[Federal Register Volume 84, Number 79 (Wednesday, April 24, 2019)]
[Proposed Rules]
[Pages 17244-17335]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 2019-07885]
[[Page 17243]]
Vol. 84
Wednesday,
No. 79
April 24, 2019
Part II
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; Inpatient Rehabilitation Facility (IRF) Prospective
Payment System for Federal Fiscal Year 2020 and Updates to the IRF
Quality Reporting Program; Proposed Rule
Federal Register / Vol. 84 , No. 79 / Wednesday, April 24, 2019 /
Proposed Rules
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DEPARTMENT OF HEALTH AND HUMAN SERVICES
Centers for Medicare & Medicaid Services
42 CFR Part 412
[CMS-1710-P]
RIN 0938-AT67
Medicare Program; Inpatient Rehabilitation Facility (IRF)
Prospective Payment System for Federal Fiscal Year 2020 and Updates to
the IRF Quality Reporting Program
AGENCY: Centers for Medicare & Medicaid Services (CMS), HHS.
ACTION: Proposed rule.
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SUMMARY: This proposed rule would update the prospective payment rates
for inpatient rehabilitation facilities (IRFs) for federal fiscal year
(FY) 2020. As required by the Social Security Act (the Act), this
proposed rule includes the classification and weighting factors for the
IRF prospective payment system's (PPS) case-mix groups (CMGs) and a
description of the methodologies and data used in computing the
prospective payment rates for FY 2020. We are proposing to rebase and
revise the IRF market basket to reflect a 2016 base year rather than
the current 2012 base year. Additionally, we are proposing to replace
the previously finalized unweighted motor score with a weighted motor
score to assign patients to CMGs and remove one item from the score
beginning with FY 2020 and to revise the CMGs and update the CMG
relative weights and average length of stay values beginning with FY
2020, based on analysis of 2 years of data (FY 2017 and FY 2018). We
are proposing to update the IRF wage index to use the concurrent FY
inpatient prospective payment system (IPPS) wage index beginning with
FY 2020. We are soliciting comments on stakeholder concerns regarding
the appropriateness of the wage index used to adjust IRF payments. We
are proposing to amend the regulations to clarify that the
determination as to whether a physician qualifies as a rehabilitation
physician (that is, a licensed physician with specialized training and
experience in inpatient rehabilitation) is made by the IRF. For the IRF
Quality Reporting Program (QRP), we are proposing to adopt two new
measures, modify an existing measure, and adopt new standardized
patient assessment data elements. We also propose to expand data
collection to all patients, regardless of payer, as well as proposing
updates related to the system used for the submission of data and
related regulation text.
DATES: To be assured consideration, comments must be received at one of
the addresses provided below, not later than 5 p.m. on June 17, 2019.
ADDRESSES: In commenting, please refer to file code CMS-1710-P. Because
of staff and resource limitations, we cannot accept comments by
facsimile (FAX) transmission.
Comments, including mass comment submissions, must be submitted in
one of the following three ways (please choose only one of the ways
listed):
1. Electronically. You may submit electronic comments on this
regulation to http://www.regulations.gov. Follow the ``Submit a
comment'' instructions.
2. By regular mail. You may mail written comments to the following
address ONLY: Centers for Medicare & Medicaid Services, Department of
Health and Human Services, Attention: CMS-1710-P, P.O. Box 8016,
Baltimore, MD 21244-8016.
Please allow sufficient time for mailed comments to be received
before the close of the comment period.
3. By express or overnight mail. You may send written comments to
the following address ONLY: Centers for Medicare & Medicaid Services,
Department of Health and Human Services, Attention: CMS-1710-P, Mail
Stop C4-26-05, 7500 Security Boulevard, Baltimore, MD 21244-1850.
For information on viewing public comments, see the beginning of
the SUPPLEMENTARY INFORMATION section.
FOR FURTHER INFORMATION CONTACT:
Gwendolyn Johnson, (410) 786-6954, for general information.
Catie Kraemer, (410) 786-0179, for information about the IRF
payment policies and payment rates.
Kadie Derby, (410) 786-0468, for information about the IRF coverage
policies.
Kate Brooks, (410) 786-7877, for information about the IRF quality
reporting program.
SUPPLEMENTARY INFORMATION: The IRF PPS Addenda along with other
supporting documents and tables referenced in this proposed rule are
available through the internet on the CMS website at http://www.cms.hhs.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/.
Executive Summary
A. Purpose
This proposed rule would update the prospective payment rates for
IRFs for FY 2020 (that is, for discharges occurring on or after October
1, 2019, and on or before September 30, 2020) as required under section
1886(j)(3)(C) of the Act. As required by section 1886(j)(5) of the Act,
this proposed rule includes the classification and weighting factors
for the IRF PPS's case-mix groups and a description of the
methodologies and data used in computing the prospective payment rates
for FY 2020. This proposed rule would also rebase and revise the IRF
market basket to reflect a 2016 base year, rather than the current 2012
base year. Additionally, this proposed rule proposes to replace the
previously finalized unweighted motor score with a weighted motor score
to assign patients to CMGs and remove one item from the score beginning
in FY 2020 and to revise the CMGs and update the CMG relative weights
and average length of stay values beginning with FY 2020, based on
analysis of 2 years of data (FY 2017 and FY 2018). We are also
proposing to update the IRF wage index to use the concurrent IPPS wage
index for the IRF PPS beginning with FY 2020. We are also soliciting
comments on stakeholder concerns regarding the appropriateness of the
wage index used to adjust IRF payments. We are also proposing to amend
the regulations at Sec. 412.622 to clarify that the determination as
to whether a physician qualifies as a rehabilitation physician (that
is, a licensed physician with specialized training and experience in
inpatient rehabilitation) is made by the IRF. For the IRF Quality
Reporting Program (QRP), we are proposing to adopt two new measures,
modify an existing measure, and adopt new standardized patient
assessment data elements. We also propose to expand data collection to
all patients, regardless of payer, as well as proposing updates related
to the system used for the submission of data and related regulation
text.
B. Summary of Major Provisions
In this proposed rule, we use the methods described in the FY 2019
IRF PPS final rule (83 FR 38514) to update the prospective payment
rates for FY 2020 using updated FY 2018 IRF claims and the most recent
available IRF cost report data, which is FY 2017 IRF cost report data.
This proposed rule also proposes to rebase and revise the IRF market
basket to reflect a 2016 base year rather than the current 2012 base
year. Additionally, this proposed rule proposes to replace the
previously finalized unweighted motor score with a weighted motor score
to assign patients to CMGs and remove one item
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from the score beginning with FY 2020 and to revise the CMGs and update
the CMG relative weights and average length of stay values beginning
with FY 2020, based on analysis of 2 years of data (FY 2017 and FY
2018). We are also proposing to use the concurrent IPPS wage index for
the IRF PPS beginning in FY 2020. We are also soliciting comments on
stakeholder concerns regarding the appropriateness of the wage index
used to adjust IRF payments. We are also proposing to amend the
regulations at Sec. 412.622 to clarify that the determination as to
whether a physician qualifies as a rehabilitation physician (that is, a
licensed physician with specialized training and experience in
inpatient rehabilitation) is made by the IRF. We are also proposing to
update requirements for the IRF QRP.
C. Summary of Impacts
[GRAPHIC] [TIFF OMITTED] TP24AP19.000
I. Background
A. Historical Overview of the IRF PPS
Section 1886(j) of the Act provides for the implementation of a
per-discharge PPS for inpatient rehabilitation hospitals and inpatient
rehabilitation units of a hospital (collectively, hereinafter referred
to as IRFs). Payments under the IRF PPS encompass inpatient operating
and capital costs of furnishing covered rehabilitation services (that
is, routine, ancillary, and capital costs), but not direct graduate
medical education costs, costs of approved nursing and allied health
education activities, bad debts, and other services or items outside
the scope of the IRF PPS. Although a complete discussion of the IRF PPS
provisions appears in the original FY 2002 IRF PPS final rule (66 FR
41316) and the FY 2006 IRF PPS final rule (70 FR 47880), we are
providing a general description of the IRF PPS for FYs 2002 through
2019.
Under the IRF PPS from FY 2002 through FY 2005, the prospective
payment rates were computed across 100 distinct CMGs, as described in
the FY 2002 IRF PPS final rule (66 FR 41316). We constructed 95 CMGs
using rehabilitation impairment categories (RICs), functional status
(both motor and cognitive), and age (in some cases, cognitive status
and age may not be a factor in defining a CMG). In addition, we
constructed five special CMGs to account for very short stays and for
patients who expire in the IRF.
For each of the CMGs, we developed relative weighting factors to
account for a patient's clinical characteristics and expected resource
needs. Thus, the weighting factors accounted for the relative
difference in resource use across all CMGs. Within each CMG, we created
tiers based on the estimated effects that certain comorbidities would
have on resource use.
We established the federal PPS rates using a standardized payment
conversion factor (formerly referred to as the budget-neutral
conversion factor). For a detailed discussion of the budget-neutral
conversion factor, please refer to our FY 2004 IRF PPS final rule (68
FR 45684 through 45685). In the FY 2006 IRF PPS final rule (70 FR
47880), we discussed in detail the methodology for determining the
standard payment conversion factor.
We applied the relative weighting factors to the standard payment
conversion factor to compute the unadjusted prospective payment rates
under the IRF PPS from FYs 2002 through 2005. Within the structure of
the payment system, we then made adjustments to account for interrupted
stays, transfers, short stays, and deaths. Finally, we applied the
applicable adjustments to account for geographic variations in wages
(wage index), the percentage of low-income patients, location in a
rural area (if applicable), and outlier payments (if applicable) to the
IRFs' unadjusted prospective payment rates.
For cost reporting periods that began on or after January 1, 2002,
and before October 1, 2002, we determined the final prospective payment
amounts using the transition methodology prescribed in section
1886(j)(1) of the Act. Under this provision, IRFs transitioning into
the PPS were paid a blend of the federal IRF PPS rate and the payment
that the IRFs would have received had the IRF PPS not been implemented.
This provision also allowed IRFs to elect to bypass this blended
payment and immediately be paid 100 percent of the federal IRF PPS
rate. The transition methodology expired as of cost reporting periods
beginning on or after October 1, 2002 (FY 2003), and payments for all
IRFs now consist of 100 percent of the federal IRF PPS rate.
Section 1886(j) of the Act confers broad statutory authority upon
the Secretary to propose refinements to the IRF PPS. In the FY 2006 IRF
PPS final rule (70 FR 47880) and in correcting amendments to the FY
2006 IRF PPS final rule (70 FR 57166), we finalized a number of
refinements to the IRF PPS case-mix classification system (the CMGs and
the corresponding relative weights) and the case-level and facility-
level adjustments. These refinements included the adoption of the
Office of Management and Budget's (OMB) Core-Based Statistical Area
(CBSA) market definitions; modifications to the CMGs, tier
comorbidities; and CMG relative weights, implementation of a new
teaching status adjustment for IRFs; rebasing and revising the market
basket index used to update IRF payments, and updates to the rural,
low-income percentage (LIP), and high-cost outlier adjustments.
Beginning with the FY 2006 IRF PPS final rule (70 FR 47908 through
47917), the market basket index used to update IRF payments was a
market basket reflecting the operating and capital cost structures for
freestanding IRFs, freestanding inpatient psychiatric facilities
(IPFs), and long-term care hospitals (LTCHs) (hereinafter referred to
as the rehabilitation, psychiatric, and long-term care (RPL) market
basket). Any reference to the FY 2006 IRF PPS final rule in this
proposed rule also includes the provisions effective in the correcting
amendments. For a detailed discussion of the final key policy changes
for FY 2006, please refer to the FY 2006 IRF PPS final rule.
In the FY 2007 IRF PPS final rule (71 FR 48354), we further refined
the IRF PPS case-mix classification system (the
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CMG relative weights) and the case-level adjustments, to ensure that
IRF PPS payments would continue to reflect as accurately as possible
the costs of care. For a detailed discussion of the FY 2007 policy
revisions, please refer to the FY 2007 IRF PPS final rule.
In the FY 2008 IRF PPS final rule (72 FR 44284), we updated the
prospective payment rates and the outlier threshold, revised the IRF
wage index policy, and clarified how we determine high-cost outlier
payments for transfer cases. For more information on the policy changes
implemented for FY 2008, please refer to the FY 2008 IRF PPS final
rule.
After publication of the FY 2008 IRF PPS final rule (72 FR 44284),
section 115 of the Medicare, Medicaid, and SCHIP Extension Act of 2007
(Pub. L. 110-173, enacted on December 29, 2007) (MMSEA) amended section
1886(j)(3)(C) of the Act to apply a zero percent increase factor for
FYs 2008 and 2009, effective for IRF discharges occurring on or after
April 1, 2008. Section 1886(j)(3)(C) of the Act required the Secretary
to develop an increase factor to update the IRF prospective payment
rates for each FY. Based on the legislative change to the increase
factor, we revised the FY 2008 prospective payment rates for IRF
discharges occurring on or after April 1, 2008. Thus, the final FY 2008
IRF prospective payment rates that were published in the FY 2008 IRF
PPS final rule (72 FR 44284) were effective for discharges occurring on
or after October 1, 2007, and on or before March 31, 2008, and the
revised FY 2008 IRF prospective payment rates were effective for
discharges occurring on or after April 1, 2008, and on or before
September 30, 2008. The revised FY 2008 prospective payment rates are
available on the CMS website at http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Data-Files.html.
In the FY 2009 IRF PPS final rule (73 FR 46370), we updated the CMG
relative weights, the average length of stay values, and the outlier
threshold; clarified IRF wage index policies regarding the treatment of
``New England deemed'' counties and multi-campus hospitals; and revised
the regulation text in response to section 115 of the MMSEA to set the
IRF compliance percentage at 60 percent (the ``60 percent rule'') and
continue the practice of including comorbidities in the calculation of
compliance percentages. We also applied a zero percent market basket
increase factor for FY 2009 in accordance with section 115 of the
MMSEA. For more information on the policy changes implemented for FY
2009, please refer to the FY 2009 IRF PPS final rule.
In the FY 2010 IRF PPS final rule (74 FR 39762) and in correcting
amendments to the FY 2010 IRF PPS final rule (74 FR 50712), we updated
the prospective payment rates, the CMG relative weights, the average
length of stay values, the rural, LIP, teaching status adjustment
factors, and the outlier threshold; implemented new IRF coverage
requirements for determining whether an IRF claim is reasonable and
necessary; and revised the regulation text to require IRFs to submit
patient assessments on Medicare Advantage (MA) (formerly called
Medicare Part C) patients for use in the 60 percent rule calculations.
Any reference to the FY 2010 IRF PPS final rule in this proposed rule
also includes the provisions effective in the correcting amendments.
For more information on the policy changes implemented for FY 2010,
please refer to the FY 2010 IRF PPS final rule.
After publication of the FY 2010 IRF PPS final rule (74 FR 39762),
section 3401(d) of the Patient Protection and Affordable Care Act (Pub.
L. 111-148, enacted on March 23, 2010), as amended by section 10319 of
the same Act and by section 1105 of the Health Care and Education
Reconciliation Act of 2010 (Pub. L. 111-152, enacted on March 30, 2010)
(collectively, hereinafter referred to as ``PPACA''), amended section
1886(j)(3)(C) of the Act and added section 1886(j)(3)(D) of the Act.
Section 1886(j)(3)(C) of the Act requires the Secretary to estimate a
multifactor productivity (MFP) adjustment to the market basket increase
factor, and to apply other adjustments as defined by the Act. The
productivity adjustment applies to FYs from 2012 forward. The other
adjustments apply to FYs 2010 to 2019.
Sections 1886(j)(3)(C)(ii)(II) and 1886(j)(3)(D)(i) of the Act
defined the adjustments that were to be applied to the market basket
increase factors in FYs 2010 and 2011. Under these provisions, the
Secretary was required to reduce the market basket increase factor in
FY 2010 by a 0.25 percentage point adjustment. Notwithstanding this
provision, in accordance with section 3401(p) of the PPACA, the
adjusted FY 2010 rate was only to be applied to discharges occurring on
or after April 1, 2010. Based on the self-implementing legislative
changes to section 1886(j)(3) of the Act, we adjusted the FY 2010
prospective payment rates as required, and applied these rates to IRF
discharges occurring on or after April 1, 2010, and on or before
September 30, 2010. Thus, the final FY 2010 IRF prospective payment
rates that were published in the FY 2010 IRF PPS final rule (74 FR
39762) were used for discharges occurring on or after October 1, 2009,
and on or before March 31, 2010, and the adjusted FY 2010 IRF
prospective payment rates applied to discharges occurring on or after
April 1, 2010, and on or before September 30, 2010. The adjusted FY
2010 prospective payment rates are available on the CMS website at
https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html.
In addition, sections 1886(j)(3)(C) and (D) of the Act also
affected the FY 2010 IRF outlier threshold amount because they required
an adjustment to the FY 2010 RPL market basket increase factor, which
changed the standard payment conversion factor for FY 2010.
Specifically, the original FY 2010 IRF outlier threshold amount was
determined based on the original estimated FY 2010 RPL market basket
increase factor of 2.5 percent and the standard payment conversion
factor of $13,661. However, as adjusted, the IRF prospective payments
were based on the adjusted RPL market basket increase factor of 2.25
percent and the revised standard payment conversion factor of $13,627.
To maintain estimated outlier payments for FY 2010 equal to the
established standard of 3 percent of total estimated IRF PPS payments
for FY 2010, we revised the IRF outlier threshold amount for FY 2010
for discharges occurring on or after April 1, 2010, and on or before
September 30, 2010. The revised IRF outlier threshold amount for FY
2010 was $10,721.
Sections 1886(j)(3)(C)(ii)(II) and 1886(j)(3)(D)(i) of the Act also
required the Secretary to reduce the market basket increase factor in
FY 2011 by a 0.25 percentage point adjustment. The FY 2011 IRF PPS
notice (75 FR 42836) and the correcting amendments to the FY 2011 IRF
PPS notice (75 FR 70013) described the required adjustments to the FY
2010 and FY 2011 IRF PPS prospective payment rates and outlier
threshold amount for IRF discharges occurring on or after April 1,
2010, and on or before September 30, 2011. It also updated the FY 2011
prospective payment rates, the CMG relative weights, and the average
length of stay values. Any reference to the FY 2011 IRF PPS notice in
this proposed rule also includes the provisions effective in the
correcting amendments. For more information on the FY 2010 and FY 2011
adjustments or the updates for FY 2011, please refer to the FY 2011 IRF
PPS notice.
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In the FY 2012 IRF PPS final rule (76 FR 47836), we updated the IRF
prospective payment rates, rebased and revised the RPL market basket,
and established a new QRP for IRFs in accordance with section
1886(j)(7) of the Act. We also consolidated, clarified, and revised
existing policies regarding IRF hospitals and IRF units of hospitals to
eliminate unnecessary confusion and enhance consistency. For more
information on the policy changes implemented for FY 2012, please refer
to the FY 2012 IRF PPS final rule.
The FY 2013 IRF PPS notice (77 FR 44618) described the required
adjustments to the FY 2013 prospective payment rates and outlier
threshold amount for IRF discharges occurring on or after October 1,
2012, and on or before September 30, 2013. It also updated the FY 2013
prospective payment rates, the CMG relative weights, and the average
length of stay values. For more information on the updates for FY 2013,
please refer to the FY 2013 IRF PPS notice.
In the FY 2014 IRF PPS final rule (78 FR 47860), we updated the
prospective payment rates, the CMG relative weights, and the outlier
threshold amount. We also updated the facility-level adjustment factors
using an enhanced estimation methodology, revised the list of diagnosis
codes that count toward an IRF's 60 percent rule compliance calculation
to determine ``presumptive compliance,'' revised sections of the
inpatient rehabilitation facility patient assessment instrument (IRF-
PAI), revised requirements for acute care hospitals that have IRF
units, clarified the IRF regulation text regarding limitation of
review, updated references to previously changed sections in the
regulations text, and updated requirements for the IRF QRP. For more
information on the policy changes implemented for FY 2014, please refer
to the FY 2014 IRF PPS final rule.
In the FY 2015 IRF PPS final rule (79 FR 45872) and the correcting
amendments to the FY 2015 IRF PPS final rule (79 FR 59121), we updated
the prospective payment rates, the CMG relative weights, and the
outlier threshold amount. We also revised the list of diagnosis codes
that count toward an IRF's 60 percent rule compliance calculation to
determine ``presumptive compliance,'' revised sections of the IRF-PAI,
and updated requirements for the IRF QRP. Any reference to the FY 2015
IRF PPS final rule in this proposed rule also includes the provisions
effective in the correcting amendments. For more information on the
policy changes implemented for FY 2015, please refer to the FY 2015 IRF
PPS final rule.
In the FY 2016 IRF PPS final rule (80 FR 47036), we updated the
prospective payment rates, the CMG relative weights, and the outlier
threshold amount. We also adopted an IRF-specific market basket that
reflects the cost structures of only IRF providers, a blended 1-year
transition wage index based on the adoption of new OMB area
delineations, a 3-year phase-out of the rural adjustment for certain
IRFs due to the new OMB area delineations, and updates for the IRF QRP.
For more information on the policy changes implemented for FY 2016,
please refer to the FY 2016 IRF PPS final rule.
In the FY 2017 IRF PPS final rule (81 FR 52056) and the correcting
amendments to the FY 2017 IRF PPS final rule (81 FR 59901), we updated
the prospective payment rates, the CMG relative weights, and the
outlier threshold amount. We also updated requirements for the IRF QRP.
Any reference to the FY 2017 IRF PPS final rule in this proposed rule
also includes the provisions effective in the correcting amendments.
For more information on the policy changes implemented for FY 2017,
please refer to the FY 2017 IRF PPS final rule.
In the FY 2018 IRF PPS final rule (82 FR 36238), we updated the
prospective payment rates, the CMG relative weights, and the outlier
threshold amount. We also revised the International Classification of
Diseases, 10th Revision, Clinical Modification (ICD-10-CM) diagnosis
codes that are used to determine presumptive compliance under the ``60
percent rule,'' removed the 25 percent payment penalty for IRF-PAI late
transmissions, removed the voluntary swallowing status item (Item 27)
from the IRF-PAI, summarized comments regarding the criteria used to
classify facilities for payment under the IRF PPS, provided for a
subregulatory process for certain annual updates to the presumptive
methodology diagnosis code lists, adopted the use of height/weight
items on the IRF-PAI to determine patient body mass index (BMI) greater
than 50 for cases of single-joint replacement under the presumptive
methodology, and updated requirements for the IRF QRP. For more
information on the policy changes implemented for FY 2018, please refer
to the FY 2018 IRF PPS final rule.
In the FY 2019 IRF PPS final rule (83 FR 38514), we updated the
prospective payment rates, the CMG relative weights, and the outlier
threshold amount. We also alleviated administrative burden for IRFs by
removing the FIMTM instrument and associated Function
Modifiers from the IRF-PAI beginning in FY 2020 and revised certain IRF
coverage requirements to reduce the amount of required paperwork in the
IRF setting beginning in FY 2019. Additionally, we incorporated certain
data items located in the Quality Indicators section of the IRF-PAI
into the IRF case-mix classification system using analysis of 2 years
of data (FY 2017 and FY 2018) beginning in FY 2020. For the IRF QRP, we
adopted a new measure removal factor, removed two measures from the IRF
QRP measure set, and codified a number of program requirements in our
regulations. For more information on the policy changes implemented for
FY 2019, please refer to the FY 2019 IRF PPS final rule.
B. Provisions of the PPACA Affecting the IRF PPS in FY 2012 and Beyond
The PPACA included several provisions that affect the IRF PPS in
FYs 2012 and beyond. In addition to what was previously discussed,
section 3401(d) of the PPACA also added section 1886(j)(3)(C)(ii)(I) of
the Act (providing for a ``productivity adjustment'' for fiscal year
2012 and each subsequent fiscal year). The productivity adjustment for
FY 2020 is discussed in section V.D. of this proposed rule. Section
1886(j)(3)(C)(ii)(II) of the Act provides that the application of the
productivity adjustment to the market basket update may result in an
update that is less than 0.0 for a fiscal year and in payment rates for
a fiscal year being less than such payment rates for the preceding
fiscal year.
Sections 3004(b) of the PPACA and section 411(b) of the Medicare
Access and CHIP Reauthorization Act of 2015 (Pub. L. 114-10, enacted on
April 16, 2015) (MACRA) also addressed the IRF PPS. Section 3004(b) of
PPACA reassigned the previously designated section 1886(j)(7) of the
Act to section 1886(j)(8) of the Act and inserted a new section
1886(j)(7) of the Act, which contains requirements for the Secretary to
establish a QRP for IRFs. Under that program, data must be submitted in
a form and manner and at a time specified by the Secretary. Beginning
in FY 2014, section 1886(j)(7)(A)(i) of the Act requires the
application of a 2 percentage point reduction to the market basket
increase factor otherwise applicable to an IRF (after application of
subparagraphs (C)(iii) and (D) of section 1886(j)(3) of the Act) for a
fiscal year if the IRF does not comply with the requirements of the IRF
QRP for that fiscal year. Application of the 2
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percentage point reduction may result in an update that is less than
0.0 for a fiscal year and in payment rates for a fiscal year being less
than such payment rates for the preceding fiscal year. Reporting-based
reductions to the market basket increase factor are not cumulative;
they only apply for the FY involved. Section 411(b) of MACRA amended
section 1886(j)(3)(C) of the Act by adding clause (iii), which required
us to apply for FY 2018, after the application of section
1886(j)(3)(C)(ii) of the Act, an increase factor of 1.0 percent to
update the IRF prospective payment rates.
C. Operational Overview of the Current IRF PPS
As described in the FY 2002 IRF PPS final rule (66 FR 41316), upon
the admission and discharge of a Medicare Part A Fee-for-Service (FFS)
patient, the IRF is required to complete the appropriate sections of a
patient assessment instrument (PAI), designated as the IRF-PAI. In
addition, beginning with IRF discharges occurring on or after October
1, 2009, the IRF is also required to complete the appropriate sections
of the IRF-PAI upon the admission and discharge of each Medicare
Advantage (MA) patient, as described in the FY 2010 IRF PPS final rule
(74 FR 39762 and 74 FR 50712). All required data must be electronically
encoded into the IRF-PAI software product. Generally, the software
product includes patient classification programming called the Grouper
software. The Grouper software uses specific IRF-PAI data elements to
classify (or group) patients into distinct CMGs and account for the
existence of any relevant comorbidities.
The Grouper software produces a five-character CMG number. The
first character is an alphabetic character that indicates the
comorbidity tier. The last four characters are numeric characters that
represent the distinct CMG number. Free downloads of the Inpatient
Rehabilitation Validation and Entry (IRVEN) software product, including
the Grouper software, are available on the CMS website at http://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Software.html.
Once a Medicare Part A FFS patient is discharged, the IRF submits a
Medicare claim as a Health Insurance Portability and Accountability Act
of 1996 (Pub. L. 104-191, enacted on August 21, 1996) (HIPAA) compliant
electronic claim or, if the Administrative Simplification Compliance
Act of 2002 (Pub. L. 107-105, enacted on December 27, 2002) (ASCA)
permits, a paper claim (a UB-04 or a CMS-1450 as appropriate) using the
five-character CMG number and sends it to the appropriate Medicare
Administrative Contractor (MAC). In addition, once a MA patient is
discharged, in accordance with the Medicare Claims Processing Manual,
chapter 3, section 20.3 (Pub. L. 100-04), hospitals (including IRFs)
must submit an informational-only bill (Type of Bill (TOB) 111), which
includes Condition Code 04 to their MAC. This will ensure that the MA
days are included in the hospital's Supplemental Security Income (SSI)
ratio (used in calculating the IRF LIP adjustment) for fiscal year 2007
and beyond. Claims submitted to Medicare must comply with both ASCA and
HIPAA.
Section 3 of the ASCA amended section 1862(a) of the Act by adding
paragraph (22), which requires the Medicare program, subject to section
1862(h) of the Act, to deny payment under Part A or Part B for any
expenses for items or services for which a claim is submitted other
than in an electronic form specified by the Secretary. Section 1862(h)
of the Act, in turn, provides that the Secretary shall waive such
denial in situations in which there is no method available for the
submission of claims in an electronic form or the entity submitting the
claim is a small provider. In addition, the Secretary also has the
authority to waive such denial in such unusual cases as the Secretary
finds appropriate. For more information, see the ``Medicare Program;
Electronic Submission of Medicare Claims'' final rule (70 FR 71008).
Our instructions for the limited number of Medicare claims submitted on
paper are available at http://www.cms.gov/manuals/downloads/clm104c25.pdf.
Section 3 of the ASCA operates in the context of the administrative
simplification provisions of HIPAA, which include, among others, the
requirements for transaction standards and code sets codified in 45 CFR
part 160 and part 162, subparts A and I through R (generally known as
the Transactions Rule). The Transactions Rule requires covered
entities, including covered health care providers, to conduct covered
electronic transactions according to the applicable transaction
standards. (See the CMS program claim memoranda at http://www.cms.gov/ElectronicBillingEDITrans/ and listed in the addenda to the Medicare
Intermediary Manual, Part 3, section 3600).
The MAC processes the claim through its software system. This
software system includes pricing programming called the ``Pricer''
software. The Pricer software uses the CMG number, along with other
specific claim data elements and provider-specific data, to adjust the
IRF's prospective payment for interrupted stays, transfers, short
stays, and deaths, and then applies the applicable adjustments to
account for the IRF's wage index, percentage of low-income patients,
rural location, and outlier payments. For discharges occurring on or
after October 1, 2005, the IRF PPS payment also reflects the teaching
status adjustment that became effective as of FY 2006, as discussed in
the FY 2006 IRF PPS final rule (70 FR 47880).
D. Advancing Health Information Exchange
The Department of Health and Human Services (HHS) has a number of
initiatives designed to encourage and support the adoption of
interoperable health information technology and to promote nationwide
health information exchange to improve health care. The Office of the
National Coordinator for Health Information Technology (ONC) and CMS
work collaboratively to advance interoperability across settings of
care, including post-acute care.
To further interoperability in post-acute care, we developed a Data
Element Library (DEL) to serve as a publicly-available centralized,
authoritative resource for standardized data elements and their
associated mappings to health IT standards. The DEL furthers CMS' goal
of data standardization and interoperability, which is also a goal of
the Improving Medicare Post-Acute Care Transformation Act of 2014
(IMPACT Act). These interoperable data elements can reduce provider
burden by allowing the use and exchange of healthcare data, support
provider exchange of electronic health information for care
coordination, person-centered care, and support real-time, data driven,
clinical decision making. Standards in the Data Element Library
(https://del.cms.gov/) can be referenced on the CMS website and in the
ONC Interoperability Standards Advisory (ISA). The 2019 ISA is
available at https://www.healthit.gov/isa.
The 21st Century Cures Act (Pub. L. 114-255, enacted on December
13, 2016) (Cures Act), requires HHS to take new steps to enable the
electronic sharing of health information ensuring interoperability for
providers and settings across the care continuum. In another important
provision, Congress defined ``information blocking'' as practices
likely to interfere with, prevent, or materially discourage access,
exchange, or use of electronic health
[[Page 17249]]
information, and established new authority for HHS to discourage these
practices. In March 2019, ONC and CMS published the proposed rules,
``21st Century Cures Act: Interoperability, Information Blocking, and
the ONC Health IT Certification Program,'' (84 FR 7424) and
``Interoperability and Patient Access'' (84 FR 7610) to promote secure
and more immediate access to health information for patients and
healthcare providers through the implementation of information blocking
provisions of the Cures Act and the use of standardized application
programming interfaces (APIs) that enable easier access to electronic
health information. These two proposed rules are open for public
comment at www.regulations.gov. We invite providers to learn more about
these important developments and how they are likely to affect IRFs.
II. Summary of Provisions of the Proposed Rule
In this proposed rule, we propose to update the IRF prospective
payment rates for FY 2020 and to rebase and revise the IRF market
basket to reflect a 2016 base year rather than the current 2012 base
year. We are also proposing to replace the previously finalized
unweighted motor score with a weighted motor score to assign patients
to CMGs and remove one item from the score beginning with FY 2020 and
to revise the CMGs and update the CMG relative weights and average
length of stay values beginning with FY 2020, based on analysis of 2
years of data (FY 2017 and FY 2018). We are also proposing to use the
concurrent IPPS wage index for the IRF PPS beginning with FY 2020. We
are also soliciting comments on stakeholder concerns regarding the
appropriateness of the wage index used to adjust IRF payments. We are
proposing to amend the regulations at Sec. 412.622 to clarify that the
determination as to whether a physician qualifies as a rehabilitation
physician (that is, a licensed physician with specialized training and
experience in inpatient rehabilitation) is made by the IRF.
The proposed policy changes and updates to the IRF prospective
payment rates for FY 2020 are as follows:
Describe a proposed weighted motor score to replace the
previously finalized unweighted motor score to assign a patient to a
CMG, the removal of one item from the score, and revisions to the CMGs
beginning on October 1, 2019, based on analysis of 2 years of data (FY
2017 and FY 2018) using the Quality Indicator items in the IRF-PAI.
This includes proposed revisions to the CMG relative weights and
average length of stay values for FY 2020, in a budget neutral manner,
as discussed in section III. of this proposed rule.
Describe the proposed rebased and revised IRF market
basket to reflect a 2016 base year rather than the current 2012 base
year as discussed in section V. of this proposed rule.
Update the IRF PPS payment rates for FY 2020 by the
proposed market basket increase factor, based upon the most current
data available, with a proposed productivity adjustment required by
section 1886(j)(3)(C)(ii)(I) of the Act, as described in section V. of
this proposed rule.
Describe the proposed update to the IRF wage index to use
the concurrent IPPS wage index and the FY 2020 proposed labor-related
share in a budget-neutral manner, as described in section V. of this
proposed rule.
Describe the continued use of FY 2014 facility-level
adjustment factors, as discussed in section IV. of this proposed rule.
Describe the calculation of the IRF standard payment
conversion factor for FY 2020, as discussed in section V. of this
proposed rule.
Update the outlier threshold amount for FY 2020, as
discussed in section VI. of this proposed rule.
Update the cost-to-charge ratio (CCR) ceiling and urban/
rural average CCRs for FY 2020, as discussed in section VI. of this
proposed rule.
Describe the proposed amendments to the regulations at
Sec. 412.622 to clarify that the determination as to whether a
physician qualifies as a rehabilitation physician (that is, a licensed
physician with specialized training and experience in inpatient
rehabilitation) is made by the IRF, as discussed in section VII. of
this proposed rule.
Updates to the requirements for the IRF QRP, as discussed
in section VIII. of this proposed rule.
III. Proposed Refinements to the Case-Mix Classification System
Beginning With FY 2020
A. Background
Section 1886(j)(2)(A) of the Act requires the Secretary to
establish case-mix groups for payment under the IRF PPS and a method of
classifying specific IRF patients within these groups. Under section
1886(j)(2)(B) of the Act, the Secretary must assign each case-mix group
an appropriate weighting factor that reflects the relative facility
resources used for patients classified within the group as compared to
patients classified within other groups. Additionally, section
1886(j)(2)(C)(i) of the Act requires the Secretary from time to time to
adjust the established classifications and weighting factors as
appropriate to reflect changes in treatment patterns, technology, case-
mix, number of payment units for which payment is made under title
XVIII of the Act, and other factors which may affect the relative use
of resources. Such adjustments must be made in a manner so that changes
in aggregate payments under the classification system are a result of
real changes and are not a result of changes in coding that are
unrelated to real changes in case mix.
In the FY 2019 IRF PPS final rule (83 FR 38533 through 38549), we
finalized the removal of the Functional Independence Measure
(FIMTM) instrument and associated Function Modifiers from
the IRF-PAI and the incorporation of an unweighted additive motor score
derived from 19 data items located in the Quality Indicators section of
the IRF-PAI beginning with FY 2020 (83 FR 38535 through 38536, 38549).
As discussed in section III.B of this proposed rule, based on further
analysis to examine the potential impact of weighting the motor score,
we are proposing to replace the previously finalized unweighted motor
score with a weighted motor score and remove one item from the score
beginning with FY 2020.
Additionally, as noted in the FY 2019 IRF PPS final rule (83 FR
38534), the incorporation of the data items from the Quality Indicator
section of the IRF-PAI into the IRF case-mix classification system
necessitates revisions to the CMGs to ensure that IRF payments are
calculated accurately. We finalized the use of data items from the
Quality Indicators section of the IRF-PAI to construct the functional
status scores used to classify IRF patients in the IRF case-mix
classification system for purposes of establishing payment under the
IRF PPS beginning with FY 2020, but modified our proposal based on
public comments to incorporate two years of data (FYs 2017 and 2018)
into our analyses used to revise the CMG definitions (83 FR 38549). We
stated that any changes to the proposed CMG definitions resulting from
the incorporation of an additional year of data (FY 2018) into the
analysis would be addressed in future rulemaking prior to their
implementation beginning in FY 2020. As discussed in section III.C of
this proposed rule, we are proposing to revise the CMGs based on
analysis of 2 years of data (FYs 2017 and 2018) beginning with FY 2020.
We are also proposing to update the relative weights and average length
of stay values
[[Page 17250]]
associated with the revised CMGs beginning with FY 2020.
B. Proposed Use of a Weighted Motor Score Beginning With FY 2020
As noted in the FY 2019 IRF PPS final rule (83 FR 38535), the IRF
case-mix classification system currently uses a weighted motor score
based on FIMTM data items to assign patients to CMGs under
the IRF PPS through FY 2019. More information on the development and
implementation of this motor score can be found in the FY 2006 IRF PPS
final rule (70 FR 47896 through 47900). In the FY 2019 IRF PPS final
rule (83 FR 38535 through 38536, 38549), we finalized the incorporation
of an unweighted additive motor score derived from 19 data items
located in the Quality Indicators section of the IRF-PAI beginning with
FY 2020. We did not propose a weighted motor score at the time, because
we believed that the unweighted motor score would facilitate greater
understanding among the provider community, as it is less complex.
However, we also noted that we would take comments in favor of a
weighted motor score into consideration in future analysis. In response
to feedback we received from various stakeholders and professional
organizations regarding the use of an unweighted motor score and
requesting that we consider weighting the motor score, we extended our
contract with Research Triangle Institute, International (RTI) to
examine the potential impact of weighting the motor score. Based on
this analysis, discussed further below, we now believe that a weighted
motor score would improve the accuracy of payments to IRFs, and we are
proposing to replace the previously finalized unweighted motor score
with a weighted motor score to assign patients to CMGs beginning with
FY 2020.
The previously finalized motor score is calculated by summing the
scores of the 19 data items, with equal weight applied to each item.
The 19 data items are (83 FR 38535):
GG0130A1 Eating.
GG0130B1 Oral hygiene.
GG0130C1 Toileting hygiene.
GG0130E1 Shower/bathe self.
GG0130F1 Upper-body dressing.
GG0130G1 Lower-body dressing.
GG0130H1 Putting on/taking off footwear.
GG0170A1 Roll left and right.
GG0170B1 Sit to lying.
GG0170C1 Lying to sitting on side of bed.
GG0170D1 Sit to stand.
GG0170E1 Chair/bed-to-chair transfer.
GG0170F1 Toilet transfer.
GG0170I1 Walk 10 feet.
GG0170J1 Walk 50 feet with two turns.
GG0170K1 Walk 150 feet.
GG0170M1 One step curb.
H0350 Bladder continence.
H0400 Bowel continence.
In response to feedback we received from various stakeholders and
professional organizations requesting that we consider applying weights
to the motor score, we extended our contract with RTI to explore the
potential of applying unique weights to each of the 19 items in the
motor score.
As part of their analysis, RTI examined the degree to which the
items used to construct the motor score were related to one another and
adjusted their weighting methodology to account for their findings. RTI
considered a number of different weighting methodologies to develop a
weighted index that would increase the predictive power of the IRF
case-mix classification system while at the same time maintaining
simplicity. RTI used regression analysis to explore the relationship of
the motor score items to costs. This analysis was undertaken to
determine the impact of each of the items on cost and then to weight
each item in the index according to its relative impact on cost. Based
on findings from this analysis, we are proposing to remove the item
GG0170A1 Roll left and right from the motor score as this item was
found to have a high degree of multicollinearity with other items in
the motor score and behaved unexpectedly across the regression models
considered in the development of the weighted index. Using the revised
motor score composed of the remaining 18 items identified above, RTI
designed a weighting methodology for the motor score that could be
applied uniformly across all RICs. For a more detailed discussion of
the analysis used to construct the weighted motor score, we refer
readers to the March 2019 technical report entitled ``Analyses to
Inform the Use of Standardized Patient Assessment Data Elements in the
Inpatient Rehabilitation Facility Prospective Payment System'',
available at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Research.html. Findings from this analysis
suggest that the use of a weighted motor score index slightly improves
the ability of the IRF PPS to predict patient costs. Based on this
analysis, we believe it is appropriate to utilize a weighted motor
score for the purpose of determining IRF payments.
Table 1 shows the proposed weights for each component of the motor
score, averaged to 1, obtained through the regression analysis.
Table 1--Proposed Motor Score Weight Index
------------------------------------------------------------------------
Item Weight
------------------------------------------------------------------------
GG0130A1--Eating............................................... 2.7
GG0130B1--Oral hygiene......................................... 0.3
GG0130C1--Toileting hygiene.................................... 2.0
GG0130E1--Shower bathe self.................................... 0.7
GG0130F1--Upper-body dressing.................................. 0.5
GG0130G1--Lower-body dressing.................................. 1.0
GG0130H1--Putting on/taking off footwear....................... 1.0
GG0170B1--Sit to lying......................................... 0.1
GG0170C1--Lying to sitting on side of bed...................... 0.1
GG0170D1--Sit to stand......................................... 1.1
GG0170E1--Chair/bed-to-chair transfer.......................... 1.1
GG0170F1--Toilet transfer...................................... 1.6
GG0170I1--Walk 10 feet......................................... 0.8
GG0170J1--Walk 50 feet with two turns.......................... 0.8
GG0170K1--Walk 150 feet........................................ 0.8
GG0170M1--One-step curb........................................ 1.4
H0350--Bladder Continence...................................... 1.3
H0400--Bowel Continence........................................ 0.7
------------------------------------------------------------------------
We are proposing to determine the motor score by applying each of
the weights indicated in Table 1 to the score of each corresponding
item, as finalized in the FY 2019 IRF PPS final rule (83 FR 38535
through 38537), and then summing the weighted scores for each of the 18
items that compose the motor score.
We invite public comments on the proposal to replace the previously
finalized unweighted motor score with a weighted motor score to assign
patients to CMGs under the IRF PPS and our proposal to remove the item
GG0170A1 Roll left and right from the calculation of the motor score
beginning with FY 2020, that is, for all discharges beginning on or
after October 1, 2019.
C. Proposed Revisions to the CMGs and Proposed Updates to the CMG
Relative Weights and Average Length of Stay Values Beginning With FY
2020
In the FY 2019 IRF PPS final rule (83 FR 38549), we finalized the
use of data items from the Quality Indicators section of the IRF-PAI to
construct the functional status scores used to classify IRF patients in
the IRF case-mix classification system for purposes of establishing
payment under the IRF PPS beginning with FY 2020, but modified our
proposal based on public comments to incorporate two years of data (FY
2017 and FY 2018) into our analyses used to revise the CMG definitions.
We stated that any changes to the proposed CMG definitions resulting
from the incorporation of an additional year of data (FY 2018) into the
analysis would be addressed in future rulemaking prior to their
implementation beginning in FY 2020. Additionally, we stated that we
would also update the relative weights and average length of stay
values
[[Page 17251]]
associated with any revised CMG definitions in future rulemaking.
We have continued our contract with RTI to support us in developing
proposed revisions to the CMGs used under the IRF PPS based on analysis
of 2 years of data (FY 2017 and FY 2018). The process RTI uses for its
analysis, which is based on a Classification and Regression Tree (CART)
algorithm, is described in detail in the FY 2019 IRF PPS final rule (83
FR 38536 through 38540). RTI has used this analysis to revise the CMGs
utilizing FY 2017 and FY 2018 claim and assessment data and to develop
revised CMGs that reflect the use of the data items collected in the
Quality Indicators section of the IRF-PAI, incorporating the proposed
weighted motor score, described in section III.B of this proposed rule.
To develop the proposed revised CMGs, RTI used CART analysis to divide
patients into payment groups based on similarities in their clinical
characteristics and relative costs. As part of this analysis, RTI
imposed some typically-used constraints on the payment group divisions
(for example, on the minimum number of cases that could be in the
resulting payment groups and the minimum dollar payment amount
differences between groups) to identify the optimal set of payment
groups. For a more detailed discussion of the analysis used to revise
the CMGs for FY 2020, we refer readers to the March 2019 technical
report entitled, ``Analyses to Inform the Use of Standardized Patient
Assessment Data Elements in the Inpatient Rehabilitation Facility
Prospective Payment System'' available at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/Research.html.
As noted in the FY 2019 IRF PPS final rule (83 FR 38533 through
38549), we finalized the construction of a motor score, a memory score,
and a communication score to be considered for use in our ongoing
analysis to revise the CMGs based on FY 2017 and FY 2018 data. In
developing the proposed CMGs using both FY 2017 and FY 2018 data,
cognitive status as reflected through the communication score emerged
as a potential split point for CMGs in RICs 12 and 16 as shown in Table
2.
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As similarly discussed in the FY 2019 IRF PPS final rule (83 FR
38537 through 38546), the inclusion of the communication score in these
CMG definitions would result in lower payments for patients with higher
cognitive deficits. As we believe it would be inappropriate to
establish lower payments for patients with higher cognitive
impairments, we are proposing to combine the CMGs within these RICs as
shown in Table 3. As the CMGs we are proposing to combine within these
RICs are only differentiated by a communication score, our proposal to
consolidate the CMGs in these 2 RICs results in the exclusion of the
communication score from the definitions of the proposed CMGs presented
in Table 3 of this proposed rule. We would like to note that while the
memory score did not emerge as a potential split point in the CART
analysis and the communication score was not ultimately selected as a
determinant for the proposed CMGs, both scores were considered as
possible elements in developing the proposed CMGs.
After developing the revised CMGs, RTI calculated the relative
weights and average length of stay values for each revised CMG using
the same methodologies that we have used to update the CMG relative
weights and average length of stay values each fiscal year since 2009
when we implemented an update to this methodology. More information
about the methodology used to update the CMG relative weights can be
found in the FY 2009 IRF PPS final rule (73 FR 46372 through 46374).
For FY 2020, we propose to use the FY 2017 and FY 2018 IRF claims and
FY 2017 IRF cost report data to update the CMG relative weights and
average length of stay values. In calculating the CMG relative weights,
we use a hospital-specific relative value method to estimate operating
(routine and ancillary services) and capital costs of IRFs. As noted in
the FY 2019 IRF PPS final rule (83 FR 38521), this is the same
methodology that we have used to update the CMG relative weights and
average length of stay values each fiscal year since we implemented an
update to the methodology in the FY 2009 IRF PPS final rule (73 FR
46372 through 46374). More information on the methodology used to
update calculate the CMG relative weights and average length of stay
values can found in the March 2019 technical report entitled ``Analyses
to Inform the Use of Standardized Patient Assessment Data Elements in
the Inpatient Rehabilitation Facility Prospective Payment System''
available at https://www.cms.gov/
[[Page 17252]]
Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/
Research.html. Consistent with the methodology that we have used to
update the IRF classification system in each instance in the past, we
are proposing to update the relative weights associated with the
revised CMGs for FY 2020 in a budget neutral manner by applying a
budget neutrality factor to the standard payment amount. To calculate
the appropriate budget neutrality factor for use in updating the FY
2020 CMG relative weights, we use the following steps:
Step 1. Calculate the estimated total amount of IRF PPS payments
for FY 2020 (with no changes to the CMG relative weights).
Step 2. Calculate the estimated total amount of IRF PPS payments
for FY 2020 by applying the changes to the CMGs and the associated CMG
relative weights (as described in this proposed rule).
Step 3. Divide the amount calculated in step 1 by the amount
calculated in step 2 to determine the budget neutrality factor (1.0016)
that would maintain the same total estimated aggregate payments in FY
2020 with and without the changes to the CMGs and the associated CMG
relative weights.
Step 4. Apply the budget neutrality factor (1.0016) to the FY 2019
IRF PPS standard payment amount after the application of the budget-
neutral wage adjustment factor.
In section V.H. of this proposed rule, we discuss the proposed use
of the existing methodology to calculate the standard payment
conversion factor for FY 2020.
In Table 3, we present the proposed revised CMGs and their
respective descriptions, as well as the comorbidity tiers,
corresponding relative weights and the average length of stay values
for each proposed CMG and tier for FY 2020. The average length of stay
for each CMG is used to determine when an IRF discharge meets the
definition of a short-stay transfer, which results in a per diem case
level adjustment.
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A list of the FY 2019 CMGs can be found in the FY 2019 IRF PPS
final rule (83 FR 38521 through 38523). The following would be the most
significant differences between the FY 2019 CMGs and the proposed
revised CMGs:
There would be more CMGs than before (97 instead of 92
currently).
There would be fewer CMGs in RICs 1, 2, 5, and 8 while
there would be more CMGs in RICs 3, 4, 10, 11, 12, 13, 16, 18, 19, and
21.
A patient's age would affect assignment for CMGs in RICs
1, 3, 4, 12, 13, 16, and 20 whereas it currently affects assignment for
CMGs in RICs 1, 4, and 8.
We are proposing to utilize the CMGs identified in Table 3 to
classify IRF patients for purposes of establishing payment under the
IRF PPS beginning with FY 2020, that is, for all discharges on or after
October 1, 2019. We are proposing to implement these revisions in a
budget neutral manner. For more information on the specific impacts of
this proposal, we refer readers to Table 4. We are also proposing to
update the CMG relative weights and average length of stay values
associated with the proposed CMGs based on the data items from the
Quality Indicators section of the IRF-PAI.
