Medicare Program; Alternative Payment Model Updates and the Increasing Organ Transplant Access (IOTA) Model, 96280-96463 [2024-27841]
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Federal Register / Vol. 89, No. 233 / Wednesday, December 4, 2024 / Rules and Regulations
Standard Provisions for Innovation
Center Models.
SUPPLEMENTARY INFORMATION:
DEPARTMENT OF HEALTH AND
HUMAN SERVICES
Centers for Medicare & Medicaid
Services
Current Procedural Terminology (CPT)
Copyright Notice
42 CFR Part 512
[CMS–5535–F]
RIN 0938–AU51
Medicare Program; Alternative
Payment Model Updates and the
Increasing Organ Transplant Access
(IOTA) Model
Centers for Medicare &
Medicaid Services (CMS), Department
of Health and Human Services (HHS).
ACTION: Final rule.
AGENCY:
This final rule describes a
new mandatory alternative payment
model, the Increasing Organ Transplant
Access Model (IOTA Model), that will
test whether performance-based upside
risk payments or downside risk
payments paid to or owed by
participating kidney transplant
hospitals increase access to kidney
transplants for patients with end-stage
renal disease (ESRD) while preserving
or enhancing the quality of care and
reducing Medicare expenditures. This
final rule also adopts standard
provisions that will apply to the
Radiation Oncology Model, the EndStage Renal Disease (ESRD) Treatment
Choices Model, and mandatory
Innovation Center models, including the
IOTA Model, whose first performance
period begins on or after January 1,
2025. The finalized standard provisions
relate to beneficiary protections;
cooperation in model evaluation and
monitoring; audits and records
retention; rights in data and intellectual
property; monitoring and compliance;
remedial action; model termination by
CMS; limitations on review;
miscellaneous provisions on bankruptcy
and other notifications; and the
reconsideration review process.
DATES: These regulations are effective
January 3, 2025.
FOR FURTHER INFORMATION CONTACT:
Thomas Duvall (410) 786–8887, for
questions related to the Increasing
Organ Transplant Access Model.
Lina Gebremariam, (410) 786–8893,
for questions related to the Increasing
Organ Transplant Access Model.
Christina McCormick (410) 786–4012,
for questions related to the Increasing
Organ Transplant Access Model.
CMMItransplant@cms.hhs.gov for
questions related to the Increasing
Organ Transplant Access Model.
CMMI-StandardProvisions@
cms.hhs.gov for questions related to the
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SUMMARY:
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Throughout this final rule, we use
CPT® codes and descriptions to refer to
a variety of services. We note that CPT®
codes and descriptions are copyright
2020 American Medical Association
(AMA). All Rights Reserved. CPT® is a
registered trademark of the American
Medical Association. Applicable
Federal Acquisition Regulations (FAR)
and Defense Federal Acquisition
Regulations (DFAR) apply.
I. Executive Summary
A. Purpose
Section 1115A of the Social Security
Act (the Act) gives the Secretary of the
Department of Health and Human
Services the authority to test innovative
payment and service delivery models to
reduce program expenditures in
Medicare, Medicaid, and the Children’s
Health Insurance Program (CHIP) while
preserving or enhancing the quality of
care furnished to individuals covered by
such programs. Specifically, section
1115A(b)(2)(a) of the Act states that ‘‘the
Secretary shall select models to be
tested from models where the Secretary
determines that there is evidence that
the model addresses a defined
population for which there are deficits
in care leading to poor clinical
outcomes or potentially avoidable
expenditures. The Secretary shall focus
on models expected to reduce program
costs under the applicable title while
preserving or enhancing the quality of
care received by individuals receiving
benefits under such title.’’ 1 This final
rule describes a new mandatory
Medicare payment model to be tested
under section 1115A of the Act—the
Increasing Organ Transplant Access
Model (IOTA Model)—which will begin
on July 1, 2025, and end on June 30,
2031. In this final rule, we address
payment policies, participation
requirements, and other provisions to
test the IOTA Model. We will test
whether performance-based incentives
(including both upside and downside
risk payments) for participating kidney
transplant hospitals can increase the
number of functioning kidney
transplants (including both living donor
and deceased donor transplants)
furnished to end stage renal disease
(ESRD) patients, encourage investments
in care processes and patterns with
1 U.S. Congress. (1940) United States Code: Social
Security Act, 42 U.S.C. 1315a(b)(2)(a).
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respect to patients who need kidney
transplants, encourage investments in
value-based care and improvement
activities, and promote greater
accountability by participating kidney
transplant hospitals by tying payments
to the value of the care provided. The
IOTA Model is also intended to advance
health equity by improving equitable
access to the transplantation ecosystem
for all patients, such as rural and
underserved populations, through
design features such as voluntary health
equity plans to address health outcome
disparities.
This final rule also includes standard
provisions that will apply to the RO
Model, the ETC model, and all
mandatory Innovation Center models
whose first performance periods begin
on or after January 1, 2025. The
standard provisions address beneficiary
protections; cooperation in model
evaluation and monitoring; audits and
record retention; rights in data and
intellectual property; monitoring and
compliance; remedial action; model
termination by CMS; limitations on
review; miscellaneous provisions on
bankruptcy and other notifications; and
the reconsideration review process.
