[Federal Register Volume 86, Number 147 (Wednesday, August 4, 2021)]
[Rules and Regulations]
[Pages 42608-42679]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2021-16336]



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Vol. 86

Wednesday,

No. 147

August 4, 2021

Part VI





Department of Health and Human Services





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Centers for Medicare & Medicaid Services





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42 CFR Part 412





Medicare Program; FY 2022 Inpatient Psychiatric Facilities Prospective 
Payment System and Quality Reporting Updates for Fiscal Year Beginning 
October 1, 2021 (FY 2022); Final Rule

  Federal Register / Vol. 86 , No. 147 / Wednesday, August 4, 2021 / 
Rules and Regulations  

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DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Medicare & Medicaid Services

42 CFR Part 412

[CMS-1750-F]
RIN 0938-AU40


Medicare Program; FY 2022 Inpatient Psychiatric Facilities 
Prospective Payment System and Quality Reporting Updates for Fiscal 
Year Beginning October 1, 2021 (FY 2022)

AGENCY: Centers for Medicare & Medicaid Services (CMS), HHS.

ACTION: Final rule.

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SUMMARY: This final rule updates the prospective payment rates, the 
outlier threshold, and the wage index for Medicare inpatient hospital 
services provided by Inpatient Psychiatric Facilities (IPF), which 
include psychiatric hospitals and excluded psychiatric units of an 
acute care hospital or critical access hospital. This rule also updates 
and clarifies the IPF teaching policy with respect to IPF hospital 
closures and displaced residents and finalizes a technical change to 
one of the 2016-based IPF market basket price proxies. In addition, 
this final rule finalizes proposals on quality measures and reporting 
requirements under the Inpatient Psychiatric Facilities Quality 
Reporting (IPFQR) Program. We note that this final rule does not 
finalize two proposals to remove quality measures. The changes 
finalized in this rule for the IPFQR Program are effective for IPF 
discharges occurring during the Fiscal Year (FY) beginning October 1, 
2021 through September 30, 2022 (FY 2022).

DATES: These regulations are effective on October 1, 2021.

FOR FURTHER INFORMATION CONTACT: 
    The IPF Payment Policy mailbox at [email protected] for 
general information.
    Mollie Knight (410) 786-7948 or Eric Laib (410) 786-9759, for 
information regarding the market basket update or the labor related 
share.
    Nick Brock (410) 786-5148 or Theresa Bean (410) 786-2287, for 
information regarding the regulatory impact analysis.
    Lauren Lowenstein, (410) 786-4507, for information regarding the 
inpatient psychiatric facilities quality reporting program.

SUPPLEMENTARY INFORMATION:

Availability of Certain Tables Exclusively Through the Internet on the 
CMS Website

    Addendum A to this final rule summarizes the FY 2022 IPF PPS 
payment rates, outlier threshold, cost of living adjustment factors 
(COLA) for Alaska and Hawaii, national and upper limit cost-to-charge 
ratios, and adjustment factors. In addition, the B Addenda to this 
final rule shows the complete listing of ICD-10 Clinical Modification 
(CM) and Procedure Coding System (PCS) codes, the FY 2022 IPF PPS 
comorbidity adjustment, and electroconvulsive therapy (ECT) procedure 
codes. The A and B Addenda are available online at: https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html.
    Tables setting forth the FY 2022 Wage Index for Urban Areas Based 
on Core-Based Statistical Area (CBSA) Labor Market Areas and the FY 
2022 Wage Index Based on CBSA Labor Market Areas for Rural Areas are 
available exclusively through the internet, on the CMS website at 
https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/IPFPPS/WageIndex.html.

I. Executive Summary

A. Purpose

    This final rule updates the prospective payment rates, the outlier 
threshold, and the wage index for Medicare inpatient hospital services 
provided by Inpatient Psychiatric Facilities (IPFs) for discharges 
occurring during FY 2022 beginning October 1, 2021 through September 
30, 2022. This rule also updates and clarifies the IPF teaching policy 
with respect to IPF hospital closures and displaced residents and 
finalizes a technical change to one of the 2016-based IPF market basket 
price proxies. In addition, the final rule finalizes proposals to adopt 
quality measures and reporting requirements under the Inpatient 
Psychiatric Facilities Quality Reporting (IPFQR) Program.

B. Summary of the Major Provisions

1. Inpatient Psychiatric Facilities Prospective Payment System (IPF 
PPS)
    For the IPF PPS, we are finalizing our proposal to--
     Update IPF PPS teaching policy with respect to IPF 
hospital closures and displaced residents.
     Replace one of the price proxies currently used for the 
For-profit Interest cost category in the 2016-based IPF market basket 
with a similar price proxy.
     Adjust the 2016-based IPF market basket update (2.7 
percent) for economy-wide productivity (0.7 percentage point) as 
required by section 1886(s)(2)(A)(i) of the Social Security Act (the 
Act), resulting in a final IPF payment rate update of 2.0 percent for 
FY 2022.
     Make technical rate setting changes: The IPF PPS payment 
rates will be adjusted annually for inflation, as well as statutory and 
other policy factors. This final rule updates:
    ++ The IPF PPS Federal per diem base rate from $815.22 to $832.94.
    ++ The IPF PPS Federal per diem base rate for providers who failed 
to report quality data to $816.61.
    ++ The Electroconvulsive therapy (ECT) payment per treatment from 
$350.97 to $358.60.
    ++ The ECT payment per treatment for providers who failed to report 
quality data to $351.57.
    ++ The labor-related share from 77.3 percent to 77.2 percent.
    ++ The wage index budget-neutrality factor from 0.9989 to 1.0017.
    ++ The fixed dollar loss threshold amount from $14,630 to $14,470 
to maintain estimated outlier payments at 2 percent of total estimated 
aggregate IPF PPS payments.
2. Inpatient Psychiatric Facilities Quality Reporting (IPFQR) Program
    In this final rule, we are:
     Adopting voluntary patient-level data reporting for chart-
abstracted measures for data submitted for the FY 2023 payment 
determination and mandatory patient-level data reporting for chart-
abstracted measures for the FY 2024 payment determination and 
subsequent years;
     Revising our regulations at 42 CFR 412.434(b)(3) by 
replacing the term ``QualityNet system administrator'' with 
``QualityNet security official'';
     Adopting the Coronavirus disease 2019 (COVID-19) 
Vaccination Coverage Among Health Care Personnel (HCP) measure for the 
FY 2023 payment determination and subsequent years;
     Adopting the Follow-up After Psychiatric Hospitalization 
(FAPH) measure for the FY 2024 payment determination and subsequent 
years; and
     Removing the following two measures for FY 2024 payment 
determination and subsequent years:
    ++ Timely Transmission of Transition Record (Discharges from an 
Inpatient Facility to Home/Self Care or Any Other Site of Care) measure 
and
    ++ Follow-up After Hospitalization for Mental Illness (FUH) 
measure.
     Not finalizing our proposals to remove the following two 
measures for

[[Page 42609]]

FY 2024 payment determination and subsequent years:
    ++ Alcohol Use Brief Intervention Provided or Offered and Alcohol 
Use Brief Intervention Provided (SUB-2/2a) measure; and
    ++ Tobacco Use Treatment Provided or Offered and Tobacco Use 
Treatment (TOB-2/2a) measure.

C. Summary of Impacts
[GRAPHIC] [TIFF OMITTED] TR04AU21.169

II. Background

A. Overview of the Legislative Requirements of the IPF PPS

    Section 124 of the Medicare, Medicaid, and State Children's Health 
Insurance Program Balanced Budget Refinement Act of 1999 (BBRA) (Pub. 
L. 106-113) required the establishment and implementation of an IPF 
PPS. Specifically, section 124 of the BBRA mandated that the Secretary 
of the Department of Health and Human Services (the Secretary) develop 
a per diem Prospective Payment System (PPS) for inpatient hospital 
services furnished in psychiatric hospitals and excluded psychiatric 
units including an adequate patient classification system that reflects 
the differences in patient resource use and costs among psychiatric 
hospitals and excluded psychiatric units. ``Excluded psychiatric unit'' 
means a psychiatric unit of an acute care hospital or of a Critical 
Access Hospital (CAH), which is excluded from payment under the 
Inpatient Prospective Payment System (IPPS) or CAH payment system, 
respectively. These excluded psychiatric units will be paid under the 
IPF PPS.
    Section 405(g)(2) of the Medicare Prescription Drug, Improvement, 
and Modernization Act of 2003 (MMA) (Pub. L. 108-173) extended the IPF 
PPS to psychiatric distinct part units of CAHs.
    Sections 3401(f) and 10322 of the Patient Protection and Affordable 
Care Act (Pub. L. 111-148) as amended by section 10319(e) of that Act 
and by section 1105(d) of the Health Care and Education Reconciliation 
Act of 2010 (Pub. L. 111-152) (hereafter referred to jointly as ``the 
Affordable Care Act'') added subsection (s) to section 1886 of the Act.
    Section 1886(s)(1) of the Act titled ``Reference to Establishment 
and Implementation of System,'' refers to section 124 of the BBRA, 
which relates to the establishment of the IPF PPS.
    Section 1886(s)(2)(A)(i) of the Act requires the application of the 
productivity adjustment described in section 1886(b)(3)(B)(xi)(II) of 
the Act to the IPF PPS for the rate year (RY) beginning in 2012 (that 
is, a RY that coincides with a FY) and each subsequent RY.
    Section 1886(s)(2)(A)(ii) of the Act required the application of an 
``other adjustment'' that reduced any update to an IPF PPS base rate by 
a percentage point amount specified in section 1886(s)(3) of the Act 
for the RY beginning in 2010 through the RY beginning in 2019. As noted 
in the FY 2020 IPF PPS final rule, for the RY beginning in 2019, 
section 1886(s)(3)(E) of the Act required that the other adjustment 
reduction be equal to 0.75 percentage point; this was the final year 
the statute required the application of this adjustment. Because FY 
2021, was a RY beginning in 2020, FY 2021 was the first-year section 
1886(s)(2)(A)(ii) did not apply since its enactment.
    Sections 1886(s)(4)(A) through (D) of the Act require that for RY 
2014 and each subsequent RY, IPFs that fail to report required quality 
data with respect to such a RY will have their annual update to a 
standard Federal rate for discharges reduced by 2.0 percentage points. 
This may result in an annual update being less than 0.0 for a RY, and 
may result in payment rates for the upcoming RY being less than such 
payment rates for the preceding RY. Any reduction for failure to report 
required quality data will apply only to the RY involved, and the 
Secretary will not take into account such reduction in computing the 
payment amount for a subsequent RY. More information about the 
specifics of the current Inpatient Psychiatric Facilities Quality 
Reporting (IPFQR) Program is available in the FY 2020 IPF PPS and 
Quality Reporting Updates for Fiscal Year Beginning October 1, 2019 
final rule (84 FR 38459 through 38468).
    To implement and periodically update these provisions, we have 
published various proposed and final rules and notices in the Federal 
Register. For more information regarding these documents, see the 
Center for Medicare & Medicaid (CMS) website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/index.html?redirect=/InpatientPsychFacilPPS/.

B. Overview of the IPF PPS

    The November 2004 IPF PPS final rule (69 FR 66922) established the 
IPF PPS, as required by section 124 of the BBRA and codified at 42 CFR 
part 412, subpart N. The November 2004 IPF PPS final rule set forth the 
Federal per diem base rate for the implementation year (the 18-month 
period from January 1, 2005 through June 30, 2006), and provided 
payment for the inpatient operating and capital costs to IPFs for 
covered psychiatric services they furnish (that is, routine, ancillary, 
and capital costs, but not costs of approved educational activities, 
bad debts, and

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other services or items that are outside the scope of the IPF PPS). 
Covered psychiatric services include services for which benefits are 
provided under the fee-for-service Part A (Hospital Insurance Program) 
of the Medicare program.
    The IPF PPS established the Federal per diem base rate for each 
patient day in an IPF derived from the national average daily routine 
operating, ancillary, and capital costs in IPFs in FY 2002. The average 
per diem cost was updated to the midpoint of the first year under the 
IPF PPS, standardized to account for the overall positive effects of 
the IPF PPS payment adjustments, and adjusted for budget-neutrality.
    The Federal per diem payment under the IPF PPS is comprised of the 
Federal per diem base rate described previously and certain patient- 
and facility-level payment adjustments for characteristics that were 
found in the regression analysis to be associated with statistically 
significant per diem cost differences with statistical significance 
defined as p less than 0.05. A complete discussion of the regression 
analysis that established the IPF PPS adjustment factors can be found 
in the November 2004 IPF PPS final rule (69 FR 66933 through 66936).
    The patient-level adjustments include age, Diagnosis-Related Group 
(DRG) assignment, and comorbidities; additionally, there are 
adjustments to reflect higher per diem costs at the beginning of a 
patient's IPF stay and lower costs for later days of the stay. 
Facility-level adjustments include adjustments for the IPF's wage 
index, rural location, teaching status, a cost-of-living adjustment for 
IPFs located in Alaska and Hawaii, and an adjustment for the presence 
of a qualifying emergency department (ED).
    The IPF PPS provides additional payment policies for outlier cases, 
interrupted stays, and a per treatment payment for patients who undergo 
electroconvulsive therapy (ECT). During the IPF PPS mandatory 3-year 
transition period, stop-loss payments were also provided; however, 
since the transition ended as of January 1, 2008, these payments are no 
longer available.

C. Annual Requirements for Updating the IPF PPS

    Section 124 of the BBRA did not specify an annual rate update 
strategy for the IPF PPS and was broadly written to give the Secretary 
discretion in establishing an update methodology. Therefore, in the 
November 2004 IPF PPS final rule, we implemented the IPF PPS using the 
following update strategy:
     Calculate the final Federal per diem base rate to be 
budget-neutral for the 18-month period of January 1, 2005 through June 
30, 2006.
     Use a July 1 through June 30 annual update cycle.
     Allow the IPF PPS first update to be effective for 
discharges on or after July 1, 2006 through June 30, 2007.
    In November 2004, we implemented the IPF PPS in a final rule that 
published on November 15, 2004 in the Federal Register (69 FR 66922). 
In developing the IPF PPS, and to ensure that the IPF PPS can account 
adequately for each IPF's case-mix, we performed an extensive 
regression analysis of the relationship between the per diem costs and 
certain patient and facility characteristics to determine those 
characteristics associated with statistically significant cost 
differences on a per diem basis. That regression analysis is described 
in detail in our November 28, 2003 IPF proposed rule (68 FR 66923; 
66928 through 66933) and our November 15, 2004 IPF final rule (69 FR 
66933 through 66960). For characteristics with statistically 
significant cost differences, we used the regression coefficients of 
those variables to determine the size of the corresponding payment 
adjustments.
    In the November 15, 2004 final rule, we explained the reasons for 
delaying an update to the adjustment factors, derived from the 
regression analysis, including waiting until we have IPF PPS data that 
yields as much information as possible regarding the patient-level 
characteristics of the population that each IPF serves. We indicated 
that we did not intend to update the regression analysis and the 
patient-level and facility-level adjustments until we complete that 
analysis. Until that analysis is complete, we stated our intention to 
publish a notice in the Federal Register each spring to update the IPF 
PPS (69 FR 66966).
    On May 6, 2011, we published a final rule in the Federal Register 
titled, ``Inpatient Psychiatric Facilities Prospective Payment System--
Update for Rate Year Beginning July 1, 2011 (RY 2012)'' (76 FR 26432), 
which changed the payment rate update period to a RY that coincides 
with a FY update. Therefore, final rules are now published in the 
Federal Register in the summer to be effective on October 1. When 
proposing changes in IPF payment policy, a proposed rule would be 
issued in the spring, and the final rule in the summer to be effective 
on October 1. For a detailed list of updates to the IPF PPS, we refer 
readers to our regulations at 42 CFR 412.428.
    The most recent IPF PPS annual update was published in a final rule 
on August 4, 2020 in the Federal Register titled, ``Medicare Program; 
FY 2021 Inpatient Psychiatric Facilities Prospective Payment System and 
Special Requirements for Psychiatric Hospitals for Fiscal Year 
Beginning October 1, 2020 (FY 2021)'' (85 FR 47042), which updated the 
IPF PPS payment rates for FY 2021. That final rule updated the IPF PPS 
Federal per diem base rates that were published in the FY 2020 IPF PPS 
Rate Update final rule (84 FR 38424) in accordance with our established 
policies.

III. Provisions of the FY 2022 IPF PPS Final Rule and Responses to 
Comments

A. Final Update to the FY 2021 Market Basket for the IPF PPS

1. Background
    Originally, the input price index that was used to develop the IPF 
PPS was the ``Excluded Hospital with Capital'' market basket. This 
market basket was based on 1997 Medicare cost reports for Medicare 
participating inpatient rehabilitation facilities (IRFs), IPFs, long-
term care hospitals (LTCHs), cancer hospitals, and children's 
hospitals. Although ``market basket'' technically describes the mix of 
goods and services used in providing health care at a given point in 
time, this term is also commonly used to denote the input price index 
(that is, cost category weights and price proxies) derived from that 
market basket. Accordingly, the term market basket as used in this 
document, refers to an input price index.
    Since the IPF PPS inception, the market basket used to update IPF 
PPS payments has been rebased and revised to reflect more recent data 
on IPF cost structures. We last rebased and revised the IPF market 
basket in the FY 2020 IPF PPS rule, where we adopted a 2016-based IPF 
market basket, using Medicare cost report data for both Medicare 
participating freestanding psychiatric hospitals and psychiatric units. 
We refer readers to the FY 2020 IPF PPS final rule for a detailed 
discussion of the 2016-based IPF PPS market basket and its development 
(84 FR 38426 through 38447). References to the historical market 
baskets used to update IPF PPS payments are listed in the FY 2016 IPF 
PPS final rule (80 FR 46656).
2. Final FY 2022 IPF Market Basket Update
    For FY 2022 (that is, beginning October 1, 2021 and ending 
September 30, 2022), we proposed to update the IPF PPS payments by a 
market basket

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increase factor with a productivity adjustment as required by section 
1886(s)(2)(A)(i) of the Act. In the FY 2022 IPF proposed rule (86 FR 
19483), we proposed to use the same methodology described in the FY 
2021 IPF PPS final rule (85 FR 47045 through 47046), with one proposed 
modification to the 2016-based IPF market basket.
    For the price proxy for the For-profit Interest cost category of 
the 2016-based IPF market basket, we proposed to use the iBoxx AAA 
Corporate Bond Yield index instead of the Moody's AAA Corporate Bond 
Yield index. Effective for December 2020, the Moody's AAA Corporate 
Bond series is no longer available for use under license to IHS Global 
Inc. (IGI), the nationally recognized economic and financial 
forecasting firm with which we contract to forecast the components of 
the market baskets and multi-factor productivity (MFP). Since IGI is no 
longer licensed to use and publish the Moody's series, IGI was required 
to discontinue the publication of the associated historical data and 
forecasts of this series. Therefore, IGI constructed a bond yield index 
(iBoxx) that closely replicates the Moody's corporate bond yield 
indices currently used in the market baskets.
    In the FY 2022 IPF PPS proposed rule, we stated that because the 
iBoxx AAA Corporate Bond Yield index captures the same technical 
concept as the current corporate bond proxy and tracks similarly to the 
current measure that is no longer available, we believed that the iBoxx 
AAA Corporate Bond Yield index is technically appropriate to use in the 
2016-based IPF market basket.
    Based on IGI's fourth quarter 2020 forecast with historical data 
through the third quarter of 2020, the proposed 2016-based IPF market 
basket increase factor for FY 2022 was projected to be 2.3 percent. We 
also proposed that if more recent data became available after the 
publication of the proposed rule and before the publication of this 
final rule (for example, a more recent estimate of the market basket 
update or MFP), we would use such data, if appropriate, to determine 
the FY 2022 market basket update in this final rule.
    Section 1886(s)(2)(A)(i) of the Act requires that, after 
establishing the increase factor for a FY, the Secretary shall reduce 
such increase factor for FY 2012 and each subsequent FY, by the 
productivity adjustment described in section 1886(b)(3)(B)(xi)(II) of 
the Act. Section 1886(b)(3)(B)(xi)(II) of the Act sets forth the 
definition of this productivity adjustment. The statute defines the 
productivity adjustment to be equal to the 10-year moving average of 
changes in annual economy-wide, private nonfarm business MFP (as 
projected by the Secretary for the 10-year period ending with the 
applicable FY, year, cost reporting period, or other annual period) 
(the ``productivity adjustment''). The U.S. Department of Labor's 
Bureau of Labor Statistics (BLS) publishes the official measure of 
private nonfarm business MFP. Please see http://www.bls.gov/mfp for the 
BLS historical published MFP data. A complete description of the MFP 
projection methodology is available on the CMS website at https://www.cms.gov/Research-Statistics-Dataand-Systems/Statistics-Trends-andReports/MedicareProgramRatesStats/MarketBasketResearch.html. We note 
that effective with FY 2022 and forward, CMS is changing the name of 
this adjustment to refer to it as the productivity adjustment rather 
than the MFP adjustment. We note that the adjustment relies on the same 
underlying data and methodology. This new terminology is more 
consistent with the statutory language described in section 
1886(s)(2)(A)(i) of the Act.
    Using IGI's fourth quarter 2020 forecast, the productivity 
adjustment for FY 2022 was projected to be 0.2 percent. We proposed to 
then reduce the proposed 2.3 percent IPF market basket update by the 
estimated productivity adjustment for FY 2022 of 0.2 percentage point. 
Therefore, the proposed FY 2022 IPF update was equal to 2.1 percent 
(2.3 percent market basket update reduced by the 0.2 percentage point 
productivity adjustment). Furthermore, we proposed that if more recent 
data became available after the publication of the proposed rule and 
before the publication of this final rule (for example, a more recent 
estimate of the market basket or MFP), we would use such data, if 
appropriate, to determine the FY 2022 market basket update and 
productivity adjustment in this final rule.
    Based on the more recent data available for this FY 2022 IPF final 
rule (that is, IGI's second quarter 2021 forecast of the 2016-based IPF 
market basket with historical data through the first quarter of 2021), 
we estimate that the IPF FY 2022 market basket update is 2.7 percent. 
The current estimate of the productivity adjustment for FY 2022 is 0.7 
percentage point. Therefore, the current estimate of the FY 2022 IPF 
increase factor is equal to 2.0 percent (2.7 percent market basket 
update reduced by 0.7 percentage point productivity adjustment).
    We invited public comment on our proposals for the FY 2022 market 
basket update and productivity adjustment. The following is a summary 
of the public comments received on the proposed FY 2022 market basket 
update and productivity adjustment and our responses:
    Comment: One commenter supported the update to the IPF payment 
rates of 2.1 percent.
    Response: We thank the commenter for their support.
    Comment: One commenter stated that given the growing behavioral 
health and substance abuse crisis made worse by the COVID-19 Public 
Health Emergency (PHE), that CMS should provide additional payment for 
IPFs in the future.
    Response: We understand the commenter's concern. We acknowledge 
that the COVID-19 PHE has amplified the growing need for behavioral 
health services in this country and remain committed to trying to find 
ways to mitigate its impact on IPFs. Our goal is to ensure that the IPF 
payment rates accurately reflect the best available data. For example, 
as discussed in section VI.C.3 of this final rule, in comparing and 
analyzing FY 2019 and FY 2020 claims, we determined that the COVID-19 
PHE appears to have significantly impacted the FY 2020 IPF claims such 
that the FY 2019 claims are the best available data to set the outlier 
fixed dollar loss threshold for FY 2022. Therefore, we deviated from 
our longstanding practice of using the most recent available year of 
claims, that is, FY 2020 claims, for estimating IPF PPS payments in FY 
2022. We will continue to analyze more recent available IPF claims data 
to better understand both the short- and long-term effects of the 
COVID-19 PHE on the IPF PPS.
    Final Decision: After consideration of the comments we received, we 
are finalizing a FY 2022 IPF update equal to 2.0 percent based on the 
more recent data available.
3. Final FY 2022 IPF Labor-Related Share
    Due to variations in geographic wage levels and other labor-related 
costs, we believe that payment rates under the IPF PPS should continue 
to be adjusted by a geographic wage index, which would apply to the 
labor-related portion of the Federal per diem base rate (hereafter 
referred to as the labor-related share). The labor-related share is 
determined by identifying the national average proportion of total 
costs that are related to, influenced by, or vary with the local labor 
market. We proposed to continue to classify a cost category as labor-
related if the costs are labor-intensive and vary with the local labor 
market.

[[Page 42612]]

    Based on our definition of the labor-related share and the cost 
categories in the 2016-based IPF market basket, we proposed to 
calculate the labor-related share for FY 2022 as the sum of the FY 2022 
relative importance of 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 the Capital-Related relative 
importance from the 2016-based IPF market basket. For more details 
regarding the methodology for determining specific cost categories for 
inclusion in the 2016-based IPF labor-related share, see the FY 2020 
IPF PPS final rule (84 FR 38445 through 38447).
    The relative importance reflects the different rates of price 
change for these cost categories between the base year (FY 2016) and FY 
2022. Based on IGI's fourth quarter 2020 forecast of the 2016-based IPF 
market basket, the sum of the FY 2022 relative importance for Wages and 
Salaries; Employee Benefits; Professional Fees: Labor-related; 
Administrative and Facilities Support Services; Installation 
Maintenance & Repair Services; and All Other: Labor related Services 
was 74.0 percent. We proposed that the portion of Capital-Related costs 
that are influenced by the local labor market is 46 percent. Since the 
relative importance for Capital- Related costs was 6.7 percent of the 
2016-based IPF market basket for FY 2022, we proposed to take 46 
percent of 6.7 percent to determine the labor-related share of Capital-
Related costs for FY 2022 of 3.1 percent. Therefore, we proposed a 
total labor-related share for FY 2022 of 77.1 percent (the sum of 74.0 
percent for the labor-related share of operating costs and 3.1 percent 
for the labor-related share of Capital-Related costs). We also proposed 
that if more recent data became available after publication of the 
proposed rule and before the publication of this final rule (for 
example, a more recent estimate of the labor-related share), we would 
use such data, if appropriate, to determine the FY 2022 IPF labor-
related share in the final rule.
    Based on IGI's second quarter 2021 forecast of the 2016-based IPF 
market basket, the sum of the FY 2022 relative importance for Wages and 
Salaries; Employee Benefits; Professional Fees: Labor-related; 
Administrative and Facilities Support Services; Installation 
Maintenance & Repair Services; and All Other: Labor-related Services is 
74.1 percent. Since the relative importance for Capital-Related costs 
is 6.7 percent of the 2016-based IPF market basket for FY 2022, we take 
46 percent of 6.7 percent to determine the labor-related share of 
Capital-Related costs for FY 2022 of 3.1 percent. Therefore, the 
current estimate of the total labor-related share for FY 2022 is equal 
to 77.2 percent (the sum of 74.1 percent for the labor-related share of 
operating costs and 3.1 percent for the labor-related share of Capital-
Related costs). Table 1 shows the final FY 2022 labor-related share and 
the final FY 2021 labor-related share using the 2016-based IPF market 
basket relative importance.
[GRAPHIC] [TIFF OMITTED] TR04AU21.170

    We invited public comments on the proposed labor-related share for 
FY 2022.
    Comment: Several commenters supported the decrease in the labor-
related share from 77.3 percent in FY 2021 to 77.1 percent in FY 2022 
noting that it will help any facility that has a wage index less than 
1.0. The commenters stated that, across this country there is a growing 
disparity between high-wage and low-wage states. Recognizing this 
disparity and slightly lowering the labor-related share provides some 
aid to hospitals in many rural and underserved communities.
    Response: We thank the commenter for their support. We agree with 
the commenters that the labor-related share should reflect the 
proportion of costs that are attributable to labor and vary 
geographically to account for differences in labor-related costs across 
geographic areas. More recent data became available; therefore, based 
on IGI's second quarter 2021 forecast with historical data through the 
first quarter 2021 the FY 2022 labor-related share for the final rule 
is 77.2 percent as shown in Table 1.
    After consideration of comments received, we are finalizing the use 
of the sum of the FY 2022 relative importance

[[Page 42613]]

for the labor-related cost categories based on the most recent forecast 
(IGI's second quarter 2021 forecast) of the 2016-based IPF market 
basket labor-related share cost weights, as proposed.

B. Final Updates to the IPF PPS Rates for FY Beginning October 1, 2021

    The IPF PPS is based on a standardized Federal per diem base rate 
calculated from the IPF average per diem costs and adjusted for budget-
neutrality in the implementation year. The Federal per diem base rate 
is used as the standard payment per day under the IPF PPS and is 
adjusted by the patient-level and facility-level adjustments that are 
applicable to the IPF stay. A detailed explanation of how we calculated 
the average per diem cost appears in the November 2004 IPF PPS final 
rule (69 FR 66926).
1. Determining the Standardized Budget-Neutral Federal per Diem Base 
Rate
    Section 124(a)(1) of the BBRA required that we implement the IPF 
PPS in a budget-neutral manner. In other words, the amount of total 
payments under the IPF PPS, including any payment adjustments, must be 
projected to be equal to the amount of total payments that would have 
been made if the IPF PPS were not implemented. Therefore, we calculated 
the budget-neutrality factor by setting the total estimated IPF PPS 
payments to be equal to the total estimated payments that would have 
been made under the Tax Equity and Fiscal Responsibility Act of 1982 
(TEFRA) (Pub. L. 97-248) methodology had the IPF PPS not been 
implemented. A step-by-step description of the methodology used to 
estimate payments under the TEFRA payment system appears in the 
November 2004 IPF PPS final rule (69 FR 66926).
    Under the IPF PPS methodology, we calculated the final Federal per 
diem base rate to be budget-neutral during the IPF PPS implementation 
period (that is, the 18-month period from January 1, 2005 through June 
30, 2006) using a July 1 update cycle. We updated the average cost per 
day to the midpoint of the IPF PPS implementation period (October 1, 
2005), and this amount was used in the payment model to establish the 
budget-neutrality adjustment.
    Next, we standardized the IPF PPS Federal per diem base rate to 
account for the overall positive effects of the IPF PPS payment 
adjustment factors by dividing total estimated payments under the TEFRA 
payment system by estimated payments under the IPF PPS. In addition, 
information concerning this standardization can be found in the 
November 2004 IPF PPS final rule (69 FR 66932) and the RY 2006 IPF PPS 
final rule (71 FR 27045). We then reduced the standardized Federal per 
diem base rate to account for the outlier policy, the stop loss 
provision, and anticipated behavioral changes. A complete discussion of 
how we calculated each component of the budget-neutrality adjustment 
appears in the November 2004 IPF PPS final rule (69 FR 66932 through 
66933) and in the RY 2007 IPF PPS final rule (71 FR 27044 through 
27046). The final standardized budget-neutral Federal per diem base 
rate established for cost reporting periods beginning on or after 
January 1, 2005 was calculated to be $575.95.
    The Federal per diem base rate has been updated in accordance with 
applicable statutory requirements and Sec.  412.428 through publication 
of annual notices or proposed and final rules. A detailed discussion on 
the standardized budget-neutral Federal per diem base rate and the 
electroconvulsive therapy (ECT) payment per treatment appears in the FY 
2014 IPF PPS update notice (78 FR 46738 through 46740). These documents 
are available on the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/index.html.
    IPFs must include a valid procedure code for ECT services provided 
to IPF beneficiaries in order to bill for ECT services, as described in 
our Medicare Claims Processing Manual, Chapter 3, Section 190.7.3 
(available at https://www.cms.gov/Regulations-and-Guidance/Guidance/Manuals/Downloads/clm104c03.pdf.) There were no changes to the ECT 
procedure codes used on IPF claims as a result of the final update to 
the ICD-10-PCS code set for FY 2022. Addendum B to this final rule 
shows the ECT procedure codes for FY 2022 and is available on our 
website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html.
2. Final Update of the Federal Per Diem Base Rate and Electroconvulsive 
Therapy Payment per Treatment
    The current (FY 2021) Federal per diem base rate is $815.22 and the 
ECT payment per treatment is $350.97. For the final FY 2022 Federal per 
diem base rate, we applied the payment rate update of 2.0 percent--that 
is, the 2016-based IPF market basket increase for FY 2022 of 2.7 
percent less the productivity adjustment of 0.7 percentage point--and 
the wage index budget-neutrality factor of 1.0017 (as discussed in 
section III.D.1 of this final rule) to the FY 2021 Federal per diem 
base rate of $815.22, yielding a final Federal per diem base rate of 
$832.94 for FY 2022. Similarly, we applied the 2.0 percent payment rate 
update and the 1.0017 wage index budget-neutrality factor to the FY 
2021 ECT payment per treatment of $350.97, yielding a final ECT payment 
per treatment of $358.60 for FY 2022.
    Section 1886(s)(4)(A)(i) of the Act requires that for RY 2014 and 
each subsequent RY, in the case of an IPF that fails to report required 
quality data with respect to such RY, the Secretary will reduce any 
annual update to a standard Federal rate for discharges during the RY 
by 2.0 percentage points. Therefore, we are applying a 2.0 percentage 
point reduction to the Federal per diem base rate and the ECT payment 
per treatment as follows:
     For IPFs that fail requirements under the IPFQR Program, 
we applied a 0.0 percent payment rate update--that is, the IPF market 
basket increase for FY 2022 of 2.7 percent less the productivity 
adjustment of 0.7 percentage point for an update of 2.0 percent, and 
further reduced by 2 percentage points in accordance with section 
1886(s)(4)(A)(i) of the Act--and the wage index budget-neutrality 
factor of 1.0017 to the FY 2021 Federal per diem base rate of $815.22, 
yielding a Federal per diem base rate of $816.61 for FY 2022.
     For IPFs that fail to meet requirements under the IPFQR 
Program, we applied the 0.0 percent annual payment rate update and the 
1.0017 wage index budget-neutrality factor to the FY 2021 ECT payment 
per treatment of $350.97, yielding an ECT payment per treatment of 
$351.57 for FY 2022.

C. Final Updates to the IPF PPS Patient-Level Adjustment Factors

1. Overview of the IPF PPS Adjustment Factors
    The IPF PPS payment adjustments were derived from a regression 
analysis of 100 percent of the FY 2002 Medicare Provider and Analysis 
Review (MedPAR) data file, which contained 483,038 cases. For a more 
detailed description of the data file used for the regression analysis, 
see the November 2004 IPF PPS final rule (69 FR 66935 through 66936). 
We are finalizing our proposal to continue to use the existing 
regression-derived adjustment factors established in 2005 for FY 2022. 
However, we have used more recent claims data to simulate payments to 
finalize the outlier fixed dollar loss threshold amount and to assess 
the impact of the IPF PPS updates.

[[Page 42614]]

2. IPF PPS Patient-Level Adjustments
    The IPF PPS includes payment adjustments for the following patient-
level characteristics: Medicare Severity Diagnosis Related Groups (MS-
DRGs) assignment of the patient's principal diagnosis, selected 
comorbidities, patient age, and the variable per diem adjustments.
a. Final Update to MS-DRG Assignment
    We believe it is important to maintain for IPFs the same diagnostic 
coding and Diagnosis Related Group (DRG) classification used under the 
IPPS for providing psychiatric care. For this reason, when the IPF PPS 
was implemented for cost reporting periods beginning on or after 
January 1, 2005, we adopted the same diagnostic code set (ICD-9-CM) and 
DRG patient classification system (MS-DRGs) that were utilized at the 
time under the IPPS. In the RY 2009 IPF PPS notice (73 FR 25709), we 
discussed CMS' effort to better recognize resource use and the severity 
of illness among patients. CMS adopted the new MS-DRGs for the IPPS in 
the FY 2008 IPPS final rule with comment period (72 FR 47130). In the 
RY 2009 IPF PPS notice (73 FR 25716), we provided a crosswalk to 
reflect changes that were made under the IPF PPS to adopt the new MS-
DRGs. For a detailed description of the mapping changes from the 
original DRG adjustment categories to the current MS-DRG adjustment 
categories, we refer readers to the RY 2009 IPF PPS notice (73 FR 
25714).
    The IPF PPS includes payment adjustments for designated psychiatric 
DRGs assigned to the claim based on the patient's principal diagnosis. 
The DRG adjustment factors were expressed relative to the most 
frequently reported psychiatric DRG in FY 2002, that is, DRG 430 
(psychoses). The coefficient values and adjustment factors were derived 
from the regression analysis discussed in detail in the November 28, 
2003 IPF proposed rule (68 FR 66923; 66928 through 66933) and the 
November 15, 2004 IPF final rule (69 FR 66933 through 66960). Mapping 
the DRGs to the MS-DRGs resulted in the current 17 IPF MS-DRGs, instead 
of the original 15 DRGs, for which the IPF PPS provides an adjustment. 
For FY 2022, we did not propose any changes to the IPF MSDRG adjustment 
factors. Therefore, we are finalizing our proposal to maintain the 
existing IPF MS-DRG adjustment factors.
    In the FY 2015 IPF PPS final rule published August 6, 2014 in the 
Federal Register titled, ``Inpatient Psychiatric Facilities Prospective 
Payment System--Update for FY Beginning October 1, 2014 (FY 2015)'' (79 
FR 45945 through 45947), we finalized conversions of the ICD-9-CM-based 
MS-DRGs to ICD-10-CM/PCS-based MS-DRGs, which were implemented on 
October 1, 2015. Further information on the ICD-10-CM/PCS MS-DRG 
conversion project can be found on the CMS ICD-10-CM website at https://www.cms.gov/Medicare/Coding/ICD10/ICD-10-MS-DRG-Conversion-Project.html.
    For FY 2022, we are finalizing our proposal to continue to make the 
existing payment adjustment for psychiatric diagnoses that group to one 
of the existing 17 IPF MS-DRGs listed in Addendum A. Addendum A is 
available on our website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html. Psychiatric 
principal diagnoses that do not group to one of the 17 designated MS-
DRGs will still receive the Federal per diem base rate and all other 
applicable adjustments, but the payment will not include an MS-DRG 
adjustment.
    The diagnoses for each IPF MS-DRG will be updated as of October 1, 
2021, using the final IPPS FY 2022 ICD-10-CM/PCS code sets. The FY 2022 
IPPS/LTCH PPS final rule includes tables of the changes to the ICD-10-
CM/PCS code sets, which underlie the FY 2022 IPF MS-DRGs. Both the FY 
2022 IPPS final rule and the tables of final changes to the ICD-10-CM/
PCS code sets, which underlie the FY 2022 MS-DRGs, are available on the 
CMS IPPS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/index.html.
Code First
    As discussed in the ICD-10-CM Official Guidelines for Coding and 
Reporting, certain conditions have both an underlying etiology and 
multiple body system manifestations due to the underlying etiology. For 
such conditions, the ICD-10-CM has a coding convention that requires 
the underlying condition be sequenced first followed by the 
manifestation. Wherever such a combination exists, there is a ``use 
additional code'' note at the etiology code, and a ``code first'' note 
at the manifestation code. These instructional notes indicate the 
proper sequencing order of the codes (etiology followed by 
manifestation). In accordance with the ICD-10-CM Official Guidelines 
for Coding and Reporting, when a primary (psychiatric) diagnosis code 
has a ``code first'' note, the provider will follow the instructions in 
the ICD-10-CM Tabular List. The submitted claim goes through the CMS 
processing system, which will identify the principal diagnosis code as 
non-psychiatric and search the secondary codes for a psychiatric code 
to assign a DRG code for adjustment. The system will continue to search 
the secondary codes for those that are appropriate for comorbidity 
adjustment.
    For more information on the code first policy, we refer our readers 
to the November 2004 IPF PPS final rule (69 FR 66945) and see sections 
I.A.13 and I.B.7 of the FY 2020 ICD-10-CM Coding Guidelines, available 
at https://www.cdc.gov/nchs/data/icd/10cmguidelines-FY2020_final.pdf. 
In the FY 2015 IPF PPS final rule, we provided a code first table for 
reference that highlights the same or similar manifestation codes where 
the code first instructions apply in ICD-10-CM that were present in 
ICD-9-CM (79 FR 46009). In FY 2018, FY 2019 and FY 2020, there were no 
changes to the final ICD-10-CM codes in the IPF Code First table. For 
FY 2021, there were 18 ICD-10-CM codes deleted from the final IPF Code 
First table. For FY 2022 there are 18 codes finalized for deletion from 
the ICD-10-CM codes in the IPF Code First table. The final FY 2022 Code 
First table is shown in Addendum B on our website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html.
b. Final Payment for Comorbid Conditions
    The intent of the comorbidity adjustments is to recognize the 
increased costs associated with comorbid conditions by providing 
additional payments for certain existing medical or psychiatric 
conditions that are expensive to treat. In our RY 2012 IPF PPS final 
rule (76 FR 26451 through 26452), we explained that the IPF PPS 
includes 17 comorbidity categories and identified the new, revised, and 
deleted ICD-9-CM diagnosis codes that generate a comorbid condition 
payment adjustment under the IPF PPS for RY 2012 (76 FR 26451).
    Comorbidities are specific patient conditions that are secondary to 
the patient's principal diagnosis and that require treatment during the 
stay. Diagnoses that relate to an earlier episode of care and have no 
bearing on the current hospital stay are excluded and must not be 
reported on IPF claims. Comorbid conditions must exist at the time of 
admission or develop subsequently, and affect the treatment received, 
length of stay (LOS), or both treatment and LOS.

[[Page 42615]]

    For each claim, an IPF may receive only one comorbidity adjustment 
within a comorbidity category, but it may receive an adjustment for 
more than one comorbidity category. Current billing instructions for 
discharge claims, on or after October 1, 2015, require IPFs to enter 
the complete ICD-10-CM codes for up to 24 additional diagnoses if they 
co-exist at the time of admission, or develop subsequently and impact 
the treatment provided.
    The comorbidity adjustments were determined based on the regression 
analysis using the diagnoses reported by IPFs in FY 2002. The principal 
diagnoses were used to establish the DRG adjustments and were not 
accounted for in establishing the comorbidity category adjustments, 
except where ICD-9-CM code first instructions applied. In a code first 
situation, the submitted claim goes through the CMS processing system, 
which will identify the principal diagnosis code as non-psychiatric and 
search the secondary codes for a psychiatric code to assign an MS-DRG 
code for adjustment. The system will continue to search the secondary 
codes for those that are appropriate for comorbidity adjustment.
    As noted previously, it is our policy to maintain the same 
diagnostic coding set for IPFs that is used under the IPPS for 
providing the same psychiatric care. The 17 comorbidity categories 
formerly defined using ICD-9-CM codes were converted to ICD-10-CM/PCS 
in our FY 2015 IPF PPS final rule (79 FR 45947 through 45955). The goal 
for converting the comorbidity categories is referred to as 
replication, meaning that the payment adjustment for a given patient 
encounter is the same after ICD-10-CM implementation as it will be if 
the same record had been coded in ICD-9-CM and submitted prior to ICD-
10-CM/PCS implementation on October 1, 2015. All conversion efforts 
were made with the intent of achieving this goal. For FY 2022, we are 
finalizing our proposal to continue to use the same comorbidity 
adjustment factors in effect in FY 2021, which are found in Addendum A, 
available on our website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html.
    We have updated the ICD-10-CM/PCS codes, which are associated with 
the existing IPF PPS comorbidity categories, based upon the final FY 
2022 update to the ICD-10-CM/PCS code set. The final FY 2022 ICD-10-CM/
PCS updates include: 8 ICD-10-CM diagnosis codes added to the Poisoning 
comorbidity category, 4 codes deleted, and 4 changes to Poisoning 
comorbidity long descriptions; 2 ICD-10-CM diagnosis codes added to the 
Developmental Disabilities comorbidity category and 1 code deleted; and 
3 ICD-10-PCS codes added to the Oncology Procedures comorbidity 
category. These updates are detailed in Addenda B of this final rule, 
which are available on our website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html.
    In accordance with the policy established in the FY 2015 IPF PPS 
final rule (79 FR 45949 through 45952), we reviewed all new FY 2022 
ICD-10-CM codes to remove codes that were site ``unspecified'' in terms 
of laterality from the FY 2022 ICD-10-CM/PCS codes in instances where 
more specific codes are available. As we stated in the FY 2015 IPF PPS 
final rule, we believe that specific diagnosis codes that narrowly 
identify anatomical sites where disease, injury, or a condition exists 
should be used when coding patients' diagnoses whenever these codes are 
available. We finalized in the FY 2015 IPF PPS rule, that we would 
remove site ``unspecified'' codes from the IPF PPS ICD-10-CM/PCS codes 
in instances when laterality codes (site specified codes) are 
available, as the clinician should be able to identify a more specific 
diagnosis based on clinical assessment at the medical encounter. None 
of the finalized additions to the FY 2022 ICD-10-CM/PCS codes were site 
``unspecified'' by laterality, therefore, we are not removing any of 
the new codes.
    Comment: A commenter requested that CMS add 13 ICD-10-CM codes for 
infectious diseases to the list of codes that qualify for the IPF PPS 
comorbidity adjustment.
    Response: As noted previously, the intent of the comorbidity 
adjustments is to recognize the increased costs associated with 
comorbid conditions by providing additional payments for certain 
existing medical or psychiatric conditions that are expensive to treat. 
Also, the comorbidity adjustments were derived through a regression 
analysis, which also includes other IPF PPS adjustments (for example, 
the age adjustment). Our established policy is to annually update the 
ICD-10-CM/PCS codes, which are associated with the existing IPF PPS 
comorbidity categories. Adding or removing codes to the existing 
comorbidity categories that are not part of the annual coding update 
would occur as part of a larger IPF PPS refinement. We did not propose 
to refine the IPF PPS in the FY 2022 IPF PPS proposed rule, and 
therefore, are not changing the policy in this final rule. However, we 
will consider the comment to potentially inform future refinements.
c. Final Patient Age Adjustments
    As explained in the November 2004 IPF PPS final rule (69 FR 66922), 
we analyzed the impact of age on per diem cost by examining the age 
variable (range of ages) for payment adjustments. In general, we found 
that the cost per day increases with age. The older age groups are 
costlier than the under 45 age group, the differences in per diem cost 
increase for each successive age group, and the differences are 
statistically significant. For FY 2022, we are finalizing our proposal 
to continue to use the patient age adjustments currently in effect in 
FY 2021, as shown in Addendum A of this rule (see https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html).
d. Final Variable Per Diem Adjustments
    We explained in the November 2004 IPF PPS final rule (69 FR 66946) 
that the regression analysis indicated that per diem cost declines as 
the length of stay (LOS) increases. The variable per diem adjustments 
to the Federal per diem base rate account for ancillary and 
administrative costs that occur disproportionately in the first days 
after admission to an IPF. As discussed in the November 2004 IPF PPS 
final rule, we used a regression analysis to estimate the average 
differences in per diem cost among stays of different lengths (69 FR 
66947 through 66950). As a result of this analysis, we established 
variable per diem adjustments that begin on day 1 and decline gradually 
until day 21 of a patient's stay. For day 22 and thereafter, the 
variable per diem adjustment remains the same each day for the 
remainder of the stay. However, the adjustment applied to day 1 depends 
upon whether the IPF has a qualifying ED. If an IPF has a qualifying 
ED, it receives a 1.31 adjustment factor for day 1 of each stay. If an 
IPF does not have a qualifying ED, it receives a 1.19 adjustment factor 
for day 1 of the stay. The ED adjustment is explained in more detail in 
section III.D.4 of this rule.
    For FY 2022, we are finalizing our proposal to continue to use the 
variable per diem adjustment factors currently in effect, as shown in 
Addendum A of this rule (available at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html). A 
complete discussion of the variable per diem adjustments appears in the 
November 2004 IPF PPS final rule (69 FR 66946).

