staffing data through the PBJ system. Since 
April 2018, we have been using PBJ and the MDS as the data sources for 
this measure for public reporting and for use in the Five-Star Quality 
Rating System. For more information, see the Final Specifications for 
the SNF VBP Program Total Nursing Hours per Resident Day Measure, at 
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/SNF-VBP/Measure.
    The CMS report ``Appropriateness of Minimum Nurse Staffing Ratios 
in Nursing Homes, Phase II,'' described earlier in this section, showed 
the relationship between quality and nurse staffing levels using 
several methods, establishing the face validity of the Total Nurse 
Staffing measure. The study included an analysis of data from 10 states 
including over 5,000 facilities and found evidence of a relationship 
between staffing ratios and the quality of nursing home care.
    We note that payroll data are considered the gold standard for 
nurse staffing measures and a significant improvement over the manual 
data previously used, wherein staffing information was calculated based 
on a form (CMS-671) filled out manually by the facility.\238\ In 
contrast, PBJ staffing data are electronically submitted and are 
auditable back to payroll and other verifiable sources. Analyses of 
PBJ-based staffing measures show a relationship between higher nurse 
staffing levels and higher ratings for other dimensions of quality such 
as health inspection survey results and quality measures.\239\
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    \238\ https://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/SurveyCertificationGenInfo/Downloads/QSO18-17-NH.pdf.
    \239\ https://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=96520.
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(a) Interested Parties and TEP Input
    In considering whether the total nurse staffing measure would be 
appropriate for the SNF VBP Program, we looked at the developmental 
history of the measure in which we employed a transparent process that 
provided interested parties and national experts the opportunity to 
provide pre-rulemaking input. We convened meetings with interested 
parties and offered engagement opportunities at all phases of measure 
development, from 2004 through 2019. Calls and meetings with interested 
parties have included patient/consumer advocates and a wide range of 
facilities throughout the country including large and small, rural and 
urban, independently owned facilities and national chains. In addition 
to input obtained through meetings with interested parties, we

[[Page 47573]]

solicited input through a dedicated email address 
([email protected]).
(b) MAP Review
    The Total Nurse Staffing measure was included in the publicly 
available ``List of Measures Under Consideration for December 1, 
2021.'' \240\ The MAP conditionally supported the Total Nurse Staffing 
measure for rulemaking, pending NQF endorsement. We refer readers to 
the final 2021-2022 MAP report available at https://www.qualityforum.org/Publications/2022/03/MAP_2021-2022_Considerations_for_Implementing_Measures_Final_Report_-_Clinicians,_Hospitals,_and_PAC-LTC.aspx.
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    \240\ https://www.cms.gov/files/document/measures-under-consideration-list-2021-report.pdf.
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(3) Data Sources
    As proposed, the Total Nurse Staffing measure is calculated using 
auditable, electronic staffing data submitted by each SNF for each 
quarter through the PBJ system, along with daily resident census 
information derived from Minimum Data Set, Version 3.0 (MDS 3.0) 
standardized patient assessments. We refer readers to the Final 
Specifications for the SNF VBP Program Total Nursing Hours per Resident 
Day Measure, at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/SNF-VBP/Measure. We 
noted that the Total Nurse Staffing measure is already reported on the 
Provider Data Catalog website and used as part of the Five-Star Quality 
Rating System and thus, there will be no additional data collection or 
submission burdens for SNFs.
(4) Inclusion and Exclusion Criteria
    The target population for the measure is all SNFs to whom the SNF 
VBP applies and that are not excluded for the reasons listed below. A 
set of exclusion criteria are used to identify facilities with highly 
improbable staffing data and these facilities are excluded. The 
exclusion criteria are as follows:
     Total nurse staffing, aggregated over all days in the 
quarter that the facility reported both residents and staff is 
excessively low (<1.5 hours per resident day).
     Total nurse staffing, aggregated over all days in the 
quarter that the facility reported both residents and staff is 
excessively high (>12 hours per resident day).
     Nurse aide staffing, aggregated over all days in the 
quarter that the facility reported both residents and staff is 
excessively high (>5.25 hours per resident day).
(5) Measure Calculation and Case-Mix Adjustment
    We proposed to calculate case-mix adjusted hours per resident day 
for each facility for each staff type using this formula:

Hours Adjusted = (Hours Reported/Hours 
Case-Mix) * Hours National Average

    The reported hours are those reported by the facility through PBJ. 
National average hours for a given staff type represent the national 
mean of case-mix hours across all facilities active on the last day of 
the quarter that submitted valid nurse staffing data for the quarter.
    The measure is case-mix adjusted based on the distribution of MDS 
assessments by RUG-IV groups. The CMS Staff Time Resource Intensity 
Verification (STRIVE) Study measured the average number of RN, LPN, and 
NA minutes associated with each RUG-IV group (using the 66-group 
version of RUG-IV).\241\ We refer to these as ``case-mix hours.'' The 
case-mix values for each facility are based on the daily distribution 
of residents by RUG-IV group in the quarter covered by the PBJ reported 
staffing and estimates of daily RN, LPN, and NA hours from the CMS 
STRIVE Study. This adjustment is based on the distribution of MDS 
assessments by RUG-IV groups to account for differences in acuity, 
functional status, and care needs of residents, and therefore is 
appropriate for the SNF VBP Program. For more information, see the 
Final Specifications for the SNF VBP Program Total Nursing Hours per 
Resident Day Measure, at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/SNF-VBP/Measure.
---------------------------------------------------------------------------

    \241\ https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/TimeStudy.
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(a) Numerator
    The numerator for the measure is total nursing hours (RN + LPN + NA 
hours). RN hours include the RN director of nursing, RNs with 
administrative duties, and RNs. LPN hours include licensed practical 
and licensed vocational nurses with administrative duties and licensed 
practical and licensed vocational nurses. NA hours include certified 
nurse aides (CNAs), aides in training, and medication aides/
technicians. We noted that the proposed PBJ staffing data include both 
facility employees (full-time and part-time) and individuals under an 
organization (agency) contract or an individual contract. The proposed 
PBJ staffing data do not include ``private duty'' nursing staff 
reimbursed by a resident or his/her family. Also, hospice staff and 
feeding assistants are not included.
(b) Denominator
    The denominator for the measure is a count of daily resident census 
derived from MDS resident assessments. It is calculated by: (1) 
identifying the reporting period (quarter) for which the census will be 
calculated; (2) extracting MDS assessment data for all residents of a 
facility beginning 1 year prior to the reporting period to identify all 
residents that may reside in the facility (that is, any resident with 
an MDS assessment); and (3) identifying discharged or deceased 
residents using specified criteria. For any date, residents whose 
assessments do not meet the criteria for being identified as discharged 
or deceased prior to that date are assumed to reside in the facility. 
The count of these residents is the census for that particular day. We 
refer readers to the Final Specifications for the SNF VBP Program Total 
Nursing Hours per Resident Day Measure for more information on the 
calculation of daily resident census used in the denominator of the 
reported nurse staffing ratios, at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/SNF-VBP/Measure.
    The currently publicly reported Total Nurse Staffing measure is 
reported on a quarterly basis. To align with other quality measures for 
the expanded SNF VBP Program, we proposed to report the measure rate 
for the SNF VBP Program for each SNF as a simple average rate of total 
nurse staffing per resident day across available quarters in the 1-year 
performance period.
(6) Confidential Feedback Reports and Public Reporting
    We refer readers to the FY 2017 SNF PPS final rule (81 FR 52006 
through 52007) for discussion of our policy to provide quarterly 
confidential feedback reports to SNFs on their measure performance. We 
also refer readers to the FY 2022 SNF PPS final rule (86 FR 42516 
through 42517) for a summary of our two-phase review and corrections 
policy for SNFs' quality measure data. Furthermore, we refer readers to 
the FY 2018 SNF PPS final rule (82 FR 36622 through 36623) and the FY 
2021 SNF PPS final rule (85 FR 47626) where we finalized our policy to 
publicly report SNF measure performance information under the SNF VBP 
Program on the Provider Data Catalog website currently hosted by HHS 
and available at https://data.cms.gov/provider-data/. We

[[Page 47574]]

proposed to update and redesignate the confidential feedback report and 
public reporting policies, which are currently codified at Sec.  
413.338(e)(1) through (3) as Sec.  413.338(f), to include the Total 
Nurse Staffing measure.
    We invited public comment on our proposal to adopt the Total Nurse 
Staffing measure beginning with the FY 2026 SNF VBP program year. We 
received the following comments and provide our responses:
    Comment: Many commenters supported our proposal to adopt a measure 
of Total Nurse Staffing, citing the strong relationship between higher 
nurse staffing levels and improved quality of care. Some commenters 
noted that they supported inclusion of the measure because, although it 
a structural measure, not an outcome measure, staffing levels are tied 
to multiple outcomes such as hospitalizations, pressure ulcers, 
emergency department use, functional improvement, weight loss and 
dehydration, and COVID-19 infection rates and deaths. Another commenter 
noted that adding the measure allows for more accountability for SNFs 
without adding data collection burden.
    Response: We agree that there is a strong, positive relationship 
between nurse staffing levels, quality of care, and patient outcomes 
and that the adoption of this measure adds an important dimension of 
quality to the Program. We refer readers to the evidence discussed in 
our proposed rule (87 FR 22771 through 22772) which demonstrates that 
nurse staffing levels are associated with various patient outcomes, 
such as hospitalizations and functional status. We also note that 
analyses of PBJ-based staffing data show a relationship between higher 
nurse staffing levels and higher ratings on other dimensions of quality 
such as health inspection survey results and various quality 
measures.\242\ We agree that the measure allows for more accountability 
for quality outcomes without adding data reporting or administrative 
burden, as SNFs already report nurse staffing data on which the measure 
is based through the PBJ system, and the Total Nurse Staffing measure 
is currently used in the Nursing Home Five-Star Quality Rating System.
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    \242\ https://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=96520.
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    Comment: Many commenters opposed our proposal to adopt a measure of 
Total Nurse Staffing. Several commenters stated that staff shortages 
have made it difficult for facilities to operate, potentially impacting 
SNFs for years to come, and suggested that we delay the measure's 
implementation in the Program.
    Response: We recognize that the COVID-19 PHE has had significant 
impacts on SNF operations and staffing. We also note that facilities 
with data indicating excessively low staffing levels are excluded from 
the measure, and based on the proposed exclusion criteria, facilities 
with <1.5 nursing hours per resident day will be excluded from the 
measure on the basis that those data are at high risk for 
inaccuracy.\243\ We refer readers to our proposed rule for further 
information on the inclusion and exclusion criteria for this measure 
(87 FR 22773). We also remain committed to the importance of value-
based care and incentivizing quality care tied to payment. SNF staffing 
is a high priority because of its central role in the quality of care 
for Medicare beneficiaries, and therefore, we continue to believe that 
this measure will provide a more comprehensive assessment of, and 
accountability for, the quality of care provided to residents.
---------------------------------------------------------------------------

    \243\ See ``Denominator Exclusions,'' Proposed Specifications 
for the Skilled Nursing Facility Value-Based Purchasing (SNF VBP) 
Program Total Nursing Hours per Resident Day Measure, available at 
https://www.cms.gov/files/document/proposed-specifications-skilled-nursing-facility-value-based-purchasing-snf-vbp-program-total.pdf.
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    Comment: One commenter stated that an operational measure is not 
appropriate for the SNF VBP Program, while another stated that the 
Program's purpose to link payments to outcomes is not served by a 
structural measure.
    Response: We recognize that the Total Nurse Staffing measure is a 
structural measure, not a patient outcome measure. However, numerous 
studies have shown that higher staffing levels are associated with 
better patient outcomes, such as fewer hospitalizations 
244 245, fewer pressure ulcers 246 247 248, more 
weight loss 249 250, and better functional status 
251 252. As a result, we believe that this measure is a 
strong indicator of quality of care and is an appropriate and important 
addition to the Program.
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    \244\ Centers for Medicare and Medicaid Services. 2001 Report to 
Congress: Appropriateness of Minimum Nurse Staffing Ratios in 
Nursing Homes, Phase II. Baltimore, MD: Centers for Medicare and 
Medicaid Services. https://phinational.org/wpcontent/https://phinational.org/wpcontent/uploads/legacy/clearinghouse/PhaseIIVolumeIofIII.pdf.
    \245\ Dorr D.A., Horn S.D., Smout R.J. Cost analysis of nursing 
home registered nurse staffing times. J Am Geriatr Soc. 2005 
May;53(5):840-5. doi: 10.1111/j.1532-5415.2005.53267.x. PMID: 
15877561. https://pubmed.ncbi.nlm.nih.gov/15877561/.
    \246\ Alexander, G.L. An analysis of nursing home quality 
measures and staffing. Qual Manag Health Care. 2008;17:242-251. 
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3006165/.
    \247\ Horn S.D., Buerhaus P., Bergstrom N., et al. RN staffing 
time and outcomes of long-stay nursing home residents: Pressure 
ulcers and other adverse outcomes are less likely as RNs spend more 
time on direct patient care. Am J Nurs 2005 6:50-53. https://pubmed.ncbi.nlm.nih.gov/16264305/.
    \248\ Bostick et al.
    \249\ Centers for Medicare and Medicaid Services. 2001 Report to 
Congress: Appropriateness of Minimum Nurse Staffing Ratios in 
Nursing Homes, Phase II. Baltimore, MD: Centers for Medicare and 
Medicaid Services. https://phinational.org/wpcontent/uploads/legacy/clearinghouse/PhaseIIVolumeIofIII.pdf.
    \250\ Bostick et al.
    \251\ Centers for Medicare and Medicaid Services. 2001 Report to 
Congress: Appropriateness of Minimum Nurse Staffing Ratios in 
Nursing Homes, Phase II. Baltimore, MD: Centers for Medicare and 
Medicaid Services. https://phinational.org/wpcontent/uploads/legacy/clearinghouse/PhaseIIVolumeIofIII.pdf.
    \252\ Bostick et al.
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    Comment: One commenter noted that the measure is unlikely to 
provide an accurate assessment of care quality because it simplifies 
the relationship between staffing levels and improved care. Another 
commenter stated that we should adopt measures of the clinical outcomes 
that are associated with nurse staffing and not reward facilities for 
simply increasing staffing rather than achieving better clinical 
outcomes. Another commenter stated that there is less evidence of the 
relationship between patient outcomes and certain types of facility 
staff, such as LPNs and nurse aides, than there is of the relationship 
between patient outcomes and RNs.
    Response: We recognize the relationship between nurse staffing and 
quality of care is multi-faceted. We refer commenters to our proposed 
rule (87 FR 22771 through 22772) where we discussed several studies 
that emphasize the evidence of a relationship between staffing levels, 
quality of care, and patient outcomes. We have selected this measure as 
a first step towards addressing this complex relationship between nurse 
staffing and quality of care. Furthermore, we are examining additional 
staffing measures to include in a future Program year to further 
account for the multi-faceted nature of the relationship between 
staffing and care quality and outcomes. We refer readers to our RFI on 
the potential inclusion of a staff turnover measure in section 
VII.I.1.a. of the

[[Page 47575]]

proposed rule (87 FR 22786 through 22787). In addition, as we discussed 
in the proposed rule (87 FR 22771 through 22772), several studies have 
identified a strong relationship between higher RN staffing and better 
quality of care. Also, studies support that other nursing staff, 
including certified nursing assistants and LPNs, play a critical role 
in providing care to Medicare beneficiaries in SNFs and, therefore, 
certified nursing assistants and LPNs, in addition to RNs, are also 
included in our proposed Total Nurse Staffing measure.\253\
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    \253\ Horn S.D., Buerhaus P., Bergstrom N., Smout R.J. RN 
staffing time and outcomes of long-stay nursing home residents: 
pressure ulcers and other adverse outcomes are less likely as RNs 
spend more time on direct patient care. Am J Nurs. 2005;105(11):58-
71. https://pubmed.ncbi.nlm.nih.gov/16264305/.
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    Comment: A few commenters recommended that the measure should be 
endorsed by NQF as soon as possible or prior to its adoption.
    Response: We intend to submit the measure for NQF endorsement in 
the next 1 to 2 years, which we believe is the most feasible timeline. 
We continue to believe the Total Nurse Staffing measure provides vital 
quality of care information; as mentioned in the proposed rule (87 FR 
22771 through 22772), studies demonstrate a strong relationship between 
nurse staffing levels, quality of care, and patient outcomes. Given its 
relationship to quality of care, we believe it is important to include 
this measure in the Program despite the lack of current NQF 
endorsement.
    Comment: One commenter expressed concern that a staffing measure 
may exacerbate care disparities because SNFs with larger minority 
patient populations tend to have lower staffing levels. Another 
commenter was concerned that the measure could cause SNFs to close, 
especially if they serve underserved populations and rural communities. 
The commenter suggested that we reexamine staffing and wage 
reimbursement levels and economic conditions before implementing the 
measure.
    Response: We recognize the commenters' concerns that this measure 
could impact disparities in care provided to SNF residents, especially 
with respect to SNFs that serve large proportions of minority patient 
populations and other underserved communities. We will monitor and 
evaluate the measure's impact on health disparities as it is 
implemented in the SNF VBP Program. Addressing and improving health 
equity is an important priority for us, and as discussed in our RFI on 
the Program's approach to measuring and improving health equity (87 FR 
22789), we remain committed to examining ways to incorporate health 
equity measurement and adjustments in our quality reporting and value-
based purchasing programs. Further, we share the commenter's concerns 
about rural health disparities and note that we remain committed to 
providing support to rural communities in an effort to improve quality 
of care. We also note that in November 2021, the US Department of 
Health and Human Services began distributing $7.5 billion in American 
Rescue Plan (ARP) Rural payments to providers and suppliers who serve 
rural Medicaid, Children's Health Insurance Program (CHIP), and 
Medicare beneficiaries.\254\ In addition, we will continue to examine 
staffing and wage reimbursement levels and economic conditions as part 
of our ongoing evaluation of the Program.
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    \254\ U.S. Department of Health and Human Services. Biden-Harris 
Administration Begins Distributing American Rescue Plan Rural 
Funding to Support Providers Impacted by Pandemic. https://www.hhs.gov/about/news/2021/11/23/biden-admin-begins-distributing-arp-prf-support-to-providers-impacted-by-pandemic.html. Published 
November 23, 2021. Accessed July 18, 2022.
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    Comment: One commenter recommended that we should only reward 
facilities with the highest staffing levels. Another commenter noted 
that literature on the effects of nursing facility staffing incentives 
is mixed and suggested that incentives may be too small or too complex 
to administer to motivate behavioral changes. Other commenters 
suggested that staffing requirements be set based on residents' acuity, 
stating that facilities that successfully provide quality services 
without increasing staffing should not be penalized.
    Response: We agree that it is important to incentivize staffing 
levels that foster the highest quality outcomes for SNF residents. As a 
reminder, the proposed Total Nurse Staffing measure calculates total 
nursing hours per resident day, and we refer readers to our proposed 
rule (87 FR 22774) to review the specific measure calculations. We 
continue to believe that scoring facilities based on their achievement 
on the Program's quality measures provides strong incentives in this 
program for those facilities already providing higher quality of care 
without prescribing specific staffing levels or practices. We believe 
this type of clinical quality assessment, which allows participating 
facilities to decide how best to achieve better care outcomes, is an 
important feature in our quality programs. However, we also believe 
that it is important to offer SNFs that provide lower levels of care 
quality in the baseline period with incentives for their successes in 
substantially improving the quality of care they provide based on their 
investments in quality improvement. Providing incentives for both 
achievement and improvement in staffing levels and other quality 
metrics provides the opportunity for the program to increase the 
quality of care for all SNF residents, and not only those residents who 
receive care from higher performing SNFs. We will continue to evaluate 
the impact on SNFs' behaviors, staffing levels, and quality outcomes as 
the measure is implemented in the Program. Regarding the commenter's 
concern that SNFs could be penalized for failing to increase staffing 
while still providing quality services, we do not believe this measure 
would penalize those SNFs as long as staffing levels are not low enough 
to imperil services provided to SNF residents. Finally, we note that 
the Total Nurse Staffing measure is case-mix adjusted based on resident 
assessments to account for differences in acuity, functional status, 
and care needs of residents.
    Comment: One commenter suggested that we use targeted surveillance 
of PBJ staffing data to monitor SNFs' staffing rather than using a 
broad count of general staff hours, noting that CMS currently monitors 
PBJ staffing data for trends such as differences in weekend and weekday 
staffing. Another commenter recommended that we align the Program's 
staffing requirements with the Five-Star Quality Rating System.
    Response: We agree that it is important to align the Program's 
measures with other quality and public reporting programs and note that 
the proposed Total Nurse Staffing measure is currently used in the 
Nursing Home Five-Star Quality Rating System. We agree that targeted 
oversight and auditing of PBJ staffing data, such as weekend staffing 
levels and staff turnover, is an important element of our efforts to 
assure sufficient staffing, and we refer readers to this memorandum for 
more information on these efforts: https://www.cms.gov/files/document/qso-22-08-nh.pdf.
    Comment: Several commenters offered technical views on the measure, 
particularly around the type of staff that are included and excluded. 
One commenter suggested that nursing hours should exclude RNs with 
administrative duties, medication aides, technicians, aides in 
training, or private duty nurses. One commenter recommended that the 
measure should include only Medicare Part A beneficiaries because the 
commenter believes that is the scope of the SNF VBP Program. Some

[[Page 47576]]

commenters recommended that we exclude Temporary Nurse Aides (TNAs) 
from the measure's calculation, or otherwise measure CNA, LPN, and RN 
time separately. Some commenters recommended that we weight agency 
staff lower in the measure.
    Response: We refer readers to the proposed rule where we more 
thoroughly discuss inclusion and exclusion criteria for SNFs under this 
measure (87 FR 22773). All SNFs to whom the SNF VBP Program applies are 
included in the measure, except for facilities where total nurse 
staffing or nurse aide staffing is excessively low or excessively high. 
As mentioned in our proposed rule (87 FR 22773), facilities where total 
nurse staffing is <1.5 hours per resident day or >12 hours per resident 
day are excluded. Also, facilities where nurse aide staffing is >5.25 
hours per resident day are excluded. Furthermore, staff included in the 
measure are RNs, LPNs, and nurse aides, such as certified nurse aides 
(CNAs), aides in training, and medication aides/technicians. We 
included a variety of SNF staff in the proposed measure, because as 
discussed in our proposed rule (87 FR 22771-22772), several studies 
demonstrate the strong relationship between these types of staff and 
patient outcomes. Private duty nurses are not included in the measure 
calculation at this time, because they are not included in PBJ staffing 
data. We will also take commenters' suggestions around excluding 
certain types of nurse staffing or calculating CNA, LPN, and RN time 
separately into account as we monitor implementation of the measure. In 
response to the commenter suggesting that we limit the measure to 
Medicare Part A beneficiaries only, we note our continued belief that 
our quality programs drive quality improvement for all patients, 
meaning that we do not believe any such limitation is appropriate at 
this time.
    Comment: A few commenters expressed concerns about the measure's 
case-mix adjustment. One commenter suggested CMS should report both 
actual staffing levels and case-mix adjusted staffing levels. Another 
commenter noted that the measure's case-mix adjustment information is 
outdated and has not been reviewed by a TEP or by NQF.
    Response: We note that the proposed case-mix adjustment is 
consistent with that currently used for the measure in the Nursing Home 
Five-Star Quality Rating System and was originally reviewed by a TEP 
(see https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/SNFPPS/TimeStudy). The case-mix values for each facility are based on 
the daily distribution of residents by RUG-IV group in the quarter 
covered by the PBJ reported staffing and estimates of daily RN, LPN and 
NA hours from the CMS STRIVE Study. We also believe it is important to 
include the case-mix adjustment to account for differences in acuity, 
functional status, and care needs of residents. For more information, 
we refer commenters to our proposed rule (87 FR 22774). We will 
consider whether any changes or updates are needed to the case-mix 
adjustment.
    Comment: One commenter expressed concern that PBJ data may not 
capture salaried individuals who work more than 40 hours per work week 
and variations in how lunch breaks are captured in the PBJ system. 
Another commenter recommended that we allow the PBJ system to capture 
patient care hours provided by other types of professionals such as 
mental health support service workers, music therapists, or respiratory 
therapists. One commenter noted that the proposed exclusion criteria 
are not appropriate for the VBP Program and should be accompanied by an 
appeals process.
    Response: We recognize the importance of various types of 
professionals in providing care and services to Medicare beneficiaries 
in SNFs, but we emphasize the strong relationship identified in the 
literature between nursing professionals and quality of care. For this 
reason, we proposed to adopt the Total Nurse Staffing measure, which 
includes the time worked by RNs, LPNs, and nurse aides, in the FY 2026 
Program. We intend to assess the impact of other types of professionals 
on quality of care. We also note that we will continue to assess the 
measure and if needed, propose measure updates in future rulemaking.
    After considering the public comments, we are finalizing our 
proposal to adopt the Total Nursing Hours per Resident Day Staffing 
(Total Nurse Staffing) measure beginning with the FY 2026 SNF VBP 
program year as proposed.
d. Adoption of the DTC--PAC Measure for SNFs (NQF #3481) Beginning With 
the FY 2027 SNF VBP Program Year
    As part of the SNF VBP Program expansion authorized under the CAA, 
we proposed to adopt the DTC PAC SNF measure for the FY 2027 SNF VBP 
Program and subsequent years. The DTC PAC SNF measure (NQF #3481) is an 
outcome measure that assesses the rate of successful discharges to 
community from a SNF setting, using 2 years of Medicare FFS claims 
data. As proposed, the measure addresses an important health care 
outcome for many SNF residents (returning to a previous living 
situation and avoiding further institutionalization) and will align the 
Program with the Seamless Care Coordination domain of CMS's Meaningful 
Measures 2.0 Framework. In addition, the DTC PAC SNF measure is 
currently part of the SNF QRP measure set.\255\ For more information on 
this measure in the SNF QRP, see 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.
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    \255\ We note that the SNF QRP refers to this measure as the 
``Discharge to Community--PAC SNF QRP'' measure. Though we are using 
a different measure short name (``DTC PAC SNF''), we are proposing 
to adopt the same measure the SNF QRP uses for purposes of the SNF 
VBP program.
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(1) Background
    As we stated in the proposed rule, we believe it is an important 
goal in post-acute care settings to return patients to their previous 
levels of independence and functioning with discharge to community 
being one of the primary goals for post-acute patients. We also stated 
our belief that it is important to improve access to community 
discharge options for SNF residents. Discharge to community is 
considered a valuable outcome to measure because it provides important 
information about patient outcomes after being discharged from a SNF 
and is a multifaceted measure that captures the patient's functional 
status, cognitive capacity, physical ability, and availability of 
social support at home.
    In 2019, 1.5 million of Medicare's FFS beneficiaries (4 percent of 
all Medicare FFS beneficiaries) utilized Medicare coverage for a SNF 
stay.\256\ However, almost half of the older adults that are admitted 
to SNFs are not discharged to the community, and for a significant 
proportion of those that are discharged back to the community, it may 
take up to 365 days.257 258 In 2017, the SNF QRP and other 
PAC QRP programs adopted this measure; however, there remains 
considerable variation in performance on this measure. In 2019, the 
lowest performing SNFs had risk-adjusted rates of successful discharge 
to the community at or below 39.5 percent,