As we stated in the notice of proposed
rulemaking, the IOTA Model will test
ways to reduce Medicare expenditures
while preserving or enhancing the
quality of care furnished to
beneficiaries. We are finalizing several,
but not all, of the provisions discussed
in the proposed rule, and we intend to
address certain other provisions
discussed in the proposed rule in future
rulemaking. We also note that some of
the public comments were outside of
the scope of the proposed rule. These
out-of-scope public comments are not
addressed in this final rule. We have
summarized the public comments that
are within the scope of the proposed
rule and have included our responses to
those public comments. However, we
note that in this final rule we are not
addressing most comments received
with respect to the provisions of the
proposed rule that we are not finalizing
at this time. Rather, we will address
them at a later time, in a subsequent
rulemaking document, as appropriate.
We are clarifying and emphasizing our
intent that if any provision of this final
rule is held to be invalid or
unenforceable by its terms, or as applied
to any person or circumstance, or stayed
pending further action, it shall be
severable from other parts of this final
rule, and from rules and regulations
currently in effect, and not affect the
remainder thereof or the application of
the provision to other persons not
similarly situated or to other, dissimilar
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Federal Register / Vol. 89, No. 233 / Wednesday, December 4, 2024 / Rules and Regulations
circumstances. Through this rule, we
adopt provisions that are intended to
and will operate independently of each
other, even if each serves the same
general purpose or policy goal. Where a
provision is necessarily dependent on
another, the context generally makes
that clear.
B. Summary of the Provisions
1. Standard Provisions for Innovation
Center Models
The standard provisions for
Innovation Center models will be
applicable to the RO Model, the ETC
Model, and all mandatory Innovation
Center models whose first performance
periods begin on or after January 1,
2025.
We are codifying these standard
provisions to increase transparency,
efficiency, and clarity in the operation
and governance of mandatory
Innovation Center models, and to avoid
the need to restate the provisions in
each model’s governing documentation.
The standard provisions include terms
that have been repeatedly memorialized,
with minimal variation, in existing
models’ governing documentation. The
standard provisions are not intended to
encompass all of the terms and
conditions that will apply to each
mandatory Innovation Center model, as
each model includes unique design
features and implementation plans that
may require additional, more tailored
provisions, including with respect to
payment methodology, care delivery
and quality measurement, that will
continue to be included in each model’s
governing documentation. We note that
while we are not finalizing our proposal
to apply the standard provisions to
voluntary Innovation Center models, we
expect to utilize the provisions in
voluntary models and will incorporate
them by reference into the models’
governing documentation as appropriate
based on the model’s design. Modelspecific provisions applicable to the
IOTA Model are described in section III
of this final rule.
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2. Model Overview—Proposed
Increasing Organ Transplant Access
Model
a. Proposed IOTA Model Overview
End-Stage Renal Disease (ESRD) is a
medical condition in which a person’s
kidneys cease functioning on a
permanent basis, leading to the need for
a regular course of long-term dialysis or
a kidney transplant to maintain life.2
2 End-Stage Renal Disease (ESRD) | CMS. (n.d.).
https://www.cms.gov/medicare/coordinationbenefits-recovery/overview/end-stage-renal-diseaseesrd.
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The best treatment for most patients
with kidney failure is kidney
transplantation. Nearly 808,000 people
in the United States are living with
ESRD, with about 69 percent on dialysis
and 31 percent with a kidney
transplant.3 Relative to dialysis, a
kidney transplant can improve survival,
reduce avoidable health care utilization
and hospital acquired conditions,
improve quality of life, and lower
Medicare expenditures.4 5 However,
despite these benefits of kidney
transplantation, evidence shows low
rates of ESRD patients placed on kidney
transplant hospitals’ waitlists, a decline
in living donors over the past 20 years,
and underutilization of available donor
kidneys, coupled with increasing rates
of donor kidney discards, and wide
variation in kidney offer acceptance
rates and donor kidney discards by
region and across kidney transplant
hospitals.6 7 Further, there are
substantial disparities in both deceased
and living donor transplantation rates
among structurally disadvantaged
populations. Strengthening and
improving the performance of the organ
transplantation system is a priority for
the Department of Health and Human
Services (HHS).8 Consistent with this
priority, and through joint efforts with
HHS’ Health Resources and Services
Administration (HRSA), the IOTA
Model will aim to reduce Medicare
expenditures and improve quality
3 United States Renal Data System. 2022 USRDS
Annual Data Report: Epidemiology of kidney
disease in the United States. National Institutes of
Health, National Institute of Diabetes and Digestive
and Kidney Diseases, Bethesda, MD, 2022.
4 Tonelli, M., Wiebe, N., Knoll, G., Bello, A.,
Browne, S., Jadhav, D., Klarenbach, S., & Gill, J.
(2011). Systematic Review: Kidney Transplantation
Compared With Dialysis in Clinically Relevant
Outcomes. American Journal of Transplantation,
11(10), 2093–2109. https://doi.org/10.1111/j.16006143.2011.03686.x.