[[Page 42616]]

D. Final Updates to the IPF PPS Facility-Level Adjustments

    The IPF PPS includes facility-level adjustments for the wage index, 
IPFs located in rural areas, teaching IPFs, cost of living adjustments 
for IPFs located in Alaska and Hawaii, and IPFs with a qualifying ED.
1. Wage Index Adjustment
a. Background
    As discussed in the RY 2007 IPF PPS final rule (71 FR 27061), RY 
2009 IPF PPS (73 FR 25719) and the RY 2010 IPF PPS notices (74 FR 
20373), in order to provide an adjustment for geographic wage levels, 
the labor-related portion of an IPF's payment is adjusted using an 
appropriate wage index. Currently, an IPF's geographic wage index value 
is determined based on the actual location of the IPF in an urban or 
rural area, as defined in Sec.  412.64(b)(1)(ii)(A) and (C).
    Due to the variation in costs and because of the differences in 
geographic wage levels, in the November 15, 2004 IPF PPS final rule, we 
required that payment rates under the IPF PPS be adjusted by a 
geographic wage index. We proposed and finalized a policy to use the 
unadjusted, pre-floor, pre-reclassified IPPS hospital wage index to 
account for geographic differences in IPF labor costs. We implemented 
use of the pre-floor, pre-reclassified IPPS hospital wage data to 
compute the IPF wage index since there was not an IPF-specific wage 
index available. We believe that IPFs generally compete in the same 
labor market as IPPS hospitals so the pre-floor, pre-reclassified IPPS 
hospital wage data should be reflective of labor costs of IPFs. We 
believe this pre-floor, pre-reclassified IPPS hospital wage index to be 
the best available data to use as proxy for an IPF specific wage index. 
As discussed in the RY 2007 IPF PPS final rule (71 FR 27061 through 
27067), under the IPF PPS, the wage index is calculated using the IPPS 
wage index for the labor market area in which the IPF is located, 
without considering geographic reclassifications, floors, and other 
adjustments made to the wage index under the IPPS. For a complete 
description of these IPPS wage index adjustments, we refer readers to 
the FY 2019 IPPS/LTCH PPS final rule (83 FR 41362 through 41390). Our 
wage index policy at Sec.  412.424(a)(2), requires us to use the best 
Medicare data available to estimate costs per day, including an 
appropriate wage index to adjust for wage differences.
    When the IPF PPS was implemented in the November 15, 2004 IPF PPS 
final rule, with an effective date of January 1, 2005, the pre-floor, 
pre-reclassified IPPS hospital wage index that was available at the 
time was the FY 2005 pre-floor, pre-reclassified IPPS hospital wage 
index. Historically, the IPF wage index for a given RY has used the 
pre-floor, pre-reclassified IPPS hospital wage index from the prior FY 
as its basis. This has been due in part to the pre-floor, pre-
reclassified IPPS hospital wage index data that were available during 
the IPF rulemaking cycle, where an annual IPF notice or IPF final rule 
was usually published in early May. This publication timeframe was 
relatively early compared to other Medicare payment rules because the 
IPF PPS follows a RY, which was defined in the implementation of the 
IPF PPS as the 12-month period from July 1 to June 30 (69 FR 66927). 
Therefore, the best available data at the time the IPF PPS was 
implemented was the pre-floor, pre-reclassified IPPS hospital wage 
index from the prior FY (for example, the RY 2006 IPF wage index was 
based on the FY 2005 pre-floor, pre-reclassified IPPS hospital wage 
index).
    In the RY 2012 IPF PPS final rule, we changed the reporting year 
timeframe for IPFs from a RY to the FY, which begins October 1 and ends 
September 30 (76 FR 26434 through 26435). In that FY 2012 IPF PPS final 
rule, we continued our established policy of using the pre-floor, pre-
reclassified IPPS hospital wage index from the prior year (that is, 
from FY 2011) as the basis for the FY 2012 IPF wage index. This policy 
of basing a wage index on the prior year's pre-floor, pre-reclassified 
IPPS hospital wage index has been followed by other Medicare payment 
systems, such as hospice and inpatient rehabilitation facilities. By 
continuing with our established policy, we remained consistent with 
other Medicare payment systems.
    In FY 2020, we finalized the IPF wage index methodology to align 
the IPF PPS wage index with the same wage data timeframe used by the 
IPPS for FY 2020 and subsequent years. Specifically, we finalized to 
use the pre-floor, pre-reclassified IPPS hospital wage index from the 
FY concurrent with the IPF FY as the basis for the IPF wage index. For 
example, the FY 2020 IPF wage index was based on the FY 2020 pre-floor, 
pre-reclassified IPPS hospital wage index rather than on the FY 2019 
pre-floor, pre-reclassified IPPS hospital wage index.
    We explained in the FY 2020 proposed rule (84 FR 16973), that using 
the concurrent pre-floor, pre-reclassified IPPS hospital wage index 
will result in the most up-to-date wage data being the basis for the 
IPF wage index. It will also result in more consistency and parity in 
the wage index methodology used by other Medicare payment systems. The 
Medicare SNF PPS already used the concurrent IPPS hospital wage index 
data as the basis for the SNF PPS wage index. Thus, the wage adjusted 
Medicare payments of various provider types will be based upon wage 
index data from the same timeframe. CMS proposed similar policies to 
use the concurrent pre-floor, pre-reclassified IPPS hospital wage index 
data in other Medicare payment systems, such as hospice and inpatient 
rehabilitation facilities. For FY 2022, we proposed to continue to use 
the concurrent pre-floor, pre-reclassified IPPS hospital wage index as 
the basis for the IPF wage index.
    Comment: Several commenters expressed concerns with our proposal to 
continue using the concurrent pre-floor, pre-reclassified IPPS hospital 
wage index as the basis for the IPF wage index. Three commenters 
recommended CMS extend the transition for the reductions in payment for 
certain IPFs resulting from the wage index changes adopted in the FY 
2021 IPF PPS final rule. Another commenter also recommended that CMS 
apply a non-budget neutral 5 percent cap on decreases to a hospital's 
wage index value to help mitigate wide annual swings that are beyond a 
hospital's ability to control.
    Response: We did not propose to modify the transition policy that 
was finalized in the FY 2021 IPF PPS final rule; therefore, we are not 
changing the previously adopted policy in this final rule. As we 
discussed in the FY 2021 IPF PPS final rule (85 FR 47058 through 
47059), the transition policy caps the estimated reduction in an IPF's 
wage index to 5 percent in FY 2021, with no cap applied in FY 2022. We 
stated our belief that implementing updated wage index values along 
with the revised OMB delineations will result in wage index values 
being more representative of the actual costs of labor in a given area. 
As evidenced by the detailed economic analysis (85 FR 47065 through 
47068), we estimated that implementing these wage index changes would 
have distributional effects, both positive and negative, among IPF 
providers. We continue to believe that applying the 5-percent cap 
transition policy in year one provided an adequate safeguard against 
any significant payment reductions, has allowed for sufficient time to 
make operational changes for future FYs, and provided a reasonable 
balance between mitigating some short-term instability in IPF payments 
and improving the accuracy of the payment adjustment for differences in 
area wage levels.

[[Page 42617]]

    We note that certain changes to wage index policy may significantly 
affect Medicare payments. These changes may arise from revisions to the 
OMB delineations of statistical areas resulting from the decennial 
census data, periodic updates to the OMB delineations in the years 
between the decennial censuses, or other wage index policy changes. 
While we consider how best to address these potential scenarios in a 
consistent and thoughtful manner, we reiterate 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, 
in notice and comment rulemaking.
    Comment: Two commenters recommended that CMS incorporate a frontier 
state floor into the IPF wage index. Another commenter requested that 
CMS implement policies to address the disparity in payments between 
rural and urban IPFs, similar to policies that have been adopted for 
IPPS hospitals.
    Response: We appreciate commenters' suggestions regarding 
opportunities to improve the accuracy of the IPF wage index. We did not 
propose the specific policies that commenters have suggested, but we 
will take them into consideration to potentially inform future 
rulemaking.
    Final Decision: For FY 2022, we are finalizing the proposal to 
continue to use the concurrent pre-floor, pre-reclassified IPPS 
hospital wage index as the basis for the IPF wage index. Since we did 
not propose any changes to the 2-year transition that was finalized in 
the FY 2021 IPF PPS final rule, there will be no cap applied to the 
reduction in the wage index for the second year (that is, FY 2022).
    We will apply the IPF wage index adjustment to the labor-related 
share of the national base rate and ECT payment per treatment. The 
labor-related share of the national rate and ECT payment per treatment 
will change from 77.3 percent in FY 2021 to 77.2 percent in FY 2022. 
This percentage reflects the labor-related share of the 2016-based IPF 
market basket for FY 2022 (see section III.A.4 of this rule).
b. Office of Management and Budget (OMB) Bulletins
(i.) Background
    The wage index used for the IPF PPS is calculated using the 
unadjusted, pre-reclassified and pre-floor IPPS wage index data and is 
assigned to the IPF on the basis of the labor market area in which the 
IPF is geographically located. IPF labor market areas are delineated 
based on the Core-Based Statistical Area (CBSAs) established by the 
OMB.
    Generally, OMB issues major revisions to statistical areas every 10 
years, based on the results of the decennial census. However, OMB 
occasionally issues minor updates and revisions to statistical areas in 
the years between the decennial censuses through OMB Bulletins. These 
bulletins contain information regarding CBSA changes, including changes 
to CBSA numbers and titles. OMB bulletins may be accessed online at 
https://www.whitehouse.gov/omb/information-for-agencies/bulletins/. In 
accordance with our established methodology, the IPF PPS has 
historically adopted any CBSA changes that are published in the OMB 
bulletin that corresponds with the IPPS hospital wage index used to 
determine the IPF wage index and, when necessary and appropriate, has 
proposed and finalized transition policies for these changes.
    In the RY 2007 IPF PPS final rule (71 FR 27061 through 27067), we 
adopted the changes discussed in the 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 OMB CBSA geographic designations in RY 2007, we did not 
provide a separate transition for the CBSA-based wage index since the 
IPF PPS was already in a transition period from TEFRA payments to PPS 
payments.
    In the RY 2009 IPF PPS notice, we incorporated the CBSA 
nomenclature changes published in the most recent OMB bulletin that 
applied to the IPPS hospital wage index used to determine the current 
IPF wage index and stated that we expected to continue to do the same 
for all the OMB CBSA nomenclature changes in future IPF PPS rules and 
notices, as necessary (73 FR 25721).
    Subsequently, CMS adopted the changes that were published in past 
OMB bulletins in the FY 2016 IPF PPS final rule (80 FR 46682 through 
46689), the FY 2018 IPF PPS rate update (82 FR 36778 through 36779), 
the FY 2020 IPF PPS final rule (84 FR 38453 through 38454), and the FY 
2021 IPF PPS final rule (85 FR 47051 through 47059). We direct readers 
to each of these rules for more information about the changes that were 
adopted and any associated transition policies.
    In part due to the scope of changes involved in adopting the CBSA 
delineations for FY 2021, we finalized a 2-year transition policy 
consistent with our past practice of using transition policies to help 
mitigate negative impacts on hospitals of certain wage index policy 
changes. We applied a 5-percent cap on wage index decreases to all IPF 
providers that had any decrease in their wage indexes, regardless of 
the circumstance causing the decline, so that an IPF's final wage index 
for FY 2021 will not be less than 95 percent of its final wage index 
for FY 2020, regardless of whether the IPF was part of an updated CBSA. 
We refer readers to the FY 2021 IPF PPS final rule (85 FR 47058 through 
47059) for a more detailed discussion about the wage index transition 
policy for FY 2021.
    On March 6, 2020 OMB issued OMB Bulletin 20-01 (available on the 
web at https://www.whitehouse.gov/wp-content/uploads/2020/03/Bulletin-20-01.pdf). In considering whether to adopt this bulletin, we analyzed 
whether the changes in this bulletin would have a material impact on 
the IPF PPS wage index. This bulletin creates only one Micropolitan 
statistical area. As discussed in further detail in section 
III.D.1.b.ii, since Micropolitan areas are considered rural for the IPF 
PPS wage index, this bulletin has no material impact on the IPF PPS 
wage index. That is, the constituent county of the new Micropolitan 
area was considered rural effective as of FY 2021 and would continue to 
be considered rural if we adopted OMB Bulletin 20-01. Therefore, we did 
not propose to adopt OMB Bulletin 20-01 in the FY 2022 IPF PPS proposed 
rule.
(ii.) Micropolitan Statistical Areas
    OMB defines a ``Micropolitan Statistical Area'' as a CBSA 
associated with at least one urban cluster that has a population of at 
least 10,000, but less than 50,000 (75 FR 37252). We refer to these as 
Micropolitan Areas. After extensive impact analysis, consistent with 
the treatment of these areas under the IPPS as discussed in the FY 2005 
IPPS final rule (69 FR 49029 through 49032), we determined the best 
course of action would be to treat Micropolitan Areas as ``rural'' and 
include them in the calculation of each state's IPF PPS rural wage 
index. We refer the reader to the FY 2007 IPF PPS final rule (71 FR 
27064 through 27065) for a complete discussion regarding treating 
Micropolitan Areas as rural.
c. Final Adjustment for Rural Location
    In the November 2004 IPF PPS final rule, (69 FR 66954) we provided 
a 17 percent payment adjustment for IPFs located in a rural area. This 
adjustment was based on the regression analysis, which indicated that 
the per diem cost

[[Page 42618]]

of rural facilities was 17 percent higher than that of urban facilities 
after accounting for the influence of the other variables included in 
the regression. This 17 percent adjustment has been part of the IPF PPS 
each year since the inception of the IPF PPS. For FY 2022, we proposed 
to continue to apply a 17 percent payment adjustment for IPFs located 
in a rural area as defined at Sec.  412.64(b)(1)(ii)(C) (see 69 FR 
66954 for a complete discussion of the adjustment for rural locations).
    Comment: We received one comment in favor of the proposed extension 
of the 17 percent payment adjustment for rural IPFs. The commenter 
acknowledged CMS' efforts to avoid disparities in payments to 
facilities in rural and underserved communities.
    Response: We appreciate this comment of support. Since the 
inception of the IPF PPS, we have applied a 17 percent adjustment for 
IPFs located in rural areas. As stated in the previous paragraph, this 
adjustment was derived from the results of our regression analysis and 
was incorporated into the payment system in order to ensure the 
accuracy of payments to rural IPFs. CMS continues to look for ways to 
ensure accuracy of payments to rural IPFs.
    Final Decision: For FY 2022, we are finalizing our proposal to 
continue to apply a 17 percent payment adjustment for IPFs located in a 
rural area as defined at Sec.  412.64(b)(1)(ii)(C).
d. Final Budget Neutrality Adjustment
    Changes to the wage index are made in a budget-neutral manner so 
that updates do not increase expenditures. Therefore, for FY 2022, we 
are finalizing our proposal to continue to apply a budget-neutrality 
adjustment in accordance with our existing budget-neutrality policy. 
This policy requires us to update the wage index in such a way that 
total estimated payments to IPFs for FY 2022 are the same with or 
without the changes (that is, in a budget-neutral manner) by applying a 
budget neutrality factor to the IPF PPS rates. We use the following 
steps to ensure that the rates reflect the FY 2022 update to the wage 
indexes (based on the FY 2018 hospital cost report data) and the labor-
related share in a budget-neutral manner:
    Step 1: Simulate estimated IPF PPS payments, using the FY 2021 IPF 
wage index values (available on the CMS website) and labor-related 
share (as published in the FY 2021 IPF PPS final rule (85 FR 47043)).
    Step 2: Simulate estimated IPF PPS payments using the final FY 2022 
IPF wage index values (available on the CMS website) and final FY 2022 
labor-related share (based on the latest available data as discussed 
previously).
    Step 3: Divide the amount calculated in step 1 by the amount 
calculated in step 2. The resulting quotient is the FY 2022 budget-
neutral wage adjustment factor of 1.0017.
    Step 4: Apply the FY 2022 budget-neutral wage adjustment factor 
from step 3 to the FY 2021 IPF PPS Federal per diem base rate after the 
application of the market basket update described in section III.A of 
this rule, to determine the FY 2022 IPF PPS Federal per diem base rate.
2. Final Teaching Adjustment
a. Background
    In the November 2004 IPF PPS final rule, we implemented regulations 
at sect; 412.424(d)(1)(iii) to establish a facility-level adjustment 
for IPFs that are, or are part of, teaching hospitals. The teaching 
adjustment accounts for the higher indirect operating costs experienced 
by hospitals that participate in graduate medical education (GME) 
programs. The payment adjustments are made based on the ratio of the 
number of full-time equivalent (FTE) interns and residents training in 
the IPF and the IPF's average daily census (ADC).
    Medicare makes direct GME payments (for direct costs such as 
resident and teaching physician salaries, and other direct teaching 
costs) to all teaching hospitals including those paid under a PPS, and 
those paid under the TEFRA rate-of-increase limits. These direct GME 
payments are made separately from payments for hospital operating costs 
and are not part of the IPF PPS. The direct GME payments do not address 
the estimated higher indirect operating costs teaching hospitals may 
face.
    The results of the regression analysis of FY 2002 IPF data 
established the basis for the payment adjustments included in the 
November 2004 IPF PPS final rule. The results showed that the indirect 
teaching cost variable is significant in explaining the higher costs of 
IPFs that have teaching programs. We calculated the teaching adjustment 
based on the IPF's ``teaching variable,'' which is (1 + (the number of 
FTE residents training in the IPF/the IPF's ADC)). The teaching 
variable is then raised to the 0.5150 power to result in the teaching 
adjustment. This formula is subject to the limitations on the number of 
FTE residents, which are described in this section of this rule.
    We established the teaching adjustment in a manner that limited the 
incentives for IPFs to add FTE residents for the purpose of increasing 
their teaching adjustment. We imposed a cap on the number of FTE 
residents that may be counted for purposes of calculating the teaching 
adjustment. The cap limits the number of FTE residents that teaching 
IPFs may count for the purpose of calculating the IPF PPS teaching 
adjustment, not the number of residents teaching institutions can hire 
or train. We calculated the number of FTE residents that trained in the 
IPF during a ``base year'' and used that FTE resident number as the 
cap. An IPF's FTE resident cap is ultimately determined based on the 
final settlement of the IPF's most recent cost report filed before 
November 15, 2004 (publication date of the IPF PPS final rule). A 
complete discussion of the temporary adjustment to the FTE cap to 
reflect residents due to hospital closure or residency program closure 
appears in the RY 2012 IPF PPS proposed rule (76 FR 5018 through 5020) 
and the RY 2012 IPF PPS final rule (76 FR 26453 through 26456). In 
section III.D.2.b of this final rule, we discuss finalized updates to 
the IPF policy on temporary adjustment to the FTE cap.
    In the regression analysis, the logarithm of the teaching variable 
had a coefficient value of 0.5150. We converted this cost effect to a 
teaching payment adjustment by treating the regression coefficient as 
an exponent and raising the teaching variable to a power equal to the 
coefficient value. We note that the coefficient value of 0.5150 was 
based on the regression analysis holding all other components of the 
payment system constant. A complete discussion of how the teaching 
adjustment was calculated appears in the November 2004 IPF PPS final 
rule (69 FR 66954 through 66957) and the RY 2009 IPF PPS notice (73 FR 
25721). As with other adjustment factors derived through the regression 
analysis, we do not plan to rerun the teaching adjustment factors in 
the regression analysis until we more fully analyze IPF PPS data. 
Therefore, in this FY 2022 final rule, we are finalizing our proposal 
to continue to retain the coefficient value of 0.5150 for the teaching 
adjustment to the Federal per diem base rate.
b. Final Update to IPF Teaching Policy on IPF Program Closures and 
Displaced Residents
    For FY 2022, we proposed to change the IPF policy regarding 
displaced residents from IPF closures and closures of IPF teaching 
programs. Specifically, we proposed to adopt conforming changes to the 
IPF PPS teaching policy

[[Page 42619]]

to align with the policy changes that the IPPS finalized in the FY 2021 
IPPS final rule (85 FR 58865 through 58870). We believe that the IPF 
IME policy relating to hospital closure and displaced students is 
susceptible to the same vulnerabilities as IPPS GME policy. Hence, if 
an IPF with a large number of residents training in its residency 
program announces that it is closing, these residents will become 
displaced and will need to find alternative positions at other IPF 
hospitals or risk being unable to become Board-certified. Although we 
proposed to adopt a policy under the IPF PPS that is consistent with an 
applicable policy under the IPPS, the actual caps under the two payment 
systems may not be commingled. In other words, the resident cap 
applicable under the IPPS is separate from the resident cap applicable 
under the IPF PPS; moreover, a provider cannot add its IPF resident cap 
to its IPPS resident cap in order to increase the number of residents 
it receives payment for under either payment system.
    As stated in the November 2004 IPF PPS final rule (69 FR 66922), we 
implemented regulations at Sec.  412.424(d)(1)(iii) to establish a 
facility-level adjustment for IPFs that are, or are part of, teaching 
hospitals. The facility-level adjustment we are providing for teaching 
hospitals under IPF PPS parallels the IME payments paid under the IPPS. 
Both payments are add on adjustments to the amount per case and both 
are based in part on the number of full-time equivalent (FTE) residents 
training at the facility.
    The regulation at 42 CFR 412.424(d)(1)(iii)(F) permits an IPF to 
temporarily adjust its FTE cap to reflect residents added because of 
another hospital or program's closure. We first implemented regulations 
regarding residents displaced by teaching hospital and program closures 
in the May 6, 2011 IPF PPS final rule (76 FR 26431). In that final 
rule, we adopted the IPPS definition of ``closure of a hospital'' at 42 
CFR 413.79(h)(1)(i) to apply to IPF closures as well, and to mean that 
the IPF terminates its Medicare provider agreement as specified in 42 
CFR 489.52. In the proposed rule, we proposed to codify this 
definition, as well as, the definition of an IPF program closure, at 
Sec.  412.402.
    Although not explicitly stated in regulatory text, our current 
policy is that a displaced resident is one that is physically present 
at the hospital training on the day prior to or the day of hospital or 
program closure. This longstanding policy derived from the fact that in 
the regulations text, there are requirements that the receiving 
hospital identifies the residents ``who have come from the closed IPF'' 
(Sec.  412.424(d)(1)(iii)(F)(1)(ii)) or identifies the residents ``who 
have come from another IPF's closed program'' (Sec.  
412.424(d)(1)(iii)(F)(2)(i)), and that the IPF that closed its program 
identifies ``the residents who were in training at the time of the 
program's closure'' (Sec.  412.424(d)(1)(iii)(F)(2)(ii)). We considered 
the residents who were physically present at the IPF to be those 
residents who were ``training at the time of the program's closure,'' 
thereby granting them the status of ``displaced residents.'' Although 
we did not want to limit the ``displaced residents'' to only those 
physically present at the time of closure, it becomes much more 
administratively challenging for the following groups of residents at 
closing IPFs/programs to continue their training: (1) Residents who 
leave the program after the closure is publicly announced to continue 
training at another IPF, but before the actual closure; (2) residents 
assigned to and training at planned rotations at other IPFs who will be 
unable to return to their rotations at the closing IPF or program; and 
(3) individuals (such as medical students or would-be fellows) who 
matched into resident programs at the closing IPF or program but have 
not yet started training at the closing IPF or program. Other groups of 
residents who, under current policy, are already considered ``displaced 
residents'' include--(1) residents who are physically training in the 
IPF on the day prior to or day of program or IPF closure; and (2) 
residents who would have been at the closing IPF or IPF program on the 
day prior to or of closure but were on approved leave at that time, and 
are unable to return to their training at the closing IPF or IPF 
program.
    We proposed to amend the IPF policy with regard to closing teaching 
IPFs and closing residency programs to address the needs of residents 
attempting to find alternative IPFs in which to complete their 
training. Additionally, this proposal addresses the incentives of 
originating and receiving IPFs with regard to ensuring we appropriately 
account for their indirect teaching costs by way of an appropriate IPF 
teaching adjustment based on each program's resident FTEs. We proposed 
to change two aspects of the current IPF policy, which are discussed in 
the following section.
    First, rather than link the status of displaced residents, for the 
purpose of the receiving IPF's request to increase their FTE cap, to 
the resident's presence at the closing IPF or program on the day prior 
to or the day of program or IPF closure, we proposed that the ideal day 
will be the day that the closure was publicly announced, (for example, 
via a press release or a formal notice to the Accreditation Council on 
Graduate Medical Education (ACGME)). This will provide greater 
flexibility for the residents to transfer while the IPF operations or 
residency programs were winding down, rather than waiting until the 
last day of IPF or program operation. This will address the needs of 
the first group of residents as previously described: Residents who 
leave the IPF program after the closure was publicly announced to 
continue training at another IPF, but before the day of actual closure.
    Second, by removing the link between the status of displaced 
residents and their presence at the closing IPF or program on the day 
prior to or the day of program or IPF closure, we proposed to also 
allow the second and third group of residents who are not physically at 
the closing IPF/closing program, but had intended to train at (or 
return to training at, in the case of residents on rotation) to be 
considered displaced residents. Thus, we proposed to revise our 
teaching policy with regard to which residents can be considered 
``displaced'' for the purpose of the receiving IPF's request to 
increase their FTE cap in the situation where an IPF announces publicly 
that it is closing or that it is closing an IPF residency program(s). 
Specifically, we are adopting the definitions of ``closure of a 
hospital'', ``closure of a hospital residency training program'', and 
``displaced resident'' as defined at 42 CFR 413.79(h) but with respect 
to IPFs and for the purposes of accounting for indirect teaching costs.
    In addition, we proposed to change another detail of the IPF 
teaching policy specific to the requirements for the receiving IPF. To 
apply for the temporary increase in the FTE resident cap, the receiving 
IPF will have to submit a letter to its Medicare Administrative 
Contractor (MAC) within 60 days of beginning the training of the 
displaced residents. As established under existing regulation at Sec.  
412.424(d)(1)(iii)(F)(1)(ii) and Sec.  412.424(d)(1)(iii)(F)(2)(i), 
this letter must identify the residents who have come from the closed 
IPF or program that have caused the receiving IPF to exceed its cap, 
and the receiving IPF must specify the length of time the adjustment is 
needed. Moreover, we want to propose clarifications on how the 
information will be delivered in this letter. Consistent with IPPS 
teaching policy, we proposed that the letter from the receiving IPF 
will have to include:

[[Page 42620]]

(1) The name of each displaced resident; (2) the last four digits of 
each displaced resident's social security number; (3) the IPF and 
program in which each resident was training previously; and (4) the 
amount of the cap increase needed for each resident (based on how much 
the receiving IPF is in excess of its cap and the length of time for 
which the adjustments are needed). We proposed to require the receiving 
hospital to only supply the last four digits of each displaced 
resident's social security number to reduce the amount of personally 
identifiable information (PII) included in these agreements.
    We also clarified, as previously discussed in the May 6, 2011 IPF 
PPS final rule (76 FR 26455), the maximum number of FTE resident cap 
slots that could be transferred to all receiving IPFs is the number of 
FTE resident cap slots belonging to the IPF that has the closed program 
or that is closing. Therefore, if the originating IPF is training 
residents in excess of its cap, then being a displaced resident does 
not guarantee that a cap slot will be transferred along with that 
resident. Therefore, if there are more IPF displaced residents than 
available cap slots, the slots may be apportioned according to the 
closing IPF's discretion. The decision to transfer a cap slot if one is 
available will be voluntary and made at the sole discretion of the 
originating IPF. However, if the originating IPF decides to do so, then 
it will be the originating IPF's responsibility to determine how much 
of an available cap slot will go with a particular resident (if any). 
We also note, as we previously discussed in the May 6, 2011 IPF PPS 
final rule (76 FR 25455), only to the extent a receiving IPF would 
exceed its FTE cap by training displaced residents would it be eligible 
for a temporary adjustment to its resident FTE cap. Displaced residents 
are factored into the receiving IPF's ratio of resident FTEs to the 
facility's average daily census.
    Comment: We received 3 comments on our proposed updates to IPF 
teaching policy. All commenters appreciate the alignment of IPF 
teaching policy with IPPS. They believe it is important to protect 
medical education. Therefore, decreasing confusion and streamlining the 
process gives residents and program directors more time to find a new 
program or rotation site, which can only help the transfer process.
    Response: We thank these commenters for their support.
    Final Decision: For FY 2022, we are finalizing the closure policy 
as proposed. Section 124 of the BBRA gives the Secretary broad 
discretion to determine the appropriate adjustment factors for the IPF 
PPS. We are finalizing our proposal to implement the policy regarding 
IPF resident caps and closures to remain consistent with the way that 
the IPPS teaching policy calculates FTE resident caps in the case of a 
receiving hospital that obtains a temporary IME and direct GME cap 
adjustment for assuming the training of displaced residents due to 
another hospital or residency program's closure. We are also finalizing 
our proposal that in the future, we will deviate from IPPS teaching 
policy as it pertains to counting displaced residents for the purposes 
of the IPF teaching adjustment only when it is necessary and 
appropriate for the IPF PPS.
    In addition, we are finalizing our proposal to amend the IPF policy 
with regard to closing teaching IPFs and closing residency programs to 
address the needs of residents attempting to find alternative IPFs in 
which to complete their training. This proposal addresses the 
incentives of originating and receiving IPFs with regard to ensuring we 
appropriately account for their indirect teaching costs by way of an 
appropriate IPF teaching adjustment based on each program's resident 
FTEs. We are also finalizing our proposal to change two aspects of the 
current IPF policy, which are discussed in the following section.
    First, rather than link the status of displaced residents for the 
purpose of the receiving IPF's request to increase their FTE cap to the 
resident's presence at the closing IPF or program on the day prior to 
or the day of program or IPF closure, we are finalizing our proposal 
that the ideal day will be the day that the closure was publicly 
announced, (for example, via a press release or a formal notice to the 
Accreditation Council on Graduate Medical Education (ACGME)). This will 
provide greater flexibility for the residents to transfer while the IPF 
operations or residency programs were winding down, rather than waiting 
until the last day of IPF or program operation. This will address the 
needs of the first group of residents as previously described: 
Residents who leave the IPF program after the closure was publicly 
announced to continue training at another IPF, but before the day of 
actual closure.
    Second, by removing the link between the status of displaced 
residents and their presence at the closing IPF or program on the day 
prior to or the day of program or IPF closure, we are finalizing to 
also allow the second and third group of residents who are not 
physically at the closing IPF/closing program, but had intended to 
train at (or return to training at, in the case of residents on 
rotation) to be considered a displaced resident. Thus, we are 
finalizing our proposal to revise our teaching policy with regard to 
which residents can be considered ``displaced'' for the purpose of the 
receiving IPF's request to increase their FTE cap in the situation 
where an IPF announces publicly that it is closing or that it is 
closing an IPF residency program(s). Specifically, we are adopting the 
definitions of ``closure of a hospital'', ``closure of a hospital 
residency training program'', and ``displaced resident'' as defined at 
42 CFR 413.79(h) but with respect to IPFs and for the purposes of 
accounting for indirect teaching costs.
    In addition, we are finalizing our proposal to change another 
detail of the IPF teaching policy specific to the requirements for the 
receiving IPF. To apply for the temporary increase in the FTE resident 
cap, the receiving IPF will have to submit a letter to its Medicare 
Administrative Contractor (MAC) within 60 days of beginning the 
training of the displaced residents. As established under existing 
regulation at Sec.  412.424(d)(1)(iii)(F)(1)(ii) and Sec.  
412.424(d)(1)(iii)(F)(2)(i), this letter must identify the residents 
who have come from the closed IPF or program that have caused the 
receiving IPF to exceed its cap, and the receiving IPF must specify the 
length of time the adjustment is needed. Moreover, we are finalizing 
the clarifications on how the information will be delivered in this 
letter. Consistent with IPPS teaching policy, the letter from the 
receiving IPF will have to include: (1) The name of each displaced 
resident; (2) the last four digits of each displaced resident's social 
security number; (3) the IPF and program in which each resident was 
training previously; and (4) the amount of the cap increase needed for 
each resident (based on how much the receiving IPF is in excess of its 
cap and the length of time for which the adjustments are needed). We 
are also finalizing our proposal to require the receiving hospital to 
only supply the last four digits of each displaced resident's social 
security number to reduce the amount of personally identifiable 
information (PII) included in these agreements.
    We are also finalizing the clarification that the maximum number of 
FTE resident cap slots that could be transferred to all receiving IPFs 
is the number of FTE resident cap slots belonging to the IPF that has 
the closed program or that is closing. Therefore, if the originating 
IPF is training residents in excess of its cap, then being a displaced 
resident does not guarantee that a cap slot will be transferred along

[[Page 42621]]

with that resident. Therefore, if there are more IPF displaced 
residents than available cap slots, the slots may be apportioned 
according to the closing IPF's discretion. The decision to transfer a 
cap slot if one is available will be voluntary and made at the sole 
discretion of the originating IPF. However, if the originating IPF 
decides to do so, then it will be the originating IPF's responsibility 
to determine how much of an available cap slot will go with a 
particular resident (if any). We also note that, as we previously 
discussed in the May 6, 2011 IPF PPS final rule (76 FR 25455), only to 
the extent a receiving IPF would exceed its FTE cap by training 
displaced residents would it be eligible for a temporary adjustment to 
its resident FTE cap. Displaced residents are factored into the 
receiving IPF's ratio of resident FTEs to the facility's average daily 
census.
3. Final Cost of Living Adjustment for IPFs Located in Alaska and 
Hawaii
    The IPF PPS includes a payment adjustment for IPFs located in 
Alaska and Hawaii based upon the area in which the IPF is located. As 
we explained in the November 2004 IPF PPS final rule, the FY 2002 data 
demonstrated that IPFs in Alaska and Hawaii had per diem costs that 
were disproportionately higher than other IPFs. Other Medicare 
prospective payment systems (for example, the IPPS and LTCH PPS) 
adopted a COLA to account for the cost differential of care furnished 
in Alaska and Hawaii.
    We analyzed the effect of applying a COLA to payments for IPFs 
located in Alaska and Hawaii. The results of our analysis demonstrated 
that a COLA for IPFs located in Alaska and Hawaii will improve payment 
equity for these facilities. As a result of this analysis, we provided 
a COLA in the November 2004 IPF PPS final rule.
    A COLA for IPFs located in Alaska and Hawaii is made by multiplying 
the non-labor-related portion of the Federal per diem base rate by the 
applicable COLA factor based on the COLA area in which the IPF is 
located.
    The COLA factors through 2009 were published by the Office of 
Personnel Management (OPM), and the OPM memo showing the 2009 COLA 
factors is available at https://www.chcoc.gov/content/nonforeign-area-retirement-equity-assurance-act.
    We note that the COLA areas for Alaska are not defined by county as 
are the COLA areas for Hawaii. In 5 CFR 591.207, the OPM established 
the following COLA areas:
     City of Anchorage, and 80-kilometer (50-mile) radius by 
road, as measured from the Federal courthouse.
     City of Fairbanks, and 80-kilometer (50-mile) radius by 
road, as measured from the Federal courthouse.
     City of Juneau, and 80-kilometer (50-mile) radius by road, 
as measured from the Federal courthouse.
     Rest of the state of Alaska.
    As stated in the November 2004 IPF PPS final rule, we update the 
COLA factors according to updates established by the OPM. However, 
sections 1911 through 1919 of the Non-foreign Area Retirement Equity 
Assurance Act, as contained in subtitle B of title XIX of the National 
Defense Authorization Act (NDAA) for FY 2010 (Pub. L. 111-84, October 
28, 2009), transitions the Alaska and Hawaii COLAs to locality pay. 
Under section 1914 of NDAA, locality pay was phased in over a 3-year 
period beginning in January 2010, with COLA rates frozen as of the date 
of enactment, October 28, 2009, and then proportionately reduced to 
reflect the phase-in of locality pay.
    When we published the proposed COLA factors in the RY 2012 IPF PPS 
proposed rule (76 FR 4998), we inadvertently selected the FY 2010 COLA 
rates, which had been reduced to account for the phase-in of locality 
pay. We did not intend to propose the reduced COLA rates because that 
would have understated the adjustment. Since the 2009 COLA rates did 
not reflect the phase-in of locality pay, we finalized the FY 2009 COLA 
rates for RY 2010 through RY 2014.
    In the FY 2013 IPPS/LTCH final rule (77 FR 53700 through 53701), we 
established a new methodology to update the COLA factors for Alaska and 
Hawaii, and adopted this methodology for the IPF PPS in the FY 2015 IPF 
final rule (79 FR 45958 through 45960). We adopted this new COLA 
methodology for the IPF PPS because IPFs are hospitals with a similar 
mix of commodities and services. We think it is appropriate to have a 
consistent policy approach with that of other hospitals in Alaska and 
Hawaii. Therefore, the IPF COLAs for FY 2015 through FY 2017 were the 
same as those applied under the IPPS in those years. As finalized in 
the FY 2013 IPPS/LTCH PPS final rule (77 FR 53700 and 53701), the COLA 
updates are determined every 4 years, when the IPPS market basket 
labor-related share is updated. Because the labor-related share of the 
IPPS market basket was updated for FY 2018, the COLA factors were 
updated in FY 2018 IPPS/LTCH rulemaking (82 FR 38529). As such, we also 
updated the IPF PPS COLA factors for FY 2018 (82 FR 36780 through 
36782) to reflect the updated COLA factors finalized in the FY 2018 
IPPS/LTCH rulemaking.
    For FY 2022, we are finalizing our proposal to update the COLA 
factors published by OPM for 2009 (as these are the last COLA factors 
OPM published prior to transitioning from COLAs to locality pay) using 
the methodology that we finalized in the FY 2013 IPPS/LTCH PPS final 
rule and adopted for the IPF PPS in the FY 2015 IPF final rule. 
Specifically, we are finalizing our proposal to update the 2009 OPM 
COLA factors by a comparison of the growth in the Consumer Price 
Indices (CPIs) for the areas of Urban Alaska and Urban Hawaii, relative 
to the growth in the CPI for the average U.S. city as published by the 
Bureau of Labor Statistics (BLS). We note that for the prior update to 
the COLA factors, we used the growth in the CPI for Anchorage and the 
CPI for Honolulu. Beginning in 2018, these indexes were renamed to the 
CPI for Urban Alaska and the CPI for Urban Hawaii due to the BLS 
updating its sample to reflect the data from the 2010 Decennial Census 
on the distribution of the urban population (https://www.bls.gov/regions/west/factsheet/2018cpirevisionwest.pdf, accessed January 22, 
2021). The CPI for Urban Alaska area covers Anchorage and Matanuska-
Susitna Borough in the State of Alaska and the CPI for Urban Hawaii 
covers Honolulu in the State of Hawaii. BLS notes that the indexes are 
considered continuous over time, regardless of name or composition 
changes.
    Because BLS publishes CPI data for only Urban Alaska and Urban 
Hawaii, using the methodology we finalized in the FY 2013 IPPS/LTCH PPS 
final rule and adopted for the IPF PPS in the FY 2015 IPF final rule, 
we are finalizing our proposal to use the comparison of the growth in 
the overall CPI relative to the growth in the CPI for those areas to 
update the COLA factors for all areas in Alaska and Hawaii, 
respectively. We believe that the relative price differences between 
these urban areas and the U.S. (as measured by the CPIs) are 
appropriate proxies for the relative price differences between the 
``other areas'' of Alaska and Hawaii and the U.S.
    BLS publishes the CPI for All Items for Urban Alaska, Urban Hawaii, 
and for the average U.S. city. However, consistent with our methodology 
finalized in the FY 2013 IPPS/LTCH PPS final rule and adopted for the 
IPF PPS in the FY 2015 IPF final rule, we are finalizing our proposal 
to create reweighted CPIs for each of the respective areas to reflect 
the underlying

[[Page 42622]]

composition of the IPPS market basket nonlabor-related share. The 
current composition of the CPI for All Items for all of the respective 
areas is approximately 40 percent commodities and 60 percent services. 
However, the IPPS nonlabor-related share is comprised of a different 
mix of commodities and services. Therefore, we are finalizing our 
proposal to create reweighted indexes for Urban Alaska, Urban Hawaii, 
and the average U.S. city using the respective CPI commodities index 
and CPI services index and proposed shares of 57 percent commodities/43 
percent. We created reweighted indexes using BLS data for 2009 through 
2020--the most recent data available at the time of this final 
rulemaking. In the FY 2018 IPPS/LTCH PPS final rule (82 FR 38530), we 
created reweighted indexes based on the 2014-based IPPS market basket 
(which was adopted for the FY 2018 IPPS update) and BLS data for 2009 
through 2016 (the most recent BLS data at the time of the FY 2018 IPPS/
LTCH PPS rulemaking), and we updated the IPF PPS COLA factors 
accordingly for FY 2018.
    We continue to believe this methodology is appropriate because we 
continue to make a COLA for hospitals located in Alaska and Hawaii by 
multiplying the nonlabor-related portion of the standardized amount by 
a COLA factor. We note that OPM's COLA factors were calculated with a 
statutorily mandated cap of 25 percent. As stated in the FY 2018 IPPS/
LTCH PPS final rule (82 FR 38530), under the COLA update methodology we 
finalized in the FY 2013 IPPS/LTCH PPS final rule, we exercised our 
discretionary authority to adjust payments to hospitals in Alaska and 
Hawaii by incorporating this cap. In applying this finalized 
methodology for updating the COLA factors, for FY 2022, we are 
finalizing our proposal to continue to use such a cap, as our policy is 
based on OPM's COLA factors (updated by the methodology described 
above).
    Applying this methodology, the COLA factors that we are finalizing 
our proposal to establish for FY 2022 to adjust the nonlabor-related 
portion of the standardized amount for IPFs located in Alaska and 
Hawaii are shown in Table 2. For comparison purposes, we also are 
showing the COLA factors effective for FY 2018 through FY 2021.
[GRAPHIC] [TIFF OMITTED] TR04AU21.171

    The final IPF PPS COLA factors for FY 2022 are also shown in 
Addendum A to this final rule, and is available at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html.
4. Final Adjustment for IPFs with a Qualifying Emergency Department 
(ED)
    The IPF PPS includes a facility-level adjustment for IPFs with 
qualifying EDs. We provide an adjustment to the Federal per diem base 
rate to account for the costs associated with maintaining a full-
service ED. The adjustment is intended to account for ED costs incurred 
by a psychiatric hospital with a qualifying ED or an excluded 
psychiatric unit of an IPPS hospital or a CAH, for preadmission 
services otherwise payable under the Medicare Hospital Outpatient 
Prospective Payment System (OPPS), furnished to a beneficiary on the 
date of the beneficiary's admission to the hospital and during the day 
immediately preceding the date of admission to the IPF (see Sec.  
413.40(c)(2)), and the overhead cost of maintaining the ED. This 
payment is a facility-level adjustment that applies to all IPF 
admissions (with one exception which we described), regardless of 
whether a particular patient receives preadmission services in the 
hospital's ED.
    The ED adjustment is incorporated into the variable per diem 
adjustment for the first day of each stay for IPFs with a qualifying 
ED. Those IPFs with a qualifying ED receive an adjustment factor of 
1.31 as the variable per diem adjustment for day 1 of each patient 
stay. If an IPF does not have a qualifying ED, it receives an 
adjustment factor of 1.19 as the variable per diem adjustment for day 1 
of each patient stay.
    The ED adjustment is made on every qualifying claim except as 
described in this section of the proposed rule. As specified in Sec.  
412.424(d)(1)(v)(B), the ED adjustment is not made when a patient is 
discharged from an IPPS hospital or CAH and admitted to the same IPPS 
hospital's or CAH's excluded psychiatric unit. We clarified in the 
November 2004 IPF PPS final rule (69 FR 66960) that an ED adjustment is 
not made in this case because the costs associated with ED services are 
reflected in the DRG payment to the IPPS hospital or through the 
reasonable cost payment made to the CAH.
    Therefore, when patients are discharged from an IPPS hospital or 
CAH and admitted to the same hospital's or CAH's excluded

[[Page 42623]]

psychiatric unit, the IPF receives the 1.19 adjustment factor as the 
variable per diem adjustment for the first day of the patient's stay in 
the IPF. For FY 2022, we are finalizing our proposal to continue to 
retain the 1.31 adjustment factor for IPFs with qualifying EDs. A 
complete discussion of the steps involved in the calculation of the ED 
adjustment factors are in the November 2004 IPF PPS final rule (69 FR 
66959 through 66960) and the RY 2007 IPF PPS final rule (71 FR 27070 
through 27072).

