[Federal Register Volume 87, Number 145 (Friday, July 29, 2022)]
[Rules and Regulations]
[Pages 46846-46878]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2022-16260]



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Vol. 87

Friday,

No. 145

July 29, 2022

Part III





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 2023 Inpatient Psychiatric Facilities Prospective 
Payment System--Rate Update and Quality Reporting--Request for 
Information; Final Rule

  Federal Register / Vol. 87, No. 145 / Friday, July 29, 2022 / Rules 
and Regulations  

[[Page 46846]]


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DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Medicare & Medicaid Services

42 CFR Part 412

[CMS-1769-F]
RIN 0938-AU80


Medicare Program; FY 2023 Inpatient Psychiatric Facilities 
Prospective Payment System--Rate Update and Quality Reporting--Request 
for Information

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

ACTION: Final rule.

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SUMMARY: This final rule updates 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 final rule 
establishes a permanent mitigation policy to smooth the impact of year-
to-year changes in IPF payments related to decreases in the IPF wage 
index. In addition, this final rule includes responses to public 
comments received on the results of the data analysis of the IPF 
Prospective Payment System (PPS) adjustments. These changes will be 
effective for IPF discharges occurring during the Fiscal Year (FY) 
beginning October 1, 2022, through September 30, 2023 (FY 2023). 
Lastly, this final rule includes public comments received in response 
to requests for information that appeared in the FY 2023 IPF PPS 
proposed rule.

DATES: Effective October 1, 2022.

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 2023 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 2023 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 2023 Wage Index for Urban Areas Based 
on Core-Based Statistical Area (CBSA) Labor Market Areas and the FY 
2023 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 Fiscal Year (FY) 2023 beginning October 1, 2022 
through September 30, 2023. This final rule establishes a permanent 
mitigation policy to smooth the impact of year-to-year changes in IPF 
payments related to changes in the IPF wage index. In addition, this 
final rule includes responses to public comments received on the 
results of the data analysis of the IPF Prospective Payment System 
(PPS) adjustments. Lastly, this final rule includes public comments 
received in response to requests for information that appeared in the 
FY 2023 IPF PPS proposed rule.

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--
     Establish a permanent mitigation policy in order to smooth 
the impact of year-to-year changes in IPF payments related to decreases 
to the IPF wage index.
     Adjust the 2016-based IPF market basket update (4.1 
percent) for economy-wide productivity (0.3 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 3.8 percent for 
FY 2023.
     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 $832.94 to $865.63.
    ++ The IPF PPS Federal per diem base rate for providers who failed 
to report quality data to $848.95.
    ++ The ECT payment per treatment from $358.60 to $372.67.
    ++ The ECT payment per treatment for providers who failed to report 
quality data to $365.49.
    ++ The labor-related share from 77.2 percent to 77.4 percent.
    ++ The wage index budget-neutrality factor to 1.0012.
    ++ The fixed dollar loss threshold amount from $16,040 to $24,630 
to maintain estimated outlier payments at 2 percent of total estimated 
aggregate IPF PPS payments.
2. Inpatient Psychiatric Facilities Quality Reporting (IPFQR) Program
    We did not propose any changes to the IPFQR Program and are not 
finalizing any changes to the IPFQR Program. We did receive many 
comments requesting that we add a patient experience of care measure to 
the IPFQR Program. Additionally, one commenter recommended that CMS 
adopt a patient and workforce safety measure for the IPF setting. We 
also received several comments recommending that CMS adopt a value-
based purchasing program for the IPF setting. Finally, one commenter 
provided input about depression screening instruments for CMS's ongoing 
work to develop a measure of improvement of depression symptoms. We 
appreciate these comments but note that they fall outside the scope of 
this rulemaking. We will consider all these comments as we continue to 
evolve the IPFQR Program in the future.
    We also included a request for information (RFI) on the Overarching 
Principles for Measuring Healthcare Quality Disparities Across CMS 
Quality Programs. Feedback provided will inform future efforts in all 
CMS Quality programs and, as applicable, may be introduced in the IPFQR 
as future RFIs or proposals.

C. Summary of Impacts

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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 payment perspective 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; that 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 consider such reduction in computing the payment 
amount for a subsequent RY. Additional information about the specifics 
of the current IPFQR Program is available in the FY 2020 IPF PPS and 
Quality Reporting Updates for FY 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 CMS 
website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/index.html?redirect=/
InpatientPsychFacilPPS/.

B. Overview of the IPF PPS

    On November 15, 2004, we published the IPF PPS final rule in the 
Federal Register (69 FR 66922). The November 2004 IPF PPS final rule 
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 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, as well as adjustments to reflect 
higher per diem costs at the beginning of a

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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 has 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. In the November 2004 
IPF PPS final rule (69 FR 66922), 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 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 2004 IPF 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 1st. When 
proposing changes in IPF payment policy, a proposed rule is issued in 
the spring, and the final rule in the summer to be effective on October 
1st. 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, 2021 in the Federal Register titled, ``Medicare Program; 
FY 2022 Inpatient Psychiatric Facilities Prospective Payment System and 
Quality Reporting Updates for Fiscal Year Beginning October 1, 2021 (FY 
2022)'' (86 FR 42608), which updated the IPF PPS payment rates for FY 
2022. That final rule updated the IPF PPS Federal per diem base rates 
that were published in the FY 2021 IPF PPS Rate Update final rule (85 
FR 47042) in accordance with our established policies.

III. Analysis of and Responses to Public Comments

    We received 396 public comments, 27 of which pertained to proposed 
IPF PPS payment policies, 20 of which pertained to the request for 
comments on addressing healthcare disparities and advancing healthcare 
equity in the IPFQR Program, and the remainder were seeking to 
encourage the addition of a patient experience of care measure into the 
IPFQR Program. Comments were from health systems, national and state-
level provider and patient advocacy organizations, MedPAC, and 
individuals. We reviewed each comment and grouped related comments, 
after which we placed them in categories based on subject matter or 
section(s) of the regulation affected. Summaries of the public comments 
received and our responses to those comments are provided in the 
appropriate sections in the preamble of this final rule.

IV. Provisions of the FY 2023 IPF PPS Final Rule and Responses to 
Comments

A. FY 2023 Market Basket Update and Productivity Adjustment 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. 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. FY 2023 IPF Market Basket Update
    For FY 2023 (beginning October 1, 2022 and ending September 30, 
2023), we proposed to update the IPF PPS payments by a market basket 
increase factor with a productivity adjustment as required by section 
1886(s)(2)(A)(i) of the Act. Consistent with historical practice, we 
proposed to estimate the market basket update for the IPF PPS based on 
the most recent forecast available at the time of rulemaking from IHS 
Global Inc. (IGI). IGI is a nationally recognized economic and 
financial forecasting firm with which CMS contracts to forecast the 
components of the market baskets and productivity adjustment. For the 
proposed rule, based on IGI's fourth quarter 2021 forecast with 
historical data through the third quarter of 2021, the proposed 2016-
based IPF market basket increase factor for FY 2023 was 3.1 percent.

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    Section 1886(s)(2)(A)(i) of the Act requires that, after 
establishing the increase factor for a FY, the Secretary of the 
Department of Health and Human Services (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. The statute defines the productivity adjustment to be equal to 
the 10-year moving average of changes in annual economy-wide, private 
nonfarm business multifactor productivity (MFP) (as projected by the 
Secretary for the 10-year period ending with the applicable FY, year, 
cost reporting period, or other annual period) (the ``productivity 
adjustment''). The United States Department of Labor's Bureau of Labor 
Statistics (BLS) publishes the official measures of productivity for 
the United States economy. We note that previously the productivity 
measure referenced in section 1886(b)(3)(B)(xi)(II) of the Act was 
published by BLS as private nonfarm business MFP. Beginning with the 
November 18, 2021 release of productivity data, BLS replaced the term 
``multifactor productivity'' with ``total factor productivity'' (TFP). 
BLS noted that this is a change in terminology only and will not affect 
the data or methodology. As a result of the BLS name change, the 
productivity measure referenced in section 1886(b)(3)(B)(xi)(II) of the 
Act is now published by BLS as private nonfarm business total factor 
productivity. However, as mentioned previously, the data and methods 
are unchanged. We refer readers to www.bls.gov for the BLS historical 
published TFP data. A complete description of IGI's TFP projection 
methodology is available on the CMS website at https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/MedicareProgramRatesStats/MarketBasketResearch. In addition, in the FY 
2022 IPF final rule (86 FR 42611), we noted that effective with FY 2022 
and forward, CMS changed the name of this adjustment to refer to it as 
the productivity adjustment rather than the MFP adjustment.
    For the FY 2023 IPF PPS proposed rule, based on IGI's fourth 
quarter 2021 forecast, the proposed productivity adjustment for FY 2023 
(the 10-year moving average growth in TFP for the period ending FY 
2023) was projected to be 0.4 percent. Accordingly, we proposed to 
reduce the proposed 3.1 percent IPF market basket update by this 
proposed 0.4 percentage point productivity adjustment, as mandated by 
the Act. This resulted in a proposed FY 2023 IPF PPS payment rate 
update of 2.7 percent (3.1-0.4 = 2.7). We also proposed that if more 
recent data became available, we would use such data, if appropriate, 
to determine the FY 2023 IPF market basket update and productivity 
adjustment for the final rule.
    Comment: Commenters appreciated the positive proposed update to the 
IPF market basket for FY 2023; however, many commenters expressed 
concern that the proposed 2.7 percent market basket update (reflecting 
a 3.1 percent market basket update less 0.4 percentage point 
productivity adjustment) was inadequate, particularly noting the 
historically high inflation rates. The commenters acknowledged that CMS 
will refresh the market basket update in the final rule but were deeply 
concerned the revised update would continue to be insufficient relative 
to input cost inflation. They stated that hospitals on the front lines 
of the ``coronavirus disease 2019'' (abbreviated ``COVID-19'') Public 
Health Emergency (PHE) during the past 2 years continue to weather a 
number of market pressures such as labor shortages (which have led to 
use of more contract labor) and supply chain issues. One commenter 
stated that the rate update does not account for the many issues that 
their system encounters, including higher acuity patients, additional 
staffing to meet acuity needs and care for underserved patients. 
Another commenter stated that unlike many of the other hospitals and 
providers, IPFs did not receive any targeted funding allocation from 
the Provider Relief Fund to address their increased costs as well as 
the increased need for mental healthcare and addiction treatment during 
this pandemic.
    Many commenters believe CMS's current methodology for updating the 
market basket is ill-suited to adequately adjust Medicare payments in a 
highly inflationary environment. Therefore, they recommended that CMS 
consider other methods and data sources to calculate the final rule 
market basket update and an alternative approach to better align the 
market basket increases with increases in cost to treat patients, 
including using the authority under section 1886(s) of the Act to 
further increase IPF rates to better adjust FY 2023 payments to IPFs to 
account for inflation.
    Response: We believe the 2016-based IPF market basket increase 
adequately reflects the average change in the price of goods and 
services hospitals purchase in order to provide IPF medical services, 
and is appropriate to use as the IPF payment update factor. As 
described in the FY 2020 IPF final rule (84 FR 38426 through 38447), 
the IPF market basket is a fixed-weight, Laspeyres-type index that 
measures price changes over time and would not reflect increases in 
costs associated with changes in the volume or intensity of input goods 
and services. As such, the IPF market basket update would reflect the 
prospective price pressures described by the commenters as increasing 
during a high inflation period (such as faster wage growth or higher 
energy prices), but would inherently not reflect other factors that 
might increase the level of costs, such as the quantity of labor used 
or any shifts between contract and staff nurses. We note that cost 
changes (that is, the product of price and quantities) would only be 
reflected when a market basket is rebased and the base year weights are 
updated to a more recent time period.
    We agree with the commenters that recent higher inflationary trends 
have impacted the outlook for price growth over the next several 
quarters. Based on IGI's fourth quarter 2021 forecast with historical 
data through the third quarter of 2021, the proposed 2016-based IPF 
market basket update for FY 2023 was 3.1 percent, reflecting forecasted 
compensation price growth of 3.5 percent (by comparison, compensation 
price growth in the IPF market basket averaged 2.2 percent from 2012-
2021). In the FY 2023 IPF PPS proposed rule, we proposed that if more 
recent data became available, we would use such data, if appropriate, 
to derive the final FY 2023 IPF market basket update for the final 
rule. For this final rule, we now have an updated forecast of the price 
proxies underlying the market basket that incorporates more recent 
historical data and reflects a revised outlook regarding the United 
States economy and expected price inflation for FY 2023 for IPFs. Based 
on IGI's second quarter 2022 forecast with historical data through the 
first quarter of 2022, the final FY 2023 IPF market basket update is 
4.1 percent (reflecting forecasted compensation price growth of 4.5 
percent) and the final FY 2023 productivity adjustment is 0.3 
percentage point. Therefore, for FY 2023, the final IPF productivity-
adjusted market basket update is 3.8 percent (4.1 percent less 0.3 
percentage point), compared to the proposed 2.7 percent productivity-
adjusted market basket update. We note that the final FY 2023 IPF 
market basket growth rate of 4.1 percent would be the highest market 
basket update we have implemented in a final rule since the beginning 
of the IPF PPS.