5 Cheng, X.S., Han, J., Braggs-Gresham, J.L., Held,
P.J., Busque, S., Roberts, J.P., Tan, J.C., Scandling,
J.D., Chertow, G.M., & Dor, A. (2022). Trends in
Cost Attributable to Kidney Transplantation
Evaluation and Waitlist Management in the United
States, 2012–2017. JAMA network open, 5(3),
e221847. https://doi:10.1001/
jamanetworkopen.2022.1847.
6 Al Ammary, F., Bowring, M.G., Massie, A.B.,
Yu, S., Waldram, M.M., Garonzik-Wang, J., Thomas,
A.G., Holscher, C.M., Qadi, M.A., Henderson, M.L.,
Wiseman, A.C., Gralla, J., Brennan, D.C., Segev,
D.L., & Muzaale, A.D. (2019). The changing
landscape of live kidney donation in the United
States from 2005 to 2017. American Journal of
Transplantation: Official Journal of the American
Society of Transplantation and the American
Society of Transplant Surgeons, 19(9), 2614–2621.
https://doi.org/10.1111/ajt.15368.
7 Mohan, S., Yu, M., King, K.L., & Husain, S.A.
(2023). Increasing Discards as an Unintended
Consequence of Recent Changes in United States
Kidney Allocation Policy. Kidney International
Reports, 8(5), 1109–1111.
8 https://doi.org/10.1016/j.ekir.2023.02.1081.
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performance and equity in kidney
transplantation by creating
performance-based incentive payments
for participating kidney transplant
hospitals tied to kidney transplant]
access and quality of care for ESRD
patients on the hospitals’ waitlists.
The IOTA Model will be a mandatory
model that will begin on July 1, 2025,
and end on June 30, 2031, resulting in
a 6-year model performance period
comprised of 6 individual performance
years (‘‘PYs’’). The IOTA Model will test
whether performance-based incentives
paid to, or owed by, participating
kidney transplant hospitals can increase
access to kidney transplants for patients
with ESRD, while preserving or
enhancing quality of care and reducing
Medicare expenditures. CMS will select
kidney transplant hospitals to
participate in the IOTA Model through
the methodology proposed in section
III.C.3.d of this final rule. As this will
be a mandatory model, the selected
kidney transplant hospitals will be
required to participate. CMS will
measure and assess the participating
kidney transplant hospitals’
performance during each PY across
three performance domains:
achievement, efficiency, and quality.
The achievement domain will assess
each participating kidney transplant
hospital on the overall number of
kidney transplants performed during a
PY, relative to a participant-specific
target. The efficiency domain will assess
the kidney organ offer acceptance rate
ratios of each participating kidney
transplant hospital relative to a national
ranking or the participating kidney
transplant hospital’s past organ offer
acceptance rate ratio. The quality
domain will assess the quality of care
provided by the participating kidney
transplant hospitals via a composite
graft survival ratio. Each participating
kidney transplant hospital’s
performance score across these three
domains will determine its final
performance score and corresponding
amount for the upside risk payment that
CMS would pay to the participating
kidney transplant hospital, or the
downside risk payment that would be
owed by the participating kidney
transplant hospital to CMS. The upside
risk payment will be a lump sum
payment paid by CMS after the end of
a PY to a participating kidney transplant
hospital with a final performance score
of 60 or greater. Conversely, beginning
in PY 2, the downside risk payment will
be a lump sum payment paid to CMS by
any participating kidney transplant
hospital with a final performance score
of 40 or lower. There is no downside
risk payment for PY 1 of the model.
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b. Model Scope
Participation in the IOTA Model will
be mandatory for approximately 50
percent of all eligible kidney transplant
hospitals in the United States. We
anticipate that a total of approximately
90 kidney transplant hospitals will be
selected to participate in the IOTA
Model. Additionally, we note that we
intend to publicly post information
regarding the selection process and how
it resulted in the list of DSAs and
kidney transplant hospitals selected to
participate in the model. As discussed
in section III.C.3.b. of this final rule, we
believe that mandatory participation is
necessary to minimize the potential for
selection bias and to ensure a
representative sample size nationally,
thereby guaranteeing that there will be
adequate data to evaluate the model test.
Eligible kidney transplant hospitals
will be those that: (1) performed at least
eleven kidney transplants for patients
18 years of age or older annually
regardless of payer type during the
three-year period ending 12 months
before the model’s start date; and (2) are
non-pediatric transplant facilities that
furnished more than 50 percent of the
hospital’s annual kidney transplants to
patients 18 years of age or older during
that same period. CMS will select the
kidney transplant hospitals that will be
required to participate in the IOTA
Model from the group of eligible kidney
transplant hospitals using a stratified
random sampling of donation service
areas (‘‘DSAs’’) to ensure that there is a
fair selection process and representative
group of participating kidney transplant
hospitals. For the purposes of this final
rule, a DSA has the same meaning given
to that term at 42 CFR 486.302.