F. Other Final Payment Adjustments and Policies

1. Outlier Payment Overview
    The IPF PPS includes an outlier adjustment to promote access to IPF 
care for those patients who require expensive care and to limit the 
financial risk of IPFs treating unusually costly patients. In the 
November 2004 IPF PPS final rule, we implemented regulations at Sec.  
412.424(d)(3)(i) to provide a per-case payment for IPF stays that are 
extraordinarily costly. Providing additional payments to IPFs for 
extremely costly cases strongly improves the accuracy of the IPF PPS in 
determining resource costs at the patient and facility level. These 
additional payments reduce the financial losses that would otherwise be 
incurred in treating patients who require costlier care, and therefore, 
reduce the incentives for IPFs to under-serve these patients. We make 
outlier payments for discharges in which an IPF's estimated total cost 
for a case exceeds a fixed dollar loss threshold amount (multiplied by 
the IPF's facility-level adjustments) plus the Federal per diem payment 
amount for the case.
    In instances when the case qualifies for an outlier payment, we pay 
80 percent of the difference between the estimated cost for the case 
and the adjusted threshold amount for days 1 through 9 of the stay 
(consistent with the median LOS for IPFs in FY 2002), and 60 percent of 
the difference for day 10 and thereafter. The adjusted threshold amount 
is equal to the outlier threshold amount adjusted for wage area, 
teaching status, rural area, and the COLA adjustment (if applicable), 
plus the amount of the Medicare IPF payment for the case. We 
established the 80 percent and 60 percent loss sharing ratios because 
we were concerned that a single ratio established at 80 percent (like 
other Medicare PPSs) might provide an incentive under the IPF per diem 
payment system to increase LOS in order to receive additional payments.
    After establishing the loss sharing ratios, we determined the 
current fixed dollar loss threshold amount through payment simulations 
designed to compute a dollar loss beyond which payments are estimated 
to meet the 2 percent outlier spending target. Each year when we update 
the IPF PPS, we simulate payments using the latest available data to 
compute the fixed dollar loss threshold so that outlier payments 
represent 2 percent of total estimated IPF PPS payments.
2. Final Update to the Outlier Fixed Dollar Loss Threshold Amount
    In accordance with the update methodology described in Sec.  
412.428(d), we are finalizing our proposal to update the fixed dollar 
loss threshold amount used under the IPF PPS outlier policy. Based on 
the regression analysis and payment simulations used to develop the IPF 
PPS, we established a 2 percent outlier policy, which strikes an 
appropriate balance between protecting IPFs from extraordinarily costly 
cases while ensuring the adequacy of the Federal per diem base rate for 
all other cases that are not outlier cases.
    Our longstanding methodology for updating the outlier fixed dollar 
loss threshold involves using the best available data, which is 
typically the most recent available data. For this final rulemaking, 
the most recent available data are the FY 2020 claims. However, during 
FY 2020, the U.S. healthcare system undertook an unprecedented response 
to the PHE declared by the Health and Human Services Secretary on 
January 31, 2020 in response to the outbreak of respiratory disease 
caused by a novel (new) coronavirus that has been named ``SARS CoV 2'' 
and the disease it causes, which has been named ``coronavirus disease 
2019'' (abbreviated ``COVID-19''). Therefore, as discussed in section 
VI.C.3 of the FY 2022 IPF PPS proposed rule (86 FR 19524 through 
195266), we considered whether the most recent available year of 
claims, FY 2020, or the prior year, FY 2019, would be the best for 
estimating IPF PPS payments in FY 2021 and FY 2022. We compared the two 
years' claims distributions as well as the impact results, and based on 
that analysis determined that the FY 2019 claims appeared to be the 
best available data at this time. We refer the reader to section VI.C.3 
of the FY 2022 IPF PPS proposed rule (86 FR 19524 through 195266 FR) 
for a detailed discussion of that analysis.
    Comment: We received 2 comments on our analysis of the FY 2019 and 
FY 2020 claims in determining the best available data for estimating 
IPF PPS payments in FY 2021 and FY 2022. Both comments were supportive 
of our proposal to use the FY 2019 claims for this purpose. One of 
these commenters expressed appreciation for the proposed reduction in 
the outlier fixed dollar loss threshold. Another commenter agreed with 
our assessment that FY 2020 claims were heavily impacted by the 
intensity of the COVID-19 pandemic.
    Response: We appreciate these commenters' support. Based on the 
revised impact analysis discussed in section VI.C.3 of this final rule, 
we continue to believe that the FY 2019 claims are the best available 
data for estimating FY 2021 and FY 2022 payments.
    Final Decision: We are finalizing as proposed to use the June 2020 
update of the FY 2019 IPF claims for updating the outlier fixed dollar 
loss threshold.
    Based on an analysis of the June 2020 update of FY 2019 IPF claims 
and the FY 2021 rate increases, we believe it is necessary to update 
the fixed dollar loss threshold amount to maintain an outlier 
percentage that equals 2 percent of total estimated IPF PPS payments. 
We are finalizing our proposal to update the IPF outlier threshold 
amount for FY 2022 using FY 2019 claims data and the same methodology 
that we used to set the initial outlier threshold amount in the RY 2007 
IPF PPS final rule (71 FR 27072 and 27073), which is also the same 
methodology that we used to update the outlier threshold amounts for 
years 2008 through 2021. Based on an analysis of these updated data, we 
estimate that IPF outlier payments as a percentage of total estimated 
payments are approximately 1.9 percent in FY 2021. Therefore, we are 
finalizing our proposal to update the outlier threshold amount to 
$14,470 to maintain estimated outlier payments at 2 percent of total 
estimated aggregate IPF payments for FY 2022. This final update is a 
decrease from the FY 2021 threshold of $14,630. In contrast, using the 
FY 2020 claims to estimate payments, the final outlier fixed dollar 
loss threshold for FY 2022 would be $22,720, which would have been an 
increase from the FY 2021 threshold of $14,630. We refer the reader to 
section VI.C.3 of this final rule for a detailed discussion of the 
estimated impacts of the final update to the outlier fixed dollar loss 
threshold.
    We note that our use of the FY 2019 claims to set the final outlier 
fixed dollar loss threshold for FY 2022 deviates from what has been our 
longstanding practice of using the most recent available year of 
claims, which is FY 2020 data. However, we are finalizing this policy 
in a way that remains otherwise consistent with the

[[Page 42624]]

established outlier update methodology. As discussed in this section 
and in section VI.C.3 of this final rule, we are finalizing our 
proposal to update the outlier fixed dollar loss threshold based on FY 
2019 IPF claims in order to maintain the appropriate outlier percentage 
in FY 2022. We are finalizing our proposal to deviate from our 
longstanding practice of using the most recent available year of claims 
only because, and to the extent that, the COVID-19 PHE appears to have 
significantly impacted the FY 2020 IPF claims. As discussed in section 
VI.C.3 of this final rule, we have analyzed more recent available IPF 
claims data and continue to believe that using FY 2019 IPF claims is 
appropriate for the FY 2022 update. We intend to continue to analyze 
further data in order to better understand both the short-term and 
long-term effects of the COVID-19 PHE on IPFs.
3. Final Update to IPF Cost-to-Charge Ratio Ceilings
    Under the IPF PPS, an outlier payment is made if an IPF's cost for 
a stay exceeds a fixed dollar loss threshold amount plus the IPF PPS 
amount. In order to establish an IPF's cost for a particular case, we 
multiply the IPF's reported charges on the discharge bill by its 
overall cost-to-charge ratio (CCR). This approach to determining an 
IPF's cost is consistent with the approach used under the IPPS and 
other PPSs. In the FY 2004 IPPS final rule (68 FR 34494), we 
implemented changes to the IPPS policy used to determine CCRs for IPPS 
hospitals, because we became aware that payment vulnerabilities 
resulted in inappropriate outlier payments. Under the IPPS, we 
established a statistical measure of accuracy for CCRs to ensure that 
aberrant CCR data did not result in inappropriate outlier payments.
    As we indicated in the November 2004 IPF PPS final rule (69 FR 
66961), we believe that the IPF outlier policy is susceptible to the 
same payment vulnerabilities as the IPPS; therefore, we adopted a 
method to ensure the statistical accuracy of CCRs under the IPF PPS. 
Specifically, we adopted the following procedure in the November 2004 
IPF PPS final rule:
     Calculated two national ceilings, one for IPFs located in 
rural areas and one for IPFs located in urban areas.
     Computed the ceilings by first calculating the national 
average and the standard deviation of the CCR for both urban and rural 
IPFs using the most recent CCRs entered in the most recent Provider 
Specific File (PSF) available.
    For FY 2022, we are finalizing our proposal to continue to follow 
this methodology.
    To determine the rural and urban ceilings, we multiplied each of 
the standard deviations by 3 and added the result to the appropriate 
national CCR average (either rural or urban). The upper threshold CCR 
for IPFs in FY 2022 is 2.0261 for rural IPFs, and 1.6879 for urban 
IPFs, based on CBSA-based geographic designations. If an IPF's CCR is 
above the applicable ceiling, the ratio is considered statistically 
inaccurate, and we assign the appropriate national (either rural or 
urban) median CCR to the IPF.
    We apply the national median CCRs to the following situations:
     New IPFs that have not yet submitted their first Medicare 
cost report. We continue to use these national median CCRs until the 
facility's actual CCR can be computed using the first tentatively or 
final settled cost report.
     IPFs whose overall CCR is in excess of three standard 
deviations above the corresponding national geometric mean (that is, 
above the ceiling).
     Other IPFs for which the MAC obtains inaccurate or 
incomplete data with which to calculate a CCR.
    We are finalizing our proposal to continue to update the FY 2022 
national median and ceiling CCRs for urban and rural IPFs based on the 
CCRs entered in the latest available IPF PPS PSF. Specifically, for FY 
2022, to be used in each of the three situations listed previously, 
using the most recent CCRs entered in the CY 2021 PSF, we provide an 
estimated national median CCR of 0.5720 for rural IPFs and a national 
median CCR of 0.4200 for urban IPFs. These calculations are based on 
the IPF's location (either urban or rural) using the CBSA-based 
geographic designations. A complete discussion regarding the national 
median CCRs appears in the November 2004 IPF PPS final rule (69 FR 
66961 through 66964).

IV. Inpatient Psychiatric Facilities Quality Reporting (IPFQR) Program

A. Background and Statutory Authority

    We refer readers to the FY 2019 IPF PPS final rule (83 FR 38589) 
for a discussion of the background and statutory authority \1\ of the 
IPFQR Program.
---------------------------------------------------------------------------

    \1\ We note that the statute uses the term ``rate year'' (RY). 
However, beginning with the annual update of the inpatient 
psychiatric facility prospective payment system (IPF PPS) that took 
effect on July 1, 2011 (RY 2012), we aligned the IPF PPS update with 
the annual update of the ICD codes, effective on October 1 of each 
year. This change allowed for annual payment updates and the ICD 
coding update to occur on the same schedule and appear in the same 
Federal Register document, promoting administrative efficiency. To 
reflect the change to the annual payment rate update cycle, we 
revised the regulations at 42 CFR 412.402 to specify that, beginning 
October 1, 2012, the IPF PPS RY means the 12-month period from 
October 1 through September 30, which we refer to as a ``fiscal 
year'' (FY) (76 FR 26435). Therefore, with respect to the IPFQR 
Program, the terms ``rate year,'' as used in the statute, and 
``fiscal year'' as used in the regulation, both refer to the period 
from October 1 through September 30. For more information regarding 
this terminology change, we refer readers to section III. of the RY 
2012 IPF PPS final rule (76 FR 26434 through 26435).
---------------------------------------------------------------------------

B. Covered Entities

    In the FY 2013 IPPS/LTCH PPS final rule (77 FR 53645), we 
established that the IPFQR Program's quality reporting requirements 
cover those psychiatric hospitals and psychiatric units paid under 
Medicare's IPF PPS (Sec.  412.404(b)). Generally, psychiatric hospitals 
and psychiatric units within acute care and critical access hospitals 
that treat Medicare patients are paid under the IPF PPS. Consistent 
with previous regulations, we continue to use the terms ``facility'' or 
IPF to refer to both inpatient psychiatric hospitals and psychiatric 
units. This usage follows the terminology in our IPF PPS regulations at 
Sec.  412.402. For more information on covered entities, we refer 
readers to the FY 2013 IPPS/LTCH PPS final rule (77 FR 53645).

C. Previously Finalized Measures and Administrative Procedures

    The current IPFQR Program includes 14 measures. For more 
information on these measures, we refer readers to Table 5 of this 
final rule and the following final rules:
     The FY 2013 IPPS/LTCH PPS final rule (77 FR 53646 through 
53652);
     The FY 2014 IPPS/LTCH PPS final rule (78 FR 50889 through 
50897);
     The FY 2015 IPF PPS final rule (79 FR 45963 through 
45975);
     The FY 2016 IPF PPS final rule (80 FR 46695 through 
46714);
     The FY 2017 IPPS/LTCH PPS final rule (81 FR 57238 through 
57247);
     The FY 2019 IPF PPS final rule (83 FR 38590 through 
38606); and
     The FY 2020 IPF PPS final rule (84 FR 38459 through 
38467).
    For more information on previously adopted procedural requirements, 
we refer readers to the following rules:
     The FY 2013 IPPS/LTCH PPS final rule (77 FR 53653 through 
53660);
     The FY 2014 IPPS/LTCH PPS final rule (78 FR 50897 through 
50903);
     The FY 2015 IPF PPS final rule (79 FR 45975 through 
45978);
     The FY 2016 IPF PPS final rule (80 FR 46715 through 
46719);

[[Page 42625]]

     The FY 2017 IPPS/LTCH PPS final rule (81 FR 57248 through 
57249);
     The FY 2018 IPPS/LTCH PPS final rule (82 FR 38471 through 
38474);
     The FY 2019 IPF PPS final rule (83 FR 38606 through 
38608); and
     The FY 2020 IPF PPS final rule (84 FR 38467 through 
38468).

D. Closing the Health Equity Gap in CMS Quality Programs--Request for 
Information (RFI)

    Persistent inequities in health care outcomes exist in the U.S., 
including among Medicare patients. In recognition of persistent health 
disparities and the importance of closing the health equity gap, we 
requested information on revising several CMS programs to make 
reporting of health disparities based on social risk factors and race 
and ethnicity more comprehensive and actionable for facilities, 
providers, and patients. The RFI that was included in the proposed rule 
is part of an ongoing effort across CMS to evaluate appropriate 
initiatives to reduce health disparities. Feedback will be used to 
inform the creation of a future, comprehensive, RFI focused on closing 
the health equity gap in CMS programs and policies.
    The RFI contained four parts:
     Background: This section provided information describing 
our commitment to health equity, and existing initiatives with an 
emphasis on reducing health disparities.
     Current CMS Disparity Methods: This section described the 
methods, measures, and indicators of social risk currently used with 
the CMS Disparity Methods.
     Future potential stratification of quality measure 
results: This section described four potential future expansions of the 
CMS Disparity Methods, including (1) Stratification of Quality Measure 
Results--Dual Eligibility; (2) Stratification of Quality Measure 
Results--Race and Ethnicity; (3) Improving Demographic Data Collection; 
and (4) Potential Creation of a Facility Equity Score to Synthesize 
Results Across Multiple Social Risk Factors.
     Solicitation of public comment: This section specified 12 
requests for feedback on these topics. We reviewed feedback on these 
topics and note our intention for an additional RFI or rulemaking on 
this topic in the future.
1. Background
    Significant and persistent inequities in health care 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; or 
being near or below the poverty level, is often associated with worse 
health outcomes.2 3 4 5 6 7 8 9 Such disparities in health 
outcomes are the result of number of factors, but importantly for CMS 
programs, although not the sole determinant, poor access and provision 
of lower quality health care contribute to health disparities. For 
instance, numerous studies have shown that among Medicare 
beneficiaries, racial and ethnic minority individuals often receive 
lower quality of care, report lower experiences of care, and experience 
more frequent hospital readmissions and operative 
complications.10 11 12 13 14 15 Readmission rates for common 
conditions in the Hospital Readmissions Reduction Program are higher 
for Black Medicare beneficiaries and higher for Hispanic Medicare 
beneficiaries with Congestive Heart Failure and Acute Myocardial 
Infarction.16 17 18 19 20 Studies have also shown that 
African Americans are significantly more likely than white Americans to 
die prematurely from heart disease, and stroke.\21\ The COVID-19 
pandemic has further illustrated many of these longstanding health 
inequities with higher rates of infection, hospitalization, and 
mortality among Black, Latino, and Indigenous and Native American 
persons relative to White persons.22 23 As noted by the 
Centers for Disease Control ``long-standing systemic health and social 
inequities have put many people from racial and ethnic minority groups 
at increased risk of getting sick and dying from COVID-19.'' \24\ One 
important strategy for addressing these important inequities is 
improving data collection to allow for better measurement and reporting 
on equity across our programs and policies.
---------------------------------------------------------------------------

    \2\ Joynt KE, Orav E, Jha AK. Thirty-Day Readmission Rates for 
Medicare Beneficiaries by Race and Site of Care. JAMA. 
2011;305(7):675-681.
    \3\ Lindenauer PK, Lagu T, Rothberg MB, et al. Income Inequality 
and 30 Day Outcomes After Acute Myocardial Infarction, Heart 
Failure, and Pneumonia: Retrospective Cohort Study. British Medical 
Journal. 2013;346.
    \4\ Trivedi AN, Nsa W, Hausmann LRM, et al. Quality and Equity 
of Care in U.S. Hospitals. New England Journal of Medicine. 
2014;371(24):2298-2308.
    \5\ Polyakova, M., et al. Racial Disparities In Excess All-Cause 
Mortality During The Early COVID-19 Pandemic Varied Substantially 
Across States. Health Affairs. 2021; 40(2): 307-316.
    \6\ Rural Health Research Gateway. Rural Communities: Age, 
Income, and Health Status. Rural Health Research Recap. November 
2018.
    \7\ https://www.minorityhealth.hhs.gov/assets/PDF/Update_HHS_Disparities_Dept-FY2020.pdf.
    \8\ www.cdc.gov/mmwr/volumes/70/wr/mm7005a1.htm.
    \9\ Poteat TC, Reisner SL, Miller M, Wirtz AL. COVID-19 
Vulnerability of Transgender Women With and Without HIV Infection in 
the Eastern and Southern U.S. Preprint. medRxiv. 
2020;2020.07.21.20159327. Published 2020 Jul 24. doi:10.1101/
2020.07.21.20159327.
    \10\ Martino, SC, Elliott, MN, Dembosky, JW, Hambarsoomian, K, 
Burkhart, Q, Klein, DJ, Gildner, J, and Haviland, AM. Racial, 
Ethnic, and Gender Disparities in Health Care in Medicare Advantage. 
Baltimore, MD: CMS Office of Minority Health. 2020.
    \11\ Guide to Reducing Disparities in Readmissions. CMS Office 
of Minority Health. Revised August 2018. Available at: https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/OMH_Readmissions_Guide.pdf.
    \12\ Singh JA, Lu X, Rosenthal GE, Ibrahim S, Cram P. Racial 
disparities in knee and hip total joint arthroplasty: an 18-year 
analysis of national Medicare data. Ann Rheum Dis. 2014 
Dec;73(12):2107-15.
    \13\ Rivera-Hernandez M, Rahman M, Mor V, Trivedi AN. Racial 
Disparities in Readmission Rates among Patients Discharged to 
Skilled Nursing Facilities. J Am Geriatr Soc. 2019 Aug;67(8):1672-
1679.
    \14\ Joynt KE, Orav E, Jha AK. Thirty-Day Readmission Rates for 
Medicare Beneficiaries by Race and Site of Care. JAMA. 
2011;305(7):675-681.
    \15\ Tsai TC, Orav EJ, Joynt KE. Disparities in surgical 30-day 
readmission rates for Medicare beneficiaries by race and site of 
care. Ann Surg. Jun 2014;259(6):1086-1090.
    \16\ Rodriguez F, Joynt KE, Lopez L, Saldana F, Jha AK. 
Readmission rates for Hispanic Medicare beneficiaries with heart 
failure and acute myocardial infarction. Am Heart J. Aug 
2011;162(2):254-261 e253.
    \17\ Centers for Medicare and Medicaid Services. Medicare 
Hospital Quality Chartbook: Performance Report on Outcome Measures; 
2014.
    \18\ Guide to Reducing Disparities in Readmissions. CMS Office 
of Minority Health. Revised August 2018. Available at: https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/OMH_Readmissions_Guide.pdf.
    \19\ Prieto-Centurion V, Gussin HA, Rolle AJ, Krishnan JA. 
Chronic obstructive pulmonary disease readmissions at minority-
serving institutions. Ann Am Thorac Soc. Dec 2013;10(6):680-684.
    \20\ Joynt KE, Orav E, Jha AK. Thirty-Day Readmission Rates for 
Medicare Beneficiaries by Race and Site of Care. JAMA. 
2011;305(7):675-681.
    \21\ HHS. Heart disease and African Americans. (March 29, 2021). 
https://www.minorityhealth.hhs.gov/omh/browse.aspx?lvl=4&lvlid=19.
    \22\ https://www.cms.gov/files/document/medicare-covid-19-data-snapshot-fact-sheet.pdf.
    \23\ Ochieng N, Cubanski J, Neuman T, Artiga S, and Damico A. 
Racial and Ethnic Health Inequities and Medicare. Kaiser Family 
Foundation. February 2021. Available at: https://www.kff.org/medicare/report/racial-and-ethnic-health-inequities-and-medicare/.
    \24\ https://www.cdc.gov/coronavirus/2019-ncov/community/health-equity/race-ethnicity.html.
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    We are committed to achieving equity in health care outcomes for 
our beneficiaries by supporting providers in quality improvement 
activities to reduce health inequities, enabling them to make more 
informed decisions, and promoting provider accountability for health 
care disparities.\25\ For the purposes of this final rule, we are using 
a definition of equity established in

[[Page 42626]]

Executive Order 13985, as ``the consistent and systematic fair, just, 
and impartial treatment of all individuals, including individuals who 
belong to underserved communities that have been denied such treatment, 
such as Black, Latino, and Indigenous and Native American persons, 
Asian Americans and Pacific Islanders and other persons of color; 
members of religious minorities; lesbian, gay, bisexual, transgender, 
and queer (LGBTQ+) persons; persons with disabilities; persons who live 
in rural areas; and persons otherwise adversely affected by persistent 
poverty or inequality.'' \26\ We note that this definition was recently 
established by the current administration, and provides a useful, 
common definition for equity across different areas of government, 
although numerous other definitions of equity exist.
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    \25\ https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/Downloads/CMS-Quality-Strategy.pdf.
    \26\ https://www.federalregister.gov/documents/2021/01/25/2021-01753/advancing-racial-equity-and-support-for-underserved-communities-through-the-Federal-government.
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    Our ongoing commitment to closing the equity gap in CMS quality 
programs is demonstrated by a portfolio of programs aimed at making 
information on the quality of health care providers and services, 
including disparities, more transparent to consumers and providers. The 
CMS Equity Plan for Improving Quality in Medicare outlines a path to 
equity which aims to support Quality Improvement Networks and Quality 
Improvement Organizations (QIN-QIOs) in their efforts to engage with 
and assist providers that care for vulnerable populations; Federal, 
state, local, and tribal organizations; providers; researchers; 
policymakers; beneficiaries and their families; and other stakeholders 
in activities to achieve health equity.\27\ The CMS Equity Plan for 
Improving Quality in Medicare focuses on three core priority areas 
which inform our policies and programs: (1) Increasing understanding 
and awareness of health disparities; (2) developing and disseminating 
solutions to achieve health equity; and (3) implementing sustainable 
actions to achieve health equity.\28\ The CMS Quality Strategy \29\ and 
Meaningful Measures Framework \30\ include elimination of racial and 
ethnic disparities as a central principle. Our efforts aimed at closing 
the health equity gap to date have included providing transparency 
about health disparities, supporting providers with evidence-informed 
solutions to achieve health equity, and reporting to providers on gaps 
in quality through the following reports and programs:
---------------------------------------------------------------------------

    \27\ Centers for Medicare and Medicaid Services Office of 
Minority Health. The CMS Equity Plan for Improving Quality in 
Medicare. 2015. https://www.cms.gov/About-CMS/Agency-Information/OMH/OMH_Dwnld-CMS_EquityPlanforMedicare_090615.pdf.
    \28\ Centers for Medicare and Medicaid Services Office of 
Minority Health. The CMS Equity Plan for Improving Quality in 
Medicare. 2015. https://www.cms.gov/About-CMS/Agency-Information/OMH/OMH_Dwnld-CMS_EquityPlanforMedicare_090615.pdf.
    \29\ Centers for Medicare Services. CMS Quality Strategy. 2016. 
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/Downloads/CMS-Quality-Strategy.pdf.
    \30\ https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/MMF/General-info-Sub-Page.
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     The CMS Mapping Medicare Disparities Tool, which is an 
interactive map that identifies areas of disparities and a starting 
point to understand and investigate geographical, racial and ethnic 
differences in health outcomes for Medicare patients.\31\
---------------------------------------------------------------------------

    \31\ https://www.cms.gov/About-CMS/Agency-Information/OMH/OMH-Mapping-Medicare-Disparities.
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     The Racial, Ethnic, and Gender Disparities in Health Care 
in Medicare Advantage Stratified Report, which highlights racial and 
ethnic differences in health care experiences and clinical care, 
compares quality of care for women and men, and looks at racial and 
ethnic differences in quality of care among women and men separately 
for Medicare Advantage plans.\32\
---------------------------------------------------------------------------

    \32\ https://www.cms.gov/About-CMS/Agency-Information/OMH/research-and-data/statistics-and-data/stratified-reporting.
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     The Rural-Urban Disparities in Health Care in Medicare 
Report, which details rural-urban differences in health care 
experiences and clinical care.\33\
---------------------------------------------------------------------------

    \33\ Centers for Medicare and Medicaid Services. Rural-Urban 
Disparities in Health Care in Medicare. 2019. https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/Rural-Urban-Disparities-in-Health-Care-in-Medicare-Report.pdf.
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     The Standardized Patient Assessment Data Elements for 
certain post-acute care Quality Reporting Programs, which now includes 
data reporting for race and ethnicity and preferred language, in 
addition to screening questions for social needs (84 FR 42536 through 
42588).
     The CMS Innovation Center's Accountable Health Communities 
Model, which include standardized data collection of health-related 
social needs data.
     The Guide to Reducing Disparities which provides an 
overview of key issues related to disparities in readmissions and 
reviews sets of activities that can help hospital leaders reduce 
readmissions in diverse populations.\34\
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    \34\ Guide to Reducing Disparities in Readmissions. CMS Office 
of Minority Health. Revised August 2018. Available at: https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/OMH_Readmissions_Guide.pdf.
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     The CMS Disparity Methods, which provide hospital-level 
confidential results stratified by dual eligibility for condition-
specific readmission measures currently included in the Hospital 
Readmission Reduction Program (84 FR 42496 through 42500).
    These programs are informed by reports by the National Academies of 
Science, Engineering and Medicine (NASEM) \35\ and the Office of the 
Assistant Secretary for Planning and Evaluation (ASPE) \36\ which have 
examined the influence of social risk factors on several of our quality 
programs. In this RFI, we addressed only the seventh initiative listed, 
the CMS Disparity Methods, which we have implemented for measures in 
the Hospital Readmissions Reduction Program and are considering in 
other programs, including the IPFQR Program. We discussed the 
implementation of these methods to date and present considerations for 
continuing to improve and expand these methods to provide providers and 
ultimately consumers with actionable information on disparities in 
health care quality to support efforts at closing the equity gap.
---------------------------------------------------------------------------

    \35\ National Academies of Sciences, Engineering, and Medicine. 
2016. Accounting for Social Risk Factors in Medicare Payment: 
Identifying Social Risk Factors. Washington, DC: The National 
Academies Press. https://doi.org/10.17226/21858.
    \36\ https://aspe.hhs.gov/pdf-report/report-congress-social-risk-factors-and-performance-under-medicares-value-based-purchasing-programs.
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2. Current CMS Disparity Methods
    We first sought public comment on potential confidential and public 
reporting of IPFQR program measure data stratified by social risk 
factors in the FY 2018 IPPS/LTCH PPS proposed rule (82 FR 20121). We 
initially focused on stratification by dual eligibility, which is 
consistent with recommendations from ASPE's First Report to Congress 
which was required by the Improving Medicare Post-Acute Care 
Transformation (IMPACT) Act of 2014 (Pub. L. 113-185).\37\ This report 
found that in the context of value-based purchasing (VBP) programs, 
dual eligibility was among the most powerful predictors of poor health 
outcomes

[[Page 42627]]

among those social risk factors that ASPE examined and tested.
---------------------------------------------------------------------------

    \37\ https://aspe.hhs.gov/pdf-report/report-congress-social-risk-factors-and-performance-under-medicares-value-based-purchasing-programs.
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    In the FY 2018 IPPS/LTCH PPS final rule we also solicited feedback 
on two potential methods for illuminating differences in outcomes rates 
among patient groups within a provider's patient population that would 
also allow for a comparison of those differences, or disparities, 
across providers for the Hospital IQR Program (82 FR 38403 through 
38409). The first method (the Within-Hospital disparity method) 
promotes quality improvement by calculating differences in outcome 
rates among patient groups within a hospital while accounting for their 
clinical risk factors. This method also allows for a comparison of the 
magnitude of disparity across hospitals, permitting hospitals to assess 
how well they are closing disparity gaps compared to other hospitals. 
The second methodological approach (the Across-Hospital method) is 
complementary and assesses hospitals' outcome rates for dual-eligible 
patients only, across hospitals, allowing for a comparison among 
hospitals on their performance caring for their patients with social 
risk factors. In the FY 2018 IPPS/LTCH PPS proposed rule under the 
IPFQR Program (82 FR 20121), we also specifically solicited feedback on 
which social risk factors provide the most valuable information to 
stakeholders. Overall, comments supported the use of dual eligibility 
as a proxy for social risk, although commenters also suggested 
investigation of additional social risk factors, and we continue to 
consider which risk factors provide the most valuable information to 
stakeholders.
    Concurrent with our comment solicitation on stratification in the 
IPFQR Program, we have considered methods for stratifying measure 
results for other quality reporting programs. For example, in the FY 
2019 IPPS/LTCH PPS final rule (82 FR 41597 through 41601), we finalized 
plans to provide confidential hospital-specific reports (HSRs) 
containing stratified results of the Pneumonia Readmission (NQF #0506) 
and Pneumonia Mortality (NQF #0468) measures including both the Across-
Hospital Disparity Method and the Within-Hospital Disparity Method 
(disparity methods), stratified by dual eligibility. In the FY 2019 
IPPS/LTCH PPS final rule (83 FR 41554 through 41556), we also removed 
six condition/procedure specific readmissions measures, including the 
Pneumonia Readmission measure (NQF #0506) and five mortality measures, 
including the Pneumonia Mortality measure (NQF #0468) (83 FR 41556 
through 41558) from the Hospital IQR Program. However, the Pneumonia 
Readmission (NQF #0506) and the other condition/procedure readmissions 
measures remained in the Hospital Readmissions Reduction Program. In 
2019, we provided hospitals with results of the Pneumonia Readmission 
measure (NQF#0506) stratified using dual eligibility. We provided this 
information in annual confidential HSRs for claims-based measures.
    We then, in the FY 2020 IPPS/LTCH PPS Final Rule (84 FR 42388 
through 42390), finalized the proposal to provide confidential hospital 
specific reports (HSRs) containing data stratified by dual-eligible 
status for all six readmission measures included in the Hospital 
Readmission Reduction Program.
3. Potential Expansion of the CMS Disparity Methods
    We are committed to advancing health equity by improving data 
collection to better measure and analyze disparities across programs 
and policies.\38\ As we previously noted, we have been considering, 
among other things, expanding our efforts to provide stratified data 
for additional social risk factors and measures, optimizing the ease-
of-use of the results, enhancing public transparency of equity results, 
and building towards provider accountability for health equity. We 
sought public comment on the potential stratification of quality 
measures in the IPFQR Program across two social risk factors: Dual 
eligibility and race/ethnicity.
---------------------------------------------------------------------------

    \38\ Centers for Medicare Services. CMS Quality Strategy. 2016. 
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/Downloads/CMS-Quality-Strategy.pdf.
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a. Stratification of Quality Measure Results--Dual Eligibility
    As described previously in this section, landmark reports by the 
National Academies of Science, Engineering and Medicine (NASEM) \39\ 
and the Office of the Assistant Secretary for Planning and Evaluation 
(ASPE),\40\ which have examined the influence of social risk factors on 
several of our quality programs, have shown that in the context of 
value-based purchasing (VBP) programs, dual eligibility, as an 
indicator of social risk, is a powerful predictor of poor health 
outcomes. We noted that the patient population of IPFs has a higher 
percentage of dually eligible patients than the general Medicare 
population. Specifically, over half (56 percent) of Medicare patients 
in IPFs are dually eligible \41\ while approximately 20 percent of all 
Medicare patients are dually eligible.\42\ We are considering 
stratification of quality measure results in the IPFQR Program and are 
considering which measures would be most appropriate for stratification 
and if dual eligibility would be a meaningful social risk factor for 
stratification.
---------------------------------------------------------------------------

    \39\ National Academies of Sciences, Engineering, and Medicine. 
2016. Accounting for Social Risk Factors in Medicare Payment: 
Identifying Social Risk Factors. Washington, DC: The National 
Academies Press. https://doi.org/10.17226/21858.
    \40\ https://aspe.hhs.gov/pdf-report/report-congress-social-risk-factors-and-performance-under-medicares-value-based-purchasing-programs.
    \41\ https://aspe.hhs.gov/basic-report/transitions-care-and-service-use-among-medicare-beneficiaries-inpatient-psychiatric-facilities-issue-brief.
    \42\ https://www.cms.gov/Medicare-Medicaid-Coordination/Medicare-and-Medicaid-Coordination/Medicare-Medicaid-Coordination-Office/DataStatisticalResources/Downloads/MedicareMedicaidDualEnrollmentEverEnrolledTrendsDataBrief2006-2018.pdf.
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    For the IPFQR Program, we would consider disparity reporting using 
two disparity methods derived from the Within-Hospital and Across-
Hospital methods, described in section IV.D.2 of this final rule. The 
first method (based on the Within-Facility disparity method) would aim 
to promote quality improvement by calculating differences in outcome 
rates between dual and non-dual eligible patient groups within a 
facility while accounting for their clinical risk factors. This method 
would allow for a comparison of those differences, or disparities, 
across facilities, so facilities could assess how well they are closing 
disparity gaps compared to other facilities. The second approach (based 
on the Across-Facility method) would be complementary and assesses 
facilities' outcome rates for subgroups of patients, such as dual 
eligible patients, across facilities, allowing for a comparison among 
facilities on their performance caring for their patients with social 
risk factors.
b. Stratification of Quality Measure Results--Race and Ethnicity
    The Administration's Executive Order on Advancing Racial Equity and 
Support for Underserved Communities Through the Federal Government 
directs agencies to assess potential barriers that underserved 
communities and individuals may face to enrollment in and access to 
benefits and services in Federal Programs. As summarized in section 
IV.D of this final rule, studies have shown that among Medicare 
beneficiaries, racial and ethnic minority persons often experience 
worse health outcomes, including more frequent hospital readmissions 
and operative

[[Page 42628]]

complications. An important part of identifying and addressing 
inequities in health care is improving data collection to allow us to 
better measure and report on equity across our programs and policies. 
We are considering stratification of quality measure results in the 
IPFQR Program by race and ethnicity and are considering which measures 
would be most appropriate for stratification.
    As outlined in the 1997 Office of Management and Budget (OMB) 
Revisions to the Standards for the Collection of Federal Data on Race 
and Ethnicity, the racial and ethnic categories, which may be used for 
reporting the disparity methods are considered to be social and 
cultural, not biological or genetic.\43\ The 1997 OMB Standard lists 
five minimum categories of race: (1) American Indian or Alaska Native; 
(2) Asian; (3) Black or African American; (4) Native Hawaiian or Other 
Pacific Islander; (5) and White. In the OMB standards, Hispanic or 
Latino is the only ethnicity category included, and since race and 
ethnicity are two separate and distinct concepts, persons who report 
themselves as Hispanic or Latino can be of any race.\44\ Another 
example, the ``Race & Ethnicity--CDC'' code system in Public Health 
Information Network (PHIN) Vocabulary Access and Distribution System 
(VADS) \45\ permits a much more granular structured recording of a 
patient's race and ethnicity with its inclusion of over 900 concepts 
for race and ethnicity. The recording and exchange of patient race and 
ethnicity at such a granular level can facilitate the accurate 
identification and analysis of health disparities based on race and 
ethnicity. Further, the ``Race & Ethnicity--CDC'' code system has a 
hierarchy that rolls up to the OMB minimum categories for race and 
ethnicity and, thus, supports aggregation and reporting using the OMB 
standard. ONC includes both the CDC and OMB standards in its criterion 
for certified health IT products.\46\ For race and ethnicity, a 
certified health IT product must be able to express both detailed races 
and ethnicities using any of the 900 plus concepts in the ``Race & 
Ethnicity--CDC'' code system in the PHIN VADS, as well as aggregate 
each one of a patient's races and ethnicities to the categories in the 
OMB standard for race and ethnicity. This approach can reduce burden on 
providers recording demographics using certified products.
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    \43\ Executive Office of the President Office of Management and 
Budget, Office of Information and Regulatory Affairs. Revisions to 
the standards for the classification of Federal data on race and 
ethnicity. Vol 62. Federal Register. 1997:58782-58790
    \44\ https://www.census.gov/topics/population/hispanic-origin/about.html.
    \45\ https://phinvads.cdc.gov/vads/ViewValueSet.action?id=67D34BBC-617F-DD11-B38D-00188B398520.
    \46\ ONC criteria for certified health IT products: https://www.healthit.gov/isa/representing-patient-race-and-ethnicity.
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    Self-reported race and ethnicity data remain the gold standard for 
classifying an individual according to race or ethnicity. However, CMS 
does not consistently collect self-reported race and ethnicity for the 
Medicare program, but instead gets the data from the Social Security 
Administration (SSA) and the data accuracy and comprehensiveness have 
proven challenging despite capabilities in the marketplace via 
certified health IT products. Historical inaccuracies in Federal data 
systems and limited collection classifications have contributed to the 
limited quality of race and ethnicity information in Medicare's 
administrative data systems.\47\ In recent decades, to address these 
data quality issues, we have undertaken numerous initiatives, including 
updating data taxonomies and conducting direct mailings to some 
beneficiaries to enable more comprehensive race and ethnic 
identification.48 49 Despite those efforts, studies reveal 
varying data accuracy in identification of racial and ethnic groups in 
Medicare administrative data, with higher sensitivity for correctly 
identifying White and Black individuals, and lower sensitivity for 
correctly identifying individuals of Hispanic ethnicity or of Asian/
Pacific Islander and American Indian/Alaskan Native race.\50\ 
Incorrectly classified race or ethnicity may result in overestimation 
or underestimation in the quality of care received by certain groups of 
beneficiaries.
---------------------------------------------------------------------------

    \47\ Eicheldinger, C., & Bonito, A. (2008). More accurate racial 
and ethnic codes for Medicare administrative data. Health Care 
Financing Review, 29(3), 27-42.
    \48\ Filice CE, Joynt KE. Examining Race and Ethnicity 
Information in Medicare Administrative Data. Med Care. 
2017;55(12):e170-e176. doi:10.1097/MLR.0000000000000608.
    \49\ Eicheldinger, C., & Bonito, A. (2008). More accurate racial 
and ethnic codes for Medicare administrative data. Health Care 
Financing Review, 29(3), 27-42.
    \50\ Centers for Medicare and Medicaid Services. Building an 
Organizational Response to Health Disparities Inventory of Resources 
for Standardized Demographic and Language Data Collection. 2020. 
https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/Data-Collection-Resources.pdf.
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    We continue to work with Federal and private partners to better 
collect and leverage data on social risk to improve our understanding 
of how these factors can be better measured in order to close the 
health equity gap. Among other things, we have developed an Inventory 
of Resources for Standardized Demographic and Language Data Collection 
\51\ and supported collection of specialized International 
Classification of Disease, 10th Revision, Clinical Modification (ICD-
10-CM) codes for describing the socioeconomic, cultural, and 
environmental determinants of health, and sponsored several initiatives 
to statistically estimate race and ethnicity information when it is 
absent.\52\ The Office of the National Coordinator for Health 
Information Technology (ONC) included social, psychological, and 
behavioral standards in the 2015 Edition health information technology 
(IT) certification criteria (2015 Edition), providing interoperability 
standards (LOINC (Logical Observation Identifiers Names and Codes) and 
SNOMED CT (Systematized Nomenclature of Medicine--Clinical Terms)) for 
financial strain, education, social connection and isolation, and 
others. Additional stakeholder efforts underway to expand capabilities 
to capture additional social determinants of health data elements 
include the Gravity Project to identify and harmonize social risk 
factor data for interoperable electronic health information exchange 
for EHR fields, as well as proposals to expand the ICD-10 
(International Classification of Diseases, Tenth Revision) Z codes, the 
alphanumeric codes used worldwide to represent diagnoses.\53\
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    \51\ Centers for Medicare and Medicaid Services. Building an 
Organizational Response to Health Disparities Inventory of Resources 
for Standardized Demographic and Language Data Collection. 2020. 
https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/Data-Collection-Resources.pdf.
    \52\ https://pubmed.ncbi.nlm.nih.gov/18567241/, https://pubmed.ncbi.nlm.nih.gov/30506674/, Eicheldinger C, Bonito A. More 
accurate racial and ethnic codes for Medicare administrative data. 
Health Care Finance Rev. 2008;29(3):27-42. Haas A, Elliott MN, 
Dembosky JW, et al. Imputation of race/ethnicity to enable 
measurement of HEDIS performance by race/ethnicity. Health Serv Res. 
2019;54(1):13-23. doi:10.1111/1475-6773.13099.
    \53\ https://aspe.hhs.gov/pdf-report/second-impact-report-to-congress.
---------------------------------------------------------------------------

    While development of sustainable and consistent programs to collect 
data on social determinants of health can be considerable undertakings, 
we recognize that another method to identify better race and ethnicity 
data is needed in the short term to address the need for reporting on 
health equity. In working with our contractors, two algorithms have 
been developed to indirectly estimate the race and ethnicity of 
Medicare beneficiaries (as described further in the following 
paragraphs). We feel that using indirect estimation can

[[Page 42629]]

help to overcome the current limitations of demographic information and 
enable timelier reporting of equity results until longer term 
collaborations to improve demographic data quality across the health 
care sector materialize. The use of indirectly estimated race and 
ethnicity for conducting stratified reporting does not place any 
additional collection or reporting burdens on facilities as these data 
are derived using existing administrative and census-linked data.
    Indirect estimation relies on a statistical imputation method for 
inferring a missing variable or improving an imperfect administrative 
variable using a related set of information that is more readily 
available.\54\ Indirectly estimated data are most commonly used at the 
population level (such as the facility or health plan-level), where 
aggregated results form a more accurate description of the population 
than existing, imperfect data sets. These methods often estimate race 
and ethnicity using a combination of other data sources which are 
predictive of self-identified race and ethnicity, such as language 
preference, information about race and ethnicity in our administrative 
records, first and last names matched to validated lists of names 
correlated to specific national origin groups, and the racial and 
ethnic composition of the surrounding neighborhood. Indirect estimation 
has been used in other settings to support population-based equity 
measurement when self-identified data are not available.\55\
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    \54\ IOM. 2009. Race, Ethnicity, and Language Data: 
Standardization for Health Care Quality Improvement. Washington, DC: 
The National Academies Press.
    \55\ IOM. 2009. Race, Ethnicity, and Language Data: 
Standardization for Health Care Quality Improvement. Washington, DC: 
The National Academies Press.
---------------------------------------------------------------------------

    As described in section IV.D.2, we have previously supported the 
development of two such methods of indirect estimation of race and 
ethnicity of Medicare beneficiaries. One indirect estimation approach, 
developed by our contractor, uses Medicare administrative data, first 
name and surname matching, derived from the U.S. Census and other 
sources, with beneficiary language preference, state of residence, and 
the source of the race and ethnicity code in Medicare administrative 
data to reclassify some beneficiaries as Hispanic or Asian/Pacific 
Islander (API).\56\ In recent years, we have also worked with another 
contractor to develop a new approach, the Medicare Bayesian Improved 
Surname Geocoding (MBISG), which combines Medicare administrative data, 
first and surname matching, geocoded residential address linked to the 
2010 U.S. Census, and uses both Bayesian updating and multinomial 
logistic regression to estimate the probability of belonging to each of 
six racial/ethnic groups.\57\
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    \56\ Bonito AJ, Bann C, Eicheldinger C, Carpenter L. Creation of 
New Race-Ethnicity Codes and Socioeconomic Status (SES) Indicators 
for Medicare Beneficiaries. Final Report, Sub-Task 2. (Prepared by 
RTI International for the Centers for Medicare and Medicaid Services 
through an interagency agreement with the Agency for Healthcare 
Research and Policy, under Contract No. 500-00-0024, Task No. 21) 
AHRQ Publication No. 08-0029-EF. Rockville, MD, Agency for 
Healthcare Research and Quality. January 2008.
    \57\ Haas, A., Elliott, M. et al (2018). Imputation of race/
ethnicity to enable measurement of HEDIS performance by race/
ethnicity. Health Services Research, 54:13-23.
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    The MBISG model is currently used to conduct the national, 
contract-level, stratified reporting of Medicare Part C & D performance 
data for Medicare Advantage Plans by race and ethnicity.\58\ Validation 
testing reveals concordances with self-reported race and ethnicity of 
0.96 through 0.99 for API, Black, Hispanic, and White beneficiaries for 
MBISG version 2.1.\59\ The algorithms under consideration are 
considerably less accurate for individuals who self-identify as 
American Indian/Alaskan Native or multiracial.\60\ Indirect estimation 
can be a statistically reliable approach for calculating population-
level equity results for groups of individuals (such as the facility-
level) and is not intended, nor being considered, as an approach for 
inferring the race and ethnicity of an individual.
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    \58\ The Office of Minority Health (2020). Racial, Ethnic, and 
Gender Disparities in Health Care in Medicare Advantage, The Centers 
for Medicare and Medicaid Services, (pg vii). https://www.cms.gov/About-CMS/Agency-Information/OMH/research-and-data/statistics-and-data/stratified-reporting.
    \59\ MBISG 2.1 validation results performed under contract #GS-
10F-0012Y/HHSM-500-2016-00097G). Pending public release of the 2021 
Part C and D Performance Data Stratified by Race, Ethnicity, and 
Gender Report, available at: https://www.cms.gov/About-CMS/Agency-Information/OMH/research-and-data/statistics-and-data/stratified-reporting.
    \60\ Haas, A., Elliott, M. et al (2018). Imputation of race/
ethnicity to enable measurement of HEDIS performance by race/
ethnicity. Health Services Research, 54:13-23 and Bonito AJ, Bann C, 
Eicheldinger C, Carpenter L. Creation of New Race-Ethnicity Codes 
and Socioeconomic Status (SES) Indicators for Medicare 
Beneficiaries. Final Report, Sub-Task 2. (Prepared by RTI 
International for the Centers for Medicare and Medicaid Services 
through an interagency agreement with the Agency for Healthcare 
Research and Policy, under Contract No. 500-00-0024, Task No. 21) 
AHRQ Publication No. 08-0029-EF. Rockville, MD, Agency for 
Healthcare Research and Quality. January 2008.
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    However, despite the high degree of statistical accuracy of the 
indirect estimation algorithms under consideration there remains the 
small risk of unintentionally introducing bias. For example, if the 
indirect estimation is not as accurate in correctly estimating race and 
ethnicity in certain geographies or populations it could lead to some 
bias in the method results. Such bias might result in slight 
overestimation or underestimation of the quality of care received by a 
given group. We feel this amount of bias is considerably less than 
would be expected if stratified reporting was conducted using the race 
and ethnicity currently contained in our administrative data. Indirect 
estimation of race and ethnicity is envisioned as an intermediate step, 
filling the pressing need for more accurate demographic information for 
the purposes of exploring inequities in service delivery, while 
allowing newer approaches, as described in the next section, for 
improving demographic data collection to progress. We expressed 
interest in learning more about, and solicited comments about, the 
potential benefits and challenges associated with measuring facility 
equity using an imputation algorithm to enhance existing administrative 
data quality for race and ethnicity until self-reported information is 
sufficiently available.
c. Improving Demographic Data Collection
    Stratified facility-level reporting using dual eligibility and 
indirectly estimated race and ethnicity would represent an important 
advance in our ability to provide equity reports to facilities. 
However, self-reported race and ethnicity data remain the gold standard 
for classifying an individual according to race or ethnicity. The CMS 
Quality Strategy outlines our commitment to strengthening 
infrastructure and data systems by ensuring that standardized 
demographic information is collected to identify disparities in health 
care delivery outcomes.\61\ Collection and sharing of a standardized 
set of social, psychological, and behavioral data by facilities, 
including race and ethnicity, using electronic data definitions which 
permit nationwide, interoperable health information exchange, can 
significantly enhance the accuracy and robustness of our equity 
reporting.\62\ This could potentially include expansion to