Table 4--Distributional Effects of the Proposed Changes to the CMGs
----------------------------------------------------------------------------------------------------------------
Estimated
Number of impact of
Facility classification Number of IRFs cases proposed CMG
revisions
(1) (2) (3) (4)
----------------------------------------------------------------------------------------------------------------
Total........................................................... 1,119 409,982 0.0
Urban unit...................................................... 696 166,872 2.5
Rural unit...................................................... 136 21,700 2.9
Urban hospital.................................................. 276 216,894 -2.2
Rural hospital.................................................. 11 4,516 -3.6
Urban For-Profit................................................ 357 211,280 -1.8
Rural For-Profit................................................ 36 7,920 0.1
Urban Non-Profit................................................ 522 150,310 1.6
Rural Non-Profit................................................ 90 15,166 2.2
Urban Government................................................ 93 22,176 3.1
Rural Government................................................ 21 3,130 4.1
Urban........................................................... 972 383,766 -0.1
[[Page 17260]]
Rural........................................................... 147 26,216 1.8
----------------------------------------------------------------------------------------------------------------
Urban by region
----------------------------------------------------------------------------------------------------------------
Urban New England............................................... 29 16,260 -2.3
Urban Middle Atlantic........................................... 135 51,539 -1.6
Urban South Atlantic............................................ 147 77,315 -0.5
Urban East North Central........................................ 165 50,466 2.3
Urban East South Central........................................ 56 27,966 -0.6
Urban West North Central........................................ 74 20,822 1.0
Urban West South Central........................................ 184 84,068 -0.5
Urban Mountain.................................................. 83 30,294 -0.6
Urban Pacific................................................... 99 25,036 2.1
----------------------------------------------------------------------------------------------------------------
Rural by region
----------------------------------------------------------------------------------------------------------------
Rural New England............................................... 5 1,317 -2.4
Rural Middle Atlantic........................................... 12 1,248 1.2
Rural South Atlantic............................................ 16 3,639 -2.4
Rural East North Central........................................ 23 4,061 1.5
Rural East South Central........................................ 21 4,523 3.9
Rural West North Central........................................ 22 3,178 2.4
Rural West South Central........................................ 40 7,332 3.6
Rural Mountain.................................................. 5 626 1.8
Rural Pacific................................................... 3 292 3.0
----------------------------------------------------------------------------------------------------------------
Teaching status
----------------------------------------------------------------------------------------------------------------
Non-teaching.................................................... 1,014 362,675 -0.2
Resident to ADC less than 10%................................... 60 34,000 0.7
Resident to ADC 10%-19%......................................... 31 11,784 2.6
Resident to ADC greater than 19%................................ 14 1,523 4.3
----------------------------------------------------------------------------------------------------------------
Disproportionate share patient percentage (DSH PP)
----------------------------------------------------------------------------------------------------------------
DSH PP = 0%..................................................... 29 5,300 -1.3
DSH PP <5%...................................................... 139 60,003 -1.6
DSH PP 5%-10%................................................... 299 127,442 -0.7
DSH PP 10%-20%.................................................. 371 139,001 0.0
DSH PP greater than 20%......................................... 281 78,236 2.1
----------------------------------------------------------------------------------------------------------------
Table 4 shows how we estimate that the application of the proposed
revisions to the case-mix system for FY 2020 would affect particular
groups. Table 4 categorizes IRFs by geographic location, including
urban or rural location, and location for CMS's 9 Census divisions of
the country. In addition, Table 4 divides IRFs into those that are
separate rehabilitation hospitals (otherwise called freestanding
hospitals in this section), those that are rehabilitation units of a
hospital (otherwise called hospital units in this section), rural or
urban facilities, ownership (otherwise called for-profit, non-profit,
and government), by teaching status, and by disproportionate share
patient percentage (DSH PP). The proposed changes to the case-mix
classification system are expected to affect the overall distribution
of payments across CMGs. Note that, because we propose to implement the
revisions to the case-mix classification system in a budget-neutral
manner, total estimated aggregate payments to IRFs would not be
affected as a result of the proposed revisions to the CMGs and the CMG
relative weights. However, these proposed revisions may affect the
distribution of payments across CMGs. For a provider specific impact
analysis of this proposed change, we refer readers to the CMS website
at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html.
We invite public comment on the proposed revisions to the CMGs
based on analysis of 2 years of data (FYs 2017 and 2018) and the
proposed updates to the relative weights and average length of stay
values associated with the revised CMGs beginning with FY 2020, that
is, for all discharges beginning on or after October 1, 2019.
IV. Facility-Level Adjustment Factors
Section 1886(j)(3)(A)(v) of the Act confers broad authority upon
the Secretary to adjust the per unit payment rate by such factors as
the Secretary determines are necessary to properly reflect variations
in necessary costs of treatment among rehabilitation facilities. Under
this authority, we currently adjust the prospective payment amount
associated with a CMG to account for facility-level characteristics
such as an IRF's LIP, teaching status, and location in a rural area, if
applicable, as described in Sec. 412.624(e).
[[Page 17261]]
Based on the substantive changes to the facility-level adjustment
factors that were adopted in the FY 2014 IRF PPS final rule (78 FR
47860, 47868 through 47872), in the FY 2015 IRF PPS final rule (79 FR
45872, 45882 through 45883), we froze the facility-level adjustment
factors at the FY 2014 levels for FY 2015 and all subsequent years
(unless and until we propose to update them again through future
notice-and-comment rulemaking). For FY 2020, we will continue to hold
the adjustment factors at the FY 2014 levels as we continue to monitor
the most current IRF claims data available and continue to evaluate and
monitor the effects of the FY 2014 changes.
V. Proposed FY 2020 IRF PPS Payment Update
A. Background
Section 1886(j)(3)(C) of the Act requires the Secretary to
establish an increase factor that reflects changes over time in the
prices of an appropriate mix of goods and services included in the
covered IRF services. According to section 1886(j)(3)(A)(i) of the Act,
the increase factor shall be used to update the IRF prospective payment
rates for each FY. Section 1886(j)(3)(C)(ii)(I) of the Act requires the
application of a productivity adjustment. Thus, we propose to update
the IRF PPS payments for FY 2020 by a market basket increase factor as
required by section 1886(j)(3)(C) of the Act based upon the most
current data available, with a productivity adjustment as required by
section 1886(j)(3)(C)(ii)(I) of the Act.
We have utilized various market baskets through the years in the
IRF PPS. For a discussion of these market baskets, we refer readers to
the FY 2016 IRF PPS final rule (80 FR 47046).
Beginning with FY 2016, we finalized the use of a 2012-based IRF
market basket, using Medicare cost report data for both freestanding
and hospital-based IRFs (80 FR 47049 through 47068). Beginning with FY
2020, we are proposing to rebase and revise the IRF market basket to
reflect a 2016 base year. In the following discussion, we provide an
overview of the proposed market basket and describe the methodologies
used to determine the operating and capital portions of the proposed
2016-based IRF market basket.
B. Overview of the Proposed 2016-Based IRF Market Basket
The proposed 2016-based IRF market basket is a fixed-weight,
Laspeyres-type price index. A Laspeyres price index measures the change
in price, over time, of the same mix of goods and services purchased in
the base period. Any changes in the quantity or mix of goods and
services (that is, intensity) purchased over time relative to a base
period are not measured.
The index itself is constructed in three steps. First, a base
period is selected (in this proposed rule, the base period is 2016),
total base period costs are estimated for a set of mutually exclusive
and exhaustive cost categories, and each category is calculated as a
proportion of total costs. These proportions are called cost weights.
Second, each cost category is matched to an appropriate price or wage
variable, referred to as a price proxy. In nearly every instance where
we have selected price proxies for the various market baskets, these
price proxies are derived from publicly available statistical series
that are published on a consistent schedule (preferably at least on a
quarterly basis). In cases where a publicly available price series is
not available (for example, a price index for malpractice insurance),
we have collected price data from other sources and subsequently
developed our own index to capture changes in prices for these types of
costs. Finally, the cost weight for each cost category is multiplied by
the established price proxy. The sum of these products (that is, the
cost weights multiplied by their price levels) for all cost categories
yields the composite index level of the market basket for the given
time period. Repeating this step for other periods produces a series of
market basket levels over time. Dividing the composite index level of
one period by the composite index level for an earlier period produces
a rate of growth in the input price index over that timeframe.
As previously noted, the market basket is described as a fixed-
weight index because it represents the change in price over time of a
constant mix (quantity and intensity) of goods and services needed to
furnish IRF services. The effects on total costs resulting from changes
in the mix of goods and services purchased after the base period are
not measured. For example, an IRF hiring more nurses after the base
period to accommodate the needs of patients would increase the volume
of goods and services purchased by the IRF, but would not be factored
into the price change measured by a fixed-weight IRF market basket.
Only when the index is rebased would changes in the quantity and
intensity be captured, with those changes being reflected in the cost
weights. Therefore, we rebase the market basket periodically so that
the cost weights reflect recent changes in the mix of goods and
services that IRFs purchase (hospital inputs) to furnish inpatient care
between base periods.
C. Proposed Rebasing and Revising of the IRF PPS Market Basket
As discussed in the FY 2016 IRF PPS final rule (80 FR 47050), the
2012-based IRF market basket reflects the Medicare cost reports for
both freestanding and hospital-based facilities.
Beginning with FY 2020, we are proposing to rebase and revise the
2012-based IRF market basket to a 2016 base year reflecting both
freestanding and hospital-based IRFs. Below we provide a detailed
description of our methodology used to develop the proposed 2016-based
IRF market basket. This proposed methodology is generally similar to
the methodology used to develop the 2012-based IRF market basket with
the exception of the proposed derivation of the Home Office Contract
Labor cost weight using the Medicare cost report data as described in
section V.C.a.(6) of this proposed rule.
1. Development of Cost Categories and Weights for the Proposed 2016-
Based IRF Market Basket
a. Use of Medicare Cost Report Data
We are proposing a 2016-based IRF market basket that consists of
seven major cost categories and a residual derived from the 2016
Medicare cost reports (CMS Form 2552-10) for freestanding and hospital-
based IRFs. The seven cost categories are Wages and Salaries, Employee
Benefits, Contract Labor, Pharmaceuticals, Professional Liability
Insurance (PLI), Home Office Contract Labor, and Capital. The residual
category reflects all remaining costs not captured in the seven cost
categories. The 2016 cost reports include providers whose cost
reporting period began on or after October 1, 2015, and prior to
September 30, 2016. We selected 2016 as the base year because we
believe that the Medicare cost reports for this year represent the most
recent, complete set of Medicare cost report data available for
developing the proposed IRF market basket at this time.
Since our goal is to establish cost weights that were reflective of
case mix and practice patterns associated with the services IRFs
provide to Medicare beneficiaries, as we did for the 2012-based IRF
market basket, we are proposing to limit the cost reports used to
establish the 2016-based IRF market basket to those from facilities
that had a Medicare average length of stay (LOS) that was relatively
similar to their
[[Page 17262]]
facility average LOS. We believe that this requirement eliminates
statistical outliers and ensures a more accurate market basket that
reflects the costs generally incurred during a Medicare-covered stay.
The Medicare average LOS for freestanding IRFs is calculated from data
reported on line 14 of Worksheet S-3, part I. The Medicare average LOS
for hospital-based IRFs is calculated from data reported on line 17 of
Worksheet S-3, part I. We propose to include the cost report data from
IRFs with a Medicare average LOS within 15 percent (that is, 15 percent
higher or lower) of the facility average LOS to establish the sample of
providers used to estimate the 2016-based IRF market basket cost
weights. We are proposing to apply this LOS edit to the data for IRFs
to exclude providers that serve a population whose LOS would indicate
that the patients served are not consistent with a LOS of a typical
Medicare patient. We note that this is the same LOS edit that we
applied to develop the 2012-based IRF market basket. This process
resulted in the exclusion of about eight percent of the freestanding
and hospital-based IRF Medicare cost reports. Of those excluded, about
18 percent were freestanding IRFs and 82 percent were hospital-based
IRFs. This ratio is relatively consistent with the ratio of the
universe of freestanding to hospital-based IRF providers.
We then used the cost reports for IRFs that met this requirement to
calculate the costs for the seven major cost categories (Wages and
Salaries, Employee Benefits, Contract Labor, Professional Liability
Insurance, Pharmaceuticals, Home Office Contract Labor, and Capital)
for the market basket. For comparison, the 2012-based IRF market basket
utilized the Bureau of Economic Analysis Benchmark Input-Output data
rather than Medicare cost report data to derive the Home Office
Contract Labor cost weight. A more detailed discussion of this
methodological change is provided in section V.C.1.a.(6). of this
proposed rule.
Similar to the 2012-based IRF market basket major cost weights, the
proposed 2016-based IRF market basket cost weights reflect Medicare
allowable costs (routine, ancillary and capital)--costs that are
eligible for reimbursement through the IRF PPS. We propose to define
Medicare allowable costs for freestanding facilities as the following
lines on Worksheet A and Worksheet, part I (CMS Form 2552-10): 30
through 35, 50 through 76 (excluding 52 and 75), 90 through 91 and 93.
We propose to define Medicare allowable costs for hospital-based
facilities as the following lines on Worksheet A and Worksheet B, part
I (CMS Form 2552-10): 41, 50 through 76 (excluding 52 and 75), 90
through 91, and 93.
For freestanding IRFs, total Medicare allowable costs would be
equal to the total costs as reported on Worksheet B, part I, column 26
for the lines listed above. For hospital-based IRFs, total Medicare
allowable costs would be equal to total costs for the IRF inpatient
unit after the allocation of overhead costs (Worksheet B, part I,
column 26, line 41) and a proportion of total ancillary costs. We
propose to calculate the portion of ancillary costs attributable to the
hospital-based IRF for a given ancillary cost center by multiplying
total facility ancillary costs for the specific cost center (as
reported on Worksheet B, part I, column 26) by the ratio of IRF
Medicare ancillary costs for the cost center (as reported on Worksheet
D-3, column 3 for hospital-based IRFs) to total Medicare ancillary
costs for the cost center (equal to the sum of Worksheet D-3, column 3
for all relevant PPS [that is, IPPS, IRF, IPF and skilled nursing
facility (SNF)]). We propose to use these methods to derive levels of
total costs for IRF providers. This is the same methodology used for
the 2012-based IRF market basket. With this work complete, we then set
about deriving cost levels for the seven major cost categories and then
derive a residual cost weight reflecting all other costs not
classified.
(1) Wages and Salaries Costs
For freestanding IRFs, we are proposing to derive Wages and
Salaries costs as the sum of routine inpatient salaries, ancillary
salaries, and a proportion of overhead (or general service cost centers
in the Medicare cost reports) salaries as reported on Worksheet A,
column 1. Since overhead salary costs are attributable to the entire
IRF, we only include the proportion attributable to the Medicare
allowable cost centers. We are proposing to estimate the proportion of
overhead salaries that are attributed to Medicare allowable costs
centers by multiplying the ratio of Medicare allowable area salaries
(Worksheet A, column 1, lines 50 through 76 (excluding 52 and 75), 90
through 91, and 93) to total salaries (Worksheet A, column 1, line 200)
times total overhead salaries (Worksheet A, column 1, lines 4 through
18). This is the same methodology used in the 2012-based IRF market
basket.
For hospital-based IRFs, we are proposing to derive Wages and
Salaries costs as the sum of inpatient routine salary costs (Worksheet
A, column 1, line 41) for the hospital-based IRF and the overhead
salary costs attributable to this IRF inpatient unit; and ancillary
salaries plus a portion of overhead salary costs attributable to the
ancillary departments utilized by the hospital-based IRF.
We are proposing to calculate hospital-based ancillary salary costs
for a specific cost center (Worksheet A, column 1, lines 50 through 76
(excluding 52 and 75), 90 through 91, and 93) using salary costs from
Worksheet A, column 1, multiplied by the ratio of IRF Medicare
ancillary costs for the cost center (as reported on Worksheet D-3,
column 3, for IRF subproviders) to total Medicare ancillary costs for
the cost center (equal to the sum of Worksheet D-3, column 3, for all
relevant PPS units [that is, IPPS, IRF, IPF and a SNF]). For example,
if hospital-based IRF Medicare physical therapy costs represent 30
percent of the total Medicare physical therapy costs for the entire
facility, then 30 percent of total facility physical therapy salaries
(as reported in Worksheet A, column 1, line 66) would be attributable
to the hospital-based IRF. We believe it is appropriate to use only a
portion of the ancillary costs in the market basket cost weight
calculations since the hospital-based IRF only utilizes a portion of
the facility's ancillary services. We believe the ratio of reported IRF
Medicare costs to reported total Medicare costs provides a reasonable
estimate of the ancillary services utilized, and costs incurred, by the
hospital-based IRF.
We are proposing to calculate the portion of overhead salary costs
attributable to hospital-based IRFs by first calculating total
noncapital overhead costs (Worksheet B, part I, columns 4-18, line 41,
less Worksheet B, part II, columns 4-18, line 41). We then multiply
total noncapital overhead costs by an overhead ratio equal to the ratio
of total facility overhead salaries (as reported on Worksheet A, column
1, lines 4-18) to total facility noncapital overhead costs (as reported
on Worksheet A, column 1 and 2, lines 4-18). This methodology assumes
the proportion of total costs related to salaries for the overhead cost
center is similar for all inpatient units (that is, acute inpatient or
inpatient rehabilitation).
We are proposing to calculate the portion of overhead salaries
attributable to each ancillary department by first calculating total
noncapital overhead costs attributable to each specific ancillary
department (Worksheet B, part I, columns 4-18 less, Worksheet B, part
II, columns 4-18). We then identify the portion of these noncapital
overhead
[[Page 17263]]
costs attributable to Wages and Salaries by multiplying these costs by
the overhead ratio defined as the ratio of total facility overhead
salaries (as reported on Worksheet A, column 1, lines 4-18) to total
overhead costs (as reported on Worksheet A, column 1 & 2, lines 4-18).
Finally, we identified the portion of these overhead salaries for each
ancillary department that is attributable to the hospital-based IRF by
multiplying by the ratio of IRF Medicare ancillary costs for the cost
center (as reported on Worksheet D-3, column 3, for hospital-based
IRFs) to total Medicare ancillary costs for the cost center (equal to
the sum of Worksheet D-3, column 3, for all relevant PPS units [that
is, IPPS, IRF, IPF and SNF]). This is the same methodology used to
derive the 2012-based IRF market basket.
(2) Employee Benefits Costs
Effective with the implementation of CMS Form 2552-10, we began
collecting Employee Benefits and Contract Labor data on Worksheet S-3,
part V.
For 2016 Medicare cost report data, the majority of providers did
not report data on Worksheet S-3, part V; particularly, approximately
48 percent of freestanding IRFs and 40 percent of hospital-based IRFs
reported data on Worksheet S-3, part V. However, we believe we have a
large enough sample to enable us to produce a reasonable Employee
Benefits cost weight. Again, we continue to encourage all providers to
report these data on the Medicare cost report.
For freestanding IRFs, we are proposing Employee Benefits costs
would be equal to the data reported on Worksheet S-3, part V, column 2,
line 2. We note that while not required to do so, freestanding IRFs
also may report Employee Benefits data on Worksheet S-3, part II, which
is applicable to only IPPS providers. For those freestanding IRFs that
report Worksheet S-3, part II, data, but not Worksheet S-3, part V, we
are proposing to use the sum of Worksheet S-3, part II, lines 17, 18,
20, and 22, to derive Employee Benefits costs. This proposed method
would allow us to obtain data from about 30 more freestanding IRFs than
if we were to only use the Worksheet S-3, part V, data as was done for
the 2012-based IRF market basket.
For hospital-based IRFs, we are proposing to calculate total
benefit costs as the sum of inpatient unit benefit costs, a portion of
ancillary benefits, and a portion of overhead benefits attributable to
the routine inpatient unit and a portion of overhead benefits
attributable to the ancillary departments. We are proposing inpatient
unit benefit costs be equal to Worksheet S-3, part V, column 2, line 4.
We are proposing that the portion of overhead benefits attributable to
the routine inpatient unit and ancillary departments be calculated by
multiplying ancillary salaries for the hospital-based IRF and overhead
salaries attributable to the hospital-based IRF (determined in the
derivation of hospital-based IRF Wages and Salaries costs as described
above) by the ratio of total facility benefits to total facility
salaries. Total facility benefits is equal to the sum of Worksheet S-3,
part II, column 4, lines 17-25, and total facility salaries is equal to
Worksheet S-3, part II, column 4, line 1.
(3) Contract Labor Costs
Contract Labor costs are primarily associated with direct patient
care services. Contract labor costs for other services such as
accounting, billing, and legal are calculated separately using other
government data sources as described in section V.C.3. of this proposed
rule. To derive contract labor costs using Worksheet S-3, part V, data,
for freestanding IRFs, we are proposing Contract Labor costs be equal
to Worksheet S-3, part V, column 1, line 2. As we noted for Employee
Benefits, freestanding IRFs also may report Contract Labor data on
Worksheet S-3, part II, which is applicable to only IPPS providers. For
those freestanding IRFs that report Worksheet S-3, part II data, but
not Worksheet S-3, part V, we are proposing to use the sum of Worksheet
S-3, part II, lines 11 and 13, to derive Contract Labor costs.
For hospital-based IRFs, we are proposing that Contract Labor costs
would be equal to Worksheet S-3, part V, column 1, line 4. As
previously noted, for 2016 Medicare cost report data, while there were
providers that did report data on Worksheet S-3, part V, many providers
did not complete this worksheet. However, we believe we have a large
enough sample to enable us to produce a reasonable Contract Labor cost
weight. We continue to encourage all providers to report these data on
the Medicare cost report.
(4) Pharmaceuticals Costs
For freestanding IRFs, we are proposing to calculate
pharmaceuticals costs using non-salary costs reported on Worksheet A,
column 7, less Worksheet A, column 1, for the pharmacy cost center
(line 15) and drugs charged to patients cost center (line 73).
For hospital-based IRFs, we are proposing to calculate
pharmaceuticals costs as the sum of a portion of the non-salary
pharmacy costs and a portion of the non-salary drugs charged to patient
costs reported for the total facility. We propose that non-salary
pharmacy costs attributable to the hospital-based IRF would be
calculated by multiplying total pharmacy costs attributable to the
hospital-based IRF (as reported on Worksheet B, part I, column 15, line
41) by the ratio of total non-salary pharmacy costs (Worksheet A,
column 2, line 15) to total pharmacy costs (sum of Worksheet A, columns
1 and 2 for line 15) for the total facility. We propose that non-salary
drugs charged to patient costs attributable to the hospital-based IRF
would be calculated by multiplying total non-salary drugs charged to
patient costs (Worksheet B, part I, column 0, line 73 plus Worksheet B,
part I, column 15, line 73, less Worksheet A, column 1, line 73) for
the total facility by the ratio of Medicare drugs charged to patient
ancillary costs for the IRF unit (as reported on Worksheet D-3 for
hospital-based IRFs, column 3, line 73) to total Medicare drugs charged
to patient ancillary costs for the total facility (equal to the sum of
Worksheet D-3, column 3, line 73 for all relevant PPS [that is, IPPS,
IRF, IPF and SNF]).
(5) Professional Liability Insurance Costs
For freestanding IRFs, we are proposing that Professional Liability
Insurance (PLI) costs (often referred to as malpractice costs) would be
equal to premiums, paid losses and self-insurance costs reported on
Worksheet S-2, columns 1 through 3, line 118. For hospital-based IRFs,
we are proposing to assume that the PLI weight for the total facility
is similar to the hospital-based IRF unit since the only data reported
on this worksheet is for the entire facility, as we currently have no
means to identify the proportion of total PLI costs that are only
attributable to the hospital-based IRF. Therefore, hospital-based IRF
PLI costs are equal to total facility PLI (as reported on Worksheet S-
2, columns 1 through 3, line 118) divided by total facility costs (as
reported on Worksheet A, columns 1 and 2, line 200) times hospital-
based IRF Medicare allowable total costs. Our assumption is that the
same proportion of expenses are used among each unit of the hospital.
We welcome comments on this proposed method of deriving the PLI costs
for hospital-based IRFs.
(6) Home Office/Related Organization Contract Labor Costs
For the 2016-based IRF market basket, we are proposing to determine
the home office/related organization contract
[[Page 17264]]
labor costs using Medicare cost report data. The 2012-based IRF market
basket used the 2007 Benchmark Input-Output (I-O) expense data
published by the Bureau of Economic Analysis (BEA) to derive these
costs (80 FR 47057). A more detailed explanation of the general
methodology using the BEA I-O data is provided in section V.C.3. of
this proposed rule. For freestanding and hospital-based IRFs, we are
proposing to calculate the home office contract labor cost weight
(using data reported on Worksheet S-3, part II, column 4, lines 14,
1401, 1402, 2550, and 2551) and total facility costs (Worksheet B, part
1, column 26, line 202). We are proposing to use total facility costs
as the denominator for calculating the home office contract labor cost
weight as these expenses reported on Worksheet S-3, part II reflect the
entire hospital facility. Our assumption is that the same proportion of
expenses are used among each unit of the hospital. For the 2012-based
IRF market basket, we calculated the home office cost weight using
expense data for North American Industry Classification System (NAICS)
code 55, Management of Companies and Enterprises (80 FR 47067).
(7) Capital Costs
For freestanding IRFs, we are proposing that capital costs would be
equal to Medicare allowable capital costs as reported on Worksheet B,
part II, column 26, lines 30 through 35, 50 through 76 (excluding 52
and 75), 90 through 91, and 93.
For hospital-based IRFs, we are proposing that capital costs would
be equal to IRF inpatient capital costs (as reported on Worksheet B,
part II, column 26, line 41) and a portion of IRF ancillary capital
costs. We calculate the portion of ancillary capital costs attributable
to the hospital-based IRF for a given cost center by multiplying total
facility ancillary capital costs for the specific ancillary cost center
(as reported on Worksheet B, part II, column 26) by the ratio of IRF
Medicare ancillary costs for the cost center (as reported on Worksheet
D-3, column 3 for hospital-based IRFs) to total Medicare ancillary
costs for the cost center (equal to the sum of Worksheet D-3, column 3
for all relevant PPS [that is, IPPS, IRF, IPF and SNF]). For example,
if hospital-based IRF Medicare physical therapy costs represent 30
percent of the total Medicare physical therapy costs for the entire
facility, then 30 percent of total facility physical therapy capital
costs (as reported in Worksheet B, part II, column 26, line 66) would
be attributable to the hospital-based IRF.
b. Final Major Cost Category Computation
After we derive costs for the major cost categories for each
provider using the Medicare cost report data as previously described,
we propose to trim the data for outliers. For the Wages and Salaries,
Employee Benefits, Contract Labor, Pharmaceuticals, Professional
Liability Insurance, and Capital cost weights, we first divide the
costs for each of these six categories by total Medicare allowable
costs calculated for the provider to obtain cost weights for the
universe of IRF providers. We then remove those providers whose derived
cost weights fall in the top and bottom 5 percent of provider specific
derived cost weights to ensure the exclusion of outliers. After the
outliers have been excluded, we sum the costs for each category across
all remaining providers. We then divide this by the sum of total
Medicare allowable costs across all remaining providers to obtain a
cost weight for the proposed 2016-based IRF market basket for the given
category.
The proposed trimming methodology for the Home Office Contract
Labor cost weight is slightly different than the proposed trimming
methodology for the other six cost categories as described above. For
the Home Office Contract Labor cost weight, since we are using total
facility data rather than Medicare-allowable costs associated with IRF
services, we are proposing to trim the freestanding and hospital-based
IRF cost weights separately. For each of the providers, we first divide
the home office contract labor costs by total facility costs to obtain
a Home Office Contract Labor cost weight for the universe of IRF
providers. We are then proposing to trim only the top 1 percent of
providers to exclude outliers while also allowing providers who have
reported zero home office costs to remain in the Home Office Contract
Labor cost weight calculations as not all providers will incur home
office costs. After removing these outliers, we are left with a trimmed
data set for both freestanding and hospital-based providers. We are
then proposing to sum the costs for each category (freestanding and
hospital-based) across all remaining providers. We next divide this by
the sum of total facility costs across all remaining providers to
obtain a freestanding and hospital-based cost weight. Lastly, we are
proposing to weight these two cost weights together using the Medicare-
allowable costs to derive a Home Office Contract Labor cost weight for
the proposed 2016-based IRF market basket.
Finally, we calculate the residual ``All Other'' cost weight that
reflects all remaining costs that are not captured in the seven cost
categories listed. See Table 5 for the resulting cost weights for these
major cost categories that we obtain from the Medicare cost reports.
Table 5--Major Cost Categories as Derived From Medicare Cost Reports
----------------------------------------------------------------------------------------------------------------
Proposed 2016- 2012-based IRF
Major cost categories based IRF market market basket
basket (percent) (percent)
----------------------------------------------------------------------------------------------------------------
Wages and Salaries.......................................................... 47.1 47.3
Employee Benefits........................................................... 11.3 11.2
Contract Labor.............................................................. 1.0 0.8
Professional Liability Insurance (Malpractice).............................. 0.7 0.9
Pharmaceuticals............................................................. 5.1 5.1
Home Office Contract Labor.................................................. 3.7 n/a
Capital..................................................................... 9.0 8.6
All Other................................................................... 22.2 26.1
----------------------------------------------------------------------------------------------------------------
* Total may not sum to 100 due to rounding.
As we did for the 2012-based IRF market basket, we are proposing to
allocate the Contract Labor cost weight to the Wages and Salaries and
Employee Benefits cost weights based on their relative proportions
under the
[[Page 17265]]
assumption that contract labor costs are comprised of both wages and
salaries and employee benefits. The Contract Labor allocation
proportion for Wages and Salaries is equal to the Wages and Salaries
cost weight as a percent of the sum of the Wages and Salaries cost
weight and the Employee Benefits cost weight. For this proposed rule,
this rounded percentage is 81 percent; therefore, we are proposing to
allocate 81 percent of the Contract Labor cost weight to the Wages and
Salaries cost weight and 19 percent to the Employee Benefits cost
weight. The 2012-based IRF market basket percentage was also 81 percent
(80 FR 47056). Table 6 shows the Wages and Salaries and Employee
Benefit cost weights after Contract Labor cost weight allocation for
both the proposed 2016-based IRF market basket and 2012-based IRF
market basket.
Table 6--Wages and Salaries and Employee Benefits Cost Weights After Contract Labor Allocation
----------------------------------------------------------------------------------------------------------------
Proposed 2016-
Major cost categories based IRF market 2012-based IRF
basket market basket
----------------------------------------------------------------------------------------------------------------
Wages and Salaries.......................................................... 47.9 47.9
Employee Benefits........................................................... 11.4 11.3
----------------------------------------------------------------------------------------------------------------
c. Derivation of the Detailed Operating Cost Weights
To further divide the ``All Other'' residual cost weight estimated
from the 2016 Medicare cost report data into more detailed cost
categories, we propose to use the 2012 Benchmark Input-Output (I-O)
``Use Tables/Before Redefinitions/Purchaser Value'' for NAICS 622000,
Hospitals, published by the Bureau of Economic Analysis (BEA). This
data is publicly available at http://www.bea.gov/industry/io_annual.htm. For the 2012-based IRF market basket, we used the 2007
Benchmark I-O data, the most recent data available at the time (80 FR
47057).
The BEA Benchmark I-O data are scheduled for publication every 5
years with the most recent data available for 2012. The 2007 Benchmark
I-O data are derived from the 2012 Economic Census and are the building
blocks for BEA's economic accounts. Thus, they represent the most
comprehensive and complete set of data on the economic processes or
mechanisms by which output is produced and distributed.\1\ BEA also
produces Annual I-O estimates; however, while based on a similar
methodology, these estimates reflect less comprehensive and less
detailed data sources and are subject to revision when benchmark data
becomes available. Instead of using the less detailed Annual I-O data,
we propose to inflate the 2012 Benchmark I-O data forward to 2016 by
applying the annual price changes from the respective price proxies to
the appropriate market basket cost categories that are obtained from
the 2012 Benchmark I-O data. We repeat this practice for each year. We
then propose to calculate the cost shares that each cost category
represents of the inflated 2012 data. These resulting 2016 cost shares
are applied to the All Other residual cost weight to obtain the
proposed detailed cost weights for the 2016-based IRF market basket.
For example, the cost for Food: Direct Purchases represents 5.0 percent
of the sum of the ``All Other'' 2012 Benchmark I-O Hospital
Expenditures inflated to 2016; therefore, the Food: Direct Purchases
cost weight represents 5.0 percent of the 2016-based IRF market
basket's ``All Other'' cost category (22.2 percent), yielding a
``final'' Food: Direct Purchases cost weight of 1.1 percent in the
proposed 2016-based IRF market basket (0.05 * 22.2 percent = 1.1
percent).
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\1\ http://www.bea.gov/papers/pdf/IOmanual_092906.pdf.
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Using this methodology, we propose to derive seventeen detailed IRF
market basket cost category weights from the proposed 2016-based IRF
market basket residual cost weight (22.2 percent). These categories
are: (1) Electricity, (2) Fuel, Oil, and Gasoline (3) Food: Direct
Purchases, (4) Food: Contract Services, (5) Chemicals, (6) Medical
Instruments, (7) Rubber & Plastics, (8) Paper and Printing Products,
(9) Miscellaneous Products, (10) Professional Fees: Labor-related, (11)
Administrative and Facilities Support Services, (12) Installation,
Maintenance, and Repair, (13) All Other Labor-related Services, (14)
Professional Fees: Nonlabor-related, (15) Financial Services, (16)
Telephone Services, and (17) All Other Nonlabor-related Services. We
note that for the 2012-based IRF market basket, we had a Water and
Sewerage cost weight. For the proposed 2016-based IRF market basket, we
are proposing to include Water and Sewerage costs in the Electricity
cost weight due to the small amount of costs in this category.
For the 2012-based IRF market basket, we used the I-O data for
NAICS 55 Management of Companies to derive the Home Office Contract
Labor cost weight, which were classified in the Professional Fees:
Labor-related and Professional Fees: Nonlabor-related cost weights. As
previously discussed, we are proposing to use the Medicare cost report
data to derive the Home Office Contract Labor cost weight, which we
would further classify into the Professional Fees: Labor-related or
Professional Fees: Nonlabor-related categories.
d. Derivation of the Detailed Capital Cost Weights
As described in section V.C.1.a.(6) of this proposed rule, we are
proposing a Capital-Related cost weight of 9.0 percent as obtained from
the 2016 Medicare cost reports for freestanding and hospital-based IRF
providers. We are proposing to then separate this total Capital-Related
cost weight into more detailed cost categories.
Using 2016 Medicare cost reports, we are able to group Capital-
Related costs into the following categories: Depreciation, Interest,
Lease, and Other Capital-Related costs. For each of these categories,
we are proposing to determine separately for hospital-based IRFs and
freestanding IRFs what proportion of total capital-related costs the
category represents.
For freestanding IRFs, we are proposing to derive the proportions
for Depreciation, Interest, Lease, and Other Capital-related costs
using the data reported by the IRF on Worksheet A-7, which is similar
to the methodology used for the 2012-based IRF market basket.
For hospital-based IRFs, data for these four categories are not
reported separately for the hospital-based IRF; therefore, we are
proposing to derive these proportions using data reported on Worksheet
A-7 for the total facility. We are assuming the cost shares for the
overall hospital are representative for the hospital-based IRF unit.
For example, if depreciation costs make up 60 percent of total capital
costs for the entire facility, we believe it is
[[Page 17266]]
reasonable to assume that the hospital-based IRF would also have a 60
percent proportion because it is a unit contained within the total
facility. This is the same methodology used for the 2012-based IRF
market basket (80 FR 47057).
To combine each detailed capital cost weight for freestanding and
hospital-based IRFs into a single capital cost weight for the proposed
2016-based IRF market basket, we are proposing to weight together the
shares for each of the categories (Depreciation, Interest, Lease, and
Other Capital-related costs) based on the share of total capital costs
each provider type represents of the total capital costs for all IRFs
for 2016. Applying this methodology results in proportions of total
capital-related costs for Depreciation, Interest, Lease and Other
Capital-related costs that are representative of the universe of IRF
providers. This is the same methodology used for the 2012-based IRF
market basket (80 FR 47057 through 47058).
Lease costs are unique in that they are not broken out as a
separate cost category in the proposed 2016-based IRF market basket.
Rather, we are proposing to proportionally distribute these costs among
the cost categories of Depreciation, Interest, and Other Capital-
Related, reflecting the assumption that the underlying cost structure
of leases is similar to that of capital-related costs in general. As
was done under the 2012-based IRF market basket, we are proposing to
assume that 10 percent of the lease costs as a proportion of total
capital-related costs represents overhead and assign those costs to the
Other Capital-Related cost category accordingly. We propose to
distribute the remaining lease costs proportionally across the three
cost categories (Depreciation, Interest, and Other Capital-Related)
based on the proportion that these categories comprise of the sum of
the Depreciation, Interest, and Other Capital-related cost categories
(excluding lease expenses). This would result in three primary capital-
related cost categories in the proposed 2016-based IRF market basket:
Depreciation, Interest, and Other Capital-Related costs. This is the
same methodology used for the 2012-based IRF market basket (80 FR
47058). The allocation of these lease expenses are shown in Table 6.
Finally, we are proposing to further divide the Depreciation and
Interest cost categories. We are proposing to separate Depreciation
into the following two categories: (1) Building and Fixed Equipment and
(2) Movable Equipment. We are proposing to separate Interest into the
following two categories: (1) Government/Nonprofit and (2) For-profit.
To disaggregate the Depreciation cost weight, we need to determine
the percent of total Depreciation costs for IRFs that is attributable
to Building and Fixed Equipment, which we hereafter refer to as the
``fixed percentage.'' For the proposed 2016-based IRF market basket, we
are proposing to use slightly different methods to obtain the fixed
percentages for hospital-based IRFs compared to freestanding IRFs.
For freestanding IRFs, we are proposing to use depreciation data
from Worksheet A-7 of the 2016 Medicare cost reports. However, for
hospital-based IRFs, we determined that the fixed percentage for the
entire facility may not be representative of the hospital-based IRF
unit due to the entire facility likely employing more sophisticated
movable assets that are not utilized by the hospital-based IRF.
Therefore, for hospital-based IRFs, we are proposing to calculate a
fixed percentage using: (1) Building and fixture capital costs
allocated to the hospital-based IRF unit as reported on Worksheet B,
part I, line 41, and (2) building and fixture capital costs for the top
five ancillary cost centers utilized by hospital-based IRFs. We propose
to weight these two fixed percentages (inpatient and ancillary) using
the proportion that each capital cost type represents of total capital
costs in the proposed 2016-based IRF market basket. We are proposing to
then weight the fixed percentages for hospital-based and freestanding
IRFs together using the proportion of total capital costs each provider
type represents. For both freestanding and hospital-based IRFs, this is
the same methodology used for the 2012-based IRF market basket (80 FR
47058).
To disaggregate the Interest cost weight, we determined the percent
of total interest costs for IRFs that are attributable to government
and nonprofit facilities, which is hereafter referred to as the
``nonprofit percentage,'' as price pressures associated with these
types of interest costs tend to differ from those for for-profit
facilities. For the 2016-based IRF market basket, we are proposing to
use interest costs data from Worksheet A-7 of the 2016 Medicare cost
reports for both freestanding and hospital-based IRFs. We are proposing
to determine the percent of total interest costs that are attributed to
government and nonprofit IRFs separately for hospital-based and
freestanding IRFs. We then are proposing to weight the nonprofit
percentages for hospital-based and freestanding IRFs together using the
proportion of total capital costs that each provider type represents.
Table 7 provides the proposed detailed capital cost share
composition estimated from the 2016 IRF Medicare cost reports. These
detailed capital cost share composition percentages are applied to the
total Capital-Related cost weight of 9.0 percent explained in detail in
section V.C.1.a.(6) of this proposed rule.
Table 7--Capital Cost Share Composition for the Proposed 2016-Based IRF Market Basket
----------------------------------------------------------------------------------------------------------------
Capital cost Capital cost
share share
composition composition
before lease after lease
expense expense
allocation (%) allocation (%)
----------------------------------------------------------------------------------------------------------------
Depreciation................................................................ 59 73
Building and Fixed Equipment................................................ 37 45
Movable Equipment........................................................... 22 28
Interest.................................................................... 13 16
Government/Nonprofit........................................................ 8 9
For Profit.................................................................. 5 7
Lease....................................................................... 21 ................
Other....................................................................... 7 11
----------------------------------------------------------------------------------------------------------------
* Detail may not add to total due to rounding.
[[Page 17267]]
e. Proposed 2016-Based IRF Market Basket Cost Categories and Weights
Table 8 compares the cost categories and weights for the proposed
2016-based IRF market basket compared to the 2012-based IRF market
basket.
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2. Selection of Price Proxies
After developing the cost weights for the proposed 2016-based IRF
market basket, we select the most appropriate wage and price proxies
currently available to represent the rate of price change for each
expenditure category. For the majority of the cost weights, we base the
price proxies on U.S. Bureau of Labor Statistics (BLS) data and group
them into one of the following BLS categories:
Employment Cost Indexes. Employment Cost Indexes (ECIs)
measure the rate of change in employment wage rates and employer costs
for employee benefits per hour worked. These indexes are fixed-weight
indexes and strictly measure the change in wage rates and employee
benefits per hour. ECIs are superior to Average Hourly Earnings (AHE)
as price proxies for input price indexes because they are not affected
by shifts in occupation or industry mix, and because they measure pure
price change and are available by both occupational group and by
industry. The industry ECIs are based on the NAICS and the occupational
ECIs are based on the Standard Occupational Classification System
(SOC).
Producer Price Indexes. Producer Price Indexes (PPIs)
measure the average change over time in the selling prices received by
domestic producers for their output. The prices included in the PPI are
from the first commercial transaction for many products and some
services (https://www.bls.gov/ppi/).
Consumer Price Indexes. Consumer Price Indexes (CPIs)
measure the average change over time in the prices paid by urban
consumers for a market basket of consumer goods and services (https://www.bls.gov/cpi/). CPIs are only used when the purchases are similar to
those of retail consumers rather than purchases at the producer level,
or if no appropriate PPIs are available.
We evaluate the price proxies using the criteria of reliability,
timeliness, availability, and relevance:
Reliability. Reliability indicates that the index is based
on valid statistical methods and has low sampling variability. Widely
accepted statistical methods ensure that the data were collected and
aggregated in a way that can be replicated. Low sampling variability is
desirable because it indicates that the sample reflects the typical
members of the population. (Sampling variability is variation that
occurs by chance because only a sample was surveyed rather than the
entire population.)
Timeliness. Timeliness implies that the proxy is published
regularly, preferably at least once a quarter. The market baskets are
updated quarterly, and therefore, it is important for the underlying
price proxies to be up-to-date, reflecting the most recent data
available. We believe that using proxies that are published regularly
(at least quarterly, whenever possible) helps to ensure that we are
using the most recent data available to update the market basket. We
strive to use publications that are disseminated frequently, because we
believe that this is an optimal way to stay abreast of the most current
data available.
Availability. Availability means that the proxy is
publicly available. We prefer that our proxies are publicly available
because this will help ensure that our market basket updates are as
transparent to the public as possible. In addition, this enables the
public to be able to obtain the price proxy data on a regular basis.
Relevance. Relevance means that the proxy is applicable
and representative of the cost category weight to which it is applied.
The CPIs, PPIs, and ECIs that we have selected to propose in this
regulation meet these criteria. Therefore, we believe that they
continue to be the best measure of price changes for the cost
categories to which they would be applied.
Table 11 lists all price proxies that we propose to use for the
proposed 2016-based IRF market basket. Below is a detailed explanation
of the price proxies we are proposing for each cost category weight.
a. Price Proxies for the Operating Portion of the Proposed 2016-Based
IRF Market Basket
(1) Wages and Salaries
We are proposing to continue to use the ECI for Wages and Salaries
for All Civilian workers in Hospitals (BLS series code
CIU1026220000000I) to measure the wage rate growth of this cost
category. This is the same price proxy used in the 2012-based IRF
market basket (80 FR 47060).
(2) Benefits
We are proposing to continue to use the ECI for Total Benefits for
All Civilian workers in Hospitals to measure price growth of this
category. This ECI is calculated using the ECI for Total Compensation
for All Civilian workers in Hospitals (BLS series code
CIU1016220000000I) and the relative importance of wages and salaries
within total compensation. This is the same price proxy used in the
2012-based IRF market basket (80 FR 47060).
(3) Electricity
We are proposing to continue to use the PPI Commodity Index for
Commercial Electric Power (BLS series code WPU0542) to measure the
price growth of this cost category. This is the same price proxy used
in the 2012-based IRF market basket (80 FR 47060).
(4) Fuel, Oil, and Gasoline
Similar to the 2012-based IRF market basket, for the 2016-based IRF
market basket, we are proposing to use a blend of the PPI for Petroleum
Refineries and the PPI Commodity for Natural Gas. Our analysis of the
Bureau of Economic Analysis' 2012 Benchmark Input-Output data (use
table before redefinitions, purchaser's value for NAICS 622000
[Hospitals]), shows that Petroleum Refineries expenses account for
approximately 90 percent and Natural Gas expenses account for
approximately 10 percent of Hospitals' (NAICS 622000) total Fuel, Oil,
and Gasoline expenses. Therefore, we propose to use a blend of 90
percent of the PPI for Petroleum Refineries (BLS series code
PCU324110324110) and 10 percent of the PPI Commodity Index for Natural
Gas (BLS series code WPU0531) as the price proxy for this cost
category. The 2012-based IRF market basket used a 70/30 blend of these
price proxies, reflecting the 2007 I-O data (80 FR 47060). We believe
that these two price proxies continue to be the most technically
appropriate indices available to measure the price growth of the Fuel,
Oil, and Gasoline cost category in the proposed 2016-based IRF market
basket.
(5) Professional Liability Insurance
We are proposing to continue to use the CMS Hospital Professional
Liability Index to measure changes in PLI premiums. To generate this
index, we collect commercial insurance premiums for a fixed level of
coverage while holding non-price factors constant (such as a change in
the level of coverage). This is the same proxy used in the 2012-based
IRF market basket (80 FR 47060).
(6) Pharmaceuticals
We are proposing to continue to use the PPI for Pharmaceuticals for
Human Use, Prescription (BLS series code WPUSI07003) to measure the
price growth of this cost category. This is the same proxy used in the
2012-based IRF market basket (80 FR 47060).
(7) Food: Direct Purchases
We are proposing to continue to use the PPI for Processed Foods and
Feeds (BLS series code WPU02) to measure the price growth of this cost
category. This
[[Page 17269]]
is the same proxy used in the 2012-based IRF market basket (80 FR
47060).
(8) Food: Contract Purchases
We are proposing to continue to use the CPI for Food Away From Home
(BLS series code CUUR0000SEFV) to measure the price growth of this cost
category. This is the same proxy used in the 2012-based IRF market
basket (80 FR 47060 through 47061).
(9) Chemicals
Similar to the 2012-based IRF market basket, we are proposing to
use a four part blended PPI as the proxy for the chemical cost category
in the proposed 2016-based IRF market basket. The proposed blend is
composed of the PPI for Industrial Gas Manufacturing, Primary Products
(BLS series code PCU325120325120P), the PPI for Other Basic Inorganic
Chemical Manufacturing (BLS series code PCU32518-32518-), the PPI for
Other Basic Organic Chemical Manufacturing (BLS series code PCU32519-
32519-), and the PPI for Other Miscellaneous Chemical Product
Manufacturing (BLS series code PCU325998325998). We note that the four
part blended PPI used in the 2012-based IRF market basket is composed
of the PPI for Industrial Gas Manufacturing (BLS series code
PCU325120325120P), the PPI for Other Basic Inorganic Chemical
Manufacturing (BLS series code PCU32518-32518-), the PPI for Other
Basic Organic Chemical Manufacturing (BLS series code PCU32519-32519-),
and the PPI for Soap and Cleaning Compound Manufacturing (BLS series
code PCU32561-32561-). For the proposed 2016-based IRF market basket,
we are proposing to derive the weights for the PPIs using the 2012
Benchmark I-O data. The 2012-based IRF market basket used the 2007
Benchmark I-O data to derive the weights for the four PPIs (80 FR
47061).
Table 9 shows the weights for each of the four PPIs used to create
the proposed blended Chemical proxy for the proposed 2016 IRF market
basket compared to the 2012-based blended Chemical proxy.
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(10) Medical Instruments
We are proposing to continue to use a blend of two PPIs for the
Medical Instruments cost category. The 2012 Benchmark Input-Output data
shows an approximate 57/43 split between Surgical and Medical
Instruments and Medical and Surgical Appliances and Supplies for this
cost category. Therefore, we propose a blend composed of 57 percent of
the commodity-based PPI for Surgical and Medical Instruments (BLS
series code WPU1562) and 43 percent of the commodity-based PPI for
Medical and Surgical Appliances and Supplies (BLS series code WPU1563).
The 2012-based IRF market basket used a 50/50 blend of these PPIs based
on the 2007 Benchmark I-O data (80 FR 47061).
(11) Rubber and Plastics
We are proposing to continue to use the PPI for Rubber and Plastic
Products (BLS series code WPU07) to measure price growth of this cost
category. This is the same proxy used in the 2012-based IRF market
basket (80 FR 47061).
(12) Paper and Printing Products
We are proposing to continue to use the PPI for Converted Paper and
Paperboard Products (BLS series code WPU0915) to measure the price
growth of this cost category. This is the same proxy used in the 2012-
based IRF market basket (80 FR 47061).
(13) Miscellaneous Products
We are proposing to continue to use the PPI for Finished Goods Less
Food and Energy (BLS series code WPUFD4131) to measure the price growth
of this cost category. This is the same proxy used in the 2012-based
IRF market basket (80 FR 47061).
(14) Professional Fees: Labor-Related
We are proposing to continue to use the ECI for Total Compensation
for Private Industry workers in Professional and Related (BLS series
code CIU2010000120000I) to measure the price growth of this category.
This is the same proxy used in the 2012-based IRF market basket (80 FR
47061).
(15) Administrative and Facilities Support Services
We are proposing to continue to use the ECI for Total Compensation
for Private Industry workers in Office and Administrative Support (BLS
series code CIU2010000220000I) to measure the price growth of this
category. This is the same proxy used in the 2012-based IRF market
basket (80 FR 47061).
(16) Installation, Maintenance, and Repair
We are proposing to continue to use the ECI for Total Compensation
for Civilian workers in Installation, Maintenance, and Repair (BLS
series code CIU1010000430000I) to measure the price growth of this cost
category. This is the same proxy used in the 2012-based IRF market
basket (80 FR 47061).
(17) All Other: Labor-Related Services
We are proposing to continue to use the ECI for Total Compensation
for Private Industry workers in Service Occupations (BLS series code
CIU2010000300000I) to measure the price growth of this cost category.
This is the same proxy used in the 2012-based IRF market basket (80 FR
47061).
(18) Professional Fees: Nonlabor-Related
We are proposing to continue to use the ECI for Total Compensation
for Private Industry workers in Professional and Related (BLS series
code CIU2010000120000I) to measure the price growth of this category.
This is the same proxy used in the 2012-based IRF market basket (80 FR
47061).
(19) Financial Services
We are proposing to continue to use the ECI for Total Compensation
for Private Industry workers in Financial
[[Page 17270]]
Activities (BLS series code CIU201520A000000I) to measure the price
growth of this cost category. This is the same proxy used in the 2012-
based IRF market basket (80 FR 47061).
(20) Telephone Services
We are proposing to continue to use the CPI for Telephone Services
(BLS series code CUUR0000SEED) to measure the price growth of this cost
category. This is the same proxy used in the 2012-based IRF market
basket (80 FR 47061).
(21) All Other: Nonlabor-Related Services
We are proposing to continue to use the CPI for All Items Less Food
and Energy (BLS series code CUUR0000SA0L1E) to measure the price growth
of this cost category. This is the same proxy used in the 2012-based
IRF market basket (80 FR 47061).
b. Price Proxies for the Capital Portion of the Proposed 2016-Based IRF
Market Basket
(1) Capital Price Proxies Prior to Vintage Weighting
We are proposing to continue to use the same price proxies for the
capital-related cost categories in the proposed 2016-based IRF market
basket as were used in the 2012-based IRF market basket (80 FR 47062),
which are provided in Table 10 and described below. Specifically, we
are proposing to proxy:
Depreciation: Building and Fixed Equipment cost category
by BEA's Chained Price Index for Nonresidential Construction for
Hospitals and Special Care Facilities (BEA Table 5.4.4. Price Indexes
for Private Fixed Investment in Structures by Type).
Depreciation: Movable Equipment cost category by the PPI
for Machinery and Equipment (BLS series code WPU11).
Nonprofit Interest cost category by the average yield on
domestic municipal bonds (Bond Buyer 20-bond index).
For-profit Interest cost category by the average yield on
Moody's Aaa bonds (Federal Reserve).
Other Capital-Related cost category by the CPI-U for Rent
of Primary Residence (BLS series code CUUS0000SEHA).
We believe these are the most appropriate proxies for IRF capital-
related costs that meet our selection criteria of relevance,
timeliness, availability, and reliability. We are also proposing to
continue to vintage weight the capital price proxies for Depreciation
and Interest to capture the long-term consumption of capital. This
vintage weighting method is similar to the method used for the 2012-
based IRF market basket (80 FR 47062) and is described below.
