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Rule2022-16260

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

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Published

July 29, 2022

Effective

October 1, 2022

Issuing agencies

Health and Human Services DepartmentCenters for Medicare & Medicaid Services

Abstract

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.

Full Text

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[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 [<a href="http://www.gpo.gov">www.gpo.gov</a>]
[FR Doc No: 2022-16260]

[[Page 46845]]

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
<a href="/cdn-cgi/l/email-protection#420b120412233b2f272c36122d2e2b213b02212f316c2a2a316c252d34">[email&#160;protected]</a> 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: <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html</a>.
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
<a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/IPFPPS/WageIndex.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/IPFPPS/WageIndex.html</a>.

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--
<bullet> 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.
<bullet> 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.
<bullet> 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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[GRAPHIC] [TIFF OMITTED] TR29JY22.655

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 <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/index.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/index.html</a>?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:
<bullet> 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.
<bullet> Use a July 1 through June 30 annual update cycle.
<bullet> 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 <a href="http://www.bls.gov">www.bls.gov</a> for the BLS historical
published TFP data. A complete description of IGI's TFP projection
methodology is available on the CMS website at <a href="https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/MedicareProgramRatesStats/MarketBasketResearch">https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/MedicareProgramRatesStats/MarketBasketResearch</a>. 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\ <a href="https://www.hrsa.gov/sites/default/files/hrsa/provider-relief/provider-relief-fund-faq-complete.pdf">https://www.hrsa.gov/sites/default/files/hrsa/provider-relief/provider-relief-fund-faq-complete.pdf</a>.
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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 (<a href="https://www.cms.gov/files/document/productivity-memo.pdf">https://www.cms.gov/files/document/productivity-memo.pdf</a>). 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 <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/index.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/index.html</a>.
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 <a href="https://www.cms.gov/Regulations-and-Guidance/Guidance/Manuals/Downloads/clm104c03.pdf">https://www.cms.gov/Regulations-and-Guidance/Guidance/Manuals/Downloads/clm104c03.pdf</a>.) 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 <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html</a>.
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:
<bullet> 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.
<bullet> 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 <a href="https://www.cms.gov/Medicare/Coding/ICD10/ICD-10-MS-DRG-Conversion-Project.html">https://www.cms.gov/Medicare/Coding/ICD10/ICD-10-MS-DRG-Conversion-Project.html</a>.
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 <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html</a>. 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 <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/index.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/index.html</a>.
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 <a href="https://www.cdc.gov/nchs/data/icd/10cmguidelines-FY2020_final.pdf">https://www.cdc.gov/nchs/data/icd/10cmguidelines-FY2020_final.pdf</a>.
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 <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html</a>.
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 <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html</a>.
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 <a href="/cdn-cgi/l/email-protection#0e475e485e6f77636b607a5e6162676d774e6d637d2066667d20696178">[email&#160;protected]</a> 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 <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html</a>.
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 <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html</a>). 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 <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html</a>. 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
<a href="https://www.whitehouse.gov/omb/information-for-agencies/bulletins/">https://www.whitehouse.gov/omb/information-for-agencies/bulletins/</a>. 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 <a href="https://www.whitehouse.gov/wp-content/uploads/2020/03/Bulletin-20-01.pdf">https://www.whitehouse.gov/wp-content/uploads/2020/03/Bulletin-20-01.pdf</a>). 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 <a href="https://www.chcoc.gov/content/nonforeign-area-retirement-equity-assurance-act">https://www.chcoc.gov/content/nonforeign-area-retirement-equity-assurance-act</a>.
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:
<bullet> City of Anchorage, and 80-kilometer (50-mile) radius by
road, as measured from the Federal courthouse.
<bullet> City of Fairbanks, and 80-kilometer (50-mile) radius by
road, as measured from the Federal courthouse.
<bullet> City of Juneau, and 80-kilometer (50-mile) radius by road,
as measured from the Federal courthouse.
<bullet> 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.