[[Page 42630]]

additional social risk factors, such as disability status, where 
accuracy of administrative data is currently limited. We are mindful 
that additional resources, including data collection and staff training 
may be necessary to ensure that conditions are created whereby all 
patients are comfortable answering all demographic questions, and that 
individual preferences for non-response are maintained.
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    \61\ The Centers for Medicare & Medicaid Services. CMS Quality 
Strategy. 2016. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityInitiativesGenInfo/Downloads/CMS-Quality-Strategy.pdf.
    \62\ The Office of the National Coordinator for Health 
Information Technology. United State Core Data for Interoperability 
Draft Version 2. 2021. https://www.healthit.gov/isa/sites/isa/files/2021-01/Draft-USCDI-Version-2-January-2021-Final.pdf.
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    We are also interested in learning about and solicited comments on 
current data collection practices by facilities to capture demographic 
data elements (such as race, ethnicity, sex, sexual orientation and 
gender identity (SOGI), primary language, and disability status). 
Further, we are interested in potential challenges facing facility 
collection, at the time of admission, of a minimum set of demographic 
data elements in alignment with national data collection standards 
(such as the standards finalized by the Affordable Care Act) \63\ and 
standards for interoperable exchange (such as the U.S. Core Data for 
Interoperability incorporated into certified health IT products as part 
of the 2015 Edition of health IT certification criteria).\64\ Advancing 
data interoperability through collection of a minimum set of 
demographic data collection, and incorporation of this demographic 
information into quality measure specifications, has the potential for 
improving the robustness of the disparity method results, potentially 
permitting reporting using more accurate, self-reported information, 
such as race and ethnicity, and expanding reporting to additional 
dimensions of equity, including stratified reporting by disability 
status.
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    \63\ https://minorityhealth.hhs.gov/assets/pdf/checked/1/Fact_Sheet_Section_4302.pdf.
    \64\ https://www.healthit.gov/sites/default/files/2020-08/2015EdCures_Update_CCG_USCDI.pdf.
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d. Potential Creation of a Facility Equity Score To Synthesize Results 
Across Multiple Social Risk Factors
    As we describe in section IV.D.3.a of this final rule, we are 
considering expanding the disparity methods to IPFs and to include two 
social risk factors (dual eligibility and race/ethnicity). This 
approach would improve the comprehensiveness of health equity 
information provided to facilities. Aggregated results from multiple 
measures and multiple social risk factors, from the CMS Disparity 
Methods, in the format of a summary score, can improve the usefulness 
of the equity results. In working with our contractors, we recently 
developed an equity summary score for Medicare Advantage contract/
plans, the Health Equity Summary Score (HESS), with application to 
stratified reporting using two social risk factors: Dual eligibility 
and race and as described in Incentivizing Excellent Care to At-Risk 
Groups with a Health Equity Summary Score.\65\
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    \65\ Agniel D, Martino SC, Burkhart Q, et al. Incentivizing 
Excellent Care to At-Risk Groups with a Health Equity Summary Score. 
J Gen Intern Med. Published online November 11, 2019 Nov 11. doi: 
10.1007/s11606-019-05473-x.
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    The HESS calculates standardized and combined performance scores 
blended across the two social risk factors. The HESS also combines 
results of the within-plan (similar to the Within-Facility method) and 
across-plan method (similar to the Across-Facility method) across 
multiple performance measures.
    We are considering building a ``Facility Equity Score,'' not yet 
developed, which would be modeled off the HESS but adapted to the 
context of risk-adjusted facility outcome measures and potentially 
other IPF quality measures. We envision that the Facility Equity Score 
would synthesize results for a range of measures and using multiple 
social risk factors, using measures and social risk factors, which 
would be reported to facilities as part of the CMS Disparity Methods. 
We believe that creation of the Facility Equity Score has the potential 
to supplement the overall measure data already reported on the Care 
Compare or successor website, by providing easy to interpret 
information regarding disparities measured within individual facilities 
and across facilities nationally. A summary score would decrease burden 
by minimizing the number of measure results provided and providing an 
overall indicator of equity.
    The Facility Equity Score under consideration would potentially:
     Summarize facility performance across multiple social 
determinants of health (initially dual eligibility and indirectly 
estimated race and ethnicity); and
     Summarize facility performance across the two disparity 
methods (that is, the Within-Facility Disparity Method and the Across-
Facility Disparity Method) and potentially for multiple measures.
    Prior to any future public reporting, if we determine that a 
Facility Equity Score can be feasibly and accurately calculated, we 
would provide results of the Facility Equity Score, in confidential 
facility specific reports, which facilities and their QIN-QIOs would be 
able to download. Any potential future proposal to display the Facility 
Equity Score on the Care Compare or successor website would be made 
through future RFI or rulemaking.
c. Solicitation of Public Comment
    We solicited public comments on the possibility of stratifying 
IPFQR Program measures by dual eligibility and race and ethnicity. We 
also solicited public comments on mechanisms of incorporating co-
occurring disability status into such stratification as well. We sought 
public comments on the application of the within-facility or across-
facility disparities methods IPFQR Program measures if we were to 
stratify IPFQR Program measures. We also solicited comment on the 
possibility of facility collection of standardized demographic 
information for the purposes of potential future quality reporting and 
measure stratification. In addition, we solicited public comments on 
the potential design of a facility equity score for calculating results 
across multiple social risk factors and measures, including race and 
disability. Any data pertaining to these areas that are recommended for 
collection for measure reporting for a CMS program and any potential 
public disclosure on Care Compare or successor website would be 
addressed through a separate and future notice- and-comment rulemaking. 
We plan to continue working with ASPE, facilities, the public, and 
other key stakeholders on this important issue to identify policy 
solutions that achieve the goals of attaining health equity for all 
patients and minimizing unintended consequences. We also noted our 
intention for additional RFIs or rulemaking on this topic in the 
future.
    Specifically, we solicited public comment on the following:
Future Potential Stratification of Quality Measure Results
     The possible stratification of facility-specific reports 
for IPFQR program measure data by dual-eligibility status given that 
over half of the patient population in IPFs are dually eligible, 
including, which measures would be most appropriate for stratification;
     The potential future application of indirect estimation of 
race and ethnicity to permit stratification of measure data for 
reporting facility-level disparity results until more accurate forms of 
self-identified demographic information are available;
     Appropriate privacy safeguards with respect to data 
produced from the indirect estimation of race and ethnicity to ensure 
that such data are properly

[[Page 42631]]

identified if/when they are shared with providers;
     Ways to address the challenges of defining and collecting 
accurate and standardized self-identified demographic information, 
including information on race and ethnicity and disability, for the 
purposes of reporting, measure stratification and other data collection 
efforts relating to quality.
     Recommendations for other types of readily available data 
elements for measuring disadvantage and discrimination for the purposes 
of reporting, measure stratification and other data collection efforts 
relating to quality, in addition, or in combination with race and 
ethnicity.
     Recommendations for types of quality measures or 
measurement domains to prioritize for stratified reporting by dual 
eligibility, race and ethnicity, and disability.
     Examples of approaches, methods, research, and 
considerations or any combination of these for use of data-driven 
technologies that do not facilitate exacerbation of health inequities, 
recognizing that biases may occur in methodology or be encoded in 
datasets.
    We received comments on these topics.
    Comments: Many commenters expressed support for the collection of 
data to support stratifying or otherwise measuring disparities in care 
related to dual-eligibility, race and ethnicity, and disability. Some 
commenters specifically supported the confidential reporting of 
stratified results to facilities. Several commenters urged CMS to 
expand data collection and measure stratification to include factors 
such as language preference, veteran status, health literacy, gender 
identity, and sexual orientation to provide a more comprehensive 
assessment of health equity. One commenter urged CMS to collect data on 
race and ethnicity specifically for patients suffering from psychiatric 
disorders, while another noted that for the IPF patient population risk 
factors, such as substance abuse, may be of more importance. One 
commenter also provided examples of how their health system has 
successfully collected and begun to analyze patient-level demographic 
data. Another commenter referred to an existing effort by the National 
Committee for Quality Assurance to improve the collection of race and 
ethnicity data as a possible model for improving data collection. This 
commenter also supported the use of indirect estimation of race and 
ethnicity for Medicare beneficiaries, noting some concern about the 
lack of granularity, especially with respect to Native American and 
Asian populations. One commenter urged CMS to explore how to best 
identify social determinants of health using current claims data.
    While many commenters expressed support for stratification of 
claims-based measures, many commenters expressed concern that the 
existing chart-abstracted measures would face limitations when 
stratified and thus felt the burden of collecting stratification data 
for these measures significantly outweighed any potential benefit of 
doing so. Specifically, commenters noted that stratifying the IPF 
patient population is more vulnerable to statistical concerns during 
the stratification process than other patient populations (for example, 
numbers of patients in one or more strata may be insufficient for 
reliable sampling and calculations) due to low patient volume in some 
facilities. One commenter suggested that for this and other reasons CMS 
should develop disparities reporting specifically for the IPF program 
rather than adopt an approach developed for a different program. A few 
commenters also questioned the value of stratification of these 
measures given the current high levels of performance by many IPFs.
    One commenter noted that stratified claims-based measures would 
exclude all privately insured care and thus be less useful. Several 
commenters stated that interoperability issues such as a lack of EHRs, 
particularly for IPFs that are smaller or not part of a large hospital 
or health system, further add to the burden of stratifying chart-
abstracted measures and may contribute to bias in the data.
    Several commenters also noted that stratification may be 
challenging due to differences in the patient population served by IPFs 
compared to other Medicare programs such as acute and long-term care 
hospitals, for example, age, proportion and reason for dual-eligibility 
(income versus disability), and substance abuse disorder prevalence. 
However, several commenters noted many of these same characteristics, 
as well as the mental and behavioral health needs of patients cared for 
by IPFs, are evidence of the need to improve data collection and 
measurement in IPFs. A commenter also recommended further analysis on 
the predictive power of social risk factors on mental and behavioral 
health patient outcomes compared to that of the diagnosis requiring 
treatment. Several commenters recommended CMS further address issues 
related to the potential stratification of data such as: Patient 
privacy and the collection and sharing of social risk factors from 
patient records or through indirect estimation, differing requirements 
for collection of race and ethnicity data, transparency regarding 
indirect estimation methods, and differing Medicaid eligibility 
requirements by state. One commenter related these concerns to public 
reporting, suggesting support for confidential reporting until these 
issues are addressed.
    We appreciate all of the comments and interest in this topic. We 
believe that this input is very valuable in the continuing development 
of the CMS health equity quality measurement efforts. We will continue 
to take all concerns, comments, and suggestions into account for future 
development and expansion of our health equity quality measurement 
efforts.
Improving Demographic Data Collection
     Experiences of users of certified health IT regarding 
local adoption of practices for collection of social, psychological, 
and behavioral data elements, the perceived value of using these data 
for improving decision-making and care delivery, and the potential 
challenges and benefits of collecting more granular, structured 
demographic information, such as the ``Race & Ethnicity--CDC'' code 
system.
     The possible collection of a minimum set of social, 
psychological, and behavioral data elements by hospitals at the time of 
admission using structured, interoperable data standards, for the 
purposes of reporting, measure stratification and other data collection 
efforts relating to quality.
    We received comments on these topics.
    Comments: We received mixed feedback regarding demographic data 
collection. Many commenters supported the need for and use of such 
data, noting that structured, interoperable electronic health data are 
the gold standard. They also noted that many barriers exist to adopting 
electronic health information technology systems necessary for capture 
of these data, particularly in freestanding psychiatric facilities. A 
commenter stated that the commenter's organization cannot support 
demographic data collection due to the workload burden it would place 
on both the IPF and patients and their families. This commenter also 
noted that the likelihood of patients and families comfortably 
answering multiple sensitive demographic questions is low, especially 
upon admission. Another commenter expressed concerns with the current 
capabilities of the industry to collect these data, specifying a lack 
of standardization in screening and data collection and need for staff 
training.

[[Page 42632]]

Multiple commenters expressed concern about the patient and family's 
perception of the organization if given a data collection questionnaire 
upon admission, noting that they may think the organization is more 
focused on data collection rather than care.
    Other commenters noted the importance of closing the health equity 
gap through measurement of demographic characteristics. A commenter 
suggested that agencies leverage the role of nurses in identifying 
sociodemographic factors and barriers to health equity. Another 
commenter supported this method, noting that although this may add 
another step to data collection processes, it would be valuable in 
addressing health equity gaps. To reduce possible workload burden on 
organizations that are new to this process, a commenter recommended a 
staggered approach to data collection, suggesting CMS require providers 
and facilities to collect data on age and sex by the end of 2022, race 
and ethnicity by the end of 2023, etc., with the goal of at least 80 
percent data completeness with 80 percent accuracy. In addition, 
commenters suggested reducing burden by adopting standardized screening 
tools to collect this information, such as ICD-Z-codes, which in 
practice would allow patients to be referred to resources and 
initiatives when appropriate. Several commenters encouraged collection 
of comprehensive social determinants of health and demographic 
information in addition to race and ethnicity, such as disability, 
sexual orientation, and primary language. Several commenters provided 
feedback on the potential use of an indirect estimation algorithm when 
race and ethnicity are missing/incorrect, and emphasized the 
sensitivity of demographic information and recommended that CMS use 
caution when using estimates from the algorithm, including assessing 
for potential bias, reporting the results of indirect estimation 
alongside direct self-report at the organizational level for 
comparison, and establishing a timeline to transition to entirely 
directly collected data. Commenters also advised that CMS be 
transparent with beneficiaries and explain why data are being collected 
and the plans to use these data. A commenter noted that information 
technology infrastructure should be established in advance to ensure 
that this information is being used and exchanged appropriately.
    We appreciate all of the comments and interest in this topic. We 
believe that this input is very valuable in the continuing development 
of the CMS health equity quality measurement efforts. We will continue 
to take all concerns, comments, and suggestions into account for future 
development and expansion of our health equity quality measurement 
efforts.
Potential Creation of a Facility Equity Score To Synthesize Results 
Across Multiple Social Risk Factors
     The possible creation and confidential reporting of a 
Facility Equity Score to synthesize results across multiple social risk 
factors and disparity measures.
     Interventions facilities could institute to improve a low 
facility equity score and how improved demographic data could assist 
with these efforts.
    We received comments on these topics.
    Comments: Commenters generally supported ongoing thoughtful 
investigation into best practices for measuring health equity.
    Many commenters expressed concerns about the potential Facility 
Equity Score. Commenters argued that the current approach used to 
generate the composite score may not lead to aggregate results, which 
would not be actionable for many facilities. Commenters also raised 
concerns about risk adjustment, limitations in stratification 
variables, and the appropriateness of the current measure set. A 
commenter noted that although they support thoughtful efforts to 
categorize performance, the HESS has been established only as a ``proof 
of concept'' and will require considerable time and resources to 
produce a valid and actionable measure. The same commenter also noted 
that HESS scoring was only feasible for less than one-half of Medicare 
Advantage (MA) plans and as such, may not be practical for many smaller 
facilities, or facilities whose enrolled populations differ in social 
risk factor distribution patterns compared to typical MA plans.
    Commenters generally did not support use of the Facility Equity 
Score in public reporting or payment incentive programs, suggesting 
that it is imperative to first understand any unintended consequences 
prior to implementation. More specifically, several commenters gave the 
example of facilities failing to raise the quality of care for at-risk 
patients while appearing to achieve greater equity due to lower quality 
of care for patients that are not at risk. A commenter stated the 
belief that CMS should begin their initiative to improve health equity 
by using structural health equity measures. Commenters also raised 
concerns about use of dual-eligibility as a social risk factor due to 
variations in state-level eligibility for Medicaid, making national 
comparisons, or benchmarking of facility scores unreliable. 
Additionally, commenters who expressed data reliability concerns 
recommended that CMS focus its resources on improving standardized data 
collection and reporting procedures for sociodemographic data before 
moving forward with a Facility Equity Score.
    We appreciate all of the comments and interest in this topic. We 
believe that this input is very valuable in the continuing development 
of the CMS health equity quality measurement efforts. We will continue 
to take all concerns, comments, and suggestions into account for future 
development and expansion of our health equity quality measurement 
efforts.
    We also received comments on the general topic of health equity in 
the IPFQR Program.
    Comments: Many commenters expressed overall support of CMS' goals 
to advance health equity. There were some comments regarding the need 
to further extend and specify the definition of equity provided in the 
proposed rule. Commenters also noted that equity initiatives should be 
based on existing disparities and population health goals, be mindful 
of the needs of the communities served, and work to bridge hospitals 
with post-acute and community-based providers. Several commenters 
encouraged CMS to be mindful about whether collection of additional 
quality measures and standardized patient assessment elements might 
increase provider burden. Several commenters noted support for 
consideration of a measure of organizational commitment to health 
equity, outlining how infrastructure supports delivery of equitable 
care. A commenter noted the importance of focusing programming on 
inequities in vaccine-preventable illness. Another commenter noted that 
CMS may expand their view of equity beyond quality reporting to payment 
and coverage policies.
    We appreciate all of the comments and interest in this topic. We 
believe that this input is very valuable in the continuing development 
of the CMS health equity quality measurement efforts. We will continue 
to take all concerns, comments, and suggestions into account for future 
development and expansion of our health equity quality measurement 
efforts.

E. Measure Adoption

    We strive to put consumers and caregivers first, ensuring they are 
empowered to make decisions about their own healthcare along with their

[[Page 42633]]

clinicians using information from data-driven insights that are 
increasingly aligned with meaningful quality measures. We support 
technology that reduces burden and allows clinicians to focus on 
providing high-quality healthcare for their patients. We also support 
innovative approaches to improve quality, accessibility, and 
affordability of care while paying particular attention to improving 
clinicians' and beneficiaries' experiences when interacting with our 
programs. In combination with other efforts across the Department of 
Health and Human Services (HHS), we believe the IPFQR Program helps to 
incentivize facilities to improve healthcare quality and value while 
giving patients and providers the tools and information needed to make 
the best decisions for them. Consistent with these goals, our objective 
in selecting quality measures is to balance the need for information on 
the full spectrum of care delivery and the need to minimize the burden 
of data collection and reporting. We have primarily focused on measures 
that evaluate critical processes of care that have significant impact 
on patient outcomes and support CMS and HHS priorities for improved 
quality and efficiency of care provided by IPFs. When possible, we also 
propose to incorporate measures that directly evaluate patient outcomes 
and experience. We refer readers to section VIII.F.4.a. of the FY 2013 
IPPS/LTCH PPS final rule (77 FR 53645 through 53646) for a detailed 
discussion of the considerations taken into account in selecting 
quality measures.
1. Measure Selection Process
    Before being proposed for inclusion in the IPFQR Program, measures 
are placed on a list of measures under consideration (MUC), which is 
published annually on behalf of CMS by the National Quality Forum 
(NQF). Following publication on the MUC list, the Measure Applications 
Partnership (MAP), a multi-stakeholder group convened by the NQF, 
reviews the measures under consideration for the IPFQR Program, among 
other Federal programs, and provides input on those measures to the 
Secretary. We consider the input and recommendations provided by the 
MAP in selecting all measures for the IPFQR Program. In our evaluation 
of the IPFQR Program measure set, we identified two measures that we 
believe are appropriate for the IPFQR Program.
2. COVID-19 Vaccination Coverage Among Health Care Personnel (HCP) 
66 Measure for the FY 2023 Payment Determination and 
Subsequent Years
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    \66\ This measure was previously titled, ``SARS-CoV-2 
Vaccination Coverage among Healthcare Personnel.''
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a. Background
    On January 31, 2020, the Secretary declared a PHE for the U.S. in 
response to the global outbreak of SARS-CoV-2, a novel (new) 
coronavirus that causes a disease named ``coronavirus disease 2019'' 
(COVID-19).\67\ COVID-19 is a contagious respiratory illness \68\ that 
can cause serious illness and death. Older individuals and those with 
underlying medical conditions are considered to be at higher risk for 
more serious complications from COVID-19.\69\
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    \67\ U.S. Dept of Health and Human Services, Office of the 
Assistant Secretary for Preparedness and Response. (2020). 
Determination that a Public Health Emergency Exists. Available at: 
https://www.phe.gov/emergency/news/healthactions/phe/Pages/2019-nCoV.aspx.
    \68\ Centers for Disease Control and Prevention. (2020). Your 
Health: Symptoms of Coronavirus. Available at: https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html.
    \69\ Centers for Disease Control and Prevention. (2020). Your 
Health: Symptoms of Coronavirus. Available at https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html.
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    As of April 2, 2021, the U.S. had reported over 30 million cases of 
COVID-19 and over 550,000 COVID-19 deaths.\70\ Hospitals and health 
systems saw significant surges of COVID-19 patients as community 
infection levels increased.\71\ From December 2, 2020 through January 
30, 2021, more than 100,000 Americans were in the hospital with COVID-
19 at the same time.\72\
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    \70\ Centers for Disease Control and Prevention. (2020). CDC 
COVID Data Tracker. Accessed on April 3, 2021 at: https://covid.cdc.gov/covid-data-tracker/#cases_casesper100klast7days.
    \71\ Associated Press. Tired to the Bone. Hospitals Overwhelmed 
with Virus Cases. November 18, 2020. Accessed on December 16, 2020, 
at https://apnews.com/article/hospitals-overwhelmed-coronavirus-cases-74a1f0dc3634917a5dc13408455cd895. Also see: New York Times. 
Just how full are U.S. intensive care units? New data paints an 
alarming picture. November 18, 2020. Accessed on December 16, 2020, 
at: https://www.nytimes.com/2020/12/09/world/just-how-full-are-us-intensive-care-units-new-data-paints-an-alarming-picture.html.
    \72\ U.S. Currently Hospitalized [verbar] The COVID Tracking 
Project https://covidtracking.com/data/charts/us-currently-hospitalized.
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    Evidence indicates that COVID-19 primarily spreads when individuals 
are in close contact with one another.\73\ The virus is typically 
transmitted through respiratory droplets or small particles created 
when someone who is infected with the virus coughs, sneezes, sings, 
talks, or breathes.\74\ Thus, the CDC advises that infections mainly 
occur through exposure to respiratory droplets when a person is in 
close contact with someone who has COVID-19.\75\ Experts believe that 
COVID-19 spreads less commonly through contact with a contaminated 
surface (although that is not thought to be a common way that COVID-19 
spreads),\76\ and that in certain circumstances, infection can occur 
through airborne transmission.\77\
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    \73\ Centers for Disease Control and Prevention. (2021). How 
COVID-19 Spreads. Accessed on April 3, 2021 at: https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/how-covid-spreads.html.
    \74\ Centers for Disease Control and Prevention. (2021). How 
COVID-19 Spreads. Accessed on April 3, 2021 at: https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/how-covid-spreads.html.
    \75\ Centers for Disease Control and Prevention. (2021). How 
COVID-19 Spreads. Accessed on April 3, 2021 at: https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/how-covid-spreads.html.
    \76\ Centers for Disease Control and Prevention. (2021). How 
COVID-19 Spreads. Accessed on April 3, 2021 at: https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/how-covid-spreads.html.
    \77\ Centers for Disease Control and Prevention. (2021). How 
COVID-19 Spreads. Accessed on April 3, 2021 at: https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/how-covid-spreads.html.
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    Subsequent to the publication of the proposed rule, the CDC 
confirmed that the three main ways that COVID-19 is spread are: (1) 
Breathing in air when close to an infected person who is exhaling small 
droplets and particles that contain the virus; (2) Having these small 
droplets and particles that contain virus land on the eyes, nose, or 
mouth, especially through splashes and sprays like a cough or sneeze; 
and (3) Touching eyes, nose, or mouth with hands that have the virus on 
them.\78\ According to the CDC, those at greatest risk of infection are 
persons who have had prolonged, unprotected close contact (that is, 
within 6 feet for 15 minutes or longer) with an individual with 
confirmed SARS-CoV-2 infection, regardless of whether the individual 
has symptoms.\79\ Although personal protective equipment (PPE) and 
other infection-control precautions can reduce the likelihood of 
transmission in health care settings, COVID-19 can spread between 
health care personnel (HCP) and patients, or from patient to patient 
given the close contact that may occur during the provision of 
care.\80\ The CDC has emphasized that health care settings, including 
long-term care

[[Page 42634]]

settings, can be high-risk places for COVID-19 exposure and 
transmission.\81\
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    \78\ Centers for Disease Control and Prevention. (2021). How 
COVID-19 Spreads. Accessed on July 15, 2021 at: https://www.cdc.gov/coronavirus/2019-ncov/prevent-getting-sick/how-covid-spreads.html.
    \79\ Centers for Disease Control and Prevention. (2021). When to 
Quarantine. Accessed on April 3, 2021 at: https://www.cdc.gov/coronavirus/2019-ncov/if-you-are-sick/quarantine.html.
    \80\ Centers for Disease Control and Prevention. (2020). Interim 
U.S. Guidance for Risk Assessment and Work Restrictions for 
Healthcare Personnel with Potential Exposure to COVID-19. Accessed 
on April 2, 2021 at: https://www.cdc.gov/coronavirus/2019-ncov/hcp/faq.html#Transmission.
    \81\ Dooling, K, McClung, M, et al. ``The Advisory Committee on 
Immunization Practices' Interim Recommendations for Allocating 
Initial Supplies of COVID-19 Vaccine--United States, 2020.'' Morb 
Mortal Wkly Rep. 2020; 69(49): 1857-1859.
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    Vaccination is a critical part of the nation's strategy to 
effectively counter the spread of COVID-19 and ultimately help restore 
societal functioning.\82\ On December 11, 2020, FDA issued the first 
Emergency Use Authorization (EUA) for a COVID-19 vaccine in the 
U.S.\83\ Subsequently, FDA issued EUAs for additional COVID-19 
vaccines.\84\
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    \82\ Centers for Disease Control and Prevention. (2020). COVID-
19 Vaccination Program Interim Playbook for Jurisdiction Operations. 
Accessed on April 3, 2021 at: https://www.cdc.gov/vaccines/imz-managers/downloads/COVID-19-Vaccination-Program-Interim_Playbook.pdf.
    \83\ U.S. Food and Drug Administration. (2020). Pfizer-BioNTech 
COVID-19 Vaccine EUA Letter of Authorization. Available at https://www.fda.gov/media/144412/download. (as reissued on May 10, 2021).
    \84\ U.S. Food and Drug Administration. (2020). Moderna COVID-19 
Vaccine EUA Letter of Authorization. Available at https://www.fda.gov/media/144636/download (as reissued on July 7, 2021); 
U.S. Food and Drug Administration. (2021). Janssen COVID-19 Vaccine 
EUA Letter of Authorization. Available at https://www.fda.gov/media/146303/download (as reissued on June 10, 2021).
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    FDA determined that it was reasonable to conclude that the known 
and potential benefits of each vaccine, when used as authorized to 
prevent COVID-19, outweighed its known and potential risks.\85\
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    \85\ U.S. Food and Drug Administration. (2020). Pfizer-BioNTech 
COVID-19 Vaccine EUA Letter of Authorization. Available at https://www.fda.gov/media/144412/download (as reissued on May 10, 2021) and 
U.S. Food and Drug Administration. (2020). Moderna COVID-19 Vaccine 
EUA Letter of Authorization. Available at https://www.fda.gov/media/144636/download (as reissued on July 7, 2021); U.S. Food and Drug 
Administration. (2021). Janssen COVID-19 Vaccine EUA Letter of 
Authorization. Available at https://www.fda.gov/media/146303/download (as reissued on June 10, 2021).
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    As part of its national strategy to address COVID-19, the Biden 
Administration stated that it would work with states and the private 
sector to execute an aggressive vaccination strategy and has outlined a 
goal of administering 200 million shots in 100 days.\86\ Although the 
goal of the U.S. government is to ensure that every American who wants 
to receive a COVID-19 vaccine can receive one, Federal agencies 
recommended that early vaccination efforts focus on those critical to 
the PHE response, including HCP providing direct care to patients with 
COVID-19, and individuals at highest risk for developing severe illness 
from COVID-19.\87\ For example, the CDC's Advisory Committee on 
Immunization Practices (ACIP) recommended that HCP should be among 
those individuals prioritized to receive the initial, limited supply of 
the COVID-19 vaccination given the potential for transmission in health 
care settings and the need to preserve health care system capacity.\88\ 
Research suggests most states followed this recommendation,\89\ and HCP 
began receiving the vaccine in mid-December of 2020.\90\
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    \86\ https://www.whitehouse.gov/briefing-room/speeches-remarks/2021/03/29/remarks-by-president-biden-on-the-covid-19-response-and-the-state-of-vaccinations/.
    \87\ Health and Human Services, Department of Defense. (2020) 
From the Factory to the Frontlines: The Operation Warp Speed 
Strategy for Distributing a COVID-19 Vaccine. Accessed December 18 
at: https://www.hhs.gov/sites/default/files/strategy-for-distributing-covid-19-vaccine.pdf; Centers for Disease Control 
(2020). COVID-19 Vaccination Program Interim Playbook for 
Jurisdiction Operations. Accessed December 18 at: https://www.cdc.gov/vaccines/imz-managers/downloads/COVID-19-Vaccination-Program-Interim_Playbook.pdf.
    \88\ Dooling, K, McClung, M, et al. ``The Advisory Committee on 
Immunization Practices' Interim Recommendations for Allocating 
Initial Supplies of COVID-19 Vaccine--United States, 2020.'' Morb. 
Mortal Wkly Rep. 2020; 69(49): 1857-1859. ACIP also recommended that 
long-term care residents be prioritized to receive the vaccine, 
given their age, high levels of underlying medical conditions, and 
congregate living situations make them high risk for severe illness 
from COVID-19.
    \89\ Kates, J, Michaud, J, Tolbert, J. ``How Are States 
Prioritizing Who Will Get the COVID-19 Vaccine First?'' Kaiser 
Family Foundation. December 14, 2020. Accessed on December 16 at 
https://www.kff.org/policy-watch/how-are-states-prioritizing-who-will-get-the-covid-19-vaccine-first/.
    \90\ Associated Press. `Healing is Coming:' US Health Workers 
Start Getting Vaccine. December 15, 2020. Accessed on December 16 
at: https://apnews.com/article/us-health-workers-coronavirus-vaccine-56df745388a9fc12ae93c6f9a0d0e81f.
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    There are approximately 18 million healthcare workers in the 
U.S.\91\ As of April 3, 2021 the CDC reported that over 162 million 
doses of COVID-19 vaccine had been administered, and approximately 60 
million people had received a complete vaccination course as described 
in IV.E.b.i of this final rule.\92\ By July 15, 2021 the CDC reported 
that over 336,000,000 doses had been administered, and approximately 
160,000,000 people had received a complete vaccination course.\93\ 
President Biden indicated on March 2, 2021 that the U.S. is on track to 
have sufficient vaccine supply for every adult by the end of May 
2021.\94\ Subsequent to the publication of the IPF PPS proposed rule, 
on June 3, 2021, the White House confirmed that there was sufficient 
vaccine supply for all Americans.\95\
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    \91\ https://www.cdc.gov/niosh/topics/healthcare/
default.html#:~:text=HEALTHCARE%20WORKERS,-
Related%20Pages&text=Healthcare%20is%20the%20fastest%2Dgrowing,of%20t
he%20healthcare%20work%20force.
    \92\ CDC. COVID Data Tracker. COVID-19 Vaccinations in the 
United States. Accessed on 4/4/21 at: https://covid.cdc.gov/covid-data-tracker/#vaccinations.
    \93\ CDC. COVID Data Tracker. COVID-19 Vaccinations in the 
United States. Accessed on 7/6/2021 at: https://covid.cdc.gov/covid-data-tracker/#vaccinations.
    \94\ The White House. Remarks by President Biden on the 
Administration's COVID-19 Vaccination Efforts. Accessed March 18, 
2021 at: https://www.whitehouse.gov/briefing-room/speeches-remarks/2021/03/02/remarks-by-president-biden-on-the-administrations-covid-19-vaccination-efforts/.
    \95\ Press Briefing by White House COVID-19 Response Team and 
Public Health Officials [verbar] The White House.
---------------------------------------------------------------------------

    We believe it is important to require that IPFs report HCP 
vaccination in their facilities in order to assess whether they are 
taking steps to protect health care workers and to help sustain the 
ability of IPFs to continue serving their communities throughout the 
PHE and beyond. Therefore, we proposed a new measure, COVID-19 
Vaccination Coverage Among HCP, beginning with the FY 2023 program 
year. For that program year, IPFs would be required to report data on 
the measure for the fourth quarter of 2021 (October 1, 2021 through 
December 31, 2021). For more information about the reporting period, 
see section V.E.2.c of this final rule. The measure would assess the 
proportion of an IPF's health care workforce that has been vaccinated 
against COVID-19.
    Although at the time of the proposed rule, data to show the 
effectiveness of COVID-19 vaccines to prevent asymptomatic infection or 
transmission of SARS-CoV-2 were limited, we stated our belief that IPFs 
should report the level of vaccination among their HCP as part of their 
efforts to assess and reduce the risk of transmission of COVID-19 
within their facilities. HCP vaccination can potentially reduce illness 
that leads to work absence and limit disruptions to care.\96\ Data from 
influenza vaccination demonstrates that provider uptake of the vaccine 
is associated with that provider recommending vaccination to 
patients,\97\ and we believe HCP COVID-19 vaccination in IPFs could 
similarly increase uptake among that patient population. We also 
believe that publishing the HCP vaccination rates would be helpful to 
many patients, including those who are at high-risk for

[[Page 42635]]

developing serious complications from COVID-19, as they choose 
facilities from which to seek treatment. Under CMS' Meaningful Measures 
Framework, the COVID-19 measure addresses the quality priority of 
``Promote Effective Prevention and Treatment of Chronic Disease'' 
through the Meaningful Measure Area of ``Preventive Care.''
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    \96\ Centers for Disease Control and Prevention. Overview of 
Influenza Vaccination among Health Care Personnel. October 2020. 
(2020) Accessed March 16, 2021 at: https://www.cdc.gov/flu/toolkit/long-term-care/why.htm.
    \97\ Measure Application Committee Coordinating Committee 
Meeting Presentation. March 15, 2021. (2021) Accessed March 16, 2021 
at: http://www.qualityforum.org/Project_Pages/MAP_Coordinating_Committee.aspx.
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b. Overview of Measure
    The COVID-19 Vaccination Coverage Among HCP measure (``COVID-19 HCP 
vaccination measure'') is a process measure developed by the CDC to 
track COVID-19 vaccination coverage among HCP in facilities such as 
IPFs.
(1). Measure Specifications
    The denominator is the number of HCP eligible to work in the IPF 
for at least 1 day during the reporting period, excluding persons with 
contraindications to COVID-19 vaccination that are described by the 
CDC.\98\
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    \98\ Centers for Disease Control and Prevention. 
Contraindications and precautions. https://www.cdc.gov/vaccines/covid-19/info-by-product/clinical-considerations.html#Contraindications.
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    The numerator is the cumulative number of HCP eligible to work in 
the IPF for at least 1 day during the reporting period and who received 
a completed vaccination course against COVID-19 since the vaccine was 
first available or on a repeated interval if revaccination on a regular 
basis is needed.\99\ Vaccination coverage for the purposes of this 
measure is defined as the estimated percentage of HCP eligible to work 
at the IPF for at least 1 day who received a completed vaccination 
course. A completed vaccination course may require one or more doses 
depending on the EUA for the specific vaccine used.
---------------------------------------------------------------------------

    \99\ Measure Application Partnership Coordinating Committee 
Meeting Presentation. March 15, 2021. (2021) Accessed March 16, 2021 
at: http://www.qualityforum.org/Project_Pages/MAP_Coordinating_Committee.aspx.
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    The finalized specifications for this measure are available at 
https://www.cdc.gov/nhsn/nqf/index.html.
(2). Review by the Measure Applications Partnership
    The COVID-19 HCP vaccination measure was included on the publicly 
available ``List of Measures under Consideration for December 21, 
2020,'' \100\ a list of measures under consideration for use in various 
Medicare programs. When the Measure Applications Partnership (MAP) 
Hospital Workgroup convened on January 11, 2021, it reviewed the MUC 
List and the COVID-19 HCP vaccination measure. The MAP recognized that 
the proposed measure represents a promising effort to advance 
measurement for an evolving national pandemic and that it would bring 
value to the IPFQR Program measure set by providing transparency about 
an important COVID-19 intervention to help prevent infections in HCP 
and patients.\101\ The MAP also stated that collecting information on 
COVID-19 vaccination coverage among HCP and providing feedback to 
facilities would allow facilities to benchmark coverage rates and 
improve coverage in their IPF, and that reducing rates of COVID-19 in 
HCP may reduce transmission among patients and reduce instances of 
staff shortages due to illness.\102\
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    \100\ https://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=94212.
    \101\ Measure Applications Partnership. MAP Preliminary 
Recommendations 2020-2021. Accessed on January 24, 2021 at: http://www.qualityforum.org/Project_Pages/MAP_Hospital_Workgroup.aspx.
    \102\ Measure Applications Partnership. MAP Preliminary 
Recommendations 2020-2021. Accessed on January 24, 2021 at: http://www.qualityforum.org/Project_Pages/MAP_Hospital_Workgroup.aspx.
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    In its preliminary recommendations, the MAP Hospital Workgroup did 
not support this measure for rulemaking, subject to potential for 
mitigation.\103\ To mitigate its concerns, the MAP believed that the 
measure needed well-documented evidence, finalized specifications, 
testing, and NQF endorsement prior to implementation.\104\ 
Subsequently, the MAP Coordinating Committee met on January 25, 2021, 
and reviewed the COVID-19 Vaccination Coverage Among HCP measure. In 
the 2020-2021 MAP Final Recommendations, the MAP offered conditional 
support for rulemaking contingent on CMS bringing the measures back to 
MAP once the specifications are further refined.\105\ The MAP 
specifically stated, ``the incomplete specifications require immediate 
mitigation and further development should continue.'' \106\ The 
spreadsheet of final recommendations no longer cited concerns regarding 
evidence, testing, or NQF endorsement.\107\ In response to the MAP 
final recommendation request that CMS bring the measure back to the MAP 
once the specifications were further refined, CMS and the CDC met with 
MAP Coordinating committee on March 15th. Additional information was 
provided to address vaccine availability, alignment of the COVID-19 
Vaccination Coverage Among HCP measure as closely as possible with the 
data collection for the Influenza HCP vaccination measure (NQF 0431), 
and clarification related to how HCP are defined. At this meeting, CMS 
and the CDC presented preliminary findings from the testing of the 
numerator of COVID-19 Vaccination Coverage Among HCP, which was in 
process at that time. These preliminary findings showed numerator data 
should be feasible and reliable. Testing of the numerator of the number 
of healthcare personnel vaccinated involves a comparison of the data 
collected through NHSN and independently reported through the Federal 
pharmacy partnership program for delivering vaccination to LTC 
facilities. These are two completely independent data collection 
systems. In initial analyses of the first month of vaccination, the 
number of healthcare workers vaccinated in approximately 1,200 
facilities, which had data from both systems, the number of healthcare 
personnel vaccinated was highly correlated between these 2 systems with 
a correlation coefficient of nearly 90 percent in the second two weeks 
of reporting.\108\ The MAP further noted that the measure would add 
value to the program measure set by providing visibility into an 
important intervention to limit COVID-19 infections in healthcare 
personnel and the patients for whom they provide care.\109\
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    \103\ Measure Applications Partnership. MAP Preliminary 
Recommendations 2020-2021. Accessed on January 24, 2021 at: http://www.qualityforum.org/Project_Pages/MAP_Hospital_Workgroup.aspx.
    \104\ Measure Applications Partnership. MAP Preliminary 
Recommendations 2020-2021. Accessed on January 24, 2021 at: http://www.qualityforum.org/Project_Pages/MAP_Hospital_Workgroup.aspx.
    \105\ Measure Applications Partnership. 2020-2021 MAP Final 
Recommendations. Accessed on February 3, 2021 at: http://www.qualityforum.org/Setting_Priorities/Partnership/Measure_Applications_Partnership.aspx.
    \106\ Measure Applications Partnership. 2020-2021 MAP Final 
Recommendations. Accessed on February 23, 2021 at: http://www.qualityforum.org/Project_Pages/MAP_Hospital_Workgroup.aspx.
    \107\ Ibid.
    \108\ For more information on testing results and other measure 
updates, please see the Meeting Materials (including Agenda, 
Recording, Presentation Slides, Summary, and Transcript) of the 
March 15, 2021 meeting available at https://www.qualityforum.org/ProjectMaterials.aspx?projectID=75367.
    \109\ Measure Applications Partnership. 2020-2021 MAP Final 
Recommendations. Accessed on February 23, 2021 at: http://www.qualityforum.org/Project_Pages/MAP_Hospital_Workgroup.aspx.
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    We value the recommendations of the MAP and considered these 
recommendations carefully. Section 1890A(a)(4) of the Act requires the 
Secretary to take into consideration input from multi-stakeholder 
groups in selecting certain quality and efficiency measures. While we 
value input from the MAP, we believe it is important to propose the 
measure as quickly as

[[Page 42636]]

possible to address the urgency of the COVID-19 PHE and its impact on 
vulnerable populations, including IPFs. We continue to engage with the 
MAP to mitigate concerns and appreciate the MAP's conditional support 
for the measure.
(3). NQF Endorsement
    Under section 1886(s)(4)(D)(i) of the Act, unless the exception of 
clause (ii) applies, measures selected for the quality reporting 
program must have been endorsed by the entity with a contract under 
section 1890(a) of the Act. The NQF currently holds this contract. 
Section 1886(s)(4)(D)(ii) of the Act provides an exception to the 
requirement for NQF endorsement of measures: 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 by the entity 
with a contract under section 1890(a) of the Act, the Secretary may 
specify a measure that is not so endorsed as long as due consideration 
is given to measures that have been endorsed or adopted by a consensus 
organization identified by the Secretary.
    This measure is not NQF endorsed and has not been submitted to NQF 
for endorsement consideration. The CDC, in collaboration with CMS, are 
planning to submit the measure for consideration in the NQF Fall 2021 
measure cycle.
    Because this measure is not NQF-endorsed, we considered other 
available measures. We found no other feasible and practical measures 
on the topic of COVID-19 vaccination among HCP, therefore, we believe 
the exception in Section 1186(s)(4)(D)(ii) of the Act applies.
c. Data Collection, Submission and Reporting
    Given the time-sensitive nature of this measure considering the 
PHE, in the FY 2022 IPF PPS proposed rule, we proposed that IPFs would 
be required to begin reporting data on the proposed COVID-19 
Vaccination Coverage Among HCP measure beginning October 1, 2021 for 
the FY 2023 IPFQR Program year (86 FR 19504). Thereafter, we proposed 
quarterly \110\ reporting periods.
---------------------------------------------------------------------------

    \110\ We note that the proposed rule incorrectly read ``annual 
reporting periods'' however the section of the proposed rule on data 
submission (IV.J.2.a) correctly described the data submission 
process and timelines.
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    To report this measure, facilities would report COVID-19 
vaccination data to the NHSN for at least one week each month, 
beginning in October 2021 for the October 1, 2021 through December 31, 
2021 reporting period affecting FY 2023 payment determination and 
continuing for each quarter in subsequent years. For more details on 
data submission, we refer readers to section V.J.2.a of this final 
rule.
    We proposed that IPFs would report the measure through the CDC 
National Healthcare Safety Network (NHSN) web-based surveillance 
system.\111\ While the IPFQR Program does not currently require use of 
the NHSN web-based surveillance system, we have previously required use 
of this system. We refer readers to the FY 2015 IPF PPS final rule in 
which we adopted the Influenza Vaccination Coverage Among Healthcare 
Personnel (NQF #0431) measure for additional information on reporting 
through the NHSN web-based surveillance system (79 FR 45968 through 
45970).
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    \111\ Centers for Disease Control and Prevention. Surveillance 
for Weekly HCP COVID-19 Vaccination. Accessed at: https://www.cdc.gov/nhsn/hps/weekly-covid-vac/index.html. on February 10, 
2021.
---------------------------------------------------------------------------