(2) Vintage Weights for Price Proxies
Because capital is acquired and paid for over time, capital-related
expenses in any given year are determined by both past and present
purchases of physical and financial capital. The vintage-weighted
capital-related portion of the proposed 2016-based IRF market basket is
intended to capture the long-term consumption of capital, using vintage
weights for depreciation (physical capital) and interest (financial
capital). These vintage weights reflect the proportion of capital-
related purchases attributable to each year of the expected life of
building and fixed equipment, movable equipment, and interest. We are
proposing to use vintage weights to compute vintage-weighted price
changes associated with depreciation and interest expenses.
Capital-related costs are inherently complicated and are determined
by complex capital-related purchasing decisions, over time, based on
such factors as interest rates and debt financing. In addition, capital
is depreciated over time instead of being consumed in the same period
it is purchased. By accounting for the vintage nature of capital, we
are able to provide an accurate and stable annual measure of price
changes. Annual non-vintage price changes for capital are unstable due
to the volatility of interest rate changes, and therefore, do not
reflect the actual annual price changes for IRF capital-related costs.
The capital-related component of the proposed 2016-based IRF market
basket reflects the underlying stability of the capital-related
acquisition process.
The methodology used to calculate the vintage weights for the
proposed 2016-based IRF market basket is the same as that used for the
2012-based IRF market basket (80 FR 47062 through 47063) with the only
difference being the inclusion of more recent data. To calculate the
vintage weights for depreciation and interest expenses, we first need a
time series of capital-related purchases for building and fixed
equipment and movable equipment. We found no single source that
provides an appropriate time series of capital-related purchases by
hospitals for all of the above components of capital purchases. The
early Medicare cost reports did not have sufficient capital-related
data to meet this need. Data we obtained from the American Hospital
Association (AHA) do not include annual capital-related purchases.
However, we are able to obtain data on total expenses back to 1963 from
the AHA. Consequently, we are proposing to use data from the AHA Panel
Survey and the AHA Annual Survey to obtain a time series of total
expenses for hospitals. We are then proposing to use data from the AHA
Panel Survey supplemented with the ratio of depreciation to total
hospital expenses obtained from the Medicare cost reports to derive a
trend of annual depreciation expenses for 1963 through 2016. We propose
to separate these depreciation expenses into annual amounts of building
and fixed equipment depreciation and movable equipment depreciation as
determined earlier. From these annual depreciation amounts, we derive
annual end-of-year book values for building and fixed equipment and
movable equipment using the expected life for each type of asset
category. While data is not available that is specific to IRFs, we
believe this information for all hospitals serves as a reasonable
alternative for the pattern of depreciation for IRFs.
To continue to calculate the vintage weights for depreciation and
interest expenses, we also need to account for the expected lives for
Building and Fixed Equipment, Movable Equipment, and Interest for the
proposed 2016-based IRF market basket. We are proposing to calculate
the expected lives using Medicare cost report data from freestanding
and hospital-based IRFs. The expected life of any asset can be
determined by dividing the value of the asset (excluding fully
depreciated assets) by its current year depreciation amount. This
calculation yields the estimated expected life of an asset if the rates
of depreciation were to continue at current year levels, assuming
straight-line depreciation. We are proposing to determine the expected
life of building and fixed equipment separately for hospital-based IRFs
and freestanding IRFs, and then weight these expected lives using the
percent of total capital costs each provider type represents. We are
proposing to apply a similar method for movable equipment. Using these
proposed methods, we determined the average expected life of building
and fixed equipment to be equal to 22 years, and the average expected
life of movable equipment to be equal to 11 years. For the expected
life of interest, we believe vintage weights for interest should
represent the average expected life of building and fixed equipment
because, based on previous research described in the FY 1997 IPPS final
rule (61 FR 46198), the expected life of hospital debt instruments and
the expected life of buildings and fixed equipment are similar. We note
that for the 2012-based
[[Page 17271]]
IRF market basket, the expected life of building and fixed equipment is
23 years, and the expected life of movable equipment is 11 years (80 FR
47062).
Multiplying these expected lives by the annual depreciation amounts
results in annual year-end asset costs for building and fixed equipment
and movable equipment. We then calculate a time series, beginning in
1964, of annual capital purchases by subtracting the previous year's
asset costs from the current year's asset costs.
For the building and fixed equipment and movable equipment vintage
weights, we are proposing to use the real annual capital-related
purchase amounts for each asset type to capture the actual amount of
the physical acquisition, net of the effect of price inflation. These
real annual capital-related purchase amounts are produced by deflating
the nominal annual purchase amount by the associated price proxy as
provided earlier in this proposed rule. For the interest vintage
weights, we are proposing to use the total nominal annual capital-
related purchase amounts to capture the value of the debt instrument
(including, but not limited to, mortgages and bonds). Using these
capital-related purchase time series specific to each asset type, we
are proposing to calculate the vintage weights for building and fixed
equipment, for movable equipment, and for interest.
The vintage weights for each asset type are deemed to represent the
average purchase pattern of the asset over its expected life (in the
case of building and fixed equipment and interest, 22 years, and in the
case of movable equipment, 11 years). For each asset type, we used the
time series of annual capital-related purchase amounts available from
2016 back to 1964. These data allow us to derive thirty-two 22-year
periods of capital-related purchases for building and fixed equipment
and interest, and forty-three 11-year periods of capital-related
purchases for movable equipment. For each 22-year period for building
and fixed equipment and interest, or 11-year period for movable
equipment, we calculate annual vintage weights by dividing the capital-
related purchase amount in any given year by the total amount of
purchases over the entire 22-year or 11-year period. This calculation
is done for each year in the 22-year or 11-year period and for each of
the periods for which we have data. We then calculate the average
vintage weight for a given year of the expected life by taking the
average of these vintage weights across the multiple periods of data.
The vintage weights for the capital-related portion of the proposed
2016-based IRF market basket and the 2012-based IRF market basket are
presented in Table 10.
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The process of creating vintage-weighted price proxies requires
applying the vintage weights to the price proxy index where the last
applied vintage weight in Table 8 is applied to the most recent data
point. We have provided on the CMS website an example of how the
vintage weighting price proxies are calculated, using
[[Page 17272]]
example vintage weights and example price indices. The example can be
found at http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/MedicareProgramRatesStats/MarketBasketResearch.html in the zip file titled ``Weight Calculations
as described in the IPPS FY 2010 Proposed Rule.''
c. Summary of Price Proxies of the Proposed 2016-Based IRF Market
Basket
Table 11 shows both the operating and capital price proxies for the
proposed 2016-based IRF market basket.
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D. Proposed FY 2020 Market Basket Update and Productivity Adjustment
1. Proposed FY 2020 Market Basket Update
For FY 2020 (that is, beginning October 1, 2019 and ending
September 30, 2020), we are proposing to use the proposed 2016-based
IRF market basket increase factor described in section V.C. of this
proposed rule to update the IRF PPS base payment rate. Consistent with
historical practice, we are proposing to estimate the market basket
update for the IRF PPS based on IHS Global Inc.'s (IGI's) forecast
using the most recent available data. IGI is a nationally recognized
economic and financial forecasting firm with which we contract to
forecast the components of the market baskets and multifactor
productivity (MFP).
Based on IGI's first quarter 2019 forecast with historical data
through the fourth quarter of 2018, the projected proposed 2016-based
IRF market basket increase factor for FY 2020 is 3.0 percent.
Therefore, consistent with our historical practice of estimating market
basket increases based on the best available data, we are proposing a
market basket increase factor of 3.0 percent for FY 2020. We are also
proposing that if more recent data are subsequently available (for
example, a more recent estimate of the market basket) we would use such
data to determine the FY 2020 update in the final rule. For comparison,
the current 2012-based IRF market basket is also projected to increase
by 3.0 percent in FY 2020 based on IGI's first quarter 2019 forecast.
Table 12 compares the proposed 2016-based IRF market basket and the
2012-based IRF market basket percent changes. On average, the two
indexes produce similar updates to one another, with the 5-year average
historical and forecasted growth rates for both IRF market baskets
equal to 2.1 percent and 3.0 percent, respectively.
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2. Proposed Productivity Adjustment
According to section 1886(j)(3)(C)(i) of the Act, the Secretary
shall establish an increase factor based on an appropriate percentage
increase in a market basket of goods and services. As described in
sections V.C and V.D.1. of this proposed rule, we are proposing to
estimate the IRF PPS increase factor for FY 2020 based on the proposed
2016-based IRF market basket. Section 1886(j)(3)(C)(ii) of the Act then
requires that, after establishing the increase factor for a FY, the
Secretary shall reduce such increase factor for FY 2012 and each
subsequent FY, by the productivity adjustment described in section
1886(b)(3)(B)(xi)(II) of the Act. Section 1886(b)(3)(B)(xi)(II) of the
Act sets forth the definition of this productivity adjustment. The
statute defines the productivity adjustment to be equal to the 10-year
moving average of changes in annual economy-wide private nonfarm
business MFP (as projected by the Secretary for the 10-year period
ending with the applicable FY, year, cost reporting period, or other
annual period) (the ``MFP adjustment''). The BLS publishes the official
measure of private nonfarm business MFP. Please see http://www.bls.gov/mfp for the BLS historical published MFP data.
MFP is derived by subtracting the contribution of labor and capital
input growth from output growth. The projections of the components of
MFP are currently produced by IGI, a nationally recognized economic
forecasting firm with which CMS contracts to forecast the components of
the market basket and MFP. For more information on the productivity
adjustment, we refer reader to the discussion in the FY 2016 IRF PPS
final rule (80 FR 47065).
Using IGI's first quarter 2019 forecast, the MFP adjustment for FY
2020 (the 10-year moving average of MFP for the period ending FY 2020)
is projected to be 0.5 percent. Thus, in accordance with section
1886(j)(3)(C) of the Act, we propose to base the FY 2020 market basket
update, which is used to determine the applicable percentage increase
for the IRF payments, on the most recent estimate of the proposed 2016-
based IRF market basket (currently estimated to be 3.0 percent based on
IGI's first quarter 2019 forecast). We propose to then reduce this
percentage increase by the current estimate of the MFP adjustment for
FY 2020 of 0.5 percentage point (the 10-year moving average of MFP for
the period ending FY 2020 based on IGI's first quarter 2019 forecast).
Therefore, the current estimate of the FY 2020 IRF update is 2.5
percent (3.0 percent market basket update, less 0.5 percentage point
MFP adjustment). Furthermore, we propose that if more recent data are
subsequently available (for example, a more recent estimate of the
market basket and MFP adjustment), we would use such data to determine
the FY 2020 market basket update and MFP adjustment in the final rule.
[[Page 17275]]
For FY 2020, the Medicare Payment Advisory Commission (MedPAC)
recommends that a decrease of 5 percent be applied to IRF PPS payment
rates. As discussed, and in accordance with section 1886(j)(3)(C) of
the Act, the Secretary proposes to update IRF PPS payment rates for FY
2020 by an adjusted market basket increase factor of 2.5 percent, as
section 1886(j)(3)(C) of the Act does not provide the Secretary with
the authority to apply a different update factor to IRF PPS payment
rates for FY 2020.
We invite public comment on these proposals.
E. Proposed Labor-Related Share for FY 2020
Section 1886(j)(6) of the Act specifies that the Secretary is to
adjust the proportion (as estimated by the Secretary from time to time)
of rehabilitation facilities' costs which are attributable to wages and
wage-related costs, of the prospective payment rates computed under
section 1886(j)(3) of the Act for area differences in wage levels by a
factor (established by the Secretary) reflecting the relative hospital
wage level in the geographic area of the rehabilitation facility
compared to the national average wage level for such facilities. 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 propose to continue to classify a cost
category as labor-related if the costs are labor-intensive and vary
with the local labor market. As stated in the FY 2016 IRF PPS final
rule (80 FR 47068), the labor-related share was defined as the sum of
the relative importance of Wages and Salaries, Employee Benefits,
Professional Fees: Labor-related Services, Administrative and
Facilities Support Services, Installation, Maintenance, and Repair, All
Other: Labor-related Services, and a portion of the Capital Costs from
the 2012-based IRF market basket.
Based on our definition of the labor-related share and the cost
categories in the proposed 2016-based IRF market basket, we are
proposing to include in the labor-related share for FY 2020 the sum of
the FY 2020 relative importance of Wages and Salaries, Employee
Benefits, Professional Fees: Labor-related, Administrative and
Facilities Support Services, Installation, Maintenance, and Repair, All
Other: Labor-related Services, and a portion of the Capital-Related
cost weight from the proposed 2016-based IRF market basket.
Similar to the 2012-based IRF market basket (80 FR 47067), the
proposed 2016-based IRF market basket includes two cost categories for
nonmedical Professional Fees (including, but not limited to, expenses
for legal, accounting, and engineering services). These are
Professional Fees: Labor-related and Professional Fees: Nonlabor-
related. For the proposed 2016-based IRF market basket, we propose to
estimate the labor-related percentage of non-medical professional fees
(and assign these expenses to the Professional Fees: Labor-related
services cost category) based on the same method that was used to
determine the labor-related percentage of professional fees in the
2012-based IRF market basket.
As was done in the 2012-based IRF market basket (80 FR 47067), we
propose to determine the proportion of legal, accounting and auditing,
engineering, and management consulting services that meet our
definition of labor-related services based on a survey of hospitals
conducted by us in 2008, a discussion of which can be found in the FY
2010 IPPS/LTCH PPS final rule (74 FR 43850 through 43856). Based on the
weighted results of the survey, we determined that hospitals purchase,
on average, the following portions of contracted professional services
outside of their local labor market:
34 percent of accounting and auditing services.
30 percent of engineering services.
33 percent of legal services.
42 percent of management consulting services.
We are proposing to apply each of these percentages to the
respective Benchmark I-O cost category underlying the professional fees
cost category to determine the Professional Fees: Nonlabor-related
costs. The Professional Fees: Labor-related costs were determined to be
the difference between the total costs for each Benchmark I-O category
and the Professional Fees: Nonlabor-related costs. This is the same
methodology that we used to separate the 2012-based IRF market basket
professional fees category into Professional Fees: Labor-related and
Professional Fees: Nonlabor-related cost categories (80 FR 47067).
In the proposed 2016-based IRF market basket, nonmedical
professional fees that are subject to allocation based on these survey
results represent 4.4 percent of total costs (and are limited to those
fees related to Accounting & Auditing, Legal, Engineering, and
Management Consulting services). Based on our survey results, we
propose to apportion 2.8 percentage points of the 4.4 percentage point
figure into the Professional Fees: Labor-related share cost category
and designate the remaining 1.6 percentage point into the Professional
Fees: Nonlabor-related cost category.
In addition to the professional services listed, for the 2016-based
IRF market basket, we are proposing to allocate a proportion of the
Home Office Contract Labor cost weight, calculated using the Medicare
cost reports as stated above, into the Professional Fees: Labor-related
and Professional Fees: Nonlabor-related cost categories. We are
proposing to classify these expenses as labor-related and nonlabor-
related as many facilities are not located in the same geographic area
as their home office, and therefore, do not meet our definition for the
labor-related share that requires the services to be purchased in the
local labor market. For the 2012-based IRF market basket, we used the
BEA I-O expense data for NAICS 55, Management of Companies and
Enterprises, to estimate the Home Office Contract Labor cost weight (80
FR 47067). We then allocated these expenses into the Professional Fess:
Labor-related and Professional Fees: Nonlabor-related cost categories.
Similar to the 2012-based IRF market basket, we are proposing for
the 2016-based IRF market basket to use the Medicare cost reports for
both freestanding IRF providers and hospital-based IRF providers to
determine the home office labor-related percentages. The Medicare cost
report requires a hospital to report information regarding their home
office provider. For the 2016-based IRF market basket, we are proposing
to start with the sample of IRF providers that passed the top 1 percent
trim used to derive the Home Office Contract Labor cost weight as
described in section V.B. of this proposed rule. For both freestanding
and hospital-based providers, we are proposing to multiply each
provider's Home Office Contract Labor cost weight (calculated using
data from the total facility) by Medicare allowable total costs. This
results in an amount of Medicare allowable home office compensation
costs for each IRF. Using information on the Medicare cost report, we
then compare the location of the IRF with the location of the IRF's
home office. We are proposing to classify an IRF with a home office
located in their respective local labor market if the IRF and its home
office are located in the same Metropolitan Statistical Area. We then
calculate the proportion of Medicare allowable home office compensation
costs that these IRFs represent of total Medicare allowable home office
compensation costs. We
[[Page 17276]]
propose to multiply this percentage (42 percent) by the Home Office
Contract Labor cost weight (3.7 percent) to determine the proportion of
costs that should be allocated to the labor-related share. Therefore,
we are allocating 1.6 percentage points of the Home Office Contract
Labor cost weight (3.7 percent times 42 percent) to the Professional
Fees: Labor-related cost weight and 2.1 percentage points of the Home
Office Contract Labor cost weight to the Professional Fees: Nonlabor-
related cost weight (3.7 percent times 58 percent). For the 2012-based
IRF market basket, we used a similar methodology but we relied on
provider counts rather than home office/related organization contract
labor compensation costs to determine the labor-related percentage (80
FR 47067).
In summary, we apportioned 2.8 percentage points of the non-medical
professional fees and 1.6 percentage points of the home office/related
organization contract labor cost weights into the Professional Fees:
Labor-related cost category. This amount was added to the portion of
professional fees that was identified to be labor-related using the I-O
data such as contracted advertising and marketing costs (approximately
0.6 percentage point of total costs) resulting in a Professional Fees:
Labor-related cost weight of 5.0 percent.
As stated previously, we are proposing to include in the labor-
related share the sum of the relative importance of Wages and Salaries,
Employee Benefits, Professional Fees: Labor- Related, Administrative
and Facilities Support Services, Installation, Maintenance, and Repair,
All Other: Labor-related Services, and a portion of the Capital-Related
cost weight from the proposed 2016-based IRF market basket. The
relative importance reflects the different rates of price change for
these cost categories between the base year (2016) and FY 2020. Based
on IGI's 1st quarter 2019 forecast for the proposed 2016-based IRF
market basket, the sum of the FY 2020 relative importance for Wages and
Salaries, Employee Benefits, Professional Fees: Labor-related,
Administrative and Facilities Support Services, Installation
Maintenance & Repair Services, and All Other: Labor-related Services is
68.7 percent. The portion of Capital costs that is influenced by the
local labor market is estimated to be 46 percent, which is the same
percentage applied to the 2012-based IRF market basket (80 FR 47068).
Since the relative importance for Capital is 8.5 percent of the
proposed 2016-based IRF market basket in FY 2020, we took 46 percent of
8.5 percent to determine the proposed labor-related share of Capital
for FY 2020 of 3.9 percent. Therefore, we are proposing a total labor-
related share for FY 2020 of 72.6 percent (the sum of 68.7 percent for
the operating costs and 3.9 percent for the labor-related share of
Capital). Table 13 shows the FY 2020 labor-related share using the
proposed 2016-based IRF market basket relative importance and the FY
2019 labor-related share using the 2012-based IRF market basket
relative importance.
Table 13--Proposed FY 2020 IRF Labor-Related Share and FY 2019 IRF Labor-Related Share
----------------------------------------------------------------------------------------------------------------
FY 2020 proposed FY 2019 final
labor-related labor related
share \1\ share \2\
----------------------------------------------------------------------------------------------------------------
Wages and Salaries.......................................................... 48.1 47.7
Employee Benefits........................................................... 11.4 11.1
Professional Fees: Labor-related \3\........................................ 5.0 3.4
Administrative and Facilities Support Services.............................. 0.8 0.8
Installation, Maintenance, and Repair....................................... 1.6 1.9
All Other: Labor-related Services........................................... 1.8 1.8
-----------------------------------
Subtotal................................................................ 68.7 66.7
----------------------------------------------------------------------------------------------------------------
Labor-related portion of capital (46%)...................................... 3.9 3.8
-----------------------------------
Total Labor-Related Share........................................... 72.6 70.5
----------------------------------------------------------------------------------------------------------------
\1\ Based on the proposed 2016-based IRF Market Basket, IHS Global Insight, Inc. 1st quarter 2019 forecast.
\2\ Based on the 2012-based IRF market basket as published in the Federal Register (83 FR 38526).
\3\ Includes all contract advertising and marketing costs and a portion of accounting, architectural,
engineering, legal, management consulting, and home office contract labor costs.
We invite public comment on the proposed labor-related share for FY
2020.
F. Proposed Update to the IRF Wage Index To Use Concurrent FY IPPS Wage
Index Beginning With FY 2020
1. Background
Section 1886(j)(6) of the Act requires the Secretary to adjust the
proportion of rehabilitation facilities' costs attributable to wages
and wage-related costs (as estimated by the Secretary from time to
time) by a factor (established by the Secretary) reflecting the
relative hospital wage level in the geographic area of the
rehabilitation facility compared to the national average wage level for
those facilities. The Secretary is required to update the IRF PPS wage
index on the basis of information available to the Secretary on the
wages and wage-related costs to furnish rehabilitation services. Any
adjustment or updates made under section 1886(j)(6) of the Act for a FY
are made in a budget-neutral manner.
2. Proposed Update to the IRF Wage Index To Use Concurrent FY IPPS Wage
Index Beginning With FY 2020
When the IRF PPS was implemented in the FY 2002 IRF PPS final rule
(66 FR 41358), we finalized the use of the IPPS wage data in the
creation of an IRF wage index. We believed that a wage index based on
IPPS wage data was the best proxy and most appropriate wage index to
use in adjusting payments to IRFs, since both IPPS hospitals and IRFs
compete in the same labor markets. For this reason, we believed, and
continue to believe, that the wage data of IPPS hospitals accurately
captures the relationship of wages and wage-related costs of IRFs in an
area as compared with the national average. Therefore, in the FY 2002
IRF PPS final rule, we finalized use of the FY 1997 IPPS wage data to
develop the wage index for the IRF PPS, as that was the most recent
final data available.
[[Page 17277]]
For all subsequent years in which the IRF PPS wage index has been
updated, we have continued to use the most recent final IPPS data
available, which has led us to use the pre-floor, pre-reclassified IPPS
wage index values from the prior fiscal year.
In the FY 2018 IRF PPS proposed rule (82 FR 20742 through 20743),
we included a request for information (RFI) to solicit comments from
stakeholders requesting information on CMS flexibilities and
efficiencies. The purpose of the RFI was to receive feedback regarding
ways in which we could reduce burden for hospitals and physicians,
improve quality of care, decrease costs and ensure that patients
receive the best care. We received comments from IRF industry
associations, state and national hospital associations, industry
groups, representing hospitals, and individual IRF providers in
response to the solicitation. One of the responses we received to the
RFI suggested that there is concern among IRF stakeholders about the
different wage index data used in the different post-acute care
settings. For the IRF PPS, we use a one-year lag of the pre-floor, pre-
reclassified IPPS wage index, meaning that for the IRF PPS for FY 2019,
we finalized use of the FY 2018 IPPS wage index (83 FR 38527). However,
we base the wage indexes for the SNF PPS and the LTCH PPS on the
concurrent year's IPPS wage index ((83 FR 39172 through 39178) and (83
FR 41731), respectively).
As we look towards a more unified post-acute care payment system,
we believe that standardizing the wage index data across post-acute
care settings is necessary. Therefore, we are proposing to change the
IRF wage index methodology to align with other post-acute care
settings. Specifically, we are proposing to change from our established
policy of using the pre-floor, pre-reclassified IPPS wage index from
the prior fiscal year as the basis for the IRF wage index to using,
instead, the pre-floor, pre-reclassified IPPS wage index from the
current fiscal year. This proposed change would use the concurrent
fiscal year's pre-floor, pre-reclassified IPPS wage index for the IRF
wage index beginning with FY 2020 and continuing for all subsequent
years. Thus, for the FY 2020 IRF wage index, we would propose to use
the FY 2020 pre-floor, pre-reclassified IPPS wage index. We are
proposing to implement these revisions in a budget neutral manner. For
more information on the impacts of this proposal, we refer readers to
Table 14. Table 14 shows the estimated effects of maintaining the
existing wage index methodology for FY 2020 compared to the effects of
implementing the proposed change to the wage index methodology as
described above. For a provider specific impact analysis of this
proposed change, we refer readers to the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html.
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Using the current pre-floor, pre-reclassified IPPS wage index would
result in the most up-to-date wage data being the basis for the IRF
wage index.
[[Page 17279]]
It would also result in more consistency and equity in the wage index
methodology used by Medicare.
We invite comments on this proposal to align the data timeframes
with that of the IPPS by using the FY 2020 pre-floor, pre-reclassified
IPPS wage index as the basis for the FY 2020 IRF wage index.
3. Proposed Wage Adjustment for FY 2020 Using Concurrent IPPS Wage
Index
Due to our proposal to use the concurrent IPPS wage index beginning
with FY 2020, for FY 2020, we are proposing to use the policy and
methodologies described in section V. of this proposed rule related to
the labor market area definitions and the wage index methodology for
areas with wage data. Thus, we propose to use the CBSA labor market
area definitions and the FY 2020 pre-reclassification and pre-floor
IPPS wage index data. In accordance with section 1886(d)(3)(E) of the
Act, the FY 2020 pre-reclassification and pre-floor IPPS wage index is
based on data submitted for hospital cost reporting periods beginning
on or after October 1, 2015 and before October 1, 2016 (that is, FY
2016 cost report data).
The labor market designations made by the OMB include some
geographic areas where there are no hospitals and, thus, no hospital
wage index data on which to base the calculation of the IRF PPS wage
index. We propose to continue to use the same methodology discussed in
the FY 2008 IRF PPS final rule (72 FR 44299) to address those
geographic areas where there are no hospitals and, thus, no hospital
wage index data on which to base the calculation for the FY 2020 IRF
PPS wage index.
We invite public comment on this proposal.
4. Core-Based Statistical Areas (CBSAs) for the Proposed FY 2020 IRF
Wage Index
The wage index used for the IRF PPS is calculated using the pre-
reclassification and pre-floor IPPS wage index data and is assigned to
the IRF on the basis of the labor market area in which the IRF is
geographically located. IRF labor market areas are delineated based on
the CBSAs established by the OMB. The current CBSA delineations (which
were implemented for the IRF PPS beginning with FY 2016) are based on
revised OMB delineations issued on February 28, 2013, in OMB Bulletin
No. 13-01. OMB Bulletin No. 13-01 established revised delineations for
Metropolitan Statistical Areas, Micropolitan Statistical Areas, and
Combined Statistical Areas in the United States and Puerto Rico based
on the 2010 Census, and provided guidance on the use of the
delineations of these statistical areas using standards published in
the June 28, 2010 Federal Register (75 FR 37246 through 37252). We
refer readers to the FY 2016 IRF PPS final rule (80 FR 47068 through
47076) for a full discussion of our implementation of the OMB labor
market area delineations beginning with the FY 2016 wage index.
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. On July 15, 2015, OMB issued
OMB Bulletin No. 15-01, which provides minor updates to and supersedes
OMB Bulletin No. 13-01 that was issued on February 28, 2013. The
attachment to OMB Bulletin No. 15-01 provides detailed information on
the update to statistical areas since February 28, 2013. The updates
provided in OMB Bulletin No. 15-01 are based on the application of the
2010 Standards for Delineating Metropolitan and Micropolitan
Statistical Areas to Census Bureau population estimates for July 1,
2012 and July 1, 2013.
In the FY 2018 IRF PPS final rule (82 FR 36250 through 36251), we
adopted the updates set forth in OMB Bulletin No. 15-01 effective
October 1, 2017, beginning with the FY 2018 IRF wage index. For a
complete discussion of the adoption of the updates set forth in OMB
Bulletin No. 15-01, we refer readers to the FY 2018 IRF PPS final rule.
In the FY 2019 IRF PPS final rule (83 FR 38527), we continued to use
the OMB delineations that were adopted beginning with FY 2016 to
calculate the area wage indexes, with updates set forth in OMB Bulletin
No. 15-01 that we adopted beginning with the FY 2018 wage index.
On August 15, 2017, OMB issued OMB Bulletin No. 17-01, which
provided updates to and superseded OMB Bulletin No. 15-01 that was
issued on July 15, 2015. The attachments to OMB Bulletin No. 17-01
provide detailed information on the update to statistical areas since
July 15, 2015, and are based on the application of the 2010 Standards
for Delineating Metropolitan and Micropolitan Statistical Areas to
Census Bureau population estimates for July 1, 2014 and July 1, 2015.
In OMB Bulletin No. 17-01, OMB announced that one Micropolitan
Statistical Area now qualifies as a Metropolitan Statistical Area. The
new urban CBSA is as follows:
Twin Falls, Idaho (CBSA 46300). This CBSA is comprised of
the principal city of Twin Falls, Idaho in Jerome County, Idaho and
Twin Falls County, Idaho.
The OMB bulletin is available on the OMB website at https://www.whitehouse.gov/sites/whitehouse.gov/files/omb/bulletins/2017/b-17-01.pdf.
As we indicated in the FY 2019 IRF PPS final rule (83 FR 38528), we
believe that it is important for the IRF PPS to use the latest labor
market area delineations available as soon as is reasonably possible to
maintain a more accurate and up-to-date payment system that reflects
the reality of population shifts and labor market conditions. As
discussed in the FY 2019 IPPS and LTCH PPS final rule (83 FR 20591),
these updated labor market area definitions were implemented under the
IPPS beginning on October 1, 2018. Therefore, we are proposing to
implement these revisions for the IRF PPS beginning October 1, 2019,
consistent with our historical practice of modeling IRF PPS adoption of
the labor market area delineations after IPPS adoption of these
delineations.
We invite public comments on these proposals.
5. Wage Adjustment
The proposed FY 2020 wage index tables (which, as discussed in
section V.F above, we propose to base on the FY 2020 pre-reclassified,
pre-floor FY 2020 IPPS wage index) are available on the CMS website at
https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html. Table A is for
urban areas, and Table B is for rural areas.
To calculate the wage-adjusted facility payment for the payment
rates set forth in this proposed rule, we would multiply the unadjusted
federal payment rate for IRFs by the FY 2020 labor-related share based
on the 2016-based IRF market basket (72.6 percent) to determine the
labor-related portion of the standard payment amount. A full discussion
of the calculation of the labor-related share is located in section V.E
of this proposed rule. We would then multiply the labor-related portion
by the applicable IRF wage index from the tables in the addendum to
this proposed rule. These tables are available on the CMS website at
https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html. Adjustments or
updates to the IRF wage index made under section 1886(j)(6) of the Act
must be made in a
[[Page 17280]]
budget-neutral manner. We propose to calculate a budget-neutral wage
adjustment factor as established in the FY 2004 IRF PPS final rule (68
FR 45689), codified at Sec. 412.624(e)(1), as described in the steps
below. We propose to use the listed steps to ensure that the proposed
FY 2020 IRF standard payment conversion factor reflects the proposed
updates to the IRF wage index (based on the FY 2020 IPPS wage index)
and the labor-related share in a budget-neutral manner:
Step 1. Determine the total amount of the estimated FY 2019 IRF PPS
payments, using the FY 2019 standard payment conversion factor and the
labor-related share and the wage indexes from FY 2019 (as published in
the FY 2019 IRF PPS final rule (83 FR 38514)).
Step 2. Calculate the total amount of estimated IRF PPS payments
using the proposed FY 2020 standard payment conversion factor and the
proposed FY 2020 labor-related share and CBSA urban and rural wage
indexes.
Step 3. Divide the amount calculated in step 1 by the amount
calculated in step 2. The resulting quotient is the proposed FY 2020
budget-neutral wage adjustment factor of 1.0076.
Step 4. Apply the proposed FY 2020 budget-neutral wage adjustment
factor from step 3 to the FY 2020 IRF PPS standard payment conversion
factor after the application of the increase factor to determine the FY
2020 proposed standard payment conversion factor.
We discuss the calculation of the proposed standard payment
conversion factor for FY 2020 in section V.H. of this proposed rule.
We invite public comment on the proposed IRF wage adjustment for FY
2020.
G. Wage Index Comment Solicitation
Historically, we have calculated the IRF wage index values using
unadjusted wage index values from another provider setting.
Stakeholders have frequently commented on certain aspects of the IRF
wage index values and their impact on payments. We are soliciting
comments on concerns stakeholders may have regarding the wage index
used to adjust IRF payments and suggestions for possible updates and
improvements to the geographic adjustment of IRF payments.
H. Description of the Proposed IRF Standard Payment Conversion Factor
and Payment Rates for FY 2020
To calculate the proposed standard payment conversion factor for FY
2020, as illustrated in Table 15, we begin by applying the proposed
increase factor for FY 2020, as adjusted in accordance with sections
1886(j)(3)(C) of the Act, to the standard payment conversion factor for
FY 2019 ($16,021). Applying the proposed 2.5 percent increase factor
for FY 2020 to the standard payment conversion factor for FY 2019 of
$16,021 yields a standard payment amount of $16,422. Then, we apply the
proposed budget neutrality factor for the FY 2020 wage index and labor-
related share of 1.0076, which results in a proposed standard payment
amount of $16,546. We next apply the proposed budget neutrality factor
for the revised CMGs and CMG relative weights of 1.0016, which results
in the proposed standard payment conversion factor of $16,573 for FY
2020.
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We invite public comment on the proposed FY 2020 standard payment
conversion factor.
After the application of the proposed CMG relative weights
described in section III. of this proposed rule to the proposed FY 2020
standard payment conversion factor ($16,573), the resulting unadjusted
IRF prospective payment rates for FY 2020 are shown in Table 16.
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[[Page 17282]]
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I. Example of the Methodology for Adjusting the Proposed Prospective
Payment Rates
Table 17 illustrates the methodology for adjusting the proposed
prospective payments (as described in section V. of this proposed
rule). The following examples are based on two hypothetical Medicare
beneficiaries, both classified into CMG 0107 (without comorbidities).
The proposed unadjusted prospective payment rate for CMG 0107 (without
comorbidities) appears in Table 16.
Example: One beneficiary is in Facility A, an IRF located in rural
Spencer County, Indiana, and another beneficiary is in Facility B, an
IRF located in urban Harrison County, Indiana. Facility A, a rural non-
teaching hospital has a Disproportionate Share Hospital (DSH)
percentage of 5 percent (which would result in a LIP adjustment of
1.0156), a wage index of 0.8281, and a rural adjustment of 14.9
percent.
[[Page 17283]]
Facility B, an urban teaching hospital, has a DSH percentage of 15
percent (which would result in a LIP adjustment of 1.0454 percent), a
wage index of 0.8809, and a teaching status adjustment of 0.0784.
To calculate each IRF's labor and non-labor portion of the proposed
prospective payment, we begin by taking the unadjusted prospective
payment rate for CMG 0107 (without comorbidities) from Table 16. Then,
we multiply the proposed labor-related share for FY 2020 (72.6 percent)
described in section V.E. of this proposed rule by the proposed
unadjusted prospective payment rate. To determine the non-labor portion
of the proposed prospective payment rate, we subtract the labor portion
of the federal payment from the proposed unadjusted prospective
payment.
To compute the proposed wage-adjusted prospective payment, we
multiply the labor portion of the proposed federal payment by the
appropriate wage index located in Tables A and B. These tables are
available on the CMS website at https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/InpatientRehabFacPPS/IRF-Rules-and-Related-Files.html.
The resulting figure is the wage-adjusted labor amount. Next, we
compute the proposed wage-adjusted federal payment by adding the wage-
adjusted labor amount to the non-labor portion of the proposed federal
payment.
Adjusting the proposed wage-adjusted federal payment by the
facility-level adjustments involves several steps. First, we take the
wage-adjusted prospective payment and multiply it by the appropriate
rural and LIP adjustments (if applicable). Second, to determine the
appropriate amount of additional payment for the teaching status
adjustment (if applicable), we multiply the teaching status adjustment
(0.0784, in this example) by the wage-adjusted and rural-adjusted
amount (if applicable). Finally, we add the additional teaching status
payments (if applicable) to the wage, rural, and LIP-adjusted
prospective payment rates. Table 17 illustrates the components of the
adjusted payment calculation.
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Thus, the proposed adjusted payment for Facility A would be
$36,906.90, and the adjusted payment for Facility B would be
$37,099.73.
VI. Proposed Update to Payments for High-Cost Outliers Under the IRF
PPS for FY 2020
A. Proposed Update to the Outlier Threshold Amount for FY 2020
Section 1886(j)(4) of the Act provides the Secretary with the
authority to make payments in addition to the basic IRF prospective
payments for cases incurring extraordinarily high costs. A case
qualifies for an outlier payment if the estimated cost of the case
exceeds the adjusted outlier threshold. We calculate the adjusted
outlier threshold by adding the IRF PPS payment for the case (that is,
the CMG payment adjusted by all of the relevant facility-level
adjustments) and the adjusted threshold amount (also adjusted by all of
the relevant facility-level adjustments). Then, we calculate the
estimated cost of a case by multiplying the IRF's overall CCR by the
Medicare allowable covered charge. If the estimated cost of the case is
higher than the adjusted outlier threshold, we make an outlier payment
for the case equal to 80 percent of the difference between the
estimated cost of the case and the outlier threshold.
In the FY 2002 IRF PPS final rule (66 FR 41362 through 41363), we
discussed our rationale for setting the outlier threshold amount for
the IRF PPS so that estimated outlier payments would equal 3 percent of
total estimated payments. For the 2002 IRF PPS final rule, we analyzed
various outlier policies using 3, 4, and 5 percent of the total
estimated payments, and we concluded that an outlier policy set at 3
percent of total estimated payments would optimize the extent to which
we could reduce the financial risk to IRFs of caring for high-cost
patients, while still providing for adequate payments for all other
(non-high cost outlier) cases.
Subsequently, we updated the IRF outlier threshold amount in the
FYs 2006 through 2019 IRF PPS final rules and the FY 2011 and FY 2013
notices (70 FR 47880, 71 FR 48354, 72 FR 44284, 73 FR 46370, 74 FR
39762, 75 FR 42836, 76 FR 47836, 76 FR 59256, 77 FR
[[Page 17284]]
44618, 78 FR 47860, 79 FR 45872, 80 FR 47036, 81 FR 52056, 82 FR 36238,
and 83 FR 38514, respectively) to maintain estimated outlier payments
at 3 percent of total estimated payments. We also stated in the FY 2009
final rule (73 FR 46370 at 46385) that we would continue to analyze the
estimated outlier payments for subsequent years and adjust the outlier
threshold amount as appropriate to maintain the 3 percent target.
To update the IRF outlier threshold amount for FY 2020, we propose
to use FY 2018 claims data and the same methodology that we used to set
the initial outlier threshold amount in the FY 2002 IRF PPS final rule
(66 FR 41316 and 41362 through 41363), which is also the same
methodology that we used to update the outlier threshold amounts for
FYs 2006 through 2019. The outlier threshold is calculated by
simulating aggregate payments and using an iterative process to
determine a threshold that results in outlier payments being equal to 3
percent of total payments under the simulation. To determine the
outlier threshold for FY 2020, we estimate the amount of FY 2020 IRF
PPS aggregate and outlier payments using the most recent claims
available (FY 2018) and the proposed FY 2020 standard payment
conversion factor, labor-related share, and wage indexes, incorporating
any applicable budget-neutrality adjustment factors. The outlier
threshold is adjusted either up or down in this simulation until the
estimated outlier payments equal 3 percent of the estimated aggregate
payments. Based on an analysis of the preliminary data used for the
proposed rule, we estimated that IRF outlier payments as a percentage
of total estimated payments would be approximately 3.2 percent in FY
2019. Therefore, we propose to update the outlier threshold amount from
$9,402 for FY 2019 to $9,935 for FY 2020 to maintain estimated outlier
payments at approximately 3 percent of total estimated aggregate IRF
payments for FY 2020.
We invite public comment on the proposed update to the FY 2020
outlier threshold amount to maintain estimated outlier payments at
approximately 3 percent of total estimated IRF payments.
B. Proposed Update to the IRF Cost-to-Charge Ratio Ceiling and Urban/
Rural Averages for FY 2020
Cost-to-charge ratios are used to adjust charges from Medicare
claims to costs and are computed annually from facility-specific data
obtained from Medicare cost reports. IRF specific cost-to-charge ratios
are used in the development of the CMG relative weights and the
calculation of outlier payments under the IRF prospective payment
system. In accordance with the methodology stated in the FY 2004 IRF
PPS final rule (68 FR 45674, 45692 through 45694), we propose to apply
a ceiling to IRFs' CCRs. Using the methodology described in that final
rule, we propose to update the national urban and rural CCRs for IRFs,
as well as the national CCR ceiling for FY 2020, based on analysis of
the most recent data that is available. We apply the national urban and
rural CCRs in the following situations:
New IRFs that have not yet submitted their first Medicare
cost report.
IRFs whose overall CCR is in excess of the national CCR
ceiling for FY 2020, as discussed below in this section.
Other IRFs for which accurate data to calculate an overall
CCR are not available.
Specifically, for FY 2020, we propose to estimate a national
average CCR of 0.500 for rural IRFs, which we calculated by taking an
average of the CCRs for all rural IRFs using their most recently
submitted cost report data. Similarly, we propose to estimate a
national average CCR of 0.406 for urban IRFs, which we calculated by
taking an average of the CCRs for all urban IRFs using their most
recently submitted cost report data. We apply weights to both of these
averages using the IRFs' estimated costs, meaning that the CCRs of IRFs
with higher total costs factor more heavily into the averages than the
CCRs of IRFs with lower total costs. For this proposed rule, we have
used the most recent available cost report data (FY 2017). This
includes all IRFs whose cost reporting periods begin on or after
October 1, 2016, and before October 1, 2017. If, for any IRF, the FY
2017 cost report was missing or had an ``as submitted'' status, we used
data from a previous fiscal year's (that is, FY 2004 through FY 2016)
settled cost report for that IRF. We do not use cost report data from
before FY 2004 for any IRF because changes in IRF utilization since FY
2004 resulting from the 60 percent rule and IRF medical review
activities suggest that these older data do not adequately reflect the
current cost of care.
In accordance with past practice, we propose to set the national
CCR ceiling at 3 standard deviations above the mean CCR. Using this
method, we propose a national CCR ceiling of 1.31 for FY 2020. This
means that, if an individual IRF's CCR were to exceed this ceiling of
1.31 for FY 2020, we would replace the IRF's CCR with the appropriate
proposed national average CCR (either rural or urban, depending on the
geographic location of the IRF). We calculated the proposed national
CCR ceiling by:
Step 1. Taking the national average CCR (weighted by each IRF's
total costs, as previously discussed) of all IRFs for which we have
sufficient cost report data (both rural and urban IRFs combined).
Step 2. Estimating the standard deviation of the national average
CCR computed in step 1.
Step 3. Multiplying the standard deviation of the national average
CCR computed in step 2 by a factor of 3 to compute a statistically
significant reliable ceiling.
Step 4. Adding the result from step 3 to the national average CCR
of all IRFs for which we have sufficient cost report data, from step 1.
The proposed national average rural and urban CCRs and the proposed
national CCR ceiling in this section will be updated in the final rule
if more recent data becomes available to use in these analyses.
We invite public comment on the proposed update to the IRF CCR
ceiling and the urban/rural averages for FY 2020.
VII. Proposed Amendments to Sec. 412.622 To Clarify the Definition of
a Rehabilitation Physician
Under Sec. 412.622(a)(3)(iv), a rehabilitation physician is
defined as ``a licensed physician with specialized training and
experience in inpatient rehabilitation.'' The term rehabilitation
physician is used in several other places in Sec. 412.622, with
corresponding references to Sec. 412.622(a)(3)(iv). The definition at
Sec. 412.622(a)(3)(iv) does not specify the level or type of training
and experience required for a licensed physician to be designated as a
rehabilitation physician because we believe that the IRFs are in the
best position to make this determination for purposes of Sec. 412.622.
Therefore, we propose to amend the definition of a rehabilitation
physician to clarify that the determination as to whether a physician
qualifies as a rehabilitation physician (that is, a licensed physician
with specialized training and experience in inpatient rehabilitation)
is made by the IRF. For clarity, we also propose to remove this
definition from Sec. 412.622(a)(3)(iv) and move it to a new paragraph
(Sec. 412.622(c)). We also propose to make corresponding technical
corrections elsewhere in Sec. 412.622(a)(3)(iv), (a)(4)(i)(A),
(a)(4)(iii)(A), and (a)(5)(i) to remove the references to Sec.
412.622(a)(3)(iv) in those paragraphs,
[[Page 17285]]
so as to reflect the new location of the definition.
We invite public comment on the proposal to clarify the definition
of a rehabilitation physician, to move the definition from Sec.
412.622(a)(3)(iv) to Sec. 412.622(c), and to make corresponding
technical corrections elsewhere in Sec. 412.622 to remove references
to the current location of the definition in Sec. 412.622(a)(3)(iv).
VIII. Proposed Revisions and Updates to the IRF Quality Reporting
Program (QRP)
A. Background
The Inpatient Rehabilitation Facility Quality Reporting Program
(IRF QRP) is authorized by section 1886(j)(7) of the Act, and it
applies to freestanding IRFs, as well as inpatient rehabilitation units
of hospitals or critical access hospitals (CAHs) paid by Medicare under
the IRF PPS. Under the IRF QRP, the Secretary must reduce the annual
increase factor for discharges occurring during such fiscal year by 2
percentage points for any IRF that does not submit data in accordance
with the requirements established by the Secretary. For more
information on the background and statutory authority for the IRF QRP,
we refer readers to the FY 2012 IRF PPS final rule (76 FR 47873 through
47874), the CY 2013 Hospital Outpatient Prospective Payment System/
Ambulatory Surgical Center (OPPS/ASC) Payment Systems and Quality
Reporting Programs final rule (77 FR 68500 through 68503), the FY 2014
IRF PPS final rule (78 FR 47902), the FY 2015 IRF PPS final rule (79 FR
45908), the FY 2016 IRF PPS final rule (80 FR 47080 through 47083), the
FY 2017 IRF PPS final rule (81 FR 52080 through 52081), the FY 2018 IRF
PPS final rule (82 FR 36269 through 36270), and the FY 2019 IRF PPS
final rule (83 FR 38555 through 38556).
B. General Considerations Used for the Selection of Measures for the
IRF QRP
For a detailed discussion of the considerations we historically
used for the selection of IRF QRP quality, resource use, and other
measures, we refer readers to the FY 2016 IRF PPS final rule (80 FR
47083 through 47084).
C. Quality Measures Currently Adopted for the FY 2021 IRF QRP
The IRF QRP currently has 15 measures for the FY 2020 program year,
which are set out in Table 18.
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D. IRF QRP Quality Measure Proposals Beginning With the FY 2022 IRF QRP
In this proposed rule, we are proposing to adopt two process
measures for the IRF QRP that would satisfy section 1899B(c)(1)(E)(ii)
of the Act, which requires that the quality measures specified by the
Secretary include measures with respect to the
[[Page 17286]]
quality measure domain titled ``Accurately communicating the existence
of and providing for the transfer of health information and care
preferences of an individual to the individual, family caregiver of the
individual, and providers of services furnishing items and services to
the individual when the individual transitions from a post-acute care
(PAC) provider to another applicable setting, including a different PAC
provider, a hospital, a critical access hospital, or the home of the
individual.'' Given the length of this domain title, hereafter, we will
refer to this quality measure domain as ``Transfer of Health
Information.''
The two measures we are proposing to adopt are: (1) Transfer of
Health Information to the Provider-Post-Acute Care (PAC); and (2)
Transfer of Health Information to the Patient-Post-Acute Care (PAC).
Both of these proposed measures support our Meaningful Measures
priority of promoting effective communication and coordination of care,
specifically the Meaningful Measure area of the transfer of health
information and interoperability.
In addition to the two measure proposals, we are proposing to
update the specifications for the Discharge to Community-Post Acute
Care (PAC) IRF QRP measure to exclude baseline nursing facility (NF)
residents from the measure.
We are seeking public comment on each of these proposals.
1. Proposed Transfer of Health Information to the Provider-Post-Acute
Care (PAC) Measure
The proposed Transfer of Health Information to the Provider-Post-
Acute Care (PAC) Measure is a process-based measure that assesses
whether or not a current reconciled medication list is given to the
subsequent provider when a patient is discharged or transferred from
his or her current PAC setting.
a. Background
In 2013, 22.3 percent of all acute hospital discharges were
discharged to PAC settings, including 11 percent who were discharged to
home under the care of a home health agency, and nine percent who were
discharged to SNFs.\2\ The proportion of patients being discharged from
an acute care hospital to a PAC setting was greater among beneficiaries
enrolled in Medicare fee-for-service (FFS). Among Medicare FFS patients
discharged from an acute hospital, 42 percent went directly to PAC
settings. Of that 42 percent, 20 percent were discharged to a SNF, 18
percent were discharged to a home health agency (HHA), 3 percent were
discharged to an IRF, and one percent were discharged to an LTCH.\3\ Of
the Medicare FFS beneficiaries with an IRF stay in FYs 2016 and 2017,
an estimated 10 percent were discharged or transferred to an acute care
hospital, 51 percent discharged home with home health services, 16
percent discharged or transferred to a SNF, and one percent discharged
or transferred to another PAC setting (for example, another IRF, a
hospice, or an LTCH).\4\
---------------------------------------------------------------------------
\2\ Tian, W. ``An all-payer view of hospital discharge to post-
acute care,'' May 2016. Available at https://www.hcup-us.ahrq.gov/reports/statbriefs/sb205-Hospital-Discharge-Postacute-Care.jsp.
\3\ Ibid.
\4\ RTI International analysis of Medicare claims data for index
stays in IRF 2016/2017. (RTI program reference: MM150).
---------------------------------------------------------------------------
The transfer and/or exchange of health information from one
provider to another can be done verbally (for example, clinician-to-
clinician communication in-person or by telephone), paper-based (for
example, faxed or printed copies of records), and via electronic
communication (for example, through a health information exchange
network using an electronic health/medical record, and/or secure
messaging). Health information, such as medication information, that is
incomplete or missing increases the likelihood of a patient or resident
safety risk, and is often life-threatening.5 6 7 8 9 10 Poor
communication and coordination across health care settings contributes
to patient complications, hospital readmissions, emergency department
visits, and medication errors.11 12 13 14 15 16 17 18 19 20
Communication has been cited as the third most frequent root cause in
sentinel events, which The Joint Commission defines \21\ as a patient
safety event that results in death, permanent harm, or severe temporary
harm. Failed or ineffective patient handoffs are estimated to play a
role in 20 percent of serious preventable adverse events.\22\ When care
transitions are enhanced through care coordination activities, such as
expedited patient information flow, these activities can reduce
duplication of care services and costs of care, resolve conflicting
care plans, and prevent medical errors. 23 24 25 26 27
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\5\ Kwan, J.L., Lo, L., Sampson, M., & Shojania, K.G.,
``Medication reconciliation during transitions of care as a patient
safety strategy: A systematic review,'' Annals of Internal Medicine,
2013, Vol. 158(5), pp. 397-403.
\6\ Boockvar, K.S., Blum, S., Kugler, A., Livote, E.,
Mergenhagen, K.A., Nebeker, J.R., & Yeh, J., ``Effect of admission
medication reconciliation on adverse drug events from admission
medication changes,'' Archives of Internal Medicine, 2011, Vol.
171(9), pp. 860-861.
\7\ Bell, C.M., Brener, S. S., Gunraj, N., Huo, C., Bierman,
A.S., Scales, D.C., & Urbach, D.R., ``Association of ICU or hospital
admission with unintentional discontinuation of medications for
chronic diseases,'' JAMA, 2011, Vol. 306(8), pp. 840-847.