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    With respect to the comment about the lack of a targeted funding 
allocation for IPFs from the Provider Relief Fund, we do not agree with 
the commenter and note that IPFs were included in the types of eligible 
specialty hospitals for rural targeted distribution payments.\1\
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    \1\ https://www.hrsa.gov/sites/default/files/hrsa/provider-relief/provider-relief-fund-faq-complete.pdf.
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    Lastly, regarding commenters' request that CMS consider other 
methods and data sources to calculate the final rule market basket 
update, including the authority under section 1886(s) of the Act, while 
we generally agree that the Secretary has broad authority under the 
statute to establish the methodology for updating the IPF PPS base 
rate, our longstanding policy since the inception of the IPF PPS has 
been to update IPF PPS payments based on an appropriate market basket. 
As discussed earlier in this section of this final rule, the market 
basket used to update IPF PPS payments has been rebased and revised 
over the history of the IPF PPS to reflect more recent data on IPF cost 
structures, and we believe it continues to appropriately reflect IPF 
cost structures. We did not propose to use other methods or data 
sources to calculate the final market basket update for FY 2023, and we 
are not finalizing such an approach for this final rule. Consistent 
with our proposal, we have used more recent data to calculate a final 
IPF productivity-adjusted market basket update of 3.8 percent for FY 
2023.
    Comment: One commenter stated that the market basket updates in FY 
2021 and FY 2022 are currently estimated to underinflate the base IPF 
rate by 1.9 percent, which means the base rate for FY 2023 is 1.9 
percent too low.
    Response: The IPF market basket updates are set prospectively, 
which means that the update relies on a mix of both historical data for 
part of the period for which the update is calculated and forecasted 
data for the remainder. For instance, the FY 2023 market basket update 
in this final rule reflects historical data through the first quarter 
of CY 2022 and forecasted data through the third quarter of CY 2023. 
While there is no precedent to adjust for market basket forecast error 
in the IPF payment update, a forecast error can be calculated by 
comparing the actual market basket increase for a given year less the 
forecasted market basket increase. Due to the uncertainty regarding 
future price trends, forecast errors can be both positive and negative. 
This was the case for the FY 2020 IPF forecast error, which was -0.7 
percentage point, and the FY 2021 IPF forecast error, which was +0.7 
percentage point; FY 2022 historical data is not yet available to 
calculate a forecast error for FY 2022. Regarding the comment that the 
FY 2023 IPF base rate is 1.9 percent too low, we disagree with this 
assertion as it does not consider years in which the base rates may 
have been overinflated. For this final rule, we have incorporated more 
recent historical data and forecasts to capture the price and wage 
pressures facing IPFs. We believe it is the best available projection 
of inflation to determine the applicable percentage increase for the 
IPF payments in FY 2023.
    Comment: One commenter stated that with the significant increase in 
inflation that has already taken place in 2022, they did not support 
using 2021 historical data to set the FY 2023 rates. The commenter 
stated that an additional increase should be added to the 2021 
historical data to help offset the significant increased costs that 
providers are currently experiencing.
    Response: In determining the FY 2023 IPF market basket update of 
4.1 percent, a combination of observed and forecasted trends were used. 
Actual experience is incorporated through first quarter 2022, and 
forecasted trends through the remaining quarters of FY 2022 and all of 
FY 2023. Likewise, the FY 2024 market basket update would reflect not 
only historical data through 2022 but also forecasted trends through FY 
2024.
    Comment: Several commenters disagreed with the assumptions 
underpinning the productivity adjustment. They stated that the 
productivity adjustment to the market basket update assumes IPFs can 
increase overall productivity at the same rate as increases in the 
broader economy, and referenced CMS Office of the Actuary analysis that 
compares private non-farm total factor productivity growth measure and 
a hospital-specific measure (https://www.cms.gov/files/document/productivity-memo.pdf). The commenters stated that IPF services are 
highly labor-intensive, and therefore, IPFs cannot improve productivity 
using strategies like offshoring or automation that are commonly 
deployed in other sectors of the economy. The commenters claimed that 
during the PHE productivity fell as result of having to use temporary 
staffing due to labor shortages.
    In addition, the commenters stated that although CMS is required by 
statute to implement a productivity adjustment to the market basket 
update, they requested that CMS work with the Congress to permanently 
eliminate the productivity adjustment. Furthermore, the commenters 
recommended that CMS use its Section 1135 waiver authority to remove 
the productivity adjustment for any FY that was covered under the PHE 
determination (that is, 2020, 2021, and 2022) from the calculation of 
market basket for FY 2023 and any year thereafter that the PHE 
continues.
    Response: Section 1886(s)(2)(A)(i) of the Act requires the 
application of a productivity adjustment to the IPF PPS market basket 
increase factor. As required by statute, the FY 2023 productivity 
adjustment is derived based on the 10-year moving average growth in 
economy-wide productivity for the period ending FY 2023. Regarding the 
suggestion that CMS consider section 1135 waiver authority to suspend 
application of the productivity adjustment, such authority is 
unavailable in this circumstance. Section 1135 of the Act authorizes 
the Secretary to waive or modify only those statutory provisions and 
regulations described at section 1135(b) of the Act, such as conditions 
of participation or providers' regulatory deadlines. Payment 
requirements, such as the application of the productivity adjustment 
under the IPF PPS, are not one of the types of requirements set out 
under this subsection.
    Final Decision: After consideration of the comments we received, we 
are finalizing a FY 2023 IPF productivity-adjusted market basket update 
equal to 3.8 percent based on the more recent data available. This 3.8 
percent update is based on a more recent forecast of the FY 2023 IPF 
market basket update of 4.1 percent reduced by a statutorily required 
productivity adjustment of 0.3 percentage point.
3. FY 2023 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.
    Based on our definition of the labor-related share and the cost 
categories in the 2016-based IPF market basket, we proposed to include 
in the labor-related share the sum of the relative importance of Wages 
and Salaries; Employee

[[Page 46851]]

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 
2023. Based on IGI's fourth quarter 2021 forecast of the 2016-based IPF 
market basket, the sum of the FY 2023 relative importance moving 
average 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 was 74.4 percent. We proposed, consistent with prior 
rulemaking, 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 6.6 percent of the 2016-based 
IPF market basket for FY 2023, we proposed to take 46 percent of 6.6 
percent to determine a labor-related share of Capital-Related costs for 
FY 2023 of 3.0 percent. Therefore, we proposed a total labor-related 
share for FY 2023 of 77.4 percent (the sum of 74.4 percent for the 
labor-related share of operating costs and 3.0 percent for the labor-
related share of Capital-Related costs). We also proposed that if more 
recent data became available, we would use such data, if appropriate to 
determine the FY 2023 labor-related share for the final rule. For more 
information on the labor-related share and its calculation, we refer 
readers to the FY 2020 IPF PPS final rule (84 FR 38445 through 38447).
    We invited public comments on the proposed labor-related share for 
FY 2023.
    Comment: One commenter did not support CMS's proposal to increase 
the labor-related share from 77.2 percent in FY 2022 to 77.4 percent in 
FY 2023, stating that any increase to the labor-related share penalizes 
facilities that have a wage index less than 1.0. The commenter also 
stated that there is a growing disparity between high-wage and low-wage 
states that harms hospitals in many rural and underserved communities. 
In addition, the commenter stated that they believe CMS should consider 
excluding the labor portion of capital related costs for FY 2023 and 
going forward.
    Response: We proposed to use the FY 2023 relative importance values 
for the labor-related cost categories from the 2016-based IPF market 
basket because it accounts for more recent data regarding price 
pressures and cost structure of IPFs. This methodology is consistent 
with the determination of the labor-related share since the 
implementation of the IPF PPS in 2007. The labor-related cost 
categories reflect IPF costs that are related to, influenced by, or 
vary with the local labor market, which would include a portion of the 
capital-related costs. Therefore, we disagree with the commenter that 
we should exclude the labor portion of capital-related costs for FY 
2023 and going forward. As stated in the FY 2023 IPF proposed rule, we 
also proposed that if more recent data became available, we would use 
such data, if appropriate, to determine the FY 2023 labor-related share 
for the final rule. Based on IHS Global Inc.'s second quarter 2022 
forecast with historical data through the first quarter of 2022, the FY 
2023 labor-related share for the final rule is 77.4 percent, unchanged 
from the proposed rule.
    Final Decision: After consideration of the comments we received, we 
are finalizing a FY 2023 labor-related share equal to 77.4 percent 
based on the latest available IGI forecast.
    Table 1 shows the FY 2023 labor-related share and the final FY 2022 
labor-related share using the 2016-based IPF market basket relative 
importance.
[GRAPHIC] [TIFF OMITTED] TR29JY22.656

B. Updates to the IPF PPS Rates for FY Beginning October 1, 2022

    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

[[Page 46852]]

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, had to 
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. The 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 2023. Addendum B to this final rule 
shows the ECT procedure codes for FY 2023 and is available on our 
website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html.
2. Update of the Federal Per Diem Base Rate and Electroconvulsive 
Therapy Payment per Treatment
    The current (FY 2022) Federal per diem base rate is $832.94 and the 
ECT payment per treatment is $358.60. For the final FY 2023 Federal per 
diem base rate, we applied the payment rate update of 3.8 percent--that 
is, the 2016-based IPF market basket increase for FY 2023 of 4.1 
percent less the productivity adjustment of 0.3 percentage point--and 
the wage index budget-neutrality factor of 1.0012 (as discussed in 
section IV.D.1 of this final rule) to the FY 2022 Federal per diem base 
rate of $832.94, yielding a final Federal per diem base rate of $865.63 
for FY 2023. Similarly, we applied the 3.8 percent payment rate update 
and the 1.0012 wage index budget-neutrality factor to the FY 2022 ECT 
payment per treatment of $358.60, yielding a final ECT payment per 
treatment of $372.67 for FY 2023.
    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 
points reduction to the Federal per diem base rate and the ECT payment 
per treatment as follows:
     For IPFs that fail to report required data under the IPFQR 
Program, we applied a 1.8 percent payment rate update--that is, the IPF 
market basket increase for FY 2023 of 4.1 percent less the productivity 
adjustment of 0.3 percentage point for an update of 3.8 percent, and 
further reduced by 2.0 percentage points in accordance with section 
1886(s)(4)(A)(i) of the Act--and the wage index budget-neutrality 
factor of 1.0012 to the FY 2022 Federal per diem base rate of $832.94, 
yielding a Federal per diem base rate of $848.95 for FY 2023.
     For IPFs that fail to report required data under the IPFQR 
Program, we applied the 1.8 percent annual payment rate update and the 
final 1.0012 wage index budget-neutrality factor to the FY 2022 ECT 
payment per treatment of $358.60, yielding an ECT payment per treatment 
of $365.49 for FY 2023.

C. 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 proposed to continue to use the existing regression-derived 
adjustment factors established in 2005 for FY 2023. 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.
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. 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,

[[Page 46853]]

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 2023, we did not propose any changes to the IPF MS-DRG 
adjustment factors. Therefore, we are retaining 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 2023, we proposed to continue making 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; however, the payment will not include an MS-DRG 
adjustment. The diagnoses for each IPF MS-DRG will be updated as of 
October 1, 2022, using the final IPPS FY 2023 ICD-10-CM/PCS code sets. 
The FY 2023 IPPS/LTCH PPS final rule includes tables of the changes to 
the ICD-10-CM/PCS code sets, which underlie the FY 2023 IPF MS-DRGs. 
Both the FY 2023 IPPS final rule and the tables of final changes to the 
ICD-10-CM/PCS code sets, which underlie the FY 2023 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, 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 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 2022 there were 18 codes finalized for 
deletion from the ICD-10-CM codes in the IPF Code First table. For FY 
2023, we proposed to delete 2 ICD-10-PCS codes and add 48 ICD-10-PCS 
codes to the IPF Code First table. For this FY 2023 IPF PPS final rule, 
we are finalizing our proposal to delete 2 ICD-10-PCS codes to add 48 
ICD-10-PCS codes to the IPF Code First table. The FY 2023 Code First 
table is shown in Addendum B on the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html.
b. 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.
    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,

[[Page 46854]]

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 2023, we 
proposed to continue to use the same comorbidity adjustment factors in 
effect in FY 2022. The FY 2023 comorbidity adjustment factors are found 
in Addendum A, available on the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html.
    For FY 2023, we proposed to add 10 ICD-10-CM/PCS codes and remove 1 
ICD-10-CM/PCS code from the Coagulation Factor category; proposed to 
add 3 ICD-10-CM/PCS codes and remove 11 ICD-10-CM/PCS codes from the 
Oncology Treatment comorbidity category; and proposed to add 4 ICD-10-
CM/PCS codes to the Poisoning comorbidity category.
    Comment: One commenter expressed concerns that the proposed FY 2023 
comorbidity codes detailed in Addenda B were not displayed on the CMS 
website at the time the proposed rule was posted.
    Response: We appreciate the concern that this commenter raised. Due 
to unanticipated technical issues, we were unable to post the B addenda 
until a few days after the display of the proposed rule. We apologize 
for any inconvenience that this delay caused, and will continue to work 
to ensure that addenda are posted as soon as possible after the display 
of the proposed rule for each FY. We encourage readers to contact the 
IPF Payment Policy mailbox at [email protected] in order to 
bring issues like this to our attention as soon as possible.
    The proposed FY 2023 comorbidity codes are shown in Addenda B, 
available on the CMS 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 2023 
ICD-10-CM codes to remove codes that were site ``unspecified'' in terms 
of laterality from the FY 2023 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. There 
were no proposed changes to the FY 2023 ICD-10-CM/PCS codes, therefore, 
we did not propose to remove any of the new codes.
c. 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 2023, we proposed continuing to use 
the patient age adjustments currently in effect in FY 2022, as shown in 
Addendum A of this rule (see https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html). We did not 
receive any comments on this proposal and are finalizing it as 
proposed.
d. 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 IV.D.4 of this final rule.
    For FY 2023, we proposed to continue to use the variable per diem 
adjustment factors currently in effect, as shown in Addendum A to this 
rule, which is available on the CMS website 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).

D. 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 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-

[[Page 46855]]

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) provides 
that we 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 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 the 
use of 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. We noted that it would also result in more consistency 
and parity in the wage index methodology used by other Medicare payment 
systems. We indicated that the Medicare skilled nursing facility (SNF) 
PPS already used the concurrent IPPS hospital wage index data as the 
basis for the SNF PPS wage index. CMS proposed and finalized 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. Thus, the wage 
adjusted Medicare payments of various provider types are based upon 
wage index data from the same timeframe. For FY 2023, 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: One commenter recommended we revise our policy so that the 
post-reclassification and post-floor hospital inpatient PPS wage index 
is used to calculate the wage index for IPFs. The commenter believe 
that the continued use of the pre-reclassification and pre-floor 
hospital inpatient wage index is unreasonable because it places IPFs at 
a disadvantage in the labor markets in which they operate relative to 
hospitals in the same markets. Another commenter recommended the 
application of a non-budget neutral wage index floor along with an 
annual cap on CBSAs with high wage indices and asserted that that the 
impact of certain wage index changes could be eliminated by allowing 
IPFs to reclassify to another CBSA as they are permitted to do under 
the IPPS.
    Response: We appreciate the commenters' recommendations. We did not 
propose the specific policies suggested by commenters, but we will take 
them into consideration to potentially inform future rulemaking. We do 
not believe that the continued use of the pre-reclassification and pre-
floor hospital inpatient wage index for FY 2023 is unreasonable or that 
this policy puts IPFs at a disadvantage relative to hospitals in the 
labor markets in which they operate. As we have previously discussed in 
the RY 2007 final rule (71 FR 27066), we believe that the actual 
location of an IPF (as opposed to the location of affiliated providers) 
is most appropriate for determining the wage adjustment because the 
prevailing wages in the area in which the IPF is located influence the 
cost of a case. In that same RY 2007 final rule (71 FR 27066), we also 
stated that we believe the ``rural floor'' is required only for the 
acute care hospital payment system, because section 4410 of the 
Balanced Budget Act of 1997 (Pub. L. 105-33) applies specifically to 
acute care hospitals and not excluded hospitals and excluded units. 
Therefore, we believe using the pre-floor, pre-reclassified IPPS 
hospital wage index is the best available data to use as a proxy for an 
IPF wage index because it best reflects the variation in local labor 
costs of IPFs in the various geographic areas in which they are located 
and uses the most recent IPPS hospital wage data without any geographic 
reclassifications, floors, or other adjustments.
    Final Decision: After consideration of the comments received, we 
are finalizing our proposal for FY 2023 to continue to use the 
concurrent pre-floor, pre-reclassified IPPS hospital wage index as the 
basis for the IPF wage index.
    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 77.2 percent in FY 2022 to 77.4 percent in FY 2023. 
This percentage reflects the labor-related share of the 2016-based IPF 
market basket for FY 2023 (see section IV.A of this rule).

[[Page 46856]]

b. Office of Management and Budget (OMB) Bulletins
1. 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 in 
the FY 2021 IPF PPS final rule 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 would 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 IV.D.1.b.ii 
of this final rule 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.
2. 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 readers to the FY 2007 IPF PPS final rule (71 FR 27064 
through 27065) for a complete discussion regarding treating 
Micropolitan Areas as rural.
c. Permanent Cap on Wage Index Decreases
    As discussed in section IV.D.1.b.(1) of this final rule, we have 
proposed and finalized temporary transition policies in the past to 
mitigate significant changes to payments due to changes to the IPF PPS 
wage index. Specifically, for FY 2016 (80 FR 46652), we implemented a 
50/50 blend for all geographic areas consisting of the wage index 
values computed using the then-current OMB area delineations and the 
wage index values computed using new area delineations based on OMB 
Bulletin No. 13-01. In FY 2021 (85 FR 47059), we implemented a 2-year 
transition to mitigate any negative effects of wage index changes by 
applying a 5-percent cap on any decrease in an IPF's wage index from 
the IPF's final wage index from FY 2020. We explained that we believe 
the 5-percent cap would provide greater transparency and would be 
administratively less complex than the prior methodology of applying a 
50/50 blended wage index. We indicated that no cap would be applied to 
the reduction in the wage index for the second year, that is, FY 2022, 
and that this transition approach struck an appropriate balance by 
providing a transition period to mitigate the resulting short-term 
instability and negative impacts on providers and time for them to 
adjust to their new labor market area delineations and wage index 
values.
    In FY 2022 (86 FR 42616 through 42617), a couple of commenters 
recommended CMS extend the transition period adopted in the FY 2021 IPF 
PPS final rule. We did not propose to modify the transition policy that 
was finalized in the FY 2021 IPF PPS final rule, and we did not extend 
the transition period for FY 2022. In the FY 2022 IPF PPS final rule, 
we stated that we continued to believe that applying the 5-percent cap 
transition policy in year one provided an adequate safeguard against 
any significant payment reductions associated with the adoption of the 
revised CBSA delineations in FY 2021, 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. However, we acknowledged that certain changes to wage 
index policy may significantly affect Medicare payments.