    IPFs would report COVID-19 vaccination data in the NHSN Healthcare 
Personnel Safety (HPS) Component by reporting the number of HCP 
eligible to have worked at the IPF that week (denominator) and the 
number of those HCP who have received a completed vaccination course of 
a COVID-19 vaccination (numerator). For additional information about 
the data reporting requirements, see IV.J.4. of this final rule.
    We invited public comment on our proposal to add a new measure, 
COVID-19 Vaccination Coverage Among HCP, to the IPFQR Program for the 
FY 2023 payment determination and subsequent years.
    Comment: Some commenters supported the proposed COVID-19 
Vaccination Coverage Among Healthcare Personnel measure. One commenter 
observed that data on vaccination coverage are important for patients 
and for individuals seeking employment at IPFs. Several commenters 
noted the importance of vaccines to reduce transmission, and one 
commenter specifically observed that vaccination is particularly 
important in settings such as IPFs because non-pharmaceutical 
interventions are challenging in such institutional settings. Another 
commenter expressed the belief that the measure is methodologically 
sound.
    Response: We thank these commenters for their support.
    Comment: Many commenters expressed concern that using NHSN for 
reporting is too burdensome and disproportionately affects smaller and 
freestanding IPFs. Some of these commenters further expressed that 
requiring reporting through NHSN is inconsistent with the removal of 
Influenza Vaccine Coverage among HCP measure because the rationale for 
removing the Influenza Vaccine Coverage among HCP measure was the high 
reporting burden associated with NHSN reporting.
    Response: We believe that there are many significant benefits to 
collecting and reporting data on COVID-19 vaccination coverage among 
HCP that outweigh its burden. As discussed in our proposal to adopt 
this measure, HCP vaccination can potentially reduce illness that leads 
to work absence and limit disruptions to care (86 FR 19502). The CDC 
has emphasized that health care settings can be high-risk places for 
COVID-19 exposure and transmission.\112\ In these settings, COVID-19 
can spread between health care personnel (HCP) and patients, or from 
patient to patient given the close contact that may occur during the 
provision of care.\113\
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    \112\ Dooling, K, McClung, M, et al. ``The Advisory Committee on 
Immunization Practices' Interim Recommendations for Allocating 
Initial Supplies of COVID-19 Vaccine--United States, 2020.'' Morb 
Mortal Wkly Rep. 2020; 69(49): 1857-1859.
    \113\ Centers for Disease Control and Prevention. (2020). 
Interim U.S. Guidance for Risk Assessment and Work Restrictions for 
Healthcare Personnel with Potential Exposure to COVID-19. Accessed 
on April 2, 2021 at: https://www.cdc.gov/coronavirus/2019-ncov/hcp/faq.html#Transmission.
---------------------------------------------------------------------------

    Subsequent to the publication of the IPF PPS proposed rule, the CDC 
updated its Science Brief on COVID-19 Vaccines and Vaccination and 
observed that the growing body of evidence indicates that people who 
are fully vaccinated with an mRNA vaccine are less likely to have 
asymptomatic infection or to transmit SARS-CoV-2 to others. The CDC 
further noted that the studies are continuing on the benefits of the 
Johnson & Johnson/Janssen vaccine.\114\ Therefore we believe that 
vaccination coverage among HCP will reduce the risk of contracting 
COVID-19 for patients in IPFs, and that IPFs reporting this information 
can help patients identify IPFs where they may have lower risk of 
COVID-19 exposure. Publishing the HCP vaccination rates will be helpful 
to many patients, including those who are at high-risk for developing 
serious complications from COVID-19, as they choose IPFs from which to 
seek treatment.
---------------------------------------------------------------------------

    \114\ https://www.cdc.gov/coronavirus/2019-ncov/science/science-briefs/fully-vaccinated-people.html.
---------------------------------------------------------------------------

    While we agree with the commenters that there is some burden 
associated with reporting this measure (see Section (V)(A)(2)(c) of 
this final rule), we believe the benefits of data collection and

[[Page 42637]]

reporting on COVID-19 vaccination coverage among HCP are sufficient to 
outweigh this burden. In addition, commenters are correct in noting 
that when we removed the Influenza Vaccination Coverage Among 
Healthcare Personnel (NQF #0431) measure from the IPFQR Program in the 
FY 2019 IPF PPS final rule, we observed that reporting measure data 
through the NHSN is relatively more burdensome for IPFs than for acute 
care hospitals and that this may be especially true for independent or 
freestanding IPFs (83 FR 38593 through 38595). However, in our analysis 
of facilities that did not receive full payment updates for FY 2018 and 
FY 2019 and the reasons these facilities did not receive full payment 
updates we observed that 98.24 percent and 99.05 percent of IPFs 
respectively, including small, independent, and freestanding IPFs, 
successfully reported data for the Influenza Vaccination Coverage Among 
Health Care Personnel (NQF #0431) measure prior to its removal from the 
IPFQR Program. For the reasons outlined above, the COVID-19 pandemic 
and associated PHE has had a much more significant effect on most 
aspects of society, including the ability of the healthcare system to 
operate smoothly, than influenza, making the benefits of the COVID-19 
Vaccination Among HCP measure greater than those of the Influenza 
Vaccination Coverage Among Health Care Personnel (NQF #0431) measure.
    Comment: Other commenters expressed concern that facilities face 
duplicative reporting requirements given that other agencies are 
requiring reporting through systems other than NHSN, such as the HHS 
TeleTracking site. A few of these commenters recommended that CMS use 
the TeleTracking site for data reporting and consumer information as 
opposed to adopting a quality measure. Other commenters recommended 
that CMS sunset TeleTracking and use NHSN for reporting COVID-19 
vaccination coverage data. One commenter recommended that CMS 
collaborate with CDC to ensure minimal reporting burden.
    Response: We recognize that this measure may lead to duplicative 
reporting requirements if facilities voluntarily report COVID-19 HCP 
vaccination information to data reporting systems other than NHSN, and 
we are collaborating with other HHS agencies, including the CDC, to 
ensure minimal reporting burden and to eliminate duplicative 
requirements to the extent feasible.
    Comment: Some commenters expressed concern about the measure 
specifications leading to increased reporting burden. Several of these 
commenters expressed that the proposed quarterly reporting of three 
weeks of data (one week per month) is excessively burdensome. Other 
commenters expressed concern that the measure specifications are not 
aligned with the Influenza Vaccination Coverage Among Healthcare 
Personnel measure (NQF #0431), specifically noting that the COVID 
Vaccination Coverage Among HCP measure requires data elements (such as 
contraindications) that are not required for Influenza Vaccination 
Coverage Among Healthcare Personnel measure (NQF #0431). One commenter 
observed that including all staff (not just clinical staff or staff 
directly employed by the IPF) makes the measure unduly burdensome. 
Another commenter observed that tracking location is challenging in 
large organizations with staff that work across locations.
    Response: We recognize commenters' concern regarding reporting 
burden associated with the specifications of this measure. We believe 
that, given the public health importance of vaccination in addressing 
the COVID-19 PHE, the benefits of requiring reporting outweigh the 
burden. We believe that reporting these data on a frequent interval 
would increase their value by allowing the CDC to better track these 
important public health data while also being a valuable quality 
measure that supports consumer choice and IPF improvement initiatives. 
Because the CDC requests data reported on a monthly basis for one week 
per month, we believe this is an appropriate reporting frequency for 
our quality measure to ensure that IPFs do not have duplicative 
reporting requirements to meet the CDC's need for public health data 
and CMS' quality measure reporting requirements. We further note that 
while we have sought to align this measure with the Influenza 
Vaccination Coverage Among HCP measure (NQF #0431), each measure 
addresses different public health initiatives and therefore complete 
alignment may not be possible. For example, because influenza 
vaccinations are provided during the influenza season (that is, October 
1 through March 31) these measures have different reporting periods.
    Further, we note that while the Influenza Vaccination Coverage 
Among HCP measure (NQF #0431) does not have a denominator exclusion for 
HCP with contraindications to the influenza vaccine, there is a 
numerator category for these HCP. Specifically, the numerator 
description is as follows: ``HCP in the denominator population who 
during the time from October 1 (or when the vaccine became available) 
through March 31 of the following year: . . . (b) were determined to 
have a medical contraindication/condition of severe allergic reaction 
to eggs or to other component(s) of the vaccine, or a history of 
Guillain-Barre Syndrome within 6 weeks after a previous influenza 
vaccination . . .'' \115\ We believe that this numerator element 
requires the IPF to track HCP's contraindications to the influenza 
vaccination. Therefore, we disagree with the commenter's statement that 
the COVID-19 Vaccination Coverage Among HCP measure is more burdensome 
than the Influenza Vaccination Coverage Among HCP measure due to 
requiring IPFs to track HCP's contraindications to the vaccine.
---------------------------------------------------------------------------

    \115\ http://www.qualityforum.org/Projects/n-r/Population_Health_Prevention/0431_InfluenzaImmunizationHCPersonnelForm_CDC.aspx.
---------------------------------------------------------------------------

    Finally, we note that CDC's guidance for entering data requires 
submission of HCP count at the IPF level \116\ and the measure requires 
reporting consistent with that guidance. We proposed the reporting 
schedule of monthly reporting of data from only one week a month to 
provide COVID-19 vaccination coverage data on a more timely basis than 
annual influenza vaccination coverage (NQF #0431) while also reducing 
burden on facilities of weekly reporting which has been the reporting 
cycle for many COVID-19-related metrics during the pandemic. As 
described in response to previous commenters, we believe that the 
public health benefits to having these data available are high, and 
that they therefore outweigh the burden of reporting for systems with 
multiple facilities or locations. In summary, we recognize that there 
may be some elements of the measure specifications that increase burden 
for some IPFs, however given the impact that the COVID-19 PHE has had 
on society and the healthcare system, we believe that the benefits 
outweigh this reporting burden.
---------------------------------------------------------------------------

    \116\ COVID-19 Vaccination Non-LTC Healthcare Personnel TOI 
(cdc.gov).
---------------------------------------------------------------------------

    Comment: Some commenters expressed concern that having some 
vaccinations require two doses creates undue reporting burden for IPFs. 
One commenter recommended modelling this measure on the measure under 
consideration for patient vaccination coverage within the Merit-Based 
Incentive Payment System (MIPS) program which would require reporting 
based on receipt of one dose, as opposed to requiring reporting on 
receipt of a full course of the vaccine. Some commenters

[[Page 42638]]

expressed concern that because it can take up to 28 days for an 
individual to be fully vaccinated, requiring reporting for HCP who have 
worked only one day of the reporting period is burdensome or that this 
disparately affects facilities without access to the one-dose vaccine.
    Response: We believe that it is appropriate to require data on HCP 
who have received complete COVID-19 vaccination courses, because an IPF 
has more long-term and regular contact with the HCP who work there than 
an ambulatory care provider, such as those being evaluated under the 
MIPS Program, has with their patient population. This gives the IPF 
more ability to track and encourage HCP to receive their complete 
vaccination course.
    We recognize that since a complete vaccination course could take up 
to 28 days, some IPFs may initially appear to have lower performance 
than others (based on having access to two dose vaccinations as opposed 
to one dose vaccination). However, we believe that with the reporting 
frequency these providers should show rapid improvement as their staff 
become fully vaccinated. We note that given the highly infectious 
nature of the COVID-19 virus, we believe it is important to encourage 
all personnel within the IPF, regardless of patient contact, role, or 
employment type, to receive the COVID-19 vaccination to prevent 
outbreaks within the IPF which may affect resource availability and 
have a negative impact on patient access to care.
    Comment: Some commenters recommended deferring measurement of 
vaccine coverage among HCP until there is at least one vaccine that has 
received full FDA approval (as opposed to an EUA). A few commenters 
expressed concern that the long-term effects of the vaccines are 
unknown and that some HCP concerned about the risk of serious adverse 
events; one commenter further expressed concerns regarding the rapid 
development and EUA timelines. A few commenters expressed concerns 
regarding HCP being unwilling to receive a vaccine which has not 
received full FDA approval.
    Response: We support widespread vaccination coverage, and note that 
in issuing the EUAs for these vaccines FDA has established that the 
known and potential benefits of these vaccines outweigh the known and 
potential risks.\117\ Furthermore, as July 15, 2021, more than 
336,000,000 doses have been administered in the United States.\118\ 
Although COVID-19 vaccines are authorized for emergency use prevent 
COVID-19 and serious health outcomes associated with COVID-19, 
including hospitalization and death,\119\ we understand that some HCP 
may be concerned about receiving the COVID-19 vaccine prior to the 
vaccine receiving full FDA approval. We also understand that some HCP 
may be concerned about long-term effects. We note that the COVID-19 
Vaccination Coverage Among HCP measure does not require HCP to receive 
the vaccination, nor does this measure reward or penalize IPFs for the 
rate of HCP who have received a COVID-19 vaccine. The COVID-19 
Vaccination Coverage Among HCP measure requires IPFs to collect and 
report COVID-19 vaccination data that would support public health 
tracking and provide beneficiaries and their caregivers information to 
support informed decision making. Therefore, we believe that it is 
appropriate to collect and report these data as soon as possible.
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    \117\ https://www.fda.gov/vaccines-blood-biologics/vaccines/emergency-use-authorization-vaccines-explained.
    \118\ CDC COVID Data Tracker.
    \119\ https://www.fda.gov/emergency-preparedness-and-response/coronavirus-disease-2019-covid-19/pfizer-biontech-covid-19-vaccine, 
https://www.fda.gov/emergency-preparedness-and-response/coronavirus-disease-2019-covid-19/moderna-covid-19-vaccine, https://www.fda.gov/emergency-preparedness-and-response/coronavirus-disease-2019-covid-19/janssen-covid-19-vaccine.
---------------------------------------------------------------------------

    Comment: One commenter observed that there are interventions 
through which an IPF can promote vaccination coverage, such as by 
removing barriers to access (through means such as extended vaccine 
clinic hours). This commenter recommended encouraging these 
interventions as opposed to promoting vaccination coverage among HCP by 
adopting the COVID-19 Vaccination Coverage Among HCP measure.
    Response: We agree with the commenter that there are interventions 
through which an IPF can increase vaccination coverage by reducing 
barriers to access. However, we believe that it is appropriate to 
propose this measure for the IPFQR Program to encourage such 
interventions by collecting data on vaccination coverage among HCP. We 
believe that vaccination is an important health intervention that can 
protect the health of vulnerable patients and the availability of the 
healthcare system (that is, limiting the number of HCP absent from work 
due to illness to ensure that patients have access to care).
    Comment: Some commenters expressed the belief that it is 
inappropriate to use IPF payment policies to drive vaccination coverage 
among HCP. Some commenters expressed concern that this measure could 
lead facilities to mandate vaccines for staff, with potential 
unintended consequences (specifically, staff quitting or legal risk for 
facilities for staff experiencing adverse events). One commenter 
expressed the belief that the tie to public reporting and potentially 
IPF payment is an indirect vaccine mandate.
    Several commenters recommended CMS not consider this measure for 
pay-for-reporting because state laws regarding mandates vary and 
therefore could lead to inconsistent performance through no fault of 
facilities. One commenter expressed the belief that this measure was 
developed for public health tracking and is not appropriate for quality 
assessment.
    Response: We note that this measure does not require vaccination 
coverage among HCP at IPFs; it requires IPFs to report of COVID-19 
vaccination rates. Therefore, we believe it is incorrect to 
characterize this measure as a ``vaccine mandate.'' Furthermore, we 
note that the historical national average of providers who had received 
the influenza vaccination, as reported on the then Hospital Compare 
website was 85 percent, 80 percent, and 82 percent respectively for the 
FY 2017, FY 2018, and FY 2019 payment determinations prior to removal 
of the Influenza Vaccination Coverage among Healthcare Personnel 
measure from the IPFQR Program. We do not believe that this represents 
performance that would be consistent with a widespread ``vaccine 
mandate'' and therefore we do not believe that a vaccination coverage 
among HCP measure, including the COVID-19 Vaccination Coverage among 
HCP measure, inherently leads to ``vaccine mandates.'' However, we 
believe that data regarding COVID-19 vaccination coverage among HCP are 
important to empower patients to make health care decisions that are 
best for them.
    Comment: Some commenters expressed concern that the measure does 
not fully account for potential reasons that HCP may not receive COVID-
19 vaccinations. One commenter recommended expanding the exclusions to 
the measure's calculation, specifically citing religious objections as 
an exclusion category. Another commenter observed that there is 
uncertainty about how effective vaccines are for certain populations, 
such as those with underlying conditions.

[[Page 42639]]

    Response: We recognize that there are many reasons, including 
religious objections or concerns regarding an individual provider's 
specific health status, which may lead individual HCP to decline 
vaccination. The CDC's NHSN tool allows facilities to report on the 
number of HCP who were offered a vaccination but declined for reasons 
including religious or philosophical objections.\120\ We agree that 
there is uncertainty about effectiveness among certain patient 
populations, including those with underlying conditions. The CDC has 
found that there is evidence of reduced antibody response to or reduced 
immunogenicity of COVID-19 mRNA vaccine among some immunosuppressed 
people.\121\ However, we note that COVID-19 vaccines may be 
administered to most people with underlying medical conditions.\122\ 
Therefore, we believe that individual HCP who may have underlying 
conditions that could affect vaccine efficacy should make the decision 
of whether to receive the COVID-19 vaccination in discussion with their 
individual care provider. We believe that vaccination coverage rates 
are meaningful data for beneficiaries to use in choosing an IPF which 
can also be used for public health tracking.
---------------------------------------------------------------------------

    \120\ https://www.cdc.gov/nhsn/forms/instr/57.220-toi-508.pdf.
    \121\ https://www.cdc.gov/coronavirus/2019-ncov/science/science-briefs/fully-vaccinated-people.htmla.
    \122\ https://www.cdc.gov/coronavirus/2019-ncov/vaccines/recommendations/underlying-conditions.html.
---------------------------------------------------------------------------

    Comment: One commenter expressed the concern that this may have an 
adverse impact on HCP as it is unclear whether in the future individual 
HCP will be required to pay for the vaccination themselves.
    Response: We understand the commenter's concerns that individual 
HCP may potentially have to pay for the COVID-19 vaccine in the future. 
In alignment with our pledge to put patients first in all our programs, 
we believe that it is important to empower patients to work with their 
doctors and make health care decisions that are best for them.\123\ 
This includes the belief that HCP should be empowered to work with 
their own healthcare providers to make the health care decisions that 
are best for them, based on the totality of their circumstances, 
including potential costs to receive the vaccine and their increased 
risks of contracting COVID-19 based on occupational exposure.
---------------------------------------------------------------------------

    \123\ Home--Centers for Medicare & Medicaid Services [verbar] 
CMS.
---------------------------------------------------------------------------

    Comment: Many commenters expressed concern that this measure should 
not be adopted until there is clarity around the impact of future 
boosters. These commenters also noted that booster availability could 
have an impact on vaccination coverage among HCP. One commenter 
specifically expressed concern regarding past supply chain disruptions 
and observed that similar issues may affect booster availability in the 
future.
    Response: The COVID-19 Vaccination Coverage among HCP measure is a 
measure of a completed vaccination course (as defined in section 
IV.E.2.b.(1) of the FY 2022 IPF PPS proposed rule (86 FR 19502 through 
19503) and does not address booster shots. Currently, the need for 
COVID-19 booster doses has not been established, and no additional 
doses are currently recommended for HCP. However, we believe that the 
numerator is sufficiently broad to include potential future boosters as 
part of a ``complete vaccination course'' and therefore the measure is 
sufficiently specified to address boosters. We acknowledge the 
potential for supply chain disruptions or other factors that affect 
vaccine availability, but we believe that the urgency of adopting the 
measure to address the current COVID-19 PHE outweighs these potential 
concerns.
    Comment: Some commenters expressed that collecting the data to 
report this measure is challenging. These commenters observed that 
because, unlike influenza vaccinations, HCP have received COVID 
vaccinations from settings outside their places of employment, 
employers may still be attaining vaccination records from employees. 
One commenter observed that the data for HCP is housed in separate 
systems from those typically used for quality reporting.
    Response: We recognize that some IPFs may still be obtaining 
vaccination records from their employees and other personnel that work 
within their facilities. However, most healthcare settings, including 
IPFs, have been reporting COVID-19 data to Federal or state agencies 
for some time and therefore have established the appropriate workflows 
or other means to obtain these records from employees or other 
personnel that work within the IPF. Therefore, we believe that IPFs 
must have the means to obtain the data, either directly from HCP or 
from other systems in which these data are housed, and that it is 
appropriate to require IPFs to report these data.
    Comment: Another commenter expressed concern that the shortened 
performance period for the first year may lead to incomplete data. One 
commenter recommended allowing voluntary reporting without publicly 
reporting data for the first performance year to account for potential 
data gaps.
    Response: Given that results would be calculated quarterly for this 
measure, facilities should show rapid progress as they obtain more 
complete data on vaccination coverage for their HCP. While we 
understand the desire for a year of voluntary reporting to account for 
potential data gaps, we believe that the importance of providing 
patients and their caregivers with data on COVID-19 Vaccination 
Coverage among HCP at individual IPFs in a timely manner outweighs this 
concern and should be accomplished as soon as practical.
    Comment: A few commenters expressed concern that due to the delay 
between data collection (which takes place during a quarter) and public 
reporting (which follows the reporting of the data collected during the 
quarter, the deadline for which is 4.5 months after the end of the 
quarter) the data would not be useful by the time they are publicly 
reported either because they are too old or because the trajectory of 
the pandemic has changed. One commenter opposed public reporting until 
data has been reported for several years.
    Response: We believe that it is important to make these data 
available as soon as possible. We agree with commenters that observe 
that there is a delay between data collection and public reporting for 
this measure, and note that such a delay exists for all measures in the 
IPFQR Program. However, we believe that the data will provide 
meaningful information to consumers in making healthcare decisions 
because the data will be able to reflect differences between IPFs in 
COVID-19 vaccination coverage among HCP even if the data do not reflect 
the current vaccination rates and we believe it will benefit consumers 
to have these data available as early as possible. We proposed the 
shortened reporting period for the first performance period to make the 
COVID-19 Vaccination among HCP measure data available as quickly as 
possible.
    Comment: One commenter observed that the data would not provide 
consumers a complete picture of infection control procedures because 
vaccines are only one tactic to prevent and control infections. Another 
commenter observed that public reporting may lead to comparisons 
between facilities. An additional commenter recommended a validation 
process to ensure that consumers can rely on the data.

[[Page 42640]]

    Response: While we recognize that the data may not fully represent 
all activities to prevent and control infections, we believe that the 
data would be useful to consumers in choosing IPFs, including making 
comparisons between facilities. We note that we do not currently have a 
validation process for any measures in the IPFQR Program and refer 
readers to section IV.J.3 of this final rule where we discuss 
considerations for a validation program for the IPFQR Program.
    Comment: Some commenters recommended deferring the measure until it 
has been fully tested and NQF endorsed. One commenter observed that the 
MAP reviewed the measure concept, not the full measure, and therefore 
it is premature to include it in the IPFQR Program without further 
review. Another commenter observed that such rapid measure adoption may 
set a precedent for future rapid measure adoption.
    Response: We believe that given the current COVID-19 PHE, it is 
important to adopt this measure as quickly as possible to allow 
tracking and reporting of COVID-19 Vaccination Coverage Among HCP in 
IPFs. This tracking would provide consumers with important information. 
We refer readers to FY 2022 IPF PPS proposed rule where we discuss our 
consideration of NQF endorsed measures on the topic of COVID-19 
vaccination coverage among healthcare personnel for additional 
information (86 FR 19503 through 19504). We note that the MAP had the 
opportunity to review and provide feedback on the full measure in the 
March 15th meeting. The CDC, in collaboration with CMS, is planning to 
submit the measure for consideration in the NQF Fall 2021 measure 
cycle. Finally, we evaluate all measures on a case-by-case basis and 
therefore the pace at which we propose to adopt one measure is 
dependent on the measure and the purpose for adopting it.
    Comment: One commenter requested clarification for the reporting 
frequency.
    Response: We recognize that the proposed required frequency for 
reporting, may have been unclear because we referred to ``annual 
reporting'' periods two times in the proposed rule. Specifically, we 
referenced annual reporting periods in the first paragraph of section 
IV.E.2.c (86 FR 19504) and in our burden estimate for the measure (86 
FR 19519). Our description of data submission under IV.J.2.a in which 
we stated that facilities would be required to report the vaccination 
data to the NHSN for at least one week each month and that if they 
reported more than one week, the most recent week's data would be used 
(86 FR 19513) is correct. In that section, we further noted that the 
CDC would calculate a single quarterly result for summarizing the data 
reported monthly. In summary, the measure would require monthly 
reporting of at least one week's data per month. This would be 
calculated into quarterly results. We note that IPFs are required to 
report to NHSN sufficient data (that is, vaccination data for at least 
one week in each month per quarter) to calculate four quarterly results 
per year, except for the first performance period which depends on only 
one quarter of data (the vaccination data for at least one week in each 
month in Q1 of FY 2022). While IPFs can report data to the NHSN at any 
time, they must report by 4.5 months following the preceding quarter 
for the purposes of measure calculation. For the first performance 
period for this measure (that is Q1 of FY 2022), 4.5 months following 
the end of the quarter is May 15, 2022.
    Comment: One commenter requested clarification on which provider 
types are considered healthcare personnel.
    Response: The provider types that are considered healthcare 
personnel, along with the specifications for this measure, are 
available at https://www.cdc.gov/nhsn/nqf/index.html. The categories of 
HCP included in this measure are ancillary services employees; nurse 
employees; aide, assistant, and technician employees; therapist 
employees; physician and licensed independent practitioner employees; 
and other HCP. For more detail about each of these categories we refer 
readers to the Table of Instructions for Completion of the Weekly 
Healthcare Personnel COVID-19 Cumulative Vaccination Summary Form for 
Non-Long-Term Care Facilities available at https://www.cdc.gov/nhsn/forms/instr/57.220-toi-508.pdf.
    Comment: One commenter observed that the definition of ``location'' 
for measure calculation is unclear.
    Response: CDC's guidance for entering data requires submission of 
HCP count at the IPF level, not at the location level within the 
IPF.\124\
---------------------------------------------------------------------------

    \124\ COVID-19 Vaccination Non-LTC Healthcare Personnel TOI 
(cdc.gov).
---------------------------------------------------------------------------

    After consideration of the public comments, we are finalizing the 
COVD-19 Vaccination Coverage Among Healthcare Personnel measure as 
proposed for the FY 2023 payment determination and subsequent years.
3. Follow-Up After Psychiatric Hospitalization (FAPH) Measure for the 
FY 2024 Payment Determination and Subsequent Years
a. Background
    We proposed one new measure, Follow-Up After Psychiatric 
Hospitalization (FAPH), for the FY 2024 payment determination and 
subsequent years. The FAPH measure would use Medicare fee-for-service 
(FFS) claims to determine the percentage of inpatient discharges from 
an inpatient psychiatric facility (IPF) stay with a principal diagnosis 
of select mental illness or substance use disorders (SUDs) for which 
the patient received a follow-up visit for treatment of mental illness 
or SUD. Two rates would be calculated for this measure: (1) The 
percentage of discharges for which the patient received follow-up 
within 7 days of discharge; and (2) the percentage of discharges for 
which the patient received follow-up within 30 days of discharge.
    The FAPH measure is an expanded and enhanced version of the Follow-
Up After Hospitalization for Mental Illness (FUH, NQF #0576) measure 
currently in the IPFQR Program. We proposed to adopt the FAPH measure 
and replace the FUH measure and refer readers to section IV.F.2.d of 
the FY 2022 IPF PPS proposed rule for our proposal to remove the FUH 
measure contingent on adoption of the FAPH measure (86 FR 19510). The 
FUH (NQF #0576) measure uses Medicare FFS claims to determine the 
percentage of inpatient discharges from an IPF stay with a principal 
diagnosis of select mental illness diagnoses for which the patient 
received a follow-up visit for treatment of mental illness, and it 
excludes patients with primary substance use diagnoses. During the 2017 
comprehensive review of NQF #0576, the NQF Behavioral Health Standing 
Committee (BHSC) recommended expanding the measure population to 
include patients hospitalized for drug and alcohol disorders, because 
these patients also require follow-up care after they are discharged.
    In 2018, CMS began development of a measure to expand the IPFQR FUH 
population to include patients with principal SUD diagnoses to address 
the NQF BHSC recommendation and the CMS Meaningful Measures priority to 
promote treatment of SUDs. The FAPH measure would expand the number of 
discharges in the denominator by about 35 percent over the current FUH 
measure by adding patients with SUD or dementia as principal diagnoses 
(including patients with any

[[Page 42641]]

combination of SUD, dementia, or behavioral health disorders), 
populations that also benefit from timely follow-up care.
    Furthermore, compared to the criteria for provider type in the 
current FUH measure, the FAPH measure does not limit the provider type 
for the follow-up visit if it is billed with a diagnosis of mental 
illness or SUD. During the measure's testing, the most frequent 
provider types for the FAPH measure were family or general practice 
physicians, internal medicine physicians, nurse practitioners, and 
physician assistants. The technical expert panel (TEP) convened by our 
contractor agreed that these provider types should be credited by the 
measure for treating mental illness and SUD and confirmed that this is 
aligned with integrated care models that aim to treat the whole 
patient. The TEP further noted that in areas where there are shortages 
of mental health or SUD clinicians, other types of providers are often 
the only choice for follow-up treatment. Allowing visits to these types 
of providers to count towards the numerator allows the measure to 
capture the rates of appropriate follow-up care more accurately in 
areas with provider shortages.
    Performance on the FAPH measure indicates that follow-up rates for 
patients hospitalized with mental illness or SUD are less than optimal 
and that room for improvement is ample. The clinical benefits of timely 
follow-up care after hospitalization, including reduced risk of 
readmission and improved adherence to medication, are well-documented 
in the published literature.125 126 127 128 129 130 131
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    \125\ Tong, L., Arnold, T., Yang, J., Tian, X., Erdmann, C., & 
Esposito, T. (2018). The association between outpatient follow-up 
visits and all-cause non-elective 30-day readmissions: A 
retrospective observational cohort study. PloS one, 13(7), e0200691. 
https://doi.org/10.1371/journal.pone.0200691.
    \126\ Terman, S. W., Reeves, M. J., Skolarus, L. E., & Burke, J. 
F. (2018). Association Between Early Outpatient Visits and 
Readmissions After Ischemic Stroke. Circulation. Cardiovascular 
quality and outcomes, 11(4), e004024. https://doi.org/10.1161/CIRCOUTCOMES.117.004024.
    \127\ First Outpatient Follow-Up After Psychiatric 
Hospitalization: Does One Size Fit All? (2014). Psychiatric 
Services, 66(6), 364-372. https://doi.org/10.1176/appi.ps.201400081.
    \128\ Terman, S. W., Reeves, M. J., Skolarus, L. E., & Burke, J. 
F. (2018). Association Between Early Outpatient Visits and 
Readmissions After Ischemic Stroke. Circulation. Cardiovascular 
quality and outcomes, 11(4), e004024. https://doi.org/10.1161/CIRCOUTCOMES.117.004024.
    \129\ Jackson, C., Shahsahebi, M., Wedlake, T., & DuBard, C. A. 
(2015). Timeliness of outpatient follow-up: An evidence-based 
approach for planning after hospital discharge. Annals of family 
medicine, 13(2), 115-122. https://doi.org/10.1370/afm.1753.
    \130\ Hernandez, A. F., Greiner, M. A., Fonarow, G. C., Hammill, 
B. G., Heidenreich, P. A., Yancy, C. W., Peterson, E. D., & Curtis, 
L. H. (2010). Relationship between early physician follow-up and 30-
day readmission among Medicare beneficiaries hospitalized for heart 
failure. JAMA, 303(17), 1716-1722. https://doi.org/10.1001/jama.2010.533.
    \131\ Nadereh Pourat, Xiao Chen, Shang-Hua Wu and Anna C. Davis. 
Timely Outpatient Follow-up Is Associated with Fewer Hospital 
Readmissions among Patients with Behavioral Health Conditions. The 
Journal of the American Board of Family Medicine. May 2019, 32 (3) 
353-361; DOI: https://doi.org/10.3122/jabfm.2019.03.180244.
---------------------------------------------------------------------------

    Behavioral health patients in particular have a number of risk 
factors that underscore the need for timely follow-up and continuity of 
care: Behavioral health patients have higher baseline hospitalization 
rates, higher hospital readmission rates, and higher health care costs 
as compared with the general population of patients.132 133 
Among patients with serious mental illness, 90 percent have comorbid 
clinical conditions such as hypertension, cardiovascular disease, 
hyperlipidemia, or diabetes.\134\ Among patients hospitalized for 
general medical conditions, those who also have a mental illness are 28 
percent more likely to be readmitted within 30 days than their 
counterparts without a psychiatric comorbidity.\135\ The high 
prevalence of clinical comorbidities among behavioral health patients, 
combined with the compounding effect of mental illness on patients with 
general medical conditions, suggests that behavioral health patients 
are uniquely vulnerable and supports the intent of the measure to 
increase follow-up after hospitalization.
---------------------------------------------------------------------------

    \132\ Germack, H.D., et al. (2019, January). Association of 
comorbid serious mental illness diagnosis with 30-day medical and 
surgical readmissions. JAMA Psychiatry.
    \133\ First Outpatient Follow-Up After Psychiatric 
Hospitalization: Does One Size Fit All? (2014). Psychiatric 
Services, 66(6), 364-372. https://doi.org/10.1176/appi.ps.201400081.
    \134\ First Outpatient Follow-Up After Psychiatric 
Hospitalization: Does One Size Fit All? (2014). Psychiatric 
Services, 66(6), 364-372. https://doi.org/10.1176/appi.ps.201400081.
    \135\ Benjenk, I., & Chen, J. (2018). Effective mental health 
interventions to reduce hospital readmission rates: A systematic 
review. Journal of hospital management and health policy, 2, 45. 
https://doi.org/10.21037/jhmhp.2018.08.05.
---------------------------------------------------------------------------

    In addition, clinical practice guidelines stress the importance of 
continuity of care between settings for patients with mental illness 
and SUD. For the treatment of SUD patients, the 2010 guidelines of the 
American Psychiatric Association (APA) state: ``It is important to 
intensify the monitoring for substance use during periods when the 
patient is at a high risk of relapsing, including during the early 
stages of treatment, times of transition to less intensive levels of 
care, and the first year after active treatment has ceased.'' \136\ 
This statement is accompanied by a grade of [I], which indicates the 
highest level of APA endorsement: ``recommended with substantial 
clinical evidence.''
---------------------------------------------------------------------------

    \136\ American Psychiatric Association. Practice guideline for 
the treatment of patients with substance use disorders. 2010. http://psychiatryonline.org/pb/assets/raw/sitewide/practice_guidelines/guidelines/substanceuse.pdf.
---------------------------------------------------------------------------

    Evidence supports that outpatient follow-up care and interventions 
after hospital discharges are associated with a decreased risk of 
readmissions for patients with mental illness.137 138 IPFs 
can influence rates of follow-up care for patients hospitalized for 
mental illness or SUD. Three studies reported that with certain 
interventions--such as pre-discharge transition interviews, appointment 
reminder letters or reminder phone calls, meetings with outpatient 
clinicians before discharge, and meetings with inpatient staff familiar 
to patients at the first post-discharge appointment--facilities 
achieved 30-day follow-up rates of 88 percent or 
more.139 140 141 This is substantially higher than the 
national rate of about 52 percent observed in the current FUH measure 
for Medicare FFS discharges between July 1, 2016, and June 30, 
2017.\142\ Medicare FFS data from July 1, 2016, to June 30, 2017, show 
the national 7-day follow-up rate to be 35.5 percent and the 30-day 
rate to be 61.0 percent. These data reveal wide variation in follow-up 
rates across facilities, with a 16.9 percent absolute difference 
between the 25th and 75th

[[Page 42642]]

percentiles for the 7-day rate and a 17.4 percent absolute difference 
for the 30-day rate. If all facilities achieved the benchmark follow-up 
rates for their Medicare FFS patients (as calculated using the AHRQ 
Achievable Benchmarks of Care method,) \143\ 53,841 additional 
discharges would have a 7-day follow-up visit, and 47,552 would have a 
30-day follow-up visit.\144\
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    \137\ Kurdyak P, Vigod SN, Newman A, Giannakeas V, Mulsant BH, 
Stukel T. Impact of Physician Follow-Up Care on Psychiatric 
Readmission Rates in a Population-Based Sample of Patients With 
Schizophrenia. Psychiatr Serv. 2018;69(1):61-68. doi: 10.1176/
appi.ps.201600507.
    \138\ Marcus SC, Chuang CC, Ng-Mak DS, Olfson M. Outpatient 
follow-up care and risk of hospital readmission in schizophrenia and 
bipolar disorder. Psychiatr Serv. 2017;68(12):1239-1246. doi: 
10.1176/appi.ps.201600498.
    \139\ Batscha C, McDevitt J, Weiden P, Dancy B. The effect of an 
inpatient transition intervention on attendance at the first 
appointment post discharge from a psychiatric hospitalization. J Am 
Psychiatr Nurses Assoc. 2011;17(5):330-338. doi: 10.1177/
1078390311417307.
    \140\ Agarin T, Okorafor E, Kailasam V, et al. Comparing kept 
appointment rates when calls are made by physicians versus behavior 
health technicians in inner city hospital: literature review and 
cost considerations. Community Ment Health J. 2015;51(3):300-304. 
doi: 10.1007/s10597-014-9812-x.
    \141\ Olfson M, Mechanic D, Boyer CA, Hansell S. Linking 
inpatients with schizophrenia to outpatient care. Psychiatr Serv. 
1998;49(7):911-917. doi: 10.1176/ps.49.7.911. Quality AFHRA. 2017 
National Healthcare Quality and Disparities Report. Rockville, MD: 
Services USDoHaH; 2018.
    \142\ https://data.cms.gov/provider-data/archived-data/hospitals.
    \143\ https://nhqrnet.ahrq.gov/inhqrdr/resources/methods#Benchmarks.
    \144\ Quality AfHRa. 2017 National Healthcare Quality and 
Disparities Report. Rockville, MD: Services USDoHaH; 2018.
---------------------------------------------------------------------------

    During the development process, we used the CMS Quality Measures 
Public Comment Page to ask for public comments on the measure.\145\ We 
accepted public comments from January 25, 2019, to February 13, 2019. 
During this period, we received comments from 29 organizations or 
individuals. Many commenters acknowledged the importance of developing 
a measure that assesses acute care providers for follow-up post-
hospitalization. Some commenters expressed skepticism about the 
measure's appropriateness as a tool for evaluating the performance of 
discharging IPFs due to factors beyond the IPFs' control that can 
affect whether a patient receives timely post-discharge follow-up care. 
Ten stakeholders expressed support for the measure based on the 
expanded list of qualifying diagnoses in the denominator and the 
inclusion of more patients who could benefit from post-discharge 
follow-up visits.\146\
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    \145\ https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/MMS/Downloads/IPF_-Follow-Up-After-Psychiatric-Hospitalization_Public-Comment-Summary.pdf.
    \146\ Mathematica. FAPH public comment summary. April 2019.
---------------------------------------------------------------------------

    We reviewed the comments we received with the TEP, whose members 
shared similar feedback regarding the importance of follow-up for 
patients with both mental health diagnoses and substance use disorders, 
as well as concerns about the ability of IPFs to influence follow-up 
care. We agree with commenters that some factors that influence follow-
up are outside of an IPF's control. However, as described previously in 
this section, we believe that there are interventions (such as pre-
discharge transition interviews, appointment reminder letters or 
reminder phone calls, meetings with outpatient clinicians before 
discharge, and meetings with inpatient staff familiar to patients at 
the first post-discharge appointment) that allow facilities to improve 
their follow-up adherence. We remain committed to monitoring follow-up 
to improve health outcomes and view this measure as an expansion of our 
ability to measure appropriate follow-up care established by FUH.
b. Overview of Measure
(1). Measure Calculation
    The FAPH measure would be calculated by dividing the number of 
discharges that meet the numerator criteria by the number that meet the 
denominator criteria. Two rates are reported for this measure: the 7-
day rate and the 30-day rate.
(a) Numerator
    The first rate that would be reported for this measure includes 
discharges from an IPF that are followed by an outpatient visit for 
treatment of mental illness or SUD within 7 days. The second rate 
reported for this measure would include discharges from an IPF that are 
followed by an outpatient visit for treatment of mental illness or SUD 
within 30 days. Outpatient visits are defined as outpatient visits, 
intensive outpatient encounters, or partial hospitalization and are 
defined by the Current Procedural Terminology (CPT), Healthcare Common 
Procedure Coding System (HCPCS), and Uniform Billing (UB) Revenue 
codes. Claims with codes for emergency room visits do not count toward 
the numerator.
(b) Denominator
    The denominator includes discharges paid under the IPF prospective 
payment system during the performance period for Medicare FFS patients 
with a principal diagnosis of mental illness or SUD. Specifically, the 
measure includes IPF discharges for which the patient was:
     Discharged with a principal diagnosis of mental illness or 
SUD that would necessitate outpatient follow-up care,
     Alive at the time of discharge,
     Enrolled in Medicare Parts A and B during the month of the 
discharge date and at least one month after the discharge date to 
ensure that data are available to capture the index admission and 
follow-up visits, and
     Age 6 or older on the date of discharge, because follow-up 
treatment for mental illness or SUD might not always be recommended for 
younger children.
    The denominator excludes IPF discharges for patients who:
     Were admitted or transferred to acute and non-acute 
inpatient facilities within the 30-day follow-up period, because 
admission or transfer to other institutions could prevent an outpatient 
follow-up visit from taking place,
     Were discharged against medical advice, because the IPF 
could have limited opportunity to complete treatment and prepare for 
discharge,
     Died during the 30-day follow-up period, or
     Use hospice services or elect to use a hospice benefit at 
any time during the measurement year regardless of when the services 
began, because hospice patients could require different follow-up 
services.
    The FAPH measure differs from FUH mostly in the expansion of the 
measure population to include SUD and other mental health diagnoses in 
the measure's denominator, but it includes some additional differences:
     The FAPH measure simplifies the exclusion of admission or 
transfer to acute or non-acute inpatient facilities within 30 days 
after discharge by aligning with the HEDIS[supreg] Inpatient Stay Value 
Set used in both the HEDIS[supreg] FUH and the HEDIS[supreg] Follow-Up 
After Emergency Department Visit for Alcohol and Other Drug Abuse or 
Dependence (FUA) measures to identify acute and non-acute inpatient 
stays. A discharge is excluded from the FAPH measure if it is followed 
by an admission or a transfer with one of the codes in the value set.
     The FAPH measure uses Medicare UB Revenue codes (rather 
than inpatient discharge status code, which the FUH measure uses) to 
identify discharge or transfer to other health care institutions. This 
is to align better with the intent of the HEDIS[supreg] FUH and 
HEDIS[supreg] FUA measures.
     The FAPH measure allows mental illness or SUD diagnoses in 
any position on the follow-up visit claim to count toward the numerator 
and does not require that it be in the primary position as the FUH 
measure does.
(2) Measure Reliability and Validity
    In 2019, CMS used the final measure specifications to complete 
reliability and validity testing, which revealed that the FAPH measure 
provides reliable and valid IPF-level rates of follow-up after 
psychiatric hospitalization. We evaluated measure reliability based on 
a signal-to-noise analysis,\147\ in which a score of 0.0 implies that 
all variation is attributed to measurement error (noise), and a score 
of 1.0 implies that all measure score variation is caused by a real 
difference in performance across IPFs. Using that approach, we 
established a minimum denominator size of 40 discharges to attain an 
overall

[[Page 42643]]

reliability score of 0.7 for both the 7-day and the 30-day rate. These 
analyses revealed that the measure can reliably distinguish differences 
in performance between IPFs with adequate denominator size.
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    \147\ For additional information on reliability tests see http://www.qualityforum.org/Measuring_Performance/Improving_NQF_Process/Measure_Testing_Task_Force_Final_Report.aspx.
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    We evaluated the validity of the measure based on its correlation 
to two conceptually related measures in the IPFQR Program: The 30-Day 
All-Cause Unplanned Readmission After Psychiatric Discharge from an IPF 
(IPF Readmission) measure, and the Medication Continuation Following 
Inpatient Psychiatric Discharge (Medication Continuation) measure. We 
observed a weak negative correlation between FAPH and the IPF 
Readmission measure for both 7-day (--0.11) and 30-day (--0.18) measure 
rates. This negative correlation is expected because a higher score is 
indicative of better quality of care for the FAPH, while a lower score 
is indicative of better quality of care for the IPF readmission measure 
(that is, a lower rate of unplanned readmissions). High rates of 
follow-up after visits after discharge and low rates of unplanned 
readmissions both indicate good care coordination during the discharge 
process. We observed a weak positive correlation between the 7-day FAPH 
measure rate and the Medication Continuation measure (0.32), and 
between the 30-day FAPH measure rate and the Medication Continuation 
measure (0.42). This result is expected because for both the FAPH and 
the Medication Continuation measures higher scores are indicative of 
better-quality care. Follow-up visits after discharge and continuation 
of medication after discharge both indicate good care coordination 
during the discharge process. After reviewing these results and the 
proposed measure specifications, all 13 TEP members who were present 
agreed that the measure had face validity.\148\
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    \148\ Face validity is defined as a subjective determination by 
experts that the measure appears to reflect quality of care, done 
through a systematic and transparent process, that explicitly 
addresses whether performance scores resulting from the measure as 
specified can be used to distinguish good from poor quality, with 
degree of consensus and any areas of disagreement provided/
discussed: https://www.qualityforum.org/Measuring_Performance/Scientific_Methods_Panel/Docs/Evaluation_Guidance.aspx.
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(3) Review by the Measure Applications Partnership and NQF
    Under section 1890A(a)(2) of the Act, this measure was included in 
a publicly available document: ``List of Measures Under Consideration 
for December 1, 2019,'' available at: https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityMeasures/Downloads/Measures-under-Consideration-List-for-2018.pdf.
    On January 15, 2020, the MAP Coordinating Committee rated the 
measure as ``Conditional Support for Rulemaking'' contingent upon NQF 
endorsement. We submitted the measure to the NQF for endorsement in the 
spring 2020 cycle. However, some members of the NQF Behavioral Health 
and Substance Use Standing Committee were concerned about the measure's 
exclusions for patients who died during the 30-day follow-up period or 
who were transferred. In addition, some members objected to combining 
persons with a diagnosis of SUD and those with a diagnosis for a mental 
health disorder into a single measure of follow-up care. Therefore, the 
NQF declined to endorse this measure. We noted that the exclusions for 
patients who died or who were admitted or transferred to an acute or 
non-acute inpatient facility during the 30-day follow up period align 
with the FUH measure currently in the IPFQR Program.
    Section 1886(s)(4)(D)(ii) of the Act authorizes the Secretary to 
specify a measure for the IPFQR Program that is not endorsed by NQF. 
The exception to the requirement to specify an endorsed measure states 
that 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 by the entity with a contract under section 
1890(a) of the Act, the Secretary may specify a measure that is not so 
endorsed as long as due consideration is given to measures that have 
been endorsed or adopted by a consensus organization.
    The FAPH measure is not NQF endorsed. We have reviewed NQF-endorsed 
and other consensus-endorsed measures related to follow-up care and 
identified the FUH measure (NQF #0576) currently in the IPFQR Program 
and Continuity of Care after Inpatient or [verbar] Residential 
Treatment for SUD (NQF #3453), we believe that the FAPH measure is an 
improvement over the current FUH measure and over the Continuity of 
Care after Inpatient or Residential Treatment of Substance Use Disorder 
because we believe that it is important to ensure appropriate access to 
follow-up treatment for the largest patient population possible and the 
FAPH measure applies to a larger patient population than either of the 
measures we considered. Therefore, we proposed to adopt the FAPH 
measure described in this section for the FY 2024 payment determination 
and subsequent years.
c. Data Collection, Submission and Reporting
    FAPH uses Medicare FFS Part A and Part B claims that are received 
by Medicare for payment purposes. The measure links Medicare FFS claims 
submitted by IPFs and subsequent outpatient providers for Medicare FFS 
IPF discharges. Therefore, no additional data collection would be 
required from IPFs. For additional information on data submission for 
this measure, see section IV.J.2.b of this final rule. The performance 
period used to identify cases in the denominator is 12 months. Data 
from this period and 30 days afterward are used to identify follow-up 
visits in the numerator. Consistent with other claims-based measures in 
the IPFQR Program, the performance period for this measure is July 1 
through June 30. For example, for the FY 2024 payment determination, 
the performance period would include discharges between July 1, 2021 
and June 30, 2022.\149\
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    \149\ If data availability or operational issues prevent use of 
this performance period, we would announce the updated performance 
period through subregulatory communications including announcement 
on a CMS website and/or on our applicable listservs.
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    We invited public comment on our proposal to add a new measure, 
Follow-Up After Psychiatric Hospitalization, to the IPFQR Program, 
beginning with the FY 2024 payment determination and subsequent years.
    We received the following comments on our proposal.
    Comment: Many commenters supported the adoption of the FAPH 
measure. Some commenters expressed that the expanded cohort would 
improve the measure's value. Some commenters expressed that expanding 
the eligible provider types for the follow-up visit would improve care 
because of the shortage of psychiatrists. A few commenters observed 
that care transitions are important, and that outpatient follow-up 
serves to improve the value of the inpatient services provided. One 
commenter expressed that adoption of this measure is timely due to the 
increased behavioral health needs associated with the COVID-19 
pandemic. One commenter recommended using this measure at the health 
system level to better identify care coordination, access, and referral 
network adequacy.
    Response: We thank these commenters for their support. We agree 
that the expanded definitions would improve the measure's applicability 
and capture more follow-up visits. Regarding the commenter's