\8\ Basey, A.J., Krska, J., Kennedy, T.D., & Mackridge, A.J.,
``Prescribing errors on admission to hospital and their potential
impact: A mixed-methods study,'' BMJ Quality & Safety, 2014, Vol.
23(1), pp. 17-25.
\9\ Desai, R., Williams, C.E., Greene, S.B., Pierson, S., &
Hansen, R.A., ``Medication errors during patient transitions into
nursing homes: Characteristics and association with patient harm,''
The American Journal of Geriatric Pharmacotherapy, 2011, Vol. 9(6),
pp. 413-422.
\10\ Boling, P.A., ``Care transitions and home health care,''
Clinical Geriatric Medicine, 2009, Vol.25(1), pp. 135-48.
\11\ Barnsteiner, J.H., ``Medication Reconciliation: Transfer of
medication information across settings--keeping it free from
error,'' The American Journal of Nursing, 2005, Vol. 105(3), pp. 31-
36.
\12\ Arbaje, A.I., Kansagara, D.L., Salanitro, A.H., Englander,
H.L., Kripalani, S., Jencks, S.F., & Lindquist, L.A., ``Regardless
of age: Incorporating principles from geriatric medicine to improve
care transitions for patients with complex needs,'' Journal of
General Internal Medicine, 2014, Vol. 29(6), pp. 932-939.
\13\ Jencks, S.F., Williams, M.V., & Coleman, E.A.,
``Rehospitalizations among patients in the Medicare fee-for-service
program,'' New England Journal of Medicine, 2009, Vol. 360(14), pp.
1418-1428.
\14\ Institute of Medicine. ``Preventing medication errors:
quality chasm series,'' Washington, DC: The National Academies Press
2007. Available at https://www.nap.edu/read/11623/chapter/1.
\15\ Kitson, N.A., Price, M., Lau, F.Y., & Showler, G.,
``Developing a medication communication framework across continuums
of care using the Circle of Care Modeling approach,'' BMC Health
Services Research, 2013, Vol. 13(1), pp. 1-10.
\16\ Mor, V., Intrator, O., Feng, Z., & Grabowski, D.C., ``The
revolving door of rehospitalization from skilled nursing
facilities,'' Health Affairs, 2010, Vol. 29(1), pp. 57-64.
\17\ Institute of Medicine. ``Preventing medication errors:
quality chasm series,'' Washington, DC: The National Academies Press
2007. Available at https://www.nap.edu/read/11623/chapter/1.
\18\ Kitson, N.A., Price, M., Lau, F.Y., & Showler, G.,
``Developing a medication communication framework across continuums
of care using the Circle of Care Modeling approach,'' BMC Health
Services Research, 2013, Vol. 13(1), pp. 1-10.
\19\ Forster, A.J., Murff, H.J., Peterson, J.F., Gandhi, T.K., &
Bates, D.W., ``The incidence and severity of adverse events
affecting patients after discharge from the hospital.'' Annals of
Internal Medicine, 2003,138(3), pp. 161-167.
\20\ King, B.J., Gilmore[hyphen]Bykovskyi, A.L., Roiland, R.A.,
Polnaszek, B.E., Bowers, B.J., & Kind, A.J. ``The consequences of
poor communication during transitions from hospital to skilled
nursing facility: A qualitative study,'' Journal of the American
Geriatrics Society, 2013, Vol. 61(7), 1095-1102.
\21\ The Joint Commission, ``Sentinel Event Policy'' available
at https://www.jointcommission.org/sentinel_event_policy_and_procedures/.
\22\ The Joint Commission. ``Sentinel Event Data Root Causes by
Event Type 2004 -2015.'' 2016. Available at https://www.jointcommission.org/assets/1/23/jconline_Mar_2_2016.pdf.
\23\ Mor, V., Intrator, O., Feng, Z., & Grabowski, D.C., ``The
revolving door of rehospitalization from skilled nursing
facilities,'' Health Affairs, 2010, Vol. 29(1), pp. 57-64.
\24\ Institute of Medicine, ``Preventing medication errors:
Quality chasm series,'' Washington, DC: The National Academies
Press, 2007. Available at https://www.nap.edu/read/11623/chapter/1.
\25\ Starmer, A.J., Sectish, T. C., Simon, D.W., Keohane, C.,
McSweeney, M.E., Chung, E.Y., Yoon, C.S., Lipsitz, S.R., Wassner,
A.J., Harper, M.B., & Landrigan, C.P., ``Rates of medical errors and
preventable adverse events among hospitalized children following
implementation of a resident handoff bundle,'' JAMA, 2013, Vol.
310(21), pp. 2262-2270.
\26\ Pronovost, P., M.M.E. Johns, S. Palmer, R.C. Bono, D.B.
Fridsma, A. Gettinger, J. Goldman, W. Johnson, M. Karney, C. Samitt,
R.D. Sriram, A. Zenooz, and Y.C. Wang, Editors. Procuring
Interoperability: Achieving High-Quality, Connected, and Person-
Centered Care. Washington, DC, 2018 National Academy of Medicine.
Available at https://nam.edu/wp-content/uploads/2018/10/Procuring-Interoperability_web.pdf.
\27\ Balaban RB, Weissman JS, Samuel PA, & Woolhandler, S.,
``Redefining and redesigning hospital discharge to enhance patient
care: A randomized controlled study,'' J Gen Intern Med, 2008, Vol.
23(8), pp. 1228-33.
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[[Page 17287]]
Care transitions across health care settings have been
characterized as complex, costly, and potentially hazardous, and may
increase the risk for multiple adverse outcomes. 28 29 The
rising incidence of preventable adverse events, complications, and
hospital readmissions have drawn attention to the importance of the
timely transfer of health information and care preferences at the time
of transition. Failures of care coordination, including poor
communication of information, were estimated to cost the U.S. health
care system between $25 billion and $45 billion in wasteful spending in
2011.\30\ The communication of health information and patient care
preferences is critical to ensuring safe and effective transitions from
one health care setting to another.31 32
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\28\ Arbaje, A.I., Kansagara, D.L., Salanitro, A.H., Englander,
H.L., Kripalani, S., Jencks, S.F., & Lindquist, L.A., ``Regardless
of age: Incorporating principles from geriatric medicine to improve
care transitions for patients with complex needs,'' Journal of
General Internal Medicine, 2014, Vol 29(6), pp. 932-939.
\29\ Simmons, S., Schnelle, J., Slagle, J., Sathe, N.A.,
Stevenson, D., Carlo, M., & McPheeters, M.L., ``Resident safety
practices in nursing home settings.'' Technical Brief No. 24
(Prepared by the Vanderbilt Evidence-based Practice Center under
Contract No. 290-2015-00003-I.) AHRQ Publication No. 16-EHC022-EF.
Rockville, MD: Agency for Healthcare Research and Quality. May 2016.
Available at https://www.ncbi.nlm.nih.gov/books/NBK384624/.
\30\ Berwick, D.M. & Hackbarth, A.D. ``Eliminating Waste in US
Health Care,'' JAMA, 2012, Vol. 307(14), pp.1513-1516.
\31\ McDonald, K.M., Sundaram, V., Bravata, D.M., Lewis, R.,
Lin, N., Kraft, S.A. & Owens, D.K. Care Coordination. Vol. 7 of:
Shojania K.G., McDonald K.M., Wachter R.M., Owens D.K., editors.
``Closing the quality gap: A critical analysis of quality
improvement strategies.'' Technical Review 9 (Prepared by the
Stanford University-UCSF Evidence-based Practice Center under
contract 290-02-0017). AHRQ Publication No. 04(07)-0051-7.
Rockville, MD: Agency for Healthcare Research and Quality. June
2006. Available at https://www.ncbi.nlm.nih.gov/books/NBK44015/.
\32\ Lattimer, C., ``When it comes to transitions in patient
care, effective communication can make all the difference,''
Generations, 2011, Vol. 35(1), pp. 69-72.
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Patients in PAC settings often have complicated medication regimens
and require efficient and effective communication and coordination of
care between settings, including detailed transfer of medication
information.33 34 35 Individuals in PAC settings may be
vulnerable to adverse health outcomes due to insufficient medication
information on the part of their health care providers, and the higher
likelihood for multiple comorbid chronic conditions, polypharmacy, and
complicated transitions between care settings.36 37
Preventable adverse drug events (ADEs) may occur after hospital
discharge in a variety of settings including PAC.\38\ A 2014 Office of
Inspector General report found that 10 percent of Medicare patients in
IRFs experienced adverse events, with most of those events being
medication related. Over 45 percent of the adverse events and temporary
harm events were clearly or likely preventable.\39\ Medication errors
and one-fifth of ADEs occur during transitions between settings,
including admission to or discharge from a hospital to home or a PAC
setting, or transfer between hospitals.40 41
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\33\ Starmer A.J., Spector N.D., Srivastava R., West, D.C.,
Rosenbluth, G., Allen, A.D., Noble, E.L., & Landrigen, C.P.,
``Changes in medical errors after implementation of a handoff
program,'' N Engl J Med, 2014, Vol. 37(1), pp. 1803-1812.
\34\ Kruse, C.S. Marquez, G., Nelson, D., & Polomares, O., ``The
use of health information exchange to augment patient handoff in
long-term care: a systematic review,'' Applied Clinical Informatics,
2018, Vol. 9(4), pp. 752-771.
\35\ Brody, A.A., Gibson, B., Tresner-Kirsch, D., Kramer, H.,
Thraen, I., Coarr, M.E., & Rupper, R., ``High prevalence of
medication discrepancies between home health referrals and Centers
for Medicare and Medicaid Services home health certification and
plan of care and their potential to affect safety of vulnerable
elderly adults,'' Journal of the American Geriatrics Society, 2016,
Vol. 64(11), pp. e166-e170.
\36\ Chhabra, P.T., Rattinger, G.B., Dutcher, S.K., Hare, M.E.,
Parsons, K.L., & Zuckerman, I.H., ``Medication reconciliation during
the transition to and from long-term care settings: a systematic
review,'' Res Social Adm Pharm, 2012, Vol. 8(1), pp. 60-75.
\37\ Levinson, D.R., & General, I., ``Adverse events in skilled
nursing facilities: national incidence among Medicare
beneficiaries.'' Washington, DC: U.S. Department of Health and Human
Services, Office of the Inspector General, February 2014. Available
at https://oig.hhs.gov/oei/reports/oei-06-11-00370.pdf.
\38\ Battles J., Azam I., Grady M., & Reback K., ``Advances in
patient safety and medical liability,'' AHRQ Publication No. 17-
0017-EF. Rockville, MD: Agency for Healthcare Research and Quality,
August 2017. Available at https://www.ahrq.gov/sites/default/files/publications/files/advances-complete_3.pdf.
\39\ Health and Human Services Office of Inspector General.
Adverse Events in Rehabilitation Hospitals: National Incidence Among
Medicare Beneficiaries. (OEI-06-14-00110). 2018. Available at
https://oig.hhs.gov/oei/reports/oei-06-14-00110.asp.
\40\ Barnsteiner, J.H., ``Medication Reconciliation: Transfer of
medication information across settings--keeping it free from
error,'' The American Journal of Nursing, 2005, Vol. 105(3), pp. 31-
36.
\41\ Gleason, K.M., Groszek, J.M., Sullivan, C., Rooney, D.,
Barnard, C., Noskin, G.A., ``Reconciliation of discrepancies in
medication histories and admission orders of newly hospitalized
patients,'' American Journal of Health System Pharmacy, 2004, Vol.
61(16), pp. 1689-1694.
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Patients in PAC settings are often taking multiple medications.
Consequently, PAC providers regularly are in the position of starting
complex new medication regimens with little knowledge of the patients
or their medication history upon admission. Furthermore, inter-facility
communication barriers delay resolving medication discrepancies during
transitions of care.\42\ Medication discrepancies are common,\43\ and
found to occur in 86 percent of all transitions, increasing the
likelihood of ADEs.\44\ \45\ \46\ Up to 90 percent of patients
experience at least one medication discrepancy in the transition from
hospital to home care, and discrepancies occur within all therapeutic
classes of medications.\47\ \48\
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\42\ Patterson M., Foust J.B., Bollinger, S., Coleman, C.,
Nguyen, D., ``Inter-facility communication barriers delay resolving
medication discrepancies during transitions of care,'' Research in
Social & Administrative Pharmacy (2018), doi: 10.1016/
j.sapharm.2018.05.124.
\43\ Manias, E., Annaikis, N., Considine, J., Weerasuriya, R., &
Kusljic, S. ``Patient-, medication- and environment-related factors
affecting medication discrepancies in older patients,'' Collegian,
2017, Vol. 24, pp. 571-577.
\44\ Tjia, J., Bonner, A., Briesacher, B.A., McGee, S., Terrill,
E., Miller, K., ``Medication discrepancies upon hospital to skilled
nursing facility transitions,'' J Gen Intern Med, 2009, Vol. 24(5),
pp. 630-635.
\45\ Sinvani, L.D., Beizer, J., Akerman, M., Pekmezaris, R.,
Nouryan, C., Lutsky, L., Cal, C., Dlugacz, Y., Masick, K., Wolf-
Klein, G., ``Medication reconciliation in continuum of care
transitions: a moving target,'' J Am Med Dir Assoc, 2013, Vol.
14(9), 668-672.
\46\ Coleman E.A., Parry C., Chalmers S., & Min, S.J., ``The
Care Transitions Intervention: results of a randomized controlled
trial,'' Arch Intern Med, 2006, Vol. 166, pp. 1822-28.
\47\ Corbett C.L., Setter S. M., Neumiller J.J., & Wood, L.D.,
``Nurse identified hospital to home medication discrepancies:
implications for improving transitional care,'' Geriatr Nurs, 2011,
Vol. 31(3), pp. 188-96.
\48\ Setter S.M., Corbett C.F., Neumiller J.J., Gates, B.J.,
Sclar, D.A., & Sonnett, T.E., ``Effectiveness of a pharmacist-nurse
intervention on resolving medication discrepancies in older patients
transitioning from hospital to home care: impact of a pharmacy/
nursing intervention,'' Am J Health Syst Pharm, 2009, Vol. 66, pp.
2027-31.
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Transfer of a medication list between providers is necessary for
medication reconciliation interventions, which have been shown to be a
cost-effective way to avoid ADEs by reducing errors,49 50 51
[[Page 17288]]
especially when medications are reviewed by a pharmacist using
electronic medical records.\52\
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\49\ Boockvar, K.S., Blum, S., Kugler, A., Livote, E.,
Mergenhagen, K.A., Nebeker, J.R., & Yeh, J., ``Effect of admission
medication reconciliation on adverse drug events from admission
medication changes,'' Archives of Internal Medicine, 2011, Vol.
171(9), pp. 860-861.
\50\ Kwan, J.L., Lo, L., Sampson, M., & Shojania, K.G.,
``Medication reconciliation during transitions of care as a patient
safety strategy: a systematic review,'' Annals of Internal Medicine,
2013, Vol. 158(5), pp. 397-403.
\51\ Chhabra, P.T., Rattinger, G.B., Dutcher, S.K., Hare, M.E.,
Parsons, K.L., & Zuckerman, I.H., ``Medication reconciliation during
the transition to and from long-term care settings: a systematic
review,'' Res Social Adm Pharm, 2012, Vol. 8(1), pp. 60-75.
\52\ Agrawal A., Wu WY. ``Reducing medication errors and
improving systems reliability using an electronic medication
reconciliation system,'' The Joint Commission Journal on Quality and
Patient Safety, 2009, Vol. 35(2), pp. 106-114.
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b. Stakeholder and Technical Expert Panel (TEP) Input
The proposed measure was developed after consideration of feedback
we received from stakeholders and four TEPs convened by our
contractors. Further, the proposed measure was developed after
evaluation of data collected during two pilot tests we conducted in
accordance with the CMS Measures Management System Blueprint.
Our measure development contractors constituted a TEP which met on
September 27, 2016 \53\, January 27, 2017, and August 3, 2017 \54\ to
provide input on a prior version of this measure. Based on this input,
we updated the measure concept in late 2017 to include the transfer of
a specific component of health information--medication information. Our
measure development contractors reconvened this TEP on April 20, 2018
for the purpose of obtaining expert input on the proposed measure,
including the measure's reliability, components of face validity, and
feasibility of being implemented across PAC settings. Overall, the TEP
was supportive of the proposed measure, affirming that the measure
provides an opportunity to improve the transfer of medication
information. A summary of the April 20, 2018 TEP proceedings titled
``Transfer of Health Information TEP Meeting 4--June 2018'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
---------------------------------------------------------------------------
\53\ Technical Expert Panel Summary Report: Development of two
quality measures to satisfy the Improving Medicare Post-Acute Care
Transformation Act of 2014 (IMPACT Act) Domain of Transfer of health
Information and Care Preferences When an Individual Transitions to
Skilled Nursing Facilities (SNFs), Inpatient Rehabilitation
Facilities (IRFs), Long Term Care Hospitals (LTCHs) and Home Health
Agencies (HHAs). Available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/Transfer-of-Health-Information-TEP_Summary_Report_Final-June-2017.pdf.
\54\ Technical Expert Panel Summary Report: Development of two
quality measures to satisfy the Improving Medicare Post-Acute Care
Transformation Act of 2014 (IMPACT Act) Domain of Transfer of health
Information and Care Preferences When an Individual Transitions to
Skilled Nursing Facilities (SNFs), Inpatient Rehabilitation
Facilities (IRFs), Long Term Care Hospitals (LTCHs) and Home Health
Agencies (HHAs). Available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/Transfer-of-Health-Information-TEP-Meetings-2-3-Summary-Report_Final_Feb2018.pdf.
---------------------------------------------------------------------------
Our measure development contractors solicited stakeholder feedback
on the proposed measure by requesting comment on the CMS Measures
Management System Blueprint website, and accepted comments that were
submitted from March 19, 2018 to May 3, 2018. The comments received
expressed overall support for the measure. Several commenters suggested
ways to improve the measure, primarily related to what types of
information should be included at transfer. We incorporated this input
into development of the proposed measure. The summary report for the
March 19 to May 3, 2018 public comment period titled ``IMPACT
Medication Profile Transferred Public Comment Summary Report'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
c. Pilot Testing
The proposed measure was tested between June and August 2018 in a
pilot test that involved 24 PAC facilities/agencies, including five
IRFs, six SNFs, six LTCHs, and seven HHAs. The 24 pilot sites submitted
a total of 801 records. Analysis of agreement between coders within
each participating facility (266 qualifying pairs) indicated a 93
percent agreement for this measure. Overall, pilot testing enabled us
to verify its reliability, components of face validity, and feasibility
of being implemented across PAC settings. Further, more than half of
the sites that participated in the pilot test stated during the
debriefing interviews that the measure could distinguish facilities or
agencies with higher quality medication information transfer from those
with lower quality medication information transfer at discharge. The
pilot test summary report titled ``Transfer of Health Information 2018
Pilot Test Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
d. Measure Applications Partnership (MAP) Review and Related Measures
We included the proposed measure in the IRF QRP section of the 2018
Measures Under Consideration (MUC) list. The MAP conditionally
supported this measure pending NQF endorsement, noting that the measure
can promote the transfer of important medication information. The MAP
also suggested that CMS consider a measure that can be adapted to
capture bi-directional information exchange, and recommended that the
medication information transferred include important information about
supplements and opioids. More information about the MAP's
recommendations for this measure is available at http://www.qualityforum.org/Publications/2019/02/MAP_2019_Considerations_for_Implementing_Measures_Final_Report_-_PAC-LTC.aspx.
As part of the measure development and selection process, we also
identified one NQF-endorsed quality measure similar to the proposed
measure, titled Documentation of Current Medications in the Medical
Record (NQF #0419, CMS eCQM ID: CMS68v8). This measure was adopted as
one of the recommended adult core clinical quality measures for
eligible professionals for the EHR Incentive Program beginning in 2014
and was also adopted under the Merit-based Incentive Payment System
(MIPS) quality performance category beginning in 2017. The measure is
calculated based on the percentage of visits for patients aged 18 years
and older for which the eligible professional or eligible clinician
attests to documenting a list of current medications using all
resources immediately available on the date of the encounter.
The proposed Transfer of Health Information to the Provider-Post-
Acute Care (PAC) measure addresses the transfer of information whereas
the NQF-endorsed measure #0419 assesses the documentation of
medications, but not the transfer of such information. This is
important as the proposed measure assesses for the transfer of
medication information for the proposed measure calculation. Further,
the proposed measure utilizes standardized patient assessment data
elements (SPADEs), which is a
[[Page 17289]]
requirement for measures specified under the Transfer of Health
Information measure domain under section 1899B(c)(1)(E) of the Act,
whereas NQF #0419 does not.
After review of the NQF-endorsed measure, we determined that the
proposed Transfer of Health Information to the Provider-Post-Acute Care
(PAC) measure better addresses the Transfer of Health Information
measure domain, which requires that at least some of the data used to
calculate the measure be collected as standardized patient assessment
data through the post-acute care assessment instruments. Section
1886(j)(7)(D)(i) of the Act requires that any measure specified by the
Secretary be endorsed by the entity with a contract under section
1890(a) of the Act, which is currently the National Quality Form (NQF).
However, when a feasible and practical measure has not been NQF
endorsed for a specified area or medical topic determined appropriate
by the Secretary, section 1886(j)(7)(D)(ii) of the Act allows the
Secretary to specify a measure that is not NQF endorsed as long as due
consideration is given to the measures that have been endorsed or
adopted by a consensus organization identified by the Secretary. For
the reasons discussed previously, we believe that there is currently no
feasible NQF-endorsed measure that we could adopt under section
1886(j)(7)(D)(ii) of the Act. However, we note that we intend to submit
the proposed measure to the NQF for consideration of endorsement when
feasible.
e. Quality Measure Calculation
The proposed Transfer of Health Information to the Provider-Post-
Acute Care (PAC) quality measure is calculated as the proportion of
patient stays with a discharge assessment indicating that a current
reconciled medication list was provided to the subsequent provider at
the time of discharge. The proposed measure denominator is the total
number of IRF patient stays ending in discharge to a subsequent
provider, which is defined as a short-term general acute-care hospital,
intermediate care (intellectual and developmental disabilities
providers), home under care of an organized home health service
organization or hospice, hospice in an institutional facility, a SNF,
an LTCH, another IRF, an inpatient psychiatric facility, or a CAH.
These health care providers were selected for inclusion in the
denominator because they are identified as subsequent providers on the
discharge destination item that is currently included on the IRF
patient assessment instrument (IRF-PAI). The proposed measure numerator
is the number of IRF patient stays with an IRF-PAI discharge assessment
indicating a current reconciled medication list was provided to the
subsequent provider at the time of discharge. For additional technical
information about this proposed measure, we refer readers to the
document titled, ``Proposed Specifications for IRF QRP Quality Measures
and Standardized Patient Assessment Data Elements,'' available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html. The data source for the proposed
quality measure is the IRF-PAI assessment instrument for IRF patients.
For more information about the data submission requirements we are
proposing for this measure, we refer readers to section VIII.G.3. of
this proposed rule.
2. Proposed Transfer of Health Information to the Patient-Post-Acute
Care (PAC) Measure
Beginning with the FY 2022 IRF QRP, we are proposing to adopt the
Transfer of Health Information to the Patient--Post Acute Care (PAC)
measure, a measure that satisfies the IMPACT Act domain of Transfer of
Health Information, with data collection for discharges beginning
October 1, 2020. This process-based measure assesses whether or not a
current reconciled medication list was provided to the patient, family,
or caregiver when the patient was discharged from a PAC setting to a
private home/apartment, a board and care home, assisted living, a group
home, transitional living or home under care of an organized home
health service organization, or a hospice.
a. Background
In 2013, 22.3 percent of all acute hospital discharges were
discharged to PAC settings, including 11 percent who were discharged to
home under the care of a home health agency.\55\ Of the Medicare FFS
beneficiaries with an IRF stay in fiscal years 2016 and 2017, an
estimated 51 percent were discharged home with home health services, 21
percent were discharged home with self-care, and .5 percent were
discharged with home hospice services.\56\
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\55\ Tian, W. ``An all-payer view of hospital discharge to
postacute care,'' May 2016. Available at https://www.hcup-us.ahrq.gov/reports/statbriefs/sb205-Hospital-Discharge-Postacute-Care.jsp.
\56\ RTI International analysis of Medicare claims data for
index stays in IRF 2016/2017. (RTI program reference: MM150).
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The communication of health information, such as a reconciled
medication list, is critical to ensuring safe and effective patient
transitions from health care settings to home and/or other community
settings. Incomplete or missing health information, such as medication
information, increases the likelihood of a patient safety risk, often
life-threatening.57 58 59 60 61 Individuals who use PAC care
services are particularly vulnerable to adverse health outcomes due to
their higher likelihood of having multiple comorbid chronic conditions,
polypharmacy, and complicated transitions between care
settings.62 63 Upon discharge to home, individuals in PAC
settings may be faced with numerous medication changes, new medication
regimes, and follow-up details.64 65 66 The efficient
[[Page 17290]]
and effective communication and coordination of medication information
may be critical to prevent potentially deadly adverse effects. When
care coordination activities enhance care transitions, these activities
can reduce duplication of care services and costs of care, resolve
conflicting care plans, and prevent medical errors.67 68
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\57\ Kwan, J.L., Lo, L., Sampson, M., & Shojania, K.G.,
``Medication reconciliation during transitions of care as a patient
safety strategy: a systematic review,'' Annals of Internal Medicine,
2013, Vol. 158(5), pp. 397-403.
\58\ Boockvar, K.S., Blum, S., Kugler, A., Livote, E.,
Mergenhagen, K.A., Nebeker, J.R., & Yeh, J., ``Effect of admission
medication reconciliation on adverse drug events from admission
medication changes,'' Archives of Internal Medicine, 2011, Vol.
171(9), pp. 860-861.
\59\ Bell, C.M., Brener, S.S., Gunraj, N., Huo, C., Bierman,
A.S., Scales, D.C., & Urbach, D.R., ``Association of ICU or hospital
admission with unintentional discontinuation of medications for
chronic diseases,'' JAMA, 2011, Vol. 306(8), pp. 840-847.
\60\ Basey, A.J., Krska, J., Kennedy, T.D., & Mackridge, A.J.,
``Prescribing errors on admission to hospital and their potential
impact: a mixed-methods study,'' BMJ Quality & Safety, 2014, Vol.
23(1), pp. 17-25.
\61\ Desai, R., Williams, C.E., Greene, S.B., Pierson, S., &
Hansen, R.A., ``Medication errors during patient transitions into
nursing homes: characteristics and association with patient harm,''
The American Journal of Geriatric Pharmacotherapy, 2011, Vol. 9(6),
pp. 413-422.
\62\ Brody, A.A., Gibson, B., Tresner-Kirsch, D., Kramer, H.,
Thraen, I., Coarr, M.E., & Rupper, R. ``High prevalence of
medication discrepancies between home health referrals and Centers
for Medicare and Medicaid Services home health certification and
plan of care and their potential to affect safety of vulnerable
elderly adults,'' Journal of the American Geriatrics Society, 2016,
Vol. 64(11), pp. e166-e170.
\63\ Chhabra, P.T., Rattinger, G.B., Dutcher, S.K., Hare, M.E.,
Parsons, K.L., & Zuckerman, I.H., ``Medication reconciliation during
the transition to and from long-term care settings: a systematic
review,'' Res Social Adm Pharm, 2012, Vol. 8(1), pp. 60-75.
\64\ Brody, A.A., Gibson, B., Tresner-Kirsch, D., Kramer, H.,
Thraen, I., Coarr, M.E., & Rupper, R. ``High prevalence of
medication discrepancies between home health referrals and Centers
for Medicare and Medicaid Services home health certification and
plan of care and their potential to affect safety of vulnerable
elderly adults,'' Journal of the American Geriatrics Society, 2016,
Vol. 64(11), pp. e166-e170.
\65\ Bell, C.M., Brener, S.S., Gunraj, N., Huo, C., Bierman,
A.S., Scales, D.C., & Urbach, D.R., ``Association of ICU or hospital
admission with unintentional discontinuation of medications for
chronic diseases,'' JAMA, 2011, Vol. 306(8), pp. 840-847.
\66\ Sheehan, O.C., Kharrazi, H., Carl, K.J., Leff, B., Wolff,
J.L., Roth, D.L., Gabbard, J., & Boyd, C.M., ``Helping older adults
improve their medication experience (HOME) by addressing medication
regimen complexity in home healthcare,'' Home Healthcare Now. 2018,
Vol. 36(1) pp. 10-19.
\67\ Mor, V., Intrator, O., Feng, Z., & Grabowski, D.C., ``The
revolving door of rehospitalization from skilled nursing
facilities,'' Health Affairs, 2010, Vol. 29(1), pp. 57-64.
\68\ Starmer, A.J., Sectish, T.C., Simon, D.W., Keohane, C.,
McSweeney, M.E., Chung, E.Y., Yoon, C.S., Lipsitz, S.R., Wassner,
A.J., Harper, M.B., & Landrigan, C.P., ``Rates of medical errors and
preventable adverse events among hospitalized children following
implementation of a resident handoff bundle,'' JAMA, 2013, Vol.
310(21), pp. 2262-2270.
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Finally, the transfer of a patient's discharge medication
information to the patient, family, or caregiver is common practice and
supported by discharge planning requirements for participation in
Medicare and Medicaid programs.69 70 Most PAC EHR systems
generate a discharge medication list to promote patient participation
in medication management, which has been shown to be potentially useful
for improving patient outcomes and transitional care.\71\
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\69\ CMS, ``Revision to state operations manual (SOM), Hospital
Appendix A--Interpretive Guidelines for 42 CFR 482.43, Discharge
Planning'' May 17, 2013. Available at https://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/SurveyCertificationGenInfo/Downloads/Survey-and-Cert-Letter-13-32.pdf.
\70\ The State Operations Manual Guidance to Surveyors for Long
Term Care Facilities (Guidance Sec. 483.21(c)(1) Rev. 11-22-17) for
discharge planning process. Available at https://www.cms.gov/Regulations-and-Guidance/Guidance/Manuals/downloads/som107ap_pp_guidelines_ltcf.pdf.
\71\ Toles, M., Colon-Emeric, C., Naylor, M.D., Asafu-Adjei, J.,
Hanson, L.C., ``Connect-home: transitional care of skilled nursing
facility patients and their caregivers,'' Am Geriatr Soc., 2017,
Vol. 65(10), pp. 2322-2328.
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b. Stakeholder and Technical Expert Panel (TEP) Input
The proposed measure was developed after consideration of feedback
we received from stakeholders and four TEPs convened by our
contractors. Further, the proposed measure was developed after
evaluation of data collected during two pilot tests we conducted in
accordance with the CMS Measures Management System Blueprint.
Our measure development contractors constituted a TEP which met on
September 27, 2016,\72\ January 27, 2017, and August 3, 2017 \73\ to
provide input on a prior version of this measure. Based on this input,
we updated the measure concept in late 2017 to include the transfer of
a specific component of health information--medication information. Our
measure development contractors reconvened this TEP on April 20, 2018
to seek expert input on the measure. Overall, the TEP members supported
the proposed measure, affirming that the measure provides an
opportunity to improve the transfer of medication information. Most of
the TEP members believed that the measure could improve the transfer of
medication information to patients, families, and caregivers. Several
TEP members emphasized the importance of transferring information to
patients and their caregivers in a clear manner using plain language. A
summary of the April 20, 2018 TEP proceedings titled ``Transfer of
Health Information TEP Meeting 4--June 2018'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
---------------------------------------------------------------------------
\72\ Technical Expert Panel Summary Report: Development of two
quality measures to satisfy the Improving Medicare Post-Acute Care
Transformation Act of 2014 (IMPACT Act) Domain of Transfer of health
Information and Care Preferences When an Individual Transitions to
Skilled Nursing Facilities (SNFs), Inpatient Rehabilitation
Facilities (IRFs), Long Term Care Hospitals (LTCHs) and Home Health
Agencies (HHAs). Available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/Transfer-of-Health-Information-TEP_Summary_Report_Final-June-2017.pdf.
\73\ Technical Expert Panel Summary Report: Development of two
quality measures to satisfy the Improving Medicare Post-Acute Care
Transformation Act of 2014 (IMPACT Act) Domain of Transfer of health
Information and Care Preferences When an Individual Transitions to
Skilled Nursing Facilities (SNFs), Inpatient Rehabilitation
Facilities (IRFs), Long Term Care Hospitals (LTCHs) and Home Health
Agencies (HHAs). Available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/Transfer-of-Health-Information-TEP-Meetings-2-3-Summary-Report_Final_Feb2018.pdf.
---------------------------------------------------------------------------
Our measure development contractors solicited stakeholder feedback
on the proposed measure by requesting comment on the CMS Measures
Management System Blueprint website, and accepted comments that were
submitted from March 19, 2018 to May 3, 2018. Several commenters noted
the importance of ensuring that the instruction provided to patients
and caregivers is clear and understandable to promote transparent
access to medical record information and meet the goals of the IMPACT
Act. The summary report for the March 19 to May 3, 2018 public comment
period titled ``IMPACT-Medication Profile Transferred Public Comment
Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
c. Pilot Testing
Between June and August 2018, we held a pilot test involving 24 PAC
facilities/agencies, including five IRFs, six SNFs, six LTCHs, and
seven HHAs. The 24 pilot sites submitted a total of 801 assessments.
Analysis of agreement between coders within each participating facility
(241 qualifying pairs) indicated an 87 percent agreement for this
measure. Overall, pilot testing enabled us to verify its reliability,
components of face validity, and feasibility of being implemented
across PAC settings. Further, more than half of the sites that
participated in the pilot test stated, during debriefing interviews,
that the measure could distinguish facilities or agencies with higher
quality medication information transfer from those with lower quality
medication information transfer at discharge. The pilot test summary
report titled ``Transfer of Health Information 2018 Pilot Test Summary
Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
d. Measure Applications Partnership (MAP) Review and Related Measures
We included the proposed measure in the IRF QRP section of the 2018
MUC list. The MAP conditionally supported this measure pending NQF
endorsement, noting that the measure can promote the transfer of
important medication information to the patient. The MAP recommended
that providers transmit medication information to patients that is easy
to understand because health literacy can impact a person's ability to
take medication as directed. More information about the MAP's
recommendations for this measure is available at http://
www.qualityforum.org/Publications/
[[Page 17291]]
2019/02/
MAP_2019_Considerations_for_Implementing_Measures_Final_Report_-_PAC-
LTC.aspx.
Section 1886(j)(7)(D)(i) of the Act, requires that any measure
specified by the Secretary be endorsed by the entity with a contract
under section 1890(a) of the Act, which is currently the NQF. However,
when a feasible and practical measure has not been NQF endorsed for a
specified area or medical topic determined appropriate by the
Secretary, section 1886(j)(7)(D)(ii) of the Act allows the Secretary to
specify a measure that is not NQF endorsed as long as due consideration
is given to the measures that have been endorsed or adopted by a
consensus organization identified by the Secretary. Therefore, in the
absence of any NQF-endorsed measures that address the proposed Transfer
of Health Information to the Patient -Post-Acute Care (PAC), which
requires that at least some of the data used to calculate the measure
be collected as standardized patient assessment data through post-acute
care assessment instruments, we believe that there is currently no
feasible NQF-endorsed measure that we could adopt under section
1886(j)(7)(D)(ii) of the Act. However, we note that we intend to submit
the proposed measure to the NQF for consideration of endorsement when
feasible.
e. Quality Measure Calculation
The calculation of the proposed Transfer of Health Information to
the Patient-Post-Acute Care (PAC) measure would be based on the
proportion of patient stays with a discharge assessment indicating that
a current reconciled medication list was provided to the patient,
family, or caregiver at the time of discharge.
The proposed measure denominator is the total number of IRF patient
stays ending in discharge to a private home/apartment, a board and care
home, assisted living, a group home, transitional living or home under
care of an organized home health service organization, or a hospice.
These locations were selected for inclusion in the denominator because
they are identified as home locations on the discharge destination item
that is currently included on the IRF-PAI. The proposed measure
numerator is the number of IRF patient stays with an IRF-PAI discharge
assessment indicating a current reconciled medication list was provided
to the patient, family, or caregiver at the time of discharge. For
technical information about this proposed measure, we refer readers to
the document titled ``Proposed Specifications for IRF QRP Quality
Measures and Standardized Patient Assessment Data Elements,'' available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html. Data for the proposed quality
measure would be calculated using data from the IRF-PAI assessment
instrument for IRF patients.
For more information about the data submission requirements we are
proposing for this measure, we refer readers to section VIII.G.3. of
this proposed rule.
3. Proposed Update to the Discharge to Community-Post Acute Care (PAC)
Inpatient Rehabilitation Facility (IRF) Quality Reporting Program (QRP)
Measure
We are proposing to update the specifications for the Discharge to
Community-PAC IRF QRP measure to exclude baseline nursing facility (NF)
residents from the measure. This measure reports an IRF's risk-
standardized rate of Medicare FFS patients who are discharged to the
community following an IRF stay, do not have an unplanned readmission
to an acute care hospital or LTCH in the 31 days following discharge to
community, and who remain alive during the 31 days following discharge
to community. We adopted this measure in the FY 2017 IRF PPS final rule
(81 FR 52095 through 52103).
In the FY 2017 IRF PPS final rule (81 FR 52099), we addressed
public comments recommending exclusion of IRF patients who were
baseline NF residents, as these patients lived in a NF prior to their
IRF stay, as these patients may not be expected to return to the
community following their IRF stay. In the FY 2018 IRF PPS final rule
(82 FR 36285), we addressed public comments expressing support for a
potential future modification of the measure that would exclude
baseline NF residents; commenters stated that the exclusion would
result in the measure more accurately portraying quality of care
provided by IRFs, while controlling for factors outside of IRF control.
We assessed the impact of excluding baseline NF residents from the
measure using CY 2015 and Cy 2016 data, and found that this exclusion
impacted both patient- and facility-level discharge to community rates.
We defined baseline NF residents as IRF patients who had a long-term NF
stay in the 180 days preceding their hospitalization and IRF stay, with
no intervening community discharge between the NF stay and qualifying
hospitalization for measure inclusion. Baseline NF residents
represented 0.3 percent of the measure population after all measure
exclusions were applied. Observed patient-level discharge to community
rates were significantly lower for baseline NF residents (20.82
percent) compared with non-NF residents (64.52 percent). The national
observed patient-level discharge to community rate was 64.41 percent
when baseline NF residents were included in the measure, increasing to
64.52 percent when they were excluded from the measure. After excluding
baseline NF residents, 26.9 percent of IRFs had an increase in their
risk-standardized discharge to community rate that exceeded the
increase in the national observed patient-level discharge to community
rate.
Based on public comments received and our impact analysis, we are
proposing to exclude baseline NF residents from the Discharge to
Community-PAC IRF QRP measure beginning with the FY 2020 IRF QRP, with
baseline NF residents defined as IRF patients who had a long-term NF
stay in the 180 days preceding their hospitalization and IRF stay, with
no intervening community discharge between the NF stay and
hospitalization.
For additional technical information regarding the Discharge to
Community-PAC IRF QRP measure, including technical information about
the proposed exclusion, we refer readers to the document titled
``Proposed Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We invite public comment on this proposal.
E. IRF QRP Quality Measures, Measure Concepts, and Standardized Patient
Assessment Data Elements Under Consideration for Future Years: Request
for Information
We are seeking input on the importance, relevance, appropriateness,
and applicability of each of the measures, standardized patient
assessment data elements (SPADEs), and concepts under consideration
listed in the Table 19 for future years in the IRF QRP.
[[Page 17292]]
Table 19--Future Measures, Measure Concepts, and Standardized Patient
Assessment Data Elements (SPADEs) Under Consideration for the IRF QRP
------------------------------------------------------------------------
-------------------------------------------------------------------------
Quality Measures and Measure Concepts
------------------------------------------------------------------------
Opioid use and frequency.
Exchange of Electronic Health Information and Interoperability.
------------------------------------------------------------------------
Standardized Patient Assessment Data Elements (SPADEs)
------------------------------------------------------------------------
Cognitive complexity, such as executive function and memory.
Dementia.
Bladder and bowel continence including appliance use and episodes of
incontinence.
Care preferences, advance care directives, and goals of care.
Caregiver Status.
Veteran Status.
Health disparities and risk factors, including education, sex and gender
identity, and sexual orientation.
------------------------------------------------------------------------
While we will not be responding to specific comments submitted in
response to this Request for Information in the FY 2020 IRF PPS final
rule, we intend to use this input to inform our future measure and
SPADE development efforts.
F. Proposed Standardized Patient Assessment Data Reporting Beginning
With the FY 2022 IRF QRP
Section 1886(j)(7)(F)(ii) of the Act requires that, for fiscal
years 2019 and each subsequent year, IRFs must report standardized
patient assessment data (SPADE), required under section 1899B(b)(1) of
the Act. Section 1899B(a)(1)(C) of the Act requires, in part, the
Secretary to modify the PAC assessment instruments in order for PAC
providers, including IRFs, to submit SPADEs under the Medicare program.
Section 1899B(b)(1)(A) of the Act requires PAC providers to submit
SPADEs under applicable reporting provisions (which, for IRFs, is the
IRF QRP) with respect to the admission and discharge of an individual
(and more frequently as the Secretary deems appropriate), and section
1899B(b)(1)(B) of the Act defines standardized patient assessment data
as data required for at least the quality measures described in section
1899B(c)(1) and that is with respect to the following categories: (1)
Functional status, such as mobility and self-care at admission to a PAC
provider and before discharge from a PAC provider; (2) cognitive
function, such as ability to express ideas and to understand, and
mental status, such as depression and dementia; (3) special services,
treatments, and interventions, such as need for ventilator use,
dialysis, chemotherapy, central line placement, and total parenteral
nutrition; (4) medical conditions and comorbidities, such as diabetes,
congestive heart failure, and pressure ulcers; (5) impairments, such as
incontinence and an impaired ability to hear, see, or swallow, and (6)
other categories deemed necessary and appropriate by the Secretary.
In the FY 2018 IRF PPS proposed rule (82 FR 20722 through 20739),
we proposed to adopt SPADEs that would satisfy the first five
categories. In the FY 2018 IRF PPS final rule (82 FR 36287 through
36289), we summarized comments that supported our adoption of SPADEs,
including support for our broader standardization goal and support for
the clinical usefulness of specific proposed SPADEs. However, we did
not finalize the majority of our SPADE proposals in recognition of the
concern raised by many commenters that we were moving too fast to adopt
the SPADEs and modify our assessment instruments in light of all of the
other requirements we were also adopting under the IMPACT Act at that
time (82 FR 36292 through 36294). In addition, commenters expressed
that we should conduct further testing of the data elements we have
proposed (82 FR 36288).
However, we finalized the adoption of SPADEs for two of the
categories described in section 1899B(b)(1)(B) of the Act: (1)
Functional status: Data elements currently reported by IRFs to
calculate the measure Application of Percent of Long-Term Care Hospital
Patients with an Admission and Discharge Functional Assessment and a
Care Plan That Addresses Function (NQF #2631); and (2) Medical
conditions and comorbidities: The data elements used to calculate the
pressure ulcer measures, Percent of Residents or Patients with Pressure
Ulcers That Are New or Worsened (Short Stay) (NQF #0678) and the
replacement measure, Changes in Skin Integrity Post-Acute Care:
Pressure Ulcer/Injury. We stated that these data elements were
important for care planning, known to be valid and reliable, and
already being reported by IRFs for the calculation of quality measures.
Since we issued the FY 2018 IRF PPS final rule, IRFs have had an
opportunity to familiarize themselves with other new reporting
requirements that we have adopted under the IMPACT Act. We have also
conducted further testing of the SPADEs, as described more fully below,
and believe that this testing supports the use of the SPADEs in our PAC
assessment instruments. Therefore, we are now proposing to adopt many
of the same SPADEs that we previously proposed to adopt, along with
other SPADEs.
We are proposing that IRFs would be required to report these SPADEs
beginning with the FY 2022 IRF QRP. If finalized as proposed, IRFs
would be required to report these data with respect to admission and
discharge for patients discharged between October 1, 2020, and December
31, 2020 for the FY 2022 IRF QRP. Beginning with the FY 2023 IRF QRP,
we propose that IRFs must report data with respect to admissions and
discharges that occur during the subsequent calendar year (for example,
CY 2021 for the FY 2023 IRF QRP, CY 2022 for the FY 2024 IRF QRP).
We are also proposing that IRFs that submit the Hearing, Vision,
Race, and Ethnicity SPADEs with respect to admission only will be
deemed to have submitted those SPADEs with respect to both admission
and discharge, because it is unlikely that the assessment of those
SPADEs at admission will differ from the assessment of the same SPADEs
at discharge.
In selecting the proposed SPADEs below, we considered the burden of
assessment-based data collection and aimed to minimize additional
burden by evaluating whether any data that is currently collected
through one or more PAC assessment instruments could be collected as
SPADE. In selecting the
[[Page 17293]]
proposed SPADEs below, we also took into consideration the following
factors with respect to each data element:
(1) Overall clinical relevance;
(2) Interoperable exchange to facilitate care coordination during
transitions in care;
(3) Ability to capture medical complexity and risk factors that can
inform both payment and quality; and
(4) Scientific reliability and validity, general consensus
agreement for its usability.
In identifying the SPADEs proposed below, we additionally drew on
input from several sources, including TEPs held by our data element
contractor, public input, and the results of a recent National Beta
Test of candidate data elements conducted by our data element
contractor (hereafter ``National Beta Test'').
The National Beta Test collected data from 3,121 patients and
residents across 143 LTCHs, SNFs, IRFs, and HHAs from November 2017 to
August 2018 to evaluate the feasibility, reliability, and validity of
the candidate data elements across PAC settings. The National Beta Test
also gathered feedback on the candidate data elements from staff who
administered the test protocol in order to understand usability and
workflow of the candidate data elements. More information on the
methods, analysis plan, and results for the National Beta Test can be
found in the document titled, ``Development and Evaluation of Candidate
Standardized Patient Assessment Data Elements: Findings from the
National Beta Test (Volume 2),'' available in the document titled,
``Development and Evaluation of Candidate Standardized Patient
Assessment Data Elements: Findings from the National Beta Test (Volume
2),'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Further, to inform the proposed SPADEs, we took into account
feedback from stakeholders, as well as from technical and clinical
experts, including feedback on whether the candidate data elements
would support the factors described above. Where relevant, we also took
into account the results of the Post-Acute Care Payment Reform
Demonstration (PAC PRD) that took place from 2006 to 2012.
G. Proposed Standardized Patient Assessment Data by Category
1. Cognitive Function and Mental Status Data
A number of underlying conditions, including dementia, stroke,
traumatic brain injury, side effects of medication, metabolic and/or
endocrine imbalances, delirium, and depression, can affect cognitive
function and mental status in PAC patient and resident populations.\74\
The assessment of cognitive function and mental status by PAC providers
is important because of the high percentage of patients and residents
with these conditions,\75\ and because these assessments provide
opportunity for improving quality of care.
---------------------------------------------------------------------------
\74\ National Institute on Aging. (2014). Assessing Cognitive
Impairment in Older Patients. A Quick Guide for Primary Care
Physicians. Retrieved from: https://www.nia.nih.gov/alzheimers/publication/assessing-cognitive-impairment-older-patients.
\75\ Gage B., Morley M., Smith L., et al. (2012). Post-Acute
Care Payment Reform Demonstration (Final report, Volume 4 of 4).
Research Triangle Park, NC: RTI International.
---------------------------------------------------------------------------
Symptoms of dementia may improve with pharmacotherapy, occupational
therapy, or physical activity,76 77 78 and promising
treatments for severe traumatic brain injury are currently being
tested.\79\ For older patients and residents diagnosed with depression,
treatment options to reduce symptoms and improve quality of life
include antidepressant medication and
psychotherapy,80 81 82 83 and targeted services, such as
therapeutic recreation, exercise, and restorative nursing, to increase
opportunities for psychosocial interaction.\84\
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\76\ Casey D.A., Antimisiaris D., O'Brien J. (2010). Drugs for
Alzheimer's Disease: Are They Effective? Pharmacology &
Therapeutics, 35, 208-11.
\77\ Graff M.J., Vernooij-Dassen M.J., Thijssen M., Dekker J.,
Hoefnagels W.H., Rikkert M.G.O. (2006). Community Based Occupational
Therapy for Patients with Dementia and their Care Givers: Randomised
Controlled Trial. BMJ, 333(7580): 1196.
\78\ Bherer L., Erickson K.I., Liu-Ambrose T. (2013). A Review
of the Effects of Physical Activity and Exercise on Cognitive and
Brain Functions in Older Adults. Journal of Aging Research, 657508.
\79\ Giacino J.T., Whyte J., Bagiella E., et al. (2012).
Placebo-controlled trial of amantadine for severe traumatic brain
injury. New England Journal of Medicine, 366(9), 819-826.
\80\ Alexopoulos G.S., Katz I.R., Reynolds C.F. 3rd, Carpenter
D., Docherty J.P., Ross R.W. (2001). Pharmacotherapy of depression
in older patients: a summary of the expert consensus guidelines.
Journal of Psychiatric Practice, 7(6), 361-376.
\81\ Arean P.A., Cook B.L. (2002). Psychotherapy and combined
psychotherapy/pharmacotherapy for late life depression. Biological
Psychiatry, 52(3), 293-303.
\82\ Hollon S.D., Jarrett R.B., Nierenberg A.A., Thase M.E.,
Trivedi M., Rush A.J. (2005). Psychotherapy and medication in the
treatment of adult and geriatric depression: which monotherapy or
combined treatment? Journal of Clinical Psychiatry, 66(4), 455-468.
\83\ Wagenaar D, Colenda CC, Kreft M, Sawade J, Gardiner J,
Poverejan E. (2003). Treating depression in nursing homes: practice
guidelines in the real world. J Am Osteopath Assoc. 103(10), 465-
469.
\84\ Crespy SD, Van Haitsma K, Kleban M, Hann CJ. Reducing
Depressive Symptoms in Nursing Home Residents: Evaluation of the
Pennsylvania Depression Collaborative Quality Improvement Program. J
Healthc Qual. 2016. Vol. 38, No. 6, pp. e76-e88.
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In alignment with our Meaningful Measures Initiative, accurate
assessment of cognitive function and mental status of patients and
residents in PAC is expected to make care safer by reducing harm caused
in the delivery of care; promote effective prevention and treatment of
chronic disease; strengthen person and family engagement as partners in
their care; and promote effective communication and coordination of
care. For example, standardized assessment of cognitive function and
mental status of patients and residents in PAC will support
establishing a baseline for identifying changes in cognitive function
and mental status (for example, delirium), anticipating the patient's
or resident's ability to understand and participate in treatments
during a PAC stay, ensuring patient and resident safety (for example,
risk of falls), and identifying appropriate support needs at the time
of discharge or transfer. Standardized patient assessment data elements
will enable or support clinical decision-making and early clinical
intervention; person-centered, high quality care through facilitating
better care continuity and coordination; better data exchange and
interoperability between settings; and longitudinal outcome analysis.
Therefore, reliable standardized patient assessment data elements
assessing cognitive function and mental status are needed to initiate a
management program that can optimize a patient's or resident's
prognosis and reduce the possibility of adverse events.