[[Page 46857]]

In addition, we reiterated that our policy principles with regard to 
the wage index include generally using the most current data and 
information available and providing that data and information, as well 
as any approaches to addressing any significant effects on Medicare 
payments resulting from these potential scenarios, in notice and 
comment rulemaking. With these policy principles in mind, we considered 
for the FY 2023 proposed rule how best to address the potential 
scenarios about which commenters raised concerns; that is, scenarios in 
which changes to wage index policy may significantly affect Medicare 
payments.
    In the past, we have established transition policies of limited 
duration to phase in significant changes to labor market areas. In 
taking this approach in the past, we sought to mitigate short-term 
instability and fluctuations that can negatively impact providers due 
to wage index changes. In accordance with the requirements of the IPF 
PPS wage index regulations at Sec.  412.424(a)(2), we use an 
appropriate wage index based on the best available data, including the 
best available labor market area delineations, to adjust IPF PPS 
payments for wage differences. We have previously stated that, because 
the wage index is a relative measure of the value of labor in 
prescribed labor market areas, we believe it is important to implement 
new labor market area delineations with as minimal a transition as is 
reasonably possible. However, we recognize that changes to the wage 
index have the potential to create instability and significant negative 
impacts on certain providers even when labor market areas do not 
change. In addition, year-to-year fluctuations in an area's wage index 
can occur due to external factors beyond a provider's control, such as 
the COVID-19 PHE, and for an individual provider, these fluctuations 
can be difficult to predict. We also recognize that predictability in 
Medicare payments is important to enable providers to budget and plan 
their operations.
    In light of these considerations, we proposed a permanent approach 
to smooth year-to-year changes in providers' wage indexes. We proposed 
a policy that we believe increases the predictability of IPF PPS 
payments for providers and mitigates instability and significant 
negative impacts to providers resulting from changes to the wage index. 
As previously discussed, we believe applying a 5-percent cap on wage 
index decreases for FY 2021 provided greater transparency and was 
administratively less complex than prior transition methodologies. In 
addition, we believe this methodology mitigated short-term instability 
and fluctuations that can negatively impact providers due to wage index 
changes. Lastly, we believe the 5-percent cap applied to all wage index 
decreases for FY 2021 provided an adequate safeguard against 
significant payment reductions related to the adoption of the revised 
CBSAs. However, as discussed earlier in this section of the proposed 
rule, we recognize there are circumstances that a 2-year mitigation 
policy, like the one adopted for FY 2021, would not effectively address 
future years in which providers continue to be negatively affected by 
significant wage index decreases.
    We explained in the FY 2023 IPF PPS proposed rule (87 FR 19424) 
that typical year-to-year variation in the IPF PPS wage index has 
historically been within 5 percent, and we expected this will continue 
to be the case in future years. Because providers are usually 
experienced with this level of wage index fluctuation, we stated that 
we believe applying a 5-percent cap on all wage index decreases each 
year, regardless of the reason for the decrease, would effectively 
mitigate instability in IPF PPS payments due to any significant wage 
index decreases that may affect providers in a year. Therefore, we 
believe this approach would address concerns about instability that 
commenters raised in the FY 2022 IPF PPS rule. In addition, we noted 
that we believe applying a 5-percent cap on all wage index decreases 
would support increased predictability about IPF PPS payments for 
providers, enabling them to more effectively budget and plan their 
operations. Lastly, because applying a 5-percent cap on all wage index 
decreases would represent a small overall impact on the labor market 
area wage index system, we believe it would ensure the wage index is a 
relative measure of the value of labor in prescribed labor market 
areas. As discussed in further detail in section IV.D.1.e of this final 
rule, we estimated that applying a 5-percent cap on all wage index 
decreases would have a very small effect on the wage index budget 
neutrality factor for FY 2023. Because the wage index is a measure of 
the value of labor (wage and wage-related costs) in a prescribed labor 
market area relative to the national average, we explained that we 
anticipated that in the absence of proposed policy changes most 
providers would not experience year-to-year wage index declines greater 
than 5 percent in any given year. Therefore, we anticipated that the 
impact to the wage index budget neutrality factor in future years would 
continue to be minimal. We also stated that we believe that the 5-
percent cap would likely be applied similarly to all IPFs in the same 
labor market area, as the hospital average hourly wage data in the CBSA 
(and any relative decreases compared to the national average hourly 
wage) will be similar. We explained that, while this policy may result 
in IPFs in a CBSA receiving a higher wage index than others in the same 
area (such as situations when delineations change), we believe the 
impact would be temporary.
    The Secretary has broad authority under section 1886(s)(1) of the 
Act and Section 124 of the BBRA to establish appropriate payment 
adjustments under the IPF PPS, including the wage index adjustment. As 
discussed earlier in this section, the IPF PPS regulations specify that 
we use an appropriate wage index based on the best available data. For 
the reasons discussed in this section, we stated in the proposed rule 
that we believe a 5-percent cap on wage index decreases would be 
appropriate for the IPF PPS (87 FR 19424). Therefore, for FY 2023 and 
subsequent years, we proposed to apply a 5-percent cap on any decrease 
to a provider's wage index from its wage index in the prior year, 
regardless of the circumstances causing the decline. That is, we 
proposed that an IPF's wage index for FY 2023 would not be less than 95 
percent of its final wage index for FY 2022, regardless of whether the 
IPF is part of an updated CBSA, and that for subsequent years, a 
provider's wage index would not be less than 95 percent of its wage 
index calculated in the prior FY. This also means that if an IPF's 
prior FY wage index is calculated with the application of the 5-percent 
cap, the following year's wage index would not be less than 95 percent 
of the IPF's capped wage index in the prior FY. For example, if an 
IPF's wage index for FY 2023 is calculated with the application of the 
5-percent cap, then its wage index for FY 2024 would not be less than 
95 percent of its capped wage index in FY 2023. Lastly, we proposed 
that a new IPF would be paid the wage index for the area in which it is 
geographically located for its first full or partial FY with no cap 
applied because a new IPF would not have a wage index in the prior FY. 
We proposed to reflect the permanent cap on wage index decreases at 
Sec.  412.424(d)(1)(i).
    Comment: We received 11 comments supporting the proposal of a 
permanent cap on wage index decreases. One commenter recommended that 
CMS consider a more gradual reduction of the

[[Page 46858]]

wage index cap, such as between 1 and 2 percent.
    Response: We appreciate commenters' support for the proposed 
permanent cap on wage index decreases. We also appreciate the 
suggestion to consider a lower threshold for the permanent cap; 
however, we are not finalizing a lower threshold for the cap. 
Furthermore, as we discussed in the FY 2023 IPF PPS proposed rule (87 
FR 19424), we believe applying a 5-percent cap on wage index decreases 
would be appropriate for the IPF PPS, because it would effectively 
mitigate instability in IPF PPS payments due to any significant wage 
index decreases, and would also represent a small overall impact on the 
labor market area wage index system and would therefore ensure the wage 
index is a relative measure of the value of labor in prescribed labor 
market areas. Based on the data used for this FY 2023 IPF PPS final 
rule, we estimate that only 1.3 percent of providers will experience 
wage index changes of more than 5 percent. In contrast, we estimate 
that approximately 12.2 percent of providers will experience wage index 
decreases of more than 2 percent, and 32.1 percent will experience wage 
index decreases of more than 1 percent. Therefore, if we were to cap 
wage index decreases at a lower threshold, for example 1 or 2 percent 
as the commenter suggested, the wage index cap would affect more 
providers and, accordingly, would result in a larger budget neutrality 
effect. Furthermore, the wage index cap policy would represent a 
relatively larger overall impact on the labor market area wage index 
system, since more IPFs in a greater number of labor market areas would 
be affected by the cap. We therefore do not believe it would be 
appropriate to apply a 1 or 2 percent cap on wage index decreases as 
the commenter suggested.
    Comment: MedPAC supported the proposal to cap wage index decreases 
at 5 percent, but suggested also applying a cap to increases of more 
than 5 percent.
    Response: We appreciate MedPAC's suggestion that the cap on wage 
index changes of more than 5 percent should also be applied to 
increases in the wage index. However, as we discussed in the proposed 
rule, one purpose of the proposed policy is to help mitigate the 
significant negative impacts of certain wage index changes. As we noted 
in the FY 2023 IPF PPS proposed rule (87 FR 19424), we believe applying 
a 5-percent cap on all wage index decreases would support increased 
predictability about IPF PPS payments for providers, enabling them to 
more effectively budget and plan their operations. That is, we proposed 
to cap decreases because we believe that a provider would be able to 
more effectively budget and plan when there is predictability about its 
expected minimum level of IPF PPS payments in the upcoming fiscal year. 
We did not propose to limit wage index increases because we do not 
believe such a policy is needed to enable IPFs to more effectively 
budget and plan their operations. Therefore, we believe it is 
appropriate for providers that experience an increase in their wage 
index value to receive that wage index value.
    Comment: Several commenters recommended that CMS apply the wage 
index cap in a non-budget neutral manner.
    Response: In accordance with our longstanding policy under the IPF 
PPS, we updated the wage index in such a way that total estimated 
payments to IPFs for FY 2023 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 proposed to apply the wage index cap in 
a budget-neutral manner in accordance with this overall budget 
neutrality policy for the IPF PPS wage index so that wage index changes 
do not increase aggregate Medicare spending. In the FY 2023 IPF PPS 
proposed rule, we noted that applying a 5-percent cap on all wage index 
decreases would have a very small effect on the wage index budget 
neutrality factor for FY 2023. We explained that we anticipate that in 
the absence of proposed policy changes most providers will not 
experience year-to-year wage index declines greater than 5 percent in 
any given year and that we expect the impact to the wage index budget 
neutrality factor in future years will continue to be minimal.
    Comment: Two commenters opposed the proposal to pay any new 
provider the wage index for the area in which it is geographically 
located for its first full or partial FY with no cap applied. One 
commenter expressed concern that this policy will create an unnecessary 
inequity in Medicare payments for IPFs in the same market. Another 
commenter asserted that new facilities will struggle to fill hospital 
beds and recruit staff if their wage index is lower than other IPFs in 
the same CBSA. This commenter further noted that ultimately, the 
addition of a new facility will most likely increase the region's wage 
index in the future.
    Response: We appreciate the concerns that commenters raised, but we 
do not agree that this proposal would create an unnecessary inequity in 
IPF PPS payments or make it more difficult for new facilities to fill 
hospital beds and recruit staff. As we discussed in the FY 2023 IPF PPS 
proposed rule (87 FR 19424), while this policy may result in IPFs in a 
CBSA receiving a higher wage index than others in the same area (such 
as situations when delineations change), we believe the impact would be 
temporary because, over time, wage levels in a CBSA will converge to 
the same level. In addition, as we have previously stated, we believe 
the IPF PPS wage index accurately reflects the cost of labor in a 
prescribed labor market area. Therefore, we believe the IPF PPS wage 
index would accurately reflect the labor costs that a new provider 
would face. As we noted earlier in this section, we proposed to apply 
the permanent 5-percent cap on wage index decreases in order to 
mitigate instability, support increased predictability about IPF PPS 
payments, and enable providers to more effectively budget and plan 
their operations. We do not believe that changes to the wage index in a 
labor market area would represent a change for a new provider in that 
labor market area. In contrast to other providers in the same area, a 
new provider would not have a prior year wage index against which to 
compare the current year wage index. Therefore, we do not believe that 
applying the cap to new providers would be appropriate.
    Comment: A commenter recommended that CMS retroactively apply the 
5-percent cap policy to the FY 2022 wage index for providers that 
experienced wage index decreases due to their transition to a new CBSA 
based on the new OMB delineations that were finalized for FY 2021.
    Response: As noted previously, in FY 2021, we implemented a 2-year 
transition to mitigate any negative effects of wage index changes by 
applying a 5-percent cap on any decrease in an IPF's wage index from 
the IPF's final wage index from FY 2020; we indicated that no cap would 
be applied to the reduction in the second year, FY 2022. In the FY 2023 
IPF PPS proposed rule, we did not propose to modify that transition 
policy to extend the transition period for FY 2022. We have 
historically implemented transitions of limited duration, as discussed 
in the FY 2016 (80 FR 46652) final rule, to address CBSA changes due to 
substantial updates to OMB delineations. In accordance with our policy 
principles that we use the most updated data and information available 
with regard to the wage index, as noted in the FY 2022 IPF PPS final 
rule (86 FR 42617), we proposed that the FY 2023 IPF PPS 5-percent cap 
wage index policy would be prospective to mitigate any significant 
decreases beginning in FY 2023.

[[Page 46859]]

    Final Decision: After consideration of the comments received, we 
are finalizing as proposed a permanent 5-percent cap on any decrease to 
a provider's wage index from its wage index in the prior year, which we 
will apply in a budget-neutral manner. We are also finalizing as 
proposed that a new IPF will be paid the wage index for the area in 
which it is geographically located for its first full or partial FY 
with no cap applied because a new IPF would not have a wage index in 
the prior FY. We are reflecting the permanent cap on wage index 
decreases at Sec.  412.424(d)(1)(i).
    As previously discussed, we believe this methodology will maintain 
the IPF PPS wage index as a relative measure of the value of labor in 
prescribed labor market areas, increase predictability of IPF PPS 
payments for providers, and mitigate instability and significant 
negative impacts to providers resulting from significant changes to the 
wage index. In section VIII.C.2 of this final rule, we estimate the 
impact to payments for providers in FY 2023 based on this policy. We 
also note that we will examine the effects of this policy on an ongoing 
basis in the future in order to assess its appropriateness.
d. 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 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 2023, 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). We did not receive any comments on 
this proposal, and we are finalizing it as proposed.
e. Budget Neutrality Adjustment
    Changes to the wage index are made in a budget-neutral manner so 
that updates do not increase expenditures. For FY 2023, we proposed 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 2023 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. As discussed in section IV.E.2 of this final rule, we used the 
March 2022 update of the FY 2021 IPF claims to calculate the final FY 
2023 IPF PPS wage index budget neutrality factor. We used the following 
steps, which include the 5-percent cap on decreases to a provider's 
wage index, to ensure that the rates reflect the FY 2023 update to the 
wage indexes (based on the FY 2019 hospital cost report data) and the 
labor-related share in a budget-neutral manner:
    Step 1: Simulate estimated IPF PPS payments, using the FY 2022 IPF 
wage index values (available on the CMS website) and labor-related 
share (as published in the FY 2022 IPF PPS final rule (86 FR 42608).
    Step 2: Simulate estimated IPF PPS payments using the final FY 2023 
IPF wage index values (available on the CMS website), including 
application of the 5-percent cap on wage index decreases, and the final 
FY 2023 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 2023 budget-
neutral wage adjustment factor of 1.0012.
    Step 4: Apply the FY 2023 budget-neutral wage adjustment factor 
from step 3 to the FY 2022 IPF PPS Federal per diem base rate after the 
application of the market basket update described in section IV.A of 
this final rule, to determine the FY 2023 IPF PPS Federal per diem base 
rate.
    As discussed in section IV.D.1.c of this final rule, we also 
followed these steps to separately calculate the budget neutrality 
factor associated with the 5-percent cap on any decrease to a 
provider's wage index from its wage index in the prior year. First, we 
calculated the budget neutrality factor associated with the FY 2023 IPF 
wage index and FY 2023 labor-related share. We divided the amount of 
simulated payments using the FY 2022 IPF wage index and labor-related 
share by the amount of simulated payments using the FY 2023 wage index 
and FY 2023 labor-related share. The resulting quotient is 1.0013.
    Next, we calculated the budget neutrality factor associated with 
the 5-percent cap on any decrease to a provider's wage index from its 
wage index in the prior year. We divided the amount of simulated 
payments using the FY 2023 wage index and FY 2023 labor-related share 
by the amount of simulated payments using the FY 2023 wage index, the 
5-percent cap on any decrease to a provider's wage index from its wage 
index in the prior year, and the FY 2023 labor-related share. The 
resulting quotient is 0.9999. The combined budget neutrality factor, 
which is the FY 2023 budget-neutral wage adjustment factor as discussed 
earlier in this section, is 1.0012.
2. Teaching Adjustment
    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 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).
    Under the IPPS, 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. In addition, 
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 the final 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

[[Page 46860]]

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 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 2023 final rule, we will continue to retain the 
coefficient value of 0.5150 for the teaching adjustment to the Federal 
per diem base rate.
3. 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 believe 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 most recently updated for FY 2022, the COLA 
factors were updated in FY 2022 IPPS/LTCH rulemaking (86 FR 45547). As 
such, we also updated the IPF PPS COLA factors for FY 2022 (86 FR 42621 
through 42622) to reflect the updated COLA factors finalized in the FY 
2022 IPPS/LTCH rulemaking. Table 2 shows the IPF PPS COLA factors 
effective for FY 2022 through FY 2025.