[[Page 42644]]

recommendation on using this measure at the health system level, we 
believe the commenter is recommending adopting this measure to evaluate 
performance of regional or local health systems (such as those 
affiliated with large hospital networks). We note that the IPFQR 
Program applies to Medicare participating freestanding psychiatric 
hospitals and psychiatric units and we believe that health systems that 
have IPFs that participate in the IPFQR Program would find this measure 
useful as they assess access and referral network adequacy within their 
systems.
    Comment: Some commenters observed that some follow-ups, especially 
for substance use disorders, may not be identifiable in claims. A few 
commenters specifically noted that some providers who often provide 
follow-ups are not covered by Medicare (for example, therapists) or 
that some follow-ups may be covered by other insurers. These commenters 
observed that this may lead the measure to undercount follow-ups 
provided. A few of these commenters did not support measure adoption 
because of this undercount. However, one commenter that expressed this 
concern supported measure adoption because the commenter believes that 
burden reduction associated with claims reporting outweighs the 
potential undercounting.
    Response: We acknowledge that, like the Follow-Up After 
Hospitalization for Mental Illness (FUH, NQF #0576) measure that we 
proposed to replace with the FAPH measure, the FAPH measure would not 
be able to capture follow-up visits provided by professionals outside 
of Medicare, or if the patient uses another payer or self-pay to cover 
the patient's follow-up care, which could lead to an undercount. 
However, we believe that the data captured by the measure would be 
sufficient to inform consumers and to provide data for quality 
improvement initiatives. Further, we agree with the commenter that the 
burden reduction associated with using claims-based measures outweighs 
the potential undercounting.
    Comment: Some commenters expressed concern that this measure may be 
difficult for some IPFs to perform well on due to factors outside of 
the IPF's control. One commenter observed that many rural hospitals 
lack community resources and therefore cannot refer patients to 
outpatient psychiatrists. Another commenter observed that some patients 
may be unwilling to see an outpatient psychiatrist. Other commenters 
observed that this measure captures patient behavior, not provider 
actions. Some of these commenters observed that lack of transportation, 
access barriers, homelessness or other patient characteristics outside 
of the IPF's control may affect performance. Some of these commenters 
expressed preference for a process measure that tracks whether IPFs 
performed interventions to improve follow-up rates before or during 
discharge.
    Response: We recognize that there is regional variation in access 
to outpatient resources and that patients have varying comfort levels 
with different provider types. However, we believe that this updated 
measure helps to address some of the commenters' concerns. 
Specifically, we note that this measure expands the definition of 
follow-up to include a wider range of outpatient providers, including 
family or general practice physicians, internal medicine physicians, 
nurse practitioners, and physician assistants. We agree with commenters 
that there are factors that influence follow-up that are outside of an 
IPF's control (including patient behavior, lack of transportation, 
access barriers, homelessness, among others).
    As described in the FY 2022 IPF PPS proposed rule (86 FR 19504 
through 19505), there are interventions that allow facilities to 
improve their follow-up adherence. We believe it is incumbent upon 
facilities to identify potential barriers to follow-up adherence and 
apply appropriate interventions to improve adherence. We believe that 
this measure is preferable to a process measure because it provides 
insight into the success of interventions by identifying follow-up 
rates. As discussed in the FY 2014 IPPS/LTCH PPS final rule (78 FR 
50894 through 50895) and the FY 2022 IPF PPS proposed rule in our 
proposal to adopt the FAPH measure (86 FR 19504 through 19507) we do 
not expect 100 percent of patients discharged from IPFs to receive 
follow-up care within 7 or 30 days of discharge because of factors both 
within and outside of the control of facilities such as availability of 
providers in the referral network.
    Comment: Some commenters opposed the FAPH measure because it is not 
NQF endorsed and because it was not fully supported by the MAP. A few 
commenters observed that the measure may undergo changes to achieve NQF 
endorsement which would create burden if the measure were in the 
program when these changes occurred. Some commenters recommended 
delaying implementation until NQF's concerns are fully addressed. One 
commenter observed that the similar NQF-endorsed FUH measure is 
available and therefore CMS has not properly considered available 
consensus endorsed measures.
    Response: We appreciate the commenters' concerns about the FAPH 
measure's lack of NQF endorsement. As we stated in the proposed rule, 
after having given due consideration to similar measures, FUH measure 
(NQF #0576) and Continuity of Care after Inpatient or Residential 
Treatment for SUD (NQF #3453), we believe that the FAPH measure is an 
improvement over the FUH measure currently in the IPFQR Program (86 FR 
19507). The FAPH measure expands the number of discharges in the 
denominator by adding patients with SUD or dementia, populations that 
also benefit from timely follow-up care. We propose updates to the 
IPFQR program measure set on an annual basis through the rulemaking 
process. During the measure evaluation process, we carefully consider 
the potential burden to clinicians, health systems, and patients of any 
updates that are under consideration.
    The primary concerns of some NQF Behavioral Health and Substance 
Use Standing Committee members with the FAPH measure were exclusions 
for patients who died during the 30-day follow-up period or who were 
transferred. While we respect the NQF's concerns, we note that these 
same exclusions align with the exclusions in the Follow-Up After 
Hospitalization for Mental Illness (FUH, NQF #0576) measure which is 
already NQF endorsed, and which we adopted under the IPFQR Program in 
the FY 2014 IPPS/LTCH PPS final rule. This measure has a very similar 
denominator (78 FR 50893 through 50895). The clinical expert work group 
and technical expert panel convened by our contractor supported these 
exclusions as being appropriate for both measures.
    After having given due consideration to similar measures, FUH 
measure (NQF #0576) and Continuity of Care after Inpatient or 
Residential Treatment for SUD (NQF #3453), we believe that the FAPH 
measure is an improvement over the FUH measure which is currently in 
the IPFQR Program, because it includes patients with SUD or dementia, 
populations that also benefit from timely follow-up care (86 FR 19504 
through 19506).
    Comment: Some commenters recommended further research or testing. 
Some commenters recommended that CMS continue to consider evidence 
supporting the expanded patient cohort.
    Response: We thank commenters for these recommendations and will

[[Page 42645]]

continue to evaluate them as part of our measure monitoring and 
evaluation process. We believe that the evidence cited in our proposal, 
including the evidence supporting the APA grade of [I] applied to the 
2010 guidelines for the treatment of SUD patients that state ``It is 
important to intensify the monitoring for substance use during periods 
when the patient is at a high risk of relapsing, including during the 
early stages of treatment, times of transition to less intensive levels 
of care, and the first year after active treatment has ceased'' \150\ 
is sufficient evidence to support measuring follow up after 
hospitalization for SUD. We note that because discharge from an IPF is 
a time of transition to less intensive levels of care these guidelines 
apply to discharge from an IPF and support the expanded patient cohort.
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    \150\ American Psychiatric Association. Practice guideline for 
the treatment of patients with substance use disorders. 2010. http://psychiatryonline.org/pb/assets/raw/sitewide/practice_guidelines/guidelines/substanceuse.pdf.
---------------------------------------------------------------------------

    Comment: One commenter requested CMS specifically consider the 
impact of the physician self-referral law (commonly referred to as 
``the Stark Law'') on an IPF's ability to ensure necessary SUD follow-
up care. Some commenters recommended that CMS evaluate additional risk 
adjustment for social risk factors. One commenter further expressed 
that this measure may not be a successful strategy for reducing 
readmissions. Another commenter recommended that CMS investigate 
whether FAPH is an appropriate replacement for the Alcohol & Other Drug 
Use Disorder Treatment Provided or Offered at Discharge and Alcohol & 
Other Drug Use Disorder Treatment at Discharge (SUB-3/3a) measure.
    Response: Section 1877 of the Act, also known as the physician 
self-referral law: (1) Prohibits a physician from making referrals for 
certain designated health services payable by Medicare to an entity 
with which he or she (or an immediate family member) has a financial 
relationship, unless an exception applies; and (2) prohibits the entity 
from filing claims with Medicare (or billing another individual, 
entity, or third party payer) for those referred services. A financial 
relationship is an ownership or investment interest in the entity or a 
compensation arrangement with the entity.\151\ We believe that the 
comment regarding the physician self-referral law relates to 
compensation arrangements between IPFs (which qualify as hospitals, and 
``entities'', for purposes of the physician self-referral law) and 
physicians who provide post-discharge SUD follow-up care that may 
implicate the physician self-referral law. To the extent an IPF enters 
into a compensation arrangement with a physician who provides SUD 
follow-up care to patients discharged from the hospital, we note that 
there are exceptions to the physician self-referral law applicable to 
such compensation arrangements, including recently finalized exceptions 
for value-based arrangements.
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    \151\ https://www.cms.gov/medicare/fraud-and-abuse/physicianselfreferral.
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    We will consider this measure for potential risk adjustment or 
stratification as we seek to close the equity gap as described in 
section IV.D of this final rule. We note that a reduction in 
readmissions is this measure's objective, though improved follow-up 
adherence may serve to reduce readmissions because of improved 
continuity of care. Finally, we will evaluate whether the FAPH measure 
is an appropriate replacement for Alcohol & Other Drug Use Disorder 
Treatment Provided or Offered at Discharge and Alcohol & Other Drug Use 
Disorder Treatment at Discharge (SUB-3/3a).
    Comment: Some commenters requested clarification regarding visits 
that would be considered post-discharge follow-up. Some commenters 
requested clarification regarding whether telehealth visits, 
specifically audio-only telehealth visits, would be considered follow-
up for purposes of the measure. A few commenters requested 
clarification regarding whether visits implemented through 
collaborative agreements with mental health providers would be 
considered follow-ups. These commenters further observed that including 
these visits would incentivize community partnerships. One commenter 
requested clarification regarding whether a visit to any HCP (including 
physicians, clinics, etc.) would be considered follow-up for purposes 
of the measure. This commenter further requested clarification 
regarding whether specific diagnosis codes would be required to be 
present on the follow-up claim.
    Response: Regarding the request for clarification about the 
eligibility of telehealth visits for FAPH measure, both in-person and 
telehealth outpatient visits are acceptable, including audio-only 
visits. The FAPH numerator defines qualifying outpatient visits as 
outpatient visits, intensive outpatient encounters or partial 
hospitalizations that occur within 7 or 30 days of discharge and are 
defined by the Current Procedural Terminology (CPT), Healthcare Common 
Procedure Coding System (HCPCS), and Uniform Billing (UB) Revenue 
codes, with or without the GT telehealth modifier. The CPT codes 99441, 
99442, and 99443, which represent telephone E/M visits, are included in 
the list of codes to identify eligible outpatient visits. With respect 
to the request for clarification regarding collaborative agreements, 
the measure is agnostic to relationships between mental health 
providers, other providers, and health systems. The codes used to 
identify outpatient visits for the FAPH measure are not limited to 
mental health providers. The outpatient visit may be any outpatient 
visit, intensive outpatient encounter or partial hospitalization that 
occurs within 7 or 30 days of discharge as defined in section 
IV.E.3.b.(1). This visit must be paired with a qualifying ICD-10-CM 
diagnosis of mental illness or substance use disorder used to define 
the denominator.
    Comment: One commenter observed that historical trending would no 
longer be available due to the transition from FUH to FAPH.
    Response: We agree with the commenter that replacing FUH with FAPH 
would mean that historical trending would no longer be available. 
However, we believe that the benefits associated with the expanded 
patient population and the expanded provider types for follow-up 
appointments outweigh the loss of trend data.
    After consideration of the public comments, we are finalizing the 
FAPH measure as proposed for the FY 2024 payment determination and 
subsequent years.

F. Removal or Retention of IPFQR Program Measures

1. Background
    In the FY 2018 IPPS/LTCH PPS final rule (82 FR 38463 through 
38465), we adopted considerations for removing or retaining measures 
within the IPFQR Program and criteria for determining when a measure is 
``topped out.'' In the FY 2019 IPF PPS final rule (83 FR 38591 through 
38593), we adopted one additional measure removal factor. We did not 
propose any changes to these removal factors, topped-out criteria, or 
retention factors and refer readers to the FY 2018 IPPS/LTCH PPS final 
rule (82 FR 38463 through 38465) and the FY 2019 IPF PPS final rule (83 
FR 38591 through 38593) for more information. We will continue to 
retain measures from each previous year's IPFQR Program measure set for 
subsequent years' measure sets, except when we specifically propose to 
remove or replace a measure. We will continue to use the notice-and-
comment rulemaking

[[Page 42646]]

process to propose measures for removal or replacement, as we described 
upon adopting these factors in the FY 2018 IPPS/LTCH PPS final rule (82 
FR 38464 through 38465).
    In the FY 2022 IPF PPS proposed rule we described that in our 
continual evaluation of the IPFQR Program measure set under our 
Meaningful Measures Framework and according to our measure removal and 
retention factors, we identified four measures that we believed were 
appropriate to propose removing from the IPFQR Program for the FY 2024 
payment determination and subsequent years (86 FR 19507). Our 
discussion of these measures follows.
2. Measures Proposed for Removal in the FY 2022 IPF PPS Proposed Rule
a. Retention of the Alcohol Use Brief Intervention Provided or Offered 
and Alcohol Use Brief Intervention (SUB- 2/2a) Measure Beginning With 
FY 2024 Payment Determination
    We proposed to remove the Alcohol Use Brief Intervention Provided 
or Offered (SUB-2) and subset measure Alcohol Use Brief Intervention 
(SUB2a) collectively referred to as the SUB-2/2a measure from the IPFQR 
Program beginning with the FY 2024 payment determination under our 
measure removal Factor 8, ``The costs associated with a measure 
outweigh the benefit of its continued use in the program.'' We adopted 
the Alcohol Use Brief Intervention Provided or Offered and Alcohol Use 
Brief Intervention (SUB- 2/2a) measure in the FY 2016 IPF PPS final 
rule (80 FR 46699 through 46701) because we believe it is important to 
address the common comorbidity of alcohol use among IPF patients. This 
measure requires facilities to chart-abstract measure data on a sample 
of IPF patient records, in accordance with established sampling 
policies (80 FR 46717 through 46719).
    We have previously stated our intent to move away from chart-
abstracted measures to reduce information collection burden in this and 
other CMS quality programs (78 FR 50808; 79 FR 50242; 80 FR 49693). 
When we adopted the SUB-2/2a measure to the IPFQR Program, the benefits 
of this measure were high because IPF performance was not consistent. 
Therefore, the measure provided a means of distinguishing IPF 
performance and incentivized facilities to improve rates of treatment 
for this common comorbidity. Between the FY 2018 payment determination 
(the first year that SUB-2/2a was included in the IPFQR Program measure 
set) and the FY 2019 payment determination, we saw substantial 
performance improvement on the SUB-2 measure (which is the portion of 
the SUB-2/2a measure that assesses whether the IPF provided or offered 
a brief intervention for alcohol use). However, for the FY 2019 and FY 
2020 payment determinations, the rate of improvement has leveled off to 
consistently high performance, as indicated in Table 3. These data 
further show that at this time there is little room for improvement in 
the SUB 2 measure, and that the quality improvement benefits from the 
measure have greatly diminished.
    As stated in the proposed rule, we continue to believe that alcohol 
use is an important comorbidity to address in the IPF setting, and that 
brief interventions are a key component of addressing this comorbidity. 
However, based on these data, we believe that most IPFs routinely offer 
alcohol use brief interventions, and that IPFs will continue to offer 
these interventions to patients, regardless of whether the SUB-2/2a 
measure is in the IPFQR Program measure set, because it has become an 
embedded part of their clinical workflows.
[GRAPHIC] [TIFF OMITTED] TR04AU21.172

    In the proposed rule, we noted that while the measure does not meet 
our criteria for ``topped-out'' status because of the TCV higher than 
0.1, we believe that this measure no longer meaningfully supports the 
program objectives of informing beneficiary choice and driving 
improvement in IPF interventions for alcohol use because it is no 
longer showing significant improvement in IPF performance (that is, in 
providing or offering alcohol use brief interventions). Furthermore, as 
we stated in the FY 2019 IPF PPS final rule, costs are multi-faceted 
and include not only the burden associated with reporting, but also the 
costs associated with implementing and maintaining the program (83 FR 
38592). For example, it may be costly for health care providers to 
maintain general administrative knowledge to report this measure. 
Additionally, CMS must expend resources in maintaining information 
collection systems, analyzing reported data, and providing public 
reporting of the collected information.
    Here, IPF information collection burden and related costs 
associated with reporting the SUB 2/2a measure to CMS are high because 
it is a chart-abstracted measure. Furthermore, CMS incurs costs 
associated with the program oversight of the measure for public 
display. As a result, we believe that the costs and burdens associated 
with this chart-abstracted measure outweigh the benefit of its 
continued use in the program.
    Therefore, we proposed to remove the Alcohol Use Brief Intervention 
Provided or Offered and Alcohol Use Brief Intervention (SUB-2/2a) 
measure from the IPFQR Program beginning with the FY 2024 payment 
determination. We welcomed public comments on our proposal to remove 
the SUB-2/2a measure from the IPFQR Program.
    We received the following comments on our proposal.
    Comment: Many commenters supported our proposal to remove the 
Alcohol Use Brief Intervention Provided or Offered and Alcohol Use 
Brief Intervention (SUB-2/2a) measure. Some commenters agreed with our 
rationale that the costs of this measure outweigh the benefit of its 
continued use in the IPFQR Program. A few commenters recommended that 
CMS remove the measure immediately, rather than beginning with FY 2024 
payment determination as proposed, to further reduce burden. One 
commenter agreed

[[Page 42647]]

that providers will continue these interventions after the measure has 
been removed. Another commenter also supported removal because the 
measure is no longer NQF endorsed and was not specified for this 
setting.
    Response: We thank the commenters for their support. While we 
continue to believe that the performance on the SUB-2/2a measure in 
recent years indicates that IPFs routinely offer alcohol use brief 
interventions, we recognize that we will not be able to monitor whether 
IPFs continue these interventions if we remove this measure. We 
considered proposing to remove the measure sooner, but because data are 
currently being collected to report during CY 2022 to inform the FY 
2023 payment determination, we proposed removing the measure following 
that payment determination, that is, for the FY 2024 payment 
determination.
    The commenter is correct that the measure is no longer NQF endorsed 
and is not specified for the IPF setting. However, we continue to 
believe that this measure is appropriate for the IPF setting. We 
reiterate that we proposed to remove this measure because of the belief 
that the costs of the measure outweigh its continued benefits in the 
IPFQR Program, not because it is no longer NQF endorsed nor because it 
was not specified for this setting.
    Comment: One commenter supported removal of the SUB-2/2a measure, 
but recommended development of more meaningful measures than SUB-2/2a 
and the Alcohol & Other Drug Use Disorder Treatment Provided or Offered 
at Discharge and Alcohol & Other Drug Use Treatment at Discharge (SUB-
3/3a) measure to address screening and intervention for substance use. 
Another commenter recommended that CMS consult with consumers to 
ascertain the benefits of measures in the IPFQR Program prior to 
proposing to remove any such measures, this commenter specifically 
recommended that CMS not finalize removal of the SUB-2/2a measure until 
fully considering input from consumers.
    Response: We appreciate this commenter's input and are continually 
seeking to improve our measure set by developing more meaningful and 
less burdensome measures. As we evaluate areas appropriate for measure 
development, we will consider additional measures or measure concepts 
that more meaningfully address alcohol use disorder treatment for the 
IPF patient population.
    In response to the request that we consult with consumers to 
ascertain the benefits of the measure, we note that we evaluate input 
from all stakeholders, including consumers, patients, caregivers, and 
patient advocacy groups that we receive in response to our proposals to 
adopt or remove measures from the IPFQR Program. As part of this 
process, we have reviewed input from consumers regarding the benefits 
of the measure and considered this input in our analysis.
    Comment: Some commenters expressed concern about removing the 
measure. A few of these commenters stated that not all facilities 
perform well on the measure and, therefore, there is still room for 
improvement. One commenter stated that the COVID-19 pandemic has led to 
increased alcohol use and expressed the belief that removing the 
measure now is poorly timed.
    Response: We note that we proposed to remove the measure because of 
the belief that the benefits of retaining it have lessened to the point 
that its costs outweigh those benefits, not because the measure is 
topped out. We agree with commenters that not all facilities perform 
uniformly well on the Alcohol Use Disorder Brief Intervention Provided 
or Offered and Alcohol Use Disorder Brief Intervention Provided (SUB-2/
2a) measure.
    We also agree that alcohol use has increased during the COVID-19 
pandemic.152 153 154 In our literature review regarding this 
comment, we also identified evidence that individuals with mental 
health and substance use conditions may be at an increased risk of 
COVID-19 complications and appropriate substance use disorder treatment 
may help mitigate these complications.155 156 To ensure that 
providers would continue to address alcohol use disorders among this 
patient population, we have maintained the Alcohol & Other Drug Use 
Disorder Treatment Provided or Offered at Discharge and Alcohol & Other 
Drug Use Treatment at Discharge (SUB-3/3a) measure. However, we note 
that a prominent model to ensure those with alcohol use disorder are 
identified and referred to treatment include both brief interventions 
and referrals.\157\ Given the increased need for alcohol use brief 
interventions due to the pandemic, the current performance levels \158\ 
(for FY 2018 payment determination, the mean performance nationally was 
approximately 80 percent of patients who screened positive for alcohol 
use disorder were offered or provided a brief intervention), and the 
importance of providing alcohol use brief interventions to improve the 
efficacy of alcohol use treatment at discharge, we believe that the 
benefits of retaining the Alcohol Use Brief Intervention Provided or 
Offered and Alcohol Use Brief Intervention (SUB-2/2a) measure are 
greater than we initially estimated in our proposal to remove this 
measure and that the measure should not be removed from the program at 
this time.
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    \152\ Pollard et. al., Changes in Adult Alcohol Use and 
Consequences During the COVID-19 Pandemic in the US, JAMA Network 
Open, 2020;3(9):e2022942. doi:10.1001/jamanetworkopen.2020.22942.
    \153\ Alcohol Consumption Rises Sharply During Pandemic 
Shutdown; Heavy Drinking by Women Rises 41%, RAND, https://www.rand.org/news/press/2020/09/29.html.
    \154\ Nemani et al., Association of Psychiatric Disorders With 
Mortality Among Patients With COVID-19, JAMA Psychiatry. 
2021;78(4):380-386. doi:10.1001/jamapsychiatry.2020.4442; COVID-19 
and people at increased risk, CDC, https://www.cdc.gov/drugoverdose/resources/covid-drugs-QA.html; U. Saengow et. al.
    \155\ Wang et. al., COVID-19 risk and outcomes in patients with 
substance use disorders: Analyses from electronic health records in 
the United States, Molecular Psychiatry volume 26, pages 30-39 
(2021), https://www.nature.com/articles/s41380-020-00880-7.
    \156\ Vai et. al., Mental disorders and risk of COVID-19-related 
mortality, hospitalisation, and intensive care unit admission: A 
systematic review and meta-analysis, Lancet Psychiatry, https://www.thelancet.com/pdfs/journals/lanpsy/PIIS2215-0366(21)00232-7.pdf.
    \157\ https://www.samhsa.gov/sbirt; https://www.samhsa.gov/sbirt/coding-reimbursement.
    \158\ For FY 2018 payment determination, the mean performance 
nationally was approximately 80 percent of patients who screened 
positive for alcohol use disorder were offered or provided a brief 
intervention.
---------------------------------------------------------------------------

    Comment: One commenter observed that this measure may be useful for 
future stratification based on race and ethnicity.
    Response: We agree with the commenter that this measure may be 
useful for future stratification based on race and ethnicity. While we 
do not believe it would be appropriate to retain this measure 
specifically for the purpose of potential future stratification, we 
agree that this potential is another benefit of the measure that we had 
not considered in our previous analysis of the benefits versus the 
costs of retaining the measure.
    Comment: One commenter observed that there are benefits to 
retaining this measure because IPFs and health systems use performance 
data on this measure as part of quality improvement initiatives to 
reduce alcohol use and that removal may affect these programs.
    Response: We thank the commenter for this input. We note that IPFs 
are responsible for abstracting the data for this measure, so we 
believe that IPFs who use these data for their own quality improvement 
initiatives have access to these data regardless of whether the measure 
is in the IPFQR Program.

[[Page 42648]]

However, we recognize that such IPFs and health systems would not have 
access to publicly reported data regarding other IPFs and that these 
data may be useful for baselining. Therefore, we agree that such IPF 
level and systemic programs to reduce alcohol use is a benefit to 
retaining the measure that we had not evaluated in our proposal to 
remove this measure.
    Comment: One commenter observed that this measure is less 
burdensome than the newly proposed COVID-19 vaccination measure and 
therefore the commenter believes that removing this measure because the 
costs, especially the information collection burden, outweigh benefits 
is inconsistent.
    Response: We evaluate measures on a case-by-case basis looking at 
the overall benefits of the measure versus the overall costs of the 
measure. Therefore, measures are not evaluated based on whether they 
are more or less burdensome than other measures. However, we now 
believe that the benefits of retaining this measure are greater than we 
had considered in our proposal to remove the measure from the IPFQR 
Program measure set.
    After consideration of the public comments, we now believe that the 
benefits of retaining this measure, which include the potential for 
IPFs to continue improving performance on this measure, the importance 
of substance use interventions due to increased substance use during 
the COVID-19 pandemic, and this measure's potential influence on other 
quality improvement activities related to substance use are greater 
than we had considered in our proposal to remove the measure from the 
IPFQR Program measure set. Accordingly, we are not finalizing our 
proposal to remove the Alcohol Use Brief Intervention Provided or 
Offered and Alcohol Use Brief Intervention (SUB-2/2a) measure beginning 
with the FY 2024 payment determination. That is, we are retaining the 
Alcohol Use Disorder Brief Intervention Provided or Offered and Alcohol 
Use Disorder Brief Intervention Provided (SUB-2/2a) measure in the 
IPFQR Program measure set.
    After consideration of the public comments, we are not finalizing 
our proposal to remove the Alcohol Use Brief Intervention Provided or 
Offered and Alcohol Use Brief Intervention (SUB-2/2a) measure beginning 
with the FY 2024 payment determination. That is, we are retaining the 
Alcohol Use Disorder Brief Intervention Provided or Offered and Alcohol 
Use Disorder Brief Intervention Provided (SUB-2/2a) measure in the 
IPFQR Program measure set.
b. Retention of the Tobacco Use Treatment Provided or Offered and 
Tobacco Treatment (TOB-2/2a) Measure Beginning With FY 2024 Payment 
Determination \159\
---------------------------------------------------------------------------

    \159\ We note that the proposed rule incorrectly referred to 
this measure as the Tobacco Use Brief Intervention Provided or 
Offered and Tobacco Use Brief Intervention (TOB-2/2a) measure, we 
have corrected it here and throughout this final rule.
---------------------------------------------------------------------------

    We proposed to remove the Tobacco Use Treatment Provided or Offered 
(TOB-2) and Treatment (TOB-2a), collectively referred to as the TOB-2/
2a measure from the IPFQR Program beginning with the FY 2024 payment 
determination under our measure removal Factor 8, ``The costs 
associated with a measure outweigh the benefit of its continued use in 
the program.'' We adopted the Tobacco Use Treatment Provided or Offered 
and Tobacco Use Treatment (TOB-2/2a) measure in the FY 2015 IPF PPS 
final rule (79 FR 45971 through 45972) because we believe it is 
important to address the common comorbidity of tobacco use among IPF 
patients. Like SUB-2/2a described in the previous subsection, this 
measure requires facilities to chart-abstract measure data on a sample 
of IPF patient records, in accordance with established sampling 
policies (80 FR 46717 through 46719).
    When we introduced the TOB-2/2a measure to the IPFQR Program, the 
benefits of this measure were high, because IPF performance was not 
consistent and therefore the measure provided a means of distinguishing 
IPF performance and incentivized facilities to improve rates of 
treatment for this common comorbidity. Between the FY 2017 payment 
determination (the first year that TOB-2/2a was included in the IPFQR 
Program's measure set) and the FY 2019 payment determination we saw 
substantial performance improvement on TOB-2. However, between the FY 
2019 and FY 2020 payment determinations, that improvement has leveled 
off to consistently high performance, as indicated in Table 4. These 
data further show that currently there is little room for improvement 
in the TOB-2 measure, and that the quality improvement benefits from 
the measure have greatly diminished. We continue to believe that 
tobacco use is an important comorbidity to address in the IPF setting, 
and that brief interventions are a key component of addressing this 
comorbidity. However, based on these data, we stated in the proposed 
rule that we believe that most IPFs routinely offer tobacco use brief 
interventions, and that IPFs will continue to offer these interventions 
to patients, regardless of whether the TOB-2/2a measure is in the IPFQR 
Program measure set, because it has become an embedded part of their 
clinical workflows.
[GRAPHIC] [TIFF OMITTED] TR04AU21.173

    While the measure does not meet our criteria for ``topped-out'' 
status because of the TCV higher than 0.1, we believe that this measure 
no longer meaningfully supports the program objectives of informing 
beneficiary choice and driving improvement in IPF interventions for 
tobacco use because it is no longer showing significant improvement in 
IPF performance (that is, in providing or offering tobacco use brief 
interventions). Furthermore, as we

[[Page 42649]]

stated in the FY 2019 IPF PPS final rule, costs are multi-faceted and 
include not only the burden associated with reporting, but also the 
costs associated with implementing and maintaining the program (83 FR 
38592). For example, it may be costly for health care providers to 
maintain general administrative knowledge to report this measure. 
Additionally, CMS must expend resources in maintaining information 
collection systems, analyzing reported data, and providing public 
reporting of the collected information. Here, IPF information 
collection burden and related costs associated with reporting this 
measure to CMS are high because the measure is a chart-abstracted 
measure. Furthermore, CMS incurs costs associated with the program 
oversight of the measure for public display. As a result, we believe 
that the costs and burdens associated with this chart-abstracted 
measure outweigh the benefit of its continued use in the program.
    Therefore, we proposed to remove the Tobacco Use Treatment Provided 
or Offered and Tobacco Use Treatment (TOB-2/2a) measure from the IPFQR 
Program beginning with the FY 2024 payment determination. We welcomed 
public comments on our proposal to remove the TOB-2/2a measure from the 
IPFQR Program.
    We received the following comments on our proposal.
    Comment: Many commenters supported our proposal to remove the 
Tobacco Use Treatment Provided or Offered and Tobacco Use Treatment 
(TOB-2/2a) measure. Some of these commenters agreed with our rationale 
that the costs of this measure outweigh the benefits of its continued 
use in the IPFQR Program. Several commenters recommended removing the 
measure immediately, rather than beginning with FY 2024 payment 
determination as proposed, to further reduce burden. One commenter 
agreed that providers will continue offering this intervention even if 
it is not being measured. Another commenter further expressed that 
removal is appropriate because the measure is no longer NQF endorsed 
and is not specified for this setting.
    Response: We thank the commenters for their support. We considered 
proposing to remove the measure sooner, but because data are currently 
being collected to report during CY 2022 to inform the FY 2023 payment 
determination, we proposed to remove the measure following that payment 
determination, that is, for the FY 2024 payment determination. While we 
continue to believe that the performance on the TOB-2/2a measure in 
recent years indicates that IPFs routinely offer tobacco use cessation 
interventions during the inpatient stay, we recognize that we will not 
be able to monitor whether IPFs continue these interventions if we 
remove this measure. The commenter is correct that the measure is no 
longer NQF endorsed and is not specified for the IPF setting. We 
reiterate that we proposed to remove this measure because of the belief 
that the costs of the measure outweigh its continued benefits in the 
IPFQR Program not because it is no longer NQF endorsed nor because it 
was not specified for this setting and we continue to believe that this 
measure is appropriate for the IPF setting.
    Comment: One commenter expressed the belief that progress in 
electronic reporting systems leads to lower burden for reporting this 
measure. This commenter expressed the belief that this reduced burden 
should factor into the consideration of whether costs outweigh benefits 
and recommended that CMS retain this measure.
    Response: We thank the commenter for this feedback. However, we 
note that because this is a chart-abstracted measure, we do not believe 
access to electronic reporting systems will significantly impact the 
burden of collecting and reporting this measure for most IPFs.
    Comment: One commenter supported removal of the Tobacco Use 
Treatment Provided or Offered and Tobacco Use Treatment Provided (TOB-
2/2a) measure, but recommended development of more meaningful measures 
than TOB-2/2a and Tobacco Use Treatment Provided or Offered at 
Discharge and Tobacco Use Treatment Provided at Discharge (TOB-3/3a) to 
address screening and intervention for tobacco use. One commenter 
recommended that CMS seek consumer input on the benefit of measures 
before proposing to remove them.
    Response: We appreciate this commenter's input and are continually 
seeking to improve our measure set by developing more meaningful and 
less burdensome measures. As we evaluate areas appropriate for measure 
development, we will consider additional measures or measure concepts 
that more meaningfully address tobacco use treatment for the IPF 
patient population.
    In response to the request that we consult with consumers to 
ascertain the benefits of the measure, we note that we evaluate input 
from all stakeholders, including consumers, patients, caregivers, and 
patient advocacy groups that we receive in response to our proposals to 
adopt or remove measures from the IPFQR Program. As part of this 
process, we have reviewed input from consumers regarding the benefits 
of the measure and considered this input in our analysis.
    Comment: Some commenters expressed concern about removing the TOB-
2/2a measure from the IPFQR Program measure set. Some of these 
commenters expressed that there continues to be significant room for 
improvement in providing interventions. One commenter specifically 
observed that the measure is not topped out. A few commenters observed 
that the proposed removal is poorly timed due to the increase in 
tobacco use during the COVID-19 pandemic. Another commenter cited 
evidence supporting the benefit of brief interventions as part of a 
comprehensive program to address topped out.
    We agree with commenters that not all facilities perform uniformly 
well on the Tobacco Use Treatment Provided or Offered and Tobacco Use 
Treatment Provided (TOB-2/2a) measure. We also agree with the 
commenter's observation that tobacco use has increased during the 
COVID-19 pandemic.\160\ In our literature review, we also identified 
evidence that individuals who use tobacco may be at an increased risk 
of COVID-19 complications and tobacco use treatment may help mitigate 
these complications.\161\ To ensure that providers would continue to 
address tobacco use among this patient population, we maintained the 
Tobacco Use Treatment Provided or Offered at Discharge and Tobacco Use 
Treatment Provided at Discharge (TOB-3/3a). However, we agree with the 
commenter who expressed that these interventions are most effective as 
part of a comprehensive tobacco treatment program. Given the increased 
need for tobacco use interventions due to the COVID-19 pandemic, that 
this measure is not topped out and there is room for improvement across 
facilities,\162\ and the importance of providing tobacco use treatment 
during the inpatient stay to improve the efficacy of tobacco use 
treatment at discharge, we believe that the benefits of retaining the 
Tobacco Use Treatment Provided or Offered and Tobacco Use Treatment 
Provided (TOB-2/2a) measure are greater than we

[[Page 42650]]

estimated in our proposal to remove this measure and that the measure 
should not be removed from the program at this time.
---------------------------------------------------------------------------

    \160\ Giovenco et. al., Multi-level drivers of tobacco use and 
purchasing behaviors during COVID-19 ``lockdown'': A qualitative 
study in the United States, International Journal of Drug Policy, 
Volume 94, August 2021, 103175.
    \161\ https://www.who.int/news/item/11-05-2020-who-statement-tobacco-use-and-covid-19.
    \162\ For the FY 2018 payment determination, the mean 
performance nationally was approximately 79 percent of patients who 
screened positive for tobacco use were provided or offered treatment 
while inpatients.
---------------------------------------------------------------------------

    Comment: Many commenters opposed removal of the measure because of 
the clinical importance of treating tobacco use in the IPF patient 
population. Many of these commenters observed that tobacco use is 
undertreated. Some of these commenters referenced CDC data stating that 
only 48.9 percent of mental health treatment facilities reported 
screening patients for tobacco use. Some commenters pointed to this 
statistic and expressed concern that without measures related to 
tobacco use treatment this care may no longer be provided in IPFs. 
These commenters observed that tobacco use is nearly three times more 
prevalent in people with serious psychological distress than in those 
without. Some of these commenters observed that this discrepancy 
contributes to a shorter life expectancy for patients with mental 
illness who smoke. These commenters expressed the belief that the 
potential to increase patient life expectancy and quality of life 
outweighs the costs of reporting the measure. A few of these commenters 
observed there are high costs associated with treating tobacco 
associated illness and that these costs could be significantly reduced 
by increased screening, intervention, and treatment.
    Some commenters stated that the 2020 Surgeon General's report 
specifically stated that tobacco dependence treatment is applicable to 
the behavioral health setting. One commenter observed that brief 
interventions are part of the ``Treating Tobacco Use and Dependence 
Clinical Practice Guidelines.'' One commenter stated that behavioral 
health patients often have limited interaction with the healthcare 
system and therefore the commenter believes that it is important to use 
these interactions to drive health behaviors.
    Response: We agree with commenters that providing or offering 
tobacco use brief intervention within the IPF setting is a valuable 
intervention because of the prevalence of this comorbidity within this 
patient population and because of the ability of this intervention to 
facilitate quitting tobacco use. We further agree that brief 
interventions are part of clinical guidelines and are appropriate to 
provide to patients receiving care for behavioral health conditions. We 
note that the tobacco screening statistics cited by commenters refer to 
all behavioral health and substance use treatment facilities, whereas 
the IPFQR Program only requires reporting on treatment provided by IPFs 
that receive Medicare payment under the IPF PPS, therefore the 
statistics cited by commenters do not directly reflect care provided by 
IPFs.\163\ However, we acknowledge that the low performance on tobacco 
use screening in the behavioral health setting does indicate that 
tobacco screening and treatment performance may lapse in the IPF 
setting without measures to address this topic, and that the inpatient 
setting may be a uniquely opportune setting for providing tobacco 
cessation interventions to some patients due to limited access to or 
utilization of the healthcare system. We also agree with commenters 
that providing tobacco use brief interventions has the potential to 
increase patient life expectancy and quality of life while reducing 
healthcare costs associated with treating tobacco associated illness. 
Given the importance of tobacco use interventions in extending life 
expectancy and improving quality of life, the concern regarding 
potential reduction in performance if measures are removed (as 
demonstrated by CDC data that show that the provision of brief 
intervention for tobacco use cessation is not the current standard of 
care across behavioral health settings as only 48.9 percent of mental 
health treatment facilities report screening patients for tobacco use), 
and the room for improvement in the current performance levels, we 
believe that the benefits of retaining the Tobacco Use Treatment 
Provided or Offered and Tobacco Use Treatment Provided (TOB-2/2a) 
measure are greater than we estimated in our proposal to remove this 
measure and that the measure should not be removed from the program at 
this time.
---------------------------------------------------------------------------

    \163\ https://www.cdc.gov/mmwr/volumes/67/wr/mm6718a3.htm.
---------------------------------------------------------------------------

    Comment: One commenter observed that there are health equity 
concerns regarding tobacco use and recommended that CMS retain this 
measure for future stratification based on race and ethnicity.
    Response: We agree with the commenter that this measure may be 
useful for future stratification based on race and ethnicity. While we 
do not believe it would be appropriate to retain this measure 
specifically for the purpose of potential future stratification, we 
agree that this potential is another benefit of the measure that we had 
not considered in our previous analysis of the benefits versus the 
costs of retaining the measure.
    Comment: One commenter observed that there are benefits to 
retaining this measure because IPFs and health systems use performance 
data on this measure as part of quality improvement initiatives to 
reduce tobacco use and that measure removal may affect those programs.
    Response: We thank the commenter for this feedback. We note that 
IPFs are responsible for abstracting the data for this measure, so we 
believe that IPFs who use these data for their own quality improvement 
initiatives have access to these data regardless of whether the measure 
is in the IPFQR Program. However, we recognize that such IPFs and 
health systems would not have access to publicly reported data 
regarding other IPFs and that these data may be useful for baselining. 
Therefore, we agree that such IPF level and systemic programs to reduce 
tobacco use is a benefit to retaining the measure that we had not 
evaluated in our proposal to remove this measure.
    Comment: Many commenters expressed the belief that without this 
measure IPFs would not continue to provide tobacco use brief 
interventions. Some commenters expressed concern that removing this 
measure would reduce providers' incentive to offer brief interventions. 
These commenters further observed that it would be difficult to 
determine whether IPFs continue to offer this intervention as the 
ability to track that depends on the continued collection of this 
measure. Some commenters further expressed concern that CMS policies 
drive the behavior of other payers and without this measure the 
healthcare system may lose focus on tobacco treatment for patients with 
behavioral health disorders.
    Response: We understand commenters' concern regarding the potential 
for IPFs and other payers to no longer focus on tobacco treatment 
without the Tobacco Use Treatment Provided or Offered and Tobacco Use 
Treatment (TOB-2/2a) quality measure in the IPFQR Program and we agree 
that ensuring continuing focus on tobacco use treatment in this setting 
is a benefit of retaining this measure in the IPFQR program. 
Additionally, we agree that tracking whether IPFs continue to offer 
this intervention is a benefit of retaining the measure in the IPFQR 
program measure set.
    Comment: One commenter observed that the Tobacco Use Treatment 
Provided or Offered and Tobacco Use Treatment (TOB-2/2a) measure is not 
as burdensome as the newly proposed COVID-19 vaccination measure and 
therefore the commenter believes that removing this measure because the 
costs, especially the information