The data elements related to cognitive function and mental status
were first proposed as standardized patient assessment data elements in
the FY 2018 IRF PPS proposed rule (82 FR 20723 through 20726). In
response to our proposals, a few commenters noted that the proposed
data elements did not capture some dimensions of cognitive function and
mental status, such as functional cognition, communication, attention,
concentration, and agitation. One commenter also suggested that other
cognitive assessments should be considered for standardization. Another
commenter stated support for the standardized assessment of cognitive
function and mental status, because it could support appropriate use of
skilled therapy for beneficiaries with
[[Page 17294]]
degenerative conditions, such as dementia, and appropriate use of
medications for behavioral and psychological symptoms of dementia.
We are inviting comment on our proposals to collect as standardized
patient assessment data the following data with respect to cognitive
function and mental status.
Brief Interview for Mental Status (BIMS)
We are proposing that the data elements that comprise the BIMS meet
the definition of standardized patient assessment data with respect to
cognitive function and mental status under section 1899B(b)(1)(B)(ii)
of the Act.
As described in the FY 2018 IRF PPS Proposed Rule (82 FR 20723
through 20724), dementia and cognitive impairment are associated with
long-term functional dependence and, consequently, poor quality of life
and increased healthcare costs and mortality.\85\ This makes assessment
of mental status and early detection of cognitive decline or impairment
critical in the PAC setting. The intensity of routine nursing care is
higher for patients and residents with cognitive impairment than those
without, and dementia is a significant variable in predicting
readmission after discharge to the community from PAC providers.\86\
---------------------------------------------------------------------------
\85\ Ag[uuml]ero-Torres, H., Fratiglioni, L., Guo, Z., Viitanen,
M., von Strauss, E., & Winblad, B. (1998). ``Dementia is the major
cause of functional dependence in the elderly: 3-year follow-up data
from a population-based study.'' Am J of Public Health 88(10): 1452-
1456.
\86\ RTI International. Proposed Measure Specifications for
Measures Proposed in the FY 2017 IRF QRP NPRM. Research Triangle
Park, NC. 2016.
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The BIMS is a performance-based cognitive assessment screening tool
that assesses repetition, recall with and without prompting, and
temporal orientation. The data elements that make up the BIMS are seven
questions on the repetition of three words, temporal orientation, and
recall that result in a cognitive function score. The BIMS was
developed to be a brief, objective screening tool, with a focus on
learning and memory. As a brief screener, the BIMS was not designed to
diagnose dementia or cognitive impairment, but rather to be a
relatively quick and easy to score assessment that could identify
cognitively impaired patients as well as those who may be at risk for
cognitive decline and require further assessment. It is currently in
use in two of the PAC assessments: The MDS used by SNFs and the IRF-PAI
used by IRFs. For more information on the BIMS, we refer readers to the
document titled ``Proposed Specifications for IRF QRP Quality Measures
and Standardized Patient Assessment Data Elements,'' available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The data elements that comprise the BIMS were first proposed as
standardized patient assessment data elements in the FY 2018 IRF PPS
proposed rule (82 FR 20723 through 20724). In that proposed rule, we
stated that the proposal was informed by input we received through a
call for input published on the CMS Measures Management System
Blueprint website. Input submitted from August 12 to September 12,
2016, expressed support for use of the BIMS, noting that it is
reliable, feasible to use across settings, and will provide useful
information about patients and residents. We also stated that the data
collected through the BIMS will provide a clearer picture of patient or
resident complexity, help with the care planning process, and be useful
during care transitions and when coordinating across providers. A
summary report for the August 12 to September 12, 2016 public comment
period titled ``SPADE August 2016 Public Comment Summary Report'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the use of the BIMS,
especially in its capacity to inform care transitions, but other
commenters were critical, noting the limitations of the BIMS to assess
mild cognitive impairment and ``functional'' cognition, and that the
BIMS cannot be completed by patients and residents who are unable to
communicate. They also stated that other cognitive assessments
available in the public domain should be considered for
standardization. One commenter suggested that CMS require use of the
BIMS with respect to discharge as well as admission.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
BIMS was included in the National Beta Test of candidate data elements
conducted by our data element contractor from November 2017 to August
2018. Results of this test found the BIMS to be feasible and reliable
for use with PAC patients and residents. More information about the
performance of the BIMS in the National Beta Test can be found in the
document titled ``Proposed Specifications for IRF QRP Quality Measures
and Standardized Patient Assessment Data Elements,'' available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements and the TEP supported the
assessment of patient or resident cognitive status with respect to both
admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our ongoing SPADE development efforts.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present the results of the National
Beta Test and solicit additional comments. General input on the testing
and item development process and concerns about burden were received
from stakeholders during this meeting and via email through February 1,
2019. Some commenters also expressed concern that the BIMS, if used
alone, may not be sensitive enough to capture the range of cognitive
impairments, including mild cognitive impairment. A summary of the
public input received from the November 27, 2018 stakeholder meeting
titled ``Input on Standardized Patient Assessment Data Elements
(SPADEs) Received After November 27, 2018 Stakeholder Meeting'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We understand the concerns raised by stakeholders that BIMS, if
used alone, may not be sensitive enough to capture the range of
cognitive impairments, including functional cognition and MCI, but note
that the purpose of the BIMS
[[Page 17295]]
data elements as SPADEs is to screen for cognitive impairment in a
broad population. We also acknowledge that further cognitive tests may
be required based on a patient's condition and will take this feedback
into consideration in the development of future standardized assessment
data elements. However, taking together the importance of assessing for
cognitive status, stakeholder input, and strong test results, we are
proposing that the BIMS data elements meet the definition of
standardized patient assessment data with respect to cognitive function
and mental status under section 1899B(b)(1)(B)(ii) of the Act and to
adopt the BIMS data elements as standardized patient assessment data
for use in the IRF QRP.
Confusion Assessment Method (CAM)
In this proposed rule, we are proposing that the data elements that
comprise the Confusion Assessment Method (CAM) meet the definition of
standardized patient assessment data with respect to cognitive function
and mental status under section 1899B(b)(1)(B)(ii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20724),
the CAM was developed to identify the signs and symptoms of delirium.
It results in a score that suggests whether a patient or resident
should be assigned a diagnosis of delirium. Because patients and
residents with multiple comorbidities receive services from PAC
providers, it is important to assess delirium, which is associated with
a high mortality rate and prolonged duration of stay in hospitalized
older adults.\87\ Assessing these signs and symptoms of delirium is
clinically relevant for care planning by PAC providers.
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\87\ Fick, D.M., Steis, M.R., Waller, J.L., & Inouye, S.K.
(2013). ``Delirium superimposed on dementia is associated with
prolonged length of stay and poor outcomes in hospitalized older
adults.'' J of Hospital Med 8(9): 500-505.
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The CAM is a patient assessment that screens for overall cognitive
impairment, as well as distinguishes delirium or reversible confusion
from other types of cognitive impairment. The CAM is currently in use
in two of the PAC assessments: A four-item version of the CAM is used
in the MDS in SNFs; and a six-item version of the CAM is used in the
LTCH CARE Data Set (LCDS) in LTCHs. We are proposing the four-item
version of the CAM that assesses acute change in mental status,
inattention, disorganized thinking, and altered level of consciousness.
For more information on the CAM, we refer readers to the document
titled ``Proposed Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The data elements that comprise the CAM were first proposed as
standardized patient assessment data elements in the FY 2018 IRF PPS
proposed rule (82 FR 20724). In that proposed rule, we stated that the
proposal was informed by public input we received on the CAM through a
call for input published on the CMS Measures Management System
Blueprint website. Input submitted from August 12 to September 12, 2016
expressed support for use of the CAM, noting that it would provide
important information for care planning and care coordination, and
therefore, contribute to quality improvement. We also stated that those
commenters had noted the CAM is particularly helpful in distinguishing
delirium and reversible confusion from other types of cognitive
impairment. A summary report for the August 12 to September 12, 2016
public comment period titled ``SPADE August 2016 Public Comment Summary
Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
one commenter supported use of the CAM for standardized patient
assessment data. However, some commenters expressed concerns that the
CAM data elements assess: The presence of behavioral symptoms, but not
the cause; the possibility of a false positive for delirium due to
patient cognitive or communication impairments; and the lack of
specificity of the assessment specifications. In addition, other
commenters noted that the CAM is not necessary because: Delirium is
easily diagnosed without a tool; the CAM and BIMS assessments are
redundant; and some CAM response options are not meaningful.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
CAM was included in the National Beta Test of candidate data elements
conducted by our data element contractor from November 2017 to August
2018. Results of this test found the CAM to be feasible and reliable
for use with PAC patients and residents. More information about the
performance of the CAM in the National Beta Test can be found in the
document titled ``Proposed Specifications for IRF QRP Quality Measures
and Standardized Patient Assessment Data Elements,'' available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although they did not
specifically discuss the CAM data elements, the TEP supported the
assessment of patient or resident cognitive status with respect to both
admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our ongoing SPADE development efforts.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present the results of the National
Beta Test and solicit additional comments. General input on the testing
and item development process and concerns about burden were received
from stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for delirium,
stakeholder input, and strong test results, we are proposing that the
CAM data elements meet the definition of standardized patient
assessment data with respect to cognitive function and mental status
under section 1899B(b)(1)(B)(ii) of the Act and to adopt the CAM data
elements as standardized patient assessment data for use in the IRF
QRP.
[[Page 17296]]
Patient Health Questionnaire--2 to 9 (PHQ-2 to 9)
In this proposed rule, we are proposing that the Patient Health
Questionnaire-2 to 9 (PHQ-2 to 9) data elements meet the definition of
standardized patient assessment data with respect to cognitive function
and mental status under section 1899B(b)(1)(B)(ii) of the Act. The
proposed data elements are based on the PHQ-2 mood interview, which
focuses on only the two cardinal symptoms of depression, and the longer
PHQ-9 mood interview, which assesses presence and frequency of nine
signs and symptoms of depression. The name of the data element, the
PHQ-2 to 9, refers to an embedded skip pattern that transitions
patients with a threshold level of symptoms in the PHQ-2 to the longer
assessment of the PHQ-9. The skip pattern is described further below.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20725 through
20726), depression is a common and under-recognized mental health
condition. Assessments of depression help PAC providers better
understand the needs of their patients and residents by: Prompting
further evaluation after establishing a diagnosis of depression;
elucidating the patient's or resident's ability to participate in
therapies for conditions other than depression during their stay; and
identifying appropriate ongoing treatment and support needs at the time
of discharge.
The proposed PHQ-2 to 9 is based on the PHQ-9 mood interview. The
PHQ-2 consists of questions about only the first two symptoms addressed
in the PHQ-9: depressed mood and anhedonia (inability to feel
pleasure), which are the cardinal symptoms of depression. The PHQ-2 has
performed well as both a screening tool for identifying depression, to
assess depression severity, and to monitor patient mood over
time.88 89 If a patient demonstrates signs of
depressed mood and anhedonia under the PHQ-2, then the patient is
administered the lengthier PHQ-9. This skip pattern (also referred to
as a gateway) is designed to reduce the length of the interview
assessment for patients who fail to report the cardinal symptoms of
depression. The design of the PHQ-2 to 9 reduces the burden that would
be associated with requiring the full PHQ-9, while ensuring that
patients and residents with indications of depressive symptoms based on
the PHQ-2 receive the longer assessment.
---------------------------------------------------------------------------
\88\ Li, C., Friedman, B., Conwell, Y., & Fiscella, K. (2007).
``Validity of the Patient Health Questionnaire 2 (PHQ[hyphen]2) in
identifying major depression in older people.'' J of the A
Geriatrics Society, 55(4): 596-602.
\89\ L[ouml]we, B., Kroenke, K., & Gr[auml]fe, K. (2005).
``Detecting and monitoring depression with a two-item questionnaire
(PHQ-2).'' J of Psychosomatic Research, 58(2): 163-171.
---------------------------------------------------------------------------
Components of the proposed data elements are currently used in the
OASIS for HHAs (PHQ-2) and the MDS for SNFs (PHQ-9). For more
information on the PHQ-2 to 9, we refer readers to the document titled
``Proposed Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We proposed the PHQ-2 data elements as SPADEs in the FY 2018 IRF
proposed rule (82 FR 20725 through 20726). In that proposed rule, we
stated that the proposal was informed by input we received from the TEP
convened by our data element contractor on April 6 and 7, 2016. The TEP
members particularly noted that the brevity of the PHQ-2 made it
feasible to administer with low burden for both assessors and PAC
patients or residents. A summary of the April 6 and 7, 2016 TEP meeting
titled ``SPADE Technical Expert Panel Summary (First Convening)'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
That rule proposal was also informed by public input that we
received through a call for input published on the CMS Measures
Management System Blueprint website. Input was submitted from August 12
to September 12, 2016 on three versions of the PHQ depression screener:
The PHQ-2; the PHQ-9; and the PHQ-2 to 9 with the skip pattern design.
Many commenters were supportive of the standardized assessment of mood
in PAC settings, given the role that depression plays in well-being.
Several commenters expressed support for an approach that would use
PHQ-2 as a gateway to the longer PHQ-9 while still potentially reducing
burden on most patients and residents, as well as test administrators,
and ensuring the administration of the PHQ-9, which exhibits higher
specificity,\90\ for patients and residents who showed signs and
symptoms of depression on the PHQ-2. A summary report for the August 12
to September 12, 2016 public comment period titled ``SPADE August 2016
Public Comment Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
---------------------------------------------------------------------------
\90\ Arroll B, Goodyear-Smith F, Crengle S, Gunn J, Kerse N,
Fishman T, et al. Validation of PHQ-2 and PHQ-9 to screen for major
depression in the primary care population. Annals of family
medicine. 2010;8(4):348-53. doi: 10.1370/afm.1139 pmid:20644190;
PubMed Central PMCID: PMC2906530.
---------------------------------------------------------------------------
In response to our proposal to use the PHQ-2 in the FY 2018 IRF PPS
proposed rule (82 FR 20725 through 20726), we received comments
agreeing to the importance of a standardized assessment of depression
in patients and residents receiving PAC services. Commenters also
raised concerns about the ability of the PHQ-2 to correctly identify
all patients and residents with signs and symptoms of depression. One
commenter supported using the PHQ-2 as a gateway assessment and
conducting a more thorough evaluation of depression symptoms with the
PHQ-9 if the PHQ-2 is positive. Another commenter expressed concern
that standardized assessment of signs and symptoms of depression via
the PHQ-2 is not appropriate in the IRF setting, as patients may have
recently experienced acute illness or injury, and routine screening may
lead to overprescribing of antidepressant medications. Another
commenter expressed concern about potential conflicts between the
results of screening assessments and documented diagnoses based on the
expertise of physicians and other clinicians. In response to these
comments, we carried out additional testing, and we provide our
findings below.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
PHQ-2 to 9 was included in the National Beta Test of candidate data
elements conducted by our data element contractor from November 2017 to
August 2018. Results of this test found the PHQ-2 to 9 to be feasible
and reliable for use with PAC patients and residents. More information
about the performance of the PHQ-2 to 9 in the National Beta Test can
be found in the document titled ``Proposed Specifications for IRF QRP
Quality Measures and Standardized Patient Assessment Data Elements,''
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of
[[Page 17297]]
soliciting input on the PHQ-2 to 9. The TEP was supportive of the PHQ-2
to 9 data element set as a screener for signs and symptoms of
depression. The TEP's discussion noted that symptoms evaluated by the
full PHQ-9 (for example, concentration, sleep, appetite) had relevance
to care planning and the overall well-being of the patient or resident,
but that the gateway approach of the PHQ-2 to 9 would be appropriate as
a depression screening assessment, as it depends on the well-validated
PHQ-2 and focuses on the cardinal symptoms of depression. A summary of
the September 17, 2018 TEP meeting titled ``SPADE Technical Expert
Panel Summary (Third Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our on-going SPADE development efforts.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present the results of the National
Beta Test and solicit additional comments. General input on the testing
and item development process and concerns about burden were received
from stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for depression,
stakeholder input, and test results, we are proposing that the PHQ-2 to
9 data elements meet the definition of standardized patient assessment
data with respect to cognitive function and mental status under section
1899B(b)(1)(B)(ii) of the Act and to adopt the PHQ-2 to 9 data elements
as standardized patient assessment data for use in the IRF QRP.
2. Special Services, Treatments, and Interventions Data
Special services, treatments, and interventions performed in PAC
can have a major effect on an individual's health status, self-image,
and quality of life. The assessment of these special services,
treatments, and interventions in PAC is important to ensure the
continuing appropriateness of care for the patients and residents
receiving them, and to support care transitions from one PAC provider
to another, an acute care hospital, or discharge. In alignment with our
Meaningful Measures Initiative, accurate assessment of special
services, treatments, and interventions of patients and residents
served by PAC providers is expected to make care safer by reducing harm
caused in the delivery of care; promote effective prevention and
treatment of chronic disease; strengthen person and family engagement
as partners in their care; and promote effective communication and
coordination of care.
For example, standardized assessment of special services,
treatments, and interventions used in PAC can promote patient and
resident safety through appropriate care planning (for example,
mitigating risks such as infection or pulmonary embolism associated
with central intravenous access), and identifying life-sustaining
treatments that must be continued, such as mechanical ventilation,
dialysis, suctioning, and chemotherapy, at the time of discharge or
transfer. Standardized assessment of these data elements will enable or
support: Clinical decision-making and early clinical intervention;
person-centered, high quality care through, for example, facilitating
better care continuity and coordination; better data exchange and
interoperability between settings; and longitudinal outcome analysis.
Therefore, reliable data elements assessing special services,
treatments, and interventions are needed to initiate a management
program that can optimize a patient's or resident's prognosis and
reduce the possibility of adverse events.
A TEP convened by our data element contractor provided input on the
proposed data elements for special services, treatments, and
interventions. In a meeting held on January 5 and 6, 2017, this TEP
found that these data elements are appropriate for standardization
because they would provide useful clinical information to inform care
planning and care coordination. The TEP affirmed that assessment of
these services and interventions is standard clinical practice, and
that the collection of these data by means of a list and checkbox
format would conform with common workflow for PAC providers. A summary
of the January 5 and 6, 2017 TEP meeting titled ``SPADE Technical
Expert Panel Summary (Second Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Comments on the category of special services, treatments, and
interventions were also submitted by stakeholders during the FY 2018
IRF PPS proposed rule (82 FR 20726 through 20736) public comment
period. One commenter supported adding the SPADEs for special services,
treatments and interventions. Others stated labor costs and staff
burden would increase for data collection. The Medicare Payment
Advisory Commission (MedPAC) suggested that a few other high-cost
services, such as cardiac monitoring and specialty bed/surfaces, may
warrant consideration for inclusion in future collection efforts. One
commenter believed that the low frequency of the special services,
treatments, and interventions in the IRF setting makes them not worth
assessing for patients given the cost of data collection and reporting.
A few commenters noted that that many of these data elements should be
obtainable from administrative data (that is, coding and Medicare
claims), and therefore, assessing them through patient record review
would be duplicated effort.
Information on data element performance in the National Beta Test,
which collected data between November 2017 and August 2018, is reported
within each data element proposal below. Clinical staff who
participated in the National Beta Test supported these data elements
because of their importance in conveying patient or resident
significant health care needs, complexity, and progress. However,
clinical staff also noted that, despite the simple ``check box'' format
of these data element, they sometimes needed to consult multiple
information sources to determine a patient's or resident's treatments.
We are inviting comment on our proposals to collect as standardized
patient assessment data the following data with respect to special
services, treatments, and interventions.
Cancer Treatment: Chemotherapy (IV, Oral, Other)
We are proposing that the Chemotherapy (IV, Oral, Other) data
element meets the definition of standardized patient assessment data
with respect to special services, treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act.
[[Page 17298]]
As described in the FY 2018 IRF PPS proposed rule (82 FR 20726
through 20727), chemotherapy is a type of cancer treatment that uses
drugs to destroy cancer cells. It is sometimes used when a patient has
a malignancy (cancer), which is a serious, often life-threatening or
life-limiting condition. Both intravenous (IV) and oral chemotherapy
have serious side effects, including nausea/vomiting, extreme fatigue,
risk of infection due to a suppressed immune system, anemia, and an
increased risk of bleeding due to low platelet counts. Oral
chemotherapy can be as potent as chemotherapy given by IV and can be
significantly more convenient and less resource-intensive to
administer. Because of the toxicity of these agents, special care must
be exercised in handling and transporting chemotherapy drugs. IV
chemotherapy is administered either peripherally, or more commonly,
given via an indwelling central line, which raises the risk of
bloodstream infections. Given the significant burden of malignancy, the
resource intensity of administering chemotherapy, and the side effects
and potential complications of these highly-toxic medications,
assessing the receipt of chemotherapy is important in the PAC setting
for care planning and determining resource use. The need for
chemotherapy predicts resource intensity, both because of the
complexity of administering these potent, toxic drug combinations under
specific protocols, and because of what the need for chemotherapy
signals about the patient's underlying medical condition. Furthermore,
the resource intensity of IV chemotherapy is higher than for oral
chemotherapy, as the protocols for administration and the care of the
central line (if present) for IV chemotherapy require significant
resources.
The Chemotherapy (IV, Oral, Other) data element consists of a
principal data element (Chemotherapy) and three response option sub-
elements: IV chemotherapy, which is generally resource-intensive; Oral
chemotherapy, which is less invasive and generally requires less
intensive administration protocols; and a third category, Other,
provided to enable the capture of other less common chemotherapeutic
approaches. This third category is potentially associated with higher
risks and is more resource intensive due to delivery by other routes
(for example, intraventricular or intrathecal). If the assessor
indicates that the patient is receiving chemotherapy on the principal
Chemotherapy data element, the assessor would then indicate by which
route or routes (for example, IV, Oral, Other) the chemotherapy is
administered.
A single Chemotherapy data element that does not include the
proposed three sub-elements is currently in use in the MDS in SNFs. For
more information on the Chemotherapy (IV, Oral, Other) data element, we
refer readers to the document titled ``Proposed Specifications for IRF
QRP Quality Measures and Standardized Patient Assessment Data
Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Chemotherapy data element was first proposed as a standardized
patient assessment data element in the FY 2018 IRF PPS proposed rule
(82 FR 20726 through 20727). In that proposed rule, we stated that the
proposal was informed by input we received through a call for input
published on the CMS Measures Management System Blueprint website.
Input submitted from August 12 to September 12, 2016 expressed support
for the IV Chemotherapy data element and suggested it be included as
standardized patient assessment data. We also stated that those
commenters had noted that assessing the use of chemotherapy services is
relevant to share across the care continuum to facilitate care
coordination and care transitions and noted the validity of the data
element. Commenters also noted the importance of capturing all types of
chemotherapy, regardless of route, and stated that collecting data only
on patients and residents who received chemotherapy by IV would limit
the usefulness of this standardized data element. A summary report for
the August 12 to September 12, 2016 public comment period titled
``SPADE August 2016 Public Comment Summary Report'' is available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general; no additional
comments were received that were specific to the Chemotherapy data
element.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Chemotherapy data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the
Chemotherapy data element to be feasible and reliable for use with PAC
patients and residents. More information about the performance of the
Chemotherapy data element in the National Beta Test can be found in the
document titled ``Proposed Specifications for IRF QRP Quality Measures
and Standardized Patient Assessment Data Elements,'' available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP members
did not specifically discuss the Chemotherapy data element, the TEP
members supported the assessment of the special services, treatments,
and interventions included in the National Beta Test with respect to
both admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our ongoing SPADE development efforts.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present the results of the National
Beta Test and solicit additional comments. General input on the testing
and item development process and concerns about burden were received
from stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
[[Page 17299]]
Taking together the importance of assessing for chemotherapy,
stakeholder input, and strong test results, we are proposing that the
Chemotherapy (IV, Oral, Other) data element with a principal data
element and three sub-elements meet the definition of standardized
patient assessment data with respect to special services, treatments,
and interventions under section 1899B(b)(1)(B)(iii) of the Act and to
adopt the Chemotherapy (IV, Oral, Other) data element as standardized
patient assessment data for use in the IRF QRP.
Cancer Treatment: Radiation
We are proposing that the Radiation data element meets the
definition of standardized patient assessment data with respect to
special services, treatments, and interventions under section
1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20727
through 20728), radiation is a type of cancer treatment that uses high-
energy radioactivity to stop cancer by damaging cancer cell DNA, but it
can also damage normal cells. Radiation is an important therapy for
particular types of cancer, and the resource utilization is high, with
frequent radiation sessions required, often daily for a period of
several weeks. Assessing whether a patient or resident is receiving
radiation therapy is important to determine resource utilization
because PAC patients and residents will need to be transported to and
from radiation treatments, and monitored and treated for side effects
after receiving this intervention. Therefore, assessing the receipt of
radiation therapy, which would compete with other care processes given
the time burden, would be important for care planning and care
coordination by PAC providers.
The proposed data element consists of the single Radiation data
element. The Radiation data element is currently in use in the MDS in
SNFs. For more information on the Radiation data element, we refer
readers to the document titled ``Proposed Specifications for IRF QRP
Quality Measures and Standardized Patient Assessment Data Elements,''
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Radiation data element was first proposed as a standardized
patient assessment data element in the FY 2018 IRF PPS proposed rule
(82 FR 20727 through 20728). In that proposed rule, we stated that the
proposal was informed by input we received through a call for input
published on the CMS Measures Management System Blueprint website.
Input submitted from August 12 to September 12, 2016 expressed support
for the Radiation data element, noting its importance and clinical
usefulness for patients and residents in PAC settings, due to the side
effects and consequences of radiation treatment on patients and
residents that need to be considered in care planning and care
transitions, the feasibility of the item, and the potential for it to
improve quality. A summary report for the August 12 to September 12,
2016 public comment period titled ``SPADE August 2016 Public Comment
Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general; no additional
comments were received that were specific to the Radiation data
element.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Radiation data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the Radiation
data element to be feasible and reliable for use with PAC patients and
residents. More information about the performance of the Radiation data
element in the National Beta Test can be found in the document titled
``Proposed Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP members
did not specifically discuss the Radiation data element, the TEP
members supported the assessment of the special services, treatments,
and interventions included in the National Beta Test with respect to
both admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our ongoing SPADE development efforts.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present results of the National Beta
Test and solicit additional comments. General input on the testing and
item development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for radiation,
stakeholder input, and strong test results, we are proposing that the
Radiation data element meets the definition of standardized patient
assessment data with respect to special services, treatments, and
interventions under section 1899B(b)(1)(B)(iii) of the Act and to adopt
the Radiation data element as standardized patient assessment data for
use in the IRF QRP.
Respiratory Treatment: Oxygen Therapy (Intermittent,
Continuous, High-concentration Oxygen Delivery System)
We are proposing that the Oxygen Therapy (Intermittent, Continuous,
High-concentration Oxygen Delivery System) data element meets the
definition of standardized patient assessment data with respect to
special services, treatments, and interventions under section
1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20728), we
proposed a similar data element related to oxygen therapy. Oxygen
therapy provides a patient or resident with extra oxygen when medical
conditions such as chronic obstructive pulmonary
[[Page 17300]]
disease, pneumonia, or severe asthma prevent the patient or resident
from getting enough oxygen from breathing. Oxygen administration is a
resource-intensive intervention, as it requires specialized equipment
such as a source of oxygen, delivery systems (for example, oxygen
concentrator, liquid oxygen containers, and high-pressure systems), the
patient interface (for example, nasal cannula or mask), and other
accessories (for example, regulators, filters, tubing). The data
element proposed here captures patient or resident use of three types
of oxygen therapy (intermittent, continuous, and high-concentration
oxygen delivery system), which reflects the intensity of care needed,
including the level of monitoring and bedside care required. Assessing
the receipt of this service is important for care planning and resource
use for PAC providers.
The proposed data element, Oxygen Therapy, consists of the
principal Oxygen Therapy data element and three response option sub-
elements: Continuous (whether the oxygen was delivered continuously,
typically defined as > =14 hours per day); Intermittent; or High-
concentration Oxygen Delivery System. Based on public comments and
input from expert advisors about the importance and clinical usefulness
of documenting the extent of oxygen use, we added a third sub-element,
high-concentration oxygen delivery system, to the sub-elements, which
previously included only intermittent and continuous. If the assessor
indicates that the patient is receiving oxygen therapy on the principal
oxygen therapy data element, the assessor then would indicate the type
of oxygen the patient receives (for example, Intermittent, Continuous,
High-concentration oxygen delivery system).
These three proposed sub-elements were developed based on similar
data elements that assess oxygen therapy, currently in use in the MDS
in SNFs (``Oxygen Therapy''), previously used in the OASIS (``Oxygen
(intermittent or continuous)''), and a data element tested in the PAC
PRD that focused on intensive oxygen therapy (``High O2 Concentration
Delivery System with FiO2 > 40 percent''). For more information on the
proposed Oxygen Therapy (Continuous, Intermittent, High-concentration
oxygen delivery system) data element, we refer readers to the document
titled ``Proposed Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Oxygen Therapy (Intermittent, Continuous) data element was
first proposed as standardized patient assessment data in the FY 2018
IRF PPS proposed rule (82 FR 20728). In that proposed rule, we stated
that the proposal was informed by input we received on the single data
element, Oxygen (inclusive of intermittent and continuous oxygen use),
through a call for input published on the CMS Measures Management
System Blueprint website. Input submitted from August 12 to September
12, 2016, expressed the importance of the Oxygen data element, noting
feasibility of this item in PAC, and the relevance of it to
facilitating care coordination and supporting care transitions, but
suggesting that the extent of oxygen use be documented. A summary
report for the August 12 to September 12, 2016 public comment period
titled ``SPADE August 2016 Public Comment Summary Report'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general; no additional
comments were received that were specific to the Oxygen Therapy
(Intermittent, Continuous) data element.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Oxygen Therapy data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the Oxygen
Therapy data element to be feasible and reliable for use with PAC
patients and residents. More information about the performance of the
Oxygen Therapy data element in the National Beta Test can be found in
the document titled ``Proposed Specifications for IRF QRP Quality
Measures and Standardized Patient Assessment Data Elements,'' available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Oxygen Therapy data element, the TEP supported
the assessment of the special services, treatments, and interventions
included in the National Beta Test with respect to both admission and
discharge. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our ongoing SPADE development efforts.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present the results of the National
Beta Test and solicit additional comments. General input on the testing
and item development process and concerns about burden were received
from stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing oxygen therapy,
stakeholder input, and strong test results, we are proposing that the
Oxygen Therapy (Intermittent, Continuous, High-concentration Oxygen
Delivery System) data element with a principal data element and three
sub-elements meets the definition of standardized patient assessment
data with respect to special services, treatments, and interventions
under section 1899B(b)(1)(B)(iii) of the Act and to adopt the Oxygen
Therapy (Intermittent, Continuous, High-concentration Oxygen Delivery
System) data element as standardized patient assessment data for use in
the IRF QRP.
Respiratory Treatment: Suctioning (Scheduled, as Needed)
We are proposing that the Suctioning (Scheduled, As needed) data
element meets the definition of standardized
[[Page 17301]]
patient assessment data with respect to special services, treatments,
and interventions under section 1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20728
through 20729), suctioning is a process used to clear secretions from
the airway when a person cannot clear those secretions on his or her
own. It is done by aspirating secretions through a catheter connected
to a suction source. Types of suctioning include oropharyngeal and
nasopharyngeal suctioning, nasotracheal suctioning, and suctioning
through an artificial airway such as a tracheostomy tube. Oropharyngeal
and nasopharyngeal suctioning are a key part of many patients' or
residents' care plans, both to prevent the accumulation of secretions
than can lead to aspiration pneumonias (a common condition in patients
and residents with inadequate gag reflexes), and to relieve
obstructions from mucus plugging during an acute or chronic respiratory
infection, which often lead to desaturations and increased respiratory
effort. Suctioning can be done on a scheduled basis if the patient is
judged to clinically benefit from regular interventions, or can be done
as needed when secretions become so prominent that gurgling or choking
is noted, or a sudden desaturation occurs from a mucus plug. As
suctioning is generally performed by a care provider rather than
independently, this intervention can be quite resource intensive if it
occurs every hour, for example, rather than once a shift. It also
signifies an underlying medical condition that prevents the patient
from clearing his/her secretions effectively (such as after a stroke,
or during an acute respiratory infection). Generally, suctioning is
necessary to ensure that the airway is clear of secretions which can
inhibit successful oxygenation of the individual. The intent of
suctioning is to maintain a patent airway, the loss of which can lead
to death or complications associated with hypoxia.
The Suctioning (Scheduled, As needed) data element consists of a
principal data element, and two sub-elements: Scheduled and As needed.
These sub-elements capture two types of suctioning. Scheduled indicates
suctioning based on a specific frequency, such as every hour. As needed
means suctioning only when indicated. If the assessor indicates that
the patient is receiving suctioning on the principal Suctioning data
element, the assessor would then indicate the frequency (for example,
Scheduled, As needed). The proposed data element is based on an item
currently in use in the MDS in SNFs which does not include our proposed
two sub-elements, as well as data elements tested in the PAC PRD that
focused on the frequency of suctioning required for patients and
residents with tracheostomies (``Trach Tube with Suctioning: Specify
most intensive frequency of suctioning during stay [Every __hours]'').
For more information on the Suctioning data element, we refer readers
to the document titled ``Proposed Specifications for IRF QRP Quality
Measures and Standardized Patient Assessment Data Elements,'' available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Suctioning data element was first proposed as standardized
patient assessment data elements in the FY 2018 IRF PPS proposed rule
(82 FR 20728 through 20729). In that proposed rule, we stated that the
proposal was informed by input we received through a call for input
published on the CMS Measures Management System Blueprint website.
Input submitted from August 12 to September 12, 2016 expressed support
for the Suctioning data element. The input noted the feasibility of
this item in PAC, and the relevance of this data element to
facilitating care coordination and supporting care transitions.
We also stated that those commenters had suggested that we examine
the frequency of suctioning to better understand the use of staff time,
the impact on a patient or resident's capacity to speak and swallow,
and intensity of care required. Based on these comments, we decided to
add two sub-elements (Scheduled and As needed) to the suctioning
element. The proposed Suctioning data element includes both the
principal Suctioning data element that is included on the MDS in SNFs
and two sub-elements, Scheduled and As needed. A summary report for the
August 12 to September 12, 2016 public comment period titled ``SPADE
August 2016 Public Comment Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general; no additional
comments were received that were specific to the Suctioning data
element.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Suctioning data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the Suctioning
data element to be feasible and reliable for use with PAC patients and
residents. More information about the performance of the Suctioning
data element in the National Beta Test can be found in the document
titled ``Proposed Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Suctioning data element, the TEP supported the
assessment of the special services, treatments, and interventions
included in the National Beta Test with respect to both admission and
discharge. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our ongoing SPADE development efforts.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present the results of the National
Beta Test and solicited additional comments. General input on the
testing and item development process and concerns about burden were
received from stakeholders during this meeting and via email through
February 1, 2019. A summary of the public input received from the
November 27, 2018 stakeholder meeting titled ``Input on Standardized
Patient Assessment Data Elements (SPADEs) Received After November 27,
2018 Stakeholder Meeting'' is available at https://www.cms.gov/
Medicare/Quality-
[[Page 17302]]
Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-
Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for suctioning,
stakeholder input, and strong test results, we are proposing that the
Suctioning (Scheduled, As needed) data element with a principal data
element and two sub-elements meets the definition of standardized
patient assessment data with respect to special services, treatments,
and interventions under section 1899B(b)(1)(B)(iii) of the Act and to
adopt the Suctioning (Scheduled, As needed) data element as
standardized patient assessment data for use in the IRF QRP.
Respiratory Treatment: Tracheostomy Care
We are proposing that the Tracheostomy Care data element meets the
definition of standardized patient assessment data with respect to
special services, treatments, and interventions under section
1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20729
through 20730), a tracheostomy provides an air passage to help a
patient or resident breathe when the usual route for breathing is
obstructed or impaired. Generally, in all of these cases, suctioning is
necessary to ensure that the tracheostomy is clear of secretions, which
can inhibit successful oxygenation of the individual. Often,
individuals with tracheostomies are also receiving supplemental
oxygenation. The presence of a tracheostomy, albeit permanent or
temporary, warrants careful monitoring and immediate intervention if
the tracheostomy becomes occluded or if the device used becomes
dislodged. While in rare cases the presence of a tracheostomy is not
associated with increased care demands (and in some of those instances,
the care of the ostomy is performed by the patient) in general the
presence of such as device is associated with increased patient risk,
and clinical care services will necessarily include close monitoring to
ensure that no life-threatening events occur as a result of the
tracheostomy. In addition, tracheostomy care, which primarily consists
of cleansing, dressing changes, and replacement of the tracheostomy
cannula (tube), is a critical part of the care plan. Regular cleansing
is important to prevent infection, such as pneumonia, and to prevent
any occlusions with which there are risks for inadequate oxygenation.
The proposed data element consists of the single Tracheostomy Care
data element. The proposed data element is currently in use in the MDS
in SNFs (``Tracheostomy care''). For more information on the
Tracheostomy Care data element, we refer readers to the document titled
``Proposed Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Tracheostomy Care data element was first proposed as a
standardized patient assessment data element in the FY 2018 IRF PPS
proposed rule (82 FR 20729 through 20730). In that proposed rule, we
stated that the proposal was informed by input we received on the
Tracheostomy Care data element through a call for input published on
the CMS Measures Management System Blueprint website. Input submitted
from August 12 to September 12, 2016 expressed support for this data
element, noting the feasibility of this item in PAC, and the relevance
of this data element to facilitating care coordination and supporting
care transitions. A summary report for the August 12 to September 12,
2016 public comment period titled ``SPADE August 2016 Public Comment
Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general; no additional
comments were received that were specific to the Tracheostomy Care data
element.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Tracheostomy Care data element was included in the National Beta Test
of candidate data elements conducted by our data element contractor
from November 2017 to August 2018. Results of this test found the
Tracheostomy Care data element to be feasible and reliable for use with
PAC patients and residents. More information about the performance of
the Tracheostomy Care data element in the National Beta Test can be
found in the document titled ``Proposed Specifications for IRF QRP
Quality Measures and Standardized Patient Assessment Data Elements,''
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Tracheostomy Care data element, the TEP
supported the assessment of the special services, treatments, and
interventions included in the National Beta Test with respect to both
admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our ongoing SPADE development efforts.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present the results of the National
Beta Test and solicit additional comments. General input on the testing
and item development process and concerns about burden were received
from stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for tracheostomy care,
stakeholder input, and strong test results, we are proposing that the
Tracheostomy Care data element meets the definition of standardized
patient assessment data with respect to special services, treatments,
and interventions under section 1899B(b)(1)(B)(iii) of the Act and to
adopt the Tracheostomy Care data element as standardized patient
assessment data for use in the IRF QRP.
[[Page 17303]]
Respiratory Treatment: Non-Invasive Mechanical Ventilator
(BiPAP, CPAP)
We are proposing that the Non-invasive Mechanical Ventilator
(Bilevel Positive Airway Pressure [BiPAP], Continuous Positive Airway
Pressure [CPAP]) data element meets the definition of standardized
patient assessment data with respect to special services, treatments,
and interventions under section 1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20730),
BiPAP and CPAP are respiratory support devices that prevent the airways
from closing by delivering slightly pressurized air via electronic
cycling throughout the breathing cycle (BiPAP) or through a mask
continuously (CPAP). Assessment of non-invasive mechanical ventilation
is important in care planning, as both CPAP and BiPAP are resource-
intensive (although less so than invasive mechanical ventilation) and
signify underlying medical conditions about the patient or resident who
requires the use of this intervention. Particularly when used in
settings of acute illness or progressive respiratory decline,
additional staff (for example, respiratory therapists) are required to
monitor and adjust the CPAP and BiPAP settings and the patient or
resident may require more nursing resources.
The proposed data element, Non-invasive Mechanical Ventilator
(BIPAP, CPAP), consists of the principal Non-invasive Mechanical
Ventilator data element and two response option sub-elements: BiPAP and
CPAP. If the assessor indicates that the patient is receiving non-
invasive mechanical ventilation on the principal Non-invasive
Mechanical Ventilator data element, the assessor would then indicate
which type (for example, BIPAP, CPAP). Data elements that assess non-
invasive mechanical ventilation are currently included on LCDS for the
LTCH setting (``Non-invasive Ventilator (BIPAP, CPAP)''), and the MDS
for the SNF setting (``Non-invasive Mechanical Ventilator (BiPAP/
CPAP)''). For more information on the Non-invasive Mechanical
Ventilator (BIPAP, CPAP) data element, we refer readers to the document
titled ``Proposed Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Non-invasive Mechanical Ventilator data element was first
proposed as standardized patient assessment data elements in the FY
2018 IRF PPS proposed rule (82 FR 20730). In that proposed rule, we
stated that the proposal was informed by input we received through a
call for input published on the CMS Measures Management System
Blueprint website. Input submitted from August 12 to September 12, 2016
on a single data element, BiPAP/CPAP, that captures equivalent clinical
information but uses a different label than the data element currently
used in the MDS in SNFs and LCDS, expressed support for this data
element, noting the feasibility of these items in PAC, and the
relevance of this data element for facilitating care coordination and
supporting care transitions. In addition, we also stated that some
commenters supported separating out BiPAP and CPAP as distinct sub-
elements, as they are therapies used for different types of patients
and residents. A summary report for the August 12 to September 12, 2016
public comment period titled ``SPADE August 2016 Public Comment Summary
Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general. One commenter
noted appreciation of the revisions to the Non-invasive Mechanical
Ventilator data element in response to comments submitted during a
public input period held from August 12 to September 12, 2016.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Non-invasive Mechanical Ventilator data element was included in the
National Beta Test of candidate data elements conducted by our data
element contractor from November 2017 to August 2018. Results of this
test found the Non-invasive Mechanical Ventilator data element to be
feasible and reliable for use with PAC patients and residents. More
information about the performance of the Non-invasive Mechanical
Ventilator data element in the National Beta Test can be found in the
document titled ``Proposed Specifications for IRF QRP Quality Measures
and Standardized Patient Assessment Data Elements,'' available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Non-invasive Mechanical Ventilator data
element, the TEP supported the assessment of the special services,
treatments, and interventions included in the National Beta Test with
respect to both admission and discharge. A summary of the September 17,
2018 TEP meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our ongoing SPADE development efforts.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present the results of the National
Beta Test and solicit additional comments. General input on the testing
and item development process and concerns about burden were received
from stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for non-invasive
mechanical ventilation, stakeholder input, and strong test results, we
are proposing that the Non-invasive Mechanical Ventilator (BiPAP, CPAP)
data element with a principal data element and two sub-elements meets
the definition of standardized patient assessment data with respect to
special services, treatments, and interventions under section
1899B(b)(1)(B)(iii) of the Act and to adopt the Non-invasive Mechanical
Ventilator (BiPAP, CPAP) data element as standardized patient
assessment data for use in the IRF QRP.
[[Page 17304]]
Respiratory Treatment: Invasive Mechanical Ventilator
We are proposing that the Invasive Mechanical Ventilator data
element meets the definition of standardized patient assessment data
with respect to special services, treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20730
through 20731), invasive mechanical ventilation includes ventilators
and respirators that ventilate the patient through a tube that extends
via the oral airway into the pulmonary region or through a surgical
opening directly into the trachea. Thus, assessment of invasive
mechanical ventilation is important in care planning and risk
mitigation. Ventilation in this manner is a resource-intensive therapy
associated with life-threatening conditions without which the patient
or resident would not survive. However, ventilator use has inherent
risks requiring close monitoring. Failure to adequately care for the
patient or resident who is ventilator dependent can lead to iatrogenic
events such as death, pneumonia, and sepsis. Mechanical ventilation
further signifies the complexity of the patient's underlying medical or
surgical condition. Of note, invasive mechanical ventilation is
associated with high daily and aggregate costs.\91\
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\91\ Wunsch, H., Linde-Zwirble, W.T., Angus, D.C., Hartman,
M.E., Milbrandt, E.B., & Kahn, J.M. (2010). ``The epidemiology of
mechanical ventilation use in the United States.'' Critical Care Med
38(10): 1947-1953.
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The proposed data element, Invasive Mechanical Ventilator, consists
of a single data element. Data elements that capture invasive
mechanical ventilation are currently in use in the MDS in SNFs and LCDS
in LTCHs. For more information on the Invasive Mechanical Ventilator
data element, we refer readers to the document titled ``Proposed
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Invasive Mechanical Ventilator data element was first proposed
as a standardized patient assessment data element in the FY 2018 IRF
PPS proposed rule (82 FR 20730 through 20731). In that proposed rule,
we stated that the proposal was informed by input we received on data
elements that assess invasive ventilator use and weaning status that
were tested in the PAC PRD (``Ventilator--Weaning'' and ``Ventilator--
Non-Weaning'') through a call for input published on the CMS Measures
Management System Blueprint website. Input submitted from August 12 to
September 12, 2016, expressed support for this data element,
highlighting the importance of this information in supporting care
coordination and care transitions. We also stated that some commenters
had expressed concern about the appropriateness for standardization
given: The prevalence of ventilator weaning across PAC providers; the
timing of administration; how weaning is defined; and how weaning
status in particular relates to quality of care. These public comments
guided our decision to propose a single data element focused on current
use of invasive mechanical ventilation only, which does not attempt to
capture weaning status. A summary report for the August 12 to September
12, 2016 public comment period titled ``SPADE August 2016 Public
Comment Summary Report'' we received is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general. Two commenters
noted their appreciation of the revisions to the Invasive Mechanical
Ventilator data element in response to comments submitted during a
public input period held from August 12 to September 12, 2016.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Invasive Mechanical Ventilator data element was included in the
National Beta Test of candidate data elements conducted by our data
element contractor from November 2017 to August 2018. Results of this
test found the Invasive Mechanical Ventilator data element to be
feasible and reliable for use with PAC patients and residents. More
information about the performance of the Invasive Mechanical Ventilator
data element in the National Beta Test can be found in the document
titled ``Proposed Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data element. Although the TEP did not
specifically discuss the Invasive Mechanical Ventilator data element,
the TEP supported the assessment of the special services, treatments,
and interventions included in the National Beta Test with respect to
both admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our ongoing SPADE development efforts.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present results of the National Beta
Test and solicit additional comments. General input on the testing and
item development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for invasive mechanical
ventilation, stakeholder input, and strong test results, we are
proposing that the Invasive Mechanical Ventilator data element that
assesses the use of an invasive mechanical ventilator meets the
definition of standardized patient assessment data with respect to
special services, treatments, and interventions under section
1899B(b)(1)(B)(iii) of the Act and to adopt the Invasive Mechanical
Ventilator data element as standardized patient assessment data for use
in the IRF QRP.
[[Page 17305]]
Intravenous (IV) Medications (Antibiotics, Anticoagulants,
Vasoactive Medications, Other)
We are proposing that the IV Medications (Antibiotics,
Anticoagulants, Vasoactive Medications, Other) data element meets the
definition of standardized patient assessment data with respect to
special services, treatments, and interventions under section
1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20731
through 20732), when we proposed a similar data element related to IV
medications, IV medications are solutions of a specific medication (for
example, antibiotics, anticoagulants) administered directly into the
venous circulation via a syringe or intravenous catheter. IV
medications are administered via intravenous push, single,
intermittent, or continuous infusion through a catheter placed into the
vein. Further, IV medications are more resource intensive to administer
than oral medications, and signify a higher patient complexity (and
often higher severity of illness).
The clinical indications for each of the sub-elements of the IV
Medications data element (Antibiotics, Anticoagulants, Vasoactive
Medications, and Other) are very different. IV antibiotics are used for
severe infections when the bioavailability of the oral form of the
medication would be inadequate to kill the pathogen or an oral form of
the medication does not exist. IV anticoagulants refer to anti-clotting
medications (that is, ``blood thinners''). IV anticoagulants are
commonly used for hospitalized patients who have deep venous
thrombosis, pulmonary embolism, or myocardial infarction, as well as
those undergoing interventional cardiac procedures. Vasoactive
medications refer to the IV administration of vasoactive drugs,
including vasopressors, vasodilators, and continuous medication for
pulmonary edema, which increase or decrease blood pressure or heart
rate. The indications, risks, and benefits of each of these classes of
IV medications are distinct, making it important to assess each
separately in PAC. Knowing whether or not patients and residents are
receiving IV medication and the type of medication provided by each PAC
provider will improve quality of care.
The IV Medications (Antibiotics, Anticoagulants, Vasoactive
Medications, and Other) data element we are proposing consists of a
principal data element (IV Medications) and four response option sub-
elements: Antibiotics, Anticoagulants, Vasoactive Medications, and
Other. The Vasoactive Medications sub-element was not proposed in the
FY 2018 IRF PPS proposed rule (82 FR 20731 through 20732). We added the
Vasoactive Medications sub-element to our proposal in order to
harmonize the proposed IV Mediciations element with the data currently
collected in the LCDS.
If the assessor indicates that the patient is receiving IV
medications on the principal IV Medications data element, the assessor
would then indicate which types of medications (for example,
Antibiotics, Anticoagulants, Vasoactive Medications, Other). An IV
Medications data element is currently in use on the MDS in SNFs and
there is a related data element in OASIS that collects information on
Intravenous and Infusion Therapies. For more information on the IV
Medications (Antibiotics, Anticoagulants, Vasoactive Medications,
Other) data element, we refer readers to the document titled ``Proposed
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
An IV Medications data element was first proposed as standardized
patient assessment data element in the FY 2018 IRF PPS proposed rule
(82 FR 20731 through 20732). In that proposed rule, we stated that the
proposal was informed by input we received on Vasoactive Medications
through a call for input published on the CMS Measures Management
System Blueprint website. Input submitted from August 12 to September
12, 2016 supported this data element with one noting the importance of
this data element in supporting care transitions. We also stated that
those commenters had criticized the need for collecting specifically
Vasoactive Medications, giving feedback that the data element was too
narrowly focused. In addition, public comment received indicated that
the clinical significance of vasoactive medications administration
alone was not high enough in PAC to merit mandated assessment, noting
that related and more useful information could be captured in an item
that assessed all IV medication use. A summary report for the August 12
to September 12, 2016 public comment period titled ``SPADE August 2016
Public Comment Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general; no additional
comments were received that were specific to the IV Medications data
element.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
IV Medications data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the IV
Medications data element to be feasible and reliable for use with PAC
patients and residents. More information about the performance of the
IV Medications data element in the National Beta Test can be found in
the document titled ``Proposed Specifications for IRF QRP Quality
Measures and Standardized Patient Assessment Data Elements,'' available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the IV Medications data element, the TEP supported
the assessment of the special services, treatments, and interventions
included in the National Beta Test with respect to both admission and
discharge. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our ongoing SPADE development efforts.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present the results of the National
Beta Test and solicit additional comments. General input on the testing
and item development process and concerns about burden were received
[[Page 17306]]
from stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for IV medications,
stakeholder input, and strong test results, we are proposing that the
IV Medications (Antibiotics, Anticoagulants, Vasoactive Medications,
Other) data element with a principal data element and four sub-elements
meets the definition of standardized patient assessment data with
respect to special services, treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act and to adopt the IV Medications
(Antibiotics, Anticoagulants, Vasoactive Medications, Other) data
element as standardized patient assessment data for use in the IRF QRP.
Transfusions
We are proposing that the Transfusions data element meets the
definition of standardized patient assessment data with respect to
special services, treatments, and interventions under section
1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20732),
transfusion refers to introducing blood or blood products into the
circulatory system of a person. Blood transfusions are based on
specific protocols, with multiple safety checks and monitoring required
during and after the infusion in case of adverse events. Coordination
with the provider's blood bank is necessary, as well as documentation
by clinical staff to ensure compliance with regulatory requirements. In
addition, the need for transfusions signifies underlying patient
complexity that is likely to require care coordination and patient
monitoring, and impacts planning for transitions of care, as
transfusions are not performed by all PAC providers.