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[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 <a href="https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html">https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientPsychFacilPPS/tools.html</a>.
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:
<bullet> Calculated two national ceilings, one for IPFs located in
rural areas and one for IPFs located in urban areas.
<bullet> 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:
<bullet> 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.
<bullet> IPFs whose overall CCR is in excess of three standard
deviations above the corresponding national geometric mean (that is,
above the ceiling).
<bullet> 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:
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\2\ The report can be accessed directly via the following link:
<a href="https://www.cms.gov/files/document/technical-report-medicare-program-inpatient-psychiatric-facilities-prospective-payment-system.pdf">https://www.cms.gov/files/document/technical-report-medicare-program-inpatient-psychiatric-facilities-prospective-payment-system.pdf</a>
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<bullet> 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.
<bullet> 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.
<bullet> 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\
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\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 <a href="https://www.ruralhealthresearch.org/assets/2200-8536/rural-communities-age-income-health-status-recap.pdf">https://www.ruralhealthresearch.org/assets/2200-8536/rural-communities-age-income-health-status-recap.pdf</a> . 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 <a href="https://www.minorityhealth.hhs.gov/assets/PDF/Update_HHS_Disparities_Dept-FY2020.pdf">https://www.minorityhealth.hhs.gov/assets/PDF/Update_HHS_Disparities_Dept-FY2020.pdf</a> . 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 <a href="https://www.cdc.gov/mmwr/volumes/70/wr/mm7005a1.htm?s_cid=mm7005a1">https://www.cdc.gov/mmwr/volumes/70/wr/mm7005a1.htm?s_cid=mm7005a1</a> _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
<a href="https://www.cms.gov/pillar/health-equity">https://www.cms.gov/pillar/health-equity</a>. Accessed February 9, 2022.
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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.
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\13\ CMS Quality Strategy. 2016. Available at <a href="https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Qualityinitiativesgeninfo/downloads/cms-quality-strategy.pdf">https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Qualityinitiativesgeninfo/downloads/cms-quality-strategy.pdf</a>. Accessed February 3, 2022.
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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\
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\14\ Centers for Medicare & Medicaid Services (CMS), HHS.
Disparity Methods Confidential Reporting. Available at <a href="https://qualitynet.cms.gov/inpatient/measures/disparity-methods">https://qualitynet.cms.gov/inpatient/measures/disparity-methods</a>. Accessed
February 3, 2022.
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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
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\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 <a href="https://www.ruralhealthresearch.org/assets/2200-8536/rural-communities-age-income-health-status-recap.pdf">https://www.ruralhealthresearch.org/assets/2200-8536/rural-communities-age-income-health-status-recap.pdf</a>. 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 <a href="https://www.minorityhealth.hhs.gov/assets/PDF/Update_HHS_Disparities_Dept-FY2020.pdf">https://www.minorityhealth.hhs.gov/assets/PDF/Update_HHS_Disparities_Dept-FY2020.pdf</a> 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:
<bullet> 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).
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\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.
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<bullet> 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.
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\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 <a href="https://www.aspe.hhs.gov/reports/report-congress-social-risk-factors-performance-under-medicares-value-based-purchasing-programs">https://www.aspe.hhs.gov/reports/report-congress-social-risk-factors-performance-under-medicares-value-based-purchasing-programs</a>. Accessed February 3, 2022.
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<bullet> 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.
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\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
<a href="https://archive.ahrq.gov/research/findings/final-reports/medicareindicators/medicareindicators1.html">https://archive.ahrq.gov/research/findings/final-reports/medicareindicators/medicareindicators1.html</a>. 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 <a href="https://www.atsdr.cdc.gov/placeandhealth/svi/img/pdf/Flanagan_2011_SVIforDisasterManagement-508.pdf">https://www.atsdr.cdc.gov/placeandhealth/svi/img/pdf/Flanagan_2011_SVIforDisasterManagement-508.pdf</a>. Accessed
February 3, 2022.
\27\ Center for Health Disparities Research. University of
Wisconsin School of Medicine and Public health. Neighborhood Atlas.
Available at <a href="https://www.neighborhoodatlas.medicine.wisc.edu/">https://www.neighborhoodatlas.medicine.wisc.edu/</a>.
Accessed February 3, 2022.
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<bullet> 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\
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\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 <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6338295/pdf/HESR-54-13.pdf">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6338295/pdf/HESR-54-13.pdf</a>.
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:
<bullet> 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.
<bullet> 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.
<bullet> 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.
<bullet> Benchmarking, or comparing individual results to state or
national average, is another potential reporting strategy.
<bullet> 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. <a href="https://doi.org/10.1186/s12982-021-00100-9">https://doi.org/10.1186/s12982-021-00100-9</a>. 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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[GRAPHIC] [TIFF OMITTED] TR29JY22.660

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:
<bullet> Measures should be actionable in terms of quality
improvement;
<bullet> Measures should help beneficiaries and their caregivers
make informed healthcare decisions;
<bullet> Measures should not create incentives to lower the quality
of care; and
<bullet> 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 <a href="https://link.springer.com/content/pdf/10.1007/s11606-019-05473-x.pdf">https://link.springer.com/content/pdf/10.1007/s11606-019-05473-x.pdf</a>. 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 <a href="https://s3.amazonaws.com/bizzabo.file.upload/83kO1DYXTs6mKHjVtuk8_1%20-%20Session%2023%20Health%20Equity%20New%20Normal%20FINAL_508.pdf">https://s3.amazonaws.com/bizzabo.file.upload/83kO1DYXTs6mKHjVtuk8_1%20-%20Session%2023%20Health%20Equity%20New%20Normal%20FINAL_508.pdf</a>.
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
<a href="https://www.cms.gov/files/document/measures-under-consideration-list-2021-report.pdf">https://www.cms.gov/files/document/measures-under-consideration-list-2021-report.pdf</a>. 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 <a href="https://www.cms.gov/files/document/measures-under-consideration-list-2021-report.pdf">https://www.cms.gov/files/document/measures-under-consideration-list-2021-report.pdf</a>. 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 <a href="https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/MMS/Quality-Measure-and-Quality-Improvement-">https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/MMS/Quality-Measure-and-Quality-Improvement-</a>-. 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 <a href="https://www.ahrq.gov/talkingquality/measures/types.html">https://www.ahrq.gov/talkingquality/measures/types.html</a>. 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:

<bullet> 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
<bullet> 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.
<bullet> 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
<bullet> Identification of Meaningful Performance Differences
++ Ways that meaningful difference in disparity results should be

[[Page 46872]]

considered.
<bullet> Guiding Principles for Reporting Disparity Measures
++ Guiding principles for the use and application of the results of
disparity measurement.
<bullet> 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 (<a href="https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityMeasures/Pre-Rulemaking">https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/QualityMeasures/Pre-Rulemaking</a>).
For more information on our ongoing effort to address health
equity, we refer readers to our recently released updated CMS Quality
Strategy (<a href="https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/CMS-Quality-Strategy">https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/CMS-Quality-Strategy</a>) and
our Framework for Health Equity (<a href="https://www.cms.gov/About-CMS/Agency-Information/OMH/equity-initiatives/framework-for-health-equity">https://www.cms.gov/About-CMS/Agency-Information/OMH/equity-initiatives/framework-for-health-equity</a>) 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

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