[[Page 46861]]

[GRAPHIC] [TIFF OMITTED] TR29JY22.657

    We did not receive any comments about the proposed COLA factors for 
FY 2023, and are finalizing them as proposed. The IPF PPS COLA factors 
for FY 2023 are also shown in Addendum A to this final rule, and is 
available on the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html.
4. 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 final 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 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 2023, we proposed to continue to retain the 1.31 adjustment factor 
for IPFs with qualifying EDs. We did not receive any comments on this 
proposal, and we are finalizing it as proposed. 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).

E. 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

[[Page 46862]]

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. Update to the Outlier Fixed Dollar Loss Threshold Amount
    In accordance with the update methodology described in Sec.  
412.428(d), we proposed 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. Last year for the FY 2022 IPF 
PPS final rule, we finalized the use of FY 2019 claims rather than the 
more recent FY 2020 claims for updating the outlier fixed dollar loss 
threshold (86 FR 42623). We noted that our use of the FY 2019 claims to 
set the final outlier fixed dollar loss threshold for FY 2022 deviated 
from our longstanding practice of using the most recent available year 
of claims, but remained otherwise consistent with the established 
outlier update methodology. We explained that we finalized 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 appeared to have significantly impacted the FY 2020 IPF 
claims. We further stated that we intended 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 (86 FR 42624).
    For the FY 2023 IPF PPS proposed rule, consistent with our 
longstanding practice, we analyzed the most recent available data for 
simulating IPF PPS payments in FY 2023. We observed a continuation of 
two main trends that we noted in our analysis of FY 2020 claims for FY 
2022--that is, an overall increase in average cost per day and an 
overall decrease in the number of covered days. However, we also 
identified that some providers had significant increases in their 
charges, resulting in higher than normal estimated cost per day that 
would skew our estimate of outlier payments for FY 2022 and FY 2023.
    In the proposed rule (87 FR 19428), we noted that historically, we 
have applied statistical trims under the IPF PPS in order to improve 
the statistical validity of the data used for ratesetting. In the 
November 2004 final rule, we explained that we applied a 3 standard 
deviation trim on cost per day prior to calculating the average per 
diem cost used to calculate the IPF PPS Federal per diem base rate (69 
FR 66927). Furthermore, as discussed in section IV.E.3 of this final 
rule, our longstanding policy applies a ceiling on a provider's cost-
to-charge ratio when it exceeds 3 standard deviations from the mean 
cost-to-charge ratio for urban or rural providers. We proposed a 
similar approach in order to address the skew in estimated cost per day 
that we observed in the FY 2021 claims. Specifically, we proposed for 
FY 2023 to exclude providers from our simulation of IPF PPS payments 
for FY 2022 and FY 2023 if their change in estimated average cost per 
day is outside 3 standard deviations from the mean.
    In the proposed rule (87 FR 19428), we stated that based on an 
analysis of the December 2021 update of FY 2021 IPF claims and the FY 
2022 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 proposed to 
update the IPF outlier threshold amount for FY 2023 using FY 2021 
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 2022. However, as 
discussed earlier in this section, we also proposed for FY 2023 to 
exclude providers from our impact simulations whose change in simulated 
cost per day is outside 3 standard deviations from the mean. Based on 
an analysis of the data available for the proposed rule, we estimated 
that IPF outlier payments as a percentage of total estimated payments 
were approximately 3.2 percent in FY 2022. Therefore, we proposed to 
update the outlier threshold amount to $24,270 to maintain estimated 
outlier payments at 2 percent of total estimated aggregate IPF payments 
for FY 2023. This proposed update was an increase from the FY 2022 
threshold of $16,040.
    Comment: Several commenters expressed concern about using CY 2021 
data because of the impact of the COVID-19 PHE and suggested that CMS 
consider alternative methodologies for estimating the outlier 
percentage and setting the outlier fixed dollar loss threshold amount. 
Some commenters expressed their belief that the proposed trimming 
methodology is not sufficient to blunt COVID-19's overstated impact on 
the IPF PPS outlier calculation. These commenters encouraged CMS to use 
an alternative inflation factor from a period before the COVID-19 PHE 
and to adjust cost-to-charge ratios (CCRs) to reflect the CCRs from 
prior to the COVID-19 PHE. Another commenter suggested that CMS 
estimate the outlier percentage using multiple years of claims, or set 
the outlier fixed dollar loss threshold amount based on an average of 
outlier thresholds from multiple years. Another commenter suggested 
that the percent increase to the outlier fixed dollar loss threshold 
amount should be limited to no more than the market basket update 
percentage.
    Response: We appreciate the suggestions from commenters regarding 
these alternative methodologies. We believe that the proposed trimming 
methodology sufficiently mitigates the significant increases in charges 
that we observed in the FY 2021 claims, which we noted would skew our 
estimate of outlier payments for FY 2022. We believe this methodology 
also appropriately accounts for the ongoing trends that we noted in 
previous analysis of FY 2020 claims for FY 2022--that is, an overall 
increase in average cost per day and an overall decrease in the number 
of covered days. In the FY 2022 IPF PPS final rule (86 FR 42624), we 
explained that we believed these trends were related to the COVID-19 
PHE and noted that we would 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. Because we observed these continued trends in FY 
2021, we believe it is reasonable to expect that they will continue to 
some extent in FY 2023.
    Regarding the recommendation to use an inflation factor from a 
different time

[[Page 46863]]

period, we do not believe it would be appropriate to do so for this FY 
2023 IPF PPS final rule. We note that whereas the IPPS uses a charge 
inflation factor calculated based on historical IPPS charge data, the 
longstanding IPF PPS methodology uses a charge inflation factor 
calculated based on the latest available forecast of the IPF PPS market 
basket price proxies. As discussed in section IV.A.2 of this final 
rule, we believe the 2016-based IPF market basket increase adequately 
reflects the average change in the price of goods and services 
hospitals purchase in order to provide IPF medical services. 
Furthermore, as discussed in that same section of this final rule, the 
updated forecast for this FY 2023 final rule incorporates more recent 
historical data and reflects a revised outlook regarding the United 
States economy and expected price inflation for FY 2023 for IPFs. 
Therefore, we believe it is more appropriate to use an inflation factor 
that is based on the latest available forecast of input price growth 
for IPFs, rather than a factor based on data from an earlier time 
period, as the commenters suggested.
    Regarding the alternative methodologies that commenters suggested 
for calculating the outlier threshold, we do not believe that averaging 
the proposed FY 2023 outlier fixed dollar loss threshold amount with 
the amounts from prior years, or limiting the increase to the outlier 
fixed dollar loss threshold amount, would be appropriate for this FY 
2023 IPF PPS final rule. As discussed earlier in this section, the 
longstanding IPF PPS 2-percent outlier policy was established based on 
the regression analysis and payment simulations used to develop the IPF 
PPS. We have previously explained that the 2-percent outlier policy 
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. 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. For this FY 2023 IPF PPS final rule, we have simulated 
payments using the latest available data, and these payment simulations 
indicate that an increase to the outlier fixed dollar loss threshold is 
necessary in order to maintain outlier payments at 2 percent of total 
payments. We are concerned that the alternative methodologies that 
commenters suggested would not appropriately target outlier payments 
such that they remain at 2 percent of total IPF PPS payments. Regarding 
the suggestion that CMS use multiple years of claims to determine the 
outlier fixed dollar loss threshold amount, we reiterate that our 
longstanding methodology uses the best available data, which is 
typically the most recent available data, to update the outlier fixed 
dollar loss threshold amount. We believe the proposed methodology 
appropriately accounts for the trends in average cost per day and the 
number of covered days reflected in the IPF PPS claims, which we expect 
are likely to continue to some extent into FY 2023. We believe the 
proposed methodology also incorporates more recent historical data and 
reflects a revised outlook regarding the United States economy and 
expected price inflation for FY 2023 for IPFs. Therefore, we are 
finalizing the use of the proposed methodology to calculate the FY 2023 
IPF PPS outlier fixed dollar loss threshold amount.
    Comment: MedPAC encouraged CMS to provide additional data about the 
increase to the outlier fixed dollar loss threshold amount for FY 2023.
    Response: As we noted in the proposed rule, two main trends that we 
observed in the FY 2020 claims continued in the FY 2021 claims. First, 
we observed that average cost per day increased approximately 12 
percent when comparing the simulated FY 2021 IPF PPS payments from the 
FY 2022 IPF PPS final rule to the simulated FY 2022 IPF PPS payments 
that we used to estimate the outlier percentage for this FY 2023 IPF 
PPS final rule. In the FY 2022 IPF PPS proposed rule (86 FR 19526), we 
explained that we estimate the costs per case based on the covered 
charges on each IPF claim and the IPF's most recent CCR. In that 
proposed rule, we noted that laboratory charges, which make up roughly 
one-third of the covered charges per IPF claim, increased approximately 
6.8 percent between FY 2019 and FY 2020. We found that laboratory 
charges continued to increase for the FY 2021 claims analyzed for this 
FY 2023 IPF PPS final rule. We found that laboratory charges per day in 
2021 were approximately 12.7 percent higher than laboratory charges per 
day in 2019. We believe these increased laboratory charges are likely 
in response to the COVID-19 PHE, and as stated earlier, we believe it 
is reasonable to expect that these increased laboratory charges will 
continue to some extent in FY 2023.
    The second continued trend that we observed was that the number of 
covered days decreased in the FY 2021 claims. As we discussed in the FY 
2022 IPF PPS proposed rule (86 FR 19524), we observed a decrease in 
covered days of approximately 15 percent from the FY 2019 claims to the 
FY 2020 claims. Before applying the statistical trim for this FY 2023 
IPF PPS final rule, the number of covered days in the FY 2021 claims 
was approximately 28 percent lower than the number of covered days in 
the FY 2019 claims used for FY 2022 final rulemaking. This decrease in 
covered days corresponds with a decrease of approximately 27 percent in 
the total simulated FY 2022 IPF PPS payments compared to total 
simulated FY 2021 IPF PPS payments used for FY 2022 final rulemaking. 
After applying the statistical trim, covered days were approximately 32 
percent lower than FY 2019, and total simulated FY 2022 IPF PPS 
payments that we used to estimate the outlier percentage for this FY 
2023 IPF PPS final rule were approximately 30 percent lower than total 
simulated FY 2021 IPF PPS payments. Because we calculate the outlier 
fixed dollar loss threshold amount so that outlier payments represent 2 
percent of total estimated IPF PPS payments, the decrease to the number 
of days and total estimated IPF PPS payments increases the percentage 
of outlier payments relative to total payments, which contributes to 
the upward trend in the outlier fixed dollar loss threshold amount.
    In our simulated FY 2022 outlier payments using the FY 2022 IPF PPS 
outlier fixed dollar loss threshold of $16,040, we estimated that 9,169 
cases will receive outlier payments, with a mean outlier payment amount 
per outlier case of $10,057.59. We observed that the distribution of 
simulated FY 2022 outlier payments is skewed right, which means that a 
large number of outlier cases receive relatively small amounts of 
outlier payments, and a smaller number of outlier cases receive 
relatively large outlier payments. Consequently, half of all simulated 
outlier cases receive outlier payments of $5,490.11 or less, and 1,231 
cases receive outlier payments of $1,000 or less. We also observed that 
outlier payments are concentrated among certain types of IPFs. As shown 
in Table 3, in section VIII.C.2 of this final rule, teaching IPFs with 
more than 10 percent interns and residents to beds are projected to 
experience the largest decreases in estimated payments as a result of 
the increase to the outlier fixed dollar loss threshold amount, because 
these providers had a larger share of outlier cases than other provider 
types. We did not observe that changes in case

[[Page 46864]]

mix appear to be driving the increase in the outlier percentage. In the 
simulated FY 2022 IPF PPS payments, we observed that approximately 79 
percent of outlier cases are for DRG 885 (Psychoses), which aligns with 
the proportion of IPF PPS cases that typically receive that DRG. We 
estimate that the average outlier payment for cases with DRG 885 is 
$10,600.21, which is comparable to the average outlier payment for all 
cases.
    Final Decision: After consideration of the comments received, we 
are finalizing our proposal to use the latest available FY 2021 claims, 
in accordance with our longstanding practice, to simulate payments for 
determining the final FY 2023 IPF PPS outlier fixed dollar loss 
threshold amount. We are also finalizing our proposal to exclude 
providers from our impact simulations whose change in simulated cost 
per day is outside 3 standard deviations from the mean.
    Based on an analysis of the March 2022 update of FY 2021 IPF claims 
and the FY 2022 rate increases, we continue to 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 estimate that IPF outlier payments as a percentage of total 
estimated payments were approximately 3.2 percent in FY 2022. 
Therefore, we are updating the outlier threshold amount to $24,630 to 
maintain estimated outlier payments at 2 percent of total estimated 
aggregate IPF payments for FY 2023. This update is an increase from the 
FY 2022 threshold of $16,040.
3. 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 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 2023, we proposed to continue to follow this methodology. We 
did not receive any comments on this proposal, and we are finalizing it 
as proposed.
    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 2023 is 2.0412 for rural IPFs, and 1.7437 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 proposed to continue to update the FY 2023 national median and 
ceiling CCRs for urban and rural IPFs based on the CCRs entered in the 
latest available IPF PPS PSF. We did not receive any comments on this 
proposal, and we are finalizing it as proposed. Specifically, for FY 
2023, to be used in each of the three situations listed previously, 
using the most recent CCRs entered in the CY 2022 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).