[[Page 47577]]

while the best performing SNFs had rates of 53.5 percent or higher, 
indicating considerable room for improvement.\259\
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    \256\ https://www.medpac.gov/wp-content/uploads/2021/10/mar21_medpac_report_ch7_sec.pdf.
    \257\ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3711511/.
    \258\ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4706779/.
    \259\ March 2021 MedPAC Report to Congress: https://www.medpac.gov/wp-content/uploads/import_data/scrape_files/docs/default-source/reports/mar21_medpac_report_to_the_congress_sec.pdf.
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    In addition to being an important outcome from a resident and 
family perspective, residents discharged to community settings, on 
average, incur lower costs over the recovery episode, compared with 
those discharged to institutional settings.260 261 As stated 
in the proposed rule, we believe including this measure in the SNF VBP 
Program will further encourage SNFs to prepare residents for discharge 
to community, when clinically appropriate, which may have significant 
cost-saving implications for the Medicare program given the high costs 
of care in institutional settings. Also, providers have discovered that 
successful discharge to community is a key factor in their ability to 
achieve savings, where capitated payments for post-acute care were in 
place.\262\ For residents who require LTC due to persistent disability, 
discharge to community could result in lower LTC costs for Medicaid and 
for residents' out-of-pocket expenditures.\263\
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    \260\ Dobrez D., Heinemann A.W., Deutsch A., Manheim L., 
Mallinson T. Impact of Medicare's prospective payment system for 
inpatient rehabilitation facilities on stroke patient outcomes. 
American Journal of Physical Medicine & Rehabilitation. 
2010;89(3):198-204. https://doi.org/10.1097/PHM.0b013e3181c9fb40.
    \261\ Gage B., Morley M., Spain P., Ingber M.. Examining Post-
Acute Care Relationships in an Integrated Hospital System. Final 
Report. RTI International;2009. https://aspe.hhs.gov/sites/default/files/private/pdf/75761/report.pdf.
    \262\ Doran J.P., Zabinski S.J. Bundled payment initiatives for 
Medicare and non-Medicare total joint arthroplasty patients at a 
community hospital: Bundles in the real world. The journal of 
arthroplasty. 2015;30(3):353-355. https://doi.org/10.1016/j.arth.2015.01.035.
    \263\ Newcomer R.J., Ko M., Kang T., Harrington C., Hulett D., 
Bindman A.B. Health Care Expenditures After Initiating Long-term 
Services and Supports in the Community Versus in a Nursing Facility. 
Medical Care. 2016; 54(3):221-228. https://doi.org/10.1097/MLR.0000000000000491.
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    Discharge to community is also an actionable health care outcome, 
as targeted interventions have been shown to successfully increase 
discharge to community rates in a variety of post-acute settings. Many 
of these interventions involve discharge planning or specific 
rehabilitation strategies, such as addressing discharge barriers and 
improving medical and functional status.264 265 266 267 
Other factors that have shown positive associations with successful 
discharge to community include patient safety culture within the SNF 
and availability of home and community-based 
services.268 269 The effectiveness of these interventions 
suggests that improvement in discharge to community rates among post-
acute care residents is possible through modifying provider-led 
processes and interventions. Therefore, including the DTC PAC SNF 
measure in the SNF VBP Program may provide further incentive for 
providers to continue improving on current interventions or implement 
new interventions.
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    \264\ Kushner D.S., Peters K.M., Johnson-Greene D. Evaluating 
Siebens Domain Management Model for Inpatient Rehabilitation to 
Increase Functional Independence and Discharge Rate to Home in 
Geriatric Patients. Archives of physical medicine and 
rehabilitation. 2015;96(7):1310-1318. https://doi.org/10.1016/j.apmr.2015.03.011.
    \265\ Wodchis W.P., Teare G.F., Naglie G., et al. Skilled 
nursing facility rehabilitation and discharge to home after stroke. 
Archives of physical medicine and rehabilitation. 2005;86(3):442-
448. https://doi.org/10.1016/j.apmr.2004.06.067.
    \266\ Berkowitz R.E., Jones R.N., Rieder R., et al. Improving 
disposition outcomes for patients in a geriatric skilled nursing 
facility. Journal of the American Geriatrics Society. 
2011;59(6):1130-1136. https://doi.org/10.1111/j.1532-5415.2011.03417.
    \267\ Kushner D.S., Peters K.M., Johnson-Greene D. Evaluating 
use of the Siebens Domain Management Model during inpatient 
rehabilitation to increase functional independence and discharge 
rate to home in stroke patients. PM & R: The journal of injury, 
function, and rehabilitation. 2015;7(4):354- 364. https://doi.org/10.1016/j.pmrj.2014.10.010.
    \268\ https://doi.org/10.1111/j.1532-5415.2011.03417 Wenhan Guo, 
Yue Li, Helena Temkin-Greener, Community Discharge Among Post-Acute 
Nursing Home Residents: An Association With Patient Safety Culture?, 
Journal of the American Medical Directors Association, Volume 22, 
Issue 11, 2021, Pages 2384-2388.e1, ISSN 1525-8610, https://doi.org/10.1016/j.jamda.2021.04.022.
    \269\ https://doi.org/10.1016/j.pmrj.2014.10.010 Wang, S., 
Temkin-Greener, H., Simning, A., Konetzka, R.T. and Cai, S. (2021), 
Outcomes after Community Discharge from Skilled Nursing Facilities: 
The Role of Medicaid Home and Community-Based Services. Health Serv 
Res, 56: 16-16. https://doi.org/10.1111/1475-6773.13737.
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(2) Overview of Measure
    This measure, which was finalized for adoption under the SNF QRP 
(81 FR 52021 through 52029), reports a SNF's risk-standardized rate of 
Medicare FFS residents who are discharged to the community following a 
SNF stay, do not have an unplanned readmission to an acute care 
hospital or LTCH in the 31 days following discharge to community, and 
remain alive during the 31 days following discharge to community. 
Community, for this measure, is defined as home or selfcare, with or 
without home health services. We proposed to adopt this measure 
beginning with the FY 2027 program year. We note that including this 
measure in the FY 2027 program year provides advanced notice for 
facilities to prepare for the inclusion of this measure in the SNF VBP 
Program. This also provides the necessary time to incorporate the 
operational processes associated with including this two-year measure 
in the SNF VBP Program.
(a) Interested Parties and TEP Input
    In considering the selection of this measure for the SNF VBP 
Program, we reviewed the developmental history of the measure, which 
employed a transparent process that provided interested parties and 
national experts the opportunity to provide pre-rulemaking input. Our 
measure development contractor convened a TEP, which was strongly 
supportive of the importance of measuring discharge to community 
outcomes and implementing the measure, Discharge to Community PAC SNF 
QRP in the SNF QRP. The panel provided input on the technical 
specifications of this measure, including the feasibility of 
implementing the measure, as well as the overall measure reliability 
and validity. We refer readers to the FY 2017 SNF PPS final rule (81 FR 
52023), as well as a summary of the TEP proceedings available on the 
PAC Quality Initiatives Downloads and Videos website available at 
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos for additional information.
(b) MAP Review
    The DTC PAC SNF measure was included in the publicly available 
``List of Measures Under Consideration for December 1, 2021,'' \270\ 
and the MAP supported the DTC PAC SNF measure for rulemaking for the 
SNF VBP Program. We refer readers to the final MAP report available at 
https://www.qualityforum.org/Publications/2022/03/MAP_2021-2022_Considerations_for_Implementing_Measures_Final_Report_-_Clinicians,_Hospitals,_and_PAC-LTC.aspx.
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    \270\ https://www.cms.gov/files/document/measures-under-consideration-list-2021-report.pdf.
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(3) Data Sources
    We proposed to use data from the Medicare FFS claims and Medicare 
eligibility files to calculate this measure. We will use data from the 
``Patient Discharge Status Code'' on Medicare FFS claims to determine 
whether a resident was discharged to a community setting for 
calculation of this measure. The eligibility files provide information 
such as date of birth, date of death, sex, reasons for Medicare 
eligibility, periods of Part A coverage, and periods in the

[[Page 47578]]

Medicare FFS program. The data elements from the Medicare FFS claims 
are those basic to the operation of the Medicare payment systems and 
include data such as date of admission, date of discharge, diagnoses, 
procedures, indicators for use of dialysis services, and indicators of 
whether the Part A benefit was exhausted. The inpatient claims data 
files contain patient-level PAC and other hospital records. SNFs will 
not need to report additional data for us to calculate this 
measure.\271\
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    \271\ https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/Downloads/Measure-Specifications-for-FY17-SNF-QRP-Final-Rule.pdf.
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    We refer readers to the FY 2017 SNF PPS final rule where we adopted 
the DTC measure for use in the SNF QRP (81 FR 52021 through 52029). In 
that rule, we provided an analysis related to the accuracy of using the 
``Patient Discharge Status Code'' in determining discharge to a 
community setting. Specifically, in all PAC settings, we tested the 
accuracy of determining discharge to a community setting using the 
``Patient Discharge Status Code'' on the PAC claim by examining whether 
discharge to community coding based on PAC claim data agreed with 
discharge to community coding based on PAC assessment data. We found 
agreement between the two data sources in all PAC settings, ranging 
from 94.6 percent to 98.8 percent. Specifically, in the SNF setting, 
using 2013 data, we found 94.6 percent agreement in discharge to 
community codes when comparing discharge status codes on claims and the 
Discharge Status (A2100) on the Minimum Data Set (MDS) 3.0 discharge 
assessment, when the claims and MDS assessment had the same discharge 
date. We further examined the accuracy of the ``Patient Discharge 
Status Code'' on the PAC claim by assessing how frequently discharges 
to an acute care hospital were confirmed by follow-up acute care 
claims. We discovered that 88 percent to 91 percent of IRF, LTCH, and 
SNF claims with acute care discharge status codes were followed by an 
acute care claim on the day of, or day after, PAC discharge. We believe 
these data support the use of the claims ``Patient Discharge Status 
Code'' for determining discharge to a community setting for this 
measure. In addition, this measure can feasibly be implemented in the 
SNF VBP Program because all data used for measure calculation are 
derived from Medicare FFS claims and eligibility files, which are 
already available to us.
(4) Inclusion and Exclusion Criteria
    We proposed that the DTC PAC SNF measure will use the same 
specifications under the SNF VBP Program as the Discharge to 
Community--PAC SNF QRP measure used in the SNF QRP, which are available 
at https://www.cms.gov/files/zip/snf-qrp-measure-calculations-and-reporting-users-manual-v301-addendum-effective-10-01-2020.zip. The 
target population for the measure is the group of Medicare FFS 
residents who are admitted to a SNF and are not excluded for the 
reasons listed in this paragraph. The measure exclusion criteria are 
determined by processing Medicare claims and eligibility data to 
determine whether the individual exclusion criteria are met. All 
measure exclusion criteria are based on administrative data. Only SNF 
stays that are preceded by a short-term acute care stay in the 30 days 
prior to the SNF admission date are included in the measure. Stays 
ending in transfers to the same level of care are excluded. The measure 
excludes residents for which the following conditions are true:
     Age under 18 years;
     No short-term acute care stay within the 30 days preceding 
SNF admission;
     Discharges to a psychiatric hospital;
     Discharges against medical advice;
     Discharges to disaster alternative care sites or Federal 
hospitals;
     Discharges to court/law enforcement;
     Residents discharged to hospice and those with a hospice 
benefit in the post-discharge observation window;
     Residents not continuously enrolled in Part A FFS Medicare 
for the 12 months prior to the post-acute admission date, and at least 
31 days after post-acute discharge date;
     Residents whose prior short-term acute care stay was for 
non-surgical treatment of cancer;
     Post-acute stays that end in transfer to the same level of 
care;
     Post-acute stays with claims data that are problematic 
(for example, anomalous records for stays that overlap wholly or in 
part, or are otherwise erroneous or contradictory);
     Planned discharges to an acute or LTCH setting;
     Medicare Part A benefits exhausted;
     Residents who received care from a facility located 
outside of the U.S., Puerto Rico or a U.S. territory; and
     Swing Bed Stays in Critical Access Hospitals.
    This measure also excludes residents who had a long-term nursing 
facility stay in the 180 days preceding their hospitalization and SNF 
stay, with no intervening community discharge between the long-term 
nursing facility stay and qualifying hospitalization.
(5) Risk-Adjustment
    The measure is risk-adjusted for variables including demographic 
and eligibility characteristics, such as age and sex, principal 
diagnosis, types of surgery or procedures from the prior short-term 
acute care stay, comorbidities, length of stay and intensive care 
utilization from the prior short-term acute care stay, ventilator 
status, ESRD status, and dialysis, among other variables. For 
additional technical information about the measure, including 
information about the measure calculation, risk-adjustment, and 
denominator exclusions, we refer readers to the document titled, Final 
Specifications for SNF QRP Quality Measures and Standardized Patient 
Assessment Data Elements, 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 note that we proposed to use the 
technical information and specifications found in this document for 
purposes of calculating this measure in the SNF VBP Program.
(6) Measure Calculation
    We proposed to adopt the DTC PAC SNF measure for the SNF VBP 
Program for FY 2027 and subsequent years. This measure is calculated 
using 2 years of data. Since Medicare FFS claims data are already 
reported to the Medicare program for payment purposes, and Medicare 
eligibility files are also available, SNFs will not be required to 
report any additional data to us for calculation of this measure.
(a) Numerator
    The measure numerator is the risk-adjusted estimate of the number 
of residents who are discharged to the community, do not have an 
unplanned readmission to an acute care hospital or LTCH in the 31-day 
post-discharge observation window, and who remain alive during the 
post-discharge observation window. This estimate starts with the 
observed discharges to community and is risk-adjusted for patient/
resident characteristics and a statistical estimate of the facility 
effect beyond case-mix. A patient/resident who is discharged to the 
community is considered to have an unfavorable outcome if they have a 
subsequent unplanned readmission to an acute care hospital or LTCH in 
the post-discharge

[[Page 47579]]

observation window, which includes the day of discharge and the 31 days 
following day of discharge. Discharge to community is determined based 
on the ``Patient Discharge Status Code'' from the PAC claim. Discharge 
to community is defined as discharge to home or self-care with or 
without home health services, which includes the following Patient 
Discharge Status Codes: 01 Discharged to home or self-care (routine 
discharge); 06 Discharged/transferred to home under care of organized 
home health service organization; 81 Discharged to home or self-care 
with a planned acute care hospital readmission; and 86 Discharged/
transferred to home under care of organized home health service 
organization with a planned acute care hospital inpatient readmission. 
Residents who are discharged to the community are also considered to 
have an unfavorable outcome if they die in the post-discharge window, 
which includes the day of discharge and the 31 days following day of 
discharge. Death in the post-discharge window is identified based on 
date of death from Medicare eligibility files.
(b) Denominator
    The denominator for the DTC PAC SNF measure is the risk-adjusted 
expected number of discharges to community. This estimate includes 
risk-adjustment for patient/resident characteristics with the facility 
effect removed. The ``expected'' number of discharges to community is 
the predicted number of risk-adjusted discharges to community if the 
same residents were treated at the average facility appropriate to the 
measure.
(7) Confidential Feedback Reports and Public Reporting
    We refer readers to the FY 2017 SNF PPS final rule (81 FR 52006 
through 52007) for discussion of our policy to provide quarterly 
confidential feedback reports to SNFs on their measure performance. We 
also refer readers to the FY 2022 SNF PPS final rule (86 FR 42516 
through 42517) for a summary of our two-phase review and corrections 
policy for SNFs' quality measure data. Furthermore, we refer readers to 
the FY 2018 SNF PPS final rule (82 FR 36622 through 36623) and the FY 
2021 SNF PPS final rule (85 FR 47626) where we finalized our policy to 
publicly report SNF measure performance information under the SNF VBP 
Program on the Provider Data Catalog website currently hosted by HHS 
and available at https://data.cms.gov/provider-data/. We proposed to 
update and redesignate the confidential feedback report and public 
reporting policies, which are currently codified at Sec.  413.338(e)(1) 
through (3) to Sec.  413.338(f), to include the DTC PAC SNF measure.
    We invited public comment on this proposal to adopt the DTC PAC SNF 
measure beginning with the FY 2027 SNF VBP program year. We received 
the following comments and provide our responses:
    Comment: Many commenters supported our proposal to adopt the DTC 
PAC SNF measure, noting its endorsement by NQF, its use in other 
quality programs, and its usefulness as an indicator of health 
outcomes. A few commenters recommended that we modify the measure to 
include post-discharge ER and observation visits within 31 days because 
they could be indicators of premature discharge from the SNF. One 
commenter suggested that we include assisted living and personal care 
homes as community settings for the measure. One commenter expressed 
concern about the length of time between baseline, performance, and 
payment periods and suggested that facilities would benefit from real-
time, actionable quality data. Another commenter suggested that we 
include those nursing home residents discharged back to the same 
nursing home in the measure's calculation. One commenter also suggested 
that we monitor how the measure will affect SNFs that care for patients 
experiencing homelessness.
    Response: We agree the measure is an important indicator of 
quality. We appreciate commenters' recommendations regarding 
adjustments to the measure specifications and we will take this into 
consideration in future rulemaking.
    Comment: Some commenters opposed our proposal to adopt the DTC PAC 
SNF measure. One commenter noted that not all Medicare beneficiaries 
are able to return home, that the measure may disadvantage those 
residents that continue to need SNF care to maintain functions or slow 
declines or deterioration in function, and that the measure only 
captures fee-for-service Medicare beneficiaries. Another commenter 
recommended that we consider a measure that assesses care coordination 
between SNFs and post-SNF care, while another commenter worried that 
the DTC PAC SNF measure may penalize SNFs based on whether a patient 
complied with discharge instructions and services.
    Response: As discussed in the proposed rule (87 FR 22774 through 
22776), returning patients to their previous levels of independence and 
functioning is a key goal of post-acute care and an important indicator 
for patients and families. When we convened a TEP for this measure's 
inclusion in the SNF QRP, experts agreed with this assessment. 
Additionally, as discussed in the proposed rule (87 FR 22775), this 
measure addresses multiple components including cognitive capacity, 
physical ability, social support as home, and other actionable 
elements, incentivizing providers to continue improving care in these 
various domains. Although we agree that not all residents will be able 
to return home or will follow all discharge instructions, the 
variability in current rates of the measure among different SNFs 
indicate that there is room for improvement. This measure is risk 
adjusted for several variables, including principal diagnosis. This 
measure should not disadvantage patients that continue to need SNF care 
to maintain functioning as it includes readmissions within 30 days of 
discharge. Thus, providers will not be incentivized to discharge 
patients inappropriately. Lastly, this measure is calculated using 
Medicare FFS claims data, which does not require SNFs to report any 
additional data. Including residents for which claims data is not 
currently available would add considerable data burden to SNFs. We will 
consider whether to address care coordination among SNFs for the SNF 
VBP Program in future rulemaking.
    Comment: Some commenters offered technical comments on the measure. 
One commenter stated that an unplanned readmission post-SNF discharge 
may not be the best measure of whether a discharge was successful. A 
few commenters suggested that we consider using the discharge planning 
process or discharge to a lower level of care instead of discharge to 
communities, noting that not all admissions are appropriate for 
community discharge. One commenter also requested clarification on 
whether we plan to adjust the measure for COVID-19.
    Response: As noted above, we recognize that not all admissions are 
appropriate for community discharge, but discharge to the community is 
an important goal for residents and families, as well as a key 
indicator of care. The measure is risk adjusted and has several 
exclusions to ensure that the appropriate population is being measured. 
Additionally, this is an NQF endorsed measure and varying performance 
rates observed among SNFs for this measure suggest that it is 
actionable. This measure also adjusts for principal diagnosis.

[[Page 47580]]

    After considering the public comments, we are finalizing our 
proposal to adopt the DTC PAC SNF measure (NQF #3481) beginning with 
the FY 2027 SNF VBP program year as proposed.

C. SNF VBP Performance Periods and Baseline Periods

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. We also refer readers 
to the FY 2022 SNF PPS final rule, where we finalized our proposal to 
use FY 2019 data for the FY 2024 baseline period (86 FR 42512 through 
42513).
2. Revised Baseline Period for the FY 2025 SNF VBP Program
    Under the policy finalized in the FY 2019 SNF PPS final rule (83 FR 
39277 through 39278), the baseline period for the SNFRM for the FY 2025 
program year will be FY 2021. However, as more fully described in the 
proposed rule (87 FR 22764 through 22765), we have determined that the 
significant decrease in SNF admissions, regional variability in COVID-
19 case rates, and changes in hospitalization patterns associated with 
the PHE for COVID-19 in FY 2021 has impacted SNFRM validity and 
reliability. Because the baseline period for this measure is used to 
calculate the performance standards under the SNF VBP Program, we 
stated that we were concerned about using COVID-19 impacted data for 
the FY 2025 baseline period for scoring and payment purposes.
    Therefore, we proposed to use a baseline period of FY 2019 for the 
FY 2025 program year. We stated that we believe using data from this 
period will provide sufficiently valid and reliable data for evaluating 
SNF performance that can be used for FY 2025 scoring. We also proposed 
to select this revised data period because it captures a full year of 
data, including any seasonal effects.
    As stated in the proposed rule, we considered using FY 2020 as the 
baseline period for the FY 2025 program. However, under the ECE, SNF 
qualifying claims for a 6-month period in FY 2020 (January 1, 2020 
through 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 a FY 2020 baseline period.
    We also considered using FY 2022 as the baseline period for the FY 
2025 program year, which will be the baseline period for the FY 2026 
program year for the SNFRM under the previously established policy for 
adopting baseline periods for future years (83 FR 39277). However, it 
is operationally infeasible for us to calculate performance standards 
using a FY 2022 baseline period for the FY 2025 program year because 
performance standards must be published at least 60 days prior to the 
start of the performance period, currently planned as FY 2023, as 
required under section 1888(h)(3)(C) of the Act. We invited public 
comment on this proposal to update the baseline period for the FY 2025 
SNF VBP Program. We received the following comments and provide our 
responses:
    Comment: Some commenters supported the proposal to revise the 
baseline period for the FY 2025 program year. One commenter recommended 
that we consider the accuracy of pre- and post-pandemic quality 
comparisons to ensure that SNFs are not penalized based on factors out 
of their control, such as lower occupancy levels, patient case-mix, and 
staffing concerns.
    Response: We appreciate the support. We will continue to consider 
for future rulemaking whether and how to take the lasting impacts of 
the COVID-19 pandemic into consideration.
    After considering the public comments, we are finalizing our 
proposal to update the baseline period to FY 2019 for the FY 2025 SNF 
VBP Program.
3. Performance Periods and Baseline Periods for the SNF HAI Measure 
Beginning With the FY 2026 SNF VBP Program
a. Performance Period for the SNF HAI Measure for the FY 2026 SNF VBP 
Program and Subsequent Years
    As stated in the proposed rule, in considering the appropriate 
performance period for the SNF HAI measure for the FY 2026 SNF VBP 
Program, we recognized that we must balance the length of the 
performance period with our need to calculate valid and reliable 
performance scores and announce the resulting payment adjustments no 
later than 60 days prior to the program year involved, in accordance 
with section 1888(h)(7) of the Act. In our testing of the measure, we 
found that a 1-year performance period produced moderately reliable 
performance scores. We refer readers to the SNF HAI Measure Technical 
Report for further information on measure testing results, available at 
https://www.cms.gov/files/document/snf-hai-technical-report.pdf. In 
addition, we refer readers to the FY 2017 SNF PPS final rule (81 FR 
51998 through 51999) for a discussion of the factors we should consider 
when specifying performance periods for the SNF VBP Program, as well as 
our stated preference for 1-year performance periods. Based on these 
considerations, we believed that a 1-year performance period for the 
SNF HAI measure is operationally feasible for the SNF VBP Program and 
provides sufficiently accurate and reliable SNF HAI measure rates and 
resulting performance scores.
    We also recognized that we must balance our desire to specify a 
performance period for a fiscal year as close to the fiscal year's 
start date as possible to ensure clear connections between quality 
measurement and value-based payment with our need to announce the net 
results of the Program's adjustments to Medicare payments not later 
than 60 days prior to the fiscal year involved, in accordance with 
section 1888(h)(7) of the Act. In considering these constraints, and in 
alignment with the SNFRM, we believed that a performance period that 
occurs 2 fiscal years prior to the applicable fiscal program year is 
most appropriate for the SNF HAI measure.
    For these reasons, we proposed to adopt a 1-year performance period 
for the SNF HAI measure. In addition, we proposed to adopt FY 2024 
(October 1, 2023 through September 30, 2024) as the performance period 
for the SNF HAI measure for the FY 2026 SNF VBP Program.
    In alignment with the current Program measure, we also proposed 
that, for the SNF HAI measure, we would automatically adopt the 
performance period for a SNF VBP program year by advancing the 
beginning of the performance period by 1 year from the previous program 
year's performance period.
    We invited public comment on these proposals related to the 
performance period for the SNF HAI measure for the FY 2026 program year 
and subsequent years. We received one public comment related to the 
performance periods for the SNF HAI measure. We summarized that comment 
and provide our response below in section VIII.C.3.b. of this final 
rule. As stated in that section, we are finalizing our proposal to 
adopt FY 2024

[[Page 47581]]