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c. Performance Assessment
CMS will assess each participating
kidney transplant hospital’s
performance across three performance
domains during each PY of the model,
with a maximum possible final
performance score of 100 points. The
three performance domains will
include: (1) an achievement domain
worth up to 60 points, (2) an efficiency
domain worth up to 20 points, and (3)
a quality domain worth up to 20 points.
The achievement domain will assess
the number of kidney transplants
performed by each IOTA participant for
attributed patients, with performance on
this domain worth up to 60 points. The
final performance score will be heavily
weighted on the achievement domain to
align with the IOTA Model’s goal to
increase access to kidney transplants to
improve the quality of care and reduce
Medicare expenditures. The IOTA
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Model theorizes that improvement
activities, including those aimed at
reducing unnecessary deceased donor
discards and increasing living donors,
may help increase access to kidney
transplants.
CMS will set a target number of
kidney transplants for each IOTA
participant for each PY to measure the
IOTA participant’s performance in the
achievement domain), as described in
section III.C.5.c of the final rule. Each
IOTA participant’s transplant target for
a given PY will be based on the IOTA
participant’s historical volume of
deceased and living donor transplants
furnished to attributed patients in the
relevant baseline years, adjusted by the
national trend rate in the number of
kidney transplants performed. Section
III.C.5.c. of this final rule describes the
variation in the number of kidney
transplants performed across kidney
transplant hospitals, which would make
it challenging to set transplant targets on
a regional or national basis. The IOTA
Model will therefore set a transplant
target that is specific to each IOTA
participant to address this concern,
while still accounting for the national
trend rate in the number of kidney
transplants performed. It is expected
that IOTA participants’ transplant
targets may change from PY to PY due
to this calculation methodology.
The efficiency domain will assess the
kidney organ offer acceptance rate ratio
for each IOTA participant. The kidney
organ offer acceptance rate ratio
measures the number of kidneys an
IOTA participant accepts for transplant
over the expected value, based on
variables such as kidney quality. CMS
will assess the kidney organ offer
acceptance rate ratio relative to either
the kidney organ offer acceptance rate
ratio across all kidney transplant
hospitals or the IOTA participant’s own
past kidney organ offer acceptance rate
ratio, with CMS using whichever
method results in the IOTA participant
receiving the most points, with
performance on the efficiency domain
being worth up to 20 points.
Finally, the quality domain will
assess IOTA participants’ performance
on a composite graft survival ratio
measuring post-transplant outcomes—
relative to the composite graft survival
ratio across all kidney transplant
hospitals, with performance on this
domain being worth up to 20 points.
Each IOTA participant’s final
performance score will be the sum of
the points earned for each domain:
achievement, efficiency, and quality.
The final performance score in a PY will
determine whether the IOTA participant
will be eligible to receive an upside risk
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payment from CMS, fall into the neutral
zone where no upside or downside risk
payment would apply, or owe a
downside risk payment to CMS for the
PY as described in section III.C.6 of this
final rule.
d. Performance-Based Upside Risk
Payment and Downside Risk Payment
Formula
Each IOTA participant’s final
performance score will determine
whether: (1) CMS will pay an upside
risk payment to the IOTA participant;
(2) the IOTA participant will fall into a
neutral zone where no performancebased incentive payment will be paid to
or owed by the IOTA participant; or (3)
the IOTA participant will owe a
downside risk payment to CMS. For a
final performance score of 60 and above,
CMS will apply the formula for the
upside risk payment, which will be
equal to the IOTA participant’s final
performance score minus 60, then
divided by 40, then multiplied by
$15,000, then multiplied by the number
of kidney transplants furnished by the
IOTA participant to attributed patients
with Medicare Fee-for-Service (FFS) as
their primary or secondary payer during
the PY. Final performance scores below
60 in PY 1 and final performance scores
of 41 to 59 (inclusive) in PYs 2–6 will
fall in the neutral zone where there will
be no payment owed to the IOTA
participant or CMS.
We will phase-in the downside risk
payment beginning in PY2. We explain
in section III.C.5.b of this final rule that
new entrants to value-based payment
models may need a ramp-up period
before they are able to accept downside
risk. Thus, the IOTA Model utilizes an
upside risk-only approach for PY 1 as an
incentive in each of the three
performance domains. This will give
IOTA participants time to consider,
invest in, and implement value-based
care and quality improvement
initiatives before downside risk
payments begin. Beginning in PY 2, for
a final performance score of 40 and
below, CMS will apply the formula for
the downside risk payment, which will
be equal to 40 minus the IOTA
participant’s final performance score,
then divided by 40, then multiplied by
$2,000, then multiplied by the number
of kidney transplants furnished by the
IOTA participant to attributed patients
with Medicare FFS as their primary or
secondary payer during the PY.
CMS will pay the upside risk payment
in a lump sum to the IOTA participant
after the PY. The IOTA participant will
pay the downside risk payment to CMS
in a lump sum after the PY.