V. Comment Solicitation on Analysis of IPF PPS Adjustments

    In the FY 2023 IPF PPS proposed rule (87 FR 19428 through 19429), 
we discussed the background of the current IPF PPS patient-level and 
facility-level adjustment factors, which are the regression-derived 
adjustment factors from the November 15, 2004 IPF PPS final rule. We 
briefly discussed past analyses and areas of concern for future 
refinement, about which we previously solicited comments. Finally, we 
described the results of the latest analysis of the IPF PPS and 
solicited comments on certain topics from the report.
    As we discussed in the proposed rule, we have undertaken further 
analysis of more recent IPF cost and claim information. In conjunction 
with the FY 2023 IPF PPS proposed rule, we posted a report on the CMS 
website,\2\ which summarizes the results of the latest analysis. We 
noted that this updated analysis finds that the existing IPF PPS model 
continues to be generally appropriate in terms of effectively aligning 
IPF PPS payments with the cost of providing IPF services, but suggests 
that certain updates to the codes, categories, adjustment factors, and 
ECT payment amount per treatment could improve payment accuracy. We 
requested comments on the results of our latest analysis as summarized 
in the report. In particular, we requested comments about the following 
topics, which are discussed in detail in the report:
---------------------------------------------------------------------------

    \2\ The report can be accessed directly via the following link: 
https://www.cms.gov/files/document/technical-report-medicare-program-inpatient-psychiatric-facilities-prospective-payment-system.pdf
---------------------------------------------------------------------------

     The report summarizes results of the analysis regarding 
patient-level characteristics, about which we requested comments:
    ++ The updated regression analysis suggests that certain technical 
changes to the DRG and comorbidity adjustment factors, consolidation of 
the age categories for the patient age adjustment, and changes to the 
adjustment factors for age and length of stay could be appropriate.
    ++ The analysis of ancillary costs for IPF stays with ECT suggests 
that a higher ECT payment amount per treatment could better align IPF 
PPS payments with the costs of furnishing ECT.
    ++ The analysis of the outlier percentage suggests that fewer IPF 
cases

[[Page 46865]]

qualify for outliers under the current 2 percent outlier target than 
were estimated when the IPF PPS was established. We estimate that 
increasing the outlier percentage will increase the number of IPF cases 
that qualify for outliers, but will have distributional effects due to 
budget neutrality.
     The report summarizes the results of analysis regarding 
facility-level characteristics, about which we requested comments:
    ++ The updated regression analysis suggests that updating the 
adjustment factors for teaching facilities, rural facilities, and 
facilities with an ED could improve payment accuracy; however, we 
estimate such changes could have positive and negative effects on 
payments for different types of IPFs.
    ++ The analysis of occupancy-related control variables included in 
the regression model indicates that these control variables are 
correlated with the rural adjustment factor, and that removal of these 
control variables from the model could result in an increase to the 
rural adjustment factor in the regression model.
     The report summarizes certain areas where we believe 
additional research is needed. We requested comments about the results 
summarized in the report. We also requested comments about additional 
analyses that we should undertake to better understand how these issues 
affect the cost of providing IPF services, and how the IPF PPS could 
better account for these costs:
    ++ We analyzed the costs associated with social determinants of 
health, but found that our analysis was confounded by a low frequency 
of IPF claims reporting the applicable ICD-10 diagnosis codes. We 
solicited public comments on the results of this analysis, and whether 
there are additional patient characteristics that affect the cost of 
providing IPF services that may not be consistently reported on claims. 
Additionally, we solicited public comments about how we could better 
identify such patient characteristics and their effects on costs.
    ++ We analyzed the costs associated with the percentage of low-
income patients that IPFs treat, based on a construction of the 
Disproportionate Share Hospitals (DSH) percentage that is used in other 
payment systems using the data currently available for IPFs. We 
solicited public comments about the results of this analysis, which 
suggest that the addition of an adjustment factor for disproportionate 
share intensity could improve the accuracy of IPF PPS payments.
    We received 10 comments in response to the FY 2023 IPF PPS 
pertaining to the report, the analysis of patient-level and facility-
level adjustment factors, and areas of interest for further research. 
Commenters included MedPAC, state-level and national provider and 
patient advocacy organizations, and individual IPF hospitals and health 
systems. We thank commenters for their detailed responses to this 
comment solicitation. We will take these comments into consideration to 
potentially inform future rulemaking.

VI. Inpatient Psychiatric Facility Quality Reporting (IPFQR) Program

A. Overarching Principles for Measuring Equity and Healthcare Quality 
Disparities Across CMS Quality Programs--Request for Information

    Significant and persistent disparities in healthcare outcomes exist 
in the United States. Belonging to an underserved community is often 
associated with worse health outcomes.\3\ \4\ \5\ \6\ \7\ \8\ \9\ \10\ 
\11\ With this in mind, CMS aims to advance health equity, by which we 
mean the attainment of the highest level of health for all people, 
where everyone has a fair and just opportunity to attain their optimal 
health regardless of race, ethnicity, disability, sexual orientation, 
gender identity, socioeconomic status, geography, preferred language, 
or other factors that affect access to care and health outcomes. CMS is 
working to advance health equity by designing, implementing, and 
operationalizing policies and programs that support health for all the 
people served by our programs, eliminating avoidable differences in 
health outcomes experienced by people who are disadvantaged or 
underserved, and providing the care and support that our beneficiaries 
need to thrive.\12\
---------------------------------------------------------------------------

    \3\ Joynt KE, Orav E, Jha AK. (2011). Thirty-day readmission 
rates for Medicare beneficiaries by race and site of care. JAMA, 
305(7):675-681.
    \4\ Lindenauer PK, Lagu T, Rothberg MB, et al. (2013). Income 
inequality and 30-day outcomes after acute myocardial infarction, 
heart failure, and pneumonia: Retrospective cohort study. British 
Medical Journal, 346.
    \5\ Trivedi AN, Nsa W, Hausmann LRM, et al. (2014). Quality and 
equity of care in U.S. hospitals. New England Journal of Medicine, 
371(24):2298- 2308.
    \6\ Polyakova, M., et al. (2021). Racial disparities in excess 
all-cause mortality during the early COVID-19 pandemic varied 
substantially across states. Health Affairs, 40(2): 307-316.
    \7\ Rural Health Research Gateway. (2018). Rural communities: 
Age, Income, and Health status. Rural Health Research Recap. 
Available at https://www.ruralhealthresearch.org/assets/2200-8536/rural-communities-age-income-health-status-recap.pdf . Accessed 
February 3, 2022.
    \8\ U.S. Department of Health and Human Services. Office of the 
Secretary. Progress Report to Congress. HHS Office of Minority 
Health. 2020 Update on the Action Plan to Reduce Racial and Ethnic 
Health Disparities. FY 2020. Available at https://www.minorityhealth.hhs.gov/assets/PDF/Update_HHS_Disparities_Dept-FY2020.pdf . Accessed February 3, 2022.
    \9\ Centers for Disease Control and Prevention. Morbidity and 
Mortality Weekly Report (MMWR). Heslin, KC, Hall JE. Sexual 
Orientation Disparities in Risk Factors for Adverse COVID-19-Related 
Outcomes, by Race/Ethnicity--Behavioral Risk Factor Surveillance 
System, United States, 2017-2019. February 5, 2021/70(5); 149-154. 
Available at https://www.cdc.gov/mmwr/volumes/70/wr/mm7005a1.htm?s_cid=mm7005a1 _w. Accessed February 3, 2022.
    \10\ Poteat TC, Reisner SL, Miller M, Wirtz AL. (2020). COVID-19 
vulnerability of transgender women with and without HIV infection in 
the Eastern and Southern U.S. preprint. medRxiv. 2020;2020.07.21. 
20159327. doi:10.1101/2020.07.21.20159327.
    \11\ Milkie Vu et al. Predictors of Delayed Healthcare Seeking 
Among American Muslim Women, Journal of Women's Health 26(6) (2016) 
at 58; S.B. Nadimpalli, et al., The Association between 
Discrimination and the Health of Sikh Asian Indians.
    \12\ Centers for Medicare and Medicaid Services. Available at 
https://www.cms.gov/pillar/health-equity. Accessed February 9, 2022.
---------------------------------------------------------------------------

    We are committed to advancing equity in healthcare outcomes for our 
beneficiaries by supporting healthcare providers' quality improvement 
activities to reduce health disparities, enabling them to make more 
informed decisions, and promoting healthcare provider accountability 
for healthcare disparities.\13\ Measuring healthcare disparities in 
quality measures is a cornerstone of our approach to advancing health 
equity. Hospital performance results that illustrate differences in 
outcomes between patient populations have been reported to hospitals 
confidentially since 2018.
---------------------------------------------------------------------------

    \13\ CMS Quality Strategy. 2016. Available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Qualityinitiativesgeninfo/downloads/cms-quality-strategy.pdf. Accessed February 3, 2022.
---------------------------------------------------------------------------

    The RFI in the proposed rule (87 FR 19429 through 19437) consisted 
of three sections. The first section discussed a general framework that 
could be utilized across CMS quality programs to assess disparities in 
healthcare quality. The next section outlined approaches that could be 
used in the IPFQR Program to assess drivers of healthcare quality 
disparities in the IPFQR Program. Additionally, this section discussed 
measures of health equity that could be adapted for use in the IPFQR 
Program. Finally, the third section solicited public comment on the 
principles and approaches listed in the first two sections as well as 
sought other thoughts about disparity measurement guidelines for the 
IPFQR Program.
1. Cross-Setting Framework To Assess Healthcare Quality Disparities
    CMS has identified five key considerations that we could apply

[[Page 46866]]

consistently across CMS programs when advancing the use of measurement 
and stratification as tools to address health care disparities and 
advance health equity. The remainder of this section describes each of 
these considerations.
a. Identification of Goals and Approaches for Measuring Healthcare 
Disparities and Using Measures Stratification Across CMS Quality 
Programs
    By quantifying healthcare disparities through measure 
stratification (that is, measuring performance differences among 
subgroups of beneficiaries), we aim to provide useful tools for 
healthcare providers to drive improvement based on data. We hope that 
these results support healthcare providers' efforts in examining the 
underlying drivers of disparities in their patients' care and to 
develop their own innovative and targeted quality improvement 
interventions. Quantification of health disparities can also support 
communities in prioritizing and engaging with healthcare providers to 
execute such interventions, as well as providing additional tools for 
accountability and decision-making.
    There are several different conceptual approaches to reporting 
health disparities in the acute care setting, including two 
complementary approaches that are already used to confidentially 
provide disparity information to hospitals for a subset of existing 
measures. The first approach, referred to as the ``within-hospital 
disparity method,'' compares measure performance results for a single 
measure between subgroups of patients with and without a given factor. 
This type of comparison directly estimates disparities in outcomes 
between subgroups and can be helpful to identify potential disparities 
in care. This type of approach can be used with most measures that 
include patient-level data. The second approach, referred to as the 
``between-hospital disparity methodology,'' provides performance on 
measures for only the subgroup of patients with a particular social 
risk factor. These approaches can be used by a healthcare provider to 
compare their own measure performance on a particular subgroup of 
patients against subgroup-specific state and national benchmarks. 
Alone, each approach may provide an incomplete picture of disparities 
in care for a particular measure, but when reported together with 
overall quality performance, these approaches may provide detailed 
information about where differences in care may exist or where 
additional scrutiny may be appropriate. For example, the between-
provider disparity method may indicate that an IPF underperformed (when 
compared to other facilities on average) for patients with a given 
social risk factor, which would signal the need to improve care for 
this population. However, if the IPF also underperformed for patients 
without that social risk factor, the measured difference, or disparity 
in care, (the ``within-hospital'' disparity, as described above) could 
be negligible even though performance for the group that has been 
historically marginalized remains poor. We refer readers to the 
technical report describing the CMS Disparity Methods in detail as well 
as the FY 2018 IPPS/LTCH PPS final rule (82 FR 38405 through 38407) and 
the posted Disparity methods Updates and Specifications Report posted 
on the QualityNet website.\14\
---------------------------------------------------------------------------

    \14\ Centers for Medicare & Medicaid Services (CMS), HHS. 
Disparity Methods Confidential Reporting. Available at https://qualitynet.cms.gov/inpatient/measures/disparity-methods. Accessed 
February 3, 2022.
---------------------------------------------------------------------------

    CMS is interested in whether similar approaches to the two 
discussed in the previous paragraph could be used to produce 
confidential stratified measure results for selected IPF QRP measures, 
as appropriate and feasible. However, final decisions regarding 
disparity reporting will be made at the program-level, as CMS intends 
to tailor the approach used in each setting to achieve the greatest 
benefit and avoid unintentional consequences or biases in measurement 
that may exacerbate disparities in care.
b. Guiding Principles for Selecting and Prioritizing Measures for 
Disparity Reporting
    We intend to expand our efforts to provide stratified reporting for 
additional clinical quality measures, provided they offer meaningful, 
actionable, and valid feedback to healthcare providers on their care 
for populations that may face social disadvantage or other forms of 
discrimination or bias. We are mindful, however, that it may not be 
possible to calculate stratified results for all quality measures, and 
that there may be situations where stratified reporting is not desired. 
To help inform prioritization of the next generation of candidate 
measures for stratified reporting, we aim to receive feedback on 
several systematic principles under consideration that we believe will 
help us prioritize measures for disparity reporting across programs:
    (1) Programs may consider stratification among existing clinical 
quality measures for further disparity reporting, prioritizing 
recognized measures which have met industry standards for measure 
reliability and validity.
    (2) Programs may consider measures for prioritization that show 
evidence that a treatment or outcome being measured is affected by 
underlying healthcare disparities for a specific social or demographic 
factor. Literature related to the measure or outcome should be reviewed 
to identify disparities related to the treatment or outcome, and should 
carefully consider both social risk factors and patient demographics. 
In addition, analysis of Medicare-specific data should be done in order 
to demonstrate evidence of disparity in care for some or most 
healthcare providers that treat Medicare patients.
    (3) Programs may consider establishing statistical reliability and 
representation standards (for example, the percent of patients with a 
social risk factor included in reporting facilities) prior to reporting 
results. They may also consider prioritizing measures that reflect 
performance on greater numbers of patients to ensure that the reported 
results of the disparity calculation are reliable and representative.
    (4) After completing stratification, programs may consider 
prioritizing the reporting of measures that show differences in measure 
performance between subgroups across healthcare providers.
c. Principles for Social Risk Factor and Demographic Data Selection and 
Use
    Social risk factors are the wide array of non-clinical drivers of 
health known to negatively impact patient outcomes. These include 
factors such as socioeconomic status, housing availability, and 
nutrition (among others), often inequitably affecting historically 
marginalized communities on the basis of race and ethnicity, rurality, 
sexual orientation and gender identity, religion, and 
disability.15 16 17 18 19 20 21 22
---------------------------------------------------------------------------

    \15\ Joynt KE, Orav E, Jha AK. (2011). Thirty-day readmission 
rates for Medicare beneficiaries by race and site of care. JAMA, 
305(7):675-681.
    \16\ Lindenauer PK, Lagu T, Rothberg MB, et al. (2013). Income 
inequality and 30-day outcomes after acute myocardial infarction, 
heart failure, and pneumonia: retrospective cohort study. British 
Medical Journal, 346.
    \17\ Trivedi AN, Nsa W, Hausmann LRM, et al. (2014). Quality and 
equity of care in U.S. hospitals. New England Journal of Medicine, 
371(24):2298- 2308.
    \18\ Polyakova, M., et al. (2021). Racial disparities in excess 
all-cause mortality during the early COVID-19 pandemic varied 
substantially across states. Health Affairs, 40(2): 307-316.
    \19\ Rural Health Research Gateway. (2018). Rural communities: 
Age, Income, and Health status. Rural Health Research Recap. 
Available at https://www.ruralhealthresearch.org/assets/2200-8536/rural-communities-age-income-health-status-recap.pdf. Accessed 
February 3, 2022.
    \20\ HHS Office of Minority Health (2020). 2020 Update on the 
Action Plan to Reduce Racial and Ethnic Health Disparities. 
Available at https://www.minorityhealth.hhs.gov/assets/PDF/Update_HHS_Disparities_Dept-FY2020.pdf Accessed February 3, 2022.
    \21\ Poteat TC, Reisner SL, Miller M, Wirtz AL. 2020. COVID-19 
vulnerability of transgender women with and without HIV infection in 
the Eastern and Southern U.S. medRxiv [Preprint]. 
2020.07.21.20159327. doi: 10.1101/2020.07.21.20159327. PMID: 
32743608; PMCID: PMC7386532.
    \22\ Milkie Vu et al. Predictors of Delayed Healthcare Seeking 
Among American Muslim Women, Journal of Women's Health 26(6) (2016) 
at 58; S.B. Nadimpalli, et al., The Association between 
Discrimination and the Health of Sikh Asian Indians.