(October 1, 2023 through September 30, 2024) as the performance period 
for the SNF HAI measure for the FY 2026 program year and finalizing our 
proposal to adopt performance periods for the SNF HAI measure for 
subsequent program years by advancing the beginning of the performance 
period by 1 year from the previous program year's performance period.
b. Baseline Period for the SNF HAI Measure for the FY 2026 SNF VBP 
Program and Subsequent Years
    We discussed in the FY 2016 SNF PPS final rule (80 FR 46422) that, 
as with other Medicare quality programs, we generally adopt a baseline 
period for a fiscal year that occurs prior to the performance period 
for that fiscal year to establish measure performance standards. In the 
FY 2016 SNF PPS final rule (80 FR 46422), we also discussed our intent 
to adopt baseline periods that are as close as possible in duration as 
the performance period for a fiscal year as well as our intent to 
seasonally align baseline periods with the performance period to avoid 
any effects on quality measurement that may result from tracking SNF 
performance during different times in a year. Therefore, to align with 
the proposed performance period length for the SNF HAI measure, we 
believed a 1-year baseline period is most appropriate for the SNF HAI 
measure.
    We also recognized that we are required to calculate and announce 
performance standards no later than 60 days prior to the start of the 
performance period, as required by section 1888(h)(3)(C) of the Act. 
Therefore, in alignment with the SNFRM baseline period, we believed 
that a baseline period that occurs 4 fiscal years prior to the 
applicable fiscal program year, and 2 fiscal years prior to the 
performance period, is most appropriate for the SNF HAI measure and 
provides sufficient time to calculate and announce performance 
standards prior to the start of the performance period.
    For these reasons, we proposed to adopt a 1-year baseline period 
for the SNF HAI measure. In addition, we proposed to adopt FY 2022 
(October 1, 2021 through September 30, 2022) as the baseline period for 
the SNF HAI measure for the FY 2026 SNF VBP Program.
    In alignment with the current Program measure, we also proposed 
that for the SNF HAI measure, we would automatically adopt the baseline 
period for a SNF VBP program year by advancing the beginning of the 
baseline period by 1 year from the previous program year's baseline 
period.
    We invited public comment on these proposals related to the 
baseline period for the SNF HAI measure for the FY 2026 program year 
and subsequent years. We received the following comment related to the 
SNF HAI measure performance and baseline periods and provide our 
response:
    Comment: One commenter supported the performance and baseline 
periods for the SNF HAI measure as proposed.
    Response: We thank the commenter for its support of the proposed 
performance and baseline periods for the SNF HAI measure.
    After considering the public comment, we are finalizing our 
proposal to adopt FY 2024 (October 1, 2023 through September 30, 2024) 
as the performance period for the SNF HAI measure for the FY 2026 
program year and finalizing our proposal to adopt performance periods 
for the SNF HAI measure for subsequent program years by advancing the 
beginning of the performance period by 1 year from the previous program 
year's performance period. Additionally, we are finalizing our proposal 
to adopt FY 2022 (October 1, 2021 through September 30, 2022) as the 
baseline period for the SNF HAI measure for the FY 2026 program year 
and finalizing our policy to adopt baseline periods for the SNF HAI 
measure for subsequent program years by advancing the beginning of the 
baseline period by 1 year from the previous program year's baseline 
period.
4. Performance Periods and Baseline Periods for the Total Nursing Hours 
per Resident Day Staffing Measure Beginning With the FY 2026 SNF VBP 
Program
a. Performance Period for the Total Nursing Hours per Resident Day 
Staffing Measure for the FY 2026 SNF VBP Program and Subsequent Years
    As stated in the proposed rule, in considering the appropriate 
performance period for the Total Nurse Staffing measure for the FY 2026 
SNF VBP Program, we recognized that we must balance the length of the 
performance period with our need to calculate valid and reliable 
performance scores and announce the resulting payment adjustments no 
later than 60 days prior to the program year involved, in accordance 
with section 1888(h)(7) of the Act. The Total Nurse Staffing measure is 
currently reported on a quarterly basis for the Nursing Home Five-Star 
Quality Rating System. For purposes of inclusion in the SNF VBP 
Program, we proposed that the measure rate would be calculated on an 
annual basis. To do so, we proposed to aggregate the quarterly measure 
rates using a simple mean of the available quarterly case-mix adjusted 
scores in a 1-year performance period. We conducted testing of the 
measure and found that the quarterly measure rate and resident census 
are stable across quarters. Further, an unweighted yearly measure 
aligns the SNF VBP Program rates with rates reported on the Provider 
Data Catalog website currently hosted by HHS, available at https://data.cms.gov/provider-data/. It can also be easily understood by, and 
is transparent to, the public. In addition, we refer readers to the FY 
2017 SNF PPS final rule (81 FR 51998 through 51999) for discussion of 
the factors we should consider when specifying performance periods for 
the SNF VBP Program as well as our preference for 1-year performance 
periods. Based on these considerations, we believed that a 1-year 
performance period for the Total Nurse Staffing measure is 
operationally feasible under the SNF VBP Program and provides 
sufficiently accurate and reliable Total Nurse Staffing measure rates 
and resulting performance scores.
    We also recognized that we must balance our desire to specify a 
performance period for a fiscal year as close to the fiscal year's 
start date as possible to ensure clear connections between quality 
measurement and value-based payment with our need to announce the net 
results of the Program's adjustments to Medicare payments not later 
than 60 days prior to the fiscal year involved, in accordance with 
section 1888(h)(7) of the Act. In considering these constraints, and in 
alignment with the SNFRM, we believed that a performance period that 
occurs 2 fiscal years prior to the applicable fiscal program year is 
most appropriate for the Total Nurse Staffing measure.
    For these reasons, we proposed to adopt a 1-year performance period 
for the Total Nurse Staffing measure. In addition, we proposed to adopt 
FY 2024 (October 1, 2023 through September 30, 2024) as the performance 
period for the Total Nurse Staffing measure for the FY 2026 SNF VBP 
program year.
    In alignment with the current Program measure, we also proposed 
that, for the Total Nurse Staffing measure, we would automatically 
adopt the performance period for a SNF VBP program year by advancing 
the beginning of the performance period by 1 year from the previous 
program year's performance period.
    We invited public comment on these proposals related to the 
performance period for the Total Nurse Staffing

[[Page 47582]]

measure for the FY 2026 program year and subsequent years. We received 
the following comment and provide our response:
    Comment: One commenter recommended that we use the calendar year 
rather than the fiscal year for the Total Nurse Staffing measure's 
performance period. The commenter stated that because data for this 
measure are collected and reported quarterly starting 45 days after the 
end of the quarter, a calendar year schedule provides CMS with enough 
time to announce the Program's adjustments to Medicare payments not 
later than 60 days prior to the fiscal year involved.
    Response: We believe that using the fiscal year as the performance 
period for the Total Nurse Staffing measure is important to maintain 
consistency with our other measures in the SNF VBP Program that use 
fiscal year performance and baseline periods. All of the measures 
proposed thus far for the SNF VBP program rely on fiscal year 
measurement periods, and we intend to use measures relying on fiscal 
year periods in the Program in the future to the extent such alignment 
is feasible and practical. We believe that this type of alignment, 
where possible, helps stakeholders understand their quality measurement 
obligations and reporting periods more easily.
    After considering the public comments, we are finalizing our 
proposal to adopt FY 2024 (October 1, 2023 through September 30, 2024) 
as the performance period for the Total Nurse Staffing measure for the 
FY 2026 program year. We are also finalizing our proposal to adopt 1-
year performance periods for the Total Nurse Staffing measure for 
subsequent program years as proposed by advancing the beginning of the 
performance period by 1 year from the previous program year's 
performance period.
b. Baseline Period for the Total Nursing Hours per Resident Day 
Staffing Measure for the FY 2026 SNF VBP Program and Subsequent Years
    We discussed in the FY 2016 SNF PPS final rule (80 FR 46422) that, 
as with other Medicare quality programs, we generally adopt a baseline 
period for a fiscal year that occurs prior to the performance period 
for that fiscal year to establish measure performance standards. In the 
FY 2016 SNF PPS final rule (80 FR 46422), we also discussed our intent 
to adopt baseline periods that are as close as possible in duration as 
the performance period for a fiscal year, as well as our intent to 
seasonally align baseline periods with the performance period to avoid 
any effects on quality measurement that may result from tracking SNF 
performance during different times in a year. Therefore, to align with 
the proposed performance period length for the Total Nurse Staffing 
measure, we believed a 1-year baseline period is most appropriate.
    We also recognized that we are required to calculate and announce 
performance standards no later than 60 days prior to the start of the 
performance period, as required by section 1888(h)(3)(C) of the Act. 
Therefore, in alignment with the SNFRM baseline period, we believed 
that a baseline period that occurs 4 fiscal years prior to the 
applicable fiscal program year, and 2 fiscal years prior to the 
performance period, is most appropriate for the Total Nurse Staffing 
measure and provides sufficient time to calculate and announce 
performance standards prior to the start of the performance period.
    For these reasons, we proposed to adopt a 1-year baseline period 
for the Total Nurse Staffing measure. In addition, we proposed to adopt 
FY 2022 (October 1, 2021 through September 30, 2022) as the baseline 
period for the Total Nurse Staffing measure for the FY 2026 SNF VBP 
Program.
    In alignment with the current Program measure, we also proposed 
that for the Total Nurse Staffing measure, we would automatically adopt 
the baseline period for a SNF VBP program year by advancing the 
beginning of the baseline period by 1 year from the previous program 
year's baseline period.
    We invited public comment on these proposals related to the 
baseline period for the Total Nurse Staffing measure for the FY 2026 
program year and subsequent years. We received the following comments 
and provide our responses:
    Comment: One commenter supported our proposal to use FY 2022 as the 
baseline period for the Total Nurse Staffing measure.
    Response: We thank the commenter for their support of the proposed 
baseline period for the Total Nurse Staffing measure.
    Comment: One commenter expressed concern about using any FY 2021 
data for the Total Nurse Staffing measure, stating that during the PHE 
for COVID-19, many nursing facilities reported severe staffing 
shortages. The commenter suggested that we adopt a different baseline 
period focusing on the year with the highest staffing levels 
nationally, on average.
    Response: We clarify that we proposed to adopt FY 2022 as the 
baseline period for the Total Nurse Staffing measure for the FY 2026 
SNF VBP Program. We also believe that adopting a baseline period for a 
fiscal year that occurs prior to the performance period for that fiscal 
year gives us enough time to establish the measure's performance 
standards in our quality programs. Further, we note that we are 
required to calculate and announce performance standards no later than 
60 days prior to the start of the performance period, as required by 
section 1888(h)(3)(C) of the Act.
    Comment: One commenter opposed our proposal to use FY 2022 as the 
baseline period for the Total Nurse Staffing measure, stating that we 
should instead use FY 2019 to assess performance from prior to the 
COVID-19 pandemic.
    Response: We believe that additional policies we adopted in 
response to the challenges presented by the COVID-19 pandemic, 
including quality measure suppression, sufficiently mitigate the 
effects of the PHE on quality measurements and allow us to adopt FY 
2022 as the baseline period.
    After considering the public comments, we are finalizing our 
proposal to adopt FY 2022 (October 1, 2021 through September 30, 2022) 
as the baseline period for the Total Nurse Staffing measure for the FY 
2026 program year. We are also finalizing our proposal to adopt 1-year 
baseline periods for the Total Nurse Staffing measure for subsequent 
program years as proposed by advancing the beginning of the baseline 
period by 1 year from the previous program year's baseline period.
5. Performance Periods and Baseline Periods for the DTC PAC Measure for 
SNFs for the FY 2027 SNF VBP Program and Subsequent Years
a. Performance Period for the DTC PAC SNF Measure for the FY 2027 SNF 
VBP Program and Subsequent Years
    Under the SNF QRP, The Discharge to Community--PAC SNF QRP measure 
has a reporting period that uses 2 consecutive years to calculate the 
measure (83 FR 39217 through 39272). In alignment with the reporting 
period that applies to the measure under the SNF QRP, we proposed to 
adopt a 2-year performance period for the DTC PAC SNF measure under the 
SNF VBP Program.
    We proposed to align our performance period with the performance 
period for the measure used by the SNF QRP to maintain streamlined data 
requirements and reduce any confusion for participating SNFs. In 
addition, we proposed to adopt FY 2024 through FY 2025 (October 1, 2023 
through September 30, 2025) as the performance

[[Page 47583]]

period for the DTC PAC SNF measure for the FY 2027 SNF VBP Program.
    We also proposed that for the DTC PAC SNF measure, we would 
automatically adopt the performance period for a SNF VBP program year 
by advancing the beginning of the performance period by 1 year from the 
previous program year's performance period.
    We invited public comment on our proposals related to the 
performance period for the DTC PAC SNF measure for FY 2027 program year 
and subsequent years. We received the following comment and provide our 
response:
    Comment: One commenter supported the proposed performance period 
for the DTC PAC SNF measure.
    Response: We thank the commenter for their support of the proposed 
performance period for the DTC PAC SNF measure.
    After considering the public comment, we are finalizing our 
proposal to adopt FY 2024 through FY 2025 (October 1, 2023 through 
September 30, 2025) as the performance period for the DTC PAC SNF 
measure for the FY 2027 program year. We are also finalizing our 
proposal to adopt performance periods for the DTC PAC SNF measure for 
subsequent program years by advancing the beginning of the performance 
period by 1 year from the previous program year's performance period.
b. Baseline Period for the DTC PAC SNF Measure for the FY 2027 SNF VBP 
Program Year and Subsequent Years
    We discussed in the FY 2016 SNF PPS final rule (80 FR 46422) that, 
as with other Medicare quality programs, we generally adopt a baseline 
period for a fiscal year that occurs prior to the performance period 
for that fiscal year to establish measure performance standards. In the 
FY 2016 SNF PPS final rule (80 FR 46422), we also discussed our intent 
to adopt baseline periods that are as close as possible in duration as 
the performance period for a fiscal year, as well as our intent to 
seasonally align baseline periods with the performance period to avoid 
any effects on quality measurement that may result from tracking SNF 
performance during different times in a year. Therefore, to align with 
the proposed performance period length for the DTC PAC SNF measure, we 
believed a 2-year baseline period is most appropriate for this measure.
    We also recognized that we are required to calculate and announce 
performance standards no later than 60 days prior to the start of the 
performance period, as required by section 1888(h)(3)(C) of the Act. 
Therefore, we believed that a baseline period that begins 6 fiscal 
years prior to the applicable fiscal program year, and 3 fiscal years 
prior to the performance period, is most appropriate for the DTC PAC 
SNF measure and provides sufficient time to calculate and announce 
performance standards prior to the start of the performance period.
    For these reasons, we proposed to calculate the performance period 
for the DTC PAC SNF measure using 2 consecutive years of data. In 
addition, we proposed to adopt FY 2021 through FY 2022 (October 1, 2020 
through September 30, 2022) as the baseline period for the DTC PAC SNF 
measure for the FY 2027 SNF VBP Program.
    In alignment with the current Program measure, we also proposed 
that for the DTC PAC SNF measure, we would automatically adopt the 
baseline period for a SNF VBP program year by advancing the beginning 
of the baseline period by 1 year from the previous program year's 
baseline period.
    We invited public comment on these proposals related to the 
baseline period for the DTC PAC SNF measure for FY 2027 program year 
and subsequent years. We received the following comment and provide our 
response:
    Comment: One commenter expressed concern about adopting a baseline 
period for the DTC PAC SNF measure that includes FY 2021 through FY 
2022 data, stating that many beneficiaries discharged during those 
years may have been discharged early due to COVID-19 fears. The 
commenter noted that the associated census declines compared to pre-PHE 
practices may adversely affect facilities' outcomes. The commenter also 
encouraged us to delay implementation of the DTC PAC SNF measure until 
the baseline period does not include quality data from other measures 
that have been suppressed.
    Response: We continue to believe that using FY 2021 through FY 2022 
as the baseline period for the DTC PAC SNF measure for the FY 2027 
program year is most appropriate and would help ensure clear 
connections between the quality measurement and value-based incentive 
payments. As stated in the proposed rule, we note that the continuation 
of the PHE for COVID-19 did not necessarily impact all measures in the 
SNF setting specifically, but measures related to hospital care, 
including the SNFRM, may be impacted because of how closely the surge 
in COVID-19 cases was related to the surge in COVID-19 related hospital 
admissions. We do not believe the DTC PAC SNF measure data has been 
affected in this way. In addition, we believe the additional policies 
we adopted in response to the challenges presented by the PHE for 
COVID-19, including quality measure suppression, sufficiently mitigate 
the effects of the PHE on quality measurement. As we have done with the 
SNFRM, we will continue to assess whether the PHE has impacted the DTC 
PAC SNF measure data. Further, we note that SNFs that do not meet the 
case minimum for the DTC PAC SNF measure during the baseline period due 
to potential census declines associated with the PHE for COVID-19 will 
continue to have the opportunity to be scored on achievement during the 
applicable performance period.
    After considering the public comment, we are finalizing our 
proposal to adopt FY 2021 through FY 2022 (October 1, 2020 through 
September 30, 2022) as the baseline period for the DTC PAC SNF measure 
for the FY 2027 program year. We are also finalizing our proposal to 
adopt baseline periods for the DTC PAC SNF measure for subsequent 
program years by advancing the beginning of the baseline period by 1 
year from the previous program year's baseline period.

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 2023 performance standards in the FY 2021 SNF PPS final rule (85 
FR 47625) and adopted the final numerical values for the FY 2024 
performance standards in the FY 2022 SNF PPS final rule (86 FR 42513). 
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 did not propose any changes to these performance standard 
policies in the 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

[[Page 47584]]

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. To improve the clarity of this policy, we proposed to amend 
the definition of ``Performance standards'' and redesignate it as Sec.  
413.338(a)(12), then add additional detail about the correction policy 
at Sec.  413.338(d)(6).
    We invited public comment on our changes to the text at Sec.  
413.338(a)(12) and (d)(6). However, we did not receive any public 
comments on this topic. Accordingly, we are finalizing our proposal to 
update the performance standards correction policy in our regulations.
3. Performance Standards for the FY 2025 Program Year
    As discussed in section VIII.C.2. of this final rule, we are 
finalizing our proposal to use FY 2019 data as the baseline period for 
the FY 2025 program year. Based on this updated baseline period and our 
previously finalized methodology for calculating performance standards 
(81 FR 51996 through 51998), the final numerical values for the FY 2025 
program year performance standards are shown in Table 17.
[GRAPHIC] [TIFF OMITTED] TR03AU22.017

E. SNF VBP Performance Scoring

1. Background
    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 ECE policy. Finally, 
we refer readers to the FY 2022 SNF PPS final rule (86 FR 42513 through 
42515), where we adopted for FY 2022 a special scoring and payment 
policy due to the impact of the PHE for COVID-19.
2. Special Scoring Policy for the FY 2023 SNF VBP Program Due to the 
Impact of the PHE for COVID-19
    In the FY 2023 SNF PPS proposed rule, we proposed to suppress the 
SNFRM for the FY 2023 program year due to the impacts of the PHE for 
COVID-19. Specifically, for FY 2023 scoring, we proposed that, for all 
SNFs participating in the FY 2023 SNF VBP Program, we will use data 
from the previously finalized performance period (FY 2021) and baseline 
period (FY 2019) to calculate each SNF's RSRR for the SNFRM. Then, we 
will assign all SNFs a performance score of zero. This will result in 
all participating SNFs receiving an identical performance score, as 
well as an identical incentive payment multiplier. We also proposed 
that SNFs that do not meet the case minimum for the SNFRM for FY 2023 
(see VIII.E.3.b. of this final rule) will be excluded from the Program 
for FY 2023. SNFs will not be ranked for the FY 2023 SNF VBP Program. 
We also proposed to update our regulation text at Sec.  413.338(i) to 
codify this scoring policy for FY 2023. As we noted in section 
VIII.B.1. of this final rule, our goal is to continue the use of 
measure data for scoring and payment adjustment purposes beginning with 
the FY 2024 program year.
    We invited public comment on our proposal to use a special scoring 
policy for the FY 2023 Program year. We received the following comments 
and provide our responses:
    Comment: Some commenters supported our proposals to adopt special 
scoring and payment policies for FY 2023.
    Response: We thank the commenters for their support.
    Comment: Some commenters opposed our proposal to adopt a special 
scoring and payment policy for FY 2023. Some commenters noted that 
awarding all SNFs a performance score of zero does not create a value-
based incentive payment as required by statute and further stated that 
CMS is required to rank SNFs for the fiscal year. Another commenter 
stated that the special scoring and payment policy will cause all SNFs 
to experience a payment reduction, which they believed is inconsistent 
with the statute. One commenter recommended that we give all SNFs an 
exemption from the payment reduction for FY 2023, while other 
commenters recommended that we adopt a 70 percent payback percentage 
for the FY 2023 Program year. One commenter suggested that we grant a 
full exemption from the adjusted Federal per diem rate reduction 
required by section 1888(h)(6) of the Act.
    Response: We stated in the proposed rule our belief that for 
purposes of scoring and payment adjustments under the SNF VBP Program, 
the SNFRM as impacted by the COVID-19 PHE should not be attributed to 
the participating facility positively or negatively. We believe that 
using SNFRM data that has been impacted by the PHE due to COVID-19 
could result in performance scores that do not accurately reflect SNF 
performance for making national comparisons and ranking purposes. Due 
to the SNFRM being the only quality measure currently authorized for 
use in the FY 2023 SNF VBP, suppression of the SNFRM would mean we 
would not be able to calculate SNF performance scores for any SNF nor 
to differentially rank SNFs. Therefore, we are finalizing a change to 
the scoring methodology to assign all SNFs a performance score of zero 
and effectively rank all SNFs equally in the FY 2023 SNF VBP program 
year.
    After considering the public comments, we are finalizing our 
proposal to adopt a special scoring policy for the FY 2023 program year 
as proposed and codifying it at Sec.  413.338(i) of our regulations.

[[Page 47585]]

3. Case Minimum and Measure Minimum Policies
a. Background
    Section 111(a)(1) of Division CC of the CAA amended section 
1888(h)(1) of the Act by adding paragraph (h)(1)(C), which established 
criteria for excluding SNFs from the SNF VBP Program. Specifically, 
with respect to payments for services furnished on or after October 1, 
2022, paragraph (h)(1)(C) precludes the SNF VBP Program from applying 
to a SNF for which there are not a minimum number of cases (as 
determined by the Secretary) for the measures that apply to the SNF for 
the performance period for the applicable fiscal year, or a minimum 
number of measures (as determined by the Secretary) that apply to the 
SNF for the performance period for the applicable fiscal year.
    To implement this provision, we proposed to establish case and 
measure minimums that SNFs must meet to be included in the Program for 
a given program year. These case and measure minimum requirements will 
serve as eligibility criteria for determining whether a SNF is included 
in, or excluded from, the Program for a given program year. Inclusion 
in the Program for a program year means that a SNF would receive a SNF 
performance score and would be eligible to receive a value-based 
incentive payment. Exclusion from the Program for a program year means 
that, for the applicable fiscal year, a SNF would not be subject to the 
requirements under Sec.  413.338 and would also not be subject to a 
payment reduction under Sec.  413.337(f). Instead, the SNF would 
receive its full Federal per diem rate under Sec.  413.337 for the 
applicable fiscal year.
    We proposed to establish a case minimum for each SNF VBP measure 
that SNFs must meet during the performance period for the program year. 
We also proposed that SNFs must have a minimum number of measures 
during the performance period for the applicable program year in order 
to be eligible to participate in the SNF VBP Program for that program 
year. We proposed to codify these changes to the applicability of the 
SNF VBP Program beginning with FY 2023 at Sec.  413.338(b).
    We proposed that the case and measure minimums would be based on 
statistical accuracy and reliability, such that only SNFs that have 
sufficient data are included in the SNF VBP Program for a program year. 
The purpose of these restrictions is to apply program requirements only 
to SNFs for which we can calculate reliable measure rates and SNF 
performance scores.
    Because the case and measure minimum policies will ensure that SNFs 
participate in the Program for a program year only if they have 
sufficient data for calculating accurate and reliable measure rates and 
SNF performance scores, we do not believe there is a continuing need to 
apply the low-volume adjustment (LVA) policy beginning with FY 2023. 
Accordingly, in the FY 2023 SNF PPS proposed rule (87 FR 22783), we 
proposed to remove the LVA policy from the Program beginning with the 
FY 2023 program year. As discussed further in section VIII.E.5. of this 
final rule, we are finalizing our proposal to remove the LVA policy.
    We did not receive any public comments on our proposal to codify 
the changes to the applicability of the SNF VBP Program beginning with 
FY 2023 at Sec.  413.338(b), and therefore, we are finalizing this 
proposal.
b. Case Minimum During a Performance Period for the SNFRM Beginning 
With the FY 2023 SNF VBP Program Year
    We proposed that beginning with the FY 2023 program year, SNFs must 
have a minimum of 25 eligible stays for the SNFRM during the applicable 
1-year performance period in order to be eligible to receive a score on 
that measure in the SNF VBP Program.
    As stated in the proposed rule, we believed this case minimum 
requirement for the SNFRM is appropriate and consistent with the 
findings of reliability tests conducted for the SNFRM, and it is also 
consistent with the case threshold we have applied under the LVA 
policy. The reliability testing results, which combined CY 2014 and 
2015 SNFRM files, indicated that a minimum of 25 eligible stays for the 
SNFRM produced sufficiently reliable measure rates. In addition, the 
testing results found that approximately 85 percent of all SNFs met the 
25 eligible stay minimum during the CY 2015 testing period. While 
excluding 15 percent of SNFs may seem high, we continue to believe that 
the 25 eligible stay minimum for the SNFRM appropriately balances 
quality measure reliability with our desire to allow as many SNFs as 
possible to participate in the Program. For further details on the 
measure testing, we refer readers to the minimum eligible stay 
threshold analysis for the SNFRM available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/Other-VBPs/SNFRM-Reliability-Testing-Memo.pdf.
    We also believed this case minimum requirement for the SNFRM 
ensures that those SNFs included in the Program receive a sufficiently 
accurate and reliable SNF performance score. However, we also proposed 
changes to our scoring and payment policies for the FY 2023 SNF VBP 
Program in the proposed rule. If finalized, beginning with the FY 2023 
SNF VBP program year, any SNF that does not meet this case minimum 
requirement for the SNFRM during the applicable performance period will 
be excluded from the Program for the affected program year, provided 
there are no other measures specified for the affected program year. 
Those SNFs will not be subject to any payment reductions under the 
Program and instead will receive their full Federal per diem rate.
    We invited public comment on our proposal to adopt a case minimum 
requirement for the SNFRM beginning with the FY 2023 SNF VBP program 
year. We received the following comments and provide our responses:
    Comment: One commenter supported the proposed case minimum for the 
SNFRM based on the evidence and rationale provided.
    Response: We thank the commenter for support of the case minimum 
for the SNFRM.
    Comment: Some commenters urged CMS to increase the case minimums 
adopted in the Program to reach a reliability standard of 0.7, which 
they stated could be achieved with a case minimum of 60. The commenters 
stated that adopting longer performance and baseline periods would 
mitigate the effects of this recommendation on excluded SNFs based on 
the higher minimum number of cases.
    Response: Our reliability testing results demonstrated that 
increasing the case minimum threshold to 50 eligible stays would 
slightly increase the measure's reliability but would approximately 
double the number of SNFs that would not meet this higher case 
minimum.\272\ Therefore, we continue to believe that a 25-eligible stay 
minimum for the SNFRM best balances quality measure reliability with 
our desire to allow as many SNFs as possible to participate in the 
Program. As we discussed in the FY 2023 SNF PPS proposed rule (87 FR 
22781), reliability testing for the SNFRM indicated that a 25 eligible 
stay minimum produces sufficiently reliable measure rates. In addition, 
our analyses found that approximately 85 percent of all SNFs met the 25 
eligible stay

[[Page 47586]]

minimum during the CY 2015 testing period.
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    \272\ https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/Other-VBPs/SNFRM-Reliability-Testing-Memo.pdf.
---------------------------------------------------------------------------