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e. Data Sharing
CMS will collect certain quality,
clinical, and administrative data from
IOTA participants for model monitoring
and evaluation activities under the
authority in 42 CFR 403.1110(b). We
will also share certain data with IOTA
participants upon request as described
in section III.C.3.a. of this final rule and
as permitted by the Health Insurance
Portability and Accountability Act of
1996 (HIPAA) Privacy Rule and other
applicable law. We will offer each IOTA
participant the opportunity to request
certain beneficiary-identifiable data for
their attributed Medicare beneficiaries
for treatment, case management, care
coordination, quality improvement
activities, and population-based
activities relating to improving health or
reducing health care costs, as permitted
by 45 CFR 164.506(c). The data uses and
sharing will be allowed only to the
extent permitted by the HIPAA Privacy
Rule and other applicable law and CMS
policies. We will also share certain
aggregate, de-identified data with IOTA
participants.
f. Other Requirements
There are several other model
requirements for selected transplant
hospitals, including transparency
requirements and public reporting
requirements. IOTA participants may
also submit a voluntary health equity
plan during the model, as described in
section III.C.8. of this rule.
(1) Transparency Requirements
Patients are often unsure whether
they qualify for a kidney transplant at a
given kidney transplant hospital. IOTA
participants will be required to publish,
on a public facing website, the criteria
they use when determining whether or
not to add a patient to the kidney
transplant waitlist.
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(2) Health Equity Requirements
An IOTA participant may submit a
health equity plan (‘‘HEP’’) to CMS. The
submission of HEPs will be voluntary
for IOTA participants for the duration of
the model. The HEP will identify health
disparities within the IOTA
participant’s population of attributed
patients and outline a course of action
to address them.
g. Medicare Payment Waivers and
Additional Flexibilities
We believe it is necessary to waive
certain requirements of title XVIII of the
Act solely for purposes of carrying out
the testing of the IOTA Model under
section 1115A of the Act. We will issue
these waivers using our waiver
authority under section 1115A(d)(1) of
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the Act, which states that the Secretary
may waive such requirements of titles
XI and XVIII and of sections 1902(a)(1),
1902(a)(13), 1903(m)(2)(A)(iii), and 1934
(other than subsections (b)(1)(A) and
(c)(5) of such section) as may be
necessary solely for purposes of carrying
out this section with respect to testing
models described in section 1115A(b) of
the Act. Each of the waivers is discussed
in detail in section III.C.11.i. of this final
rule.
h. Overlaps With Other Innovation
Center Models and CMS Programs
We expect that there could be
situations where a Medicare beneficiary
attributed to an IOTA participant is also
assigned, aligned, or attributed to
another Innovation Center model or
CMS program. Overlap could also occur
among providers and suppliers at the
individual or organization level, such as
where an IOTA participant or one of
their providers participates in multiple
Innovation Center models. We believe
that the IOTA Model will be compatible
with existing models and programs that
provide opportunities to improve care
and reduce spending. The IOTA Model
will not be replacing any covered
services or changing the payments that
participating hospitals receive through
the inpatient prospective payment
system (IPPS) or outpatient prospective
payment system (OPPS). Rather, the
IOTA Model implements performancebased payments separate from what
participants will be paid by CMS for
furnishing kidney transplants to
Medicare beneficiaries. Additionally,
we will work to resolve any potential
overlaps between the IOTA Model and
other Innovation Center models or CMS
programs that could result in
duplicative payments for services, or
duplicative counting of savings or other
reductions in expenditures. Therefore,
we are allowing overlaps between the
IOTA Model and other Innovation
Center models and CMS programs.
i. Monitoring
We will closely monitor the
implementation and outcomes of the
IOTA Model throughout its duration
consistent with the monitoring
requirements in the Standard Provisions
for Innovation Center models in section
II of this final rule and the requirements
in section III.C.13. of this final rule. The
purpose of this monitoring will be to
ensure that the IOTA Model is
implemented safely and appropriately,
that the quality and experience of care
for beneficiaries is not harmed, and that
adequate patient and program integrity
safeguards are in place.
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j. Beneficiary Protections
As mentioned in section III.C.10. of
this final rule, CMS will not allow
beneficiaries or patients to opt out of
attribution to an IOTA participant;
however, the IOTA Model will not
restrict a beneficiary’s freedom to
choose another kidney transplant
hospital or any other provider or
supplier for healthcare services, and
IOTA participants will be subject to the
Standard Provisions for Innovation
Center Models outlined in section II of
this final rule protecting Medicare
beneficiary freedom of choice and
access to medically necessary services.
We also require that IOTA participants
notify Medicare beneficiaries of the
IOTA participant’s participation in the
IOTA Model by, at a minimum,
prominently displaying informational
materials in offices or facilities where
beneficiaries receive care.
C. Summary of Costs and Benefits
The IOTA Model aims to incentivize
transplant hospitals to overcome
system-level barriers to kidney
transplantation. The chronic shortfall in
kidney transplants results in poorer
outcomes for patients and increases the
burden on Medicare in terms of
payments for dialysis and dialysis-based
enrollment in the program. Based on
quantitative and qualitative analyses,
there is reasonable evidence that the
savings to Medicare resulting from an
incremental growth in transplantation
as a result of the IOTA Model will
potentially exceed the payments
projected under the model’s incentive
structure.