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[[Page 46867]]

    Identifying and prioritizing social risk or demographic variables 
to consider for disparity reporting can be challenging. This is due to 
the high number of variables that have been identified in the 
literature as risk factors for poorer health outcomes and the limited 
availability of many self-reported social risk factors and demographic 
factors across the healthcare sector. Several proxy data sources, such 
as area-based indicators of social risk and imputation methods, may be 
used if individual patient-level data is not available. Each source of 
data has advantages and disadvantages for disparity reporting:
     Patient-reported data are considered to be the gold 
standard for evaluating quality of care for patients with social risk 
factors.\23\ While data sources for many social risk factors and 
demographic variables are still developing among several CMS settings, 
the IPFQR Program will begin collecting mandatory patient-level data 
for certain chart-abstracted measures the FY 2024 payment determination 
and subsequent years (86 FR 42608).
---------------------------------------------------------------------------

    \23\ Jarr[iacute]n OF, Nyandege AN, Grafova IB, Dong X, Lin H. 
(2020). Validity of race and ethnicity codes in Medicare 
administrative data compared with gold-standard self-reported race 
collected during routine home health care visits. Med Care, 
58(1):e1-e8. doi: 10.1097/MLR.0000000000001216. PMID: 31688554; 
PMCID: PMC6904433.
---------------------------------------------------------------------------

     CMS Administrative Claims data have long been used for 
quality measurement due to their availability and will continue to be 
evaluated for usability in measure development and or stratification. 
Using these existing data allows for high impact analyses with 
negligible healthcare provider burden. For example, dual eligibility 
for Medicare and Medicaid has been found to be an effective indicator 
of social risk in beneficiary populations.\24\ There are, however, 
limitations in these data's usability for stratification analysis.
---------------------------------------------------------------------------

    \24\ Office of the Assistant Secretary for Planning and 
Evaluation. Report to Congress: Social Risk factors and Performance 
Under Medicare's Value-Based Purchasing Program. December 20, 2016. 
Available at https://www.aspe.hhs.gov/reports/report-congress-social-risk-factors-performance-under-medicares-value-based-purchasing-programs. Accessed February 3, 2022.
---------------------------------------------------------------------------

     Area-based indicators of social risk create approximations 
of patient risk based on the neighborhood or context that a patient 
resides in. Several indexes, such as Agency for Healthcare Research and 
Quality (AHRQ) Socioeconomic Status (SES) Index,\25\ Centers for 
Disease Control and Prevention/Agency for Toxic Substances and Disease 
Registry (CDC/ATSDR) Social Vulnerability Index (SVI),\26\ and Health 
Resources and Services Administration (HRSA) Area Deprivation Index 
(ADI),\27\ provide multifaceted contextual information about an area 
and may be considered as an efficient way to stratify measures that 
include many social risk factors.
---------------------------------------------------------------------------

    \25\ Bonito A., 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. Available at 
https://archive.ahrq.gov/research/findings/final-reports/medicareindicators/medicareindicators1.html. Accessed February 7, 
2022.
    \26\ Flanagan, B.E., Gregory, E.W., Hallisey, E.J., Heitgerd, 
J.L., Lewis, B. (2011). A social vulnerability index for disaster 
management. Journal of Homeland Security and Emergency Management, 
8(1). Available at https://www.atsdr.cdc.gov/placeandhealth/svi/img/pdf/Flanagan_2011_SVIforDisasterManagement-508.pdf. Accessed 
February 3, 2022.
    \27\ Center for Health Disparities Research. University of 
Wisconsin School of Medicine and Public health. Neighborhood Atlas. 
Available at https://www.neighborhoodatlas.medicine.wisc.edu/. 
Accessed February 3, 2022.
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     Imputed data sources use statistical techniques to 
estimate patient-reported factors, including race and ethnicity. One 
such tool is the Medicare Bayesian Improved Surname Geocoding (MBISG) 
method (currently in version 2.1), which combines information from 
administrative data, surname, and residential location to estimate 
patient race and ethnicity. \28\
---------------------------------------------------------------------------

    \28\ Haas A., Elliott M.N., Dembosky J.W., Adams J.L., Wilson-
Frederick S.M., Mallett J.S.,, Gaillot S, Haffer S.C., Haviland A.M. 
(2019). Imputation of race/ethnicity to enable measurement of HEDIS 
performance by race/ethnicity. Health Serv Res, 54(1):13-23. doi: 
10.1111/1475-6773.13099. Epub 2018 Dec 3. PMID: 30506674; PMCID: 
PMC6338295. Imputation of race/ethnicity to enable measurement of 
HEDIS performance by race/ethnicity. Available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6338295/pdf/HESR-54-13.pdf. 
Accessed February 3, 2022.
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d. Identifying Meaningful Performance Differences
    While we aim to use standardized approaches where possible, 
identifying differences in performance on stratified results will be 
made at the program level due to contextual variations across programs 
and settings. We requested comments on the benefits and limitations of 
the possible reporting approaches described below:
     Statistical approaches could be used to reliably group 
results, such as using confidence intervals, creating cut points based 
on standard deviations, or using a clustering algorithm.
     Programs could use a ranked ordering and percentile 
approach, ordering healthcare providers in a ranked system based on 
their performance on disparity measures to quickly allow them to 
compare their performance to other similar healthcare providers.
     Healthcare providers could be categorized into groups 
based on their performance using defined thresholds, such as fixed 
intervals of results of disparity measures, indicating different levels 
of performance.
     Benchmarking, or comparing individual results to state or 
national average, is another potential reporting strategy.
     Finally, a ranking system may not be appropriate for all 
programs and care settings, and some programs may only report disparity 
results.
e. Guiding Principles for Reporting Disparity Measures
    Reporting of the results discussed above can be employed in several 
ways to drive improvements in quality. Confidential reporting, or 
reporting results privately to healthcare providers, is generally used 
for new programs or new measures recently adopted for programs through 
notice and comment rulemaking to give healthcare providers an 
opportunity to become more familiar with calculation methods and to 
improve before other forms of reporting are used. In addition, many 
results are reported publicly, in accordance with the statute. This 
method provides all stakeholders with important information on 
healthcare provider quality, and in turn, relies on market forces to 
incentivize healthcare providers to improve and become more competitive 
in their markets without directly influencing payment from CMS. One 
important consideration is to assess differential impact on IPFs, such 
as those located in rural, or critical access areas, to ensure that 
reporting does not disadvantage already resource-limited

[[Page 46868]]

settings. The type of reporting chosen by programs will depend on the 
program context.
    Regardless of the methods used to report results, it is important 
to report stratified measure data alongside overall measure results. 
Review of both measures results along with stratified results can 
illuminate greater levels of detail about quality of care for subgroups 
of patients, providing important information to drive quality 
improvement. Unstratified quality measure results address general 
differences in quality of care between healthcare providers and promote 
improvement for all patients, but unless stratified results are 
available, it is unclear if there are subgroups of patients that 
benefit most from initiatives. Notably, even if overall quality measure 
scores improve, without identifying and measuring differences in 
outcomes between groups of patients, it is impossible to track progress 
in reducing disparity for patients with heightened risk of poor 
outcomes.

B. Approaches to Assessing Drivers of Healthcare Quality Disparities 
and Developing Measures of Healthcare Equity in the IPFQR Program

    This section presents information on two approaches for the IPFQR 
Program. The first section presents information about a method that 
could be used to assist IPFs in identifying potential drivers of 
healthcare quality disparities. The second section describes measures 
of health equity that might be appropriate for inclusion in the IPFQR 
Program.
a. Performance Disparity Decomposition
    In response to the FY 2022 IPF PPS proposed rule's RFI (86 FR 19494 
through 19500), ``Closing the Health Equity Gap in CMS Quality 
Programs,'' some stakeholders noted that identifying which factors are 
contributing to the performance gaps may not always be straightforward, 
especially if the IPF has limited information or resources to determine 
the extent to which a patient's driver of health or other mediating 
factors (for example: health histories) explain a given disparity. An 
additional complicating factor is the reality that there are likely 
multiple social determinants of health (SDOH) and other mediating 
factors responsible for a given disparity, and it may not be obvious to 
the IPF which of these factors are the primary drivers.
    Consequently, CMS may consider methods to use the data already 
available in enrollment, claims, and assessment data to estimate the 
extent to which various SDOH (for example, transportation, health 
literacy) and other mediating factors drive disparities in an effort to 
provide more actionable information. Researchers have utilized 
decomposition techniques to examine inequality in health care and, 
specifically, as a way to understand and explain the underlying causes 
of inequality.\29\ At a high level, regression decomposition is a 
method that allows one to estimate the extent to which disparities 
(that is, differences) in measure performance between subgroups of 
patient populations are due to specific factors. These factors can be 
either non-clinical (for example, SDOH) or clinical. Similarly, CMS may 
utilize regression decomposition to identify and calculate the specific 
contribution of SDOHs and other mediating factors to observed 
disparities. This approach may better inform our understanding of the 
extent to which providers and policy-makers may be able to narrow the 
gap in healthcare outcomes. Additionally, provider-specific 
decomposition results could be shared through confidential results so 
that IPFs can see the disparities within their facility with more 
granularity, allowing them to set priority targets in some performance 
areas while knowing which areas of their care are already relatively 
equitable. Importantly, these results could help IPFs identify reasons 
for disparities that might not be obvious without having access to 
additional data sources (for example: the ability to link data across 
providers).
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    \29\ Rahimi E, Hashemi Nazari S. A detailed explanation and 
graphical representation of the Blinder-Oaxaca decomposition method 
with its application in health inequalities. Emerg Themes Epidemiol. 
(2021)18:12. https://doi.org/10.1186/s12982-021-00100-9. Retrieved 
2/24/2022.
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    To more explicitly demonstrate the types of information that could 
be provided through decomposition of a measure disparity, consider the 
following example for a given IPF. Figures 1 through 3 depict an 
example (using hypothetical data) of how a disparity in a measure of 
Medicare Spending Per Beneficiary (MSPB) between dual eligible 
beneficiaries (that is, those enrolled in Medicare and Medicaid) and 
non-dual eligible beneficiaries (that is, those with Medicare only) 
could be decomposed among two mediating factors, one SDOH and one 
clinical factor: (1) low health literacy and (2) high volume of 
emergency department (ED) use. These examples were selected because 
they are factors the healthcare provider could mitigate the effects of, 
if they were shown to be drivers of disparity in their IPF. 
Additionally, high volume ED use is used as a potential mediating 
factor that could be difficult for IPFs to determine on their own, as 
it will require having longitudinal data for patients across multiple 
facilities.
    In Figure 1, the overall Medicare spending disparity is $1,000: 
spending, on average, is $5,000 per non-dual beneficiary and $6,000 per 
dual beneficiary. We can also see from Figure 2 that in this IPF, the 
dual population has twice the prevalence of beneficiaries with low 
health literacy and high ED use compared to the non-dual population. 
Using regression techniques, the difference in overall spending between 
non-dual and dual beneficiaries can be divided into three causes: (1) a 
difference in the prevalence of mediating factors (for example: low 
health literacy and high ED use) between the two groups; (2) a 
difference in how much spending is observed for beneficiaries with 
these mediating factors between the two groups; and (3) differences in 
baseline spending that are not due to either (1) or (2). In Figure 3, 
the `Non-Dual Beneficiaries' column breaks down the overall spending 
per non-dual beneficiary, $5,000, into a baseline spending of $4,600 
plus the effects of the higher spending for the 10 percent of non-dual 
beneficiaries with low health literacy ($300) and the 5 percent with 
high ED use ($100). The `Dual Beneficiaries' column similarly 
decomposes the overall spending per dual beneficiary ($6,000) into a 
baseline spending of $5,000, plus the amounts due to dual 
beneficiaries' 20 percent prevalence of low health literacy ($600, 
twice as large as the figure for non-dual beneficiaries because the 
prevalence is twice as high), and dual beneficiaries' 10 percent 
prevalence of high-volume ED use ($200, similarly twice as high as for 
non-duals beneficiaries due to higher prevalence). This column also 
includes an additional $100 per risk factor because dual beneficiaries 
experience a higher cost than non-dual beneficiaries within the low 
health literacy risk factor, and similarly within the high ED use risk 
factor. Based on this information, an IPF can determine that the 
overall $1,000 disparity can be divided into differences simply due to 
risk factor prevalence ($300 + $100 = $400 or 40 percent of the total 
disparity), disparities in costs for beneficiaries with risk factors 
($100 + $100 = $200 or 20 percent) and disparities that remain 
unexplained (differences in baseline costs: $400 or 40 percent).
    In particular, the IPF can see that simply having more patients 
with low