    We also disagree with the commenters' suggestion to adopt longer 
performance and baseline periods as a method for increasing measure 
reliability. As we discussed in the FY 2016 SNF PPS final rule (80 FR 
46422) and the FY 2017 SNF PPS final rule (81 FR 51998 through 51999), 
we continue to believe that 1-year performance and baseline periods 
provide sufficient levels of data accuracy and reliability for scoring 
performance on the SNFRM, while also allowing us to link SNF 
performance on the measure as closely as possible to the payment year 
to ensure clear connections between quality measurement and value-based 
payment. We also believe that adopting longer performance and baseline 
periods would create a time gap that would hinder our ability to 
clearly connect the quality data with SNFs' value-based payment, as 
well as limit the actionability of such quality data for SNFs to make 
quality improvements.
    After considering the public comments, we are finalizing our 
proposal to adopt a 25 eligible stay minimum requirement during a 
performance period for the SNFRM beginning with the FY 2023 program 
year.
c. Case Minimums During a Performance Period for the SNF HAI, Total 
Nurse Staffing, and DTC PAC SNF Measures
    In the FY 2023 SNF PPS proposed rule (87 FR 22767 through 22777), 
we proposed to adopt the SNF HAI and Total Nurse Staffing measures 
beginning with the FY 2026 program year, as well as the DTC PAC SNF 
measure beginning with the FY 2027 program year.
    For the SNF HAI measure, we proposed that SNFs must have a minimum 
of 25 eligible stays during the applicable 1-year performance period in 
order to be eligible to receive a score on the measure. As stated in 
the proposed rule, we believed this case minimum requirement for the 
SNF HAI measure is appropriate and consistent with the findings of 
measure testing analyses. For example, testing results indicated that a 
25 eligible stay minimum produced moderately reliable measure rates for 
purposes of public reporting under the SNF QRP. In addition, testing 
results found that 85 percent of SNFs met the 25 eligible stay minimum 
for public reporting under the SNF QRP. We believed these case minimum 
standards for public reporting purposes are also appropriate standards 
for establishing a case minimum for this measure under the SNF VBP 
Program. In addition, we believed these testing results for the 25 
eligible stay minimum support our objective, which is to establish case 
minimums that appropriately balance quality measure reliability with 
our continuing desire to score as many SNFs as possible on this 
measure. For further details on SNF HAI measure testing for the SNF 
QRP, we refer readers to the SNF HAI Measure Technical Report available 
at https://www.cms.gov/files/document/snf-hai-technical-report.pdf.
    For the Total Nurse Staffing measure, we proposed that SNFs must 
have a minimum of 25 residents, on average, across all available 
quarters during the applicable 1-year performance period in order to be 
eligible to receive a score on the measure. As discussed in the 
proposed rule, we tested three potential case minimums for this 
measure: a 25-resident minimum, a minimum of one quarter of PBJ data, 
and a minimum of two quarters of PBJ data. Over 94 percent of SNFs 
satisfied the case minimum under all three alternatives tested. There 
were very minimal differences observed between the case minimums 
tested, and this finding held for most subgroups tested as well, 
including rural SNFs, large SNFs, and those SNFs serving the highest 
proportion of dually eligible beneficiaries. The only notable observed 
difference occurred within small SNFs, defined as those with fewer than 
46 beds as a proxy for size. About 90 percent of small SNFs reported 
two quarters of PBJ data, and about 92 percent of small SNFs reported 
one quarter of PBJ data, but only about 63 percent of small SNFs 
satisfied the 25-resident minimum, indicating that even after two 
quarters of successful PBJ reporting there was a substantial proportion 
of small SNFs (about 27 percent) reporting minimal numbers of 
residents, calling into question the utility of their limited staffing 
data. After considering these alternatives, we determined that the 
proposed 25-resident minimum best balances quality measure reliability 
with our desire to score as many SNFs as possible on this measure. We 
also noted that the 25-resident minimum for this measure aligns with 
the case minimums we are proposing for the other proposed measures.
    Further, for the DTC PAC SNF measure, we proposed that SNFs must 
have a minimum of 25 eligible stays during the applicable 2-year 
performance period in order to be eligible to receive a score on the 
measure. As stated in the proposed rule, we believed this case minimum 
requirement for the DTC PAC SNF measure is appropriate and consistent 
with the findings of measure testing analyses. Analyses conducted by 
CMS contractors found that a 25 eligible stay minimum produced good to 
excellent measure score reliability. In addition, analyses using 2015 
through 2016 Medicare FFS claims data found that 94 percent of SNFs met 
the 25 eligible stay minimum during the 2-year performance period. We 
believed these testing results for the 25 eligible stay minimum support 
our objective, which is to establish case minimums that appropriately 
balance quality measure reliability with our continuing desire to score 
as many SNFs as possible on this measure. The complete measure testing 
results conducted by our contractors that we included as part of the 
documentation supporting our request for NQF to endorse the measure are 
available at https://www.qualityforum.org/QPS/3481.
    We invited public comment on our proposal to adopt case minimums 
for the SNF HAI, Total Nurse Staffing, and DTC PAC SNF measures. We 
received the following comments and provide our responses:
    Comment: One commenter supported the proposed case minimums for the 
SNF HAI, DTC PAC SNF, and Total Nurse Staffing measures as proposed.
    Response: We thank the commenter for support of the case minimums 
for the SNF HAI, DTC PAC SNF, and Total Nurse Staffing measures.
    Comment: One commenter recommended increasing the proposed minimum 
number of stays to at least 60 to mitigate the effects of a larger 
Medicare Advantage population and nursing homes that have had to limit 
or reduce admissions due to staff shortages.
    Response: We continue to believe that a 25 eligible stay minimum 
for the SNF HAI measure; a 25-resident minimum, on average, across all 
available quarters for the Total Nurse Staffing measure; and a 25 
eligible stay minimum for the DTC PAC SNF measure best balance quality 
measure reliability with our desire to score as many SNFs as possible 
on these measures. We recognize the growing Medicare Advantage 
population as well as the impact of staff shortages on the ability of a 
SNF to admit residents and we intend to continue assessing these topics 
in the future.
    After considering the public comments, we are finalizing our 
proposal to adopt a 25 eligible stay minimum for the SNF HAI measure; a 
25-resident minimum, on average, across all available quarters for the 
Total Nurse Staffing measure; and a 25

[[Page 47587]]

eligible stay minimum for the DTC PAC SNF measure.
d. Measure Minimums for the FY 2026 and FY 2027 Program Years
    We proposed to adopt measure minimums for the FY 2026 and FY 2027 
program years. Under these policies, only SNFs that have the minimum 
number of measures applicable to the program year would be eligible for 
inclusion in the Program for that program year.
    In the proposed rule, we proposed to adopt two new quality measures 
(SNF HAI and Total Nurse Staffing measures) beginning with the FY 2026 
Program. If finalized, the SNF VBP Program would consist of three 
quality measures in FY 2026 (SNF Readmission Measure, SNF HAI, and 
Total Nurse Staffing measures). We proposed that for FY 2026, SNFs must 
have the minimum number of cases for two of these three measures during 
the performance period to receive a performance score and value-based 
incentive payment. SNFs that do not meet these minimum requirements 
will be excluded from the FY 2026 program and will receive their full 
Federal per diem rate for that fiscal year. Under these minimum 
requirements, we estimated that approximately 14 percent of SNFs would 
be excluded from the FY 2026 Program. Alternatively, if we required 
SNFs to have the minimum number of cases for all three measures during 
the performance period, approximately 21 percent of SNFs would be 
excluded from the FY 2026 Program. We also assessed the consistency of 
value-based incentive payment adjustment factors, or incentive payment 
multipliers (IPMs), between time periods as a proxy for performance 
score reliability under the different measure minimum options. The 
testing results indicated that the reliability of the SNF performance 
score would be relatively consistent across the different measure 
minimum requirements. Based on these testing results, we believed the 
minimum of two out of three measures for FY 2026 best balances SNF 
performance score reliability with our desire to ensure that as many 
SNFs as possible can receive a performance score and value-based 
incentive payment.
    We also proposed to adopt an additional quality measure (DTC PAC 
SNF measure) beginning with the FY 2027 Program. If finalized, the SNF 
VBP Program would consist of four quality measures in FY 2027 (SNF 
Readmission Measure, SNF HAI, Total Nurse Staffing, and DTC PAC SNF 
measures). We proposed that for FY 2027, SNFs must have the minimum 
number of cases for three of the four measures during a performance 
period to receive a performance score and value-based incentive 
payment. SNFs that do not meet these minimum requirements will be 
excluded from the FY 2027 program and will receive their full Federal 
per diem rate for that fiscal year. Under these minimum requirements, 
we estimated that approximately 16 percent of SNFs would be excluded 
from the FY 2027 Program. Alternatively, if we required SNFs to have 
the minimum number of cases for all four measures, we estimated that 
approximately 24 percent of SNFs would be excluded from the FY 2027 
Program. We also assessed the consistency of incentive payment 
multipliers (IPMs) between time periods as a proxy for performance 
score reliability under the different measure minimum options. The 
testing results indicated that the reliability of the SNF performance 
score for the FY 2027 program year would be relatively consistent 
across the different measure minimum requirements. Based on these 
testing results, we believed the minimum of three out of four measures 
for FY 2027 best balances SNF performance score reliability with our 
desire to ensure that as many SNFs as possible can receive a 
performance score and value-based incentive payment.
    Under these measure minimums, we estimated that 14 percent of SNFs 
would be excluded from the Program for the FY 2026 program year, but 
that the excluded SNFs would, as a whole, provide care to approximately 
2 percent of the total number of eligible SNF stays. Similarly, for the 
FY 2027 Program, we estimated that 16 percent of SNFs would be excluded 
from the Program, but that the excluded SNFs, as a whole, provide care 
to approximately 2 percent of the total number of eligible SNF stays.
    We invited public comment on our proposal to adopt measure minimums 
for the FY 2026 and FY 2027 SNF VBP program years. We received the 
following comment and provide our response:
    Comment: One commenter supported the measure minimums for FY 2026 
and FY 2027 as proposed.
    Response: We thank the commenter for support of the measure 
minimums for the FY 2026 and FY 2027 program years.
    After considering the public comment, we are finalizing our 
proposal for FY 2026 that SNFs must have the minimum number of cases 
for two of the three measures during the performance period to receive 
a performance score and value-based incentive payment, and finalizing 
our proposal for FY 2027 that SNFs must have the minimum number of 
cases for three of the four measures during a performance period to 
receive a performance score and value-based incentive payment.
4. Updated Scoring Policy for SNFs Without Sufficient Baseline Period 
Data Beginning With the FY 2026 Program Year
    In the FY 2019 SNF PPS final rule (83 FR 39278), we finalized a 
policy to score SNFs based only on their achievement during the 
performance period for any program year for which they do not have 
sufficient baseline period data, which we defined as SNFs with fewer 
than 25 eligible stays during the baseline period for a fiscal year. We 
codified this policy at Sec.  413.338(d)(1)(iv) of our regulations.
    We continue to be concerned that measuring SNF performance on a 
given measure for which the SNF does not have sufficient baseline 
period data may result in unreliable improvement scores for that 
measure and, as a result, unreliable SNF performance scores. However, 
the current policy was designed for a SNF VBP Program with only one 
measure. As we continue to add measures to the Program, we aim to 
maintain the reliability of our SNF performance scoring. Therefore, we 
proposed to update our policy beginning with the FY 2026 program year. 
Under this updated policy, we will not award improvement points to a 
SNF on a measure for a program year if the SNF has not met the case 
minimum for that measure during the baseline period that applies to the 
measure for the program year. That is, if a SNF does not meet a case 
minimum threshold for a given measure during the applicable baseline 
period, that SNF will only be eligible to be scored on achievement for 
that measure during the performance period for that measure for the 
applicable fiscal year.
    For example, if a SNF has fewer than the minimum of 25 eligible 
stays during the applicable 1-year baseline period for the SNF HAI 
measure for FY 2026, that SNF would only be scored on achievement 
during the performance period for the SNF HAI measure for FY 2026, so 
long as that SNF meets the case minimum for that measure during the 
applicable performance period.
    We proposed to codify this update in our regulation text at Sec.  
413.338(e)(1)(iv).
    We invited public comment on this proposal to update the policy for 
scoring SNFs that do not have sufficient baseline period data. We 
received the following comment and provide our response:

[[Page 47588]]

    Comment: One commenter supported our proposal to not award 
improvement points to SNFs that do not meet the case minimums during 
the applicable baseline periods.
    Response: We thank the commenter for support of this proposal.
    After considering the public comment, we are finalizing our 
proposal to update the policy for scoring SNFs that do not have 
sufficient baseline period data such that we would not award 
improvement points to a SNF on a measure for a program year if that SNF 
does not meet the case minimum for that measure during the baseline 
period that applies to the measure for the program year. We are also 
finalizing our proposal to codify this update at Sec.  
413.338(e)(1)(iv) of our regulations.
5. Removal of the LVA Policy From the SNF VBP Program Beginning With 
the FY 2023 Program Year
    In the FY 2019 SNF PPS final rule (83 FR 39278 through 39280), we 
finalized our LVA policy, which provides an adjustment to the Program's 
scoring methodology to ensure low-volume SNFs receive sufficiently 
reliable performance scores for the SNF readmission measure. In that 
final rule, we also codified the LVA policy in Sec.  413.338(d)(3) of 
our regulations. As we discussed in the FY 2019 SNF PPS final rule, we 
found that the reliability of the SNFRM measure rates and resulting 
performance scores were adversely affected if SNFs had fewer than 25 
eligible stays during the performance period for a program year (83 FR 
39279). Therefore, we believed that assigning a performance score that 
results in a value-based incentive payment amount that is equal to the 
adjusted Federal per diem rate that the SNF would have received in the 
absence of the Program, to any SNF with fewer than 25 eligible stays 
for the SNFRM during the performance period, was the most appropriate 
adjustment for ensuring reliable performance scores.
    However, as discussed in the proposed rule, we no longer believe 
the LVA policy is necessary because we are now required under the 
statute to have case and measure minimum policies for the SNF VBP 
Program, and those policies will achieve the same payment objective as 
the LVA policy. Therefore, we proposed to remove the LVA Policy from 
the SNF VBP Program's scoring methodology beginning with the FY 2023 
program year. With the removal of the LVA policy, the total amount 
available for a fiscal year will no longer be increased as appropriate 
for each fiscal year to account for the assignment of a performance 
score to low-volume SNFs. We proposed to update the total amount 
available for a fiscal year to 60 percent of the total amount of the 
reduction to the adjusted SNF PPS payments for that fiscal year, as 
estimated by us, in our regulations atSec.  413.338(c)(2)(i). We 
proposed to update the LVA policy at Sec.  413.338(d)(3) to reflect its 
removal from the Program.
    We invited public comment on our proposal to remove the LVA policy 
from the SNF VBP Program beginning with the FY 2023 program year. We 
received the following comment and provide our response:
    Comment: One commenter supported our proposed removal of the LVA 
policy.
    Response: We thank the commenter for their support of this 
proposal.
    After considering the public comment, we are finalizing our 
proposal to remove the LVA policy from the SNF VBP Program beginning 
with the FY 2023 program year and finalizing our proposal to update our 
regulations at Sec.  413.338(d)(3) to reflect its removal from the 
Program.
6. Updates to the SNF VBP Scoring Methodology Beginning in the FY 2026 
Program Year
a. Background
    In the FY 2017 SNF PPS final rule (81 FR 52000 through 52005), we 
adopted a scoring methodology for the SNF VBP Program where we score 
SNFs on their performance on the SNFRM, award between zero and 100 
points to each SNF (with up to 90 points available for improvement) and 
award each SNF a SNF performance score consisting of the higher of its 
scores for achievement and improvement. The SNF performance score is 
then translated into a value-based incentive payment multiplier that 
can be applied to each SNF's Medicare claims during the SNF VBP Program 
year using an exchange function. Additionally, in the FY 2018 SNF PPS 
final rule (82 FR 36615), we adopted a clarification of our rounding 
policy in SNF VBP scoring to award SNF performance scores that are 
rounded to the nearest ten-thousandth of a point, or with no more than 
five significant digits to the right of the decimal point. We have also 
codified numerous aspects of the SNF VBP Program's policies in our 
regulations at Sec.  413.338, and our scoring policies appear in 
paragraph (d) of that section.
    We refer readers to the FY 2017 rule cited above for a detailed 
discussion of the SNF VBP Program's scoring methodology, public 
comments on the proposed policies, and examples of our scoring 
calculations.
b. Measure-Level Scoring Update
    We proposed to update our achievement and improvement scoring 
methodology to allow a SNF to earn a maximum of 10 points on each 
measure for achievement, and a maximum of nine points on each measure 
for improvement. For purposes of determining these points, we proposed 
to define the benchmark as the mean of the top decile of SNF 
performance on a measure during the baseline period and the achievement 
threshold as the 25th percentile of national SNF performance on a 
measure during the baseline period.
    We proposed to award achievement points to SNFs based on their 
performance period measure rate for each measure according to the 
following:
     If a SNF's performance period measure rate was equal to or 
greater than the benchmark, the SNF would be awarded 10 points for 
achievement.
     If a SNF's performance period measure rate was less than 
the achievement threshold, the SNF would receive zero points for 
achievement.
     If a SNF's performance period measure rate was equal to or 
greater than the achievement threshold, but less than the benchmark, we 
would award between zero and 10 points according to the following 
formula:
[GRAPHIC] [TIFF OMITTED] TR03AU22.018


[[Page 47589]]


    We also proposed to award improvement points to SNFs based on their 
performance period measure rate according to the following:
     If a SNF's performance period measure rate was equal to or 
lower than its baseline period measure rate, the SNF would be awarded 
zero points for improvement.
     If a SNF's performance period measure rate was equal to or 
higher than the benchmark, the SNF would be awarded nine points for 
improvement.
     If a SNF's performance period measure rate was greater 
than its baseline period measure rate but less than the benchmark, we 
would award between zero and nine points according to the following 
formula:
[GRAPHIC] [TIFF OMITTED] TR03AU22.019

    As proposed, we will score SNFs' performance on achievement and 
improvement for each measure and award them the higher of the two 
scores for each measure to be included in the SNF performance score, 
except in the instance that the SNF does not meet the case minimum 
threshold for the measure during the applicable baseline period, in 
which case we proposed that the SNF would only be scored on 
achievement, as discussed in section VIII.E.4. of this final rule. As 
discussed in the following section of this final rule, we will then sum 
each SNFs' measure points and normalize them to arrive at a SNF 
performance score that ranges between zero and 100 points. We believe 
that this policy appropriately recognizes the best performers on each 
measure and reserves the maximum points for their performance levels 
while also recognizing that improvement over time is important and 
should also be rewarded.
    We further proposed that this change would apply beginning with the 
FY 2026 SNF VBP program year. As proposed, all measures in the expanded 
SNF VBP Program would be weighted equally, as we believe that an equal 
weighting approach is simple for participating SNFs to understand and 
assigns significant scoring weight (that is, 33.33 percentage points if 
a SNF has sufficient data on all three measures proposed for FY 2026) 
to each measure topic covered by the expanded SNF VBP Program. However, 
as we consider whether we should propose to adopt additional measures, 
we also intend to consider whether we should group the measures into 
domains and weight them, similar to what we do under the Hospital VBP 
Program scoring methodology.
    We view this change to the measure-level scoring as a necessary 
update to the SNF VBP Program's scoring methodology to incorporate 
additional quality measures and to allow us to add more measures in the 
future. We also proposed to codify these updates to our scoring 
methodology in our regulation text by revising the heading for 
paragraph (d) and adding paragraph (e)(1) at Sec.  413.338.
    We invited public comment on this proposal. We received the 
following comments and provide our responses:
    Comment: Some commenters supported our proposed measure-level 
scoring updates. One commenter recommended adding decimal gradations to 
the nine and 10-point scales to allow additional variation and ensure 
that providers are not being disadvantaged by the scoring methodology.
    Response: We did not propose to round the measure-level scores that 
result from use of the scoring formulas specified earlier in this 
section, and we will award measure-level scores with decimal gradations 
as the commenter suggested.
    Comment: One commenter opposed the use of the mean of the top 
decile of SNFs' performance during the baseline period as the 
benchmark, stating that only about 5 percent of SNFs can meet such 
performance levels. The commenter argued that this methodology 
discriminates against certain types of SNFs, such as urban SNFs and 
those that provide care to larger minority populations. The commenter 
recommended placing the benchmark at the 10th decile of SNFs' 
performance and presenting analytical findings to a TEP for review and 
connection to clinical goals.
    Response: We thank the commenter for this feedback. While the 
commenter is correct that only a small percentage of SNFs are likely to 
qualify for the maximum number of points available on any given measure 
in a SNF VBP Program year, we believe this policy appropriately rewards 
top performers on the Program's quality measures. In our view, a value-
based purchasing program correctly provides incentives to all 
participating providers to achieve the best performance possible on the 
Program's measures. We note further that all SNFs whose performance on 
a quality measure exceeds the 25th percentile of performance from the 
baseline period can receive achievement points on a quality measure 
under the Program's scoring methodology. Further, all SNFs whose 
performance improves between the baseline and performance period can 
quality for improvement points under the Program's methodology. We 
therefore do not agree with the commenter's view that our performance 
standards policy discriminates against any SNFs, and we continue to 
believe that the performance standards policy, including the definition 
of the term ``benchmark,'' appropriately balances our desire to reward 
top performers while also recognizing SNFs whose performance improves 
over time.
    Comment: One commenter stated that we should consider adopting a 
form of risk-adjustment for SNF VBP scores, noting that some facilities 
do not have enough data to calculate some quality measures.
    Response: We thank the commenter for this suggestion. However, we 
are finalizing policies in this final rule that are designed to 
accommodate SNFs that do not have enough data to calculate some quality 
measures, specifically including a minimum number of measures required 
to receive a SNF performance score. We believe that this policy 
appropriately balances our desire to allow as much participation in the 
Program as possible while ensuring that those SNFs' performance scores 
are based on sufficiently reliable data.
    Comment: One commenter stated that we should review adjustments and 
incentives for clinically complex residents, stating that capturing 
multiple diagnoses and residents' overarching socioeconomic needs is 
important for care coordination.
    Response: We agree with the commenter that clinically complex 
residents may present challenges to SNFs attempting to provide the best 
possible care, and we will continue

[[Page 47590]]

examining this topic as part of our monitoring and evaluation efforts. 
However, we would like to clarify that we already incorporate clinical 
risk adjustment and certain exclusions in the specifications for many 
of our quality measures. The SNFRM accounts for variation across SNFs 
in both case mix and patient characteristics.\273\ The SNF HAI measure 
incorporates risk adjustment that estimates both the average predictive 
effect of resident characteristics across all SNFs, and the degree to 
which each SNF has an effect on the outcome that differs from that of 
the average SNF.\274\ Finally, the DTC PAC measure includes a 
statistical model for risk adjustment that estimates both the average 
predictive effect of the resident characteristics across all facilities 
and the degree to which each facility has an effect on discharge to 
community that differs from that of the average facility, as well as 
exclusions from the measure's calculations for situations where 
discharge to the community may not be clinically appropriate.\275\ We 
also refer readers to the FY 2023 SNF PPS proposed rule for our 
discussion of risk-adjustments for the SNF HAI measure (87 FR 22770), 
the DTC PAC SNF measure (87 FR 22776), and case-mix adjustment for the 
Total Nurse Staffing measure (87 FR 22774).
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    \273\ See Skilled Nursing Facility 30-Day All-Cause Readmission 
Measure (SNFRM) NQF #2510: All-Cause Risk-Standardized Readmission 
Measure Technical Report Supplement--2019 Update. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/SNF-VBP/Downloads/SNFRM-TechReportSupp-2019-.pdf.
    \274\ See 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-0.
    \275\ See 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.
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    After considering the public comments, we are finalizing our 
proposal to adopt a measure-level scoring policy beginning with the FY 
2026 program year as described above, and to update our regulations at 
Sec.  413.338 to reflect the new policy.
c. Normalization Policy
    We continue to believe that awarding SNF performance scores out of 
a total of 100 points helps interested parties more easily understand 
the performance evaluation that we provide through the SNF VBP Program. 
Therefore, we believe that continuing to award SNF performance scores 
out of 100 points would help interested parties understand the revised 
scoring methodology and would allow the scoring methodology to 
accommodate additional measures in the future without more 
methodological changes.
    Therefore, we considered how we could construct the SNF performance 
score such that the scores continue to range between zero and 100 
points. We considered our past experience in our VBP programs, 
specifically including our experience with the Hospital VBP Program, 
where we award between zero and 10 points to participating providers 
for their performance on each measure, and to arrive at a Total 
Performance Score that ranges between zero and 100 points regardless of 
the number of measures on which the hospital has sufficient data, we 
normalize hospitals' scores. We believe the Hospital VBP Program's 
success in comprehensible measure-level scoring provides a strong model 
for the expanded SNF VBP Program.
    We proposed to adopt a ``normalization'' policy for SNF performance 
scores under the expanded SNF VBP Program, effective in the FY 2026 
program year and subsequent years. As proposed, we will calculate a raw 
point total for each SNF by adding up the SNF's score on each of the 
measures. For example, a SNF that met the case minimum to receive a 
score on three quality measures would receive a score between zero to 
30 points, while a SNF that met the case minimum to receive a score on 
two quality measures would receive a score between zero to 20 points. 
We will then normalize the raw point totals by converting them to a 
100-point scale, with the normalized values being awarded as the SNF 
performance score. For example, we would normalize a SNF's raw point 
total of 27 points out of 30 by converting that total to a 100-point 
scale, with the result that the SNF would receive a SNF performance 
score of 90.
    In addition to allowing us to maintain a 100-point total 
performance score scale, this policy enables us to adopt additional 
quality measures for the program without making further changes to the 
scoring methodology. If, for example, we proposed to adopt a total of 
seven quality measures in the future, the normalization policy would 
enable us to continue to award SNF performance scores on a 100-point 
scale, even though the maximum raw point total would be 70 points.
    We view this normalization policy as a useful update to the SNF VBP 
Program's scoring methodology to accommodate additional quality 
measures and to ensure that the public understands the SNF performance 
scores that we award. We also proposed to codify these updates to our 
scoring methodology by adding paragraph (e)(2) to our regulation text 
at Sec.  413.338.
    We invited public comment on our proposal. However, we did not 
receive any comments specific to the normalization policy. Therefore, 
we are finalizing our proposal to adopt a normalization policy for SNF 
performance scores under the SNF VBP Program beginning with the FY 2026 
program year, and to update our regulations at Sec.  413.338 to reflect 
the new policy.

F. Adoption of a Validation Process for the SNF VBP Program Beginning 
With the FY 2023 Program Year

    Section 1888(h)(12) of the Act (as added by Division CC, section 
111(a)(4) of the Consolidated Appropriations Act, 2021 (Pub. L. 116-
120)), requires the Secretary to apply a process to validate SNF VBP 
program measures and data, as appropriate. We proposed to adopt a 
validation process for the Program beginning with the FY 2023 program 
year.
    For the SNFRM, we proposed that the process we currently use to 
ensure the accuracy of the SNFRM satisfies this statutory requirement. 
Information reported through claims for the SNFRM are validated for 
accuracy by Medicare Administrative Contractors (MACs) to ensure 
accurate Medicare payments. MACs use software to determine whether 
billed services are medically necessary and should be covered by 
Medicare, review claims to identify any ambiguities or irregularities, 
and use a quality assurance process to help ensure quality and 
consistency in claim review and processing. They conduct pre-payment 
and post-payment audits of Medicare claims, using both random selection 
and targeted reviews based on analyses of claims data. We proposed to 
codify these proposals for the FY 2023 SNF VBP in our regulation text 
at Sec.  413.338(j).
    We are considering additional validation methods that may be 
appropriate to include in the future for the SNF HAI, DTC PAC SNF, and 
Total Nurse Staffing measures, as well as for other new measures we may 
consider for the program, and for other SNF quality measures and 
assessment data. In the FY 2023 SNF PPS proposed rule (87 FR 22788 
through 22789), we requested public comment on potential future 
approaches for data validation in the Request for Information on the 
Validation of SNF Measures and Assessment Data.

[[Page 47591]]

    We invited public comment on our proposal to adopt a validation 
process for the SNF VBP Program beginning with the FY 2023 program 
year. We received the following comment and provide our response:
    Comment: One commenter supported our proposed approach to SNFRM 
validation.
    Response: We thank the commenter for their support.
    After considering the public comment, we are finalizing our 
proposal to adopt a validation process for the SNF VBP Program 
beginning with the FY 2023 program year as proposed and codifying it at 
Sec.  413.338(j) of our regulations.

G. SNF Value-Based Incentive Payments for FY 2023

    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).
    As discussed in the FY 2023 SNF PPS proposed rule, we proposed to 
suppress the SNFRM for the FY 2023 program year and assign all SNFs a 
performance score of zero, which will result in all participating SNFs 
receiving an identical performance score, as well as an identical 
incentive payment multiplier. We also proposed that we will not rank 
SNFs for FY 2023. We also proposed to reduce each participating SNF's 
adjusted Federal per diem rate for FY 2023 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 2023 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 considering our 
proposal to award a performance score of zero to all SNFs. We also 
proposed that those SNFs that do not meet the proposed case minimum for 
the SNFRM for FY 2023 will be excluded from the Program for FY 2023. We 
proposed to update Sec.  413.338(i) to reflect that this special 
scoring and payment policy will apply for FY 2023 in addition to FY 
2022. As noted in section VIII.B.1. of this final rule, our goal is to 
resume use of the scoring methodology we finalized for the program 
prior to the PHE beginning with the FY 2024 program year.
    We invited public comment on this proposed change to the SNF VBP 
Program's payment policy for the FY 2023 program year. However, we did 
not receive any public comments on this policy. We are therefore 
finalizing our proposal to adopt a special payment policy for the FY 
2023 program year and codifying it at Sec.  413.338(i) of our 
regulations.