II. Standard Provisions for Innovation
Center Models
A. Introduction
Section 1115A of the Act authorizes
the Center for Medicare and Medicaid
Innovation (the ‘‘Innovation Center’’) to
‘‘test innovative payment and service
delivery models to reduce program
expenditures under the applicable titles
[Medicare, Medicaid, and CHIP] while
preserving or enhancing the quality of
care furnished to individuals under
such titles . . . In selecting such
models, the Secretary shall give
preference to models that also improve
the coordination, quality, and efficiency
of health care services . . .’’ We have
designed and tested both voluntary
Innovation Center models—governed by
participation agreements, cooperative
agreements, and model-specific
addenda to existing contracts with
CMS—and mandatory Innovation
Center models that are governed by
regulations. Each voluntary and
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mandatory model features its own
specific payment methodology, quality
metrics, and certain other applicable
policies, but each model also features
numerous provisions of a similar or
identical nature, including provisions
regarding cooperation in model
evaluation; monitoring and compliance;
and beneficiary protections.
On September 29, 2020, we published
in the Federal Register a final rule titled
‘‘Medicare Program; Specialty Care
Models To Improve Quality of Care and
Reduce Expenditures’’ (85 FR 61114)
(hereinafter the ‘‘Specialty Care Models
final rule’’), in which we adopted
General Provisions Related to
Innovation Center models at 42 CFR
part 512 subpart A that apply to the
End-Stage Renal Disease Treatment
Choices (ETC) Model and the Radiation
Oncology (RO) Model.9 The Specialty
9 In the autumn of 2020, due to the Secretary of
Health and Human Services’ Determination that a
Public Health Emergency Exists for the Coronavirus
disease 2019 (COVID–19) (https://aspr.hhs.gov/
legal/PHE/Pages/2019-nCoV.aspx), CMS revised the
RO Model’s performance period to begin on July 1,
2021, and to end on December 31, 2025, in the CY
2021 Hospital Outpatient Prospective Payment
(OPPS) and Ambulatory Surgical Center (ASC)
Payment Systems and Quality Reporting Programs
final rule with comment period (85 FR 85866).
Section 133 of the Consolidated Appropriations Act
(CAA), 2021 (Pub. L. 116–260) (hereinafter referred
to as ‘‘CAA, 2021’’), enacted on December 27, 2020,
included a provision that prohibited
implementation of the RO Model before January 1,
2022. This congressional action superseded the July
1, 2021, start date that we had established in the
CY 2021 OPPS/ASC IFC. To align the RO Model
regulations with the requirements of the CAA, 2021,
we proposed to modify the definition of ‘‘model
performance period’’ in 42 CFR 512.205 to provide
for a 5-year model performance period starting on
January 1, 2022, unless the RO Model was
prohibited by law from starting on January 1, 2022,
in which case the model performance period would
begin on the earliest date permitted by law that is
January 1, April 1, or July 1. We also proposed other
modifications both related and unrelated to the
timing of the RO Model in the proposed rule that
appeared in the August 4, 2021, Federal Register
titled ‘‘Medicare Program: Hospital Outpatient
Prospective Payment and Ambulatory Surgical
Center Payment Systems and Quality Reporting
Programs; Price Transparency of Hospital Standard
Charges; Radiation Oncology Model; Request for
Information on Rural Emergency Hospitals’’ (86 FR
42018). These provisions were finalized in a final
rule with comment period titled ‘‘Medicare
Program: Hospital Outpatient Prospective Payment
and Ambulatory Surgical Center Payment Systems
and Quality Reporting Programs; Price
Transparency of Hospital Standard Charges;
Radiation Oncology Model’’ that appeared in the
November 16, 2021 Federal Register (86 FR 63458)
(hereinafter referred to as the ‘‘CY 2022 OPPS/ASC
FC’’).
On December 10, 2021, the Protecting Medicare
and American Farmers from Sequester Cuts Act
(Pub. L. 117–71) was enacted, which included a
provision that prohibits implementation of the RO
Model prior to January 1, 2023. The CY 2022 OPPS/
ASC final rule with comment period specified that
if the RO Model was prohibited by law from
beginning on January 1, 2022, the model
performance period would begin on the earliest
date permitted by law that is January 1, April 1, or
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Care Models final rule codified general
provisions regarding beneficiary
protections, cooperation in model
evaluation and monitoring, audits and
record retention, rights in data and
intellectual property, monitoring and
compliance, remedial action, model
termination by CMS, limitations on
review, and bankruptcy and other
notifications. These general provisions
were adopted only for the ETC and RO
Models (and, in practice, applied only
to the ETC Model). However, in the
notice of proposed rulemaking, we
explained that we now believe the
general provisions should apply to
Innovation Center models more broadly.
As we noted, the Innovation Center
models share numerous similar
provisions, and we explained that we
believed codifying the general
provisions in regulation to expand their
applicability across models, except
where otherwise explicitly specified in
a model’s governing documentation,
would promote transparency, efficiency,
clarity, and ensure consistency across
models to the extent appropriate, while
avoiding the need to restate the
provisions in each model’s governing
documentation.