The proposed data element consists of the single Transfusions data
element. A data element on transfusion is currently in use in the MDS
in SNFs (``Transfusions'') and a data element tested in the PAC PRD
(``Blood Transfusions'') was found feasible for use in each of the four
PAC settings. For more information on the Transfusions data element, we
refer readers to the document titled ``Proposed Specifications for IRF
QRP Quality Measures and Standardized Patient Assessment Data
Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Transfusions data element was first proposed as a standardized
patient assessment data element in the FY 2018 IRF PPS proposed rule
(82 FR 20732). In response to our proposal in the FY 2018 IRF PPS
proposed rule, we received public comments in support of the special
services, treatments, and interventions data elements in general; no
additional comments were received that were specific to the
Transfusions data element.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Transfusions data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the
Transfusions data element to be feasible and reliable for use with PAC
patients and residents. More information about the performance of the
Transfusions data element in the National Beta Test can be found in the
document titled ``Proposed Specifications for IRF QRP Quality Measures
and Standardized Patient Assessment Data Elements,'' available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Transfusions data element, the TEP supported
the assessment of the special services, treatments, and interventions
included in the National Beta Test with respect to both admission and
discharge. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our ongoing SPADE development efforts.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present the results of the National
Beta Test and solicit additional comments. General input on the testing
and item development process and concerns about burden were received
from stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for transfusions,
stakeholder input, and strong test results, we are proposing that the
Transfusions data element meets the definition of standardized patient
assessment data with respect to special services, treatments, and
interventions under section 1899B(b)(1)(B)(iii) of the Act and to adopt
the Transfusions data element as standardized patient assessment data
for use in the IRF QRP.
Dialysis (Hemodialysis, Peritoneal Dialysis)
We are proposing that the Dialysis (Hemodialysis, Peritoneal
dialysis) data element meets the definition of standardized patient
assessment data with respect to special services, treatments, and
interventions under section 1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20732
through 20733), dialysis is a treatment primarily used to provide
replacement for lost kidney function. Both forms of dialysis
(hemodialysis and peritoneal dialysis) are resource intensive, not only
during the actual dialysis process but before, during, and following.
Patients and residents who need and undergo dialysis procedures are at
high risk for physiologic and hemodynamic instability from fluid shifts
and electrolyte disturbances, as well as infections that can lead to
sepsis. Further, patients or residents receiving hemodialysis are often
transported to a different facility, or at a minimum, to a different
location in the same facility for treatment. Close monitoring for fluid
[[Page 17307]]
shifts, blood pressure abnormalities, and other adverse effects is
required prior to, during, and following each dialysis session. Nursing
staff typically perform peritoneal dialysis at the bedside, and as with
hemodialysis, close monitoring is required.
The proposed data element, Dialysis (Hemodialysis, Peritoneal
dialysis) consists of the principal Dialysis data element and two
response option sub-elements: Hemodialysis and Peritoneal dialysis. If
the assessor indicates that the patient is receiving dialysis on the
principal Dialysis data element, the assessor would then indicate which
type (Hemodialysis or Peritoneal dialysis). The principal Dialysis data
element is currently included on the MDS in SNFs and the LCDS for LTCHs
and assesses the overall use of dialysis.
As the result public feedback described below, in this proposed
rule, we are proposing a data element that includes the principal
Dialysis data element and two sub-elements (Hemodialysis and Peritoneal
dialysis). For more information on the Dialysis data element, we refer
readers to the document titled ``Proposed Specifications for IRF QRP
Quality Measures and Standardized Patient Assessment Data Elements,''
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Dialysis data element was first proposed as standardized
patient assessment data in the FY 2018 IRF PPS proposed rule (82 FR
20732 through 20733). In that proposed rule, we stated that the
proposal was informed by input we received on a singular Hemodialysis
data element through a call for input published on the CMS Measures
Management System Blueprint website. Input submitted from August 12 to
September 12, 2016 supported the assessment of hemodialysis and
recommended that the data element be expanded to include peritoneal
dialysis. We also stated that those commenters had supported the
singular Hemodialysis data element, noting the relevance of this
information for sharing across the care continuum to facilitate care
coordination and care transitions, the potential for this data element
to be used to improve quality, and the feasibility for use in PAC. In
addition, we received comments that the item would be useful in
improving patient and resident transitions of care. We also noted that
several commenters had stated that peritoneal dialysis should be
included in a standardized data element on dialysis and recommended
collecting information on peritoneal dialysis in addition to
hemodialysis. The rationale for including peritoneal dialysis from
commenters included the fact that patients and residents receiving
peritoneal dialysis will have different needs at post-acute discharge
compared to those receiving hemodialysis or not having any dialysis.
Based on these comments, the Hemodialysis data element was expanded to
include a principal Dialysis data element and two sub-elements,
Hemodialysis and Peritoneal dialysis. We are proposing the version of
the Dialysis element that includes two types of dialysis. A summary
report for the August 12 to September 12, 2016 public comment period
titled ``SPADE August 2016 Public Comment Summary Report'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received comments in support of the special services, treatments,
and interventions data elements in general. One commenter noted that
they appreciated the revisions to the Dialysis data element in response
to comments submitted during a public input period held from August 12
to September 12, 2016.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Dialysis data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the Dialysis
data element to be feasible and reliable for use with PAC patients and
residents. More information about the performance of the Dialysis data
element in the National Beta Test can be found in the document titled
``Proposed Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although they did not
specifically discuss the Dialysis data element, the TEP supported the
assessment of the special services, treatments, and interventions
included in the National Beta Test with respect to both admission and
discharge. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our ongoing SPADE development efforts.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present the results of the National
Beta Test and solicit additional comments. General input on the testing
and item development process and concerns about burden were received
from stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for dialysis,
stakeholder input, and strong test results, we are proposing that the
Dialysis (Hemodialysis, Peritoneal dialysis) data element with a
principal data element and two sub-elements meets the definition of
standardized patient assessment data with respect to special services,
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the
Act and to adopt the Dialysis (Hemodialysis, Peritoneal dialysis) data
element as standardized patient assessment data for use in the IRF QRP.
Intravenous (IV) Access (Peripheral IV, Midline, Central line)
We are proposing that the IV Access (Peripheral IV, Midline,
Central line) data element meets the definition of standardized patient
assessment data with respect to special services, treatments, and
interventions under section 1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20733
through 20734), patients or residents with central lines, including
those peripherally inserted or who have
[[Page 17308]]
subcutaneous central line ``port'' access, always require vigilant
nursing care to keep patency of the lines and ensure that such invasive
lines remain free from any potentially life-threatening events such as
infection, air embolism, or bleeding from an open lumen. Clinically
complex patients and residents are likely to be receiving medications
or nutrition intravenously. The sub-elements included in the IV Access
data elements distinguish between peripheral access and different types
of central access. The rationale for distinguishing between a
peripheral IV and central IV access is that central lines confer higher
risks associated with life-threatening events such as pulmonary
embolism, infection, and bleeding.
The proposed data element, IV Access (Peripheral IV, Midline,
Central line), consists of the principal IV Access data element and
three response option sub-elements: Peripheral IV, Midline, and Central
line. The proposed IV Access data element is not currently included on
any of the PAC assessment instruments. For more information on the IV
Access data element, we refer readers to the document titled ``Proposed
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The IV Access data element was first proposed as standardized
patient assessment data elements in the FY 2018 IRF PPS proposed rule
(82 FR 20733 through 20734). In that proposed rule, we stated that the
proposal was informed by input we received on one of the PAC PRD data
elements, Central Line Management, through a call for input published
on the CMS Measures Management System Blueprint website. A central line
is a type of IV access. Input submitted from August 12 to September 12,
2016 supported the assessment of central line management and
recommended that the data element be broadened to also include other
types of IV access. Several commenters noted feasibility and importance
for facilitating care coordination and care transitions. However, a few
commenters recommended that the definition of this data element be
broadened to include peripherally inserted central catheters (``PICC
lines'') and midline IVs. Based on public comment feedback and in
consultation with expert input, described below, we created an
overarching IV Access data element with sub-elements for other types of
IV access in addition to central lines (that is, peripheral IV and
midline). This expanded version of IV Access is the data element being
proposed. A summary report for the August 12 to September 12, 2016
public comment period titled ``SPADE August 2016 Public Comment Summary
Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general. One commenter
noted appreciation of the revisions to the IV Access data element in
response to comments submitted during a public input period held from
August 12 to September 12, 2016.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
IV Access data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the IV Access
data element to be feasible and reliable for use with PAC patients and
residents. More information about the performance of the IV Access data
element in the National Beta Test can be found in the document titled
``Proposed Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the IV Access data element, the TEP supported the
assessment of the special services, treatments, and interventions
included in the National Beta Test with respect to both admission and
discharge. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our ongoing SPADE development efforts.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present results of the National Beta
Test and solicit additional comments. General input on the testing and
item development process and concerns about burden were received from
stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for IV access,
stakeholder input, and strong test results, we are proposing that the
IV access (Peripheral IV, Midline, Central line) data element with a
principal data element and three sub-elements meets the definition of
standardized patient assessment data with respect to special services,
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the
Act and to adopt the IV Access (Peripheral IV, Midline, Central line)
data element as standardized patient assessment data for use in the IRF
QRP.
Nutritional Approach: Parenteral/IV Feeding
We are proposing that the Parenteral/IV Feeding data element meets
the definition of standardized patient assessment data with respect to
special services, treatments, and interventions under section
1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20734),
parenteral nutrition/IV feeding refers to a patient or resident being
fed intravenously using an infusion pump, bypassing the usual process
of eating and digestion. The need for IV/parenteral feeding indicates a
clinical complexity that prevents the patient or resident from meeting
his or her nutritional needs enterally, and is more resource intensive
than other forms of nutrition, as it often requires monitoring of blood
chemistries and the maintenance of a central line. Therefore, assessing
a patient's or resident's need for parenteral feeding is important for
care
[[Page 17309]]
planning and resource use. In addition to the risks associated with
central and peripheral intravenous access, total parenteral nutrition
is associated with significant risks, such as air embolism and sepsis.
The proposed data element consists of the single Parenteral/IV
Feeding data element. The proposed Parenteral/IV Feeding data element
is currently in use in the MDS in SNFs, and equivalent or related data
elements are in use in the LCDS, IRF-PAI, and OASIS. We are proposing
to rename the existing Tube/Parenteral feeding item in the IRF-PAI to
be the Parenteral/IV Feeding data element. For more information on the
Parenteral/IV Feeding data element, we refer readers to the document
titled ``Proposed Specifications for IRF QRP Quality Measures and
Standardized Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Parenteral/IV Feeding data element was first proposed as a
standardized patient assessment data element in the FY 2018 IRF PPS
proposed rule (82 FR 20734). In that proposed rule, we stated that the
proposal was informed by input we received on Total Parenteral
Nutrition (an item with nearly the same meaning as the proposed data
element, but with the label used in the PAC PRD), through a call for
input published on the CMS Measures Management System Blueprint
website. Input submitted from August 12 to September 12, 2016 supported
this data element, noting its relevance to facilitating care
coordination and supporting care transitions. After the public comment
period, the Total Parenteral Nutrition data element was renamed
Parenteral/IV Feeding, to be consistent with how this data element is
referred to in the MDS in SNFs. A summary report for the August 12 to
September 12, 2016 public comment period titled ``SPADE August 2016
Public Comment Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received comments in support of the special services, treatments,
and interventions data elements in general; no additional comments were
received that were specific to the Parenteral/IV Feeding data element.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Parenteral/IV Feeding data element was included in the National Beta
Test of candidate data elements conducted by our data element
contractor from November 2017 to August 2018. Results of this test
found the Parenteral/IV Feeding data element to be feasible and
reliable for use with PAC patients and residents. More information
about the performance of the Parenteral/IV Feeding data element in the
National Beta Test can be found in the document titled ``Proposed
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Parenteral/IV Feeding data element, the TEP
supported the assessment of the special services, treatments, and
interventions included in the National Beta Test with respect to both
admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our ongoing SPADE development efforts.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present the results of the National
Beta Test and solicit additional comments. General input on the testing
and item development process and concerns about burden were received
from stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for parenteral/IV
feeding, stakeholder input, and strong test results, we are proposing
that the Parenteral/IV Feeding data element meets the definition of
standardized patient assessment data with respect to special services,
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the
Act and to adopt the Parenteral/IV Feeding data element as standardized
patient assessment data for use in the IRF QRP.
Nutritional Approach: Feeding Tube
We are proposing that the Feeding Tube data element meets the
definition of standardized patient assessment data with respect to
special services, treatments, and interventions under section
1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20734
through 20735), the majority of patients admitted to acute care
hospitals experience deterioration of their nutritional status during
their hospital stay, making assessment of nutritional status and method
of feeding if unable to eat orally very important in PAC. A feeding
tube can be inserted through the nose or the skin on the abdomen to
deliver liquid nutrition into the stomach or small intestine. Feeding
tubes are resource intensive, and therefore, are important to assess
for care planning and resource use. Patients with severe malnutrition
are at higher risk for a variety of complications.\92\ In PAC settings,
there are a variety of reasons that patients and residents may not be
able to eat orally (including clinical or cognitive status).
---------------------------------------------------------------------------
\92\ Dempsey, D.T., Mullen, J.L., & Buzby, G.P. (1988). ``The
link between nutritional status and clinical outcome: Can
nutritional intervention modify it?'' Am J of Clinical Nutrition,
47(2): 352-356.
---------------------------------------------------------------------------
The proposed data element consists of the single Feeding Tube data
element. The Feeding Tube data element is currently included in the MDS
for SNFs, and in the OASIS for HHAs, where it is labeled Enteral
Nutrition. A related data element, collected in the IRF-PAI for IRFs
(Tube/Parenteral Feeding), assesses use of both feeding tubes and
parenteral nutrition. We are proposing to rename the existing Tube/
Parenteral feeding item in the IRF-PAI to the Feeding Tube data
element. For more information on the Feeding Tube data element, we
refer readers to the document titled
[[Page 17310]]
``Proposed Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Feeding Tube data element was first proposed as a standardized
patient assessment data element in the FY 2018 IRF PPS proposed rule
(82 FR 20734 through 20735). In that proposed rule, we stated that the
proposal was informed by input we received on an Enteral Nutrition data
element (the Enteral Nutrition data item is the same as the data
element we are proposing in this proposed rule, but is used in the
OASIS under a different name) through a call for input published on the
CMS Measures Management System Blueprint website. Input submitted from
August 12 to September 12, 2016 supported the data element, noting the
importance of assessing enteral nutrition status for facilitating care
coordination and care transitions. After the public comment period, the
Enteral Nutrition data element used in public comment was renamed
Feeding Tube, indicating the presence of an assistive device. A summary
report for the August 12 to September 12, 2016 public comment period
titled ``SPADE August 2016 Public Comment Summary Report'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of the special services,
treatments, and interventions data elements in general. In addition, a
commenter recommended that the term ``enteral feeding'' be used instead
of ``feeding tube''.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Feeding Tube data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the Feeding
Tube data element to be feasible and reliable for use with PAC patients
and residents. More information about the performance of the Feeding
Tube data element in the National Beta Test can be found in the
document titled ``Proposed Specifications for IRF QRP Quality Measures
and Standardized Patient Assessment Data Elements,'' available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Feeding Tube data element, the TEP supported
the assessment of the special services, treatments, and interventions
included in the National Beta Test with respect to both admission and
discharge. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our ongoing SPADE development efforts.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present the results of the National
Beta Test and solicit additional comments. General input on the testing
and item development process and concerns about burden were received
from stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for feeding tubes,
stakeholder input, and strong test results, we are proposing that the
Feeding Tube data element meets the definition of standardized patient
assessment data with respect to special services, treatments, and
interventions under section 1899B(b)(1)(B)(iii) of the Act and to adopt
the Feeding Tube data element as standardized patient assessment data
for use in the IRF QRP.
Nutritional Approach: Mechanically Altered Diet
We are proposing that the Mechanically Altered Diet data element
meets the definition of standardized patient assessment data with
respect to special services, treatments, and interventions under
section 1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20735
through 20736), the Mechanically Altered Diet data element refers to
food that has been altered to make it easier for the patient or
resident to chew and swallow, and this type of diet is used for
patients and residents who have difficulty performing these functions.
Patients with severe malnutrition are at higher risk for a variety of
complications.\93\
---------------------------------------------------------------------------
\93\ Dempsey, D.T., Mullen, J.L., & Buzby, G.P. (1988). ``The
link between nutritional status and clinical outcome: Can
nutritional intervention modify it? '' Am J of Clinical Nutrition,
47(2): 352-356.
---------------------------------------------------------------------------
In PAC settings, there are a variety of reasons that patients and
residents may have impairments related to oral feedings, including
clinical or cognitive status. The provision of a mechanically altered
diet may be resource intensive, and can signal difficulties associated
with swallowing/eating safety, including dysphagia. In other cases, it
signifies the type of altered food source, such as ground or puree that
will enable the safe and thorough ingestion of nutritional substances
and ensure safe and adequate delivery of nourishment to the patient.
Often, patients and residents on mechanically altered diets also
require additional nursing support, such as individual feeding or
direct observation, to ensure the safe consumption of the food product.
Therefore, assessing whether a patient or resident requires a
mechanically altered diet is important for care planning and resource
identification.
The proposed data element consists of the single Mechanically
Altered Diet data element. The proposed data element is currently
included on the MDS for SNFs. A related data element (``Modified food
consistency/supervision'') is currently included on the IRF-PAI for
IRFs. Another related data element is included in the OASIS for HHAs
that collects information about independent eating that requires ``a
liquid, pureed or ground meat diet.'' We are proposing to replace the
existing Modified food consistency/supervision data element in the IRF-
PAI to the Mechanically Altered Diet data element. For more information
on the Mechanically Altered Diet data element, we refer readers to the
document titled
[[Page 17311]]
``Proposed Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Mechanically Altered Diet data element was first proposed as a
standardized patient assessment data element in the FY 2018 IRF PPS
proposed rule (82 FR 20735 through 20736). In response to our proposal
in the FY 2018 IRF PPS proposed rule, we received public comments in
support of the special services, treatments, and interventions data
elements in general; no additional comments were received that were
specific to the Mechanically Altered Diet data element.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Mechanically Altered Diet data element was included in the National
Beta Test of candidate data elements conducted by our data element
contractor from November 2017 to August 2018. Results of this test
found the Mechanically Altered Diet data element to be feasible and
reliable for use with PAC patients and residents. More information
about the performance of the Mechanically Altered Diet data element in
the National Beta Test can be found in the document titled ``Proposed
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Mechanically Altered Diet data element, the
TEP supported the assessment of the special services, treatments, and
interventions included in the National Beta Test with respect to both
admission and discharge. A summary of the September 17, 2018 TEP
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our ongoing SPADE development efforts.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present the results of the National
Beta Test and solicit additional comments. General input on the testing
and item development process and concerns about burden were received
from stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for mechanically
altered diet, stakeholder input, and strong test results, we are
proposing that the Mechanically Altered Diet data element meets the
definition of standardized patient assessment data with respect to
special services, treatments, and interventions under section
1899B(b)(1)(B)(iii) of the Act and to adopt the Mechanically Altered
Diet data element as standardized patient assessment data for use in
the IRF QRP.
Nutritional Approach: Therapeutic Diet
We are proposing that the Therapeutic Diet data element meets the
definition of standardized patient assessment data with respect to
special services, treatments, and interventions under section
1899B(b)(1)(B)(iii) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20736), a
therapeutic diet refers to meals planned to increase, decrease, or
eliminate specific foods or nutrients in a patient's or resident's
diet, such as a low-salt diet, for the purpose of treating a medical
condition. The use of therapeutic diets among patients and residents in
PAC provides insight on the clinical complexity of these patients and
residents and their multiple comorbidities. Therapeutic diets are less
resource intensive from the bedside nursing perspective, but do signify
one or more underlying clinical conditions that preclude the patient
from eating a regular diet. The communication among PAC providers about
whether a patient is receiving a particular therapeutic diet is
critical to ensure safe transitions of care.
The proposed data element consists of the single Therapeutic Diet
data element. This data element is currently in use in the MDS in SNFs.
For more information on the Therapeutic Diet data element, we refer
readers to the document titled ``Proposed Specifications for IRF QRP
Quality Measures and Standardized Patient Assessment Data Elements,''
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Therapeutic Diet data element was first proposed as a
standardized patient assessment data element in the FY 2018 IRF PPS
proposed rule (82 FR 20736). In response to our proposal in the FY 2018
IRF PPS proposed rule, we received public comments in support of the
special services, treatments, and interventions data elements in
general. One commenter recommended that the definition of Therapeutic
Diet be aligned with the Academy of Nutrition and Dietetics' definition
and that ``medically altered diet'' be added to the list of nutritional
approaches.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Therapeutic Diet data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the
Therapeutic Diet data element to be feasible and reliable for use with
PAC patients and residents. More information about the performance of
the Therapeutic Diet data element in the National Beta Test can be
found in the document titled ``Proposed Specifications for IRF QRP
Quality Measures and Standardized Patient Assessment Data Elements,''
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. Although the TEP did not
specifically discuss the Therapeutic Diet data element, the TEP
supported the assessment of the special services, treatments, and
interventions included in the National Beta Test with respect to both
admission and discharge. A summary of the September 17, 2018 TEP
[[Page 17312]]
meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our ongoing SPADE development efforts.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present the results of the National
Beta Test and solicit additional comments. General input on the testing
and item development process and concerns about burden were received
from stakeholders during this meeting and via email through February 1,
2019. A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for therapeutic diet,
stakeholder input, and strong test results, we are proposing that the
Therapeutic Diet data element meets the definition of standardized
patient assessment data with respect to special services, treatments,
and interventions under section 1899B(b)(1)(B)(iii) of the Act and to
adopt the Therapeutic Diet data element as standardized patient
assessment data for use in the IRF QRP.
High-Risk Drug Classes: Use and Indication
We are proposing that the High-Risk Drug Classes: Use and
Indication data element meets the definition of standardized patient
assessment data with respect to special services, treatments, and
interventions under section 1899B(b)(1)(B)(iii) of the Act.
Most patients and residents receiving PAC services depend on short-
and long-term medications to manage their medical conditions. However,
as a treatment, medications are not without risk; medications are, in
fact, a leading cause of adverse events. A study by the U.S. Department
of Health and Human Services found that 31 percent of adverse events
that occurred in 2008 among hospitalized Medicare beneficiaries were
related to medication.\94\ Moreover, changes in a patient's condition,
medications, and transitions between care settings put patients at risk
of medication errors and adverse drug events (ADEs). ADEs may be caused
by medication errors such as drug omissions, errors in dosage, and
errors in dosing frequency.\95\
---------------------------------------------------------------------------
\94\ U.S. Department of Health and Human Services. Office of
Inspector General. Daniel R. Levinson. Adverse Events in Hospitals:
National Incidence Among Medicare Beneficiaries. OEI-06-09-00090.
November 2010.
\95\ Boockvar KS, Liu S, Goldstein N, Nebeker J, Siu A, Fried T.
Prescribing discrepancies likely to cause adverse drug events after
patient transfer. Qual Saf Health Care. 2009;18(1):32-6.
---------------------------------------------------------------------------
ADEs are known to occur across different types of healthcare
settings. For example, the incidence of ADEs in the outpatient setting
has been estimated at 1.15 ADEs per 100 person-months,\96\ while the
rate of ADEs in the long-term care setting is approximately 9.80 ADEs
per 100 resident-months.\97\ In the hospital setting, the incidence has
been estimated at 15 ADEs per 100 admissions.\98\ In addition,
approximately half of all hospital-related medication errors and 20
percent of ADEs occur during transitions within, admission to, transfer
to, or discharge from a hospital.99 100 101 ADEs are more
common among older adults, who make up most patients receiving PAC
services. The rate of emergency department visits for ADEs is three
times higher among adults 65 years of age and older compared to that
among those younger than age 65.\102\
---------------------------------------------------------------------------
\96\ Gandhi TK, Seger AC, Overhage JM, et al. Outpatient adverse
drug events identified by screening electronic health records. J
Patient Saf 2010;6:91-6.doi:10.1097/PTS.0b013e3181dcae06.
\97\ Gurwitz JH, Field TS, Judge J, Rochon P, Harrold LR,
Cadoret C, et al. The incidence of adverse drug events in two large
academic long-term care facilities. Am J Med. 2005; 118(3):2518. Epub 2005/03/05. https://doi.org/10.1016/j.amjmed.2004.09.018 PMID: 15745723.
\98\ Hug BL, Witkowski DJ, Sox CM, Keohane CA, Seger DL, Yoon C,
Matheny ME, Bates DW. Occurrence of adverse, often preventable,
events in community hospitals involving nephrotoxic drugs or those
excreted by the kidney. Kidney Int. 2009; 76:1192-1198. [PubMed:
19759525].
\99\ Barnsteiner JH. Medication reconciliation: transfer of
medication information across settings-keeping it free from error. J
Infus Nurs. 2005;28(2 Suppl):31-36.
\100\ Rozich J, Roger, R. Medication safety: one organization's
approach to the challenge. Journal of Clinical Outcomes Management.
2001(8):27-34.
\101\ Gleason KM, Groszek JM, Sullivan C, Rooney D, Barnard C,
Noskin GA. Reconciliation of discrepancies in medication histories
and admission orders of newly hospitalized patients. Am J Health
Syst Pharm. 2004;61(16):1689-1695.
\102\ Shehab N, Lovegrove MC, Geller AI, Rose KO, Weidle NJ,
Budnitz DS. US emergency department visits for outpatient adverse
drug events, 2013-2014. JAMA. doi: 10.1001/jama.2016.16201.
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Understanding the types of medication a patient is taking, and the
reason for its use, are key facets of a patient's treatment with
respect to medication. Some classes of drugs are associated with more
risk than others.\103\ We are proposing one High-Risk Drug Class data
element with six sub-elements. The six medication classes response
options are: Anticoagulants, antiplatelets, hypoglycemics (including
insulin), opioids, antipsychotics, and antibiotics. These drug classes
are high-risk due to the adverse effects that may result from use. In
particular, bleeding risk is associated with anticoagulants and
antiplatelets; 104 105 fluid retention, heart failure, and
lactic acidosis are associated with hypoglycemics; \106\ misuse is
associated with opioids; \107\ fractures and strokes are associated
with antipsychotics; 108 109 and various adverse events,
such as central nervous systems effects and gastrointestinal
intolerance, are associated with antimicrobials,\110\ the larger
category of medications that include antibiotics. Moreover, some
medications in five of the six drug classes included in this data
element are included in the 2019 Updated Beers Criteria[supreg] list as
potentially inappropriate medications for use in older adults.\111\
Finally, although a complete medication list should record several
important attributes of each medication (for example, dosage, route,
stop date),
[[Page 17313]]
recording an indication for the drug is of crucial importance.\112\
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\103\ Ibid.
\104\ Shoeb M, Fang MC. Assessing bleeding risk in patients
taking anticoagulants. J Thromb Thrombolysis. 2013;35(3):312-319.
doi: 10.1007/s11239-013-0899-7.
\105\ Melkonian M, Jarzebowski W, Pautas E. Bleeding risk of
antiplatelet drugs compared with oral anticoagulants in older
patients with atrial fibrillation: a systematic review and
meta[hyphen]analysis. J Thromb Haemost. 2017;15:1500-1510. DOI:
10.1111/jth.13697.
\106\ Hamnvik OP, McMahon GT. Balancing Risk and Benefit with
Oral Hypoglycemic Drugs. The Mount Sinai journal of medicine, New
York. 2009; 76:234-243.
\107\ Naples JG, Gellad WF, Hanlon JT. The Role of Opioid
Analgesics in Geriatric Pain Management. Clin Geriatr Med.
2016;32(4):725-735.
\108\ Rigler SK, Shireman TI, Cook-Wiens GJ, Ellerbeck EF,
Whittle JC, Mehr DR, Mahnken JD. Fracture risk in nursing home
residents initiating antipsychotic medications. J Am Geriatr Soc.
2013; 61(5):715-722. [PubMed: 23590366].
\109\ Wang S, Linkletter C, Dore D et al. Age, antipsychotics,
and the risk of ischemic stroke in the Veterans Health
Administration. Stroke 2012;43:28-31. doi:10.1161/
STROKEAHA.111.617191.
\110\ Faulkner CM, Cox HL, Williamson JC. Unique aspects of
antimicrobial use in older adults. Clin Infect Dis. 2005;40(7):997-
1004.
\111\ American Geriatrics Society 2019 Beers Criteria Update
Expert Panel. American Geriatrics Society 2019 Updated Beers
Criteria for Potentially Inappropriate Medication Use in Older
Adults. J Am Geriatr Soc 2019; 00:1-21.
\112\ Li Y, Salmasian H, Harpaz R, Chase H, Friedman C.
Determining the reasons for medication prescriptions in the EHR
using knowledge and natural language processing. AMIA Annu Symp
Proc. 2011;2011:768-76.
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The High-Risk Drug Classes: Use and Indication data element
requires an assessor to record whether or not a patient is taking any
medications within six the drug classes. The six response options for
this data element are high-risk drug classes with particular relevance
to PAC patients and residents, as identified by our data element
contractor. The six data element response options are Anticoagulants,
Antiplatelets, Hypoglycemics, Opioids, Antipsychotics, and Antibiotics.
For each drug class, the assessor is asked to indicate if the patient
is taking any medications within the class, and, for drug classes in
which medications were being taken, whether indications for all drugs
in the class are noted in the medical record. For example, for the
response option Anticoagulants, if the assessor indicates that the
patient has received anticoagulant medication, the assessor would then
indicate if an indication is recorded in the medication record for the
anticoagulant(s).
The High-Risk Drug Classes: Use and Indication data element that is
being proposed as a SPADE was developed as part of a larger set of data
elements to assess medication reconciliation, the process of obtaining
a patient's multiple medication lists and reconciling any
discrepancies. For more information on the High-Risk Drug Classes: Use
and Indication data element, we refer readers to the document titled
``Proposed Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We sought public input on the relevance of conducting assessments
on medication reconciliation and specifically on the proposed High-Risk
Drug Classes: Use and Indication data element. Our data element
contractor presented data elements related to medication reconciliation
to the TEP convened on April 6 and 7, 2016. The TEP supported a focus
on high-risk drugs, because of higher potential for harm to patients
and residents, and were in favor of a data element to capture whether
or not indications for medications were recorded in the medical record.
A summary of the April 6 and 7, 2016 TEP meeting titled ``SPADE
Technical Expert Panel Summary (First Convening)'' is available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html. Medication reconciliation data
elements were also discussed at a second TEP meeting on January 5 and
6, 2017, convened by our data element contractor. At this meeting, the
TEP agreed about the importance of evaluating the medication
reconciliation process, but disagreed about how this could be
accomplished through standardized assessment. The TEP also disagreed
about the usability and appropriateness of using the Beers Criteria to
identify high-risk medications.\113\ A summary of the January 5 and 6,
2017 TEP meeting titled ``SPADE Technical Expert Panel Summary (Second
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
---------------------------------------------------------------------------
\113\ American Geriatrics Society 2015 Beers Criteria Update
Expert Panel. American Geriatrics Society. Updated Beers Criteria
for Potentially Inappropriate Medication Use in Older Adults. J Am
Geriatr Soc 2015; 63:2227-2246.
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We also solicited public input on data elements related to
medication reconciliation during a public input period from April 26 to
June 26, 2017. Several commenters expressed support for the medication
reconciliation data elements that were put on display, noting the
importance of medication reconciliation in preventing medication errors
and stated that the items seemed feasible and clinically useful. A few
commenters were critical of the choice of 10 drug classes posted during
that comment period, arguing that ADEs are not limited to high-risk
drugs, and raised issues related to training assessors to correctly
complete a valid assessment of medication reconciliation. A summary
report for the April 26 to June 26, 2017 public comment period titled
``SPADE May-June 2017 Public Comment Summary Report'' is available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The High-Risk Drug Classes: Use and Indication data element was
included in the National Beta Test of candidate data elements conducted
by our data element contractor from November 2017 to August 2018.
Results of this test found the High-Risk Drug Classes: Use and
Indication data element to be feasible and reliable for use with PAC
patients and residents. More information about the performance of the
High-Risk Drug Classes: Use and Indication data element in the National
Beta Test can be found in the document titled ``Proposed Specifications
for IRF QRP Quality Measures and Standardized Patient Assessment Data
Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018, for the purpose of soliciting input on the proposed
standardized patient assessment data elements. The TEP acknowledged the
challenges of assessing medication safety, but were supportive of some
of the data elements focused on medication reconciliation that were
tested in the National Beta Test. The TEP was especially supportive of
the focus on the six high-risk drug classes and using these classes to
assess whether the indication for a drug is recorded. A summary of the
September 17, 2018 TEP meeting titled ``SPADE Technical Expert Panel
Summary (Third Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our ongoing SPADE development efforts. These
activities provided updates on the field-testing work and solicited
feedback on data elements considered for standardization, including the
High-Risk Drug Classes: Use and Indication data element. One
stakeholder group was critical of the six drug classes included as
response options in the High-Risk Drug Classes: Use and Indication data
element, noting that potentially risky medications (for example, muscle
relaxants) are not included in this list; that there may be important
differences between drugs within classes (for example, more recent
versus older style antidepressants); and that drug allergy information
is not captured. Finally, on November 27, 2018, our data element
contractor hosted a public meeting of stakeholders
[[Page 17314]]
to present the results of the National Beta Test and solicit additional
comments. General input on the testing and item development process and
concerns about burden were received from stakeholders during this
meeting and via email through February 1, 2019. Additionally, one
commenter questioned whether the time to complete the High-Risk Drug
Classes: Use and Indication data element would differ across settings.
A summary of the public input received from the November 27, 2018
stakeholder meeting titled ``Input on Standardized Patient Assessment
Data Elements (SPADEs) Received After November 27, 2018 Stakeholder
Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing high-risk drugs and for
whether or not indications are noted for high-risk drugs, stakeholder
input, and strong test results, we are proposing that the High-Risk
Drug Classes: Use and Indication data element meets the definition of
standardized patient assessment data with respect to special services,
treatments, and interventions under section 1899B(b)(1)(B)(iii) of the
Act and to adopt the High-Risk Drug Classes: Use and Indication data
element as standardized patient assessment data for use in the IRF QRP.
3. Medical Condition and Comorbidity Data
Assessing medical conditions and comorbidities is critically
important for care planning and safety for patients and residents
receiving PAC services, and the standardized assessment of selected
medical conditions and comorbidities across PAC providers is important
for managing care transitions and understanding medical complexity.
Below we discuss our proposals for data elements related to the
medical condition of pain as standardized patient assessment data.
Appropriate pain management begins with a standardized assessment, and
thereafter establishing and implementing an overall plan of care that
is person-centered, multi-modal, and includes the treatment team and
the patient. Assessing and documenting the effect of pain on sleep,
participation in therapy, and other activities may provide information
on undiagnosed conditions and comorbidities and the level of care
required, and do so more objectively than subjective numerical scores.
With that, we assess that taken separately and together, these proposed
data elements are essential for care planning, consistency across
transitions of care, and identifying medical complexities including
undiagnosed conditions. We also conclude that it is the standard of
care to always consider the risks and benefits associated with a
personalized care plan, including the risks of any pharmacological
therapy, especially opioids.\114\ We also conclude that in addition to
assessing and appropriately treating pain through the optimum mix of
pharmacologic, non-pharmacologic, and alternative therapies, while
being cognizant of current prescribing guidelines, clinicians in
partnership with patients are best able to mitigate factors that
contribute to the current opioid crisis.115 116 117
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\114\ Department of Health and Human Services: Pain Management
Best Practices Inter-Agency Task Force. Draft Report on Pain
Management Best Practices: Updates, Gaps, Inconsistencies, and
Recommendations. Accessed April 1, 2019. https://www.hhs.gov/sites/default/files/final-pmtf-draft-report-on-pain-management%20-best-practices-2018-12-12-html-ready-clean.pdf.
\115\ Department of Health and Human Services: Pain Management
Best Practices Inter-Agency Task Force. Draft Report on Pain
Management Best Practices: Updates, Gaps, Inconsistencies, and
Recommendations. Accessed April 1, 2019. https://www.hhs.gov/sites/default/files/final-pmtf-draft-report-on-pain-management%20-best-practices-2018-12-12-html-ready-clean.pdf.
\116\ Fishman SM, Carr DB, Hogans B, et al. Scope and Nature of
Pain- and Analgesia-Related Content of the United States Medical
Licensing Examination (USMLE). Pain Med Malden Mass. 2018;19(3):449-
459. doi:10.1093/pm/pnx336.
\117\ Fishman SM, Young HM, Lucas Arwood E, et al. Core
competencies for pain management: results of an interprofessional
consensus summit. Pain Med Malden Mass. 2013;14(7):971-981.
doi:10.1111/pme.12107.
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In alignment with our Meaningful Measures Initiative, accurate
assessment of medical conditions and comorbidities of patients and
residents in PAC is expected to make care safer by reducing harm caused
in the delivery of care; promote effective prevention and treatment of
chronic disease; strengthen person and family engagement as partners in
their care; and promote effective communication and coordination of
care. The SPADEs will enable or support: Clinical decision-making and
early clinical intervention; person-centered, high quality care
through: Facilitating better care continuity and coordination; better
data exchange and interoperability between settings; and longitudinal
outcome analysis. Therefore, reliable data elements assessing medical
conditions and comorbidities are needed to initiate a management
program that can optimize a patient's or resident's prognosis and
reduce the possibility of adverse events.
We are inviting comment that applies specifically to the
standardized patient assessment data for the category of medical
conditions and co-morbidities, specifically on:
Pain Interference (Pain Effect on Sleep, Pain Interference
With Therapy Activities, and Pain Interference With Day-to-Day
Activities)
In acknowledgement of the opioid crisis, we specifically are
seeking comment on whether or not we should add these pain items in
light of those concerns. Commenters should address to what extent the
collection of the SPADES described below through patient queries might
encourage providers to prescribe opioids.
We are proposing that a set of three data elements on the topic of
Pain Interference (Pain Effect on Sleep, Pain Interference with Therapy
Activities, and Pain Interference with Day-to-Day Activities) meet the
definition of standardized patient assessment data with respect to
medical condition and comorbidity data under section 1899B(b)(1)(B)(iv)
of the Act.
The practice of pain management began to undergo significant
changes in the 1990s because the inadequate, non-standardized, non-
evidence-based assessment and treatment of pain became a public health
issue.\118\ In pain management, a critical part of providing
comprehensive care is performance of a thorough initial evaluation,
including assessment of both the medical and any biopsychosocial
factors causing or contributing to the pain, with a treatment plan to
address the causes of pain and to manage pain that persists over
time.\119\ Quality pain management, based on current guidelines and
evidence-based practices, can minimize unnecessary opioid prescribing
both by offering alternatives or supplemental treatment to opioids and
by clearly stating when they may be appropriate, and how to utilize
risk-benefit analysis for opioid and non-opioid treatment
modalities.\120\
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\118\ Institute of Medicine. Relieving Pain in America: A
Blueprint for Transforming Prevention, Care, Education, and
Research. Washington (DC): National Academies Press (U.S.); 2011.
http://www.ncbi.nlm.nih.gov/books/NBK91497/.
\119\ Department of Health and Human Services: Pain Management
Best Practices Inter-Agency Task Force. Draft Report on Pain
Management Best Practices: Updates, Gaps, Inconsistencies, and
Recommendations. Accessed April 1, 2019. https://www.hhs.gov/sites/default/files/final-pmtf-draft-report-on-pain-management%20-best-practices-2018-12-12-html-ready-clean.pdf.
\120\ National Academies. Pain Management and the Opioid
Epidemic: Balancing Societal and Individual Benefits and Risks of
Prescription Opioid Use. Washington, DC National Academies of
Sciences, Engineering, and Medicine,; 2017.
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[[Page 17315]]
Pain is not a surprising symptom in PAC patients and residents,
where healing, recovery, and rehabilitation often require regaining
mobility and other functions after an acute event. Standardized
assessment of pain that interferes with function is an important first
step towards appropriate pain management in PAC settings. The National
Pain Strategy called for refined assessment items on the topic of pain,
and describes the need for these improved measures to be implemented in
PAC assessments.\121\ Further, the focus on pain interference, as
opposed to pain intensity or pain frequency, was supported by the TEP
convened by our data element contractor as an appropriate and
actionable metric for assessing pain. A summary of the September 17,
2018 TEP meeting titled ``SPADE Technical Expert Panel Summary (Third
Convening)'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
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\121\ National Pain Strategy: A Comprehensive Population-Health
Level Strategy for Pain. https://iprcc.nih.gov/sites/default/files/HHSNational_Pain_Strategy_508C.pdf.
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We appreciate the important concerns related to the misuse and
overuse of opioids in the treatment of pain and to that end we note
that in this proposed rule we have also proposed a SPADE that assess
for the use of, as well as importantly the indication for that use of,
high risk drugs, including opioids. Further, in the FY 2017 IRF PPS
final rule (81 FR 52111) we adopted the Drug Regimen Review Conducted
With Follow-Up for Identified Issues--Post Acute Care (PAC) IRF QRP
measure which assesses whether PAC providers were responsive to
potential or actual clinically significant medication issue(s), which
includes issues associated with use and misuse of opioids for pain
management, when such issues were identified.
We also note that the proposed SPADE related to pain assessment are
not associated with any particular approach to management. Since the
use of opioids is associated with serious complications, particularly
in the elderly,122 123 124 an array of successful non-
pharmacologic and non-opioid approaches to pain management may be
considered. PAC providers have historically used a range of pain
management strategies, including non-steroidal anti-inflammatory drugs,
ice, transcutaneous electrical nerve stimulation (TENS) therapy,
supportive devices, acupuncture, and the like. In addition, non-
pharmacological interventions for pain management include, but are not
limited to, biofeedback, application of heat/cold, massage, physical
therapy, nerve block, stretching and strengthening exercises,
chiropractic, electrical stimulation, radiotherapy, and
ultrasound.125 126 127
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\122\ Chau, D.L., Walker, V., Pai, L., & Cho, L.M. (2008).
Opiates and elderly: use and side effects. Clinical interventions in
aging, 3(2), 273-8.
\123\ Fine, P.G. (2009). Chronic Pain Management in Older
Adults: Special Considerations. Journal of Pain and Symptom
Management, 38(2): S4-S14.
\124\ Solomon, D.H., Rassen, J.A., Glynn, R.J., Garneau, K.,
Levin, R., Lee, J., & Schneeweiss, S. (2010). Archives Internal
Medicine, 170(22):1979-1986.
\125\ Byrd L. Managing chronic pain in older adults: a long-term
care perspective. Annals of Long-Term Care: Clinical Care and Aging.
2013;21(12):34-40.
\126\ Kligler, B., Bair, M.J., Banerjea, R. et al. (2018).
Clinical Policy Recommendations from the VHA State-of-the-Art
Conference on Non-Pharmacological Approaches to Chronic
Musculoskeletal Pain. Journal of General Internal Medicine, 33(Suppl
1): 16. https://doi.org/10.1007/s11606-018-4323-z.
\127\ Chou, R., Deyo, R., Friedly, J., et al. (2017).
Nonpharmacologic Therapies for Low Back Pain: A Systematic Review
for an American College of Physicians Clinical Practice Guideline.
Annals of Internal Medicine, 166(7):493-505.
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We believe that standardized assessment of pain interference will
support PAC clinicians in applying best-practices in pain management
for chronic and acute pain, consistent with current clinical
guidelines. For example, the standardized assessment of both opioids
and pain interference would support providers in successfully tapering
patients/residents who arrive in the PAC setting with long-term opioid
use off of opioids onto non-pharmacologic treatments and non-opioid
medications, as recommended by the Society for Post-Acute and Long-Term
Care Medicine,\128\ and consistent with HHS's 5-Point Strategy To
Combat the Opioid Crisis \129\ which includes ``Better Pain
Management.''
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\128\ Society for Post-Acute and Long-Term Care Medicine (AMDA).
(2018). Opioids in Nursing Homes: Position Statement. https://paltc.org/opioids%20in%20nursing%20homes.
\129\ https://www.hhs.gov/opioids/about-the-epidemic/hhs-response/index.html.
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The Pain Interference data elements consist of three data elements:
Pain Effect on Sleep, Pain Interference with Therapy Activities, and
Pain Interference with Day-to-Day Activities. Pain Effect on Sleep
assesses the frequency with which pain effects a resident's sleep. Pain
Interference with Therapy Activities assesses the frequency with which
pain interferes with a resident's ability to participate in therapies.
The Pain Interference with Day-to-Day Activities assesses the extent to
which pain interferes with a resident's ability to participate in day-
to-day activities excluding therapy.
A similar data element on the effect of pain on activities is
currently included in the OASIS. A similar data element on the effect
on sleep is currently included in the MDS instrument. For more
information on the Pain Interference data elements, we refer readers to
the document titled ``Proposed Specifications for IRF QRP Quality
Measures and Standardized Patient Assessment Data Elements,'' available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We sought public input on the relevance of conducting assessments
on pain and specifically on the larger set of Pain Interview data
elements included in the National Beta Test. The proposed data elements
were supported by comments from the TEP meeting held by our data
element contractor on April 7 to 8, 2016. The TEP affirmed the
feasibility and clinical utility of pain as a concept in a standardized
assessment. The TEP agreed that data elements on pain interference with
ability to participate in therapies versus other activities should be
addressed. Further, during a more recent convening of the same TEP on
September 17, 2018, the TEP supported the interview-based pain data
elements included in the National Beta Test. The TEP members were
particularly supportive of the items that focused on how pain
interferes with activities (that is, Pain Interference data elements),
because understanding the extent to which pain interferes with function
would enable clinicians to determine the need for appropriate pain
treatment. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We held a public input period in 2016 to solicit feedback on the
standardization of pain and several other items that were under
development in prior efforts. From the prior public comment period, we
included several pain data elements (Pain Effect on Sleep; Pain
Interference--Therapy Activities; Pain Interference--Other Activities)
in a second call for public input, open from April 26 to June 26, 2017.
The items we sought comment on were modified from
[[Page 17316]]
all stakeholder and test efforts. Commenters provided general comments
about pain assessment in general in addition to feedback on the
specific pain items. A few commenters shared their support for
assessing pain, the potential for pain assessment to improve the
quality of care, and for the validity and reliability of the data
elements. Commenters affirmed that the item of pain and the effect on
sleep would be suitable for PAC settings. Commenters' main concerns
included redundancy with existing data elements, feasibility and
utility for cross-setting use, and the applicability of interview-based
items to patients and residents with cognitive or communication
impairments, and deficits. A summary report for the April 26 to June
26, 2017 public comment period titled ``SPADE May-June 2017 Public
Comment Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Pain Interference data elements were included in the National
Beta Test of candidate data elements conducted by our data element
contractor from November 2017 to August 2018. Results of this test
found the Pain Interference data elements to be feasible and reliable
for use with PAC patients and residents. More information about the
performance of the Pain Interference data elements in the National Beta
Test can be found in the document titled ``Proposed Specifications for
SNF QRP Quality Measures and Standardized Patient Assessment Data
Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on
September 17, 2018 for the purpose of soliciting input on the
standardized patient assessment data elements. The TEP supported the
interview-based pain data elements included in the National Beta Test.
The TEP members were particularly supportive of the items that focused
on how pain interferes with activities (that is, Pain Interference data
elements), because understanding the extent to which pain interferes
with function would enable clinicians to determine the need for pain
treatment. A summary of the September 17, 2018 TEP meeting titled
``SPADE Technical Expert Panel Summary (Third Convening)'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our on-going SPADE development efforts.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present the results of the National
Beta Test and solicit additional comments. General input on the testing
and item development process and concerns about burden were received
from stakeholders during this meeting and via email through February 1,
2019. Additionally, one commenter expressed strong support for the Pain
data elements and was encouraged by the fact that this portion of the
assessment goes beyond merely measuring the presence of pain. A summary
of the public input received from the November 27, 2018 stakeholder
meeting titled ``Input on Standardized Patient Assessment Data Elements
(SPADEs) Received After November 27, 2018 Stakeholder Meeting'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Taking together the importance of assessing for the effect of pain
on function, stakeholder input, and strong test results, we are
proposing that the three Pain Interference data elements (Pain Effect
on Sleep, Pain Interference with Therapy Activities, and Pain
Interference with Day-to-Day Activities) meet the definition of
standardized patient assessment data with respect to medical conditions
and comorbidities under section 1899B(b)(1)(B)(iv) of the Act and to
adopt the Pain Interference data elements (Pain Effect on Sleep; Pain
Interference with Therapy Activities; and Pain Interference with Day-
to-Day Activities) as standardized patient assessment data for use in
the IRF QRP.
4. Impairment Data
Hearing and vision impairments are conditions that, if unaddressed,
affect activities of daily living, communication, physical functioning,
rehabilitation outcomes, and overall quality of life. Sensory
limitations can lead to confusion in new settings, increase isolation,
contribute to mood disorders, and impede accurate assessment of other
medical conditions. Failure to appropriately assess, accommodate, and
treat these conditions increases the likelihood that patients and
residents will require more intensive and prolonged treatment. Onset of
these conditions can be gradual, so individualized assessment with
accurate screening tools and follow-up evaluations are essential to
determining which patients and residents need hearing- or vision-
specific medical attention or assistive devices and accommodations,
including auxiliary aids and/or services, and to ensure that person-
directed care plans are developed to accommodate a patient's or
resident's needs. Accurate diagnosis and management of hearing or
vision impairment would likely improve rehabilitation outcomes and care
transitions, including transition from institutional-based care to the
community. Accurate assessment of hearing and vision impairment would
be expected to lead to appropriate treatment, accommodations, including
the provision of auxiliary aids and services during the stay, and
ensure that patients and residents continue to have their vision and
hearing needs met when they leave the facility.
In alignment with our Meaningful Measures Initiative, we expect
accurate and individualized assessment, treatment, and accommodation of
hearing and vision impairments of patients and residents in PAC to make
care safer by reducing harm caused in the delivery of care; promote
effective prevention and treatment of chronic disease; strengthen
person and family engagement as partners in their care; and promote
effective communication and coordination of care. For example,
standardized assessment of hearing and vision impairments used in PAC
will support ensuring patient safety (for example, risk of falls),
identifying accommodations needed during the stay, and appropriate
support needs at the time of discharge or transfer. Standardized
assessment of these data elements will: Enable or support clinical
decision-making and early clinical intervention; person-centered, high
quality care (for example, facilitating better care continuity and
coordination); better data exchange and interoperability between
settings; and longitudinal outcome analysis. Therefore, reliable data
elements assessing hearing and vision impairments are needed to
initiate a management program that can optimize a patient's or
resident's prognosis and reduce the possibility of adverse events.
Comments on the category of impairments were also submitted by
[[Page 17317]]
stakeholders during the FY 2018 IRF PPS proposed rule (82 FR 20737
through 20739) public comment period. A commenter stated hearing and
vision assessments should be administered at the beginning of the
assessment process to provide evidence about any sensory deficits that
may affect the patient's ability to participate in the assessment and
to allow the assessor to offer an assistive device.
We are inviting comment on our proposals to collect as standardized
patient assessment data the following data with respect to impairments.
Hearing
We are proposing that the Hearing data element meets the definition
of standardized patient assessment data with respect to impairments
under section 1899B(b)(1)(B)(v) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20737
through 20738), accurate assessment of hearing impairment is important
in the PAC setting for care planning and resource use. Hearing
impairment has been associated with lower quality of life, including
poorer physical, mental, social functioning, and emotional
health.130 131 Treatment and accommodation of hearing
impairment led to improved health outcomes including, but not limited
to, quality of life.\132\ For example, hearing loss in elderly
individuals has been associated with depression and cognitive
impairment,133 134 135 higher rates of incident cognitive
impairment and cognitive decline,\136\ and less time in occupational
therapy.\137\ Accurate assessment of hearing impairment is important in
the PAC setting for care planning and defining resource use.