H. Public Reporting on the Provider Data Catalog 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 Catalog 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 
interested parties with the necessary information to evaluate SNF's 
performance under the Program (82 FR 36623).
    As discussed in section VIII.B.1. of this final rule, we are 
finalizing our proposal to suppress the SNFRM for the FY 2023 program 
year due to the impacts of the PHE for COVID-19. Under this finalized 
policy, for all SNFs participating in the FY 2023 SNF VBP Program, we 
will use the performance period (FY 2021, October 1, 2020 through 
September 30, 2021) we adopted in the FY 2021 SNF PPS final rule (85 FR 
47624), as well as the previously finalized baseline period (FY 2019, 
October 1, 2018 through September 30, 2019) to calculate each SNF's 
RSRR for the SNFRM. We are also finalizing our proposal to assign all 
SNFs a performance score of zero. This will result in all participating 
SNFs receiving an identical performance score, as well as an identical 
incentive payment multiplier.
    While we will publicly report the SNFRM rates for the FY 2023 
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 2023 SNF VBP Program given 
the significant changes in SNF patient case volume and facility-level 
case-mix described earlier.
2. Changes to the Data Suppression Policy for Low-Volume SNFs Beginning 
With the FY 2023 SNF VBP Program Year
    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

[[Page 47592]]

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 this policy in the FY 2021 SNF PPS final rule (85 FR 47626) 
at Sec.  413.338(e)(3)(i) through (iii).
    As discussed in section VIII.B.1. of this final rule, we are 
finalizing our proposal to suppress the SNFRM for the FY 2023 program 
year, and we are finalizing a special scoring and payment policy for FY 
2023. In addition, as discussed in section VIII.E.3.b. of this final 
rule, we are finalizing our proposal to adopt a new case minimum that 
will apply to the SNFRM beginning with FY 2023, new case minimums that 
will apply to the SNF HAI and Total Nurse Staffing measures and a 
measure minimum that will apply beginning with FY 2026, a new case 
minimum that will apply to the DTC PAC SNF measure and a new measure 
minimum that will apply beginning with FY 2027. As a result of these 
policies, and in order to implement them for purposes of clarity and 
transparency in our public reporting, we proposed revising the data 
suppression policy as follows:
    (1) If a SNF does not have the minimum number of cases during the 
baseline period that applies to a measure for a program year, we would 
publicly report the SNF's measure rate and achievement score if the SNF 
had minimum number of cases for the measure during the performance 
period for the program year;
    (2) If a SNF does not have the minimum number of cases during the 
performance period that applies to a measure for a program year, we 
would not publicly report any information on the SNF's performance on 
that measure for the program year;
    (3) If a SNF does not have the minimum number of measures during 
the performance period for a program year, we would not publicly report 
any data for that SNF for the program year.
    We proposed to codify this policy at Sec.  413.338(f)(4).
    We invited public comment on these proposals. However, we did not 
receive any public comments on this topic. We are therefore finalizing 
our proposal to revise our data suppression policy and codify those 
revisions at Sec.  413.338(f)(4) of our regulations.

I. Requests for Comment Related to Future SNF VBP Program Expansion 
Policies

1. Requests for Comment on Additional SNF VBP Program Measure 
Considerations for Future Years
a. Request for Comment on Including a Staffing Turnover Measure in a 
Future SNF VBP Program Year
    In the FY 2022 SNF PPS final rule (86 FR 42507 through 42511), we 
summarized feedback from interested parties on our RFI related to 
potential future measures for the SNF VBP Program, including a specific 
RFI on measures that focus on staffing turnover. Specifically, we noted 
that we have been developing measures of staff turnover with data that 
are required to be submitted under section 1128I(g)(4) of the Act, with 
the goal of making the information publicly available. We stated that, 
through our implementation of the PBJ staffing data collection program, 
we will be reporting rates of employee turnover in the future (for more 
information on this program, see CMS memorandum QSO-18-17-NH \276\). We 
refer readers to the FY 2022 SNF PPS final rule for additional details 
on this RFI and a summary of the public comments we received (86 FR 
42507 through 42511).
---------------------------------------------------------------------------

    \276\ https://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/SurveyCertificationGenInfo/Downloads/QSO18-17-NH.pdf.
---------------------------------------------------------------------------

    Nursing staff turnover has long been identified as a meaningful 
factor in nursing home quality of care.\277\ Studies have shown a 
relationship between staff turnover and quality outcomes; for example, 
higher staff turnover is associated with an increased likelihood of 
receiving an infection control citation.\278\ The collection of 
auditable payroll-based daily staffing data through the PBJ system has 
provided an opportunity to calculate, compare, and publicly report 
turnover rates; examine facility characteristics associated with higher 
or lower turnover rates; and further measure the relationship between 
turnover and quality outcomes. For example, a recent study using PBJ 
data found that nursing staff turnover is higher than previously 
understood, variable across facilities, and correlated with 
organizational characteristics such as for-profit status, chain 
ownership, and higher Medicaid census.\279\ In addition, we have found 
that higher overall star ratings are associated with lower average 
staff turnover rates, suggesting that lower staff turnover rates are 
associated with higher overall nursing home quality.\280\
---------------------------------------------------------------------------

    \277\ Centers for Medicare and Medicaid Services. 2001 Report to 
Congress: Appropriateness of Minimum Nurse Staffing Ratios in 
Nursing Homes, Phase II. Baltimore, MD: Centers for Medicare and 
Medicaid Services. https://phinational.org/wp-content/uploads/legacy/clearinghouse/PhaseIIVolumeIofIII.pdf.
    \278\ Lacey Loomer, David C. Grabowski, Ashvin Gandhi, 
Association between Nursing Home Staff Turnover and Infection 
Control Citations, SSRN Electronic Journal, 10.2139/ssrn.3766377, 
(2020). https://onlinelibrary.wiley.com/doi/abs/10.1111/1475-6773.13877.
    \279\ Gandhi, A., Yu, H., & Grabowski, D., ``High Nursing 
Staff Turnover in Nursing Homes Offers Important Quality 
Information'' (2021) Health Affairs, 40(3), 384-391. doi:10.1377/
hlthaff.2020.00957. https://www.healthaffairs.org/doi/full/10.1377/hlthaff.2020.00957.
    \280\ https://www.cms.gov/files/document/qso-22-08-nh.pdf.
---------------------------------------------------------------------------

    In January of 2022, we began publicly reporting a staffing turnover 
measure on the Compare tool currently hosted by HHS, available at 
https://www.medicare.gov/care-compare, and this information will be 
included in the Nursing Home Five-Star Quality Rating System in July 
2022. We refer readers to the Nursing Home Staff Turnover and Weekend 
Staffing Levels Memo for additional information related to this measure 
at https://www.cms.gov/files/document/qso-22-08-nh.pdf. We believe 
staffing turnover is an important indicator of quality of care provided 
in nursing homes and SNFs. Additionally, in response to our RFI on a 
staffing turnover measure, interested parties strongly recommended that 
we consider measures of staffing turnover to assess patterns and 
consistency in staffing levels. As a part of our goals to build a 
robust and comprehensive measure set for the SNF VBP Program and in 
alignment with recommendations from interested parties, we stated our 
intent to propose to adopt a staffing turnover measure in the SNF VBP 
Program in the FY 2024 SNF PPS proposed rule. Specifically, the measure 
we intend to include in the SNF VBP Program is the percent of total 
nurse staff that have left the facility over the last year. Total nurse 
staff include RNs, LPNs, and nurse aides. More information on this 
measure, can be found in the Five-Star Rating Technical Users' Guide at 
https://www.cms.gov/medicare/provider-enrollment-and-certification/certificationandcomplianc/downloads/usersguide.pdf.
    The Biden-Harris Administration is committed to improving the 
quality of care in nursing homes. As stated in a fact sheet entitled 
``Protecting Seniors by Improving Safety and Quality of Care in the 
Nation's Nursing Homes,'' we are committed to strengthening the SNF VBP 
Program and have begun to measure and publish staff turnover and 
weekend staffing levels, metrics which

[[Page 47593]]

closely align with the quality of care provided in a nursing home. We 
stated our intent to propose new measures based on staffing adequacy, 
the resident experience, as well as how well facilities retain staff. 
Accordingly, we seek commenters' feedback on including the staff 
turnover measure that captures the percent of total nurse staff that 
have left the facility over the last year for the SNF VBP Program as 
currently specified or whether the measure should be revised before 
being proposed for inclusion in the SNF VBP Program.
    In addition, we are interested in whether we should explore the 
development of a composite measure that would capture multiple aspects 
of staffing, including both total nurse hours and the staff turnover 
measure rather than having separate but related measures related to 
nursing home staffing, such a measure could potentially replace the 
initial measure we intend to propose to include in SNF VBP for FY 2024. 
Preliminary analyses using the staff turnover data on the Medicare.gov 
Care Compare website have indicated that as the lower average staff 
turnover decreases, the overall star ratings for facilities increases, 
suggesting that lower turnover is associated with higher overall 
quality,\281\ and research has indicated that staff turnover has been 
linked with increased infection control issues.\282\ We believe it is 
important to capture and tie aspects of both staffing levels and 
staffing turnover to quality payment and welcome commenter's feedback 
for how to balance those goals under the SNF VBP Program. We are also 
interested to hear about actions SNFs may take or have taken to reduce 
staff turnover in their facilities, and for SNFs that did reduce staff 
turnover, the reduction's observed impact on quality of care. In 
particular, we are interested in best practices for maintaining 
continuity of staffing among both nursing and nurse aide staff. 
Finally, we are interested in commenters feedback on any considerations 
we should take into account related to the impact that including a 
Nursing Home Staff Turnover measure may have on health equity. Before 
proposing to include this measure in the SNF VBP Program in the FY 2024 
SNF PPS proposed rule, we would include the measure on a list of 
measures under consideration, as described in section 1890A of the Act.
---------------------------------------------------------------------------

    \281\ To Advance Information on Quality of Care, CMS Makes 
Nursing Home Staffing Data Available, available at: https://www.cms.gov/newsroom/press-releases/advance-information-quality-care-cms-makes-nursing-home-staffing-data-available.
    \282\ Lacey Loomer, David C. Grabowski, Ashvin Gandhi, 
Association between Nursing Home Staff Turnover and Infection 
Control Citations, SSRN Electronic Journal, 10.2139/ssrn.3766377, 
(2020). https://onlinelibrary.wiley.com/doi/abs/10.1111/1475-6773.13877.
---------------------------------------------------------------------------

    We welcomed public comment on the potential future adoption of a 
staffing turnover measure. The following is a summary of the public 
comments we received on this RFI.
    Comment: Many commenters supported a staffing turnover measure in 
the SNF VBP Program, citing growing evidence that staffing turnover 
affects quality of care for residents. One commenter suggested that we 
consider using a turnover measure from the Five-Star rating system 
rather than developing a new measure and suggested that we limit the 
Program's incentive payments to those facilities that achieve the 
lowest turnover rates. One commenter stated that we should assess both 
total nurse staff turnover and RN staff turnover and suggested that 
only nurses providing direct care should be included in the measure. 
Another commenter suggested that the measure make a distinction between 
voluntary and involuntary turnover, such as termination of staff that 
do not meet expectations. The commenter also suggested examining 
facility turnover by characteristics such as size and ownership. Some 
commenters suggested that CMS focus more on staff retention rather than 
turnover. Some commenters stated that facilities able to achieve lower 
levels of staff turnover have higher overall star ratings and better 
performance on Medicare's claims-based quality measures. One commenter 
noted that successfully reducing turnover is important to 
implementation of minimum staffing standards.
    Some commenters opposed a staffing turnover measure on the basis 
that facilities face challenges when mitigating turnover. Some 
commenters stated that facilities have trouble maintaining staff due to 
the COVID-19 pandemic. Additionally, one commenter stated that cases 
where agency staff work assignments or where specialized teams travel 
to multiple facilities should not be counted as turnover. Another 
commenter similarly stated that short-term agency staff should not be 
included in a measure of staffing turnover and suggested that extended 
leaves of absence should also be excluded. The commenter also suggested 
that the resulting turnover does not indicate low quality of care and 
that measuring staffing turnover would result in payment cuts to 
facilities that are already struggling with staffing costs. Another 
commenter stated that many factors outside of SNFs' control affect 
turnover. Another commenter stated that all health care providers are 
struggling with staffing and suggested that we limit the number of 
staffing agencies that contribute to the problem. Another commenter 
stated that not all turnover is detrimental and that it may be 
beneficial to dismiss staff that do not have the patience or 
disposition to work in a nursing facility. One commenter suggested that 
we add administrative and facility turnover to reduce management 
turnover, which the commenter believed contributes to lower quality of 
care.
    Some commenters expressed concern that a staffing turnover measure 
could impact the financial situation of SNFs with higher minority 
populations, which they believed tend to have higher turnover rates. 
One commenter worried that a staffing turnover measure would cause SNFs 
to focus narrowly on staff retention rather than care quality. One 
commenter recommended against a composite measure, stating that 
separate measures will provide consumers with clearer information and 
allow more stratification by facility type, staff members, and resident 
characteristics. One commenter expressed concern that the resources 
necessary for measure validation for the Total Nurse Staffing measure 
may shift facilities' efforts to those reviews rather than beneficiary 
care. The commenter also stated that both PBJ and MDS data are already 
reviewed for accuracy during health inspections.
    Response: We will take this feedback into consideration as we 
develop our policies for the FY 2024 SNF PPS proposed rule. In 
addition, as previously indicated, we have been posting measures of 
staff turnover since January 2022 and including SNF employee turnover 
information as part of the staffing domain of the Nursing Home Five 
Star Quality Rating System on the Medicare.gov Care Compare website 
since July 2022.
b. Request for Comment on Including the National Healthcare Safety 
Network (NHSN) COVID-19 Vaccination Coverage Among Healthcare Personnel 
Measure in a Future SNF VBP Program Year
    In addition to the staffing turnover measure and the other 
potential future measures listed in the FY 2022 SNF PPS final rule, we 
are also considering the inclusion of the NHSN COVID-19 Vaccination 
Coverage among Healthcare Personnel measure, which measures the 
percentage of healthcare personnel who receive a complete COVID-19 
vaccination course. This measure data is collected by the CDC NHSN and 
the measure was finalized for use in the SNF QRP in the FY 2022 SNF PPS 
final

[[Page 47594]]

rule (86 FR 42480 through 42489). We seek commenters' feedback on 
whether to propose to include this measure in a future SNF VBP program 
year. Before proposing to include any such measure, we would include 
the measure on a list of measures under consideration, as required by 
section 1890A of the Act.
    We welcomed public comment on the potential future adoption of the 
NHSN COVID-19 Vaccination Coverage among Healthcare Personnel measure. 
The following is a summary of the public comments received on this RFI.
    Comment: Some commenters supported a COVID-19 vaccination measure 
for healthcare personnel in the SNF VBP Program. One commenter stated 
that the measure is an important safety measure for beneficiaries and 
families. Another commenter suggested that the measure is best placed 
in the SNF QRP until long-term vaccination needs can be assessed.
    Some commenters expressed concerns about a future COVID-19 
vaccination measure for healthcare personnel in the SNF VBP Program. 
One commenter noted that the measure uses CDC processes and believed 
that may create interagency barriers and challenges. Another commenter 
stated that the measure specifications are likely to change as the 
definition of a completed COVID-19 vaccination course may change. One 
commenter stated that vaccination decisions are made by staffs' 
personal preferences, not the SNF. Another commenter noted that CMS 
already requires LTC facilities to report residents' and staffs' COVID-
19 vaccination rates and suggested that such a measure in the SNF VBP 
Program would be duplicative. Another commenter stated that exemptions 
create variation in vaccination rates. One commenter stated that the 
measure is not a patient outcome measure and thus does not align with 
the Program's purpose.
    Response: We will take this feedback into consideration as we 
develop our policies for future rulemaking.
2. Request for Comment on Updating the SNF VBP Program Exchange 
Function
    In the FY 2018 SNF PPS final rule (82 FR 36616 through 36619), we 
adopted an exchange function methodology for translating SNFs' 
performance scores into value-based incentive payments. We illustrated 
four possibilities for the functional forms that we considered--linear, 
cube, cube root, and logistic--and discussed how we assessed how each 
of the four possible exchange function forms would affect SNFs' 
incentive payments under the Program. We also discussed several 
important factors that we considered when adopting an exchange 
function, including the numbers of SNFs that receive more in value-
based incentive payments in each scenario compared to the number of 
SNFs for which a reduction is applied to their Medicare payments, as 
well as the resulting incentives for SNFs to reduce hospital 
readmissions. We also evaluated the distributions of value-based 
incentive payment adjustments and the functions' results for compliance 
with the Program's statutory requirements. We found that the logistic 
function maximized the number of SNFs with positive payment adjustments 
among SNFs measured using the SNFRM. We also found that the logistic 
function best fulfilled the requirement that SNFs in the lowest 40 
percent of the Program's ranking receive a lower payment rate than 
would otherwise apply, resulted in an appropriate distribution of 
value-based incentive payment percentages, and otherwise fulfilled the 
Program's requirements specified in statute.
    Additionally, we published a technical paper describing the 
analyses of the SNF VBP Program exchange function forms and payback 
percentages that informed the policies that we adopted in the FY 2018 
SNF PPS final rule. The paper is available on our website at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/Other-VBPs/SNF-VBP-exchange-function-analysis.pdf.
    As discussed earlier, we proposed numerous policy changes to expand 
the SNF VBP Program's measure set based on authority provided by the 
Consolidated Appropriations Act, 2021, including additional quality 
measures and adjustments to the Program's scoring methodology to 
accommodate the presence of more than one quality measure. We are also 
considering whether we should propose a new form for the exchange 
function or modify the logistic exchange function in future years.
    When we adopted the logistic function for the SNF VBP Program, we 
focused on that function's ability, coupled with the 60 percent payback 
percentage, to provide net-positive value-based incentive payments to 
as many top-performing SNFs as possible. We believed that structuring 
the Program's incentive payments in this manner enabled us to reward 
the Program's top-performing participants and provide significant 
incentives for SNFs that were not performing as well to improve over 
time.
    We continue to believe that these considerations are important and 
that net-positive incentive payments help drive quality improvement in 
the SNF VBP Program. However, in the context of a value-based 
purchasing program employing multiple measures, we are considering 
whether a new functional form or modifications to the existing logistic 
exchange function may provide the best incentives to SNFs to improve on 
the Program's measures.
    If finalized, the additional measures that we are proposing for the 
SNF VBP Program would align the Program more closely with the Hospital 
VBP Program, on which some of SNF VBP's policies, like the exchange 
function methodology, are based. The Hospital VBP Program employs a 
linear exchange function to translate its Total Performance Scores into 
value-based incentive payment percentages that can be applied to 
hospitals' Medicare claims. A linear exchange function is somewhat 
simpler for interested parties to understand but presents less of an 
opportunity to reward top performers than the logistic form that we 
currently employ in the SNF VBP Program at https://data.cms.gov/provider-data/ or https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/SNF-VBP/SNF-VBP-Page.
    We requested feedback from interested parties on whether we should 
consider proposing either a new functional form or modified logistic 
exchange function for the SNF VBP Program. Specifically, we requested 
comments on whether the proposed addition of new quality measures in 
the Program should weigh in favor of a new exchange function form, a 
modified logistic exchange function, or no change to the existing 
exchange function, whether interested parties believe that the 
increased incentive payment percentages for top performers offered by 
the logistic function should outweigh the simplicity of the linear 
function, and whether we should further consider either the cube, cube 
root, or other functional forms.
    We welcomed public comment on potential future updates to the 
Program exchange function. The following is a summary of the public 
comments we received on this RFI.
    Comment: One commenter recommended providing more information to 
SNFs on how their value-based incentive payments would change with an 
updated exchange function. The commenter also noted that the current 
system may disadvantage smaller SNFs, as well as those that treat 
sicker patients and a higher proportion of dual-eligible

[[Page 47595]]

patients. The commenter requested that CMS explore how the SNF VBP 
Program could ensure more equitable opportunity for these SNFs to 
achieve a positive value-based incentive payment, including utilizing 
peer groups. One commenter recommended that any change to the exchange 
function should be consistent with the rationale used for adopting the 
logistic function. The commenter also recommended that all options be 
further evaluated to ensure a potential exchange function does not 
create incentives at the higher end of performance to deny needed care. 
One commenter stated that, based on quality measures' typical 
distribution in a bell curve, the Program's exchange function 
methodology prevents many facilities from reaching top performance. The 
commenter stated that every facility should have the opportunity to be 
a top performer if they meet measure requirements.
    Response: We will take this feedback into consideration as we 
develop our policies for future rulemaking.
3. Request for Comment on the Validation of SNF Measures and Assessment 
Data
    We have proposed to adopt measures for the SNF VBP Program that are 
calculated using data from a variety of sources, including Medicare FFS 
claims, the minimum data set (MDS), and the PBJ system, and we are 
seeking feedback on the adoption of additional validation procedures. 
In addition, section 1888(h)(12) of the Act requires the Secretary to 
apply a process to validate SNF VBP program measures, quality measure 
data, and assessment data as appropriate. MDS information is 
transmitted electronically by nursing homes to the national MDS 
database at CMS. The data set was updated in 2010 from MDS 2.0 to MDS 
3.0 to address concerns about the quality and validity of the MDS 2.0 
data. Final testing of MDS 3.0 showed strong results, with the updated 
database outperforming MDS 2.0 in terms of accuracy, validity for 
cognitive and mood items, and clinical relevance.\283\ Research has 
also shown that MDS 3.0 discharge data match Medicare enrollment and 
hospitalization claims data with a high degree of accuracy.\284\
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    \283\ RAND MDS 3.0 Final Study Report: https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/NursingHomeQualityInits/Downloads/MDS30FinalReport-Appendices.zip.
    \284\ Rahman M., Tyler D., Acquah J.K., Lima J., Mor V.. 
Sensitivity and specificity of the Minimum Data Set 3.0 discharge 
data relative to Medicare claims. J Am Med Dir Assoc. 
2014;15(11):819-824. doi:10.1016/j.jamda.2014.06.017: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4731611/.
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    Although the MDS data sets are assessed for accuracy, as described 
above, we are interested in ensuring the validity of the data reported 
by skilled nursing facilities because use of this data would have 
payment implications under the SNF VBP Program. Accordingly, we 
requested feedback from interested parties on the feasibility and need 
to select SNFs for validation via a chart review to determine the 
accuracy of elements entered into MDS 3.0 and PBJ. Additionally, we 
requested feedback on data validation methods and procedures that could 
be utilized to ensure data element validity and accuracy.
    We noted that other programs, including the Hospital IQR (85 FR 
58946) and Hospital OQR programs (76 FR 74485), have developed 
validation processes for chart-abstracted measures and electronic 
clinical quality measures (eCQMs), data sources not utilized for the 
SNF VBP Program. However, there are other elements of existing 
programs' validation procedures that may be considered for a future SNF 
VBP Program validation effort. For example, we request feedback on the 
volume of facilities to select for validation under the SNF VBP 
Program. We estimate that 3,300 hospitals report data under the 
Hospital OQR (86 FR 63961) and Hospital IQR (86 FR 45508) Programs. We 
estimate that over 15,000 SNFs are eligible for the SNF VBP Program. 
The Hospital OQR Program randomly selects the majority of hospitals 
(450 hospitals) for validation and additionally select a subset of 
targeted hospitals (50 hospitals) (86 FR 63872). Under the Hospital IQR 
Program, 400 hospitals are selected randomly and up to 200 hospitals 
are targeted for chart-abstracted data validation and up to 200 
hospitals are randomly selected for eCQM data validation (86 FR 45424). 
We sample approximately 10 records from 300 randomly selected 
facilities under the ESRD QIP Program (82 FR 50766).
    We also requested feedback from interested parties on the use of 
both random and targeted selection of facilities for validation. The 
Hospital OQR program identifies hospitals for targeted validation based 
on whether they have previously failed validation or have reported an 
outlier value deviating markedly from the measure values for other 
hospitals (more than 3 standard deviations of the mean) (76 FR 74485). 
Validation targeting criteria utilized by the Hospital IQR Program 
include factors such as: (1) abnormal, conflicting or rapidly changing 
data patterns; (2) facilities which have joined the program within the 
previous 3 years, and which have not been previously validated or 
facilities which have not been randomly selected for validation in any 
of the previous 3 years; and (3) any hospital that passed validation in 
the previous year, but had a two-tailed confidence interval that 
included 75 percent (85 FR 58946).
    Finally, we requested feedback from interested parties on the 
implementation timeline for additional SNF VBP Program validation 
processes, as well as validation processes for other quality measures 
and assessment data. We believe it may be feasible to implement 
additional validation procedures beginning with data from the FY 2026 
program year, at the earliest. Additionally, we may consider the 
adoption of a pilot of additional data validation processes; such an 
approach would be consistent with the implementation of the ESRD QIP 
data validation procedures, which began with a pilot in CY 2014 (82 FR 
50766).
    We welcomed public comments on the data validation considerations 
for the SNF VBP Program discussed previously in this section. The 
following is a summary of the public comments we received on this RFI.
    Comment: Some commenters supported adopting a chart review process 
for SNF VBP validation. One commenter specifically recommended that we 
assess how MDS coding is equated with medical review. Another commenter 
noted MDS reviews could be included in a SNF VBP validation program 
structured similarly to hospital validation processes. Another 
commenter recommended that we consider the burden placed on SNFs, 
particularly chart reviews, that may take staff away from patient care. 
One commenter recommended that we consider the HVBP Program's 
experience with validation. The commenter also urged us to involve 
patients and families when developing validation to ensure that results 
are meaningful to consumers. Another commenter recommended that we 
adopt a pilot validation program first. One commenter suggested that we 
adopt the same types of validation procedures for the DTC and HAI 
measures as we proposed for the SNFRM. Another commenter requested that 
we work with relevant interested parties to develop and make available 
evidence-based practices on validation processes. Another commenter 
requested that we confirm whether a multidisciplinary care team can 
participate in MDS completion. Some commenters stated that additional 
validation processes are unnecessary because measures or data