[[Page 46869]]

health literacy and high ED use accounts for a disparity of $400. In 
addition, there is still a $200 disparity stemming from differences in 
costs between non-dual and dual patients for a given risk factor, and 
another $400 that is not explained by either low health literacy or 
high ED use. These differences may instead be explained by other SDOH 
that have not yet been included in this breakdown, or by the 
distinctive pattern of care decisions made by providers for dual and 
non-dual beneficiaries. These cost estimates will provide additional 
information that facilities could use when determining where to devote 
resources aimed at achieving equitable health outcomes (for example, 
facilities may choose to focus efforts on the largest drivers of a 
disparity).
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b. Measures Related to Health Equity
    Beyond identifying disparities in individual health outcomes and by 
individual risk factors, there is interest in developing more 
comprehensive measures of health equity that reflect organizational 
performance. When determining which equity measures could be 
prioritized for development for the IPFQRP Program, CMS may consider 
the following:
     Measures should be actionable in terms of quality 
improvement;
     Measures should help beneficiaries and their caregivers 
make informed healthcare decisions;
     Measures should not create incentives to lower the quality 
of care; and
     Measures should adhere to high scientific acceptability 
standards.
    CMS has developed measures assessing health equity, or designed to 
promote health equity, in other settings outside of the IPF. As a 
result, there may be measures that could be adapted for use in the 
IPFQR Program. The remainder of this section discusses two such 
measures, beginning with the Health Equity Summary Score (HESS), and 
then a structural measure assessing the degree of hospital leadership 
engagement in health equity performance data.
(1) Health Equity Summary Score
    The HESS measure was developed by the CMS OMH 30 31 to 
identify and to reward healthcare providers (that is, Medicare 
Advantage [MA] plans) that perform relatively well on measures of care 
provided to beneficiaries with social risk factors (SRFs), as well as 
to discourage the non-treatment of patients who are potentially high-
risk, in the context of value-based purchasing. Additionally, a version 
of the HESS is under consideration for the Hospital Inpatient Quality 
Reporting (HIQR) program.\32\ The HESS composite measure provides a 
summary of equity of care delivery by combining performance and 
improvement across multiple measures and multiple at-risk groups. The 
HESS was developed with the following goals: allow for ``multiple 
grouping variables, not all of which will be measurable for all 
plans,'' allow for ``disaggregation by grouping variable for nuanced 
insights,'' and allow for the future usage of additional and different 
SRFs for grouping.\33\
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    \30\ Agniel D., Martino S.C., Burkhart Q., Hambarsoomian K., Orr 
N., Beckett M.K., James C., Scholle S.H., WilsonFrederick S., Ng J., 
Elliott M.N. (2021). Incentivizing excellent care to at-risk groups 
with a health equity summary score. J Gen Intern Med, 36(7):1847-
1857. doi: 10.1007/s11606-019-05473-x. Epub 2019 Nov 11. PMID: 
31713030; PMCID: PMC8298664. Available at https://link.springer.com/content/pdf/10.1007/s11606-019-05473-x.pdf. Accessed February 3, 
2022.
    \31\ 2021 Quality Conference. Health Equity as a ``New Normal'': 
CMS Efforts to Address the Causes of Health Disparities. Available 
at https://s3.amazonaws.com/bizzabo.file.upload/83kO1DYXTs6mKHjVtuk8_1%20-%20Session%2023%20Health%20Equity%20New%20Normal%20FINAL_508.pdf. 
Accessed March 2, 2022.
    \32\ Centers for Medicare & Medicaid Services, FY 2022 IPPS/LTCH 
PPS Proposed Rule. 88 FR 25560. May 10, 2021.
    \33\ Centers for Medicare & Medicaid Services Office of Minority 
Health (CMS OMH). 2021b. ``Health Equity as a `New Normal': CMS 
Efforts to Address the Causes of Health Disparities.'' Presented at 
CMS Quality Conference, March 2-3, 2021.
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    The HESS computes across-provider disparity in performance, as well 
as within-provider and across-provider disparity improvement in 
performance. Calculation starts with a cross-sectional score and an 
overall improvement score for each SRF of race/ethnicity and dual 
eligibility, for each plan. The overall improvement score is based on 
two separate improvement metrics: within-plan improvement and 
nationally benchmarked improvement. Within-plan improvement is defined 
as how that plan improves the care of patients with SRFs relative to 
higher-performing patients between the baseline period and performance 
period, and is targeted at eliminating within-plan disparities. 
Nationally benchmarked improvement is improvement of care for 
beneficiaries with SRFs served by that MA plan, relative to the 
improvement of care for similar beneficiaries across all MA plans, and 
is targeted at improving the overall care of populations with SRFs. 
Within-plan improvement and nationally benchmarked improvement are then 
combined into an overall

[[Page 46871]]

improvement score. Meanwhile, the cross-sectional score measures 
overall measure performance among beneficiaries with SRFs during the 
performance period, regardless of improvement.
    To calculate a provider's overall score, the HESS uses a composite 
of five clinical quality measures based on HEDIS data and seven MA 
Consumer Assessment of Healthcare Providers and Systems (CAHPS) patient 
experience measures. A provider's overall HESS score is calculated once 
using only CAHPS-based measures and once using only HEDIS-based 
measures, due to incompatibility between the two data sources. The HESS 
uses a composite of these measures to form a cross-sectional score, a 
nationally benchmarked improvement score, and a within-plan improvement 
score, one for each SRF. These scores are combined to produce an SRF-
specific blended score, which is then combined with the blended score 
for another SRF to produce the overall HESS.
(2) Degree of Hospital Leadership Engagement in Health Equity 
Performance Data
    CMS has developed a structural measure for use in acute care 
hospitals assessing the degree to which hospital leadership is engaged 
in the collection of health equity performance data, with the 
motivation that organizational leadership and culture can play an 
essential role in advancing equity goals. This structural measure, 
entitled the Hospital Commitment to Health Equity measure (MUC2021-106) 
was included on the 2021 CMS List of Measures Under Consideration (MUC 
List) \34\ for acute inpatient hospitals and assesses hospital 
commitment to health equity using a suite of equity-focused 
organizational competencies aimed at achieving health equity for racial 
and ethnic minorities, people with disabilities, sexual and gender 
minorities, individuals with limited English proficiency, rural 
populations, religious minorities, and people facing socioeconomic 
challenges. The measure would include five attestation-based questions, 
each representing a separate domain of commitment. A hospital would 
receive a point for each domain where they attest to the corresponding 
statement (for a total of 5 points). At a high level, the five domains 
cover the following areas: (1) strategic plan to reduce health 
disparities; (2) approach to collecting valid and reliable demographic 
and SDOH data; (3) analyses performed to assess disparities; (4) 
engagement in quality improvement activities; \35\ and (5) leadership 
involvement in activities designed to reduce disparities. The specific 
questions requested within each domain, as well as the detailed measure 
specification are found in the CMS MUC List for December 2021 at 
https://www.cms.gov/files/document/measures-under-consideration-list-2021-report.pdf. A hospital could receive a point for each domain where 
data are submitted through a CMS portal to reflect actions taken by the 
hospital for each corresponding domain (for a point total). If we were 
to consider this measure for the IPFQR Program, we would include it for 
this program on a future MUC list.
---------------------------------------------------------------------------

    \34\ Centers for Medicare & Medicaid Services. List of Measures 
Under Consideration for December 1, 2021. Available at https://www.cms.gov/files/document/measures-under-consideration-list-2021-report.pdf. Accessed 3/1/2022.
    \35\ As described in our guide to quality measurement and 
quality improvement, the National Academy of Medicine defines 
quality as the degree to which health services for individuals and 
populations increase the likelihood of desired health outcomes and 
are consistent with current professional knowledge. Quality 
improvement is the framework used to systematically improve care. 
Quality improvement seeks to standardize processes and structure to 
reduce variation, achieve predictable results, and improve outcomes 
for patients, healthcare systems, and organizations. Structure 
includes things like technology, culture, leadership, and physical 
capital; process includes knowledge capital (for example, standard 
operating procedures) or human capital (for example, education and 
training). Available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/MMS/Quality-Measure-and-Quality-Improvement--. Accessed 3/1/2022.
---------------------------------------------------------------------------

    CMS believes this type of organizational commitment structural 
measure may complement the health disparities approach described in 
previous sections, and support IPFs in quality improvement, efficient, 
effective use of resources, and leveraging available data. As defined 
by AHRQ, structural measures aim to ``give consumers a sense of a 
healthcare provider's capacity, systems, and processes to provide high-
quality care.'' \36\ We acknowledge that collection of this structural 
measure may impose administrative and/or reporting requirements for 
IPFs.
---------------------------------------------------------------------------

    \36\ Agency for Healthcare Research and Quality. Types of Health 
Care Quality Measures. 2015. Available at https://www.ahrq.gov/talkingquality/measures/types.html. Accessed February 3, 2022.
---------------------------------------------------------------------------

    We requested feedback from stakeholders on conceptual and 
measurement priorities for the IPFQR Program to better illuminate 
organizational commitment to health equity.

C. Solicitation of Public Comment

    We requested information with the goal to describe key principles 
and approaches that we will consider when advancing the use of quality 
measure development and stratification to address healthcare 
disparities and advance health equity across our programs.
    We invited general comments on the principles and approaches 
described previously in this section of the rule, as well as additional 
thoughts about disparity measurement or stratification guidelines 
suitable for overarching consideration across CMS' QRP programs. 
Specifically, we invited comment on:

 Identification of Goals and Approaches for Measuring 
Healthcare Disparities and Using Measure Stratification Across CMS 
Quality Reporting Programs
    ++ The use of the within- and between-provider disparity methods in 
IPFs to present stratified measure results
    ++ The use of decomposition approaches to explain possible causes 
of measure performance disparities
    ++ Alternative methods to identify disparities and the drivers of 
disparities
 Guiding Principles for Selecting and Prioritizing Measures for 
Disparity Reporting
    ++ Principles to consider for prioritization of health equity 
measures and measures for disparity reporting, including prioritizing 
stratification for validated clinical quality measures, those measures 
with established disparities in care, measures that have adequate 
sample size and representation among healthcare providers and outcomes, 
and measures of appropriate access and care.
 Principles for Social Risk Factor and Demographic Data 
Selection and Use
    ++ Principles to be considered for the selection of social risk 
factors and demographic data for use in collecting disparity data 
including the importance of expanding variables used in measure 
stratification to consider a wide range of social risk factors, 
demographic variables and other markers of historic disadvantage. In 
the absence of patient-reported data we will consider use of 
administrative data, area-based indicators and imputed variables as 
appropriate
 Identification of Meaningful Performance Differences
    ++ Ways that meaningful difference in disparity results should be

[[Page 46872]]

considered.
 Guiding Principles for Reporting Disparity Measures
    ++ Guiding principles for the use and application of the results of 
disparity measurement.
 Measures Related to Health Equity
    ++ The usefulness of a HESS score for IPFs, both in terms of 
provider actionability to improve health equity, and in terms of 
whether this information would support Care Compare website users in 
making informed healthcare decisions.
    ++ The potential for a structural measure assessing an IPF's 
commitment to health equity, the specific domains that should be 
captured, and options for reporting this data in a manner that would 
minimize burden.
    ++ Options to collect facility-level information that could be used 
to support the calculation of a structural measure of health equity.
    ++ Other options for measures that address health equity.
    Consistent with what we stated in the proposed rule, we will not be 
responding to specific comments submitted in response to this RFI in 
this final rule, we will actively consider all input as we develop 
future policies that address these issues. Any updates to specific 
program requirements related to quality measurement and reporting 
provisions would be addressed through separate and future notice-and-
comment rulemaking, as necessary. Below is a summary of the comments we 
received in response to this request for information.
    We received the following comments in response to our request for 
information.
    Comment: Many commenters expressed support for reporting stratified 
IPF measures, specifically recommending providing these data in 
confidential reports prior to public reporting. Some commenters 
described potential benefits of public reporting including improved 
transparency, increased provider accountability, and use of market 
forces to drive improvement. Several commenters provided 
recommendations for developing a stratified reporting strategy, 
including focusing on data that cannot be calculated independently by 
IPFs, providing support to the public in interpreting the data, and 
analyzing the effects of potential confounders when developing reports. 
One commenter recommended that IPFs only be compared to other IPFs in 
between-provider analyses.
    Some commenters expressed concerns regarding stratified data 
reporting. One commenter expressed that publicly reported stratified 
data could lead to the perception that it is acceptable for some 
subgroups to experience worse care. This commenter recommended the use 
of performance benchmarks or national thresholds instead of the 
between-provider disparity method. Several commenters expressed concern 
that the burden of collecting data for stratifying the chart-based 
measure outweighs the potential benefit of stratifying these measures, 
especially given small numbers of patients in each stratum and high 
overall performance on the measures. Some of these commenters 
specifically stated that IPFs do not have widespread electronic health 
technology to support this data collection. Several commenters were 
concerned that there may be unintended consequences of reporting data 
based on a small sample and recommended that CMS establish a minimum 
sample size for subgroup reporting. Another commenter recommended using 
estimates of variability (that is, confidence intervals) when reporting 
data. Another commenter observed that while stratification of claims-
based measures is less burdensome, this reporting would exclude 
patients with private insurance coverage and rely on data, which are 
not self-reported. Some commenters recommended that CMS analyze the 
predictive power of drivers of health compared to the predictive power 
of the diagnosis requiring treatment prior to stratifying any measures 
by drivers of health. Another commenter recommended further analysis of 
regression decomposition prior to considering this technique in data 
reporting. Some commenters expressed that stratification based on dual-
eligibility creates bias due to state-level variation in Medicaid 
eligibility. One commenter recommended stratifying based on eligibility 
for the low-income subsidy (LIS) instead. One commenter cautioned CMS 
to ensure patient privacy is safeguarded, especially when reporting on 
small samples.
    Many commenters expressed support for the collection of data 
(including race, ethnicity, language, and other factors) to support 
increased reporting of stratified data, though these commenters 
observed that there are not currently industry standards for most of 
these data and recommended developing standard terminology prior to 
proceeding. One commenter expressed that this data collection could 
improve provider interventions and performance in providing care. Some 
commenters recommended that CMS partner with other entities such as 
states and private payors to align data collection requirements. Some 
commenters recommended that CMS evaluate use of claims to identify 
drivers of health, such as by using payment programs to incentive the 
use of ICD-10 Z Codes. One commenter observed that if CMS were to adopt 
a patient experience of care measure in this setting the same 
collection instrument could be used to collect self-reported 
demographic data. Other commenters supported use of proxy variables, 
such as indices or other data sets, when self-reported data are 
unavailable. Some commenters supported further research into 
statistical imputation prior to use in stratification.
    Many commenters expressed concerns about potentially adapting the 
HESS for this setting. Some commenters observed that an aggregated 
score may not be actionable for many facilities, with one commenter 
recommending only reporting such a measure with all its component 
scores. One commenter cautioned that in using a composite score a 
single risk factor could mask the effects of other risk factors. 
Another commenter stated that HESS scoring may not be practical for 
many smaller facilities, or facilities whose enrolled populations 
differ in drivers of health distribution patterns compared to typical 
MA plans. Several commenters expressed the belief that the measures 
underlying the HESS (HEDIS and CAHPS) are not applicable for the IPF 
settings. Another commenter observed that calculation of a HESS-type 
measure would require standardized demographic data collection for all 
patients. One commenter recommended that if CMS were to develop a 
summary measure for quality reporting programs for settings other than 
IPFs, it should include behavioral health measures in the composite 
because socially at-risk groups often experience poor mental health 
outcomes.
    Many commenters supported the Degree of Hospital Leadership 
Engagement in Health Equity Performance Data measure concept. Some of 
these commenters recommended that CMS adopt this structural measure 
before process or outcome measures related to health equity. However, 
several commenters provided recommendations or expressed concerns about 
this measure. Several commenters observed that the measure as specified 
would be difficult for many IPFs to report due to the requirement to 
use certified electronic health record technology (CEHRT). One 
commenter expressed that there is no evidence that performance on this

[[Page 46873]]

measure is associated with improved patient outcomes. One commenter 
recommended adopting an audit procedure along with this measure. 
Another commenter recommended adding a different attestation measure on 
other efforts to gauge hospital data collection efforts (for example, 
the Leapfrog Hospital Survey).
    Many commenters observed that there are measures of patient 
experience of care for other settings and that having such a measure in 
the IPF setting would improve public accountability and quality of 
care. A few commenters stated that a patient experience of care measure 
is necessary to improve the equity of care provided by IPFs.
    Several commenters stated that improving health equity would 
require government investment in addressing social needs, such as 
reducing financial barriers to access. One commenter observed that 
having such an investment would reduce provider frustration with data 
collection requirements.
    Several commenters recommended linking payment to equity 
performance; these commenters specifically recommended the use of 
incentives to avoid unintended consequences for socially at-risk 
patients. One commenter recommended the use of peer grouping (that is, 
comparing each provider's performance with providers with similar mixes 
of patients, that is, its ``peers,'' to determine rewards or penalties 
based on performance) within value based purchasing (VBP) programs.
    Several commenters supported the suggested criteria for 
prioritizing equity measures and recommended additional criteria 
including building on existing health equity strategies, balancing 
administrative burden, allowing flexibility, relying on existing data 
sources, relying on measures that include self-reported data in the 
measure structure, providing timely feedback, expanding to include 
resource use measures, and aligning with states and other payors.
    Some commenters provided general feedback on the concept of using 
quality reporting programs to reduce healthcare disparities. Several 
commenters observed that quality improvement initiatives are often 
initiated at the system level and therefore measurement should be at 
the system level to avoid duplicative reporting requirements. Another 
commenter expressed the belief that it would be appropriate to update 
the conditions of participation to address health equity. Other 
commenters recommended that any effort to use quality reporting to 
reduce healthcare disparities should include detailed definitions of 
all variables (for example, health outcomes, hospital leadership).
    Response: 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 
note that in the FY 2023 IPPS/LTCH PPS proposed rule, we proposed 
several measures related to health equity for the Hospital Inpatient 
Quality Reporting (IQR) program. Specifically, we proposed the Hospital 
Commitment to Health Equity measure (87 FR 28492 through 28497) and two 
social drivers of health measures (87 FR 28497 through 28506). and we 
may consider these or similar measures for other quality reporting 
programs, such as the IPFQR Program in the future. Additionally, we 
refer readers to the FY 2022 IPF PPS final rule in which we described 
our initial request for information on the concept of an equity summary 
score for the IPF setting and summarized the input we received (86 FR 
42625 through 42632). We will continue to take all concerns, comments, 
and suggestions into account for future development and expansion of 
our health equity quality measurement efforts. If we determine that a 
measure, including a patient experience of care measure, a health 
equity measure, or any other measure is appropriate for the IPFQR 
program we will follow the pre-rulemaking process as described on our 
website (https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityMeasures/Pre-Rulemaking).
    For more information on our ongoing effort to address health 
equity, we refer readers to our recently released updated CMS Quality 
Strategy (https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/CMS-Quality-Strategy) and 
our Framework for Health Equity (https://www.cms.gov/About-CMS/Agency-Information/OMH/equity-initiatives/framework-for-health-equity) in 
which we describe our five priorities for advancing health equity.