We also proposed a new provision
pertaining to the reconsideration review
process that would apply to Innovation
Center models that waive the appeals
processes provided under section 1869
of the Act.
governing documentation for a
particular model dictates otherwise) and
promote program efficiency and
consistency that would benefit CMS’
program administration and model
participants.
As such, we proposed to expand the
applicability of the 42 CFR part 512
subpart A ‘‘General Provisions Related
to Innovation Center Models’’ to all
Innovation Center models whose first
performance periods begin on or after
January 1, 2025, unless otherwise
specified in the models’ governing
documentation, and also to any
Innovation Center models whose first
performance periods begin prior to
January 1, 2025 if incorporated by
reference into the models’ governing
documentation. To accomplish this, we
proposed that the provisions codified at
42 CFR part 512 subpart A for the ETC
and RO Models, including those with
respect to definitions, beneficiary
protections, cooperation in model
evaluation and monitoring, audits and
record retention, rights in data and
intellectual property, monitoring and
compliance, remedial action, Innovation
Center model termination by CMS, and
limitations on review, would be
designated as the newly defined
‘‘standard provisions for Innovation
Center models’’ and would apply to all
Innovation Center models as described
previously. We proposed specific
revisions that would be necessary to
expand the scope of several of the
B. General Provisions Codified in the
current general provisions, but
Code of Federal Regulations That Would
otherwise proposed that the general
Apply to Innovation Center Models
provisions (which would be referred to
Each Innovation Center model
as the ‘‘standard provisions for
features many unique aspects that must
Innovation Center models’’) would not
be memorialized in its governing
change. In particular, we proposed that
documentation, but each model also
the substance of the following
includes certain provisions that are
provisions would not change, except
common to most or all models. We
that they would apply to all Innovation
explained that we believe codifying
Center Models as opposed to just the
these common provisions would
ETC and RO Models: § 512.120
facilitate their uniform application
Beneficiary protections; § 512.130
across models (except where the
Cooperation in model evaluation and
monitoring; § 512.135 Audits and record
July 1. As a result, under the current definition for
retention; § 512.140 Rights in data and
model performance period at § 512.205, the RO
intellectual property: § 512.150
Model would have started on January 1, 2023,
Monitoring and compliance; § 512.160
because that date is the earliest date permitted by
law. However, given the multiple delays to date,
Remedial action; § 512.165 Innovation
and because both CMS and RO participants must
center model termination by CMS;
invest operational resources in preparation for
§ 512.170 Limitations on review; and
implementation of the RO Model, we have
§ 512.180 Miscellaneous provisions on
considered how best to proceed under these
circumstances. In a final rule titled ‘‘Radiation
bankruptcy and other notifications.
Oncology (RO) Model,’’ which appeared in the
Federal Register on August 29, 2022 (87 FR 52698),
we delayed the start date of the RO Model to a date
to be determined through future rulemaking, and
modified the definition of the model performance
period at § 512.205 to provide that the start and end
dates of the model performance period for the RO
Model would be established in future rulemaking.
We have not undertaken rulemaking to determine
the start date for the RO Model and, thus, the model
is not active at this time.
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C. Revisions to the Titles, Basis and
Scope Provision, and Effective Date
We proposed to amend the title of
part 512 to read ‘‘Standard Provisions
for Innovation Center Models and
Specific Provisions for the Radiation
Oncology Model and the End Stage
Renal Disease Model’’ so that it more
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closely aligns with the other changes we
proposed and to ensure that the title
indicates that part 512 includes both
standard provisions for Innovation
Center models and specific provisions
for the RO and ETC Models. We also
proposed to amend the title of subpart
A to read ‘‘Standard Provisions for
Innovation Center Models’’ to use the
term we proposed to define the
provisions codified at 42 CFR part 512
subpart A.
Additionally, we proposed to amend
§ 512.100(a) and (b) so that the standard
provisions would take effect on January
1, 2025, and would apply to each
Innovation Center model where that
model’s first performance period begins
on or after January 1, 2025, unless the
model’s governing documentation
indicates otherwise, as well as any
Innovation Center model that begins
testing its first performance period prior
to January 1, 2025, if the model’s
governing documentation incorporates
the provisions by reference in whole or
in part. We proposed to determine on a
case-by-case basis, based on each
model’s unique features and design,
whether the standard provisions would
apply to a particular model, or whether
we would specify alternate terms in the
model’s governing documentation.
We noted in the proposed rule that
these standard provisions are necessary
for the testing of the IOTA Model. As
such, as an alternative to the previous
proposal, we proposed making these
standard provisions for Innovation
Center models applicable to, and
effective for, the IOTA Model beginning
on January 1, 2025, absent extending the
standard provisions to all Innovation
Center models. Under such an
alternative, the general provisions in the
Specialty Care Models final rule would
also still be applicable to the ETC Model
and the RO Model.
We specified in the proposed rule that
these proposed standard provisions
would not, except as specifically noted
in section II of the proposed rule, affect
the applicability of other provisions
affecting providers and suppliers under
Medicare fee-for-service (FFS). We
invited public comment on these
proposed changes.