[[Page 47596]]

collection processes already include methods to ensure their accuracy.
    One commenter supported additional validation of SNF VBP measures, 
including auditing measures based on MDS data. The commenter was 
concerned that facilities may report inaccurate or inflated MDS data to 
increase their Five-Star measure ratings. One commenter stated that MDS 
data have already been shown to be accurate. One commenter suggested 
that we consider a mix of random and targeted selection of providers in 
the validation process, and one commenter supported both random and 
targeted facility selection for validation. One commenter supported 
implementing a validation program beginning with FY 2026 data.
    Response: We will take this feedback into consideration as we 
develop our policies for future rulemaking.
4. Request for Comment on a SNF VBP Program Approach To Measuring and 
Improving Health Equity
    Significant and persistent inequities in healthcare outcomes exist 
in the U.S. 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; living in a rural area; 
being a member of a religious minority; or being near or below the 
poverty level, is often associated with worse health 
outcomes.285 286 287 288 289 290 291 292 293 In accordance 
with Executive Order 13985 of January 20, 2021 on Advancing Racial 
Equity and Support for Underserved Communities Through the Federal 
Government, equity is defined as 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 (86 FR 7009). In February 2022, we further 
expanded on this definition by defining health equity as the attainment 
of the highest level of health for all people, where everyone has a 
fair and just opportunity to attain their optimal health regardless of 
race, ethnicity, disability, sexual orientation, gender identity, sex, 
socioeconomic status, geography, preferred language, or other factors 
that affect access to care and health outcomes. We are working to 
advance health equity by designing, implementing, and operationalizing 
policies and programs that support health for all the people served by 
our programs, eliminating avoidable differences in health outcomes 
experienced by people who are disadvantaged or underserved, and 
providing the care and support that our enrollees need to thrive. Over 
the past decade we have enacted a suite of programs and policies aimed 
at reducing health care disparities including the CMS Mapping Medicare 
Disparities Tool,\294\ the CMS Innovation Center's Accountable Health 
Communities Model,\295\ the CMS Disparity Methods stratified reporting 
program,\296\ and efforts to expand social risk factor data collection, 
such as the collection of Standardized Patient Assessment Data Elements 
in the post-acute care setting.\297\
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    \285\ Joynt K.E., Orav E., Jha A.K. (2011). Thirty-day 
readmission rates for Medicare beneficiaries by race and site of 
care. JAMA, 305(7):675-681.
    \286\ Lindenauer P.K., Lagu T., Rothberg M.B., et al. (2013). 
Income inequality and 30-day outcomes after acute myocardial 
infarction, heart failure, and pneumonia: Retrospective cohort 
study. British Medical Journal, 346.
    \287\ Trivedi A.N., Nsa W., Hausmann L.R.M., et al. (2014). 
Quality and equity of care in U.S. hospitals. New England Journal of 
Medicine, 371(24):2298- 2308.
    \288\ Polyakova, M., et al. (2021). Racial disparities in excess 
all-cause mortality during the early COVID-19 pandemic varied 
substantially across states. Health Affairs, 40(2): 307-316.
    \289\ Rural Health Research Gateway. (2018). Rural communities: 
age, income, and health status. Rural Health Research Recap. https://www.ruralhealthresearch.org/assets/2200-8536/rural-communities-age-incomehealth-status-recap.pdf.
    \290\ https://www.minorityhealth.hhs.gov/assets/PDF/Update_HHS_Disparities_Dept-FY2020.pdf.
    \291\ https://www.cdc.gov/mmwr/volumes/70/wr/mm7005a1.htm.
    \292\ Milkie Vu et al. Predictors of Delayed Healthcare Seeking 
Among American Muslim Women, Journal of Women's Health 26(6) (2016) 
at 58; S.B. Nadimpalli, et al., The Association between 
Discrimination and the Health of Sikh Asian Indians Health Psychol. 
2016 Apr; 35(4): 351-355.
    \293\ Poteat T.C., Reisner S.L., Miller M., Wirtz A.L. (2020). 
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. doi:10.1101/2020.07.21.20159327.
    \294\ https://www.cms.gov/About-CMS/Agency-Information/OMH/OMH-Mapping-Medicare-Disparities.
    \295\ https://innovation.cms.gov/innovation-models/ahcm.
    \296\ https://qualitynet.cms.gov/inpatient/measures/disparity-methods.
    \297\ https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/-IMPACT-Act-Standardized-Patient-Assessment-Data-Elements.
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    As we continue to leverage our value-based purchasing programs to 
improve quality of care across settings, we are interested in exploring 
the role of health equity in creating better health outcomes for all 
populations in these programs. As the March 2020 ASPE Report to 
Congress on Social Risk Factors and Performance in Medicare's VBP 
Program notes, it is important to implement strategies that cut across 
all programs and health care settings to create aligned incentives that 
drive providers to improve health outcomes for all beneficiaries.\298\ 
Therefore, in the proposed rule, we requested feedback from interested 
parties on guiding principles for a general framework that could be 
utilized across our quality programs to assess disparities in 
healthcare quality in a broader RFI in section VI.E. of the proposed 
rule. We refer readers to this RFI titled, ``Overarching Principles for 
Measuring Healthcare Quality Disparities Across CMS Quality Programs--A 
Request for Information,'' which includes a complete discussion on the 
key considerations that we intend to consider when determining how to 
address healthcare disparities and advance health equity across all of 
our quality programs. Additionally, we are interested in feedback from 
interested parties on specific actions the SNF VBP Program can take to 
align with other value-based purchasing and quality programs to address 
healthcare disparities and advance health equity.
---------------------------------------------------------------------------

    \298\ Office of the Assistant Secretary for Planning and 
Evaluation, U.S. Department of Health & Human Services. Second 
Report to Congress on Social Risk Factors and Performance in 
Medicare's Value-Based Purchasing Program. 2020. https://aspe.hhs.gov/social-risk-factors-and-medicares-value-basedpurchasing-programs.
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    As we continue assessing the SNF VBP Program's policies in light of 
its operation and its expansion as directed by the CAA, we requested 
public comments on policy changes that we should consider on the topic 
of health equity. We specifically requested comments on whether we 
should consider incorporating adjustments into the SNF VBP Program to 
reflect the varied patient populations that SNFs serve around the 
country and tie health equity outcomes to SNF payments under the 
Program. These adjustments could occur at the measure level in forms 
such as stratification (for example, based on dual status or other 
metrics) or including measures of social determinants of health (SDOH). 
These adjustments could also be incorporated at the scoring or 
incentive payment level in forms such as modified benchmarks, points 
adjustments, or modified incentive payment multipliers (for example, 
peer comparison groups based on whether the facility includes a

[[Page 47597]]

high proportion of dual eligible beneficiaries or other metrics). We 
requested commenters' views on which of these adjustments, if any, 
would be most effective for the SNF VBP Program at accounting for any 
health equity issues that we may observe in the SNF population.
    We welcomed public comment on potential approaches to measuring and 
improving health equity in the SNF VBP Program. The following is a 
summary of the public comments we received on this RFI.
    Comment: Many commenters supported our commitment to health equity 
for SNF residents. Some commenters suggested that we examine factors 
that may lead to care inequities and suggested that we incorporated 
patient-reported outcomes and experiences in shaping our equity 
strategies. Another commenter suggested that we consider balancing 
short-stay and long-stay residents' needs when developing equity 
adjustments. Some commenters recommended that we report quality data 
stratified by race and ethnicity to assess health equity issues in the 
SNF sector. Another commenter suggested that we adopt a risk-adjustment 
or incentive payment policy for facilities that accept residents that 
other facilities will not. Another commenter recommended that we engage 
with interested parties throughout any health equity policy development 
so that facilities can implement proper data collection. One commenter 
recommended that we pair clinical data measures with social risk 
metrics to help providers deliver more comprehensive care. One 
commenter recommended against tying quality measures involving race and 
ethnicity to payment, stating that such policies may be 
unconstitutional and could lead to ineffective or biased clinical care. 
The commenter stated that categories such as dual eligibility status or 
social determinants of health would be better ways to stratify measures 
than racial or ethnic categories. One commenter supported measures 
emphasizing and incorporating social determinants of health but 
recommended delaying their implementation on the basis that additional 
administrative burden on providers is inappropriate at this time.
    Response: We will take this feedback into consideration as we 
develop our policies for future rulemaking.

IX. Changes to the Requirements for the Director of Food and Nutrition 
Services and Physical Environment Requirements in Long-Term (LTC) 
Facilities and Summary of Public Comments and Responses to the Request 
for Information on Revising the Requirements for Long-Term Care 
Facilities To Establish Mandatory Minimum Staffing Levels

A. Changes to the Requirements for the Director of Food and Nutrition 
Services and Physical Environment Requirements in Long-Term (LTC) 
Facilities

    On July 18, 2019, we published a proposed rule entitled, 
``Requirements for Long-Term Care (LTC) Facilities: Provisions to 
Promote Efficiency and Transparency'' (84 FR 34737). In combination 
with our internal review of the existing regulations, we used feedback 
from interested parties to inform our policy decisions about the 
proposals we set forth. We specifically considered how each 
recommendation could potentially reduce burden or increase flexibility 
for providers without impinging on the health and safety of residents. 
In the proposed rule, we included a detailed discussion regarding 
interested parties' response to our solicitations for suggestions to 
reduce provider burden. In response to the proposed rule, we received a 
total of 1,503 public comments. In this final rule, we are finalizing 
two of the proposals, which we believe will have a significant impact 
on a facility's ability to recruit and retain qualified staff as well 
as, allowing older existing nursing homes to remain in compliance 
without having to completely rebuild their facility or have to use the 
Fire Safety Evaluation System (FSES). On July 14, 2022, we published a 
notice to extend the timeframe allowed to finalize the remaining 
proposals in the July 18, 2019 rule (87 FR 42137). We are continuing to 
evaluate those proposals and will issue an additional final rule if we 
choose to proceed with further rulemaking.
Responses to Public Comments and Provisions of the Final Rule
1. Food and Nutrition Services (Sec.  483.60)
    Dietary standards for residents of LTC facilities are critical to 
both quality of care and quality of life. LTC interested parties have 
shared concerns regarding the current requirement that existing dietary 
staff include certified dietary managers or food service managers. 
Specifically, interested parties have concerns regarding the need for 
existing dietary staff, who are experienced in the duties of a dietary 
manager and currently operate in the position, to obtain new or 
additional training to become qualified under the current regulatory 
requirements. We believe that effective management and oversight of the 
food and nutrition service is critical to the safety and well-being of 
all residents of a nursing facility. Therefore, we continue to believe 
that it is important that there are standards for the individuals who 
will lead this service. However, to address concerns from interested 
parties we proposed to revise the standards at Sec.  483.60(a)(2) to 
increase flexibility, while providing that the director of food and 
nutrition services is an individual who has the appropriate 
competencies and skills necessary to oversee the functions of the food 
and nutrition services. Specifically, we proposed to revise the 
standards at Sec.  483.60(a)(2)(i) and (ii) to provide that at a 
minimum an individual designated as the director of food and nutrition 
services would have 2 or more years of experience in the position of a 
director of food and nutrition services, or have completed a minimum 
course of study in food safety that would include topics integral to 
managing dietary operations such as, but not limited to, foodborne 
illness, sanitation procedures, and food purchasing/receiving. We are 
retaining the existing requirement at Sec.  483.60(a)(2)(iii) which 
specifies that the director of food and nutrition services must receive 
frequently scheduled consultations from a qualified dietitian or other 
clinically qualified nutrition professional. We noted in the proposed 
rule that these revisions will maintain established standards for the 
director of food and nutrition services given the critical aspects of 
their job function, while addressing concerns related to costs 
associated with training existing staff and the potential need to hire 
new staff.
    We received public comments on these proposals. The following is a 
summary of the comments we received and our responses.
    Comment: Some commenters supported the proposal stating that the 
changes would increase flexibility for providers to be able to recruit 
and retain important staff members, and also allow experienced 
professionals to remain in their roles. Other commenters had 
significant concerns and stated that the proposed qualification 
requirements were insufficient since some knowledge necessary for the 
position could not be gained through experience alone. For example, 
commenters noted that the knowledge and expertise received during the 
Certified Dietary Manager

[[Page 47598]]

(CDM) certification required courses are not necessarily skills staff 
would learn from experience. These commenters encouraged CMS to retain 
the current requirements for the director of food and nutrition 
services.
    Response: We appreciate the feedback and agree that increased 
flexibility for recruitment and staff retention is important. However, 
we also acknowledge that some knowledge obtained through education may 
not be easily gained through experience alone. We agree with the 
commenters that certain training/education should be required for 
anyone seeking to qualify as the director of food and nutrition 
services, including those experienced staff. Therefore, we are revising 
the proposal to allow a person who has 2 or more years of experience in 
the position and has completed a minimum course of study in food safety 
to meet the requirement by October 1, 2023, to qualify. These 
modifications to the requirements at Sec.  483.60 will allow for more 
flexibility and will help providers with recruiting and retaining 
qualified staff, while also providing for an adequate minimum standard 
of education for the position. We believe that there are many paths to 
obtaining the knowledge and skills necessary to meet these 
requirements. Therefore, the experience qualifier is only one option 
for meeting the requirements for the director of food and nutrition 
services.
    Therefore, the director of food and nutrition services must meet 
the following requirements, some of which remain unchanged from our 
current regulations:
     In States that have established standards for food service 
managers or dietary managers, meets State requirements for food service 
managers or dietary managers (existing Sec.  483.60(a)(2)(ii)); and
     Receive frequently scheduled consultations from a 
qualified dietitian or other clinically qualified nutrition 
professional (existing Sec.  483.60(a)(2)(iii)).
    In addition, the director will need to meet the conditions of one 
of the following five options, four of which are retained from the 
existing rule:
     Have 2 or more years of experience in the position of a 
director of food and nutrition services, and have completed a minimum 
course of study in food safety, by no later than 1 year following the 
effective date of this rule, that includes topics integral to managing 
dietary operations such as, but not limited to, foodborne illness, 
sanitation procedures, food purchasing/receiving, etc. (new Sec.  
483.60(a)(2)(i)(E)) (we note that this would essentially be the 
equivalent of a ServSafe Food Manager certification); or
     Be a certified dietary manager (existing Sec.  
483.60(a)(2)(i)(A)); or
     Be a certified food service manager (existing Sec.  
483.60(a)(2)(i)(B)); or
     Have similar national certification for food service 
management and safety from a national certifying body(existing Sec.  
483.60(a)(2)(i)(C)); or
     Have an associate's or higher degree in food service 
management or in hospitality, if the course study includes food service 
or restaurant management, from an accredited institution of higher 
learning (existing Sec.  483.60(a)(2)(i)(D)).
    We believe that maintaining qualified and trained food and 
nutrition personnel is critical to the health and safety of residents 
in LTC facilities. We note that issues with food and nutrition 
requirements are the 3rd most frequently cited deficiencies in LTC 
facilities. We believe that these requirements will help ensure 
resident safety while also allowing facilities the flexibility to staff 
according to their unique needs and resources.
    Comment: Many commenters recommended this requirement be phased in 
over 3 years to allow providers and professionals the time they need to 
obtain the necessary certifications, which require 15 to 18 months and 
an investment of more than $2,000 for the course, textbooks, fees, and 
to sit for the exam.
    Response: We do not agree that a phase-in is necessary. As 
discussed in detail in the previous response, we have revised the 
requirements to allow 1 year for an experienced director of food and 
nutrition services to obtain training necessary to qualify for the 
position. Experience plus a minimum course of study is one of five ways 
to qualify for the position of the director of food and nutrition 
services. Given the many options available to qualify as well as the 
importance of food and safety in nursing homes, we do not believe that 
a 3-year delay in implementing the requirements is necessary or in the 
best interest of resident health and safety. We believe that all 
required staff will be able to meet the requirements.
    After consideration of public comments, we are finalizing our 
proposal with the following changes--
     We are withdrawing our proposal at Sec.  483.60(a)(2) to 
replace the existing qualifications for the director of food and 
nutrition services with an experience qualification and minimum course 
of study exclusively.
     We are revising Sec.  483.60(a)(2)(i), to add experience 
in the position as one of the ways to qualify for the position of the 
director of food and nutrition services. Specifically, an individual 
who, on the effective date of this final rule, has 2 or more years of 
experience in the position of director of food and nutrition services 
in a nursing facility setting and has completed a course of study in 
food safety and management by no later than October 1, 2023, along with 
the other requirements set out at Sec.  483.60(a)(2), is qualified to 
be the director of food and nutrition services.
2. Physical Environment (Sec.  483.90)
a. Life Safety Code
    On May 4, 2016, we published a final rule entitled, ``Medicare and 
Medicaid; Fire Safety Requirements for Certain Health Care 
Facilities,'' adopting the 2012 edition of the National Fire Protection 
Association (NFPA) 101 (81 FR 26871), also known as the Life Safety 
Code (LSC). One of the references in the LSC is NFPA 101A, Guide on 
Alternative Approaches to Life Safety, also known as the Fire Safety 
Evaluation System (FSES). The FSES was developed as a means of 
achieving and documenting an equivalent level of life safety without 
requiring literal compliance with the Life Safety Code. The FSES is a 
point score system which establishes the general overall level of fire 
safety for health care facilities as compared to explicit conformance 
to individual requirements outlined in the Life Safety Code. The system 
uses combinations of widely accepted fire safety systems and 
arrangements to provide a level of fire safety which has been judged to 
be at least equivalent to the level achieved through strict compliance 
with the Life Safety Code. Some LTC facilities that utilized the FSES 
in order to determine compliance with the containment, extinguishment 
and people movement requirements of the LSC were no longer able to 
achieve a passing score, on the FSES, because of a change in scoring.
    To address this need, in the July 2019 rule, we proposed to allow 
those existing LTC facilities (those that were Medicare or Medicaid 
certified before July 5, 2016) that have previously used the FSES to 
determine equivalent fire protection levels, to use an alternate 
scoring methodology to meet the requirements. Specifically, we proposed 
to have facilities use the mandatory values provided in the proposed 
regulations text at Sec.  483.90(a)(1)(iii) when determining compliance 
for containment, extinguishment and people movement requirements. In 
the proposed rule, we noted that allowing the use of the provided 
mandatory scoring values will continue to provide the same amount of 
safety for residents

[[Page 47599]]

and staff as has been provided since we began utilizing the score 
values set out in the FSES. We also indicated that the proposed values 
would allow existing LTC facilities that previously met the FSES 
requirements to continue to do so without incurring great expense to 
change their construction types. We proposed to use the mandatory 
scoring values as shown in Table 18.
[GRAPHIC] [TIFF OMITTED] TR03AU22.020

    We proposed to include Table 18 at Sec.  483.90(a)(1)(iii).
    We received public comments on these proposals. The following is a 
summary of the comments we received and our responses.
    Comment: Many commenters supported the proposed changes to allow 
LTC facilities to use the provided mandatory values found at Sec.  
483.90(a)(1)(iii) when determining compliance for containment, 
extinguishment and people movement requirements, especially the LTC 
facilities that are currently affected by this issue. Commenters stated 
that using the 2013 NFPA 101A (FSES) values create substantial and 
unnecessary hardships for providers, residents and staff. Since the 
adoption of the 2013 NFPA 101A several nursing homes have struggled to 
remain in compliance, and using the provided mandatory values is a 
much-needed change. Many facilities stated that they meet the 2001 
FSES, but the 2013 FSES would require retrofitting and essentially put 
them out of business due to financial hardship. Using the FSES 
mandatory values would allow existing facilities that previously met 
the FSES requirements to continue to do so without incurring great 
expense to change construction type that will not substantially improve 
the safety of residents.
    Response: We agree that using the proposed mandatory values at 
Sec.  483.90(a)(1)(iii) would allow existing facilities to continue to 
operate without incurring additional expenses that might otherwise be 
necessary to achieve compliance. All of the affected facilities are 
completely sprinklered and would not be lowering their safety standards 
at all. We agree that using the mandatory values set forth in the chart 
at Sec.  483.90(a)(1)(iii) would allow us to resolve the scoring issue 
immediately for the affected providers. Therefore, this fix will remain 
in place until CMS adopts a newer version of the LSC.
    Comment: One commenter stated that revisions to the construction 
limits for existing nursing homes were proposed for the 2021 edition of 
NFPA 101 based on input from the long-term care industry and believe 
that the effectiveness and dependability of automatic sprinkler systems 
could allow facilities to continue to operate. The commenter stated 
that existing facilities installed automatic sprinklers in good faith 
to compensate for construction deficiencies and demonstrate equivalency 
via NFPA 101A-2001 prior to the adoption of the 2012 edition of the 
NFPA 101. The commenters stated that since facilities would be in 
compliance with the revised construction requirements of the 2021 
edition of the NFPA 101, equivalency would not need to be demonstrated 
via an FSES. The commenter suggested that we not finalize this 
proposal, and instead institute a categorical waiver process for the 
affected facilities until CMS incorporated by reference the standards 
of the 2021 edition of the NFPA 101.
    Response: We are aware that revisions to the NFPA 101 were 
finalized and issued August 11, 2021. We will need to go through notice 
and comment rulemaking in order to adopt the 2021 edition or a newer 
edition of the LSC, which could take up to 3 additional years. Using 
the values found in the chart at Sec.  483.90(a)(1)(iii) will allow us 
to address the problem immediately and will remain in place until we 
adopt a newer version of the LSC.
    Comment: Many commenters agreed that the FSES chart resulting from 
adoption of the 2012 Life Safety Code has created a huge unanticipated 
negative effect on certain types of existing building construction, 
which may result in such buildings being forced to relocate residents 
and close within the next 2 years without any reduction in the overall 
fire safety features such as smoke detectors, sprinklers, fire alarm 
systems and building construction. Modifying the FSES mandatory scoring 
values as proposed by CMS solves this problem.
    Response: We do not want any facilities to potentially have to 
close or completely reconstruct their building because of the scoring 
system for the FSES. LTC facilities are currently required to meet the 
required health and safety standards based on the 2012 edition of the 
LSC and Health Care Facilities Code (NFPA 99). By using the FSES these 
facilities can demonstrate that although they may not meet a certain 
requirement such as the construction type for the current LSC 
requirements, they are able to demonstrate that they have other 
measures in place to provide the same or higher level of safety for 
residents and staff. We also know that all LTC facilities are fully 
sprinklered, which helps them maintain this higher level of safety. We 
are finalizing this provision as proposed to avoid any facility 
closures or displacement for residents and to avoid significant 
facility expenditures that may not be necessary.
    After consideration of public comments, we are finalizing our 
proposed changes without modifications.

B. Summary of Public Comments and Responses to the Request for 
Information on Revising the Requirements for Long-Term Care Facilities 
To Establish Mandatory Minimum Staffing Levels

    The COVID-19 Public Health Emergency has highlighted and 
exacerbated longstanding concerns with inadequate staffing in long-term 
care (LTC) facilities. The Biden-Harris Administration is committed to 
improving the quality of U.S. nursing

[[Page 47600]]

homes so that seniors and others living in nursing homes get the 
reliable, high-quality care they deserve. As a result, we intend to 
propose in future rulemaking the minimum standards for staffing 
adequacy that nursing homes would be required to meet. We will conduct 
a new research study to help inform policy decisions related to 
determining the level and type of staffing needed to ensure safe and 
quality care and expect to issue proposed rules within one year. In the 
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 
2023; Request for Information on Revising the Requirements for Long-
Term Care Facilities To Establish Mandatory Minimum Staffing Levels 
proposed rule (87 FR 22720), we solicited public comments on 
opportunities to improve our health and safety standards to promote 
thoughtful, informed staffing plans and decisions within LTC facilities 
that aim to meet resident needs, including maintaining or improving 
resident function and quality of life. We stated that such an approach 
is essential to effective person-centered care and that we are 
considering policy options for future rulemaking to establish specific 
minimum direct care staffing standards and are seeking stakeholder 
input to inform our policy decisions.
    Specifically, we solicited stakeholder input on options for future 
rulemaking regarding adequate staffing levels and we asked questions 
that we should consider as we evaluate future policy options (87 FR 
22794 through 22795).
    Comment: We received 3,129 comments from a variety of interested 
parties involved in long-term care issues, including advocacy groups, 
long-term care ombudsmen, industry associations (providers), labor 
unions and organizations, nursing home staff and administrators, 
industry experts and other researchers, family members and caretakers 
of nursing home residents. Overall, commenters were generally 
supportive of establishing a minimum staffing requirement, whereas 
other commenters were opposed. Commenters supporting the establishment 
of a minimum staffing requirement voiced safety concerns regarding 
residents not receiving adequate care due to chronic understaffing in 
facilities. Commenters offered examples of residents going entire 
shifts without receiving toileting assistance, which can lead to an 
increase in falls or presence of pressure ulcers. Other commenters 
shared stories of residents wearing the same outfit for a week without 
a change of clothing or a shower. These commenters highlighted the 
contributions of facility staff and greatly attributed these incidences 
and lack of quality care to insufficient staffing levels. Commenters 
offered recommendations for implementing minimum staffing requirements, 
with some commenters suggesting that CMS focus on implementing an 
acuity staffing model per shift instead of a minimum staffing 
requirement, while others recommended that minimum staffing levels be 
established for residents with the lowest care needs, assessed using 
the MDS 3.0 assessment forms, citing concerns that acuity-based 
minimums will be more susceptible to gaming. Commenters also provided 
information on several resident and facility factors for consideration 
when assessing a facility's ability to meet any mandated staffing 
standard, including whether or not the facility may have a higher 
Medicaid census, larger bed size, for profit ownership, higher county 
SNF competition, and, for staffing RNs specifically, higher community 
poverty and lower Medicare census. Other commenters stated that 
resident acuity should be a primary determinant in establishing minimum 
staffing standards, noting that CMS pays nursing homes based on 
resident acuity level.
    We also received comments on factors impacting facilities' ability 
to recruit and retain staff, with most commenters in support of 
creating avenues for competitive wages for nursing home staff to 
address issues of recruitment and retention and other commenters 
suggesting that skilled nursing facility payments are continuing to be 
cut, complicating facilities ability to increase staff wages and 
benefits.
    Finally, we received comments on the cost impacts of establishing 
staffing standards, payment, and study design. Some commenters pointed 
to the variability of Medicaid labor reimbursement amounts and how many 
States' Medicaid rates do not keep pace with rising labor costs while 
others noted that evidence shows most facilities have adequate 
resources to increase their staffing levels without additional Medicaid 
resources and pointed to a recent study documenting that most major 
publicly traded nursing home companies were highly profitable, even 
during the COVID pandemic. Commenters provided robust feedback on the 
action design and method for implementing a nurse staffing requirement, 
with some noting that resident acuity could change on a daily basis and 
recommended that CMS establish benchmarks rather than absolute values 
in staffing requirements. Other commenters recommended using both 
minimum nursing hours per resident day (hprd) and nurse to resident 
ratios.
    Response: We appreciate the robust response we received on this 
RFI. As noted, staff levels in nursing homes have a substantial impact 
on the quality of care and outcomes residents experience. The input 
received will be used in conjunction with a new research study being 
conducted by CMS to determine the level and type of nursing home 
staffing needed to ensure safe and quality care. CMS intends to issue 
proposed rules on a minimum staffing level measure within one year. We 
will consider the feedback that we have received on this RFI for the 
upcoming rulemaking and changes to the LTC facility requirements for 
participation. This feedback from a wide range of interested parties 
will help to establish minimum staffing requirements that ensure all 
residents are provided safe, quality care, and that workers have the 
support they need to provide high-quality care.