VII. Collection of Information Requirements

    This final rule updates the prospective payment rates, outlier 
threshold, and wage index for Medicare inpatient hospital services 
provided by IPFs. It also establishes a permanent mitigation policy for 
providers negatively affected by changes to the IPF PPS wage index. 
While discussed in section IV (Comment Solicitation on Analysis of IPF 
PPS Adjustments) of this preamble, the active requirements and burden 
associated with our hospital cost report form CMS-2552-10 (OMB control 
number 0938-0050) are unaffected by this rule.
    Therefore, this document does not impose information collection 
requirements, that is, reporting, recordkeeping or third-party 
disclosure requirements. Consequently, there is no need for review by 
the Office of Management and Budget under the authority of the 
Paperwork Reduction Act of 1995 (44 U.S.C. 3501 et seq.).

VIII. 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 2023 (October 1, 2022 through September 30, 2023). 
We are finalizing our proposal to apply the 2016-based IPF market 
basket increase of 4.1 percent, less the productivity adjustment of 0.3 
percentage point as required by section 1886(s)(2)(A)(i) of the Act for 
a total FY 2023 payment rate update of 3.8 percent. In this final rule, 
we are finalizing our proposal to update the outlier fixed dollar loss 
threshold amount, update the IPF labor-related share, and update the 
IPF wage index to reflect the FY 2023 hospital inpatient wage index. 
Lastly, for FY 2023 and subsequent years, we will apply a 5-percent cap 
on any decrease to a provider's wage index from its wage index in the 
prior year, regardless of the circumstances causing the decline.

B. Overall Impact

    We have examined the impacts of this rule as required by Executive 
Order 12866 on Regulatory Planning and Review (September 30, 1993), 
Executive Order 13563 on Improving Regulation and Regulatory Review 
(January 18, 2011), the Regulatory Flexibility Act (RFA) (September 19, 
1980, Pub. L. 96-354), section 1102(b) of the Social Security 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

[[Page 46874]]

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 and/or with economically 
significant effects ($100 million or more in any 1 year). We estimate 
that the total impact of these changes for FY 2023 payments compared to 
FY 2022 payments will be a net increase of approximately $90 million. 
This reflects a $130 million increase from the update to the payment 
rates (+$140 million from the second quarter 2022 IGI forecast of the 
2016-based IPF market basket of 4.1 percent, and -$10 million for the 
productivity adjustment of 0.3 percentage point), as well as a $40 
million decrease as a result of the update to the outlier threshold 
amount. Outlier payments are estimated to change from 3.2 percent in FY 
2022 to 2.0 percent of total estimated IPF payments in FY 2023.
    Based on our estimates, OMB's Office of Information and Regulatory 
Affairs has determined this rulemaking is ``economically significant'' 
as measured by the $100 million threshold, 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). 
Accordingly, we have prepared a Regulatory Impact Analysis that to the 
best of our ability presents the costs and benefits of the rulemaking. 
Therefore, OMB has reviewed these final regulations, and the 
Departments have provided the following assessment of their impact.

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. This 
budget neutrality factor included 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 IV.D.1 of this final rule, we are updating 
the wage index and labor-related share, as well as applying the 5-
percent cap on any decrease to a provider's wage index from its wage 
index in the prior year, 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 2023 of 4.1 percent (see section IV.A.2 of this final 
rule) less the productivity adjustment of 0.3 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 2023 impact will be a net increase of $90 
million in payments to IPF providers. This reflects an estimated $130 
million increase from the update to the payment rates and a $40 million 
decrease due to the update to the outlier threshold amount to set total 
estimated outlier payments at 2.0 percent of total estimated payments 
in FY 2023. This estimate does not include the implementation of the 
required 2.0 percentage point reduction of the productivity-adjusted 
market basket update factor for any IPF that fails to meet the IPF 
quality reporting requirements (as discussed in section IV.B.2. 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 2023 versus those under FY 2022. We 
determined the percent change in the estimated FY 2023 IPF PPS payments 
compared to the estimated FY 2022 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 labor-related share and the 5-percent cap on any decrease 
to a provider's wage index from its wage index in the prior year; and 
the market basket update for FY 2023, as reduced by the productivity 
adjustment according to section 1886(s)(2)(A)(i) of the Act. To 
illustrate the impacts of the FY 2023 changes in this final rule, our 
analysis begins with FY 2021 IPF PPS claims (based on the 2021 MedPAR 
claims, March 2022 update). As discussed in section IV.E.2 of this 
final rule, we are excluding providers from our impact simulations 
whose change in estimated cost per day is outside 3 standard deviations 
from the mean. We estimate FY 2022 IPF PPS payments using these 2021 
claims, the finalized FY 2022 IPF PPS Federal per diem base rates, and 
the finalized FY 2022 IPF PPS patient and facility level adjustment 
factors (as published in the FY 2022 IPF PPS final rule (86 FR 42608)). 
We then estimate the FY 2022 outlier payments based on these simulated 
FY 2022 IPF PPS payments using the same methodology as finalized in the 
FY 2022 IPF PPS final rule (86 FR 42623 through 42624) where total 
outlier payments are maintained at 2 percent of total estimated FY 2022 
IPF PPS payments. Each of the following changes is added incrementally 
to this baseline model in order to isolate the effects of each change:
     The final update to the outlier fixed dollar loss 
threshold amount.
     The final FY 2023 IPF wage index, the 5-percent cap on any 
decrease to a provider's wage index from its wage index in the prior 
year, and the FY 2023 labor-related share.
     The final market basket update for FY 2023 of 4.1 percent 
less the productivity adjustment of 0.3 percentage point in accordance 
with section 1886(s)(2)(A)(i) of the Act for a payment rate update of 
3.8 percent.
    Our column comparison in Table 3 illustrates the percent change in 
payments from FY 2022 (that is, October 1, 2021, to September 30, 2022) 
to FY 2023 (that is, October 1, 2022, to September 30, 2023) including 
all the payment policy changes.

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3. Impact Results
    Table 3 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 1,417 IPFs 
included in the analysis. In column 2, we present the number of 
facilities of each type that had information available in the PSF, had 
claims in the MedPAR dataset for FY 2021, and were not excluded due to 
the trim on providers whose change in estimated cost per day is outside 
3 standard deviations from the mean.
    In column 3, we present the effects of the update to the outlier 
fixed dollar loss threshold amount. We estimate that IPF outlier 
payments as a percentage of total IPF payments are 3.2 percent in FY 
2022. Therefore, we adjusted the outlier threshold amount to set total 
estimated outlier payments equal to 2.0 percent of total payments in FY 
2023. The estimated change in total IPF payments for FY 2023, 
therefore, includes an approximate 1.2 percent decrease in payments 
because we expect the outlier portion of total payments to decrease 
from approximately 3.2 percent to 2.0 percent.
    The overall impact of the estimated decrease to payments due to 
updating the outlier fixed dollar loss threshold (as shown in column 3 
of Table 3), across all hospital groups, is a 1.2 percent decrease. The 
largest decrease in payments due to this change is estimated to be 3.4 
percent for teaching IPFs with 10 percent to 30 percent interns and 
residents to beds.
    In column 4, we present the effects of the budget-neutral update to 
the IPF wage index, the labor-related share (LRS), and the 5-percent 
cap on any decrease to a provider's wage index from its wage index in 
the prior year discussed in section IV.D.2 of this final rule. This 
represents the effect of using the concurrent hospital wage data as 
discussed in section IV.D.1.a of this final rule. That is, the impact 
represented in this column reflects the update from the FY 2022 IPF 
wage index to the FY 2023 IPF wage index, which includes basing the FY 
2023 IPF wage index on the FY 2023 pre-floor, pre-reclassified IPPS 
hospital wage index data, applying a 5-percent cap on any decrease to a 
provider's wage index from its wage index in the prior year, and 
updating the LRS from 77.2 percent in FY 2022 to 77.4 percent in FY 
2023. We note that there is no projected change in aggregate payments 
to IPFs, as indicated in the first row of column 4; however, there are 
distributional effects among different categories of IPFs. For example, 
we estimate the largest increase in payments to be 0.8 percent for 
Pacific IPFs, and the largest decrease in payments to be 0.5 percent 
for New England IPFs.
    Overall, IPFs are estimated to experience a net increase in 
payments of 2.5 percent as a result of the updates in this final rule. 
IPF payments are therefore estimated to increase by 2.5 percent in 
urban areas and 2.9 percent in rural areas. The largest payment 
increases are estimated at 3.7 percent for freestanding urban for-
profit IPFs, IPFs located in the West South Central region, and IPF 
hospitals with 25 to 49 beds.
4. Effect on Beneficiaries
    Under the FY 2023 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 in this final 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 2023 IPF PPS will enhance the efficiency of the 
Medicare program.
5. Regulatory Review Costs
    If regulations impose administrative costs on private entities, 
such as the time needed to read and interpret this

[[Page 46877]]

final rule, we 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 the FY 2023 IPF PPS final rule, the most recent IPF proposed 
rule was the FY 2023 IPF PPS proposed rule, and we received 396 unique 
comments on this proposed rule. We believe that the number of past 
commenters on the most recent IPF proposed rule would be a fair 
estimate of the number of reviewers of this final rule. We acknowledge 
that this assumption may understate or overstate the costs of reviewing 
this final rule. It is possible that not all commenters reviewed the FY 
2023 IPF PPS proposed rule in detail, and it is also possible that some 
reviewers chose not to comment on that proposed rule. We solicited 
comments on this assumption and did not receive any comments on it.
    We also recognize that different types of entities are in many 
cases affected by mutually exclusive sections of the proposed rule, and 
therefore for the purposes of our estimate we assume that each reviewer 
reads approximately 50 percent of the rule. Using the May 2021 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 $115.22 per hour, including fringe benefits and other 
indirect costs (https://www.bls.gov/oes/current/oes119111). Assuming an 
average reading speed of 250 words per minute, we estimate that it 
would take approximately 64 minutes (1.07 hours) for the staff to 
review half of this final rule, which contains a total of approximately 
32,000 words. For each IPF that reviews the final rule, the estimated 
cost is $123.29 (1.07 x $115.22). Therefore, we estimate that the total 
cost of reviewing this final rule is $48,822.84 ($123.29 x 396 
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 2023 of 4.1 percent, reduced by the statutorily required 
productivity adjustment of 0.3 percentage point along with the wage 
index budget neutrality adjustment to update the payment rates; and 
finalizing a FY 2023 IPF wage index which uses the FY 2023 pre-floor, 
pre-reclassified IPPS hospital wage index as its basis. Additionally, 
we are applying a 5-percent cap on any decrease to a provider's wage 
index from its wage index in the prior year. Lastly, we are excluding 
providers from our simulation of IPF PPS payments for FY 2022 and FY 
2023 if their change in estimated cost per day is outside 3 standard 
deviations from the mean.

E. Accounting Statement

    As required by OMB Circular A-4 (available at https://www.whitehouse.gov/wp-content/uploads/legacy_drupal_files/omb/circulars/A4/a-4.pdf), in Table 4, 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 4 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 1,417 IPFs with data available in 
the PSF, with claims in our FY 2021 MedPAR claims dataset, and which 
were not excluded due to the trim on providers whose change in 
estimated cost per day is outside 3 standard deviations from the mean. 
Lastly, Table 4 also includes our best estimate of the costs of 
reviewing and understanding this final rule.
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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 3, we estimate that the overall revenue impact 
of this final rule on all IPFs is to increase estimated Medicare 
payments by approximately 2.5 percent. As a result, the estimated 
impact of this final rule is a net increase in revenue across almost 
all categories of IPFs. Therefore, 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 Social Security 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

[[Page 46878]]

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 VIII.C.2 of this final 
rule, the rates and policies set forth in this rule will not have an 
adverse impact on the rural hospitals based on the data of the 210 
rural excluded psychiatric units and 57 rural psychiatric hospitals in 
our database of 1,417 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 Mandates 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 
million in 1995 dollars, updated annually for inflation. In 2022, that 
threshold is approximately $165 million. This final rule does not 
mandate any requirements for state, local, or tribal governments, or 
for the private sector. This final rule will 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 $165 million 
in any 1 year.

H. Federalism

    Executive Order 13132 establishes certain requirements that an 
agency must meet when it promulgates a proposed rule (and subsequent 
final rule) that imposes substantial direct requirement costs on state 
and local governments, preempts state law, or otherwise has Federalism 
implications. This final rule does not impose substantial direct costs 
on state or local governments or preempt state law.
    This final regulation is subject to the Congressional Review Act 
provisions of the Small Business Regulatory Enforcement Fairness Act of 
1996 (5 U.S.C. 801 et seq.) and has been transmitted to the Congress 
and the Comptroller General for review.
    Chiquita Brooks-LaSure, Administrator of the Centers for Medicare & 
Medicaid Services, approved this document on July 25, 2022.

List of Subjects in 42 CFR Part 412

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

    For the reasons set forth in the preamble, the Centers for Medicare 
& Medicaid Services amends 42 CFR part 412 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 412.424 is amended by revising paragraph (d)(1)(i) to read 
as follows:


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

* * * * *
    (d) * * *
    (1) * * *
    (i) Adjustment for wages. CMS adjusts the labor portion of the 
Federal per diem base rate to account for geographic differences in the 
area wage levels using an appropriate wage index.
    (A) The application of the wage index is made on the basis of the 
location of the inpatient psychiatric facility in an urban or rural 
area as defined in Sec.  412.402.
    (B) Beginning October 1, 2022, CMS applies a cap on decreases to 
the wage index, such that the wage index applied to an inpatient 
psychiatric facility is not less than 95 percent of the wage index 
applied to that inpatient psychiatric facility in the prior fiscal 
year.
* * * * *

    Dated: July 25, 2022.
Xavier Becerra,
Secretary, Department of Health and Human Services.
[FR Doc. 2022-16260 Filed 7-27-22; 4:15 pm]
BILLING CODE 4120-01-P