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\130\ Dalton DS, Cruickshanks KJ, Klein BE, Klein R, Wiley TL,
Nondahl DM. The impact of hearing loss on quality of life in older
adults. Gerontologist. 2003;43(5):661-668.
\131\ Hawkins K, Bottone FG, Jr., Ozminkowski RJ, et al. The
prevalence of hearing impairment and its burden on the quality of
life among adults with Medicare Supplement Insurance. Qual Life Res.
2012;21(7):1135-1147.
\132\ Horn KL, McMahon NB, McMahon DC, Lewis JS, Barker M,
Gherini S. Functional use of the Nucleus 22-channel cochlear implant
in the elderly. The Laryngoscope. 1991;101(3):284-288.
\133\ Sprinzl GM, Riechelmann H. Current trends in treating
hearing loss in elderly people: a review of the technology and
treatment options--a mini-review. Gerontology. 2010;56(3):351-358.
\134\ Lin FR, Thorpe R, Gordon-Salant S, Ferrucci L. Hearing
Loss Prevalence and Risk Factors Among Older Adults in the United
States. The Journals of Gerontology Series A: Biological Sciences
and Medical Sciences. 2011;66A(5):582-590.
\135\ Hawkins K, Bottone FG, Jr., Ozminkowski RJ, et al. The
prevalence of hearing impairment and its burden on the quality of
life among adults with Medicare Supplement Insurance. Qual Life Res.
2012;21(7):1135-1147.
\136\ Lin FR, Metter EJ, O'Brien RJ, Resnick SM, Zonderman AB,
Ferrucci L. Hearing Loss and Incident Dementia. Arch Neurol.
2011;68(2):214-220.
\137\ Cimarolli VR, Jung S. Intensity of Occupational Therapy
Utilization in Nursing Home Residents: The Role of Sensory
Impairments. J Am Med Dir Assoc. 2016;17(10):939-942.
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The proposed data element consists of the single Hearing data
element. This data consists of one question that assesses level of
hearing impairment. This data element is currently in use in the MDS in
SNFs. For more information on the Hearing data element, we refer
readers to the document titled ``Proposed Specifications for IRF QRP
Quality Measures and Standardized Patient Assessment Data Elements,''
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Hearing data element was first proposed as a standardized
patient assessment data element in the FY 2018 IRF PPS proposed rule
(82 FR 20737 through 20738). In that proposed rule, we stated that the
proposal was informed by input we received on the PAC PRD form of the
data element (``Ability to Hear'') through a call for input published
on the CMS Measures Management System Blueprint website. Input
submitted from August 12 to September 12, 2016 recommended that
hearing, vision, and communication assessments be administered at the
beginning of patient assessment process. A summary report for the
August 12 to September 12, 2016 public comment period titled ``SPADE
August 2016 Public Comment Summary Report'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received public comments in support of adopting the Hearing data
element for standardized cross-setting use, noting that it would help
address the needs of patient and residents with disabilities and that
failing to identify impairments during the initial assessment can
result in inaccurate diagnoses of impaired language or cognition and
can invalidate other information obtained from patient assessment.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Hearing data element was included in the National Beta Test of
candidate data elements conducted by our data element contractor from
November 2017 to August 2018. Results of this test found the Hearing
data element to be feasible and reliable for use with PAC patients and
residents. More information about the performance of the Hearing data
element in the National Beta Test can be found in the document titled
``Proposed Specifications for IRF QRP Quality Measures and Standardized
Patient Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on January
5 and 6, 2017 for the purpose of soliciting input on all the SPADEs,
including the Hearing data element. The TEP affirmed the importance of
standardized assessment of hearing impairment in PAC patients and
residents. A summary of the January 5 and 6, 2017 TEP meeting titled
``SPADE Technical Expert Panel Summary (Second Convening)'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our ongoing SPADE development efforts.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present the results of the National
Beta Test and solicit additional comments. General input on the testing
and item development process and concerns about burden were received
from stakeholders during this meeting and via email through February 1,
2019. Additionally, a commenter expressed support for the Hearing data
element and suggested administration at the beginning of the patient
assessment to maximize utility. A summary of the public input received
from the November 27, 2018 stakeholder meeting titled ``Input on
Standardized Patient Assessment Data Elements (SPADEs) Received After
November 27, 2018 Stakeholder Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Due to the relatively stable nature of hearing impairment, it is
unlikely that a
[[Page 17318]]
patient's score on this assessment would change between the start and
end of the IRF stay. Therefore, we are proposing that IRFs that submit
the Hearing data element with respect to admission will be considered
to have submitted with respect to discharge as well.
Taking together the importance of assessing for hearing,
stakeholder input, and strong test results, we are proposing that the
Hearing data element meets the definition of standardized patient
assessment data with respect to impairments under section
1899B(b)(1)(B)(v) of the Act and to adopt the Hearing data element as
standardized patient assessment data for use in the IRF QRP.
Vision
We are proposing that the Vision data element meets the definition
of standardized patient assessment data with respect to impairments
under section 1899B(b)(1)(B)(v) of the Act.
As described in the FY 2018 IRF PPS proposed rule (82 FR 20738
through 20739), evaluation of an individual's ability to see is
important for assessing for risks such as falls and provides
opportunities for improvement through treatment and the provision of
accommodations, including auxiliary aids and services, which can
safeguard patients and residents and improve their overall quality of
life. Further, vision impairment is often a treatable risk factor
associated with adverse events and poor quality of life. For example,
individuals with visual impairment are more likely to experience falls
and hip fracture, have less mobility, and report depressive
symptoms.138 139 140 141 142 143 144 Individualized initial
screening can lead to life-improving interventions such as
accommodations, including the provision of auxiliary aids and services,
during the stay and/or treatments that can improve vision and prevent
or slow further vision loss. In addition, vision impairment is often a
treatable risk factor associated with adverse events which can be
prevented and accommodated during the stay. Accurate assessment of
vision impairment is important in the IRF setting for care planning and
defining resource use.
---------------------------------------------------------------------------
\138\ Colon-Emeric CS, Biggs DP, Schenck AP, Lyles KW. Risk
factors for hip fracture in skilled nursing facilities: Who should
be evaluated? Osteoporos Int. 2003;14(6):484-489.
\139\ Freeman EE, Munoz B, Rubin G, West SK. Visual field loss
increases the risk of falls in older adults: The Salisbury eye
evaluation. Invest Ophthalmol Vis Sci. 2007;48(10):4445-4450.
\140\ Keepnews D, Capitman JA, Rosati RJ. Measuring patient-
level clinical outcomes of home health care. J Nurs Scholarsh.
2004;36(1):79-85.
\141\ Nguyen HT, Black SA, Ray LA, Espino DV, Markides KS.
Predictors of decline in MMSE scores among older Mexican Americans.
J Gerontol A Biol Sci Med Sci. 2002;57(3):M181-185.
\142\ Prager AJ, Liebmann JM, Cioffi GA, Blumberg DM. Self-
reported Function, Health Resource Use, and Total Health Care Costs
Among Medicare Beneficiaries With Glaucoma. JAMA ophthalmology.
2016;134(4):357-365.
\143\ Rovner BW, Ganguli M. Depression and disability associated
with impaired vision: The MoVies Project. J Am Geriatr Soc.
1998;46(5):617-619.
\144\ Tinetti ME, Ginter SF. The nursing home life-space
diameter. A measure of extent and frequency of mobility among
nursing home residents. J Am Geriatr Soc. 1990;38(12):1311-1315.
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The proposed data element consists of the single Vision data
element (Ability To See in Adequate Light) that consists of one
question with five response categories. The Vision data element that we
are proposing for standardization was tested as part of the development
of the MDS and is currently in use in that assessment in SNFs. Similar
data elements, but with different wording and fewer response option
categories, are in use in the OASIS. For more information on the Vision
data element, we refer readers to the document titled ``Proposed
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
The Vision data element was first proposed as a standardized
patient assessment data element in the FY 2018 IRF PPS proposed rule
(82 FR 20738 through 20739).
In that proposed rule, we stated that the proposal was informed by
input we received on the Ability to See in Adequate Light data element
(version tested in the PAC PRD with three response categories) through
a call for input published on the CMS Measures Management System
Blueprint website. Although the data element in public comment differed
from the proposed data element, input submitted from August 12 to
September 12, 2016 supported assessing vision in PAC settings and the
useful information a vision data element would provide. We also stated
that commenters had noted that the Ability to See item would provide
important information that would facilitate care coordination and care
planning, and consequently improve the quality of care. Other
commenters suggested it would be helpful as an indicator of resource
use and noted that the item would provide useful information about the
abilities of patients and residents to care for themselves. Additional
commenters noted that the item could feasibly be implemented across PAC
providers and that its kappa scores from the PAC PRD support its
validity. Some commenters noted a preference for MDS version of the
Vision data element in SNFs over the form put forward in public
comment, citing the widespread use of this data element. A summary
report for the August 12 to September 12, 2016 public comment period
titled ``SPADE August 2016 Public Comment Summary Report'' is available
at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In response to our proposal in the FY 2018 IRF PPS proposed rule,
we received a comment supporting having a standardized patient
assessment data element for vision across PAC settings, but it stated
the proposed data element captures only basic information for risk
adjustment, and more detailed information would need to be collected to
use it as an outcome measure.
Subsequent to receiving comments on the FY 2018 IRF PPS rule, the
Vision data element was included in the National Beta Test of candidate
data elements conducted by our data element contractor from November
2017 to August 2018. Results of this test found the Vision data element
to be feasible and reliable for use with PAC patients and residents.
More information about the performance of the Vision data element in
the National Beta Test can be found in the document titled ``Proposed
Specifications for IRF QRP Quality Measures and Standardized Patient
Assessment Data Elements,'' available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In addition, our data element contractor convened a TEP on January
5 and 6, 2017 for the purpose of soliciting input on all the SPADEs
including the Vision data element. The TEP affirmed the importance of
standardized assessment of vision impairment in PAC patients and
residents. A summary of the January 5 and 6, 2017 TEP meeting titled
``SPADE Technical Expert Panel Summary (Second Convening)'' is
available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
[[Page 17319]]
We also held Special Open Door Forums and small-group discussions
with PAC providers and other stakeholders in 2018 for the purpose of
updating the public about our ongoing SPADE development efforts.
Finally, on November 27, 2018, our data element contractor hosted a
public meeting of stakeholders to present the results of the National
Beta Test and solicit additional comments. General input on the testing
and item development process and concerns about burden were received
from stakeholders during this meeting and via email through February 1,
2019. Additionally, a commenter expressed support for the Vision data
element and suggested administration at the beginning of the patient
assessment to maximize utility. A summary of the public input received
from the November 27, 2018 stakeholder meeting titled ``Input on
Standardized Patient Assessment Data Elements (SPADEs) Received After
November 27, 2018 Stakeholder Meeting'' is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
Due to the relatively stable nature of vision impairment, it is
unlikely that a patient's score on this assessment would change between
the start and end of the IRF stay. Therefore, we are proposing that
IRFs that submit the Vision data element with respect to admission will
be considered to have submitted with respect to discharge as well.
Taking together the importance of assessing for vision, stakeholder
input, and strong test results, we are proposing that the Vision data
element meets the definition of standardized patient assessment data
with respect to impairments under section 1899B(b)(1)(B)(v) of the Act
and to adopt the Vision data element as standardized patient assessment
data for use in the IRF QRP.
4. Proposed New Category: Social Determinants of Health
a. Proposed Social Determinants of Health Data Collection To Inform
Measures and Other Purposes
Subparagraph (A) of section 2(d)(2) of the IMPACT Act requires CMS
to assess appropriate adjustments to quality measures, resource
measures and other measures, and to assess and implement appropriate
adjustments to payment under Medicare, based on those measures, after
taking into account studies conducted by ASPE on social risk factors
(described below) and other information, and based on an individual's
health status and other factors. Subparagraph (C) of section 2(d)(2) of
the IMPACT Act further requires the Secretary to carry out periodic
analyses, at least every three years, based on the factors referred to
in subparagraph (A) so as to monitor changes in possible relationships.
Subparagraph (B) of section 2(d)(2) of the IMPACT Act requires CMS to
collect or otherwise obtain access to data necessary to carry out the
requirement of the paragraph (both assessing adjustments described
above in such subparagraph (A) and for periodic analyses in such
subparagraph (C)). Accordingly we are proposing to use our authority
under subparagraph (B) of section 2(d)(2) of the IMPACT Act to
establish a new data source for information to meet the requirements of
subparagraphs (A) and (C) of section 2(d)(2) of the IMPACT Act. In this
rule, we are proposing to collect and access data about social
determinants of health (SDOH) in order to perform CMS' responsibilities
under subparagraphs (A) and (C) of section 2(d)(2) of the IMPACT Act,
as explained in more detail below. Social determinants of health, also
known as social risk factors, or health-related social needs, are the
socioeconomic, cultural and environmental circumstances in which
individuals live that impact their health. We are proposing to collect
information on seven proposed SDOH SPADE data elements relating to
race, ethnicity, preferred language, interpreter services, health
literacy, transportation, and social isolation; a detailed discussion
of each of the proposed SDOH data elements is found in section
VII.G.5.b. of this proposed rule.
We are also proposing to use the assessment instrument for the IRF
QRP, the IRF-PAI, described as a PAC assessment instrument under
section 1899B(a)(2)(B) of the Act, to collect these data via an
existing data collection mechanism. We believe this approach will
provide CMS with access to data with respect to the requirements of
section 2(d)(2) of the IMPACT Act, while minimizing the reporting
burden on PAC health care providers by relying on a data reporting
mechanism already used and an existing system to which PAC health care
providers are already accustomed.
The IMPACT Act includes several requirements applicable to the
Secretary, in addition to those imposing new data reporting obligations
on certain PAC providers as discussed in VII.G.5.b. of this proposed
rule. Subparagraphs (A) and (B) of sections 2(d)(1) of the IMPACT Act
require the Secretary, acting through the Office of the Assistant
Secretary for Planning and Evaluation (ASPE), to conduct two studies
that examine the effect of risk factors, including individuals'
socioeconomic status, on quality, resource use and other measures under
the Medicare program. The first ASPE study was completed in December
2016 and is discussed below, and the second study is to be completed in
the fall of 2019. We recognize that ASPE, in its studies, is
considering a broader range of social risk factors than the SDOH data
elements in this proposal, and address both PAC and non-PAC settings.
We acknowledge that other data elements may be useful to understand,
and that some of those elements may be of particular interest in non-
PAC settings. For example, for beneficiaries receiving care in the
community, as opposed to an in-patient facility, housing stability and
food insecurity may be more relevant. We will continue to take into
account the findings from both of ASPE's reports in future policy
making.
One of the ASPE's first actions under the IMPACT Act was to
commission the National Academies of Sciences, Engineering, and
Medicine (NASEM) to define and conceptualize socioeconomic status for
the purposes of ASPE's two studies under section 2(d)(1) of the IMPACT
Act. The NASEM convened a panel of experts in the field and conducted
an extensive literature review. Based on the information collected, the
2016 NASEM panel report titled, ``Accounting for Social Risk Factors in
Medicare Payment: Identifying Social Risk Factors'', concluded that the
best way to assess how social processes and social relationships
influence key health-related outcomes in Medicare beneficiaries is
through a framework of social risk factors instead of socioeconomic
status. Social risk factors discussed in the NASEM report include
socioeconomic position, race, ethnicity, gender, social context, and
community context. These factors are discussed at length in chapter 2
of the NASEM report, titled ``Social Risk Factors.'' \145\ Consequently
NASEM framed the results of its report in terms of ``social risk
factors'' rather than ``socioeconomic status'' or ``sociodemographic
status.'' The full text of the ``Social Risk Factors'' NASEM report is
available for reading on the website at https://www.nap.edu/read/21858/chapter/1.
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\145\ National Academies of Sciences, Engineering, and Medicine.
2016. Accounting for social risk factors in Medicare payment:
Identifying social risk factors. Chapter 2. Washington, DC: The
National Academies Press.
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[[Page 17320]]
Each of the data elements we are proposing to collect and access
under our authority under section 2(d)(2)(B) of the IMPACT Act is
identified in the 2016 NASEM report as a social risk factor that has
been shown to impact care use, cost and outcomes for Medicare
beneficiaries. CMS uses the term social determinants of health (SDOH)
to denote social risk factors, which is consistent with the objectives
of Healthy People 2020.\146\
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\146\ Social Determinants of Health. Healthy People 2020.
https://www.healthypeople.gov/2020/topics-objectives/topic/social-determinants-of-health. (February 2019).
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ASPE issued its first Report to Congress, titled ``Social Risk
Factors and Performance Under Medicare's Value-Based Purchasing
Programs,'' under section 2(d)(1)(A) of the IMPACT Act on December 21,
2016.\147\ Using NASEM's social risk factors framework, ASPE focused on
the following social risk factors, in addition to disability: (1) Dual
enrollment in Medicare and Medicaid as a marker for low income, (2)
residence in a low-income area, (3) Black race, (4) Hispanic ethnicity;
and (5) residence in a rural area. ASPE acknowledged that the social
risk factors examined in its report were limited due to data
availability. The report also noted that the data necessary to
meaningfully attempt to reduce disparities and identify and reward
improved outcomes for beneficiaries with social risk factors have not
been collected consistently on a national level in post-acute care
settings. Where these data have been collected, the collection
frequently involves lengthy questionnaires. More information on the
Report to Congress on Social Risk Factors and Performance under
Medicare's Value-Based Purchasing Programs, including the full report,
is available on the website at https://aspe.hhs.gov/social-risk-factors-and-medicares-value-based-purchasing-programs-reports.
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\147\ U.S. Department of Health and Human Services, Office of
the Assistant Secretary for Planning and Evaluation. 2016. Report to
Congress: Social Risk Factors and Performance Under Medicare's
Value-Based Payment Programs. Washington, DC.
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Section 2(d)(2) of the IMPACT Act relates to CMS activities and
imposes several responsibilities on the Secretary relating to quality,
resource use, and other measures under Medicare. As mentioned
previously, under subparagraph (A) of section 2(d)(2) of the IMPACT
Act, the Secretary is required, on an ongoing basis, taking into
account the ASPE studies and other information, and based on an
individual's health status and other factors, to assess appropriate
adjustments to quality, resource use, and other measures, and to assess
and implement appropriate adjustments to Medicare payments based on
those measures. Section 2(d)(2)(A)(i) of the IMPACT Act applies to
measures adopted under subsections (c) and (d) of section 1899B of the
Act and to other measures under Medicare. However, CMS' ability to
perform these analyses, and assess and make appropriate adjustments is
hindered by limits of existing data collections on SDOH data elements
for Medicare beneficiaries. In its first study in 2016, in discussing
the second study, ASPE noted that information relating to many of the
specific factors listed in the IMPACT Act, such as health literacy,
limited English proficiency, and Medicare beneficiary activation, are
not available in Medicare data.
Subparagraph 2(d)(2)(A) of the IMPACT Act specifically requires the
Secretary to take the studies and considerations from ASPE's reports to
Congress, as well as other information as appropriate, into account in
assessing and implementing adjustments to measures and related payments
based on measures in Medicare. The results of the ASPE's first study
demonstrated that Medicare beneficiaries with social risk factors
tended to have worse outcomes on many quality measures, and providers
who treated a disproportionate share of beneficiaries with social risk
factors tended to have worse performance on quality measures. As a
result of these findings, ASPE suggested a three-pronged strategy to
guide the development of value-based payment programs under which all
Medicare beneficiaries receive the highest quality healthcare services
possible. The three components of this strategy are to: (1) Measure and
report quality of care for beneficiaries with social risk factors; (2)
set high, fair quality standards for care provided to all
beneficiaries; and (3) reward and support better outcomes for
beneficiaries with social risk factors. In discussing how measuring and
reporting quality for beneficiaries with social risk factors can be
applied to Medicare quality payment programs, the report offered nine
considerations across the three-pronged strategy, including enhancing
data collection and developing statistical techniques to allow
measurement and reporting of performance for beneficiaries with social
risk factors on key quality and resource use measures.
Congress, in section 2(d)(2)(B) of the IMPACT Act, required the
Secretary to collect or otherwise obtain access to the data necessary
to carry out the provisions of paragraph (2) of section 2(d) of the
IMPACT Act through both new and existing data sources. Taking into
consideration NASEM's conceptual framework for social risk factors
discussed above, ASPE's study, and considerations under section
2(d)(1)(A) of the IMPACT Act, as well as the current data constraints
of ASPE's first study and its suggested considerations, we are
proposing to collect and access data about SDOH under section 2(d)(2)
of the IMPACT Act. Our collection and use of the SDOH data described in
section VII.G.5.b.(1) of this proposed rule, under section 2(d)(2) of
the IMPACT Act would be independent of our proposal below (in section
VII.G.5.b.(2) of this proposed rule) and our authority to require
submission of that data for use as SPADE under section 1899B(a)(1)(B)
of the Act.
Accessing standardized data relating to the SDOH data elements on a
national level is necessary to permit CMS to conduct periodic analyses,
to assess appropriate adjustments to quality measures, resource use
measures, and other measures, and to assess and implement appropriate
adjustments to Medicare payments based on those measures. We agree with
ASPE's observations, in the value-based purchasing context, that the
ability to measure and track quality, outcomes, and costs for
beneficiaries with social risk factors over time is critical as
policymakers and providers seek to reduce disparities and improve care
for these groups. Collecting the data as proposed will provide the
basis for our periodic analyses of the relationship between an
individual's health status and other factors and quality, resource use,
and other measures, as required by section 2(d)(2) of the IMPACT Act,
and to assess appropriate adjustments. These data will also permit us
to develop the statistical tools necessary to maximize the value of
Medicare data, reduce costs and improve the quality of care for all
beneficiaries. Collecting and accessing SDOH data in this way also
supports the three-part strategy put forth in the first ASPE report,
specifically ASPE's consideration to enhance data collection and
develop statistical techniques to allow measurement and reporting of
performance for beneficiaries with social risk factors on key quality
and resource use measures.
For the reasons discussed above, we are proposing under section
2(d)(2) of the IMPACT Act, to collect the data on the following SDOH:
(1) Race, as described in section VII.G.5.b.(1) of this proposed rule;
(2) Ethnicity, as described in section VII.G5.b.(1) of this
[[Page 17321]]
proposed rule; (3) Preferred Language, as described in section
VII.G.5.b.(2) of this proposed rule; (4) Interpreter Services, as
described in section VII.G.5.b.(2) of this proposed rule; (5) Health
Literacy, as described in section VII.G.5.b.(3) of this proposed rule;
(6) Transportation, as described in section VII.G.5.b.(4) of this
proposed rule; and (7) Social Isolation, as described in section
VII.G.5.b.(5) of this proposed rule. These data elements are discussed
in more detail below in section VII.G.5.b of this proposed rule. We
welcome comment on this proposal.
b. Standardized Patient Assessment Data
Section 1899B(b)(1)(B)(vi) of the Act authorizes the Secretary to
collect SPADEs with respect to other categories deemed necessary and
appropriate. Below we are proposing to create a Social Determinants of
Health SPADE category under section 1899B(b)(1)(B)(vi) of the Act. In
addition to collecting SDOH data for the purposes outlined above under
section 2(d)(2)(B), we are also proposing to collect as SPADE these
same data elements (race, ethnicity, preferred language, interpreter
services, health literacy, transportation, and social isolation) under
section 1899B(b)(1)(B)(vi) of the Act. We believe that this proposed
new category of Social Determinants of Health will inform provider
understanding of individual patient risk factors and treatment
preferences, facilitate coordinated care and care planning, and improve
patient outcomes. We are proposing to deem this category necessary and
appropriate, for the purposes of SPADE, because using common standards
and definitions for PAC data elements is important in ensuring
interoperable exchange of longitudinal information between PAC
providers and other providers to facilitate coordinated care,
continuity in care planning, and the discharge planning process from
post-acute care settings.
All of the Social Determinants of Health data elements we are
proposing under section 1899B(b)(1)(B)(vi) of the Act have the capacity
to take into account treatment preferences and care goals of patients,
and to inform our understanding of patient complexity and risk factors
that may affect care outcomes. While acknowledging the existence and
importance of additional social determinants of health, we are
proposing to assess some of the factors relevant for patients receiving
post-acute care that PAC settings are in a position to impact through
the provision of services and supports, such as connecting patients
with identified needs with transportation programs, certified
interpreters, or social support programs.
As previously mentioned, and described in more detail below, we are
proposing to adopt the following seven data elements as SPADE under the
proposed Social Determinants of Health category: Race, ethnicity,
preferred language, interpreter services, health literacy,
transportation, and social isolation. To select these data elements, we
reviewed the research literature, a number of validated assessment
tools and frameworks for addressing SDOH currently in use (for example,
Health Leads, NASEM, Protocol for Responding to and Assessing Patients'
Assets, Risks, and Experiences (PRAPARE), and ICD-10), and we engaged
in discussions with stakeholders. We also prioritized balancing the
reporting burden for PAC providers with our policy objective to collect
SPADEs that will inform care planning and coordination and quality
improvement across care settings. Furthermore, incorporating SDOH data
elements into care planning has the potential to reduce readmissions
and help beneficiaries achieve and maintain their health goals.
We also considered feedback received during a listening session
that we held on December 13, 2018. The purpose of the listening session
was to solicit feedback from health systems, research organizations,
advocacy organizations and state agencies and other members of the
public on collecting patient-level data on SDOH across care settings,
including consideration of race, ethnicity, spoken language, health
literacy, social isolation, transportation, sex, gender identity, and
sexual orientation. We also gave participants an option to submit
written comments. A full summary of the listening session, titled
``Listening Session on Social Determinants of Health Data Elements:
Summary of Findings,'' includes a list of participating stakeholders
and their affiliations, and is available at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
(1) Race and Ethnicity
The persistence of racial and ethnic disparities in health and
health care is widely documented including in PAC settings.\148\ \149\
\150\ \151\ \152\ Despite the trend toward overall improvements in
quality of care and health outcomes, the Agency for Healthcare Research
and Quality, in its National Healthcare Quality and Disparities
Reports, consistently indicates that racial and ethnic disparities
persist, even after controlling for factors such as income, geography,
and insurance.\153\ For example, racial and ethnic minorities tend to
have higher rates of infant mortality, diabetes and other chronic
conditions, and visits to the emergency department, and lower rates of
having a usual source of care and receiving immunizations such as the
flu vaccine.\154\ Studies have also shown that African Americans are
significantly more likely than white Americans to die prematurely from
heart disease and stroke.\155\ However, our ability to identify and
address racial and ethnic health disparities has historically been
constrained by data limitations, particularly for smaller populations
groups such as Asians, American Indians and Alaska Natives, and Native
Hawaiians and other Pacific Islanders.\156\
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\148\ 2017 National Healthcare Quality and Disparities Report.
Rockville, MD: Agency for Healthcare Research and Quality; September
2018. AHRQ Pub. No. 18-0033-EF.
\149\ Fiscella, K. and Sanders, M.R. Racial and Ethnic
Disparities in the Quality of Health Care. (2016). Annual Review of
Public Health. 37:375-394.
\150\ 2018 National Impact Assessment of the Centers for
Medicare & Medicaid Services (CMS) Quality Measures Reports.
Baltimore, MD: U.S. Department of Health and Human Services, Centers
for Medicare and Medicaid Services; February 28, 2018.
\151\ Smedley, B.D., Stith, A.Y., & Nelson, A.R. (2003). Unequal
treatment: confronting racial and ethnic disparities in health care.
Washington, DC, National Academy Press.
\152\ Chase, J., Huang, L. and Russell, D. (2017). Racial/ethnic
disparities in disability outcomes among post-acute home care
patients. J of Aging and Health. 30(9):1406-1426.
\153\ National Healthcare Quality and Disparities Reports.
(December 2018). Agency for Healthcare Research and Quality,
Rockville, MD. http://www.ahrq.gov/research/findings/nhqrdr/index.html.
\154\ National Center for Health Statistics. Health, United
States, 2017: With special feature on mortality. Hyattsville,
Maryland. 2018.
\155\ HHS. Heart disease and African Americans. 2016b. (October
24, 2016). http://minorityhealth.hhs.gov/omh/browse.aspx?lvl=4&lvlid=19.
\156\ National Academies of Sciences, Engineering, and Medicine;
Health and Medicine Division; Board on Population Health and Public
Health Practice; Committee on Community-Based Solutions to Promote
Health Equity in the United States; Baciu A, Negussie Y, Geller A,
et al., editors. Communities in Action: Pathways to Health Equity.
Washington (DC): National Academies Press (US); 2017 Jan 11. 2, The
State of Health Disparities in the United States. Available from:
https://www.ncbi.nlm.nih.gov/books/NBK425844/.
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The ability to improve understanding of and address racial and
ethnic disparities in PAC outcomes requires
[[Page 17322]]
the availability of better data. There is currently a Race and
Ethnicity data element, collected in the MDS, LCDS, IRF-PAI, and OASIS,
that consists of a single question, which aligns with the 1997 Office
of Management and Budget (OMB) minimum data standards for federal data
collection efforts.\157\ The 1997 OMB Standard lists five minimum
categories of race: (1) American Indian or Alaska Native; (2) Asian;
(3) Black or African American; (4) Native Hawaiian or Other Pacific
Islander; (5) and White. The 1997 OMB Standard also lists two minimum
categories of ethnicity: (1) Hispanic or Latino and (2) Not Hispanic or
Latino. The 2011 HHS Data Standards requires a two-question format when
self-identification is used to collect data on race and ethnicity.
Large federal surveys such as the National Health Interview Survey,
Behavioral Risk Factor Surveillance System, and the National Survey on
Drug Use and Health, have implemented the 2011 HHS race and ethnicity
data standards. CMS has similarly updated the Medicare Current
Beneficiary Survey, Medicare Health Outcomes Survey, and the Health
Insurance Marketplace Application for Health Coverage with the 2011 HHS
data standards. More information about the HHS Race and Ethnicity Data
Standards are available on the website at https://minorityhealth.hhs.gov/omh/browse.aspx?lvl=3&lvlid=54.
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\157\ ``Revisions to the Standards for the Classification of
Federal Data on Race and Ethnicity (Notice of Decision)''. Federal
Register 62:210 (October 30, 1997) pp. 58782-58790. Available from:
https://www.govinfo.gov/content/pkg/FR-1997-10-30/pdf/97-28653.pdf.
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We are proposing to revise the current Race and Ethnicity data
element for purposes of this proposal to conform to the 2011 HHS Data
Standards for person-level data collection, while also meeting the 1997
OMB minimum data standards for race and ethnicity. Rather than one data
element that assesses both race and ethnicity, we are proposing two
separate data elements: One for Race and one for Ethnicity, that would
conform with the 2011 HHS Data Standards and the 1997 OMB Standard. In
accordance with the 2011 HHS Data Standards a two-question format would
be used for the proposed race and ethnicity data elements.
The proposed Race data element asks, ``What is your race? We are
proposing to include fourteen response options under the race data
element: (1) White; (2) Black or African American; (3) American Indian
or Alaska Native; (4) Asian Indian; (5) Chinese; (6) Filipino; (7)
Japanese; (8) Korean; (9) Vietnamese; (10) Other Asian; (11) Native
Hawaiian; (12) Guamanian or Chamorro; (13) Samoan; and, (14) Other
Pacific Islander.
The proposed Ethnicity data element asks, ``Are you Hispanic,
Latino/a, or Spanish origin?'' We are proposing to include five
response options under the ethnicity data element: (1) Not of Hispanic,
Latino/a, or Spanish origin; (2) Mexican, Mexican American, Chicano/a;
(3) Puerto Rican; (4) Cuban; and, (5) Another Hispanic, Latino, or
Spanish Origin.
We believe that the two proposed data elements for race and
ethnicity conform to the 2011 HHS Data Standards for person-level data
collection, while also meeting the 1997 OMB minimum data standards for
race and ethnicity, because under those standards, more detailed
information on population groups can be collected if those additional
categories can be aggregated into the OMB minimum standard set of
categories.
In addition, we received stakeholder feedback during the December
13, 2018 SDOH listening session on the importance of improving response
options for race and ethnicity as a component of health care
assessments and for monitoring disparities. Some stakeholders
emphasized the importance of allowing for self-identification of race
and ethnicity for more categories than are included in the 2011 HHS
Standard to better reflect state and local diversity, while
acknowledging the burden of coding an open-ended health care assessment
question across different settings.
We believe that the proposed modified race and ethnicity data
elements more accurately reflect the diversity of the U.S. population
than the current race/ethnicity data element included in MDS, LCDS,
IRF-PAI, and OASIS.\158\ \159\ \160\ \161\ We believe, and research
consistently shows, that improving how race and ethnicity data are
collected is an important first step in improving quality of care and
health outcomes. Addressing disparities in access to care, quality of
care, and health outcomes for Medicare beneficiaries begins with
identifying and analyzing how SDOH, such as race and ethnicity, align
with disparities in these areas.\162\ Standardizing self-reported data
collection for race and ethnicity allows for the equal comparison of
data across multiple healthcare entities.\163\ By collecting and
analyzing these data, CMS and other healthcare entities will be able to
identify challenges and monitor progress. The growing diversity of the
US population and knowledge of racial and ethnic disparities within and
across population groups supports the collection of more granular data
beyond the 1997 OMB minimum standard for reporting categories. The 2011
HHS race and ethnicity data standard includes additional detail that
may be used by PAC providers to target quality improvement efforts for
racial and ethnic groups experiencing disparate outcomes. For more
information on the Race and Ethnicity data elements, we refer readers
to the document titled ``Proposed Specifications for IRF QRP Measures
and Standardized Patient Assessment Data Elements,'' available at
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
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\158\ Penman-Aguilar, A., Talih, M., Huang, D., Moonesinghe, R.,
Bouye, K., Beckles, G. (2016). Measurement of Health Disparities,
Health Inequities, and Social Determinants of Health to Support the
Advancement of Health Equity. J Public Health Manag Pract. 22 Suppl
1: S33-42.
\159\ Ramos, R., Davis, J.L., Ross, T., Grant, C.G., Green, B.L.
(2012). Measuring health disparities and health inequities: do you
have REGAL data? Qual Manag Health Care. 21(3):176-87.
\160\ IOM (Institute of Medicine). 2009. Race, Ethnicity, and
Language Data: Standardization for Health Care Quality Improvement.
Washington, DC: The National Academies Press.
\161\ ``Revision of Standards for Maintaining, Collecting, and
Presenting Federal Data on Race and Ethnicity: Proposals From
Federal Interagency Working Group (Notice and Request for
Comments).'' Federal Register 82: 39 (March 1, 2017) p. 12242.
\162\ National Academies of Sciences, Engineering, and Medicine;
Health and Medicine Division; Board on Population Health and Public
Health Practice; Committee on Community-Based Solutions to Promote
Health Equity in the United States; Baciu A, Negussie Y, Geller A,
et al., editors. Communities in Action: Pathways to Health Equity.
Washington (DC): National Academies Press (US); 2017 Jan 11. 2, The
State of Health Disparities in the United States. Available from:
https://www.ncbi.nlm.nih.gov/books/NBK425844/.
\163\ IOM (Institute of Medicine). 2009. Race, Ethnicity, and
Language Data: Standardization for Health Care Quality Improvement.
Washington, DC: The National Academies Press.
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In an effort to standardize the submission of race and ethnicity
data among IRFs, HHAs, SNFs and LTCHs, for the purposes outlined in
section 1899B(a)(1)(B) of the Act, while minimizing the reporting
burden, we are proposing to adopt the Race and Ethnicity data elements
described above as SPADEs with respect to the proposed Social
Determinants of Health category.
Specifically, we are proposing to replace the current Race/
Ethnicity data element with the proposed Race and Ethnicity data
elements on the IRF-PAI. We are also proposing that IRFs that submit
the Race and Ethnicity data
[[Page 17323]]
elements with respect to admission will be considered to have submitted
with respect to discharge as well, because it is unlikely that the
results of these assessment findings will change between the start and
end of the IRF stay, making the information submitted with respect to a
patient's admission the same with respect to a patient's discharge.
(2) Preferred Language and Interpreter Services
More than 64 million Americans speak a language other than English
at home, and nearly 40 million of those individuals have limited
English proficiency (LEP).\164\ Individuals with LEP have been shown to
receive worse care and have poorer health outcomes, including higher
readmission rates.\165\ \166\ \167\ Communication with individuals with
LEP is an important component of high quality health care, which starts
by understanding the population in need of language services.
Unaddressed language barriers between a patient and provider care team
negatively affects the ability to identify and address individual
medical and non-medical care needs, to convey and understand clinical
information, as well as discharge and follow up instructions, all of
which are necessary for providing high quality care. Understanding the
communication assistance needs of patients with LEP, including
individuals who are Deaf or hard of hearing, is critical for ensuring
good outcomes.
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\164\ U.S. Census Bureau, 2013-2017 American Community Survey 5-
Year Estimates.
\165\ Karliner LS, Kim SE, Meltzer DO, Auerbach AD. Influence of
language barriers on outcomes of hospital care for general medicine
inpatients. J Hosp Med. 2010 May-Jun;5(5):276-82. doi: 10.1002/
jhm.658.
\166\ Kim EJ, Kim T, Paasche-Orlow MK, et al. Disparities in
Hypertension Associated with Limited English Proficiency. J Gen
Intern Med. 2017 Jun;32(6):632-639. doi: 10.1007/s11606-017-3999-9.
\167\ National Academies of Sciences, Engineering, and Medicine.
2016. Accounting for social risk factors in Medicare payment:
Identifying social risk factors. Washington, DC: The National
Academies Press.
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Presently, the preferred language of patients and residents and
need for interpreter services are assessed in two PAC assessment tools.
The LCDS and the MDS use the same two data elements to assess preferred
language and whether a patient or resident needs or wants an
interpreter to communicate with health care staff. The MDS initially
implemented preferred language and interpreter services data elements
to assess the needs of SNF residents and patients and inform care
planning. For alignment purposes, the LCDS later adopted the same data
elements for LTCHs. The 2009 NASEM (formerly Institute of Medicine)
report on standardizing data for health care quality improvement
emphasizes that language and communication needs should be assessed as
a standard part of health care delivery and quality improvement
strategies.\168\
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\168\ IOM (Institute of Medicine). 2009. Race, Ethnicity, and
Language Data: Standardization for Health Care Quality Improvement.
Washington, DC: The National Academies Press.
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In developing our proposal for a standardized language data element
across PAC settings, we considered the current preferred language and
interpreter services data elements that are in LCDS and MDS. We also
considered the 2011 HHS Primary Language Data Standard and peer-
reviewed research. The current preferred language data element in LCDS
and MDS asks, ``What is your preferred language?'' Because the
preferred language data element is open-ended, the patient or resident
is able to identify their preferred language, including American Sign
Language (ASL). Finally, we considered the recommendations from the
2009 NASEM (formerly Institute of Medicine) report, ``Race, Ethnicity,
and Language Data: Standardization for Health Care Quality
Improvement.'' In it, the committee recommended that organizations
evaluating a patient's language and communication needs for health care
purposes, should collect data on the preferred spoken language and on
an individual's assessment of his/her level of English proficiency.
A second language data element in LCDS and MDS asks, ``Do you want
or need an interpreter to communicate with a doctor or health care
staff?'' and includes yes or no response options. In contrast, the 2011
HHS Primary Language Data Standard recommends either a single question
to assess how well someone speaks English or, if more granular
information is needed, a two-part question to assess whether a language
other than English is spoken at home and if so, identify that language.
However, neither option allows for a direct assessment of a patient's
or resident's preferred spoken or written language nor whether they
want or need interpreter services for communication with a doctor or
care team, both of which are an important part of assessing patient/
resident needs and the care planning process. More information about
the HHS Data Standard for Primary Language is available on the website
at https://minorityhealth.hhs.gov/omh/browse.aspx?lvl=3&lvlid=54.
Research consistently recommends collecting information about an
individual's preferred spoken language and evaluating those responses
for purposes of determining language access needs in health care.\169\
However, using ``preferred spoken language'' as the metric does not
adequately account for people whose preferred language is ASL, which
would necessitate adopting an additional data element to identify
visual language. The need to improve the assessment of language
preferences and communication needs across PAC settings should be
balanced with the burden associated with data collection on the
provider and patient. Therefore we are proposing to retain the
Preferred Language and Interpreter Services data elements currently in
use on the MDS and LCDS on the IRF-PAI.
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\169\ Guerino, P. and James, C. Race, Ethnicity, and Language
Preference in the Health Insurance Marketplaces 2017 Open Enrollment
Period. Centers for Medicare & Medicaid Services, Office of Minority
Health. Data Highlight: Volume 7--April 2017. Available at https://www.cms.gov/About-CMS/Agency-Information/OMH/Downloads/Data-Highlight-Race-Ethnicity-and-Language-Preference-Marketplace.pdf.
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In addition, we received feedback during the December 13, 2018
listening session on the importance of evaluating and acting on
language preferences early to facilitate communication and allowing for
patient self-identification of preferred language. Although the
discussion about language was focused on preferred spoken language,
there was general consensus among participants that stated language
preferences may or may not accurately indicate the need for interpreter
services, which supports collecting and evaluating data to determine
language preference, as well as the need for interpreter services. An
alternate suggestion was made to inquire about preferred language
specifically for discussing health or health care needs. While this
suggestion does allow for ASL as a response option, we do not have data
indicating how useful this question might be for assessing the desired
information and thus we are not including this question in our
proposal.
Improving how preferred language and need for interpreter services
data are collected is an important component of improving quality by
helping PAC providers and other providers understand patient needs and
develop plans to address them. For more information on the Preferred
Language and Interpreter Services data elements, we refer readers to
the document titled ``Proposed Specifications for IRF QRP
[[Page 17324]]
Measures and Standardized Patient Assessment Data Elements,'' available
on the website at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In an effort to standardize the submission of language data among
IRFs, HHAs, SNFs and LTCHs, for the purposes outlined in section
1899B(a)(1)(B) of the Act, while minimizing the reporting burden, we
are proposing to adopt the Preferred Language and Interpreter Services
data elements currently used on the MDS and LCDS, and described above,
as SPADEs with respect to the Social Determinants of Health category.
We are proposing to add the current Preferred Language and Interpreter
Services data elements from the MDS and LCDS to the IRF-PAI.
(3) Health Literacy
The Department of Health and Human Services defines health literacy
as ``the degree to which individuals have the capacity to obtain,
process, and understand basic health information and services needed to
make appropriate health decisions.'' \170\ Similar to language
barriers, low health literacy can interfere with communication between
the provider and patient and the ability for patients or their
caregivers to understand and follow treatment plans, including
medication management. Poor health literacy is linked to lower levels
of knowledge about health, worse health outcomes, and the receipt of
fewer preventive services, but higher medical costs and rates of
emergency department use.\171\
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\170\ U.S. Department of Health and Human Services, Office of
Disease Prevention and Health Promotion. National action plan to
improve health literacy. Washington (DC): Author; 2010.
\171\ National Academies of Sciences, Engineering, and Medicine.
2016. Accounting for social risk factors in Medicare payment:
Identifying social risk factors. Washington, DC: The National
Academies Press.
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Health literacy is prioritized by Healthy People 2020 as an
SDOH.\172\ Healthy People 2020 is a long-term, evidence-based effort
led by the Department of Health and Human Services that aims to
identify nationwide health improvement priorities and improve the
health of all Americans. Although not designated as a social risk
factor in NASEM's 2016 report on accounting for social risk factors in
Medicare payment, the NASEM noted that health literacy is impacted by
other social risk factors and can affect access to care as well as
quality of care and health outcomes.\173\ Assessing for health literacy
across PAC settings would facilitate better care coordination and
discharge planning. A significant challenge in assessing the health
literacy of individuals is avoiding excessive burden on patients and
health care providers. The majority of existing, validated health
literacy assessment tools use multiple screening items, generally with
no fewer than four, which would make them burdensome if adopted in MDS,
LCDS, IRF-PAI, and OASIS. The Single Item Literacy Screener (SILS)
question asks, ``How often do you need to have someone help you when
you read instructions, pamphlets, or other written material from your
doctor or pharmacy?'' Possible response options are: (1) Never; (2)
Rarely; (3) Sometimes; (4) Often; and (5) Always. The SILS question,
which assesses reading ability, (a primary component of health
literacy), tested reasonably well against the 36 item Short Test of
Functional Health Literacy in Adults (S-TOFHLA), a thoroughly vetted
and widely adopted health literacy test, in assessing the likelihood of
low health literacy in an adult sample from primary care practices
participating in the Vermont Diabetes Information
System.174 175 The S-TOFHLA is a more complex assessment
instrument developed using actual hospital related materials such as
prescription bottle labels and appointment slips, and often considered
the instrument of choice for a detailed evaluation of health
literacy.\176\ Furthermore, the S-TOFHLA instrument is proprietary and
subject to purchase for individual entities or users.\177\ Given that
SILS is publicly available, shorter and easier to administer than the
full health literacy screen, and research found that a positive result
on the SILS demonstrates an increased likelihood that an individual has
low health literacy, we are proposing to use the single-item reading
question for health literacy in the standardized data collection across
PAC settings. We believe that use of this data element will provide
sufficient information about the health literacy of IRF patients to
facilitate appropriate care planning, care coordination, and
interoperable data exchange across PAC settings.
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\172\ Social Determinants of Health. Healthy People 2020.
https://www.healthypeople.gov/2020/topics-objectives/topic/social-determinants-of-health. (February 2019).
\173\ U.S. Department of Health & Human Services, Office of the
Assistant Secretary for Planning and Evaluation. Report to Congress:
Social Risk Factors and Performance Under Medicare's Value-Based
Purchasing Programs. Available at https://aspe.hhs.gov/pdf-report/report-congress-social-risk-factors-and-performance-under-medicares-value-based-purchasing-programs. Washington, DC: 2016.
\174\ Morris, N.S., MacLean, C.D., Chew, L.D., & Littenberg, B.
(2006). The Single Item Literacy Screener: evaluation of a brief
instrument to identify limited reading ability. BMC family practice,
7, 21. doi:10.1186/1471-2296-7-21.
\175\ Brice, J.H., Foster, M.B., Principe, S., Moss, C., Shofer,
F.S., Falk, R.J., Ferris, M.E., DeWalt, D.A. (2013). Single-item or
two-item literacy screener to predict the S-TOFHLA among adult
hemodialysis patients. Patient Educ Couns. 94(1):71-5.
\176\ University of Miami, School of Nursing & Health Studies,
Center of Excellence for Health Disparities Research. Test of
Functional Health Literacy in Adults (TOFHLA). (March 2019).
Available from: https://elcentro.sonhs.miami.edu/research/measures-library/tofhla/index.html.
\177\ Nurss, J.R., Parker, R.M., Williams, M.V., &Baker, D.W.
David W. (2001). TOFHLA. Peppercorn Books & Press. Available from:
http://www.peppercornbooks.com/catalog/information.php?info_id=5.
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In addition, we received feedback during the December 13, 2018 SDOH
listening session on the importance of recognizing health literacy as
more than understanding written materials and filling out forms, as it
is also important to evaluate whether patients understand their
conditions. However, the NASEM recently recommended that health care
providers implement health literacy universal precautions instead of
taking steps to ensure care is provided at an appropriate literacy
level based on individualized assessment of health literacy.\178\ Given
the dearth of Medicare data on health literacy and gaps in addressing
health literacy in practice, we recommend the addition of a health
literacy data element.
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\178\ Hudson, S., Rikard, R.V., Staiculescu, I. & Edison, K.
(2017). Improving health and the bottom line: The case for health
literacy. In Building the case for health literacy: Proceedings of a
workshop. Washington, DC: The National Academies Press.
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The proposed Health Literacy data element is consistent with
considerations raised by NASEM and other stakeholders and research on
health literacy, which demonstrates an impact on health care use, cost,
and outcomes.\179\ For more information on the proposed Health Literacy
data element, we refer readers to the document titled ``Proposed
Specifications for IRF QRP Measures and Standardized Patient Assessment
Data Elements,'' available on the website at https://www.cms.gov/
Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-
Care-Quality-Initiatives/IMPACT-Act-of-
[[Page 17325]]
2014/IMPACT-Act-Downloads-and-Videos.html.
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\179\ National Academies of Sciences, Engineering, and Medicine.
2016. Accounting for Social Risk Factors in Medicare Payment:
Identifying Social Risk Factors. Washington, DC: The National
Academies Press.
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In an effort to standardize the submission of health literacy data
among IRFs, HHAs, SNFs and LTCHs, for the purposes outlined in section
1899B(a)(1)(B) of the Act, while minimizing the reporting burden, we
are proposing to adopt SILS question described above for the Health
Literacy data element as SPADE under the Social Determinants of Health
Category. We are proposing to add the Health Literacy data element to
the IRF-PAI.
(4) Transportation
Transportation barriers commonly affect access to necessary health
care, causing missed appointments, delayed care, and unfilled
prescriptions, all of which can have a negative impact on health
outcomes.\180\ Access to transportation for ongoing health care and
medication access needs, particularly for those with chronic diseases,
is essential to successful chronic disease management. Adopting a data
element to collect and analyze information regarding transportation
needs across PAC settings would facilitate the connection to programs
that can address identified needs. We are therefore proposing to adopt
as SPADE a single transportation data element that is from the Protocol
for Responding to and Assessing Patients' Assets, Risks, and
Experiences (PRAPARE) assessment tool and currently part of the
Accountable Health Communities (AHC) Screening Tool.
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\180\ Syed, S.T., Gerber, B.S., and Sharp, L.K. (2013).
Traveling Towards Disease: Transportation Barriers to Health Care
Access. J Community Health. 38(5): 976-993.
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The proposed Transportation data element from the PRAPARE tool
asks, ``Has lack of transportation kept you from medical appointments,
meetings, work, or from getting things needed for daily living?'' The
three response options are: (1) Yes, it has kept me from medical
appointments or from getting my medications; (2) Yes, it has kept me
from non-medical meetings, appointments, work, or from getting things
that I need; and (3) No. The patient would be given the option to
select all responses that apply. We are proposing to use the
transportation data element from the PRAPARE Tool, with permission from
National Association of Community Health Centers (NACHC), after
considering research on the importance of addressing transportation
needs as a critical SDOH.\181\
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\181\ Health Research & Educational Trust. (2017, November).
Social determinants of health series: Transportation and the role of
hospitals. Chicago, IL. Available at www.aha.org/transportation.www.aha.org/transportation.
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The proposed data element is responsive to research on the
importance of addressing transportation needs as a critical SDOH and
would adopt the Transportation item from the PRAPARE tool.\182\ This
data element comes from the national PRAPARE social determinants of
health assessment protocol, developed and owned by NACHC, in
partnership with the Association of Asian Pacific Community Health
Organization, the Oregon Primary Care Association, and the Institute
for Alternative Futures. Similarly the Transportation data element used
in the AHC Screening Tool was adapted from the PRAPARE tool. The AHC
screening tool was implemented by the Center for Medicare and Medicaid
Innovation's AHC Model and developed by a panel of interdisciplinary
experts that looked at evidence-based ways to measure SDOH, including
transportation. While the transportation access data element in the AHC
screening tool serves the same purposes as our proposed SPADE
collection about transportation barriers, the AHC tool has binary yes
or no response options that do not differentiate between challenges for
medical versus non-medical appointments and activities. We believe that
this is an important nuance for informing PAC discharge planning to a
community setting, as transportation needs for non-medical activities
may differ than for medical activities and should be taken into
account.\183\ We believe that use of this data element will provide
sufficient information about transportation barriers to medical and
non-medical care for IRF patients to facilitate appropriate discharge
planning and care coordination across PAC settings. As such, we are
proposing to adopt the Transportation data element from PRAPARE. More
information about development of the PRAPARE tool is available on the
website at https://protect2.fireeye.com/url?k=7cb6eb44-20e2f238-7cb6da7b-0cc47adc5fa2-1751cb986c8c2f8c&u=http://www.nachc.org/prapare.