X. Collection of Information Requirements

    As explained below, this final rule will not impose any new or 
revised ``collection of information'' requirements or burden. 
Consequently, this final rule is not subject to the requirements of the 
Paperwork Reduction Act of 1995 (PRA) (44 U.S.C. 3501 et seq.). For the 
purpose of this section, collection of information is defined under 5 
CFR 1320.3(c) of the PRA's implementing regulations.
    With regard to the SNF QRP, in section VI.C.1. of this final rule, 
we are finalizing our proposal that SNFs submit data on the Influenza 
Vaccination Coverage among HCP measure beginning with the FY 2024 SNF 
QRP. We noted in the proposed rule that the CDC has a PRA waiver for 
the collection and reporting of vaccination data under section 321 of 
the National Childhood Vaccine Injury Act (NCVIA) (Pub. L. 99-660, 
enacted November 14, 1986).\299\ Since the burden is exempt from the 
requirements of the PRA, we set out such burden under the economic 
analysis section (see section X.A.5.) of the proposed rule. While the 
waiver is specific to the

[[Page 47601]]

PRA's requirements (``Chapter 35 of Title 44, United States Code''), 
our economic analysis requirements are not waived by any such statutes. 
We refer readers to section X.A.5. of the proposed rule, where we 
provided an estimate of the burden to SNFs.
---------------------------------------------------------------------------

    \299\ 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.C., but can be found in a 
note at 42 U.S.C. 300aa-1.
---------------------------------------------------------------------------

    In section VI.C.2. of this final rule, we are finalizing our 
proposal to revise the compliance date for certain SNF QRP reporting 
requirements including the Transfer of Health information measures and 
certain standardized patient assessment data elements (including race, 
ethnicity, preferred language, need for interpreter, health literacy, 
and social isolation). The finalized change in compliance date will 
have no impact on any requirements or burden estimates; both proposals 
are active and accounted for under OMB control number 0938-1140 (CMS-
10387). Consequently, we did not finalize any changes under that 
control number.
    In section VI.C.3. of this final rule, we are finalizing our 
proposed revisions to the regulatory text. The finalized revisions will 
have no collection of information implications.
    With regard to the SNF VBP Program, in section VIII.B.1.b. of this 
final rule, we are finalizing our proposal to suppress the SNFRM for 
scoring and payment purposes for the FY 2023 SNF VBP program year. This 
measure is calculated using Medicare FFS claims data, and our 
suppression of data on this measure for the FY 2023 program year will 
not create any new reporting burden for SNFs. We will publicly report 
the SNFRM rates for the FY 2023 program year, and 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 
2023 SNF VBP Program given the significant changes in SNF patient case 
volume and facility-level case mix, as described in section VIII.H.1. 
of this final rule. In sections VIII.B.3.b. and VIII.B.3.c. of this 
final rule, we are finalizing the adoption of two additional measures 
(the SNF Healthcare-Associated Infections (HAI) Requiring 
Hospitalization and the Total Nursing Hours per Resident Day/Payroll-
Based Journal (Total Nurse Staffing) measures) beginning with the FY 
2026 Program. The SNF HAI measure is calculated using Medicare FFS 
claims data, therefore, this measure will not create any new reporting 
burden for SNFs. The Total Nurse Staffing measure is calculated using 
data that SNFs currently report to CMS under the Nursing Home Five-Star 
Quality Rating System, and therefore, this will not create new 
reporting burden for SNFs.
    In section VIII.B.3.d. of this final rule, we are finalizing the 
adoption of the DTC PAC Measure for SNFs beginning with the FY 2027 
Program. The DTC PAC SNF measure is calculated using Medicare FFS 
claims data; therefore, this measure will not create a new reporting 
burden for SNFs.
    The aforementioned FFS-related claims submission requirements and 
burden are active and approved by OMB under control number 0938-1140 
(CMS-10387). This rule's changes will have no impact on the 
requirements and burden that are currently approved under that control 
number.

XI. Economic Analyses

A. Regulatory Impact Analysis

1. Statement of Need
a. Statutory Provisions
    This final rule updates the FY 2023 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. These are statutory 
provisions that prescribe a detailed methodology for calculating and 
disseminating payment rates under the SNF PPS, and we do not have the 
discretion to adopt an alternative approach on these issues.
    With respect to the SNF QRP, this final rule updates the FY 2024 
SNF QRP requirements. Section 1888(e)(6) of the Act authorizes the SNF 
QRP and applies to freestanding SNFs, SNFs affiliated with acute care 
facilities, and all non-critical access hospital (CAH) swing-bed rural 
hospitals. We finalize one new measure which we believe will encourage 
healthcare personnel to receive the influenza vaccine, resulting in 
fewer cases, less hospitalizations, and lower mortality associated with 
the virus. We finalize a revision to the compliance date for certain 
SNF QRP reporting requirements to improve data collection to allow for 
better measurement and reporting on equity across post-acute care 
programs and policies. For consistency in our regulations, we are also 
finalizing conforming revisions to the Requirements under the SNF QRP 
at Sec.  413.360.
    With respect to the SNF VBP Program, this final rule updates SNF 
VBP Program requirements for FY 2023 and subsequent years, including a 
policy to suppress the Skilled Nursing Facility 30-Day All-Cause 
Readmission Measure (SNFRM) for the FY 2023 SNF VBP Program Year for 
scoring and payment adjustment purposes. In addition, section 
1888(h)(3) of the Act requires the Secretary to establish and announce 
performance standards for SNF VBP Program measures no later than 60 
days before the performance period, and this final rule finalizes 
numerical values of the performance standards for the all-cause, all-
condition hospital readmission measure. Section 1888(h)(2)(A)(ii) of 
the Act (as amended by section 111(a)(2)(C) of the Consolidated 
Appropriations Act, 2021 (Pub. L. 116-120)) allows the Secretary to add 
up to nine new measures to the SNF VBP Program, and in this final rule 
we are also adding two new measures to the SNF VBP Program beginning 
with the FY 2026 SNF VBP program year and one new measure beginning 
with the FY 2027 program year and finalizing several updates to the 
scoring methodology beginning with the FY 2026 program year. We have 
updated regulations at Sec.  413.338 in accordance with these updates.
    With respect to LTC physical environment changes and the changes to 
the requirements for the Director of Food and Nutrition Services in LTC 
facilities, sections 1819 and 1919 of the Act, authorize the Secretary 
to issue requirements for participation in Medicare and Medicaid, 
including such regulations as may be necessary to protect the health 
and safety of residents (sections 1819(d)(4)(B) and 1919(d)(4)(B) of 
the Act). Such regulations are codified in the implementing regulations 
at 42 CFR part 483, subpart B.
b. Discretionary Provisions
    In addition, this final rule includes the following discretionary 
provisions:
(1) Recalibrating the Patient Driven Payment Model (PDPM) Parity 
Adjustment
    As a policy decision to ensure on-going budget neutral 
implementation of the new case mix system, the PDPM, we proposed a 
recalibration of the PDPM parity adjustment. Since October 1, 2019, we 
have been monitoring the implementation of PDPM and our analysis of FY 
2020 and FY 2021 data reveals that the PDPM implementation led to an 
increase in Medicare Part A SNF spending, even after accounting for the 
effects of the COVID-19 PHE. We noted that recalibrating the PDPM 
parity adjustment and reducing SNF spending by 4.6 percent, or $1.7 
billion, in FY 2023 with no delayed implementation

[[Page 47602]]

or phase-in period would allow for the most rapid establishment of 
payments at the appropriate level. This would work to ensure that PDPM 
will be budget-neutral as intended and prevent continuing accumulation 
of excess SNF payments, which we cannot recoup. However, while we 
received few comments on the methodology used to calculate the PDPM 
parity adjustment, we received a significant number of comments 
recommending that CMS use a phased approach in implementing the 
recalibration of the parity adjustment. These comments, and our 
responses, are discussed in section VI.C of this final rule. 
Considering these comments, in this final rule, we are finalizing the 
proposed recalibration of the PDPM parity adjustment with a 2-year 
phase-in, resulting in a reduction in FY 2023 of 2.3 percent, or $780 
million, and a reduction in FY 2024 of 2.3 percent.
(2) SNF Forecast Error Adjustment
    Each year, we evaluate the market basket forecast error for the 
most recent year for which historical data is available. The forecast 
error is determined by comparing the projected market basket increase 
in a given year with the actual market basket increase in that year. In 
evaluating the data for FY 2021, we found that the forecast error for 
FY 2021 was 1.5 percentage point, exceeding the 0.5 percentage point 
threshold we established in regulation for proposing adjustments to 
correct for forecast error. Given that the forecast error exceeds the 
0.5 percentage threshold, current regulations require that the SNF 
market basket percentage change for FY 2023 be increased by 1.5 
percentage point.
(3) Proposed Permanent Cap on Wage Index Decreases
    The Secretary has broad authority to establish appropriate payment 
adjustments under the SNF PPS, including the wage index adjustment. As 
discussed earlier in this section, the SNF PPS regulations require us 
to use an appropriate wage index based on the best available data. For 
the reasons discussed earlier in this section, we believe that a 5-
percent cap on wage index decreases would be appropriate for the SNF 
PPS. Therefore, for FY 2023 and subsequent years, we proposed to apply 
a permanent 5-percent cap on any decrease to a provider's wage index 
from its wage index in the prior year, regardless of the circumstances 
causing the decline. In this final rule, we are finalizing this 
proposed cap, as proposed.
(4) Technical Updates to ICD-10 Mappings
    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 of each year. In the proposed rule, 
we proposed several changes to the ICD-10 code mappings and lists. In 
this final rule, we are finalizing these proposed changes to the PDPM 
ICD-10 mappings, as proposed.
2. Introduction
    We have examined the impacts of this final 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. Based on our estimates, OMB's Office of Information and 
Regulatory Affairs has determined this rulemaking is ``economically 
significant'' as measured by the $100 million threshold. Accordingly, 
we have prepared a regulatory impact analysis (RIA) as further 
discussed below.
3. Overall Impacts
    This rule updates the SNF PPS rates contained in the SNF PPS final 
rule for FY 2022 (86 FR 42424). We estimated in the proposed rule that 
the aggregate impact would be a decrease of approximately $320 million 
(0.9 percent) in Part A payments to SNFs in FY 2023. This reflected a 
$1.4 billion (3.9 percent) increase from the proposed update to the 
payment rates and a $1.7 billion (4.6 percent) decrease from the 
proposed reduction to the SNF payment rates to account for the 
recalibrated parity adjustment. We noted in the proposed rule that 
these impact numbers do not incorporate the SNF VBP Program reductions 
that we estimated would total $185.55 million in FY 2023. We noted in 
the proposed rule 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.
    For this final rule, as noted in section IV.B. of this final rule, 
we have updated the productivity-adjusted market basket increase factor 
for FY 2023 based on a more recent forecast. Additionally, as discussed 
in section VI.C of this final rule, we are finalizing a 2-year phase-in 
for recalibrating the PDPM parity adjustment. As a result, we estimate 
that the aggregate impact of the provisions in this final rule will 
result in an estimated net increase in SNF payments of 2.7 percent, or 
$904 million, for FY 2023. This reflects a 5.1 percent increase from 
the final update to the payment rates and a 2.3 percent decrease from 
the reduction to the SNF payment rates to account for the recalibrated 
parity adjustment, using the formula to multiply the percentage change 
described in section X.A.4. of this final rule.
    In accordance with sections 1888(e)(4)(E) and (e)(5) of the Act and 
implementing regulations at Sec.  413.337(d), we are updating the FY 
2022 payment rates by a factor equal to the market basket index 
percentage change increased by the forecast error adjustment and 
reduced by the productivity adjustment to determine the payment rates 
for FY 2023. The impact to Medicare is included in the total column of 
Table 19. When we proposed the SNF PPS rates for FY 2023, we proposed a 
number of standard annual revisions and clarifications as mentioned in 
the proposed rule.
    The annual update in this rule applies to SNF PPS payments in FY 
2023. 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

[[Page 47603]]

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 2023 SNF PPS payment impacts appear in Table 19. Using the 
most recently available data, in this case FY 2021 we apply the current 
FY 2022 CMIs, wage index and labor-related share value to the number of 
payment days to simulate FY 2022 payments. Then, using the same FY 2021 
data, we apply the FY 2023 CMIs, wage index and labor-related share 
value to simulate FY 2023 payments. We noted in the proposed rule that, 
given that this same data is being used for both parts of this 
calculation, as compared to other analyses discussed in the proposed 
rule which compare data from FY 2020 to data from other fiscal years, 
any issues discussed throughout this rule with regard to data collected 
in FY 2020 will not cause any difference in this economic analysis. We 
tabulate the resulting payments according to the classifications in 
Table 19 (for example, facility type, geographic region, facility 
ownership), and compare the simulated FY 2022 payments to the simulated 
FY 2023 payments to determine the overall impact. The breakdown of the 
various categories of data in Table 19 is as 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 parity 
adjustment recalibration discussed in section V.C. of this final rule.
     The fourth column shows the effect of the annual update to 
the wage index. This represents the effect of using the most recent 
wage data available as well as accounts for the 5 percent cap on wage 
index transitions, discussed in section VI.A. of this final rule. The 
total impact of this change is 0.0 percent; however, there are 
distributional effects of the proposed change.
     The fifth column shows the effect of all of the changes on 
the FY 2023 payments. The update of 5.1 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 5.1 
percent, assuming facilities do not change their care delivery and 
billing practices in response.
    As illustrated in Table 19, the combined effects of all of the 
changes vary by specific types of providers and by location. For 
example, due to changes in this final rule, rural providers would 
experience a 2.5 percent increase in FY 2023 total payments.
    In this chart and throughout the rule, we use a multiplicative 
formula to derive total percentage change. This formula is:

(1 + Parity Adjustment Percentage) * (1 + Wage Index Update Percentage) 
* (1 + Payment Rate Update Percentage)-1 = Total Percentage Change

    For example, the figures shown in Column 5 of Table 19 are 
calculated by multiplying the percentage changes using this formula. 
Thus, the Total Change figure for the Total Group Category is 2.7 
percent, which is (1-2.3%) * (1 + 0.0%) * (1 + 5.1%)-1.
    As a result of rounding and the use of this multiplicative formula 
based on percentage, derived dollar estimates may not sum.
BILLING CODE 4120-01-P

[[Page 47604]]

[GRAPHIC] [TIFF OMITTED] TR03AU22.021

BILLING CODE 4120-01-C
5. Impacts for the Skilled Nursing Facility Quality Reporting Program 
(SNF QRP) for FY 2023
    Estimated impacts for the SNF QRP are based on analysis discussed 
in section IX.B. of the proposed rule.
    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 the proposed rule, we discussed 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.1. of the proposed rule, we proposed 
the adoption of one new measure to the SNF QRP beginning with the FY 
2024 SNF QRP, the Influenza Vaccination Coverage among HCP (NQF #0431) 
measure. 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 Influenza Vaccination Coverage among HCP (NQF #0431) measure 
is not accounted for under the Centers for Diseases Control and 
Prevention Paperwork Reduction Act (CDC PRA) package due to the NCVIA 
waiver discussed in section IX. of this final rule, the cost and burden 
are discussed here.
    Consistent with the CDC's experience of collecting data using the 
NHSN, we estimated that it would take each SNF an average of 15 minutes 
per year to collect data for the Influenza Vaccination Coverage among 
HCP (NQF #0431) measure and enter it into NHSN. We did not estimate 
that it will take SNFs additional time to input their data into NHSN, 
once they have logged onto the system for the purpose of submitting 
their monthly COVID-19 vaccine report. We believe it would take an 
administrative assistant 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 2020 National Occupational Employment 
and

[[Page 47605]]

Wage Estimates.\300\ To account for overhead and fringe benefits, we 
have doubled the hourly wage. These amounts are detailed in Table 20.
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    \300\ https://www.bls.gov/oes/current/oes_nat.htm. Accessed 
February 1, 2022.
[GRAPHIC] [TIFF OMITTED] TR03AU22.022

    Based on this time range, it would cost each SNF an average cost of 
$9.38 each year. We believe the data submission for the Influenza 
Vaccination Coverage among HCP (NQF #0431) measure would cause SNFs to 
incur additional average burden of 15 minutes per year for each SNF and 
a total annual burden of 3,868 hours across all SNFs. The estimated 
annual cost across all 15,472 SNFs in the U.S. for the submission of 
the Influenza Vaccination Coverage among HCP (NQF #0431) measure would 
be an average of $145,127.36.
    As discussed in section VII.C.2. of the proposed rule, we proposed 
that SNFs would begin collecting data on two quality measures and 
certain standardized patient assessment data elements beginning with 
discharges on October 1, 2023. CMS estimated the impacts for collecting 
the new data elements in the FY 2020 SNF PPS final rule (84 FR 38829). 
When we delayed the compliance date for certain reporting requirements 
under the SNF QRP in the May 8th COVID-19 IFC, we did not remove the 
impacts for the new reporting requirements. However, we are providing 
updated impact information.
    For these two quality measures, we are adding 4 data elements on 
discharge which would require an additional 1.2 minutes of nursing 
staff time per discharge. We estimate these data elements for these 
quality measures would be completed by registered nurses (25 percent of 
the time or 0.30 minutes) and by licensed practical and vocational 
nurses (75 percent of the time or 0.90 minutes). 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 2020 National Occupational Employment and Wage 
Estimates.\301\ To account for overhead and fringe benefits, we have 
doubled the hourly wage. These amounts are detailed in Table 21.
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    \301\ https://www.bls.gov/oes/current/oes_nat.htm. Accessed 
February 1, 2022.
[GRAPHIC] [TIFF OMITTED] TR03AU22.023

    With 2,406,401 discharges from 15,472 SNFs annually, we estimate an 
annual burden of 48,128 additional hours (2,406,401 discharges x 1.2 
min/60) at a cost of $2,664,127 (2,406,401 x [(0.30/60 x $76.94/hr) + 
(0.90/60 x $48.16/hr)]). For each SNF we estimate an annual burden of 
3.11 hours (48,128 hr/15,472 SNFs) at a cost of $172.19 ($2,664,127/
15,472 SNFs).
    We also proposed SNFs would begin collecting data on certain 
standardized patient assessment data elements, beginning with 
admissions and discharges (except for the preferred language, need for 
interpreter services, hearing, vision, race, and ethnicity standardized 
patient assessment data elements, which would be collected at admission 
only) on October 1, 2023. If finalized as proposed, SNFs would use the 
MDS 3.0 V1.18.11 to submit SNF QRP data. We are finalizing requirements 
to collect 55.5 standardized patient assessment data elements 
consisting of 8 data elements on admission and 47.5 data elements on 
discharge beginning with the FY 2024 SNF QRP. We estimate that the data 
elements would take an additional 12.675 minutes of nursing staff time 
consisting of 1.725 minutes to report on each admission and 10.95 
minutes to report on each discharge. We assume the added data elements 
would be performed by both registered nurses (25 percent of the time or 
3.169 minutes) and licensed practical and vocational (75 percent of the 
time or 9.506 minutes). We estimate the reporting of these assessment 
items will impose an annual burden of 508,352 total hours (2,406,401 
discharges x 12.675 min/60) at a cost of $28,139,825 ((508,352 hr x 
0.25 x $76.94/hr) + (508,352 hr x 0.75 x $48.16/hr)). For each SNF the 
annual burden is 32.86 hours (508,352 hr/15,472 SNFs) at a cost of 
$1,818.76 ($28,139,825/15,472 SNFs). The overall annual cost of the 
finalized changes associated with the newly added 59.5 assessment items 
is estimated at $1,990.95 per SNF annually ($172.19 + $1,818.76), or 
$30,803,952 ($2,664,127 + $28,139,825) for all 15,472 SNFs annually.

[[Page 47606]]

    We proposed in section VI.C.3. of the proposed rule to make certain 
revisions in the regulation text itself at Sec.  413.360 to include new 
paragraph (f) to reflect all the data completion thresholds required 
for SNFs to meet the compliance threshold for the annual payment 
update, as well as certain conforming revisions. As discussed in 
section IX. of the final rule, this change would not affect the 
information collection burden for the SNF QRP.
    We welcomed comments on the estimated time to collect influenza 
vaccination data and enter it into NHSN. We received public comments on 
this issue. The following is a summary of the comments we received and 
our responses.
    Comment: One commenter expressed concern with respect to CMS' 15-
minute burden estimate for reporting the measure, noting it may be an 
underestimation.
    Response: The burden associated with the proposed measure is the 
time it takes to sign into the NHSN, complete the required NHSN forms 
and submit the data. We estimate that data collection and reporting of 
the measure into the NHSN should take approximately 15-minutes 
annually, and can be completed once they have logged onto the system 
for the purpose of submitting their monthly COVID-19 vaccine report. 
The commenter did not provide additional information to support why 
CMS' estimate did not capture the full burden for the reporting 
requirements. We are confident with this estimation since the measure 
has been reported in the IRF and LTCH quality reporting programs for 
several years. Additionally, all SNF providers have been using the NHSN 
for data submission for approximately 15 months, and therefore, have 
familiarity with it. Without additional information, we are unable to 
respond further.
    Although we did not seek comment on the proposal to Revise the 
Compliance Date for the Transition of Health (TOH) information measures 
and certain standardized patient assessment data elements beginning 
with the FY 2024 QRP, we did receive one comment.
    Comment: A commenter expressed concern with CMS' burden estimate of 
3.11 hours annually for reporting of the TOH Information measures and 
32.86 hours annually for the collection of the standardized patient 
assessment data elements, noting that it may not capture the full 
actual burden of the new reporting requirements.
    Response: We interpret the commenter to be referring to CMS' 
estimated impacts for collecting the new data elements published in the 
FY 2020 SNF PPS final rule (84 FR 38829). However, the commenter did 
not provide additional information to support why CMS' estimate did not 
capture the full burden for the reporting requirements. The estimate is 
based on CMS' assumption that the data elements would be performed by 
both Registered Nurses and Licensed Practical Nurses. Without 
additional information, we are unable to respond further.
    After consideration of public comments, we are finalizing our 
burden estimate for the data submission for the Influenza Vaccination 
Coverage among HCP (NQF #0431) measure. The burden estimate for the 
reporting of the TOH Information measures and collection of the 
standardized patient assessment data elements was finalized in the FY 
2020 SNF PPS final rule (84 FR 38829).
6. Impacts for the SNF VBP Program
    The estimated impacts of the FY 2023 SNF VBP Program are based on 
historical data and appear in Table 22. We modeled SNF performance in 
the Program using SNFRM data from FY 2018 as the baseline period and 
April 1st through December 1st, 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).
    However, in section VIII.B.1 of this final rule, we discuss the 
suppression of the SNFRM for the FY 2023 program year. As finalized, we 
will award each participating SNF 60 percent of their 2 percent 
withhold. Additionally, we finalized our proposal to apply a case 
minimum requirement for the SNFRM in section VIII.E.3.b. of this final 
rule. In section VIII.E.5. of this final rule, we also finalized our 
proposal to remove the Low-Volume Adjustment policy beginning with the 
FY 2023 Program year. As a result of these provisions, SNFs that do not 
meet the case minimum specified for the FY 2023 program year will be 
excluded from the Program and will receive their full Federal per diem 
rate for that fiscal year. As finalized, this policy will maintain the 
overall payback percentage at 60 percent.
    Based on the 60 percent payback percentage, we estimated that we 
will redistribute approximately $278.32 million (of the estimated 
$463.86 million in withheld funds) in value-based incentive payments to 
SNFs in FY 2023, which means that the SNF VBP Program is estimated to 
result in approximately $185.55 million in savings to the Medicare 
Program in FY 2023.
    Our detailed analysis of the impacts of the FY 2023 SNF VBP Program 
is shown in Table 22.

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    In section VIII.B.2. of this final rule, we are adopting two 
additional measures (the SNF HAI and Total Nurse Staffing measures) 
beginning with the FY 2026 program year. Additionally, we finalized our 
proposal to apply a case minimum requirement for the SNF HAI and Total 
Nurse Staffing measures in section VIII.E.3.c. of this final rule. In 
section VIII.E.3.d. of this final rule, we also finalized our proposal 
to adopt a measure minimum policy for the FY 2026 program year. 
Therefore, we are providing estimated impacts of the FY 2026 SNF VBP 
Program, which are based on historical data and appear in Table 23. We 
modeled SNF performance in the Program using measure data from FY 2018 
as the baseline period and FY 2019 as the performance period for the 
SNFRM, SNF HAI, and Total Nurse Staffing measures. 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 noted that the logistic exchange function and 
payback percentage policies could be reconsidered in a future 
rulemaking. Based on the 60 percent payback percentage, we estimated 
that we will redistribute approximately $296.44 million (of the 
estimated $494.07 million in withheld funds) in value-based incentive 
payments to SNFs in FY 2026, which means that the SNF VBP Program is 
estimated to result in approximately $197.63 million in

[[Page 47608]]

savings to the Medicare Program in FY 2026.
    Our detailed analysis of the impacts of the FY 2026 SNF VBP Program 
is shown in Table 23.
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    In section VIII.B.2. of this final rule, we are adopting one 
additional measure (the DTC PAC SNF measure) beginning with the FY 2027 
program year. Additionally, we finalized our proposal to apply a case 
minimum requirement for the DTC PAC SNF measure in section VIII.E.3.c. 
of this final rule. In section VIII.E.3.d, of this final rule, we also 
finalized our proposal to adopt a measure minimum policy for the FY 
2027 program year. Therefore, we are providing estimated impacts of the 
FY 2027 SNF VBP Program, which are based on historical data and appear 
in

[[Page 47609]]

Table 24. We modeled SNF performance in the Program using measure data 
from FY 2018 (the SNFRM, SNF HAI, and Total Nurse Staffing measures) 
and FY 2017 through FY 2018 (the DTC PAC SNF measure) as the baseline 
period and FY 2019 (the SNFRM, SNF HAI, and Total Nurse Staffing 
measures) and FY 2019 through FY 2020 (the DTC PAC SNF measure) 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 
noted that the logistic exchange function and payback percentage 
policies could be reconsidered in a future rule. Based on the 60 
percent payback percentage, we estimated that we will redistribute 
approximately $294.67 million (of the estimated $491.12 million in 
withheld funds) in value-based incentive payments to SNFs in FY 2027, 
which means that the SNF VBP Program is estimated to result in 
approximately $196.45 million in savings to the Medicare Program in FY 
2027.
    Our detailed analysis of the impacts of the FY 2027 SNF VBP Program 
is shown in Table 24.
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7. Impacts for LTC Physical Environment Changes
    As discussed at section IX. of this rule, we are finalizing our 
proposal at Sec.  483.90(a)(1)(iii) based on public comments. We are 
allowing those existing LTC facilities (those that were Medicare or 
Medicaid certified before July 5, 2016) that have previously used the 
FSES to determine equivalent fire protection levels, to continue to use 
the 2001 FSES mandatory values when determining compliance for 
containment, extinguishment and people movement requirements. This will 
allow existing LTC facilities that previously met the FSES requirements 
to continue to do so without incurring great expense to change 
construction type--essentially undertake an effort to completely 
rebuild.
    While we do not have information on the number of facilities that 
undertake reconstruction in a given year, we can estimate the number of 
facilities placed at risk of a deficiency citation by these 
requirements, and thus the risk of being required to rebuild the 
structure in order to update the building's construction type, by 
considering the age of the facility and the building methodologies used 
in given time periods. We consulted with CMS Regional Office survey 
staff, and based on information received from them, we estimate that 50 
facilities are directly impacted by the change in the scoring of the 
FSES and would no longer achieve a passing score on the FSES. We 
estimate the average size of the affected nursing homes to be roughly 
25,000 sq. ft. The cost of construction per sq. ft. is estimated at 
$180 in 2013 dollars (https://www.rsmeans.com/model-pages/nursing-home.aspx). Assuming a construction cost increase over this period of 
10.33 percent using GDP deflator, the 2019 construction cost per square 
foot would be about $199 a square foot. The total savings from this 
proposal in 2019 dollars would be approximately $248,750,000 (25,000 
sq. ft. x $199 per sq. ft. x 50 facilities).
    This estimate assumes that essentially all these facilities would 
be replaced. Based on our research, we assume that there are two major 
and offsetting trends affecting the nursing home care market in coming 
decades: the increasing preference and ability of elderly and disabled 
adults to finance and obtain long term nursing care in their own homes; 
and the increasing number of elderly and disabled adults as the baby 
boom population ages.302 303 Assuming, absent specific 
evidence, that these two trends roughly offset each other, the 
preceding estimates are a reasonable projection of likely investment 
costs in new (or totally reconstructed) facilities. For purposes of 
annual cost estimates, we assume that those costs would be spread over 
5 years, and would therefore be approximately $49,750,000 million 
annually in those years ($248,750,000 million/5 years). There are 
additional uncertainties in these estimates and we therefore provide 
estimates that are 25 percent lower and higher in Table 28.
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    \302\ https://www.cbo.gov/sites/default/files/cbofiles/attachments/44363-LTC.pdf.
    \303\ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1464018/.
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8. Impacts for Changes to the Requirements for the Director of Food and 
Nutrition Services in LTC Facilities
    As discussed in section IX. of this final rule, we are revising our 
proposal to revise the required qualifications for a director of food 
and nutrition services to provide that those with several years of 
experience performing as the director of food and nutrition services in 
a facility can continue to do so. In addition to the existing 
credentialing requirements for the director of food and nutrition 
services to include being a ``certified food service manager,'' or 
``certified dietary manager,'' or ``has similar national certification 
from a national certifying body,'' or ``has an associate's or higher 
degree in food service or restaurant management'', we have added that 
an individual with 2 or more years of experience and completion of a 
course in food safety and management may also meet the required 
qualifications. Under the October 2016 final rule, a significant 
fraction of current directors of food and nutrition services would have 
had to be replaced or, at great expense, have had to attend an 
institution of higher education to obtain required credentials.
    The current annual cost for the director of food and nutrition 
services is an estimated $122,400 annually (updated to reflect current 
salary information and including fringe benefits and overhead costs). 
We previously estimated that 10 percent of facilities would need to 
pursue additional candidates that meet the new qualifications for a 
director of food and nutrition services. Assuming that, on average, 
there is a 10 percent wage differential between those with experience 
but no further credentials, and those who would have met the standards 
of the October 2016 final rule for director of food and nutrition 
services either as specified in that rule, or by meeting the even 
higher standards for ``qualified dietician,'' this means that removing 
those standards would reduce costs to facilities by $18,929,840.00 (10 
percent of 15,266 facilities x $12,400). In this calculation, the wage 
differential is assumed to be about 10 percent because there are 
offsetting costs to the facility for retaining staff who are qualified 
by experience but who may need expert help, such as the proposed 
requirement for frequently scheduled consultation with a qualified 
dietician.
    We are requiring that an individual may also be designated as the 
director of food and nutrition services if they have 2 or more years of 
experience in the position and has completed a minimum course of study 
in food safety. These revisions will provide an experience qualifier 
that will likely eliminate the need for many facilities to hire 
additional or higher salaried staff.
9. Alternatives Considered
    As described in this section, we estimate that the aggregate impact 
of the provisions in this final rule will result in an estimated net 
increase in SNF payments of 2.7 percent, or $904 million, for FY 2023. 
This reflects a 5.1 percent increase from the final update to the 
payment rates and a 2.3 percent decrease from the reduction to the SNF 
payment rates to account for the recalibrated parity adjustment, using 
the formula to multiply the percentage change described in section 
X.A.4. of this final rule.
    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.