[[Page 42651]]

collection burden, outweigh benefits is inconsistent.
    Response: We evaluate measures on a case-by-case basis looking at 
the overall benefits of the measure versus the overall costs of the 
measure. Therefore, measures are not evaluated based on whether they 
are more or less burdensome than other measures. However, we now 
believe that the benefits of retaining this measure are greater than we 
had considered in our proposal to remove the measure from the IPFQR 
Program measure set.
    After consideration of the public comments, we now believe that the 
benefits of retaining this measure, which include the potential for 
IPFs to continue improving performance on this measure, the importance 
of tobacco use interventions due to increased tobacco use during the 
COVID-19 pandemic, and this measure's potential influence on other 
quality improvement activities related to tobacco use, are greater than 
we had considered in our proposal to remove the measure from the IPFQR 
Program measure set. Accordingly, we are not finalizing our proposal to 
remove the Tobacco Use Treatment Provided or Offered and Tobacco Use 
Treatment (TOB-2/2a) measure beginning with the FY 2024 payment 
determination. That is, we are retaining the Tobacco Use Treatment 
Provided or Offered and Tobacco Use Treatment (TOB-2/2a) measure in the 
IPFQR Program measure set.
c. Removal of the Timely Transmission of Transition Record (Discharges 
From an Inpatient Facility to Home/Self Care or Any Other Site of Care) 
Measure Beginning With FY 2024 Payment Determination
    We proposed to remove the Timely Transmission of Transition Record 
(Discharges from an Inpatient Facility to Home/Self Care or Any Other 
Site of Care) measure from the IPFQR Program beginning with the FY 2024 
payment determination under our measure removal Factor 8, ``The costs 
associated with a measure outweigh the benefit of its continued use in 
the program.''
    We adopted the Timely Transmission of Transition Record (Discharges 
from an Inpatient Facility to Home/Self Care or Any Other Site of Care) 
measure in the FY 2016 IPF PPS final rule (80 FR 46706 through 46709) 
because more timely communication of vital information regarding the 
inpatient hospitalization results in better care, reduction of systemic 
medical errors, and improved patient outcomes. The Timely Transmission 
of Transition Record (Discharges from an Inpatient Facility to Home/
Self Care or Any Other Site of Care) measure builds on the Transition 
Record with Specified Elements Received by Discharged Patients 
(Discharges from an Inpatient Facility to Home/Self Care or Any Other 
Site of Care) measure, which requires facilities to provide a discharge 
record with 11 specified elements to patients at discharge.
    We continue to believe that the 11 elements required by the 
Transition Record with Specified Elements measure provide meaningful 
information about the quality of care provided by IPFs, and we 
therefore did not propose to remove that measure from the IPFQR 
Program. However, we believe that the benefits of requiring facilities 
to transmit the discharge record with 11 specified elements to the next 
level care provided within 24 hours, as required by the Timely 
Transmission of Transition Record (Discharges from an Inpatient 
Facility to Home/Self Care or Any Other Site of Care) measure, have 
been reduced. Reporting this measure requires facilities to chart-
abstract measure data on a sample of IPF patient records, in accordance 
with established sampling policies (80 FR 46717 through 46719). On May 
1, 2020, we updated the Conditions of Participation (CoPs) for IPFs 
participating in the Medicare program in the Medicare and Medicaid 
Programs; Patient Protection and Affordable Care Act; Interoperability 
and Patient Access for Medicare Advantage Organization and Medicaid 
Managed Care Plans, State Medicaid Agencies, CHIP Agencies and CHIP 
Managed Care Entities, Issuers of Qualified Health Plans on the 
Federally Facilitated Exchanges, and Health Care Providers final rule 
(85 FR 25588).
    In the May 1, 2020 update to the CoPs, we adopted a requirement for 
psychiatric hospitals that possess EHR or other administrative systems 
with the technical capacity to generate information for electronic 
patient event notifications to send electronic patient event 
notifications of a patient's admission, discharge, transfer to another 
health care facility or to another community provider, or combination 
of patient events at the time of a patient's discharge or transfer. 
Because these updated CoP requirements overlap with, but are not the 
same as, the requirements for the Timely Transmission of Transition 
Record (Discharges from an Inpatient Facility to Home/Self Care or Any 
Other Site of Care) measure (which requires transmission of a discharge 
record with 11 specified elements to the next level care provider 
within 24 hours of the patient's discharge rather that requiring 
notification regarding the patient's inpatient stay to be transmitted 
at discharge), we believe that the adoption of these updated CoPs 
increases the costs of the Timely Transmission of Transition Record 
(Discharges from an Inpatient Facility to Home/Self Care or Any Other 
Site of Care) measure while decreasing its benefit. Specifically, we 
believe that the costs of this measure are increased because facilities 
to which the new CoPs apply (that is, facilities that possess EHR or 
other administrative systems with the technical capacity to generate 
information for electronic patient event notifications as defined in 
the CoP) could bear increased cost if they separately implement the 
patient event notifications meeting both the criteria for the updated 
CoPs and the capacity to share a transition record that meets the 
requirements of our measure. We noted that the updated CoPs do not 
include the level of detail regarding data to be transferred at 
discharge that our Timely Transmission of Transition Record (Discharges 
from an Inpatient Facility to Home/Self Care or Any Other Site of Care) 
measure requires. While the set of information in the CoP notification 
policy is a minimal set of information, we believe that it would 
continue to be appropriate for providers to transmit the transition 
record that they will continue to be providing to patients under our 
Transition Record Received by Discharged Patients (Discharges from an 
Inpatient Facility to Home/Self Care or Any Other Site of Care) 
measure, we further note that the CoPs referenced in the proposed rule 
are not an exhaustive list of data transfer requirements.
    We believe the different requirements regarding both timeliness of 
notification and contents of notification could lead some providers to 
send two separate discharge notifications to meet the separate 
requirements. Further, we believe that the benefits of the measure are 
reduced because all facilities to which the new CoPs apply will be 
sending patient discharge information to the next level of care 
provider as required by the CoPs. Therefore, the benefits of this 
measure are reduced because it is less likely to ensure that these 
facilities provide patient discharge information to the next level care 
provider, and it is less likely to provide information to help 
consumers differentiate quality between facilities. While these updated 
CoPs do not directly address transmission of patient event 
notifications for facilities that do not possess EHR systems with the 
capacity to generate information for electronic patient event 
notifications,

[[Page 42652]]

such facilities should continue to transmit data using their existing 
infrastructure and timelines.
    Because we believe that the costs are now increased and the 
benefits are now reduced, we believe that the costs and burdens 
associated with this chart-abstracted measure outweigh the benefit of 
its continued use in the IPFQR Program.
    Therefore, we proposed to remove the Timely Transmission of 
Transition Record (Discharges from an Inpatient Facility to Home/Self 
Care or Any Other Site of Care) measure from the IPFQR Program 
beginning with the FY 2024 payment determination. We welcomed public 
comments on our proposal to remove the Timely Transmission of 
Transition Record (Discharges from an Inpatient Facility to Home/Self 
Care or Any Other Site of Care) measure from the IPFQR Program.
    We received the following comments on our proposal.
    Comment: Many commenters supported the removal of the Timely 
Transmission of Transition Record (Discharges from an Inpatient 
Facility to Home/Self Care or Any Other Site of Care) measure. One 
commenter recommended immediate removal to further reduce burden. 
Another commenter expressed that this measure was not developed for 
IPFs and has been difficult to report because the specifications are 
not appropriate for the setting. Another commenter further noted that 
the measure is no longer NQF endorsed.
    Response: We thank the commenters for their support. We considered 
removing the measure sooner, but because data are currently being 
collected to report during CY 2022 to inform the FY 2023 payment 
determination, we decided to propose removing the measure following 
that payment determination, therefore we proposed removal for the FY 
2024 payment determination. The commenter is correct that the measure 
is no longer NQF endorsed and is not specified for the IPF setting; 
however we continue to believe that this measure is appropriate for the 
setting. We reiterate that removal of the measure is because we believe 
that the costs of the measure outweigh its continued benefits in the 
IPFQR Program.
    Comment: Some commenters observed that the updated CoPs will not 
apply to many IPFs, especially freestanding IPFs that are not part of 
larger healthcare facilities, because IPFs were excluded from 
Meaningful Use incentives and therefore often do not have electronic 
data systems capable of meeting the standards in the updated CoPs.
    Response: We acknowledge that there are a large number of IPFs that 
do not possess EHR systems with the technical capacity to generate 
information for electronic patient event notifications of a patient's 
admission, discharge, or transfer to another health care facility or to 
another community provider, or combination of patient events at the 
time of a patient's discharge or transfer. However, for those IPFs that 
can meet these requirements, we believe that retaining the Timely 
Transmission of Transition Record (Discharges from an Inpatient 
Facility to Home/Self Care or Any Other Site of Care) measure could be 
burdensome depending on how facilities implement new requirements. 
Therefore, while for some IPFs the benefits may outweigh the costs, 
overall, for the IPFQR Program we believe the costs now outweigh the 
benefits. We reiterate that for IPFs that do not possess EHR systems 
with the capacity to generate information for patient event 
notifications as defined in the CoP regulations set forth at 42 CFR 
482.24(d), such facilities should continue to transmit data using their 
existing infrastructure and timelines.
    Comment: A few commenters recommended that CMS retain the Timely 
Transmission of Transition Record (Discharges from an Inpatient 
Facility to Home/Self Care or Any Other Site of Care) measure. Some of 
these commenters believe that the measure's benefits are more 
significant than the burden. One commenter recommended that CMS seek 
consumer input on benefits prior to proposing measures for removal.
    Response: We reiterate that we do not believe that the benefits of 
transmitting the transition record within 24 hours of discharge are 
reduced, or are lower than the costs of reporting; we believe that 
given the updates to the CoPs which overlap with this measure the 
benefits of retaining the Timely Transmission of Transition Record 
(Discharges from an Inpatient Facility to Home/Self Care or Any Other 
Site of Care) measure are no longer sufficient to justify retention. We 
used the notice and comment rulemaking process to solicit input on 
measure benefits from all stakeholders, including consumers.
    After consideration of the public comments, we are finalizing our 
proposal to remove the Timely Transmission of Transition Record 
(Discharges from an Inpatient Facility to Home/Self Care or Any Other 
Site of Care) measure beginning with the FY 2024 payment determination.
d. Removal of the Follow-Up After Hospitalization for Mental Illness 
(FUH, NQF #0576) Beginning With FY 2024 Payment Determination
    In the FY 2022 IPF PPS proposed rule we stated that if we finalize 
adoption of the Follow-Up After Psychiatric Hospitalization measure 
described in section IV.E.3, we believed that our current measure 
removal Factor 3 would apply to the existing Follow-Up After 
Hospitalization for Mental Illness (FUH, NQF #0576) measure (86 FR 
19510). Measure removal Factor 3 applies when a ``measure can be 
replaced by a more broadly applicable measure (across settings or 
populations) or a measure that is more proximal in time to desired 
patient outcomes for the particular topics.'' We adopted removal factor 
3 in the FY 2017 IPPS/LTCH PPS final rule (82 FR 38463 through 38465). 
The FAPH measure expands the patient population from patients with 
mental illness to also include patients with primary SUD diagnoses 
while addressing the same important aspect of care transitions. Because 
this FAPH measure uses the same methodology to address the same element 
of care for a broader patient population than the FUH measure, we 
believe that it is more broadly applicable across populations.
    Therefore, we proposed to remove the FUH measure under measure 
removal Factor 3 only if we finalized our proposal to adopt of the FAPH 
measure. We noted that if we did not adopt the FAPH measure, we would 
retain the FUH measure because we believe this measure addresses an 
important clinical topic. We welcomed public comments on our proposal 
to remove FUH if we were to adopt FAPH.
    We received the following comments on our proposal.
    Comment: Many commenters supported removal of this measure. Some 
commenters specifically noted that FAPH is more broadly applicable and 
therefore preferable.
    Response: We thank these commenters for their support.
    Comment: One commenter does not support either the FUH measure or 
the FAPH measure due to the belief that measures of follow-up after 
hospitalization are not appropriate for the IPFQR Program and 
recommended removing the FUH measure but not adopting the FAPH measure.
    Response: For the reasons set forth in the FY 2014 IPPS/LTCH PPS 
final rule (78 FR 50894 through 50895) and the FY 2022 IPF PPS proposed 
rule in our proposal to adopt the FAPH measure (86 FR 19504 through 
19507), we believe that a measure of follow-after

[[Page 42653]]

hospitalization is an important concept for the inpatient psychiatric 
setting. Therefore, we do not believe it would be appropriate to remove 
the FUH measure without adopting the FAPH measure.
    Comment: One commenter observed that the FUH measure is an NQF-
endorsed measure, while the NQF declined to endorse the FAPH measure. 
This commenter recommended retaining the FUH measure because it is 
endorsed.
    Response: The commenter is correct that the FUH measure is NQF 
endorsed and that the NQF declined to endorse the FAPH measure. 
However, as discussed in the FY 2022 IPF PPS proposed rule, the FUH 
measure does not apply to as broad a patient population, nor does it 
allow for follow-up care to be provided by as many provider types (86 
FR 19507). Further, for the reasons we discussed in the FY 2022 IPF PPS 
proposed rule, we believe the exception under section 1886(s)(4)(D)(ii) 
of the Act applies (86 FR 19507). Because the FAPH measure is a more 
broadly applicable measure we believe it is appropriate for adoption 
into the IPFQR Program.
    After consideration of the public comments, we are finalizing our 
proposal to remove Follow-Up After Hospitalization for Mental Illness 
(FUH, NQF #0576) measure beginning with the FY 2024 payment 
determination.

G. Summary of IPFQR Program Measures

1. IPFQR Program Measures for the FY 2023 Payment Determination and 
Subsequent Years
    There are 14 previously finalized measures for the FY 2023 payment 
determination and subsequent years. In this final rule, we are adopting 
one measure for the FY 2023 payment determination and subsequent years. 
The 15 measures which will be in the program are shown in Table 5.
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2. IPFQR Program Measures for the FY 2024 Payment Determination and 
Subsequent Years
    There are 14 previously finalized measures for the FY 2024 payment 
determination and subsequent years. In this final rule, we are adopting 
one measure for the FY 2023 payment determination and subsequent years. 
Additionally, we are finalizing our proposal to remove one measure and 
replace one measure for the FY 2024 payment determination and 
subsequent years. We are not finalizing our proposals to remove two 
measures for the FY 2024 payment determination and subsequent years. 
The 14 measures which will be in the program for FY 2024 payment 
determination and subsequent years are shown in Table 6.

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H. Considerations for Future Measure Topics

    As we have previously indicated, we seek to develop a comprehensive 
set of quality measures to be available for widespread use for informed 
decision-making and quality improvement in the IPF setting (79 FR 45974 
through 45975). Therefore, through future rulemaking, we intend to 
propose new measures for development or adoption that will help further 
our goals of achieving better healthcare and improved health for 
individuals who obtain inpatient psychiatric services through the 
widespread dissemination and use of quality information. In 2017, we 
introduced the Meaningful Measures Framework as a tool to foster 
operational efficiencies and reduce costs including collection and 
reporting burden while producing quality measurement that is more 
focused on meaningful outcomes (83 FR 38591). As we continue to evolve 
the Meaningful Measures Framework, we have stated that we intend to 
better address health care priorities and gaps, emphasize digital 
quality measurement, and promote patient perspectives.\164\ As we work 
to align the IPFQR Program's measure set with these priorities, we have 
identified the following areas that we believe are important to 
stakeholders, but which are not covered in the current IPFQR Program 
measure set: Patient Experience of Care, Functional Outcomes 
Measurement, and digital measures. As described in the following 
subsections, we sought public comment on each of these topics and other 
future measure considerations which stakeholders believe are important.
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    \164\ https://www.cms.gov/meaningful-measures-20-moving-measure-reduction-modernization.
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    We received the following public comment on measure considerations 
which stakeholders believe are important.
    Comments: Many commenters suggested measure areas that they believe 
are important for IPFs. These areas were: (1) Suicide evaluation and 
reduction; (2) patient experience; (3) patient improvement; (4) 
clinical processes that impact significant numbers of patients in 
important clinical domains; (5) patient and workforce safety; (6) 
caregiver engagement; (7) safety culture; (8) workforce engagement, (9) 
immunization status; (10) measures that more rigorously capture data on 
tobacco and substance use interventions; and (11) discharge planning 
measures. Some commenters recommended developing improved discharge 
planning measures. One commenter recommended that CMS ensure that the 
role of nurse practitioners is included in measures. One commenter 
recommend that CMS engage with patients and their caregivers to 
identify topics they find important. Another commenter recommended that 
CMS seek industry input on measure considerations.
    Response: We thank these commenters for this input. We will 
consider these recommendations as we seek to develop a more 
comprehensive measure set for the IPFQR Program.
1. Patient Experience of Care Data Collection Instrument
    When we finalized removal of the Assessment of Patient Experience 
of

[[Page 42655]]

Care attestation measure in the FY 2019 IPF PPS final rule (83 FR 
38596) we stated that we believed we had collected sufficient 
information to inform development of a patient experience of care 
measure that would capture data on the results of such a survey. In the 
FY 2020 IPF PPS proposed rule (84 FR 16986 through 16987), we solicited 
input on how providers had implemented the Hospital Consumer Assessment 
of Healthcare Providers and Systems (HCAHPS) survey in their 
facilities. We also sought public comment on other potential surveys 
that commenters believed would be appropriate to adopt for the IPFQR 
Program. We received many comments on this subject, and many of these 
comments expressed that there is not one survey used predominantly 
across IPFs (84 FR 38467). Additional commenters expressed concerns 
that the HCAHPS survey may not be appropriate for the IPF setting 
because it does not include some of the unique aspects of inpatient 
psychiatric care including, group therapy, non-physician providers, and 
involuntary admissions. While we did not solicit public comment on this 
issue in the FY 2021 IPF PPS proposed rule, we received many comments 
addressing this issue (85 FR 47043). We continue to seek to identify a 
minimally burdensome patient experience of care instrument that would 
be appropriate for the IPF setting. Therefore, in the FY 2022 IPF PPS 
proposed rule (86 FR 19511 through 19512) we sought public comment on 
instruments currently in use in the IPF setting, input on whether the 
HCAHPS survey may be appropriate for this setting, and information on 
how facilities that currently use the HCAHPS survey have addressed 
challenges with using this survey within this setting (that is, 
concerns regarding unique aspects of inpatient psychiatric care).
    We received the following comments in response to our request.
    Comment: Many commenters expressed support for development of a 
uniform patient experience of care measure because this is a gap in the 
IPFQR measure set. Many commenters expressed that there is currently no 
patient experience of care measure in the IPFQR Program and expressed 
the belief that such a survey could improve provider accountability, 
show respect for patients, and drive quality improvement. Some 
commenters observed that patients should be given the opportunity to 
share their experiences regardless of diagnosis. One commenter observed 
that evaluations of patient experience of care can be a driver of 
health equity.
    Many commenters shared personal or family experiences in IPFs and 
indicated that being able to share such experiences in a formal survey 
would allow patients and caregivers to have a voice, provide valuable 
feedback, feel respected, provide information for quality improvements, 
and inform other potential patients. One commenter observed that 
allowing proxies would be valuable. Some commenters observed that not 
collecting patient experience of care data leads to the perception that 
patients' opinions are not valid and expressed the concern that this 
message may further objectify and traumatize a vulnerable patient 
population in a stressful and potentially stigmatizing situation (that 
is, psychiatric hospitalization). Other commenters expressed that not 
collecting such data normalizes poor treatment of psychiatric patients. 
Some commenters observed that patients with psychiatric illness are not 
less likely to be competent to express their experience of care than 
patients with other acute care needs.
    Many commenters recommended that CMS identify a minimum set of 
items to include in surveys, as opposed to requiring a specific survey. 
These commenters observed that the net promoter score (NPS) used by the 
National Health Service in the UK may be a good model to consider. Some 
commenters observed that many facilities have designed their own 
surveys tailored to their patient populations (for example, pediatric 
patients, involuntarily admitted, etc.) and that it would be preferable 
for these facilities to add questions to meet a minimum set rather than 
to replace their surveys.
    Many commenters expressed that they do not support HCAHPS for the 
IPF setting. These commenters expressed that (1) the HCAHPS was 
developed for patients with non-psych primary diagnoses and not for 
behavioral health diagnoses therefore the questions on HCAHPS do not 
address patients' top concerns regarding IPF care; (2) the survey 
protocols which allow for administration of the survey up to 6 weeks 
post-discharge may negatively impact completion rates due to the 
transient nature of the patient population; (3) the protocols do not 
have a web-interface for survey administration nor email or text survey 
invites; and (4) HCAHPS does not account for involuntary admissions. 
Some commenters also expressed concern that HCAHPS is not validated, 
nor has it been through psychometric testing in this setting. Some 
commenters observed the HCAHPS survey is due for a redesign and 
observed that CMS could potentially address concerns with the HCAHPS 
survey as part of the intended redesign. Other commenters recommended 
that CMS develop a survey unique to this setting that addresses aspects 
of care specific to the setting (such as group therapy, treatment by 
therapists, involuntary admission, medication treatment, consistency of 
treatment). One commenter recommended that CMS collaborate with AHRQ in 
survey design and development. Some commenters recommended that CMS 
ensure proper risk adjustment because patient characteristic can affect 
patient experience.
    Some commenters observed that the questions on HCAHPS apply to IPF 
patients and recommended that CMS test HCAHPS for this setting. A few 
of these commenters observed that using the same measure across 
settings would improve behavioral health parity, facility comparison, 
and reduce burden for facilities that are distinct part units in acute 
care hospitals that use HCAHPS. A few commenters expressed concern that 
excluding psychiatric patients from HCAHPS is discrimination based on a 
disability which, because of the benefits derived from patient 
experience surveys, denies patients with psychiatric diagnoses equal 
treatment. Other commenters observed that minimizing burden is not a 
factor in establishing patient experience of care measures in other 
settings and that therefore it should not be a consideration in this 
setting. Some commenters observed that CMS has requested and received 
input on this subject for several years and requested a specific plan 
of action.
    A few commenters recommended that CMS collaborate with IPFs to 
determine how to assess patients' experience of care, several 
commenters recommended that CMS establish a technical expert panel 
(TEP) with IPF members.
    One commenter recommended that CMS reintroduce the attestation 
measure until a solution for assessing patient experience of care is 
identified.
    Response: We thank these commenters for their input. We agree that 
Patient Experience of Care is a gap in the current IPFQR Program 
measure set and we agree with commenters that adoption of such a 
measure would be a meaningful step towards ensuring that patients have 
a voice regarding the care they receive. We appreciate the input from 
patients and their caregivers explaining how meaningful such a measure 
would be for these stakeholders. We intend to use the feedback provided 
here and in past requests to identify the most appropriate

[[Page 42656]]

path forward towards adopting such a measure as soon as possible.
2. Functional Outcomes Instrument for Use in a Patient Reported 
Outcomes Measure
    When we introduced the Meaningful Measures Framework, we stated 
that we wanted to focus on meaningful outcomes (83 FR 38591). As we 
have assessed the IPFQR Program measure set against the Meaningful 
Measures Framework, we have identified functional outcomes as a 
potential gap area in the IPFQR Program's measure set. Therefore, we 
are evaluating whether a patient reported outcomes measure that 
assesses functional outcomes, such as global functioning, interpersonal 
problems, psychotic symptoms, alcohol or drug use, emotional lability, 
and self-harm, would be an appropriate measure to include in the IPFQR 
program measure set. If we were to develop such a measure, we would 
develop a measure that compares a patient's responses to a standardized 
functional outcomes assessment instrument at admission with the 
patient's results on the same assessment instrument at discharge. We 
sought public comment on the value of such a measure in the IPFQR 
program measure set, what would be an appropriate functional outcome 
assessment instrument to use in the potential development of such a 
measure, and any additional topics or concepts stakeholders believe 
would be appropriate for patient reported outcomes measures.
    We received the following comments in response to our request.
    Comment: Many commenters supported the concept of a functional 
outcomes measure and recommended preceding development of such a 
measure with an attestation measure which asks IPFs whether they use an 
assessment, and if so which one.
    Some commenters expressed concern regarding outcome measures in 
this setting. One commenter specifically observed that short lengths of 
stay often lead to minimal progress on outcomes. One commenter 
mentioned the lack of endorsed, public domain outcome measures for this 
setting.
    A few commenters recommended that CMS convene a technical expert 
panel (TEP) on patient reported outcomes for this setting.
    One commenter uses PHQ-9 to assess outcomes. Another commenter uses 
BASIS-32 or CABA-Y depending on the patient population.
    Response: We thank the commenters for their input and will consider 
this feedback as we continue to evaluate a functional outcomes measure 
for this setting.
3. Measures for Electronic Data Reporting
    As we seek to improve digital measurement across our quality 
reporting and value-based payment programs, we are considering measures 
both within and appropriate to adopt for the IPFQR Program measure set 
that would be appropriate for digital data collection. In our 
assessment of the current measure set, we identified the Transition 
Record with Specified Elements Received by Discharged Patients 
(Discharges from an Inpatient Facility to Home/Self Care or Any Other 
Site of Care) measure as a potential option for digital data 
collection. We sought stakeholder input on the current data collection 
burden associated with this measure, concerns regarding potential 
electronic specification and data collection for this measure, and 
other measures that may be appropriate for electronic data collection, 
either those currently in the IPFQR Program measure set, or those that 
we could adopt in the future.
    We received the following comments in response to our request.
    Comment: Several commenters supported transitioning the IPFQR 
Program to electronic reporting.
    Many commenters observed that IPFs have not received Federal 
incentives to support EHR adoption and expressed the belief that 
electronic data reporting without such funding is premature.
    Some commenters observed that the Transition Record measure is a 
complicated measure for e-specification. Some of these commenters noted 
that this measure requires a large number of data elements, some of 
which are not available in structured fields. One commenter recommended 
considering Metabolic Screening or Influenza Immunization for 
electronic specification as these measures have fewer data elements and 
those elements are available in structured fields. Another commenter 
observed that e-specification of existing chart measures often does not 
provide comparable results.
    Response: We thank commenters for this input. We acknowledge that 
IPFs were not eligible to receive prior Federal incentives to support 
EHR adoption and will consider this and other input as we seek to 
transition the IPFQR Program to electronic data reporting.

I. Public Display and Review Requirements

    We refer readers to the FY 2013 IPPS/LTCH PPS final rule (77 FR 
53653 through 53654), the FY 2014 IPPS/LTCH PPS final rule (78 FR 50897 
through 50898), and the FY 2017 IPPS/LTCH PPS final rule (81 FR 57248 
through 57249) for discussion of our previously finalized public 
display and review requirements. We did not propose any changes to 
these requirements.

J. Form, Manner, and Timing of Quality Data Submission for the FY 2022 
Payment Determination and Subsequent Years

1. Procedural Requirements for the FY 2023 Payment Determination and 
Subsequent Years
    We refer readers to the FY 2013 IPPS/LTCH PPS final rule (77 FR 
53654 through 53655), the FY 2014 IPPS/LTCH PPS final rule (78 FR 50898 
through 50899), and the FY 2018 IPPS/LTCH PPS final rule (82 FR 38471 
through 38472) for our previously finalized procedural requirements. In 
this final rule, we are finalizing our proposal to use the term 
``QualityNet security official'' instead of ``QualityNet system 
administrator,'' finalizing our proposal to revise Sec.  412.434(b)(3) 
by replacing the term ``QualityNet system administrator'' with the term 
``QualityNet security official,'' and clarifying our policy under the 
previously finalized requirement that hospitals ``[i]dentify a 
QualityNet Administrator who follows the registration process located 
on the QualityNet website'' (77 FR 53654).
a. Updated References to QualityNet System Administrator and to No 
Longer Require Active Account To Qualify for Payment
    The previously finalized QualityNet security administrator 
requirements, including those for setting up a QualityNet account and 
the associated timelines, are described in the FY 2013 IPPS/LTCH final 
rule (77 FR 53654).
    In the FY 2022 IPF PPS proposed rule, we proposed to use the term 
``QualityNet security official'' instead of ``QualityNet system 
administrator'' to denote the exercise of authority invested in the 
role and align with the Hospital Outpatient Quality Reporting Program 
and other programs (86 FR 19512). The term ``security official'' would 
refer to ``the individual(s)'' who have responsibilities for security 
and account management requirements for a IPF's QualityNet account. To 
clarify, this update in terminology will not change the individual's 
responsibilities or add burden.
    We invited public comment on our proposal to replace the term 
``QualityNet system administrator'' with ``QualityNet security 
official.''

[[Page 42657]]

    We did not receive any public comments on this proposal.
    We are finalizing our proposal to replace the term ``Quality Net 
system administrator'' with ``QualityNet security official'' as 
proposed.
    Additionally, we proposed to no longer require IPFs to maintain an 
active QualityNet security official account to qualify for payment. As 
we reviewed the requirements for the security official role and the 
basic user \165\ role to identify the most appropriate language to 
describe the distinguishing authority invested in the security official 
role, we recognized that the QualityNet security official is not 
required for submitting data--a basic user can serve in this role--but 
remains necessary to set up QualityNet basic user accounts and for 
security purposes. Therefore, consistent with adopting the security 
official term to differentiate the unique security authority and 
responsibilities of the role from the data submission responsibilities 
of the basic user role, we would continue to require a QualityNet basic 
user account to meet IPFQR Program requirements, including data 
submission and administrative requirements, while recommending, but not 
requiring, that hospitals maintain an active QualityNet security 
official account.
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    \165\ We also noted that a basic user is a QualityNet user who 
(1) does not have the registration access described for security 
officials, (2) has the appropriate data entry roles and permissions 
for program participation, (3) can submit and review measures and 
non-measure data, (4) signs and submits the Data Accuracy 
Completeness Acknowledgement (DACA) form, and (5) refreshes their 
QualityNet account password every 180 days to ensure that the 
facility's IPFQR Program Notice of Participation status is 
``Participating.''
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    We welcomed public comments on our proposal to no longer require 
facilities to maintain an active QualityNet security official account 
to qualify for payment.
    We received the following comments in response to our proposal.
    Comment: Many commenters supported removal of the requirement to 
have an active QualityNet Security Official for the complete year to 
meet IPFQR Program requirements and therefore be eligible to receive a 
full payment update.
    Response: We thank these commenters for their support. We note that 
IPFs that do not meet all IPFQR Program requirements must receive a 2 
percent reduction to their annual payment update.
    After review of the public comments received, we are finalizing our 
proposal to no longer require facilities to maintain an active 
QualityNet security official account to qualify for payment as 
proposed.
b. Updated Reference to QualityNet Administrator in Code of Federal 
Regulations
    We proposed to revise our regulation at Sec.  412.434(b)(3) by 
replacing ``QualityNet system administrator'' with ``QualityNet 
security official.'' The term ``QualityNet security official'' refers 
to the individual(s) who have responsibilities for security and account 
management requirements for a hospital's QualityNet account. To 
clarify, this update in terminology would not change the individual's 
responsibilities or add burden. The revised paragraph (b)(3) reads: 
``Contact information for the inpatient psychiatric facility's chief 
executive officer and QualityNet security official, including each 
individual's name, email address, telephone number, and physical 
mailing address.''
    We invited public comment on our proposal to replace the term 
``QualityNet system administrator'' with ``QualityNet security 
official'' at Sec.  412.434(b)(3).
    We did not receive any public comments in response to our proposal.
    We are finalizing our proposal to no longer require facilities to 
replace the term ``QualityNet system administrator'' with ``QualityNet 
security official'' at Sec.  412.434(b)(3) as proposed.
2. Data Submission Requirements
    We refer readers to the FY 2013 IPPS/LTCH PPS final rule (77 FR 
53655 through 53657), the FY 2014 IPPS/LTCH PPS final rule (78 FR 50899 
through 50900), and the FY 2018 IPPS/LTCH PPS final rule (82 FR 38472 
through 38473) for our previously finalized data submission 
requirements. In this final rule, we are finalizing our proposal to 
adopt one measure for the FY 2023 payment determination and subsequent 
years and one measure for the FY 2024 payment determination and 
subsequent years. Data submission requirements for each of these 
measures are described in the following subsections. Additionally, we 
are finalizing our proposal to adopt patient level data submission for 
certain chart abstracted measures beginning with data submitted for the 
FY 2023 payment determination and subsequent years; details of this 
proposal are in subsection c. of this section.
a. Data Submission Requirements for FY 2023 Payment Determination and 
Subsequent Years
    The measure we are finalizing for FY 2023 payment determination and 
subsequent years (the COVID-19 Vaccination Coverage Among HCP measure) 
requires facilities to report data on the number of HCP who have 
received completed vaccination course of a COVID-19 vaccine through the 
CDC's National Healthcare Safety Network (NHSN). Specific details on 
data submission for this measure can be found in the CDC's Overview of 
the Healthcare Safety Component, available at https://www.cdc.gov/nhsn/PDFs/slides/NHSN-Overview-HPS_Aug2012.pdf. For each CMS Certification 
Number (CCN), a percentage of the HCP who received a completed vaccine 
course of the COVID-19 vaccination would be calculated and publicly 
reported, so that the public would know what percentage of the HCP have 
been vaccinated in each IPF.
    For the COVID-19 HCP Vaccination measure, we proposed that 
facilities would report the numerator and denominator for the COVID-19 
HCP vaccination measure to the NHSN for at least one week each month, 
beginning in October 2021 for the October 1, 2021 through December 31, 
2021 reporting period affecting the FY 2023 payment determination. If 
facilities report more than one week of data in a month, the most 
recent week's data would be used to calculate the measure. Each 
quarter, the CDC would calculate a single quarterly result of COVID-19 
vaccination coverage which would summarize the data submitted by IPFs 
for each of the three weeks of data submitted over the three-month 
period. CMS will publicly report the CDC's quarterly summary of COVID-
19 vaccination coverage for IPFs.
    We invited public comment on our proposal to require facilities to 
report the COVID-19 HCP vaccination measure.
    We did not receive any comments in response to our proposal.
    We are finalizing our proposal to require facilities to report the 
COVID-19 HCP vaccination measure as proposed.
b. Data Submission Requirements for FY 2024 Payment Determination and 
Subsequent Years
    Because the Follow-Up After Psychiatric Hospitalization (FAPH) 
measure would be calculated by CMS using Medicare Fee-for-Service 
claims, there will be no additional data submission requirements for 
the FY 2024 payment determination and subsequent years. Therefore, we 
did not propose any changes to our data submission policies associated 
with the proposal to adopt this measure.

[[Page 42658]]

c. Patient-Level Reporting for Certain Chart-Abstracted Measures 
Beginning With FY 2024 Payment Determination and Subsequent Years
    In the FY 2013 IPPS/LTCH PPS final rule (77 FR 53655 through 
53657), we finalized that IPFs participating in the IPFQR Program must 
submit data to the Web-Based Measures Tool found in the Inpatient 
Psychiatric Facility section of the QualityNet website's secure portal 
between July 1 and August 15 of each year. We noted that the data input 
forms within the Quality Net secure portal require submission of 
aggregate data for each separate quarter. In the FY 2014 IPPS/LTCH PPS 
final rule, we clarified our intent to require that IPFs submit 
aggregate data on measures on an annual basis via the Web-Based 
Measures Tool found in the IPF section of the Quality Net website's 
secure portal and that the forms available require aggregate data for 
each separate quarter (78 FR 50899 through 50900). In the FY 2016 IPF 
PPS final rule (80 FR 46716), we updated our data submission 
requirements to require facilities to report data for chart-abstracted 
measures to the Web-Based Measures Tool on an aggregate basis by year, 
rather than by quarter. Additionally, we discontinued the requirement 
for reporting by age group. We updated these policies in the FY 2018 
IPPS/LTCH PPS final rule (82 FR 38472 through 38473) to change the 
specification of the submission deadline from exact dates to a 45-day 
submission period beginning at least 30 days following the end of the 
data collection period.
    In the FY 2019 IPF PPS final rule (83 FR 38607), we observed that 
reporting aggregate measure data increases the possibility of human 
error, such as making typographical errors while entering data, which 
cannot be detected by CMS or by data submission systems. We noted that 
unlike patient-level data reporting, aggregate measure data reporting 
does not allow for data accuracy validation, thereby lowering the 
ability to detect error. We stated that we were considering requiring 
patient-level data reporting (data regarding each patient included in a 
measure and whether the patient was included in each numerator and 
denominator of the measure) of IPFQR measure data in the future. We 
sought public comment on including patient-level data collection in the 
IPFQR program. Several commenters expressed support for patient-level 
data collection, observing that it provides greater confidence in the 
data's validity and reliability. Other commenters recommended that CMS 
use a system that has already been tested and used for IPF data 
reporting or work with IPFs in selecting a system so that any selected 
system would avoid additional burden.
    We believe that patient-level data reporting would improve the 
accuracy of the submitted and publicly reported data without increasing 
burden. As we considered the current IPFQR measure set, we determined 
that patient-level reporting of the Hours of Physical Restraint Use 
(HBIPS-2, NQF #0640) measure and Hours of Seclusion Use (HBIPS-3,\166\ 
NQF #0641) measure would be appropriate for the numerators of these 
measures only, because these measures are calculated with a denominator 
of 1,000 hours rather than a denominator of patients who meet specific 
criteria for inclusion in the measure. Therefore, we proposed to 
require reporting patient-level information for the numerators of these 
measures only. For the remainder of the chart-abstracted measures in 
the IPFQR Program we proposed to require patient-level reporting of the 
both the numerator and the denominator. Table 7 lists the proposed FY 
2023 IPFQR measure set categorized by whether we would require patient-
level data submission through the QualityNet secure portal.
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    \166\ We note that in the FY 2022 IPF PPS proposed rule this 
incorrectly read HBIPS-2 (86 FR 19514). We have corrected it to 
HBIPS-3 here.
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    Submission of aggregate data requires facilities to abstract 
patient-level data, then calculate measure performance prior to 
submitting data through the QualityNet website's secure portal. For 
measures for which we would require patient-level data submission, we 
would allow facilities to submit data using a tool such as the CMS 
Abstraction & Reporting Tool (CART). This is the tool we use in our 
other quality reporting and value-based purchasing programs, and 
therefore, we believe that many facilities may already have familiarity 
with using this tool to abstract and report data. Additionally, the 
tool has been specifically designed to facilitate data reporting and 
minimize provider burden.
    We note that under aggregate data reporting, facilities submit 
aggregate numerators and aggregate denominators for all measures to CMS 
in the Hospital Quality Reporting (HQR) system. These aggregate 
numerators and denominators are generally calculated by manually 
abstracting the medical record of each included patient using the 
algorithm, a paper tool, or a vendor abstraction tool. After each 
required medical record has been abstracted, the numerator and 
denominator results are added up and submitted as aggregate values in 
the HQR system. Under our patient level data reporting proposal, 
facilities would still manually abstract the medical record using 
either a vendor abstraction tool or an abstraction tool provided by 
CMS. The vendor abstraction tool or the CMS tool would then produce an 
individual XML file for each of the cases abstracted. Instead of 
submitting the aggregate data, the IPF would log into HQR and upload 
batches of XML files that contain patient level data for each measure 
with data from all patients whose records were abstracted, and CMS 
would calculate the aggregate numerators, aggregate denominators, and 
measure rates from those XML file submissions. Because facilities must 
abstract patient-level data as one step in calculating measure results, 
we do not believe that requiring patient-level data submission would 
increase provider costs or burden associated with measure submission.

[[Page 42660]]

    Because we believe that patient-level data would improve the data 
accuracy without increasing provider burden, we proposed to adopt 
patient-level data reporting for numerators only for the Hours of 
Physical Restraint Use (HBIPS-2, NQF #0640) and the Hours of Seclusion 
Use (HBIPS-3, NQF #0631) for numerators and denominators for the 
following 9 chart-abstracted IPFQR Program measures as detailed in 
Table 7: Patients Discharged on Multiple Antipsychotic Medications with 
Appropriate Justification (NQF #0560); Alcohol Use Brief Intervention 
Provided or Offered and SUB-2a Alcohol Use Brief Intervention; Alcohol 
and Other Drug Use Disorder Treatment Provided or Offered at Discharge 
and SUB-3a Alcohol and Other Drug Use Disorder Treatment at Discharge; 
Tobacco Use Treatment Provided or Offered and TOB-2a Tobacco Use 
Treatment; Tobacco Use Treatment Provided or Offered at Discharge and 
TOB-3a Tobacco Use Treatment at Discharge; Influenza Immunization (NQF 
#1659); Transition Record with Specified Elements Received by 
Discharged Patients (discharges from an Inpatient Facility to Home/Self 
Care or Any Other Site of Care); Timely Transmission of Transition 
Record (Discharges from an Inpatient Facility to Home/Self Care or any 
Other Site of Care); and Screening for Metabolic Disorders.
    We believe that it is appropriate to transition to patient-level 
reporting incrementally. This would allow facilities to become familiar 
with the data submission systems and to provide feedback on any 
challenges they face in reporting data to us. Therefore, we proposed to 
allow voluntary patient-level data submission for the FY 2023 payment 
determination (that is, data submitted during CY 2022). We note that 
because participation in patient-level reporting for these chart-
abstracted measures would be voluntary for this one-year period, 
facilities would be able to choose whether to submit measure data in 
aggregate or at the patient level, and would not face a payment 
reduction as long as they submit all measure data either at the patient 
level or in aggregate for each measure for which reporting is required, 
and as long as they met all other IPFQR Program requirements. 
Therefore, we are proposed to allow voluntary patient-level reporting 
prior to requiring such data submission for one year prior to the FY 
2024 payment determination. We will ensure that facilities have 
guidance available through our standard communications channels (that 
is, listserv announcements, educational webinars, and training material 
on the QualityNet website).
    We also proposed to require patient-level data submission for these 
chart-abstracted measures for the FY 2024 payment determination (that 
is, data submitted during CY 2023) and subsequent years.
    We welcomed comment on our proposals to allow voluntary patient-
level data reporting for these chart-abstracted measures for the FY 
2023 payment determination and then to require patient-level data 
reporting for the FY 2024 payment determination and subsequent years.
    We received the following comments in response to our proposal.
    Comment: Many commenters supported the adoption of patient-level 
reporting. Many of these commenters supported initiating the process 
with one year of voluntary participation. One commenter observed that 
having patient level data would help accurately identify trends and 
improve outcomes and with demographic data could help identify health 
disparities. One commenter specifically supported the numerator only 
patient-level reporting for HBIPS-2 and HBIPS-3. One commenter observed 
that HBIPS-2 was listed twice in the proposed rule (86 FR 19514).
    Response: We thank these commenters for their support.
    Comment: Some commenters recommended that CMS use a more gradual 
transition to patient-level reporting. One commenter specifically 
recommended two cycles of voluntary reporting to ensure that the data 
submission system works properly. Others recommended that CMS provide 
additional guidance and education, including XML specifications or 
other reporting templates prior to the voluntary reporting period. One 
commenter recommended aligning guidance across programs. One commenter 
observed that the start date for collecting data for the mandatory 
reporting period is before the data submission timeframe for the 
voluntary reporting period.
    Response: We recognize that IPFs will need additional guidance and 
education in preparation for patient-level reporting. We will provide 
templates, guidance, and education and outreach sessions prior to 
beginning patient level reporting. We note that, to the extent 
feasible, we will align guidance across programs. We do not believe 
that it is necessary to have a longer voluntary reporting period 
because many IPFs also have experience with these tools already and we 
have extensive experience with patient-level reporting, both using 
electronic data reporting systems, and using tools such as the CMS 
Abstraction & Reporting Tool (CART) in our other quality reporting 
programs and intend to provide templates, guidance and education and 
outreach to IPFs.
    Comment: Some commenters recommended that CMS not require patient 
level reporting for measures proposed for removal.
    Response: We note that the measure being removed from the IPFQR 
Program (Timely Transmission of Transition Record (Discharges from an 
Inpatient Facility to Home/Self Care or any Other Site of Care)) is 
being removed for FY 2024 payment determination and subsequent years. 
The first year of mandatory patient-level reporting is FY 2024 payment 
determination. Therefore, this measure will no longer be in the program 
when patient-level reporting is required. We further note that we are 
not finalizing our proposals to remove Alcohol Use Brief Intervention 
Provided or Offered and Alcohol Use Brief Intervention (SUB-2/2a) and 
Tobacco Use Treatment Provided or Offered and Tobacco Use Treatment 
(TOB-2/2a); and therefore these patient-level data reporting will be 
required for these measures beginning with the FY 2024 payment 
determination.
    Comment: Some commenters oppose patient level reporting because of 
a lack of technology. Some commenters observed that CMS should assist 
with development of EHRs in the same way they did for acute care 
hospitals. One commenter observed that patient-level reporting would be 
burdensome without EHR technology.
    Response: We disagree with commenters that EHR technology is 
necessary for patient level reporting and note that acute care 
hospitals reported patient-level data for the Hospital IQR Program 
prior to the introduction of the HITECH act and associated meaningful 
use incentives. We further note that because IPFs must abstract the 
same data from patient records regardless of whether they are reporting 
at the patient-level or in aggregate, we do not believe that submitting 
patient-level data is more burdensome than aggregate data reporting for 
providers whether or not they have EHR technology.
    Comment: One commenter requested clarification on the start date 
for voluntary patient-level data submission for FY 2023. This commenter 
specifically requested clarification on whether that would be for 
discharges beginning for FY 2023 or CY 2023.
    Response: The voluntary patient-level data submission period is for 
FY 2023 payment determination. This applies to the data submitted 
during CY 2022

[[Page 42661]]

(which affects FY 2023 payment determination). Data submitted during CY 
2022 covers discharges that occur during CY 2021.
    After review of the public comments we received, we are finalizing 
our proposal to allow voluntary patient-level data reporting for these 
chart-abstracted measures for the FY 2023 payment determination and 
then to require patient-level data reporting for the FY 2024 payment 
determination and subsequent years as proposed.
3. Considerations for Data Validation Pilot
    As discussed in section IV.J.4 and in the FY 2019 IPF PPS final 
rule, we are concerned about the limitations of aggregate data 
submission (83 FR 28607). One such concern was that the ability to 
detect error is lower for aggregate measure data reporting than for 
patient-level data reporting (that is, data regarding each patient 
included in a measure and whether the patient was included in the 
numerator and denominator of the measure). In the FY 2022 IPF PPS 
proposed rule, we noted that if we finalize our proposal to adopt 
patient-level data requirements, we would be able to adopt a data 
validation policy for the IPFQR Program in the future (86 FR 19515). We 
believe that it would be appropriate to develop such a policy 
incrementally through adoption of a data validation pilot prior to 
national implementation of data validation within the IPFQR Program. We 
sought public input on elements of a potential data validation pilot, 
for example, the number of measures to validate, number of 
participating facilities, whether the pilot should be mandatory or 
voluntary, potential thresholds for determining measure accuracy, or 
any other policies that commenters believe would be appropriate to 
include in a data validation pilot or eventual data validation policy.
    We received the following comments in response to our request.
    Comment: Many commenters supported the concept of data validation 
but recommended that CMS ensure a stable and successful patient-level 
reporting process prior to developing a data validation plan.
    One commenter recommended using two measures and 200 hospitals to 
pilot data validation.
    Some commenters did not support eventual adoption of validation for 
the IPFQR program because of the belief that data validation would be 
burdensome. One commenter observed data validation is only necessary in 
pay-for-performance programs.
    Response: We thank these commenters for this input and will take it 
into consideration if we develop a data validation program for the 
IPFQR Program.
4. Reporting Requirements for the FY 2022 Payment Determination and 
Subsequent Years
    We refer readers to the FY 2013 IPPS/LTCH PPS final rule (77 FR 
53656 through 53657), the FY 2014 IPPS/LTCH PPS final rule (78 FR 50900 
through 50901), and the FY 2015 IPF PPS final rule (79 FR 45976 through 
45977) for our previously finalized reporting requirements. We did not 
propose any changes to these policies.
5. Quality Measure Sampling Requirements
    We refer readers to the FY 2013 IPPS/LTCH PPS final rule (77 FR 
53657 through 53658), the FY 2014 IPPS/LTCH PPS final rule (78 FR 50901 
through 50902), the FY 2016 IPF PPS final rule (80 FR 46717 through 
46719), and the FY 2019 IPF PPS final rule (83 FR 38607 through 38608) 
for discussions of our previously finalized sampling policies. In the 
FY 2022 IPF PPS proposed rule, we noted that neither the measure we 
proposed to remove (FUH--NQF #0576) nor the measure we proposed to 
adopt (FAPH) if we remove the FUH-NQF #0576 are affected by our 
sampling policies because these are both calculated by CMS using 
Medicare Fee-for-Service claims and, therefore, apply to all Medicare 
patients in the denominator (86 FR 19515). Furthermore, the denominator 
of the COVID-19 Healthcare Personnel Vaccination measure we are 
adopting in this final rule is all healthcare personnel, and therefore, 
this measure is not eligible for sampling. We did not propose any 
changes to these policies.
6. Non-Measure Data Collection
    We refer readers to the FY 2015 IPF PPS final rule (79 FR 45973), 
the FY 2016 IPF PPS final rule (80 FR 46717), and the FY 2019 IPF PPS 
final rule (83 FR 38608) for our previously finalized non-measure data 
collection policies. We did not propose any changes to these policies.
7. Data Accuracy and Completeness Acknowledgement (DACA) Requirements
    We refer readers to the FY 2013 IPPS/LTCH PPS final rule (77 FR 
53658) for our previously finalized DACA requirements. We did not 
propose any changes to these policies.