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\182\ Health Research & Educational Trust. (2017, November).
Social determinants of health series: Transportation and the role of
hospitals. Chicago, IL. Available at www.aha.org/transportation.
\183\ Northwestern University. (2017). PROMIS Item Bank v. 1.0--
Emotional Distress--Anger--Short Form 1.
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In addition, we received stakeholder feedback during the December
13, 2018 SDOH listening session on the impact of transportation
barriers on unmet care needs. While recognizing that there is no
consensus in the field about whether providers should have
responsibility for resolving patient transportation needs, discussion
focused on the importance of assessing transportation barriers to
facilitate connections with available community resources.
Adding a Transportation data element to the collection of SPADE
would be an important step to identifying and addressing SDOH that
impact health outcomes and patient experience for Medicare
beneficiaries. For more information on the Transportation data element,
we refer readers to the document titled ``Proposed Specifications for
IRF QRP Measures and Standardized Patient Assessment Data Elements,''
available on the website at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In an effort to standardize the submission of transportation data
among IRFs, HHAs, SNFs and LTCHs, for the purposes outlined in section
1899B(a)(1)(B) of the Act, while minimizing the reporting burden, we
are proposing to adopt the Transportation data element described above
as SPADE with respect to the proposed Social Determinants of Health
category. If finalized as proposed, we would add the Transportation
data element to the IRF-PAI.
(5) Social Isolation
Distinct from loneliness, social isolation refers to an actual or
perceived lack of contact with other people, such as living alone or
residing in a remote area.184 185 Social isolation tends to
increase with age, is a risk factor for physical and mental illness,
and a predictor of mortality.186 187 188 Post-
[[Page 17326]]
acute care providers are well-suited to design and implement programs
to increase social engagement of patients, while also taking into
account individual needs and preferences. Adopting a data element to
collect and analyze information about social isolation in IRFs and
across PAC settings would facilitate the identification of patients who
are socially isolated and who may benefit from engagement efforts.
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\184\ Tomaka, J., Thompson, S., and Palacios, R. (2006). The
Relation of Social Isolation, Loneliness, and Social Support to
Disease Outcomes Among the Elderly. J of Aging and Health. 18(3):
359-384.
\185\ Social Connectedness and Engagement Technology for Long-
Term and Post-Acute Care: A Primer and Provider Selection Guide.
(2019). Leading Age. Available at https://www.leadingage.org/white-papers/social-connectedness-and-engagement-technology-long-term-and-post-acute-care-primer-and#1.1.
\186\ Landeiro, F., Barrows, P., Nuttall Musson, E., Gray, A.M.,
and Leal, J. (2017). Reducing Social Loneliness in Older People: A
Systematic Review Protocol. BMJ Open. 7(5): e013778.
\187\ Ong, A.D., Uchino, B.N., and Wethington, E. (2016).
Loneliness and Health in Older Adults: A Mini-Review and Synthesis.
Gerontology. 62:443-449.
\188\ Leigh-Hunt, N., Bagguley, D., Bash, K., Turner, V.,
Turnbull, S., Valtorta, N., and Caan, W. (2017). An overview of
systematic reviews on the public health consequences of social
isolation and loneliness. Public Health. 152:157-171.
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We are proposing to adopt as SPADE a single social isolation data
element that is currently part of the AHC Screening Tool. The AHC item
was selected from the Patient-Reported Outcomes Measurement Information
System (PROMIS[supreg]) Item Bank on Emotional Distress and asks, ``How
often do you feel lonely or isolated from those around you?'' The five
response options are: (1) Never; (2) Rarely; (3) Sometimes; (4) Often;
and (5) Always.\189\ The AHC Screening Tool was developed by a panel of
interdisciplinary experts that looked at evidence-based ways to measure
SDOH, including social isolation. More information about the AHC
Screening Tool is available on the website at https://innovation.cms.gov/Files/worksheets/ahcm-screeningtool.pdf.
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\189\ Northwestern University. (2017). PROMIS Item Bank v. 1.0--
Emotional Distress--Anger--Short Form 1.
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In addition, we received stakeholder feedback during the December
13, 2018 SDOH listening session on the value of receiving information
on social isolation for purposes of care planning. Some stakeholders
also recommended assessing social isolation as an SDOH as opposed to
social support.
The proposed Social Isolation data element is consistent with NASEM
considerations about social isolation as a function of social
relationships that impacts health outcomes and increases mortality
risk, as well as the current work of a NASEM committee examining how
social isolation and loneliness impact health outcomes in adults 50
years and older. We believe that adding a Social Isolation data element
would be an important component of better understanding patient
complexity and the care goals of patients, thereby facilitating care
coordination and continuity in care planning across PAC settings. For
more information on the Social Isolation data element, we refer readers
to the document titled ``Proposed Specifications for IRF QRP Measures
and Standardized Patient Assessment Data Elements,'' available on the
website at https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/IMPACT-Act-of-2014/IMPACT-Act-Downloads-and-Videos.html.
In an effort to standardize the submission of social isolation data
among IRFs, HHAs, SNFs and LTCHs, for the purposes outlined in section
1899B(a)(1)(B) of the Act, while minimizing the reporting burden, we
are proposing to adopt the Social Isolation data element described
above as SPADE with respect to the proposed Social Determinants of
Health category. We are proposing to add the Social Isolation data
element to the IRF-PAI.
We are soliciting comment on this proposal.
H. Form, Manner, and Timing of Data Submission Under the IRF QRP
1. Background
We refer readers to Sec. 412.634(b) for information regarding the
current policies for reporting IRF QRP data.
2. Update to the CMS System for Reporting Quality Measures and
Standardized Patient Assessment Data and Associated Procedural
Proposals
IRFs are currently required to submit IRF-PAI data to CMS using the
Quality Improvement and Evaluation System (QIES) Assessment and
Submission Processing (ASAP) system. We will be migrating to a new
internet Quality Improvement and Evaluation System (iQIES) that will
enable real-time upgrades, and we are proposing to designate that
system as the data submission system for the IRF QRP beginning October
1, 2019. We are proposing to revise Sec. 412.634(a)(1) by replacing
``Certification and Survey Provider Enhanced Reports (CASPER)'' with
``CMS designated data submission''. We are proposing to revise Sec.
412.634(d)(1) by replacing the reference to ``Quality Improvement and
Evaluation System Assessment Submission and Processing (QIES ASAP)
system'' with ``CMS designated data submission system''. We are
proposing to revise Sec. 412.634(d)(5) by replacing reference to the
``QIES ASAP'' with ``CMS designated data submission''. We are also
proposing to revise Sec. 412.634(f)(1) by replacing ``QIES'' with
``CMS designated data submission system''. In addition, we are
proposing to notify the public of any future changes to the CMS
designated system using subregulatory mechanisms, such as website
postings, listserv messaging, and webinars.
We invite public comment on our proposals.
3. Proposed Schedule for Reporting the Transfer of Health Information
Quality Measures Beginning With the FY 2022 IRF QRP
As discussed in section VIII.D. of this proposed rule, we are
proposing to adopt the Transfer of Health Information to the Provider-
Post-Acute Care (PAC) and Transfer of Health Information to the
Patient-Post-Acute Care (PAC) quality measures beginning with the FY
2022 IRF QRP. We also are proposing that IRFs would report the data on
those measures using the IRF-PAI. IRFs would be required to collect
data on both measures for patients beginning with patients discharged
on or after October 1, 2020. We refer readers to the FY 2018 IRF PPS
final rule (82 FR 36291 through 36292) for the data collection and
submission timeframes that we finalized for the IRF QRP.
We invite public comment on this proposal.
4. Proposed Schedule for Reporting Standardized Patient Assessment Data
Elements Beginning With the FY 2022 IRF QRP
As discussed in section IV.F. of this proposed rule, we are
proposing to adopt SPADEs beginning with the FY 2022 IRF QRP. We are
proposing that IRFs would report the data using the IRF-PAI. Similar to
the proposed schedule for reporting the Transfer of Health Information
to the Provider-Post-Acute Care (PAC) and Transfer of Health
Information to the Patient-Post-Acute Care (PAC) quality measures, IRFs
would be required to collect the SPADEs for all patients discharged on
or after October 1, 2020, at both admission and discharge. IRFs that
submit data with respect to admission for the Hearing, Vision, Race,
and Ethnicity SPADEs would be considered to have submitted data with
respect to discharges. We refer readers to the FY 2018 IRF PPS final
rule (82 FR 36291 through 36292) for the data collection and submission
timeframes that we finalized for the IRF QRP.
We invite public comment on this proposal.
5. Proposed Data Reporting on Patients for the IRF Quality Reporting
Program Beginning With the FY 2022 IRF QRP
We received public input suggesting that the quality measures used
in the IRF QRP should be calculated using data collected from all IRF
patients, regardless of the patients' payer. This input was provided to
us via comments requested about quality measure development on the CMS
Measures Management System Blueprint
[[Page 17327]]
website,\190\ as well as through comments we received from stakeholders
via our IRF QRP mailbox, and feedback received from the NQF-convened
MAP as part of their recommendations on Coordination Strategy for Post-
Acute Care and Long-Term Care Performance Measurement.\191\ Further, in
the FY 2018 IRF PPS proposed rule (82 FR 20740), we sought input on
expanding the reporting of quality measures to include all patients,
regardless of payer, so as to ensure that the IRF QRP makes publicly
available information regarding the quality of the services furnished
to the IRF population as a whole, rather than just those patients who
have Medicare.
---------------------------------------------------------------------------
\190\ Public Comment Summary Report Posting for Transfer of
Health Information and Care Preferences. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Post-Acute-Care-Quality-Initiatives/Downloads/Development-of-Cross-Setting-Transfer-of-Health-Information-Quality-Meas.pdf.
\191\ MAP Coordination Strategy for Post-Acute Care and Long-
Term Care Performance Measurement. Feb 2012. http://www.qualityforum.org/Publications/2012/02/MAP_Coordination_Strategy_for_Post-Acute_Care_and_Long-Term_Care_Performance_Measurement.aspx.
---------------------------------------------------------------------------
In response to that request for public input, several commenters,
including MedPAC, submitted comments stating that they would be
supportive of an effort to collect data specified under the IRF QRP
from all IRF patients regardless of their payer. Many commenters noted
that this would not be overly burdensome, as most of their
organizations' members currently complete the IRF-PAI on all patients,
regardless of their payer. A few commenters had concerns, including
recommending that CMS continue to align the patient assessment
instruments across PAC settings and whether the use of the data would
outweigh any additional reporting burden. For a more detailed
discussion, we refer readers to the FY 2018 IRF final rule (82 FR
36292). We have taken these concerns under consideration in proposing
this policy.
Further, given that we do not have access to other payer claims, we
believe that the most accurate representation of the quality provided
in IRFs would be best conveyed using data collected via the IRF-PAI on
all IRF patients, regardless of payer, for the purposes of the IRF QRP.
Medicare is the primary payer for approximately 60 percent of IRF
patients.\192\
---------------------------------------------------------------------------
\192\ National Academies of Sciences, Engineering, and Medicine.
2016. Accounting for social risk factors in Medicare payment:
Identifying social risk factors. Washington, DC: The National
Academies Press.
---------------------------------------------------------------------------
We also believe that data reporting on standardized patient
assessment data elements using IRF-PAI should include all IRF patients
for the same reasons for collecting data on all residents for the IRF
QRP's quality measures: To promote higher quality and more efficient
health care for Medicare beneficiaries and all patients receiving IRF
services, for example through the exchange of information and
longitudinal analysis of the data. With that, we believe that
collecting quality measure and standardized patient assessment data via
the IRF-PAI on all IRF patients ensures that quality care is provided
for Medicare beneficiaries, and patients receiving IRF services as a
whole. While we appreciate that collecting quality data on all patients
regardless of payer may create additional burden, we also note that the
effort to separate out Medicare beneficiaries from other patients is
also burdensome. We are aware that it is common practice for IRFs to
collect IRF-PAI data on all patients, regardless of their payer.
Further, we believe that patients may utilize various payer sources
for services received during their stay, for example being admitted
under one payer source including Medicare, and the payer source may
change during the patient stay which would require the restart of the
data collection and reporting in the midst of services rather than at
the actual admission. Collecting data on all IRF patients will provide
us with the most robust, accurate reflection of the quality of care
delivered to Medicare beneficiaries as compared with non-Medicare
patients and residents, and we intend to display the calculation of
this data on IRF Compare in the future. Accordingly, we are proposing
that IRFs collect data on all IRF patients to ensure that all patients,
regardless of their payer, are receiving the same care and that
provider metrics measure performance across the spectrum of patients.
Therefore, to meet the quality reporting requirements for IRFs for
the FY 2022 payment determination and each subsequent year, we propose
to expand the reporting of IRF-PAI data used for the IRF QRP to include
data on all patients, regardless of their payer, beginning with
patients discharged on or after October 1, 2020 for the FY 2022 IRF QRP
and the IRF-PAI V4.0, effective October 1, 2020.
We invite public comment on this proposal.
I. Proposed Policies Regarding Public Display of Measure Data for the
IRF QRP
Section 1886(j)(7)(E) of the Act requires the Secretary to
establish procedures for making the IRF QRP data available to the
public after ensuring that IRFs have the opportunity to review their
data prior to public display. Measure data are currently displayed on
the Inpatient Rehabilitation Facility Compare website, an interactive
web tool that assists individuals by providing information on IRF
quality of care. For more information on IRF Compare, we refer readers
to the website at https://www.medicare.gov/inpatientrehabilitationfacilitycompare/. For a more detailed discussion
about our policies regarding public display of IRF QRP measure data and
procedures for the opportunity to review and correct data and
information, we refer readers to the FY 2017 IRF PPS final rule (81 FR
52125 through 52131).
In this proposed rule, we are proposing to begin publicly
displaying data for the Drug Regimen Review Conducted With Follow-Up
for Identified Issues--PAC IRF QRP measure beginning CY 2020 or as soon
as technically feasible. We finalized the Drug Regimen Review Conducted
With Follow-Up for Identified Issues--PAC IRF QRP measure in the FY
2017 IRF PPS final rule (81 FR 52111 through 52116).
Data collection for this assessment-based measure began with
patients discharged on or after October 1, 2018. We are proposing to
display data based on four rolling quarters, initially using discharges
from January 1, 2019 through December 31, 2019 (Quarter 1 2019 through
Quarter 4 2019). To ensure the statistical reliability of the data, we
are proposing that we would not publicly report an IRF's performance on
the measure if the IRF had fewer than 20 eligible cases in any four
consecutive rolling quarters. IRFs that have fewer than 20 eligible
cases would be distinguished with a footnote that states, ``The number
of cases/patient stays is too small to publicly report.''
We invite public comment on these proposals.
J. Proposed Removal of the List of Compliant IRFs
In the FY 2016 IRF PPS final rule (80 FR 47125 through 47127), we
finalized that we would publish a list of IRFs that successfully met
the reporting requirements for the applicable payment determination on
the IRF QRP website and update the list on an annual basis.
We have received feedback from stakeholders that this list offers
minimal benefit. Although the posting of successful providers was the
final step in the applicable payment determination process, it does not
provide new information or clarification to the providers regarding
their annual
[[Page 17328]]
payment update status. Therefore, in this proposed rule, we are
proposing that we will no longer publish a list of compliant IRFs on
the IRF QRP website, effective beginning with the FY 2020 payment
determination.
We invite public comment on this proposal.
K. Method for Applying the Reduction to the FY 2020 IRF Increase Factor
for IRFs That Fail To Meet the Quality Reporting Requirements
As previously noted, section 1886(j)(7)(A)(i) of the Act requires
the application of a 2-percentage point reduction of the applicable
market basket increase factor for payments for discharges occurring
during such fiscal year for IRFs that fail to comply with the quality
data submission requirements. We propose to apply a 2-percentage point
reduction to the applicable FY 2020 proposed market basket increase
factor in calculating an adjusted FY 2020 proposed standard payment
conversion factor to apply to payments for only those IRFs that failed
to comply with the data submission requirements. As previously noted,
application of the 2-percentage point reduction may result in an update
that is less than 0.0 for a fiscal year and in payment rates for a
fiscal year being less than such payment rates for the preceding fiscal
year. Also, reporting-based reductions to the market basket increase
factor will not be cumulative; they will only apply for the FY
involved.
We invite public comment on the proposed method for applying the
reduction to the FY 2020 IRF increase factor for IRFs that fail to meet
the quality reporting requirements.
Table 20 shows the calculation of the proposed adjusted FY 2020
standard payment conversion factor that will be used to compute IRF PPS
payment rates for any IRF that failed to meet the quality reporting
requirements for the applicable reporting period.
[GRAPHIC] [TIFF OMITTED] TP24AP19.020
IX. Collection of Information Requirements
A. Statutory Requirement for Solicitation of Comments
Under the Paperwork Reduction Act of 1995 (PRA), we are required to
provide 60-day notice in the Federal Register and solicit public
comment before a collection of information requirement is submitted to
the OMB for review and approval. To fairly evaluate whether an
information collection should be approved by OMB, section 3506(c)(2)(A)
of the PRA requires that we solicit comment on the following issues:
The need for the information collection and its usefulness
in carrying out the proper functions of our agency;
The accuracy of our estimate of the information collection
burden;
The quality, utility, and clarity of the information to be
collected; and
Recommendations to minimize the information collection
burden on the affected public, including automated collection
techniques.
This proposed rule makes reference to associated information
collections that are not discussed in the regulation text contained in
this document.
B. Collection of Information Requirements for Updates Related to the
IRF QRP
An IRF that does not meet the requirements of the IRF QRP for a
fiscal year will receive a 2 percentage point reduction to its
otherwise applicable annual increase factor for that fiscal year.
Information is not currently available to determine the precise number
of IRFs that will receive less than the full annual increase factor for
FY 2020 due to non-compliance with the requirements of the IRF QRP.
We believe that the burden associated with the IRF QRP is the time
and effort associated with complying with the requirements of the IRF
QRP. As of February 1, 2019, there are approximately 1,119 IRFs
reporting quality data to CMS. For the purposes of calculating the
costs associated with the collection of information requirements, we
obtained mean hourly wages for these staff from the U.S. Bureau of
Labor Statistics' May 2017 National Occupational Employment and Wage
Estimates (http://www.bls.gov/oes/current/oes_nat.htm). To account for
overhead and fringe benefits, we have doubled the hourly wage. These
amounts are detailed in Table 21.
[GRAPHIC] [TIFF OMITTED] TP24AP19.021
[[Page 17329]]
As discussed in section VIII.D. of this proposed rule, we are
proposing to adopt two new measures, (1) Transfer of Health Information
to the Provider-Post-Acute Care (PAC); and (2) Transfer of Health
Information to the Patient-Post-Acute Care (PAC), beginning with the FY
2022 IRF QRP. As a result, the estimated burden and cost for IRFs for
complying with requirements of the FY 2022 IRF QRP will increase.
Specifically, we believe that there will be a 0.9 minute addition in
clinical staff time to report data per patient stay. We estimate
409,982 discharges from 1,119 IRFs annually. This equates to an
increase of 8,200 hours in burden for all IRFs (0.02 hours per
assessment x 409,982 discharges). Given 0.5 minutes of RN time at
$70.72 per hour and 0.4 minutes of LVN time at $43.96 per hour, we
estimate that the total cost will be increased by $330 per IRF
annually, or $369,082 for all IRFs annually. This increase in burden
will be accounted for in the information collection under OMB control
number (0938-0842), which expires December 31, 2021.
In addition, we are proposing to add the standardized patient
assessment data elements described in section VIII.F beginning with the
FY 2022 IRF QRP. As a result, the estimated burden and cost for IRFs
for complying with requirements of the FY 2022 IRF QRP will be
increased. Specifically, we believe that there will be an addition of
7.4 minutes on admission, and 11.1 minutes on discharge, for a total of
8.9 minutes of additional clinical staff time to report data per
patient stay. We estimate 409,982 discharges from 1,119 IRFs annually.
This equates to an increase of 131,194 hours in burden for all IRFs
(0.32 hours per assessment x 409,982 discharges). Given 11.3 minutes of
RN time at $70.72 per hour and 7.6 minutes of LVN time at $43.96 per
hour, we estimate that the total cost will be increased by $6,926 per
IRF annually, or $7,750,194 for all IRFs annually. This increase in
burden will be accounted for in the information collection under OMB
control number (0938-0842), which expires December 31, 2021.
In summary, the proposed IRF QRP quality measures and standardized
patient assessment data elements will result in a burden addition of
$7,256 per IRF annually, and $8,119,276 for all IRFs annually.
C. Submission of PRA-Related Comments
We have submitted a copy of this rule's information collection and
recordkeeping requirements to OMB for review and approval. These
requirements are not effective until they have been approved by the
OMB.
To obtain copies of the supporting statement and any related forms
for the proposed collections discussed above, please visit CMS's
website at www.cms.hhs.gov/PaperworkReductionActof1995, or call the
Reports Clearance Office at 410-786-1326.
We invite public comments on these potential information collection
requirements. If you wish to comment, please refer to the DATES and
ADDRESSES sections of this rulemaking for instructions. We will
consider all ICR-related comments received by the date and time
specified in the DATES section, and, when we proceed with a subsequent
document, we will respond to the comments in the preamble to that
document.
X. Response to Comments
Because of the large number of public comments we normally receive
on Federal Register documents, we are not able to acknowledge or
respond to them individually. We will consider all comments we receive
by the date and time specified in the DATES section of this preamble,
and, when we proceed with a subsequent document, we will respond to the
comments in the preamble to that document.
XI. Regulatory Impact Analysis
A. Statement of Need
This proposed rule updates the IRF prospective payment rates for FY
2020 as required under section 1886(j)(3)(C) of the Act. It responds to
section 1886(j)(5) of the Act, which requires the Secretary to publish
in the Federal Register on or before the August 1 that precedes the
start of each fiscal year, the classification and weighting factors for
the IRF PPS's case-mix groups, and a description of the methodology and
data used in computing the prospective payment rates for that fiscal
year.
This proposed rule also implements sections 1886(j)(3)(C) of the
Act. Section 1886(j)(3)(C)(ii)(I) of the Act requires the Secretary to
apply a multifactor productivity adjustment to the market basket
increase factor. The productivity adjustment applies to FYs from 2012
forward.
Furthermore, this proposed rule also adopts policy changes under
the statutory discretion afforded to the Secretary under section
1886(j)(7) of the Act. Specifically, we are proposing to rebase and
revise the IRF market basket to reflect a 2016 base year rather than
the current 2012 base year, revise the CMGs, make a technical
correction to the regulatory language to indicate that that the
determination of whether a treating physician has specialized training
and experience in inpatient rehabilitation is made by the IRF and
update regulatory language related to IRF QRP data collection.
B. Overall Impact
We have examined the impacts of this rule as required by Executive
Order 12866 on Regulatory Planning and Review (September 30, 1993),
Executive Order 13563 on Improving Regulation and Regulatory Review
(January 18, 2011), the Regulatory Flexibility Act (RFA) (September 19,
1980, Pub. L. 96-354), section 1102(b) of the Act, section 202 of the
Unfunded Mandates Reform Act of 1995 (March 22, 1995; Pub. L. 104-4),
Executive Order 13132 on Federalism (August 4, 1999), the Congressional
Review Act (5 U.S.C. 804(2) and Executive Order 13771 on Reducing
Regulation and Controlling Regulatory Costs (January 30, 2017).
Executive Orders 12866 and 13563 direct agencies to assess all
costs and benefits of available regulatory alternatives and, if
regulation is necessary, to select regulatory approaches that maximize
net benefits (including potential economic, environmental, public
health and safety effects, distributive impacts, and equity). Section
3(f) of Executive Order 12866 defines a ``significant regulatory
action'' as an action that is likely to result in a rule: (1) Having an
annual effect on the economy of $100 million or more in any 1 year, or
adversely and materially affecting a sector of the economy,
productivity, competition, jobs, the environment, public health or
safety, or state, local or tribal governments or communities (also
referred to as ``economically significant''); (2) creating a serious
inconsistency or otherwise interfering with an action taken or planned
by another agency; (3) materially altering the budgetary impacts of
entitlement grants, user fees, or loan programs or the rights and
obligations of recipients thereof; or (4) raising novel legal or policy
issues arising out of legal mandates, the President's priorities, or
the principles set forth in the Executive Order.
A regulatory impact analysis (RIA) must be prepared for major rules
with economically significant effects ($100 million or more in any 1
year). We estimate the total impact of the policy updates described in
this proposed rule by comparing the estimated payments in FY 2020 with
those in FY 2019. This analysis results in an estimated $195 million
increase for FY 2020 IRF PPS payments. Additionally we estimate that
[[Page 17330]]
costs associated with the proposals to update the reporting
requirements under the IRF quality reporting program result in an
estimated $8.1 million addition in costs in FY 2020 for IRFs. We
estimate that this rulemaking is ``economically significant'' as
measured by the $100 million threshold, and hence also a major rule
under the Congressional Review Act. Also, the rule has been reviewed by
OMB. Accordingly, we have prepared a Regulatory Impact Analysis that,
to the best of our ability, presents the costs and benefits of the
rulemaking.
C. Anticipated Effects
1. Effects on IRFs
The RFA requires agencies to analyze options for regulatory relief
of small entities, if a rule has a significant impact on a substantial
number of small entities. For purposes of the RFA, small entities
include small businesses, nonprofit organizations, and small
governmental jurisdictions. Most IRFs and most other providers and
suppliers are small entities, either by having revenues of $7.5 million
to $38.5 million or less in any 1 year depending on industry
classification, or by being nonprofit organizations that are not
dominant in their markets. (For details, see the Small Business
Administration's final rule that set forth size standards for health
care industries, at 65 FR 69432 at http://www.sba.gov/sites/default/files/files/Size_Standards_Table.pdf, effective March 26, 2012 and
updated on February 26, 2016.) Because we lack data on individual
hospital receipts, we cannot determine the number of small proprietary
IRFs or the proportion of IRFs' revenue that is derived from Medicare
payments. Therefore, we assume that all IRFs (an approximate total of
1,120 IRFs, of which approximately 55 percent are nonprofit facilities)
are considered small entities and that Medicare payment constitutes the
majority of their revenues. The HHS generally uses a revenue impact of
3 to 5 percent as a significance threshold under the RFA. As shown in
Table 22, we estimate that the net revenue impact of this proposed rule
on all IRFs is to increase estimated payments by approximately 2.3
percent. The rates and policies set forth in this proposed rule will
not have a significant impact (not greater than 3 percent) on a
substantial number of small entities. Medicare Administrative
Contractors are not considered to be small entities. Individuals and
states are not included in the definition of a small entity.
In addition, section 1102(b) of the Act requires us to prepare a
regulatory impact analysis if a rule may have a significant impact on
the operations of a substantial number of small rural hospitals. This
analysis must conform to the provisions of section 603 of the RFA. For
purposes of section 1102(b) of the Act, we define a small rural
hospital as a hospital that is located outside of a Metropolitan
Statistical Area and has fewer than 100 beds. As discussed in detail
below in this section, the rates and policies set forth in this
proposed rule will not have a significant impact (not greater than 3
percent) on a substantial number of rural hospitals based on the data
of the 136 rural units and 11 rural hospitals in our database of 1,119
IRFs for which data were available.
Section 202 of the Unfunded Mandates Reform Act of 1995 (Pub. L.
104-04, enacted on March 22, 1995) (UMRA) also requires that agencies
assess anticipated costs and benefits before issuing any rule whose
mandates require spending in any 1 year of $100 million in 1995
dollars, updated annually for inflation. In 2019, that threshold is
approximately $154 million. This proposed rule does not mandate any
requirements for State, local, or tribal governments, or for the
private sector.
Executive Order 13132 establishes certain requirements that an
agency must meet when it issues a proposed rule (and subsequent final
rule) that imposes substantial direct requirement costs on state and
local governments, preempts state law, or otherwise has federalism
implications. As stated, this proposed rule will not have a substantial
effect on state and local governments, preempt state law, or otherwise
have a federalism implication.
Executive Order 13771, titled Reducing Regulation and Controlling
Regulatory Costs, was issued on January 30, 2017 and requires that the
costs associated with significant new regulations ``shall, to the
extent permitted by law, be offset by the elimination of existing costs
associated with at least two prior regulations.'' This proposed rule is
considered an E.O. 13771 deregulatory action. We estimate that this
rule would generate $6.18 million in annualized cost, discounted at 7
percent relative to year 2016, over a perpetual time horizon. Details
on the estimated costs of this rule can be found in the preceding
analyses.
2. Detailed Economic Analysis
This proposed rule updates to the IRF PPS rates contained in the FY
2019 IRF PPS final rule (83 FR 38514). Specifically, this proposed rule
updates the CMG relative weights and average length of stay values, the
wage index, and the outlier threshold for high-cost cases. This
proposed rule applies a MFP adjustment to the FY 2020 IRF market basket
increase factor in accordance with section 1886(j)(3)(C)(ii)(I) of the
Act. Further, this proposed rule proposes to rebase and revise the IRF
market basket to reflect a 2016 base year rather than the current 2012
base year, revise the CMGs based on FY 2017 and 2018 data and to make a
technical correction to the regulatory language to indicate that the
determination of whether a treating physician has specialized training
and experience in inpatient rehabilitation is made by the IRF.
We estimate that the impact of the changes and updates described in
this proposed rule would be a net estimated increase of $195 million in
payments to IRF providers. This estimate does not include the
implementation of the required 2 percentage point reduction of the
market basket increase factor for any IRF that fails to meet the IRF
quality reporting requirements (as discussed in section VIII.J. of this
proposed rule). The impact analysis in Table 22 of this proposed rule
represents the projected effects of the updates to IRF PPS payments for
FY 2020 compared with the estimated IRF PPS payments in FY 2019. We
determine the effects by estimating payments while holding all other
payment variables constant. We use the best data available, but we do
not attempt to predict behavioral responses to these changes, and we do
not make adjustments for future changes in such variables as number of
discharges or case-mix.
We note that certain events may combine to limit the scope or
accuracy of our impact analysis, because such an analysis is future-
oriented and, thus, susceptible to forecasting errors because of other
changes in the forecasted impact time period. Some examples could be
legislative changes made by the Congress to the Medicare program that
would impact program funding, or changes specifically related to IRFs.
Although some of these changes may not necessarily be specific to the
IRF PPS, the nature of the Medicare program is such that the changes
may interact, and the complexity of the interaction of these changes
could make it difficult to predict accurately the full scope of the
impact upon IRFs.
In updating the rates for FY 2020, we are proposing standard annual
revisions described in this proposed rule (for example, the update to
the wage and market basket indexes used to adjust the
[[Page 17331]]
federal rates). We are also implementing a productivity adjustment to
the FY 2020 IRF market basket increase factor in accordance with
section 1886(j)(3)(C)(ii)(I) of the Act. We estimate the total increase
in payments to IRFs in FY 2020, relative to FY 2019, will be
approximately $195 million.
This estimate is derived from the application of the FY 2020 IRF
market basket increase factor, as reduced by a productivity adjustment
in accordance with section 1886(j)(3)(C)(ii)(I) of the Act, which
yields an estimated increase in aggregate payments to IRFs of $210
million. Furthermore, there is an additional estimated $15 million
decrease in aggregate payments to IRFs due to the proposed update to
the outlier threshold amount. Outlier payments are estimated to
decrease from approximately 3.2 percent in FY 2019 to 3.0 percent in FY
2020. Therefore, summed together, we estimate that these updates will
result in a net increase in estimated payments of $195 million from FY
2019 to FY 2020.
The effects of the proposed updates that impact IRF PPS payment
rates are shown in Table 22. The following proposed updates that affect
the IRF PPS payment rates are discussed separately below:
The effects of the proposed update to the outlier
threshold amount, from approximately 3.2 percent to 3.0 percent of
total estimated payments for FY 2020, consistent with section
1886(j)(4) of the Act.
The effects of the proposed annual market basket update
(using the IRF market basket) to IRF PPS payment rates, as required by
section 1886(j)(3)(A)(i) and section 1886(j)(3)(C) of the Act,
including a productivity adjustment in accordance with section
1886(j)(3)(C)(i)(I) of the Act.
The effects of applying the proposed budget-neutral labor-
related share and wage index adjustment, as required under section
1886(j)(6) of the Act.
The effects of the proposed budget-neutral changes to the
CMGs, relative weights and average length of stay values, under the
authority of section 1886(j)(2)(C)(i) of the Act.
The total change in estimated payments based on the
proposed FY 2020 payment changes relative to the estimated FY 2019
payments.
3. Description of Table 22
Table 22 categorizes IRFs by geographic location, including urban
or rural location, and location for CMS's 9 Census divisions (as
defined on the cost report) of the country. In addition, the table
divides IRFs into those that are separate rehabilitation hospitals
(otherwise called freestanding hospitals in this section), those that
are rehabilitation units of a hospital (otherwise called hospital units
in this section), rural or urban facilities, ownership (otherwise
called for-profit, non-profit, and government), by teaching status, and
by DSH PP. The top row of Table 22 shows the overall impact on the
1,119 IRFs included in the analysis.
The next 12 rows of Table 22 contain IRFs categorized according to
their geographic location, designation as either a freestanding
hospital or a unit of a hospital, and by type of ownership; all urban,
which is further divided into urban units of a hospital, urban
freestanding hospitals, and by type of ownership; and all rural, which
is further divided into rural units of a hospital, rural freestanding
hospitals, and by type of ownership. There are 972 IRFs located in
urban areas included in our analysis. Among these, there are 696 IRF
units of hospitals located in urban areas and 276 freestanding IRF
hospitals located in urban areas. There are 147 IRFs located in rural
areas included in our analysis. Among these, there are 136 IRF units of
hospitals located in rural areas and 11 freestanding IRF hospitals
located in rural areas. There are 393 for-profit IRFs. Among these,
there are 357 IRFs in urban areas and 36 IRFs in rural areas. There are
612 non-profit IRFs. Among these, there are 522 urban IRFs and 90 rural
IRFs. There are 114 government-owned IRFs. Among these, there are 93
urban IRFs and 21 rural IRFs.
The remaining four parts of Table 22 show IRFs grouped by their
geographic location within a region, by teaching status, and by DSH PP.
First, IRFs located in urban areas are categorized for their location
within a particular one of the nine Census geographic regions. Second,
IRFs located in rural areas are categorized for their location within a
particular one of the nine Census geographic regions. In some cases,
especially for rural IRFs located in the New England, Mountain, and
Pacific regions, the number of IRFs represented is small. IRFs are then
grouped by teaching status, including non-teaching IRFs, IRFs with an
intern and resident to average daily census (ADC) ratio less than 10
percent, IRFs with an intern and resident to ADC ratio greater than or
equal to 10 percent and less than or equal to 19 percent, and IRFs with
an intern and resident to ADC ratio greater than 19 percent. Finally,
IRFs are grouped by DSH PP, including IRFs with zero DSH PP, IRFs with
a DSH PP less than 5 percent, IRFs with a DSH PP between 5 and less
than 10 percent, IRFs with a DSH PP between 10 and 20 percent, and IRFs
with a DSH PP greater than 20 percent.
The estimated impacts of each policy described in this rule to the
facility categories listed are shown in the columns of Table 22. The
description of each column is as follows:
Column (1) shows the facility classification categories.
Column (2) shows the number of IRFs in each category in
our FY 2020 analysis file.
Column (3) shows the number of cases in each category in
our FY 2020 analysis file.
Column (4) shows the estimated effect of the proposed
adjustment to the outlier threshold amount.
Column (5) shows the estimated effect of the proposed
update to the IRF labor-related share and wage index, in a budget-
neutral manner.
Column (6) shows the estimated effect of the proposed
update to the CMGs, relative weights, and average length of stay
values, in a budget-neutral manner.
Column (7) compares our estimates of the payments per
discharge, incorporating all of the policies reflected in this proposed
rule for FY 2020 to our estimates of payments per discharge in FY 2019.
The average estimated increase for all IRFs is approximately 2.3
percent. This estimated net increase includes the effects of the
proposed IRF market basket increase factor for FY 2020 of 3.0 percent,
reduced by a productivity adjustment of 0.5 percentage point in
accordance with section 1886(j)(3)(C)(ii)(I) of the Act. It also
includes the approximate 0.2 percent overall decrease in estimated IRF
outlier payments from the proposed update to the outlier threshold
amount. Since we are making the updates to the IRF wage index and the
CMG relative weights in a budget-neutral manner, they will not be
expected to affect total estimated IRF payments in the aggregate.
However, as described in more detail in each section, they will be
expected to affect the estimated distribution of payments among
providers.
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4. Impact of the Proposed Update to the Outlier Threshold Amount
The estimated effects of the proposed update to the outlier
threshold adjustment are presented in column 4 of Table 22. In the FY
2019 IRF PPS final rule (83 FR 38531 through 38532), we used FY 2017
IRF claims data (the best, most complete data available at that time)
to set the outlier threshold amount for FY 2019 so that estimated
outlier payments would equal 3 percent of total estimated payments for
FY 2019.
For this proposed rule, we are using preliminary FY 2018 IRF claims
data, and, based on that preliminary analysis, we estimated that IRF
outlier payments as a percentage of total estimated IRF payments would
be 3.2 percent in FY 2019. Thus, we propose to adjust the outlier
threshold amount in this proposed rule to set total estimated outlier
payments equal to 3 percent of total estimated payments in FY 2020.The
estimated change in total IRF payments for FY 2020, therefore, includes
an approximate 0.2 percent decrease in payments because the estimated
outlier portion of total payments is estimated to decrease from
approximately 3.2 percent to 3 percent.
The impact of this proposed outlier adjustment update (as shown in
column 4 of Table 22) is to decrease estimated overall payments to IRFs
by about 0.2 percent. We estimate the largest decrease in payments from
the update to the outlier threshold amount to be 0.6 percent for rural
IRFs in the Pacific region.
5. Impact of the Proposed CBSA Wage Index and Labor-Related Share
In column 5 of Table 22, we present the effects of the proposed
budget-neutral update of the wage index and labor-related share. The
proposed changes to the wage index and the labor-related share are
discussed together because the wage index is applied to the labor-
related share portion of payments, so the proposed changes in the two
have a combined effect on payments to providers. As discussed in
section V.E. of this proposed rule, we are proposing to update the
labor-related share from 70.5 percent in FY 2019 to 72.6 percent in FY
2020.
6. Impact of the Proposed Update to the CMG Relative Weights and
Average Length of Stay Values.
In column 6 of Table 22, we present the effects of the proposed
budget-neutral update of the CMGs, relative weights and average length
of stay values. In the aggregate, we do not estimate that these
proposed updates will affect overall estimated payments of IRFs.
However, we do expect these updates to have small distributional
effects.
7. Effects of the Requirements for the IRF QRP for FY 2020
In accordance with section 1886(j)(7)(A) of the Act, the Secretary
must reduce by 2 percentage points the market basket increase factor
otherwise applicable to an IRF for a fiscal year if the IRF does not
comply with the requirements of the IRF QRP for that fiscal year. In
section VIII.J of this proposed rule, we discuss the proposed method
for applying the 2 percentage point reduction to IRFs that fail to meet
the IRF QRP requirements.
As discussed in section VIII.D. of this proposed rule, we are
proposing to add two measures to the IRF QRP (1) Transfer of Health
Information to the Provider--Post-Acute Care (PAC); and (2) Transfer of
Health Information to the Patient--Post-Acute Care (PAC), beginning
with the FY 2022 IRF QRP. We are also proposing to add standardized
patient assessment data elements, as discussed in section IV.G of this
proposed rule. We describe the estimated burden and cost reductions for
both of these measures in section VIII.C of this proposed rule. In
summary, the proposed changes to the IRF QRP will result in a burden
addition of $7,806 per IRF annually, and $8,119,276 for all IRFs
annually.
We intend to continue to closely monitor the effects of the IRF QRP
on IRFs and to help perpetuate successful reporting outcomes through
ongoing stakeholder education, national trainings, IRF announcements,
website postings, CMS Open Door Forums, and general and technical help
desks.
D. Alternatives Considered
The following is a discussion of the alternatives considered for
the IRF PPS updates contained in this proposed rule.
Section 1886(j)(3)(C) of the Act requires the Secretary to update
the IRF PPS payment rates by an increase factor that reflects changes
over time in the prices of an appropriate mix of goods and services
included in the covered IRF services.
We are proposing a market basket increase factor for FY 2020 that
is based on a proposed rebased market basket reflecting a 2016 base
year. We considered the alternative of continuing to use the IRF market
basket without rebasing to determine the market basket increase factor
for FY 2020. However, we typically rebase and revise the market baskets
for the various PPS every 4 to 5 years so that the cost weights and
price proxies reflect more recent data. Therefore, we believe it is
more technically appropriate to use a 2016-based IRF market basket
since it allows for the FY 2020 market basket increase factor to
reflect a more up-to-date cost structure experienced by IRFs.
As noted previously in this proposed rule, section
1886(j)(3)(C)(ii)(I) of the Act requires the Secretary to apply a
productivity adjustment to the market basket increase factor for FY
2020. Thus, in accordance with section 1886(j)(3)(C) of the Act, we
propose to update the IRF prospective payments in this proposed rule by
2.5 percent (which equals the
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proposed 3.0 percent estimated IRF market basket increase factor for FY
2020 reduced by a 0.5 percentage point proposed productivity adjustment
as determined under section 1886(b)(3)(B)(xi)(II) of the Act (as
required by section 1886(j)(3)(C)(ii)(I) of the Act)).
As we finalized in the FY 2019 IRF PPS final rule (83 FR 38514) use
of the Quality Indicators items in determining payment and the
associated CMG and CMG relative weight revisions using two years of
data (FY 2017 and FY 2018) beginning with FY 2020, we did not consider
any alternative to proposing these changes.
However, we did consider whether or not to apply a weighting
methodology to the IRF motor score that was finalized in the FY 2019
IRF PPS final rule (83 FR 38514) to assign patients to CMGs beginning
in FY 2020. In light of recent analysis that indicates that weighting
the motor score would improve the accuracy of payments under the IRF
PPS, we believe that it is appropriate to propose to weight the motor
score that would be effective on October 1, 2019.
We considered not removing the item GG0170A1 Roll left and right
from the composition of the motor score. However, this item did not
behave as expected in the models considered to develop the weights.
Therefore, we believe it is appropriate to propose to remove this item
from the construction of the weighted motor score.
We considered updating facility-level adjustment factors for FY
2020. However, as discussed in more detail in the FY 2015 final rule
(79 FR 45872), we believe that freezing the facility-level adjustments
at FY 2014 levels for FY 2015 and all subsequent years (unless and
until the data indicate that they need to be further updated) will
allow us an opportunity to monitor the effects of the substantial
changes to the adjustment factors for FY 2014, and will allow IRFs time
to adjust to the previous changes.
We considered not updating the IRF wage index to use the concurrent
fiscal year's IPPS wage index and instead continuing to use a one-year
lag of the IPPS wage index. However, we believe that updating the IRF
wage index based on the concurrent year's IPPS wage index will better
align the data across acute and post-acute care settings in support of
our efforts to move toward more unified Medicare payments across post-
acute care settings.
We considered maintaining the existing outlier threshold amount for
FY 2020. However, analysis of updated FY 2020 data indicates that
estimated outlier payments would be higher than 3 percent of total
estimated payments for FY 2020, by approximately 0.2 percent, unless we
updated the outlier threshold amount. Consequently, we propose
adjusting the outlier threshold amount in this proposed rule to reflect
a 0.2 percent decrease thereby setting the total outlier payments equal
to 3 percent, instead of 3.2 percent, of aggregate estimated payments
in FY 2020.
We considered not amending Sec. 412.622(a)(3)(iv) to clarify that
the determination as to whether a physician qualifies as a
rehabilitation physician (that is, a licensed physician with
specialized training and experience in inpatient rehabilitation is made
by the IRF. However, we believe that it is important to clarify this
definition to ensure that IRF providers and Medicare contractors have a
shared understanding of these regulatory requirements.
E. Regulatory Review Costs
If regulations impose administrative costs on private entities,
such as the time needed to read and interpret this proposed rule, we
should estimate the cost associated with regulatory review. Due to the
uncertainty involved with accurately quantifying the number of entities
that will review the rule, we assume that the total number of unique
commenters on the FY 2019 IRF PPS proposed rule will be the number of
reviewers of this proposed rule. We acknowledge that this assumption
may understate or overstate the costs of reviewing this proposed rule.
It is possible that not all commenters reviewed the FY 2019 IRF PPS
proposed rule in detail, and it is also possible that some reviewers
chose not to comment on the proposed rule. For these reasons we thought
that the number of past commenters would be a fair estimate of the
number of reviewers of this proposed rule.
We also recognize that different types of entities are in many
cases affected by mutually exclusive sections of this proposed rule,
and therefore for the purposes of our estimate we assume that each
reviewer reads approximately 50 percent of the rule. We sought comments
on this assumption.
Using the wage information from the BLS for medical and health
service managers (Code 11-9111), we estimate that the cost of reviewing
this rule is $107.38 per hour, including overhead and fringe benefits
(https://www.bls.gov/oes/current/oes_nat.htm). Assuming an average
reading speed, we estimate that it would take approximately 2 hours for
the staff to review half of this proposed rule. For each IRF that
reviews the rule, the estimated cost is $214.76 (2 hours x $107.38).
Therefore, we estimate that the total cost of reviewing this regulation
is $23,194.08 ($214.76 x 108 reviewers).
F. Accounting Statement and Table
As required by OMB Circular A-4 (available at http://www.whitehouse.gov/sites/default/files/omb/assets/omb/circulars/a004/a-4.pdf), in Table 23, we have prepared an accounting statement showing
the classification of the expenditures associated with the provisions
of this proposed rule. Table 23 provides our best estimate of the
increase in Medicare payments under the IRF PPS as a result of the
proposed updates presented in this proposed rule based on the data for
1,119 IRFs in our database. In addition, Table 23 presents the costs
associated with the new IRF quality reporting program requirements for
FY 2020.
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G. Conclusion
Overall, the estimated payments per discharge for IRFs in FY 2020
are projected to increase by 2.3 percent, compared with the estimated
payments in FY 2019, as reflected in column 7 of Table 22.
IRF payments per discharge are estimated to increase by 2.2 percent
in urban areas and 4.3 percent in rural areas, compared with estimated
FY 2019 payments. Payments per discharge to rehabilitation units are
estimated to increase 4.8 percent in urban areas and 5.6 percent in
rural areas. Payments per discharge to freestanding rehabilitation
hospitals are estimated to increase 0.0 percent in urban areas and
decrease 2.0 percent in rural areas.
Overall, IRFs are estimated to experience a net increase in
payments as a result of the proposed policies in this proposed rule.
The largest payment increase is estimated to be a 6.9 percent increase
for rural government IRFs. The analysis above, together with the
remainder of this preamble, provides a Regulatory Impact Analysis.
In accordance with the provisions of Executive Order 12866, this
regulation was reviewed by the Office of Management and Budget.
List of Subjects in 42 CFR Part 412
Administrative practice and procedure, Health facilities, Medicare,
Puerto Rico, Reporting and recordkeeping requirements.
For the reasons set forth in the preamble, the Centers for Medicare
& Medicaid Services proposes to amend 42 CFR chapter IV as follows:
PART 412--PROSPECTIVE PAYMENT SYSTEMS FOR INPATIENT HOSPITAL
SERVICES
0
1. The authority citation for part 412 is revised to read as follows:
Authority: 42 U.S.C. 1302 and 1395hh.
0
2. Section 412.622 is amended by--
0
a. Revising paragraphs (a)(3)(iv), (a)(4)(i)(A), (a)(4)(iii)(A), and
(a)(5)(i); and
0
b. Adding paragraph (c).
The revisions and addition read as follows:
Sec. 412.622 Basis of payment.
(a) * * *
(3) * * *
(iv) Requires physician supervision by a rehabilitation physician.
The requirement for medical supervision means that the rehabilitation
physician must conduct face-to-face visits with the patient at least 3
days per week throughout the patient's stay in the IRF to assess the
patient both medically and functionally, as well as to modify the
course of treatment as needed to maximize the patient's capacity to
benefit from the rehabilitation process. The post-admission physician
evaluation described in paragraph (a)(4)(ii) of this section may count
as one of the face-to-face visits.
(4) * * *
(i) * * *
(A) It is conducted by a licensed or certified clinician(s)
designated by a rehabilitation physician within the 48 hours
immediately preceding the IRF admission. A preadmission screening that
includes all of the required elements, but that is conducted more than
48 hours immediately preceding the IRF admission, will be accepted as
long as an update is conducted in person or by telephone to update the
patient's medical and functional status within the 48 hours immediately
preceding the IRF admission and is documented in the patient's medical
record.
* * * * *
(iii) * * *
(A) It is developed by a rehabilitation physician with input from
the interdisciplinary team within 4 days of the patient's admission to
the IRF.
* * * * *
(5) * * *
(i) The team meetings are led by a rehabilitation physician and
further consist of a registered nurse with specialized training or
experience in rehabilitation; a social worker or case manager (or
both); and a licensed or certified therapist from each therapy
discipline involved in treating the patient. All team members must have
current knowledge of the patient's medical and functional status. The
rehabilitation physician may lead the interdisciplinary team meeting
remotely via a mode of communication such as video or telephone
conferencing.
* * * * *
(c) Definitions. As used in this section--
Rehabilitation physician means a licensed physician who is
determined by the IRF to have specialized training and experience in
inpatient rehabilitation.
0
3. Section 412.634 is amended by revising paragraphs (a)(1), (d)(1) and
(5), and (f)(1) to read as follows:
Sec. 412.634 Requirements under the Inpatient Rehabilitation Facility
(IRF) Quality Reporting Program (QRP).
(a) * * *
(1) For the FY 2018 payment determination and subsequent years, an
IRF must begin reporting data under the IRF QRP requirements no later
than the first day of the calendar quarter subsequent to 30 days after
the date on its CMS Certification Number (CCN) notification letter,
which designates the IRF as operating in the CMS designated data
submission system.
* * * * *
(d) * * *
(1) IRFs that do not meet the requirement in paragraph (b) of this
section for a program year will receive a written notification of non-
compliance through at least one of the following methods: The CMS
designated data submission system, the United States Postal Service, or
via an email from the Medicare Administrative Contractor (MAC).
* * * * *
(5) CMS will notify IRFs, in writing, of its final decision
regarding any reconsideration request through at least one of the
following methods: The CMS designated data submission system, the
United States Postal Service, or via an email from the Medicare
Administrative Contractor (MAC).
* * * * *
(f) * * *
(1) IRFs must meet or exceed two separate data completeness
thresholds: One threshold set at 95 percent for completion of required
quality measures data and standardized patient assessment data
collected using the IRF-PAI submitted through the CMS designated data
submission system; and a second threshold set at 100 percent for
measures data collected and submitted using the CDC NHSN.
* * * * *
Dated: March 26, 2019.
Seema Verma,
Administrator, Centers for Medicare & Medicaid Services.
Dated: March 28, 2019.
Alex M. Azar II,
Secretary, Department of Health and Human Services.
[FR Doc. 2019-07885 Filed 4-17-19; 4:15 pm]
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