[[Page 47613]]

    With regard to the alternatives considered related to the 
methodology for calculating the proposed parity adjustment to the 
rates, we considered numerous alternative approaches to the 
methodology, including alternative data sets, applying the parity 
adjustment to targeted components of the payment system, and delaying 
or phasing-in the parity adjustment. These alternatives were described 
in full detail in section V.C. of the proposed rule.
    With regard to the proposal to add the HCP Influenza Vaccine 
measure to the SNF QRP Program, the COVID-19 pandemic has exposed the 
importance of implementing infection prevention strategies, including 
the promotion of HCP influenza vaccination. We believe this measure 
will encourage healthcare personnel to receive the influenza vaccine, 
resulting in fewer cases, less hospitalizations, and lower mortality 
associated with the virus, but were unable to identify any alternative 
methods for collecting the data. A compelling public need exists to 
target quality improvement among SNF providers and this proposed 
measure has the potential to generate actionable data on HCP 
vaccination rates.
    With regard to the proposal to revise the compliance date for the 
MDS v1.18.11, section 1888(d)(6)(B)(i)(III) of the Act requires that, 
for fiscal years 2019 and each subsequent year, SNFs must report 
standardized patient assessment data required under section 1899B(b)(1) 
of the Act. Section 1899(a)(1)(C) of the Act requires, in part, the 
Secretary to modify the PAC assessment instruments in order for PAC 
providers, including SNFs, to submit standardized patient assessment 
data under the Medicare program. Further delay of collecting this data 
would delay compliance with the current regulations.
    As discussed previously the burden for these proposals is minimal, 
and we believe the importance of the information necessitates these 
provisions.
    With regard to the proposals for the SNF VBP Program, we discussed 
alternatives considered within those sections. In section VIII.B.2. of 
this final rule, we considered 4 options to adjust for COVID-19 in a 
technical update to the SNFRM. None of the alternatives will change the 
analysis of the impacts of the FY 2023 SNF VBP Program described in 
section VIII.B.2. of this final rule. In section VIII.C.2. of this 
final rule, we finalized our proposal to revise the baseline period for 
the FY 2025 SNF VBP Program to FY 2019. We considered using alternative 
baseline periods, including FY 2020 and FY 2022, but these options are 
operationally infeasible.
    In section VIII.E.3.c. of this final rule, we finalized our 
proposal that SNFs must have a minimum of 25 residents, on average, 
across all available quarters during the applicable 1-year performance 
period in order to be eligible to receive a score on the Total Nurse 
Staffing measure. We tested three alternative case minimums for this 
measure: a 25-resident minimum, a minimum of one quarter of PBJ data, 
and a minimum of two quarters of PBJ data. After considering these 
alternatives, we determined that the proposed 25-resident minimum best 
balances quality measure reliability with our desire to score as many 
SNFs as possible on this measure.
    In section VIII.E.3.d. of this final rule, we finalized our 
proposed measure minimums for the FY 2026 and FY 2027 SNF VBP Programs. 
SNFs that do not meet these minimum requirements would be excluded from 
the Program and would receive their full Federal per diem rate for that 
fiscal year. We also discussed alternatives, which are detailed below, 
that would result in more SNFs being excluded from the Program.
    We finalized that for FY 2026, SNFs must have the minimum number of 
cases for two of the three measures during the performance period to 
receive a performance score and value-based incentive payment. Under 
these minimum requirements for the FY 2026 program year, we estimated 
that approximately 14 percent of SNFs would be excluded from the FY 
2026 Program. Alternatively, if we required SNFs to have the minimum 
number of cases for all three measures during the performance period, 
approximately 21 percent of SNFs would be excluded from the FY 2026 
Program. We also assessed the consistency of incentive payment 
multipliers (IPMs) between time periods as a proxy for performance 
score reliability under the different measure minimum options. The 
testing results indicated that the reliability of the SNF performance 
score would be relatively consistent across the different measure 
minimum requirements. Specifically, for the FY 2026 program year, we 
estimated that under the proposed minimum of two measures, 82 percent 
of SNFs receiving a net-negative IPM in the first testing period also 
received a net-negative IPM in the second testing period. 
Alternatively, under a minimum of three measures for the FY 2026 
program year, we found that the consistency was 81 percent. Based on 
these testing results, we believe the minimum of two out of three 
measures for FY 2026 best balances SNF performance score reliability 
with our desire to ensure that as many SNFs as possible can receive a 
performance score and value-based incentive payment.
    We finalized that for FY 2027, SNFs must have the minimum number of 
cases for three of the four measures during a performance period to 
receive a performance score and value-based incentive payment. Under 
these minimum requirements, we estimated that approximately 16 percent 
of SNFs would be excluded from the FY 2027 Program. Alternatively, if 
we required SNFs to report the minimum number of cases for all four 
measures, we estimated that approximately 24 percent of SNFs would be 
excluded from the FY 2027 Program. We also assessed the consistency of 
incentive payment multipliers (IPMs) between time periods as a proxy 
for performance score reliability under the different measure minimum 
options. The testing results indicated that the reliability of the SNF 
performance score for the FY 2027 program year would be relatively 
consistent across the different measure minimum requirements. That is, 
among the different measure minimums for the FY 2027 program year, a 
strong majority (between 85 and 87 percent) of the SNFs receiving a 
net-negative IPM for the first testing period also received a net-
negative IPM for the second testing period. These findings indicated 
that increasing the measure minimum requirements did not meaningfully 
increase the consistency of the performance score. Based on these 
testing results, we believe the minimum of three out of four measures 
for FY 2027 best balances SNF performance score reliability with our 
desire to ensure that as many SNFs as possible can receive a 
performance score and value-based incentive payment.
10. Accounting Statement
    As required by OMB Circular A-4 (available online at https://obamawhitehouse.archives.gov/omb/circulars_a004_a-4/), in Tables 25 
through 27, we have prepared an accounting statement showing the 
classification of the expenditures associated with the provisions of 
this final rule for FY 2023. Tables 19 and 25 provide our best estimate 
of the possible changes in Medicare payments under the SNF PPS as a 
result of the policies in this final rule, based on the data for 15,541 
SNFs in our database. Table 26 provides our best estimate of the 
possible changes in Medicare payments under the SNF VBP as a result of 
the policies for this program. Tables 20 and

[[Page 47614]]

27 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 final 
rule. Table 28 provides our best estimate of the costs avoided by 
Medicare and Medicaid SNFs/NFs. This is our estimate of the aggregate 
costs of SNFs nationwide to rebuild facility structures for compliance 
for fire protection or LTC Physical Environment Changes. These costs 
will be avoided as a result of the policies in this final rule. Table 
29 provides our best estimate of the amount saved by Medicare and 
Medicaid-participating SNFs/NFs to designate a director of Food and 
Nutrition (F&N) Services as a result of the policies in this final 
rule.
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BILLING CODE 4120-01-C
11. Conclusion
    This rule updates the SNF PPS rates contained in the SNF PPS final 
rule for FY 2022 (86 FR 42424). Based on the above, we estimate that 
the overall payments for SNFs under the SNF PPS in FY 2023 are 
projected to increase by approximately $904 million, or 2.7 percent, 
compared with those in FY 2022. We estimate that in FY 2023, SNFs in 
urban and rural areas would experience, on average, a 2.7 percent 
increase and 2.5 percent increase, respectively, in estimated payments 
compared with FY 2022. Providers in the urban Pacific region would 
experience the largest estimated increase in payments of approximately 
3.6 percent. Providers in the urban Outlying region would experience 
the smallest estimated increase in payments of 1.4 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 https://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 updates the SNF PPS rates contained in the SNF PPS final 
rule for FY 2022 (86 FR 42424). Based on the above, we estimate that 
the aggregate impact for FY 2023 will be an increase of $904 million in 
payments to SNFs, resulting from the final SNF market basket update to 
the payment rates, reduced by the parity adjustment discussed in 
section VI.C. of this final rule, using the formula described in 
section X.A.4. of this rule. While it is projected in Table 19 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 2023 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 2022 Report to Congress 
(available at https://www.medpac.gov/wp-content/uploads/2022/03/Mar22_MedPAC_ReportToCongress_Ch7_SEC.pdf), MedPAC states that Medicare 
covers approximately 10 percent of total patient days in freestanding 
facilities and 17 percent of facility revenue (March 2022 MedPAC Report 
to Congress, 238). As indicated in Table 19, the effect on facilities 
is projected to be an aggregate positive impact of 2.7 percent for FY 
2023. 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 final rule 
will not have a significant impact on a substantial number of small 
entities for FY 2023.
    In addition, section 1102(b) of the Act requires us to prepare a 
regulatory impact analysis if a rule may have a significant impact on 
the operations of a substantial number of small rural hospitals. This 
analysis must conform to the provisions of section 604 of the RFA. For 
purposes of section 1102(b) of the Act, we define a small rural 
hospital as a hospital that is located outside of an MSA and has fewer 
than 100 beds. This final rule will 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 similar to the impact on SNF providers overall. 
Moreover, as noted in previous SNF PPS final rules (most recently, the 
one for FY 2022 (86 FR 42424)), the category of small rural hospitals 
is included within the analysis of the impact of this final rule on 
small entities in general. As indicated in Table 19, the effect on 
facilities for FY 2023 is projected to be an aggregate positive impact 
of 2.7 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 final rule will not have a significant impact 
on a substantial number of small rural hospitals for FY 2023.

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 2022, that 
threshold is approximately $165 million. This final rule will 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 final rule will have no substantial direct effect on 
State and local governments, preempt State law, or otherwise have 
federalism implications.

E. Regulatory Review Costs

    If regulations impose administrative costs on private entities, 
such as the time needed to read and interpret this

[[Page 47616]]

final rule, we should estimate the cost associated with regulatory 
review. Due to the uncertainty involved with accurately quantifying the 
number of entities that will review the rule, we assume that the total 
number of unique commenters on this year's proposed rule will be the 
number of reviewers of this year's final rule. We acknowledge that this 
assumption may understate or overstate the costs of reviewing this 
rule. It is possible that not all commenters reviewed this 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 this year's proposed rule is a 
fair estimate of the number of reviewers of this year's final rule.
    We also recognize that different types of entities are in many 
cases affected by mutually exclusive sections of this final rule, and 
therefore, for the purposes of our estimate we assume that each 
reviewer reads approximately 50 percent of the rule.
    Using the national mean hourly wage data from the May 2020 BLS 
Occupational Employment Statistics (OES) for medical and health service 
managers (SOC 11-9111), we estimate that the cost of reviewing this 
rule is $114.24 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 final rule. For each SNF that reviews 
the rule, the estimated cost is $456.96 (4 hours x $114.24). Therefore, 
we estimate that the total cost of reviewing this regulation is 
$3,185,011.20 ($456.96 x 6,970 reviewers).
    In accordance with the provisions of Executive Order 12866, this 
final rule was reviewed by the Office of Management and Budget.

Chiquita Brooks-LaSure,
Administrator of the Centers for Medicare & Medicaid Services, approved 
this document on July 25, 2022.

List of Subjects

42 CFR Part 413

    Diseases, Health facilities, Medicare, Puerto Rico, Reporting and 
recordkeeping requirements.

42 CFR Part 483

    Grant programs--health, Health facilities, Health professions, 
Health records, Medicaid, Medicare, Nursing homes, Nutrition, Reporting 
and recordkeeping requirements, Safety.

    For the reasons set forth in the preamble, the Centers for Medicare 
& Medicaid Services amends 42 CFR chapter IV as set forth below:

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
1. The authority citation for part 413 continues to read as follows:

    Authority:  42 U.S.C. 1302, 1395d(d), 1395f(b), 1395g, 1395I(a), 
(i), and (n), 1395x(v), 1395hh, 1395rr, 1395tt, and 1395ww.

0
2. Amend Sec.  413.337 by revising paragraph (b)(4) to read as follows:


Sec.  413.337   Methodology for calculating the prospective payment 
rates.

* * * * *
    (b) * * *
    (4) Standardization of data for variation in area wage levels and 
case-mix. The cost data described in paragraph (b)(2) of this section 
are standardized to remove the effects of geographic variation in wage 
levels and facility variation in case-mix.
    (i) The cost data are standardized for geographic variation in wage 
levels using the wage index. The application of the wage index is made 
on the basis of the location of the facility in an urban or rural area 
as defined in Sec.  413.333.
    (ii) Starting on October 1, 2022, CMS applies a cap on decreases to 
the wage index such that the wage index applied to a SNF is not less 
than 95 percent of the wage index applied to that SNF in the prior FY.
    (iii) The cost data are standardized for facility variation in 
case-mix using the case-mix indices and other data that indicate 
facility case-mix.
* * * * *

0
3. Amend Sec.  413.338 by--
0
a. Revising paragraphs (a)(1) and (4) through (17);
0
b. Revising paragraphs (b) and (c)(2)(i), paragraph (d) paragraph 
heading, and paragraph (d)(3);
0
c. Adding paragraphs (d)(5) and (6);
0
d. Redesignating paragraphs (e) through (g) as paragraphs (f) through 
(h);
0
e. Adding a new paragraph (e);
0
f. Revising newly redesignated paragraph (f)(1) and paragraph (f)(3) 
introductory text; and
0
g. Adding paragraphs (f)(4), (i), and (j).

    The revisions and additions read as follows:


Sec.  413.338   Skilled nursing facility value-based purchasing 
program.

    (a) * * *
    (1) Achievement threshold (or achievement performance standard) 
means the 25th percentile of SNF performance on a measure during the 
baseline period for a fiscal year.
* * * * *
    (4) Baseline period means the time period used to calculate the 
achievement threshold, benchmark, and improvement threshold that apply 
to a measure for a fiscal year.
    (5) Benchmark means, for a fiscal year, the arithmetic mean of the 
top decile of SNF performance on a measure during the baseline period 
for that fiscal year.
    (6) Eligible stay means, for purposes of the SNF readmission 
measure, an index SNF admission that would be included in the 
denominator of that measure.
    (7) Improvement threshold (or improvement performance standard) 
means an individual SNF's performance on a measure during the 
applicable baseline period for that fiscal year.
    (8) Logistic exchange function means the function used to translate 
a SNF's performance score into a value-based incentive payment 
percentage.
    (9) Low-volume SNF means a SNF with fewer than 25 eligible stays 
included in the SNF readmission measure denominator during the 
performance period for each of fiscal years 2019 through 2022.
    (10) Performance period means the time period during which SNF 
performance on a measure is calculated for a fiscal year.
    (11) Performance score means the numeric score ranging from 0 to 
100 awarded to each SNF based on its performance under the SNF VBP 
Program for a fiscal year.
    (12) Performance standards are the levels of performance that SNFs 
must meet or exceed to earn points on a measure under the SNF VBP 
Program for a fiscal year.
    (13) Ranking means the ordering of SNFs based on each SNF's 
performance score under the SNF VBP Program for a fiscal year.
    (14) SNF readmission measure means, prior to October 1, 2019, the 
all-cause all-condition hospital readmission measure (SNFRM) or the 
all-condition risk-adjusted potentially preventable hospital 
readmission rate (SNFPPR) specified by CMS for application in the SNF 
Value-Based Purchasing Program. Beginning October 1, 2019, the term SNF 
readmission measure means the all-cause all-condition hospital

[[Page 47617]]

readmission measure (SNFRM) or the all-condition risk-adjusted 
potentially preventable hospital readmission rate (Skilled Nursing 
Facility Potentially Preventable Readmissions after Hospital Discharge 
measure) specified by CMS for application in the SNF VBP Program.
    (15) SNF Value-Based Purchasing (VBP) Program means the program 
required under section 1888(h) of the Act.
    (16) Value-based incentive payment adjustment factor is the number 
that will be multiplied by the adjusted Federal per diem rate for 
services furnished by a SNF during a fiscal year, based on its 
performance score for that fiscal year, and after such rate is reduced 
by the applicable percent.
    (17) Value-based incentive payment amount is the portion of a SNF's 
adjusted Federal per diem rate that is attributable to the SNF VBP 
Program.
    (b) Applicability of the SNF VBP Program. The SNF VBP Program 
applies to SNFs, including facilities described in section 
1888(e)(7)(B) of the Act. Beginning with fiscal year 2023, the SNF VBP 
Program does not include a SNF, with respect to a fiscal year, if:
    (1) The SNF does not have the minimum number of cases that applies 
to each measure for the fiscal year, as specified by CMS; or
    (2) The SNF does not have the minimum number of measures for the 
fiscal year, as specified by CMS.
    (c) * * *
    (2) * * *
    (i) Total amount available for a fiscal year. The total amount 
available for value-based incentive payments for a fiscal year is at 
least 60 percent of the total amount of the reduction to the adjusted 
SNF PPS payments for that fiscal year, as estimated by CMS, and will be 
increased as appropriate for each fiscal year to account for the 
assignment of a performance score to low-volume SNFs under paragraph 
(d)(3) of this section. Beginning with the FY 2023 SNF VBP, the total 
amount for value-based incentive payments for a fiscal year is 60 
percent of the total amount of the reduction to the adjusted SNF PPS 
payments for that fiscal year, as estimated by CMS.
* * * * *
    (d) Performance scoring under the SNF VBP Program (applicable, as 
described in this paragraph, to fiscal year 2019 through and including 
fiscal year 2025).
* * * * *
    (3) If, with respect to a fiscal year beginning with fiscal year 
2019 through and including fiscal year 2022, CMS determines that a SNF 
is a low-volume SNF, CMS will assign a performance score to the SNF for 
the fiscal year that, when used to calculate the value-based incentive 
payment amount (as defined in paragraph (a)(17) of this section), 
results in a value-based incentive payment amount that is equal to the 
adjusted Federal per diem rate (as defined in paragraph (a)(2) of this 
section) that would apply to the SNF for the fiscal year without 
application of Sec.  413.337(f).
* * * * *
    (5) CMS will specify the measures for application in the SNF VBP 
Program for a given fiscal year.
    (6)(i) Performance standards are announced no later than 60 days 
prior to the start of the performance period that applies to that 
measure for that fiscal year.
    (ii) Beginning with the performance standards that apply to FY 
2021, if CMS discovers an error in the performance standard 
calculations subsequent to publishing their numerical values for a 
fiscal year, CMS will update the numerical values to correct the error. 
If CMS subsequently discovers one or more other errors with respect to 
the same fiscal year, CMS will not further update the numerical values 
for that fiscal year.
    (e) Performance scoring under the SNF VBP Program beginning with 
fiscal year 2026. (1) Points awarded based on SNF performance. CMS will 
award points to SNFs based on their performance on each measure for 
which the SNF reports the applicable minimum number of cases during the 
performance period applicable to that fiscal year as follows:
    (i) CMS will award from 1 to 9 points for achievement to each SNF 
whose performance on a measure during the applicable performance period 
meets or exceeds the achievement threshold for that measure but is less 
than the benchmark for that measure.
    (ii) CMS will award 10 points for achievement to a SNF whose 
performance on a measure during the applicable performance period meets 
or exceeds the benchmark for that measure.
    (iii) CMS will award from 0 to 9 points for improvement to each SNF 
whose performance on a measure during the applicable performance period 
exceeds the improvement threshold but is less than the benchmark for 
that measure.
    (iv) CMS will not award points for improvement to a SNF that does 
not meet the case minimum for a measure for the applicable baseline 
period.
    (v) The highest of the SNF's achievement and improvement score for 
a given measure will be the SNF's score on that measure for the 
applicable fiscal year.
    (2) Calculation of the SNF performance score. The SNF performance 
score for a fiscal year is calculated as follows:
    (i) CMS will sum all points awarded to a SNF as described in 
paragraph (e)(1) of this section for each measure applicable to a 
fiscal year to calculate the SNF's point total.
    (ii) CMS will normalize the point total such that the resulting SNF 
performance score is expressed as a number of points earned out of a 
total of 100.
    (f) * * *
    (1) CMS will provide quarterly confidential feedback reports to 
SNFs on their performance on each measure specified for the fiscal 
year. 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 measure 
rates contained in that report. The administrative claims data used to 
calculate measure rates are not subject to review and correction under 
paragraph (f)(1) of this section. All correction requests must be 
accompanied by appropriate evidence showing the basis for the 
correction to each of the applicable measure rates.
* * * * *
    (3) CMS will publicly report the information described in 
paragraphs (f)(1) and (2) of this section on the Nursing Home Compare 
website or a successor website. Beginning with information publicly 
reported on or after October 1, 2019, and ending with information 
publicly reported on September 30, 2022 the following exceptions apply:
* * * * *
    (4) Beginning with the information publicly reported on or after 
October 1, 2022, the following exceptions apply:
    (i) If a SNF does not have the minimum number of cases during the 
baseline period that applies to a measure for a fiscal year, CMS will 
not publicly report the SNF's baseline period measure rate for that 
particular measure, although CMS will publicly report the SNF's 
performance period measure rate and achievement score if the SNF had 
the minimum number of cases for the measure during the performance 
period of the same program year;
    (ii) If a SNF does not have the minimum number of cases during the

[[Page 47618]]

performance period that applies to a measure for a fiscal year, CMS 
will not publicly report any information with respect to the SNF's 
performance on that measure for the fiscal year;
    (iii) If a SNF does not have the minimum number of measures during 
the performance period for a fiscal year, CMS will not publicly report 
any data for that SNF for the fiscal year.
* * * * *
    (i) Special rules for the FY 2023 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 2023, 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.
    (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.
    (3) CMS will provide confidential feedback reports to SNFs on their 
performance on the SNF readmission measure in accordance with 
paragraphs (f)(1) and (2) of this section.
    (4) CMS will publicly report SNF performance on the SNF readmission 
measure in accordance with paragraph (f)(3) of this section.
    (j) Validation. (1) Beginning with the FY 2023 Program year, for 
the SNFRM measure, information reported through claims for the SNFRM 
measure are validated for accuracy by Medicare Administrative 
Contractors (MACs) to ensure accurate Medicare payments.
    (2) [Reserved]

0
4. Amend Sec.  413.360 by--
0
a. Removing paragraph (b)(2);
0
b. Redesignating paragraph (b)(3) as paragraph (b)(2); and
0
c. Adding paragraph (f).
    The addition reads as follows:


Sec.  413.360   Requirements under the Skilled Nursing Facility (SNF) 
Quality Reporting Program (QRP).

* * * * *
    (f) Data completion threshold. (1) SNFs must meet or exceed two 
separate data completeness thresholds: One threshold set at 80 percent 
for completion of required quality measures data and standardized 
patient assessment data collected using the MDS submitted through the 
CMS designated data submission system; beginning with FY 2018 and for 
all subsequent payment updates; and a second threshold set at 100 
percent for measures data collected and submitted using the CDC NHSN, 
beginning with FY 2023 and for all subsequent payment updates.
    (2) These thresholds (80 percent for completion of required quality 
measures data and standardized patient assessment data on the MDS; 100 
percent for CDC NHSN data) will apply to all measures and standardized 
patient assessment data requirements adopted into the SNF QRP.
    (3) A SNF must meet or exceed both thresholds to avoid receiving a 
2-percentage point reduction to their annual payment update for a given 
fiscal year.

PART 483--REQUIREMENTS FOR STATES AND LONG TERM CARE FACILITIES

0
5. The authority citation for part 483 continues to read as follows:

    Authority:  42 U.S.C. 1302, 1320a-7, 1395i, 1395hh and 1396r.

0
6. Amend Sec.  483.60 by--
0
a. Revising paragraphs (a)(2) introductory text, and (a)(2)(i) 
introductory text;
0
b. Removing the word ``or'' at the end of paragraphs (a)(2)(i)(C);
0
c. Revising paragraph (a)(2)(i)(D); and
0
d. Adding paragraph (a)(2)(i)(E).

    The revisions and addition read as follows:


Sec.  483.60   Food and nutrition services.

* * * * *
    (a) * * *
    (2) If a qualified dietitian or other clinically qualified 
nutrition professional is not employed full-time, the facility must 
designate a person to serve as the director of food and nutrition 
services.
    (i) The director of food and nutrition services must at a minimum 
meet one of the following qualifications--
* * * * *
    (D) Has an associate's or higher degree in food service management 
or in hospitality, if the course study includes food service or 
restaurant management, from an accredited institution of higher 
learning; or
    (E) Has 2 or more years of experience in the position of director 
of food and nutrition services in a nursing facility setting and has 
completed a course of study in food safety and management, by no later 
than October 1, 2023, that includes topics integral to managing dietary 
operations including, but not limited to, foodborne illness, sanitation 
procedures, and food purchasing/receiving; and
* * * * *

0
7. Amend Sec.  483.90 by adding paragraph (a)(1)(iii) to read as 
follows:


Sec.  483.90   Physical environment.

    (a) * * *
    (1) * * *
    (iii) If a facility is Medicare- or Medicaid-certified before July 
5, 2016 and the facility has previously used the Fire Safety Evaluation 
System for compliance, the facility may use the scoring values in the 
following Mandatory Values Chart:
[GRAPHIC] [TIFF OMITTED] TR03AU22.033


[[Page 47619]]


* * * * *

Xavier Becerra,
Secretary, Department of Health and Human Services.
[FR Doc. 2022-16457 Filed 7-29-22; 4:15 pm]
BILLING CODE 4120-01-P