K. Reconsideration and Appeals Procedures

    We refer readers to 42 CFR 412.434 for the IPFQR Program's 
reconsideration and appeals procedures. We did not propose any changes 
to these policies.

L. Extraordinary Circumstances Exceptions (ECE) Policy

    We refer readers to the FY 2013 IPPS/LTCH PPS final rule (77 FR 
53659 through 53660), the FY 2014 IPPS/LTCH PPS final rule (78 FR 
50903), the FY 2015 IPF PPS final rule (79 FR 45978), and the FY 2018 
IPPS/LTCH PPS final rule (82 FR 38473 through 38474) for our previously 
finalized ECE policies. We did not propose any changes to these 
policies.

V. Collection of Information Requirements

    Under the Paperwork Reduction Act of 1995 (PRA) (44 U.S.C. 3501 et 
seq.), we are required to provide 60-day notice in the Federal Register 
and solicit public comment before a ``collection of information'' (as 
defined under 5 CFR 1320.3(c) of the PRA's implementing regulations) 
requirement is submitted to the Office of Management and Budget (OMB) 
for review and approval. In order to fairly evaluate whether an 
information collection should be approved by OMB, section 3506(c)(2)(A) 
of the PRA requires that we solicit comment on the following issues:
     The need for the information collection and its usefulness 
in carrying out the proper functions of our agency.
     The accuracy of our estimate of the information collection 
burden.
     The quality, utility, and clarity of the information to be 
collected.
     Recommendations to minimize the information collection 
burden on the affected public, including automated collection 
techniques.
    In the FY 2022 IPF PPS proposed rule (86 FR 19480) we solicited 
public comment on each of the section 3506(c)(2)(A)-required issues for 
the following information collection requirements (ICRs). As indicated 
in section V.2.c.(1) of this final rule, we received some comments that 
generally discuss the burden of reporting through NHSN, but not 
comments specific to our information collection estimates. We have not 
made any changes from what was proposed.

A. Final ICRs for the (IPFQR) Program

    The following final requirement and burden changes will be 
submitted to OMB for approval under control number 0938-1171 (CMS-
10432).

[[Page 42662]]

1. Wage Estimates
    In the FY 2020 IPF PPS final rule (84 FR 38468), which was the most 
recent rule in which we adopted updates to the IPFQR Program, we 
estimated that reporting measures for the IPFQR Program could be 
accomplished by a Medical Records and Health Information Technician 
(BLS Occupation Code: 29-2071) with a median hourly wage of $18.83/hr 
(May 2017). In May 2019, the U.S. Bureau of Labor Statistics (BLS) 
revised their $18.83/hr wage figure to $20.50/hr (May 2019).\167\ In 
response, we proposed to adjust our cost estimates using the updated 
median wage rate figure of $20.50/hr., an increase of $1.67/hr. We are 
finalizing our proposal to use the $20.50/hr wage in this FY 2022 final 
rule.
---------------------------------------------------------------------------

    \167\ https://www.bls.gov/oes/current/oes292098.htm (Accessed on 
June 28, 2021).
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    Under OMB Circular A-76, in calculating direct labor, agencies 
should not only include salaries and wages, but also ``other 
entitlements'' such as fringe benefits and overhead.\168\ Consistent 
with our past approach, we continue to calculate the cost of fringe 
benefits and overhead at 100 percent of the median hourly wage (81 FR 
57266). This is necessarily a rough adjustment, both because fringe 
benefits and overhead costs vary significantly from employer to 
employer, and methods of estimating these costs vary widely from study 
to study. Therefore, using these assumptions, we estimate an hourly 
labor cost increase from $37.66/hr ($18.83/hr base salary + $18.83/hr 
fringe benefits and overhead) to $41.00/hr ($20.50/hr base salary + 
$20.50/hr fringe benefits and overhead). Table 8 presents these 
assumptions.
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    \168\ http://www.whitehouse.gov/omb/circulars_a076_a76_incl_tech_correction.
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2. ICRs Regarding the Inpatient Psychiatric Facility Quality Reporting 
(IPFQR) Program
    In subsection 2.a., we restate our currently approved burden 
estimates. In subsection 2.b., we estimate the adjustments in burden 
associated with the updated BLS wage rate, our facility estimates, and 
our case estimates. In subsection 2.c., we estimate the changes in 
burden associated with the finalized policies in this rule. Finally, in 
subsection 2.d., we provide an overview of the total estimated burden.
a. Currently Approved Burden
    For a detailed discussion of the burden for the IPFQR Program 
requirements that we have previously adopted, we refer readers to the 
following rules:
     The FY 2013 IPPS/LTCH PPS final rule (77 FR 53673);
     The FY 2014 IPPS/LTCH PPS final rule (78 FR 50964);
     The FY 2015 IPF PPS final rule (79 FR 45978 through 
45980);
     The FY 2016 IPF PPS final rule (80 FR 46720 through 
46721);
     The FY 2017 IPPS/LTCH PPS final rule (81 FR 57265 through 
57266);
     The FY 2018 IPPS/LTCH PPS final rule (82 FR 38507 through 
38508);
     The FY 2019 IPF PPS final rule (83 FR 38609 through 
38612); and
     The FY 2020 IPF PPS final rule (84 FR 38468 through 
38476).
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    \169\ https://www.reginfo.gov/public/do/PRAViewDocument?ref_nbr=201908-0938-011.
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    Tables 9, 10, and 11 provide an overview of our currently approved 
burden. These tables use our previous estimate of $37.66/hr ($18.83/hr 
base salary plus $18.83/hr fringe benefits and overhead) hourly labor 
cost. For more information on our currently approved burden estimates, 
please see Supporting Statement A on the Office of Information and 
Regulatory Affairs (OIRA) website.\169\
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[[Page 42664]]


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b. Final Adjustments in Burden due to Updated Wage, Facility Count, and 
Case Count Estimates
    In the FY 2020 IPF PPS final rule (84 FR 38468), which is the most 
recent rule, that updated the IPFQR Program policies, we estimated that 
there were 1,679 participating IPFs and that (for measures that require 
reporting on the entire patient population) these facilities will 
report on an average of 1,283 cases per facility. In this FY 2022 rule, 
we are finalizing our proposal to update our facility count and case 
estimates by using the most recent data available. Specifically, we 
estimate that there are now approximately 1,634 facilities (a decrease 
of 45 facilities) and an average of 1,346 cases per facility (an 
increase of 63 cases per facility). Tables 12, 13, and 14, depict the 
effects of these updates, as well as the wage rate update to $41.00/hr 
described in section V.A.1 of the preamble of this final rule, on our 
previously estimated burden.

[[Page 42666]]

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[[Page 42667]]


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[[Page 42668]]


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BILLING CODE 4120-01-C
c. Changes in Burden due to This Final Rule
(1) Updates Due to Final Measure Adoptions
    In section IV.E of this preamble, we are adopting the following two 
measures:
     COVID-19 Vaccination Among HCP for FY 2023 Payment 
Determination and Subsequent Years; and
     Follow-Up After Psychiatric Hospitalization (FAPH) for FY 
2024 Payment Determination and Subsequent Years.
    We are adopting the COVID-19 Vaccination among HCP measure 
beginning with an initial reporting period from October 1 to December 
31, 2021 affecting the FY 2023 payment determination followed by 
quarterly reporting beginning with the FY 2024 payment determination 
and subsequent years. IPFs will submit data through the CDC's NHSN. The 
NHSN is a secure, internet-based system that is maintained by the CDC 
and provided free. The CDC does not estimate burden for COVID-19 
vaccination reporting since the department has been granted a waiver 
under Section 321 of the National Childhood Vaccine Injury Act of 1986 
(NCVIA).\170\
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    \170\ Section 321 of the National Childhood Vaccine Injury Act 
(NCVIA) provides the PRA waiver for activities that come under the 
NCVIA, including those in the NCVIA at section 2102 of the Public 
Health Service Act (42 U.S.C. 300aa-2). Section 321 is not codified 
in the U.S. Code, but can be found in a note at 42 U.S.C. 300aa-1.
---------------------------------------------------------------------------

    Although the burden associated with the COVID-19 HCP Vaccination 
measure is not accounted for due to the NCVIA waiver, the burden is set 
forth here and will be accounted for by the CDC under OMB control 
number 0920-1317.
    Consistent with the CDC's experience of collecting data using the 
NHSN, we estimate that it will take each IPF on average approximately 1 
hour per month to collect data for the COVID-19 Vaccination Coverage 
among HCP measure and enter it into NHSN. We have estimated the time to 
complete this entire activity, since it could vary based on provider 
systems and staff availability. This burden is comprised of 
administrative time and wages. We believe it would take an 
Administrative Assistant \171\ between 45 minutes (0.75 hr) and 1 hour 
and 15 minutes (1.25 hr) to enter the data into NHSN. For the CY 2021 
reporting period (consisting of October 1, 2021 through December 31, 
2021) 3 months are required. For the CY 2021 reporting period/FY 2023 
payment determination, IPFs would incur an additional burden between 
2.25 hours (0.75 hours * 3 responses at 1 response per month) and 3.75 
hours (1.25 hours * 3 responses at 1 response per month) per IPF. For 
all 1,634 IPFs, the total time would range from 3,676.5 hours (2.25 
hours * 1,634 IPFs) and 6,127.5 hours (3.75 hours * 1,634 IPFs).
---------------------------------------------------------------------------

    \171\ https://www.bls.gov/oes/current/oes436013.htm (accessed on 
March 30, 2021). The hourly rate of $36.62 includes an adjustment of 
100 percent of the median hourly wage to account for the cost of 
overhead, including fringe benefits.
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    Each IPF would incur an estimated cost of between $27.47 (0.75 hour 
* $36.62/hr) and $45.78 (1.25 hours * $36.62/hr) monthly and between 
$82.40 (2.25 hours * $36.62/hr) and $137.33 (3.75 hours * $36.62/hr) in 
total over the CY 2021 reporting period to complete this task. 
Thereafter, 12 months of data are required annually. Therefore, IPFs 
would incur an additional annual burden between 9 hours (0.75 hours/
month * 12 months) and 15 hours (1.25 hours/month * 12 months) per IPF 
and between 14,706 hours (9 hours/IPF * 1,634 IPFs) and 24,510 hours 
(15 hours/IPF * 1,634 IPFs) for all IPFs. Each IPF would incur an 
estimated cost of between $329.58 (9 hours x $36.62/hr) and $549.30 
annually (15 hours x $36.62/hr). The estimated cost across all 1,634 
IPFs would be between $134,641.60 ($82.40/IPF * 1,634 IPFs) and 
$224,397.22 ($137.33/IPF * 1,634 IPFs) for the CY 2021 reporting 
period. The estimated cost across all 1,634 IPFs would be between 
$538,533.72 ($329.58/IPF * 1,634 IPFs) and $897,556.20 ($549.30/IPF * 
1,634 IPFs) annually thereafter. Since the burden falls under the 
authority of the CDC, we have not added such burden to Table 16.
    We recognize that many healthcare facilities are also reporting 
other COVID-19 data to HHS. We believe the benefits of requiring IPFs 
to report data on the COVID-19 HCP Vaccination measure to assess 
whether they are taking steps to limit the spread of

[[Page 42669]]

COVID-19 among their healthcare workers and to help sustain the ability 
of IPFs to continue serving their communities throughout the PHE and 
beyond outweigh the costs of reporting. In our proposed rule, we 
welcomed comments on the time to collect data and enter it into the 
NHSN. While we did receive some comments addressing the burden of NHSN 
reporting, which we address in section IV.E.2 of this rule, we did not 
receive any public comments on the estimated time to collect and submit 
such data.
    We further note that as described in section IV.E.3 of this 
preamble, we will calculate the FAPH measure using Medicare Part A and 
Part B claims that IPFs and other providers (specifically outpatient 
providers who provide the follow-up care) submit for payment. Since 
this is a claims-based measure, there is no additional burden outside 
of submitting the claim. The claim submission is approved by OMB under 
control number 0938-0050 (CMS-2552-10). This rule does not warrant any 
changes under that control number.
(2) Updates Due to Final Measure Removals
    In section IV.F. of this preamble, we are finalizing our proposals 
to remove the following two measures for the FY 2024 payment 
determination and subsequent years:
     Timely Transmission of Transition Record (Discharges from 
an Inpatient Facility to Home/Self Care or Any Other Site of Care); and
     FUH--Follow-Up After Hospitalization for Mental Illness 
(NQF #0576).
    We note that we are not finalizing our proposals to remove the 
following two measures:
     SUB-2--Alcohol Use Brief Intervention Provided or Offered 
and the subset measure SUB-2a Alcohol Use Brief Intervention Provided; 
and
     TOB-2--Tobacco Use Treatment Provided or Offered and the 
subset measure TOB-2a Tobacco Use Treatment.
    For the FY 2024 payment determination, data on CY 2022 performance 
would be reported during the summer of 2023. Therefore, we are applying 
the burden reduction that would occur to the FY 2023 burden 
calculation. One of the measures we are removing (the Timely 
Transmission of Transition Record (Discharges from an Inpatient 
Facility to Home/Self Care or Any Other Site of Care) measure) falls 
under our previously finalized ``global sample'' (80 FR 46717 through 
46718) and, therefore, would require abstraction of 609 records. We 
estimate that removing this measure would result in a decrease in 
burden of 152.25 hours per facility (609 cases per facility * 0.25 
hours per case), or 248,776.5 hours (152.25 hours/facility x 1,634 
facilities) across all IPFs. Therefore, the decrease in costs for each 
measure is approximately $6,242.25 per IPF ($41.00/hr * 152.25 hours), 
or $10,199,836.50 across all IPFs ($6,242.25/facility * 1,634 
facilities).
    We have previously estimated that the FUH (NQF #0576) measure does 
not have any reporting burden because it is calculated from Medicare 
FFS claims. Therefore, we do not anticipate a reduction in facility 
burden associated with the removal of this measure. Table 15 describes 
our estimated reduction in burden associated with removing these two 
measures.
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[[Page 42670]]


(3) Updates Due to Final Administrative Policies
(a) Updates Associated With Final Updated Reference to QualityNet 
System Administrator
    In section IV.J.1.a of this preamble, we are finalizing our 
proposal to use the term ``QualityNet security official'' instead of 
``QualityNet system administrator.'' Because this final update will not 
change the individual's responsibilities, we do not believe there would 
be any changes to the information collection burden as a result of this 
update. We also do not believe that removing the requirement for 
facilities to have an active QualityNet security official account to 
qualify for payment updates will affect burden because we continue to 
recommend that facilities maintain an active QualityNet security 
official account.
(b) Updates Associated With Adoption of Patient-Level Reporting for 
Certain Chart Abstracted Measures
    In section IV.J.2.c of this preamble, we are adopting patient-level 
data submission for the 11 chart-abstracted measures currently in the 
IPFQR Program measure set (for more details on these measures we refer 
readers to Table 7). Because submission of aggregate data requires 
facilities to abstract patient-level data, then calculate measure 
performance prior to submitting data through the QualityNet website's 
secure portal, facilities must already abstract patient-level data. 
Therefore, we do not believe that submitting data that facilities must 
already calculate through a tool that facilities already have 
experience using will change provider burden.
d. Overall Burden Summary
    Table 16 summarizes the estimated burden associated with the IPFQR 
Program.
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[[Page 42672]]


    The total change in burden associated with this final rule 
(including all updates to wage rate, case counts, facility numbers, and 
the measures and administrative policies) is a reduction of 287,924 
hours and $512,065 from our currently approved burden of 3,381,086 
hours and $127,331,707. We refer readers to Table 17 for details.
[GRAPHIC] [TIFF OMITTED] TR04AU21.188

BILLING CODE 4120-01-C

VI. Regulatory Impact Analysis

A. Statement of Need

    This rule finalizes updates to the prospective payment rates for 
Medicare inpatient hospital services provided by IPFs for discharges 
occurring during FY 2022 (October 1, 2021 through September 30, 2022). 
We are finalizing our proposal to apply the 2016-based IPF market 
basket increase of 2.7 percent, less the productivity adjustment of 0.7 
percentage point as required by 1886(s)(2)(A)(i) of the Act for a final 
total FY 2022 payment rate update of 2.0 percent. In this final rule, 
we are finalizing our proposal to update the IPF labor-related share 
and update the IPF wage index to reflect the FY 2022 hospital inpatient 
wage index.

B. Overall Impact

    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 Social 
Security Act (the Act), section 202 of the Unfunded Mandates Reform Act 
of 1995 (March 22, 1995; Pub. L. 104-4), Executive Order 13132 on 
Federalism (August 4, 1999), 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). Section 
3(f) of Executive Order 12866 defines a ``significant regulatory 
action'' as an action that is likely to result in a rule: (1) Having an 
annual effect on the economy of $100 million or more in any 1 year, or 
adversely and materially affecting a sector of the economy, 
productivity, competition, jobs, the environment, public health or 
safety, or state, local or tribal governments or communities (also 
referred to as ``economically significant''); (2) creating a serious 
inconsistency or otherwise interfering with an action taken or planned 
by another agency; (3) materially altering the budgetary impacts of 
entitlement grants, user fees, or loan programs or the rights and 
obligations of recipients thereof; or (4) raising novel legal or policy 
issues arising out of legal mandates, the President's priorities, or 
the principles set forth in the Executive Order.
    A regulatory impact analysis (RIA) must be prepared for major rules 
with significant regulatory action/s or with economically significant 
effects ($100 million or more in any 1 year).
    We estimate that the total impact of these changes for FY 2022 
payments compared to FY 2021 payments will be a net increase of 
approximately $80 million. This reflects an $75 million increase from 
the update to the payment rates (+$100 million from the 2nd quarter 
2021 IGI forecast of the 2016-based IPF market basket of 2.7 percent, 
and -$25 million for the productivity adjustment of 0.7 percentage 
point), as well as a $5 million increase as a result of the update to 
the outlier threshold amount. Outlier payments are estimated to change 
from 1.9 percent in FY 2021 to 2.0 percent of total estimated IPF 
payments in FY 2022.
    Based on our estimates, OMB's Office of Information and Regulatory 
Affairs has determined that this rulemaking is ``economically 
significant,'' and hence also a major rule under Subtitle E of the 
Small Business Regulatory Enforcement Fairness Act of 1996 (also known 
as the Congressional Review Act).

C. Detailed Economic Analysis

    In this section, we discuss the historical background of the IPF 
PPS and the impact of this final rule on the Federal Medicare budget 
and on IPFs.
1. Budgetary Impact
    As discussed in the November 2004 and RY 2007 IPF PPS final rules, 
we applied a budget neutrality factor to the Federal per diem base rate 
and ECT payment per treatment to ensure that total estimated payments 
under the IPF PPS in the implementation period would equal the amount 
that would have been paid if the IPF PPS had not been implemented. The 
budget neutrality factor includes the following components: Outlier 
adjustment, stop-loss adjustment, and the behavioral offset. As 
discussed in the RY 2009 IPF PPS notice (73 FR 25711), the stop-loss 
adjustment is no longer applicable under the IPF PPS.
    As discussed in section III.D.1 of this final rule, we are updating 
the wage index and labor-related share in a budget neutral manner by 
applying a wage index budget neutrality factor to the Federal per diem 
base rate and ECT payment per treatment. Therefore, the budgetary 
impact to the Medicare program of this final rule will be due to the 
market basket update for FY 2022 of 2.7 percent (see section III.A.4 of 
this final rule) less the productivity adjustment of 0.7 percentage 
point required by section 1886(s)(2)(A)(i) of the Act and the update to 
the outlier fixed dollar loss threshold amount.
    We estimate that the FY 2022 impact will be a net increase of $80 
million in payments to IPF providers. This reflects an estimated $75 
million increase from the update to the payment rates and a $5 million 
increase due to the update to the outlier threshold amount to set total

[[Page 42673]]

estimated outlier payments at 2.0 percent of total estimated payments 
in FY 2022. This estimate does not include the implementation of the 
required 2.0 percentage point reduction of the market basket update 
factor for any IPF that fails to meet the IPF quality reporting 
requirements (as discussed in section V.A. of this final rule).
2. Impact on Providers
    To show the impact on providers of the changes to the IPF PPS 
discussed in this final rule, we compare estimated payments under the 
IPF PPS rates and factors for FY 2022 versus those under FY 2021. We 
determined the percent change in the estimated FY 2022 IPF PPS payments 
compared to the estimated FY 2021 IPF PPS payments for each category of 
IPFs. In addition, for each category of IPFs, we have included the 
estimated percent change in payments resulting from the update to the 
outlier fixed dollar loss threshold amount; the updated wage index data 
including the updated labor-related share; and the market basket update 
for FY 2022, as reduced by the productivity adjustment according to 
section 1886(s)(2)(A)(i) of the Act.
    Our longstanding methodology uses the best available data as the 
basis for our estimates of payments. Typically, this is the most recent 
update of the latest available fiscal year of IPF PPS claims, and for 
this final rulemaking, that would be the FY 2020 claims. However, as 
discussed in section III.F.2 of this final rule, the U.S. healthcare 
system undertook an unprecedented response to the COVID-19 PHE during 
FY 2020. Therefore, we considered whether the most recent available 
year of claims, FY 2020, or the prior year, FY 2019, would be the best 
for estimating IPF PPS payments in FY 2021 and FY 2022.
    As discussed in the FY 2022 IPF PPS proposed rule (86 FR 19524 
through 19526), we examined the differences between the FY 2019 and FY 
2020 claims distributions to better understand the disparity in the 
estimate of outlier payments as a percentage of total PPS payments 
between the two years, which was driving the divergent results in our 
proposed rule impacts between FY 2019 claims and FY 2020 claims. Based 
on our analysis, we stated that we believe it is likely that the 
response to the COVID-19 PHE in FY 2020 has contributed to increases in 
estimated outlier payments and to decreases in estimated total PPS 
payments in the FY 2020 claims. Therefore, we proposed, in contrast to 
our usual methodology, to use the FY 2019 claims to calculate the 
outlier fixed dollar loss threshold and wage index budget neutrality 
factor.
    We requested comments from stakeholders about likely explanations 
for the declines in total PPS payments, covered IPF days, and covered 
IPF stays in FY 2020. Additionally, we requested comments from 
stakeholders about likely explanations for the observed fluctuations 
and overall increases in covered lab charges per claim and per day, 
which we identified through our analysis. Lastly, we requested comments 
regarding likely explanations for the increases in estimated cost per 
stay relative to estimated IPF Federal per diem payment amounts per 
stay.
    Comment: We received 1 comment regarding our analysis of FY 2020 
claims and 3 comments in support of our proposal to use FY 2019 claims 
for calculating the outlier fixed dollar loss threshold and wage index 
budget neutrality factor for FY 2022. One commenter appreciated CMS' 
recognition of the impact of the COVID-19 PHE on providers. Another 
commenter agreed with our analysis about the effect of the COVID-19 PHE 
on the FY 2020 claims, stating their belief that FY 2020 cases were 
heavily impacted by the intensity of the COVID-19 pandemic, which 
continues to subside.
    Response: We appreciate the support from these commenters. As we 
discuss later in this section of this final rule, based on the results 
of our final impact analysis, we continue to believe that the FY 2019 
claims are the best available data for estimating payments in this FY 
2022 final rulemaking, due to the likely impact of the COVID-19 PHE on 
IPF utilization in FY 2020. We will continue to analyze data in order 
to understand its short-term and long-term effects on IPF utilization.
    Final Decision: In light of the comments received and after 
analyzing more recently updated FY 2020 claims, we are finalizing our 
proposal to use the FY 2019 claims to calculate the outlier fixed 
dollar loss threshold and wage index budget neutrality factor.
    To illustrate the impacts of the FY 2022 changes in this final 
rule, our analysis presents a side-by-side comparison of payments 
estimated using FY 2019 claims versus payments estimated using FY 2020 
claims. We begin with FY 2019 IPF PPS claims (based on the 2019 MedPAR 
claims, June 2020 update) and FY 2020 IPF PPS claims (based on the 2020 
MedPAR claims, March 2021 update). We estimate FY 2021 IPF PPS payments 
using these 2019 and 2020 claims, the finalized FY 2021 IPF PPS Federal 
per diem base rates, and the finalized FY 2021 IPF PPS patient and 
facility level adjustment factors (as published in the FY 2021 IPF PPS 
final rule (85 FR 47042 through 47070)). We then estimate the FY 2021 
outlier payments based on these simulated FY 2021 IPF PPS payments 
using the same methodology as finalized in the FY 2021 IPF PPS final 
rule (85 FR 47061 through 47062) where total outlier payments are 
maintained at 2 percent of total estimated FY 2021 IPF PPS payments.
    Each of the following changes is added incrementally to this 
baseline model in order for us to isolate the effects of each change:
     The final update to the outlier fixed dollar loss 
threshold amount.
     The final FY 2022 IPF wage index, the final FY 2022 labor-
related share, and the final updated COLA factors.
     The final market basket update for FY 2022 of 2.7 percent 
less the productivity adjustment of 0.7 percentage point in accordance 
with section 1886(s)(2)(A)(i) of the Act for a payment rate update of 
2.0 percent.
    Our final column comparison in Table 18 illustrates the percent 
change in payments from FY 2021 (that is, October 1, 2020, to September 
30, 2021) to FY 2022 (that is, October 1, 2021, to September 30, 2022) 
including all the payment policy changes in this final rule. For each 
column, Table 18 presents a side-by-side comparison of the results 
using FY 2019 and FY 2020 IPF PPS claims.
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BILLING CODE 4120-01-C
3. Impact Results
    Table 18 displays the results of our analysis. The table groups 
IPFs into the categories listed here based on characteristics provided 
in the Provider of Services file, the IPF PSF, and cost report data 
from the Healthcare Cost Report Information System:
     Facility Type.
     Location.
     Teaching Status Adjustment.
     Census Region.
     Size.
    The top row of the table shows the overall impact on the 1,519 IPFs 
included in the analysis for FY 2019 claims or the 1,534 IPFs included 
in the analysis for FY 2020 claims. In column 2, we present the number 
of facilities of each type that had information available in the PSF 
and also had claims in the MedPAR dataset for FY 2019 or FY 2020. The 
number of providers in each category therefore differs slightly between 
the two years.
    In column 3, we present the effects of the update to the outlier 
fixed dollar loss threshold amount. Based on the FY 2019 claims, we 
would estimate that IPF outlier payments as a percentage of total IPF 
payments are 1.9 percent in FY 2021. Alternatively, based on the FY 
2020 claims, we would estimate that IPF outlier payments as a 
percentage of total IPF payments are 3.1 percent in FY 2021.
    Thus, we are finalizing our proposal to adjust the outlier 
threshold amount in this final rule to set total estimated outlier 
payments equal to 2.0 percent of total payments in FY 2022. Based on 
the FY 2019 claims, the estimated change in total IPF payments for FY 
2022 would include an approximate 0.1 percent increase in payments 
because we would expect the outlier portion of total payments to 
increase from approximately 1.9 percent to 2.0 percent. Alternatively, 
based on the FY 2020 claims, the estimated change in total IPF payments 
for FY 2022 would include an approximate 1.1 percent decrease in 
payments because we would expect the outlier portion of total payments 
to decrease from approximately 3.1 percent to 2.0 percent.
    The overall impact of the estimated increase or decrease to 
payments due to updating the outlier fixed dollar loss threshold (as 
shown in column 3 of Table 18), across all hospital groups, is 0.1 
percent based on the FY 2019 claims, or -1.1 percent based on the FY 
2020 claims. Based on the FY 2019 claims, the largest increase in 
payments due to this change is estimated to be 0.4 percent for teaching 
IPFs with more than 30 percent interns and residents to beds. Among 
teaching IPFs, this same provider facility type would experience the 
largest estimated decrease in payments if we were to instead increase 
the outlier fixed dollar loss threshold based on the FY 2020 claims 
distribution.
    In column 4, we present the effects of the budget-neutral update to 
the IPF wage index, the Labor-Related Share (LRS), and the final 
updated COLA factors discussed in section III.D.3. This represents the 
effect of using the concurrent hospital wage data as discussed in 
section III.D.1.a of this final rule. That is, the impact represented 
in this column reflects the final updated COLA factors and the update 
from the FY 2021 IPF wage index to the final FY 2022 IPF wage index, 
which includes basing the FY 2022 IPF wage index on the FY 2022 pre-
floor, pre-reclassified IPPS hospital wage index data and updating the 
LRS from 77.3 percent in FY 2021 to 77.2 percent in FY 2022. We note 
that there is no projected change in aggregate payments to IPFs, as 
indicated in the first row of column 4; however, there will be 
distributional effects among different categories of IPFs. We also note 
that when comparing the results using

[[Page 42676]]

FY 2019 and FY 2020 claims, the distributional effects are very 
similar. For example, we estimate the largest increase in payments to 
be 0.6 percent for IPFs in the South Atlantic region, and the largest 
decrease in payments to be -0.5 percent for IPFs in the East South 
Central region, based on either the FY 2019 or FY 2020 claims.
    Finally, column 5 compares the total final changes reflected in 
this final rule for FY 2022 to the estimates for FY 2021 (without these 
changes). The average estimated increase for all IPFs is approximately 
2.1 percent based on the FY 2019 claims, or 0.9 percent based on the FY 
2020 claims. These estimated net increases include the effects of the 
2016-based market basket update of 2.7 percent reduced by the 
productivity adjustment of 0.7 percentage point, as required by section 
1886(s)(2)(A)(i) of the Act. They also include the overall estimated 
0.1 percent increase in estimated IPF outlier payments as a percent of 
total payments from updating the outlier fixed dollar loss threshold 
amount. In addition, column 5 includes the distributional effects of 
the final updates to the IPF wage index, the labor-related share, and 
the final updated COLA factors, whose impacts are displayed in column 
4. Based on the FY 2020 claims distribution, the increase to estimated 
payments due to the market basket update factor are offset in large 
part for some provider types by the increase to the outlier fixed 
dollar loss threshold.
    In summary, comparing the impact results for the FY 2019 and FY 
2020 claims, the largest difference in the results continues to be due 
to the update to the outlier fixed dollar loss threshold, which is the 
same result we observed in the FY 2022 IPF PPS proposed rule (86 FR 
19524). Estimated outlier payments increased and estimated total PPS 
payments decreased, when comparing FY 2020 to FY 2019. As a result, we 
continue to believe that FY 2019 claims, rather than FY 2020 claims, 
are the best available data for setting the FY 2022 final outlier fixed 
dollar loss threshold. Furthermore, the distributional effects of the 
updates presented in column 4 of Table 18 (the budget-neutral update to 
the IPF wage index, the LRS, and the final updated COLA factors) are 
very similar when using the FY 2019 or FY 2020 claims data. Therefore, 
we believe the FY 2019 claims are the best available data for 
estimating payments in this FY 2022 final rulemaking, and we are 
finalizing our proposal to use the FY 2019 claims to calculate the 
outlier fixed dollar loss threshold and wage index budget neutrality 
factor.
    IPF payments are therefore estimated to increase by 2.1 percent in 
urban areas and 2.2 percent in rural areas based on this finalized 
policy. Overall, IPFs are estimated to experience a net increase in 
payments as a result of the updates in this final rule. The largest 
payment increase is estimated at 2.7 percent for IPFs in the South 
Atlantic region.
4. Effect on Beneficiaries
    Under the FY 2022 IPF PPS, IPFs will continue to receive payment 
based on the average resources consumed by patients for each day. Our 
longstanding payment methodology reflects the differences in patient 
resource use and costs among IPFs, as required under section 124 of the 
BBRA. We expect that updating IPF PPS rates as finalized in this rule 
will improve or maintain beneficiary access to high quality care by 
ensuring that payment rates reflect the best available data on the 
resources involved in inpatient psychiatric care and the costs of these 
resources. We continue to expect that paying prospectively for IPF 
services under the FY 2022 IPF PPS will enhance the efficiency of the 
Medicare program.
    As discussed in sections IV.E.2, IV.E.3, and V.A.2.d of this final 
rule, we expect that additional program measures will improve follow-up 
for patients with both mental health and substance use disorders and 
ensure health-care personnel COVID-19 vaccinations. We also estimate an 
annualized estimate of $512,065 reduction in information collection 
burden as a result our measure removals. Therefore, we expect that the 
final updates to the IPFQR program will improve quality for 
beneficiaries.
5. Effects of Updates to the IPFQR Program
    As discussed in section V. of this final rule and in accordance 
with section 1886(s)(4)(A)(i) of the Act, we will apply a 2 percentage 
point reduction to the FY 2022 market basket update for IPFs that have 
failed to comply with the IPFQR Program requirements for FY 2022, 
including reporting on the required measures. In section V. of this 
final rule, we discuss how the 2 percentage point reduction will be 
applied. For FY 2021, of the 1,634 IPFs eligible for the IPFQR Program, 
43 IPFs (2.6 percent) did not receive the full market basket update 
because of the IPFQR Program; 31 of these IPFs chose not to participate 
and 12 did not meet the requirements of the program. We anticipate that 
even fewer IPFs would receive the reduction for FY 2022 as IPFs become 
more familiar with the requirements. Thus, we estimate that the IPFQR 
Program will have a negligible impact on overall IPF payments for FY 
2022.
    Based on the IPFQR Program policies finalized in this final rule, 
we estimate a total decrease in burden of 287,924 hours across all 
IPFs, resulting in a total decrease in information collection burden of 
$512,065 across all IPFs. As discussed in section VI. of this final 
rule, we will attribute the cost savings associated with the proposals 
to the year in which these savings begin; for the purposes of all the 
policies in this final rule, that year is FY 2023. Further information 
on these estimates can be found in section VI. of this final rule.
    We intend to closely monitor the effects of the IPFQR Program on 
IPFs and help facilitate successful reporting outcomes through ongoing 
stakeholder education, national trainings, and a technical help desk.
6. Regulatory Review Costs
    If regulations impose administrative costs on private entities, 
such as the time needed to read and interpret this final rule, we 
should estimate the cost associated with regulatory review. Due to the 
uncertainty involved with accurately quantifying the number of entities 
that will be directly impacted and will review this final rule, we 
assume that the total number of unique commenters on the most recent 
IPF proposed rule will be the number of reviewers of this final rule. 
For this FY 2022 IPF PPS final rule, the most recent IPF proposed rule 
was the FY 2022 IPF PPS proposed rule, and we received 898 unique 
comments on this proposed rule. We acknowledge that this assumption may 
understate or overstate the costs of reviewing this final rule. It is 
possible that not all commenters reviewed the FY 2021 IPF proposed rule 
in detail, and it is also possible that some reviewers chose not to 
comment on that proposed rule. For these reasons, we thought that the 
number of commenters would be a fair estimate of the number of 
reviewers who are directly impacted by this final rule. We solicited 
comments on this assumption.
    We also recognize that different types of entities are in many 
cases affected by mutually exclusive sections of this final rule; 
therefore, for the purposes of our estimate, we assume that each 
reviewer reads approximately 50 percent of this final rule.
    Using the May, 2020 mean (average) wage information from the BLS 
for medical and health service managers (Code 11-9111), we estimate 
that the cost of reviewing this final rule is $114.24 per hour, 
including overhead and fringe benefits (https://www.bls.gov/oes/current/oes119111.htm). Assuming

[[Page 42677]]

an average reading speed of 250 words per minute, we estimate that it 
would take approximately 128 minutes (2.13 hours) for the staff to 
review half of this final rule, which is approximately 32,000 words. 
For each IPF that reviews the final rule, the estimated cost is (2.13 x 
$114.24) or $243.33. Therefore, we estimate that the total cost of 
reviewing this final rule is $ 218,510.34 ($243.33 x 898 reviewers).

D. Alternatives Considered

    The statute does not specify an update strategy for the IPF PPS and 
is broadly written to give the Secretary discretion in establishing an 
update methodology. We continue to believe it is appropriate to 
routinely update the IPF PPS so that it reflects the best available 
data about differences in patient resource use and costs among IPFs as 
required by the statute. Therefore, we are finalizing our proposal to 
update the IPF PPS using the methodology published in the November 2004 
IPF PPS final rule; applying the 2016-based IPF PPS market basket 
update for FY 2022 of 2.7 percent, reduced by the statutorily required 
productivity adjustment of 0.7 percentage point along with the wage 
index budget neutrality adjustment to update the payment rates; and 
finalizing a FY 2022 IPF wage index which uses the FY 2022 pre-floor, 
pre-reclassified IPPS hospital wage index as its basis.
    As discussed in section VI.C.3 of this final rule, we also 
considered using FY 2020 claims data to determine the final FY 2022 
outlier fixed dollar loss threshold, wage index budget neutrality 
factor, per diem base rate, and ECT rate. For the reasons discussed in 
that section, we are finalizing our proposal to use FY 2019 claims 
data.

E. Accounting Statement

    As required by OMB Circular A-4 (available at www.whitehouse.gov/sites/whitehouse.gov/files/omb/circulars/A4/a-4.pdf), in Table 19, we 
have prepared an accounting statement showing the classification of the 
expenditures associated with the updates to the IPF wage index and 
payment rates in this final rule. Table 19 provides our best estimate 
of the increase in Medicare payments under the IPF PPS as a result of 
the changes presented in this final rule and based on the data for 
1,519 IPFs with data available in the PSF and with claims in our FY 
2019 MedPAR claims dataset. Table 19 also includes our best estimate of 
the cost savings for the 1,634 IPFs eligible for the IPFQR Program. 
Lastly, Table 19 also includes our best estimate of the costs of 
reviewing and understanding this final rule.
[GRAPHIC] [TIFF OMITTED] TR04AU21.191

F. Regulatory Flexibility Act

    The RFA requires agencies to analyze options for regulatory relief 
of small entities if a rule has a significant impact on a substantial 
number of small entities. For purposes of the RFA, small entities 
include small businesses, nonprofit organizations, and small 
governmental jurisdictions. Most IPFs and most other providers and 
suppliers are small entities, either by nonprofit status or having 
revenues of $8 million to $41.5 million or less in any 1 year. 
Individuals and states are not included in the definition of a small 
entity.
    Because we lack data on individual hospital receipts, we cannot 
determine the number of small proprietary IPFs or the proportion of 
IPFs' revenue derived from Medicare payments. Therefore, we assume that 
all IPFs are considered small entities.
    The Department of Health and Human Services generally uses a 
revenue impact of 3 to 5 percent as a significance threshold under the 
RFA. As shown in Table 18, we estimate that the overall revenue impact 
of this final rule on all IPFs is to increase estimated Medicare 
payments by approximately 2.1 percent. As a result, since the estimated 
impact of this final rule is a net increase in revenue across almost 
all categories of IPFs, the Secretary has determined that this final 
rule will have a positive revenue impact on a substantial number of 
small entities.
    In addition, section 1102(b) of the Act requires us to prepare a 
regulatory impact analysis if a rule may have a significant impact on 
the operations of a substantial number of small rural hospitals. This 
analysis must conform to the provisions of section 604 of the RFA. For 
purposes of section 1102(b) of the Act, we define a small rural 
hospital as a hospital that is located outside of a metropolitan 
statistical area and has fewer than 100 beds. As discussed in section 
V.C.1 of this final rule, the rates and policies set forth in this 
final rule will not have an adverse impact on the rural hospitals based 
on the data of the 239 rural excluded psychiatric units and 60 rural 
psychiatric hospitals in our database of 1,519 IPFs for which data were 
available. Therefore, the Secretary has certified that this final rule 
will not have a significant impact on the operations of a substantial 
number of small rural hospitals.

G. Unfunded Mandate Reform Act (UMRA)

    Section 202 of the Unfunded Mandates Reform Act of 1995 (UMRA) also 
requires that agencies assess anticipated costs and benefits before 
issuing any rule whose mandates require spending in any 1 year of $100

[[Page 42678]]

million in 1995 dollars, updated annually for inflation. In 2021, that 
threshold is approximately $158 million. This final rule does not 
mandate any requirements for state, local, or tribal governments, or 
for the private sector. This final rule would not impose a mandate that 
will result in the expenditure by state, local, and Tribal Governments, 
in the aggregate, or by the private sector, of more than $158 million 
in any one year.

H. Federalism

    Executive Order 13132 establishes certain requirements that an 
agency must meet when it promulgates a proposed rule that imposes 
substantial direct requirement costs on state and local governments, 
preempts state law, or otherwise has Federalism implications. This 
final rule does not impose substantial direct costs on state or local 
governments or preempt state law.
    I, Chiquita Brooks-LaSure, Administrator of the Centers for 
Medicare & Medicaid Services, approved this document on July 23, 2021.

List of Subjects in 42 CFR Part 412

    Administrative practice and procedure, Health facilities, Medicare, 
Puerto Rico, Reporting and recordkeeping requirements.

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

PART 412--PROSPECTIVE PAYMENT SYSTEMS FOR INPATIENT HOSPITAL 
SERVICES

0
1. The authority citation for part 412 continues to read as follows:

    Authority: 42 U.S.C. 1302 and 1395hh.


0
2. Section 412.402 is amended by adding definitions for ``Closure of an 
IPF'', ``Closure of an IPF's residency training program'', and 
``Displaced resident'' in alphabetical order to read as follows:


Sec.  412.402  Definitions.

* * * * *
    Closure of an IPF means closure of a hospital as defined in Sec.  
413.79(h)(1)(i) by an IPF meeting the requirements of Sec.  412.404(b) 
for the purposes of accounting for indirect teaching costs.
    Closure of an IPF's residency training program means closure of a 
hospital residency training program as defined in Sec.  
413.79(h)(1)(ii) by an IPF meeting the requirements of Sec.  412.404(b) 
for the purposes of accounting for indirect teaching costs.
* * * * *
    Displaced resident means a displaced resident as defined in Sec.  
413.79(h)(1)(iii) for the purposes of accounting for indirect teaching 
costs.
* * * * *

0
3. Section 412.424 is amended by revising paragraph (d)(1)(iii)(F) to 
read as follows:


Sec.  412.424  Methodology for calculating the Federal per diem payment 
system.

* * * * *
    (d) * * *
    (1) * * *
    (iii) * * *
    (F) Closure of an IPF or IPF residency training program--(1) 
Closure of an IPF. For cost reporting periods beginning on or after 
July 1, 2011, an IPF may receive a temporary adjustment to its FTE cap 
to reflect displaced residents added because of another IPF's closure 
if the IPF meets the following criteria:
    (i) The IPF is training additional displaced residents from an IPF 
that closed on or after July 1, 2011.
    (ii) No later than 60 days after the IPF begins to train the 
displaced residents, the IPF submits a request to its Medicare 
contractor for a temporary adjustment to its cap, documents that the 
IPF is eligible for this temporary adjustment by identifying the 
displaced residents who have come from the closed IPF and have caused 
the IPF to exceed its cap, and specifies the length of time the 
adjustment is needed.
    (2) Closure of an IPF's residency training program. If an IPF that 
closes its residency training program on or after July 1, 2011, agrees 
to temporarily reduce its FTE cap according to the criteria specified 
in paragraph (d)(1)(iii)(F)(2)(ii) of this section, another IPF(s) may 
receive a temporary adjustment to its FTE cap to reflect displaced 
residents added because of the closure of the residency training 
program if the criteria specified in paragraph (d)(1)(iii)(F)(2)(i) of 
this section are met.
    (i) Receiving IPF(s). For cost reporting periods beginning on or 
after July 1, 2011, an IPF may receive a temporary adjustment to its 
FTE cap to reflect displaced residents added because of the closure of 
another IPF's residency training program if the IPF is training 
additional displaced residents from the residency training program of 
an IPF that closed a program; and if no later than 60 days after the 
IPF begins to train the displaced residents, the IPF submits to its 
Medicare Contractor a request for a temporary adjustment to its FTE 
cap, documents that it is eligible for this temporary adjustment by 
identifying the displaced residents who have come from another IPF's 
closed program and have caused the IPF to exceed its cap, specifies the 
length of time the adjustment is needed, and submits to its Medicare 
contractor a copy of the FTE reduction statement by the hospital that 
closed its program, as specified in paragraph (d)(1)(iii)(F)(2)(ii) of 
this section.
    (ii) IPF that closed its program. An IPF that agrees to train 
displaced residents who have been displaced by the closure of another 
IPF's program may receive a temporary FTE cap adjustment only if the 
hospital with the closed program temporarily reduces its FTE cap based 
on the FTE of displaced residents in each program year training in the 
program at the time of the program's closure. This yearly reduction in 
the FTE cap will be determined based on the number of those displaced 
residents who would have been training in the program during that year 
had the program not closed. No later than 60 days after the displaced 
residents who were in the closed program begin training at another 
hospital, the hospital with the closed program must submit to its 
Medicare contractor a statement signed and dated by its representative 
that specifies that it agrees to the temporary reduction in its FTE cap 
to allow the IPF training the displaced residents to obtain a temporary 
adjustment to its cap; identifies the displaced residents who were in 
training at the time of the program's closure; identifies the IPFs to 
which the displaced residents are transferring once the program closes; 
and specifies the reduction for the applicable program years.
* * * * *

0
4. Section 412.434 is amended by revising paragraph (b)(3) to read as 
follows:


Sec.  412.434  Reconsideration and appeals procedures of Inpatient 
Psychiatric Facilities Quality Reporting (IPFQR) Program decisions

* * * * *
    (b) * * *
    (3) Contact information for the inpatient psychiatric facility's 
chief executive officer and QualityNet security official, including 
each individual's name, email address, telephone number, and physical 
mailing address;
* * * * *


[[Page 42679]]


    Dated: July 27, 2021.
Xavier Becerra,
Secretary, Department of Health and Human Services.
[FR Doc. 2021-16336 Filed 7-29-21; 4:15 pm]
BILLING CODE 4120-01-P