Comment: We received a comment
that emphasized that the proposed
standard provisions should not affect
the applicability of other provisions
affecting providers and suppliers under
Medicare fee-for-service. The
commenter believed that
standardization of provisions across
models would decrease administrative
burden for providers and simplify
understanding of complex models.
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Response: We thank the commenter
for their comment. We agree. We are
finalizing the proposed regulation text
at § 512.100(b)(3) to provide that, except
as specifically noted in subpart A of Part
512, the standard provisions will not
affect the applicability of other
provisions affecting providers and
suppliers under Medicare fee-forservice, including provisions regarding
payment, coverage, and program
integrity. We agree with the commenter
that the standardization of provisions
across models will decrease
administrative burden and simplify
understanding of our Innovation Center
models.
After consideration of the public
comment we received, we are finalizing
the proposed revisions to the titles for
42 CFR part 512 and for subpart A as
described later in this section. Further,
we are finalizing the proposed revisions
to the basis and scope provision at 42
CFR 512.100 with modification to apply
the standard provisions to mandatory
Innovation Center models that begin
their performance periods on or after
January 1, 2025, rather than to both
mandatory and voluntary Innovation
Center models. After further
consideration, we do not believe it is
necessary to adopt the standard
provisions for voluntary models because
we can include those provisions, or
other provisions, if necessary, in the
models’ governing documentation. We
also are not including in the final
regulation text the reference to applying
the standard provisions ‘‘unless
otherwise specified in the Innovation
Center model’s governing
documentation’’ at proposed
§ 512.100(b)(ii) because we are able to
include the standard provisions, or
other provisions as appropriate, in
voluntary Innovation Center model
participation agreements. We anticipate
utilizing the standard provisions in
most voluntary Innovation Center model
participation agreements and will
reference them or incorporate them by
reference as appropriate.
We also are not codifying the
proposed regulation text at
§ 512.100(b)(i), which provided that the
standard provisions would apply to
each Innovation Center model that
began its first performance period before
January 1, 2025, if incorporated by
reference, in whole or in part, into the
Innovation Center model’s governing
documentation. If we believe it is
appropriate to apply the standard
provisions, in whole or in part, to an
Innovation Center model for which the
first performance period began before
January 1, 2025, we will amend the
model’s governing documentation as
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appropriate, including through notice
and comment rulemaking if necessary.
We are finalizing that the standard
provisions will apply to the RO and ETC
Models as well as all other mandatory
Innovation Center models, including the
IOTA model.
We are finalizing revised titles for 42
CFR part 512 and subpart A that refer
to ‘‘Standard Provisions for Mandatory
Innovation Center Models.’’ We are
revising § 512.100(a)—‘‘Basis’’—to
provide that the standard provisions
apply to ‘‘certain’’ Innovation Center
models. At § 512.100(b)—‘‘Scope’’—we
are adding language to provide that the
standard provisions apply to the RO
Model, the ETC Model, and to
Innovation Center Models ‘‘for which
participation by Model participants is
mandatory.’’
D. Provisions Revising Certain
Definitions
We proposed to amend the definition
of ‘‘Innovation Center model’’ at 42 CFR
512.110 by replacing the specific
references to the RO and ETC Models
with a definition consistent with section
1115A of the Act and intended to
encompass all Innovation Center
models. We proposed to amend the
definition for ‘‘Innovation Center
model’’ to read as follows: ‘‘an
innovative payment and service
delivery model tested under the
authority of section 1115A(b) of the Act,
including a model expansion under
section 1115A(c) of the Act.’’
We proposed to add a new definition
of the term ‘‘governing documentation’’
at § 512.110 to mean, ‘‘the applicable
Federal regulations, and the modelspecific participation agreement,
cooperative agreement, and any
addendum to an existing contract with
CMS, that collectively specify the terms
of the Innovation Center model.’’ We
proposed to add a new definition,
‘‘standard provisions for Innovation
Center models,’’ at § 512.110 to mean
the provisions codified in 42 CFR part
512 subpart A. We proposed to add a
new definition, ‘‘performance period,’’
at § 512.110 to mean, ‘‘the period of
time during which an Innovation Center
model is tested and model participants
are held accountable for cost and quality
of care; the performance period for each
Innovation Center model is specified in
the governing documentation.’’
Further, we proposed to amend the
definitions of ‘‘Innovation Center model
activities,’’ ‘‘model beneficiary,’’ and
‘‘model participant’’ to pertain to all
‘‘Innovation Center models,’’ as we
proposed to define that term, instead of
just the models previously implemented
under part 512. As such, we proposed
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to define ‘‘Innovation Center model
activities’’ to mean ‘‘any activities
affecting the care of model beneficiaries
related to the test of the Innovation
Center model.’’ We proposed to define
‘‘model beneficiary’’ to mean ‘‘a
beneficiary attributed to a model
participant or otherwise included in an
Innovation Center model.’’ We proposed
to define ‘‘model participant’’ to mean
‘‘an individual or entity that is
identified as a participant in the
Innovation Center model.’’
We invited public comment on these
proposed changes to the definitions of
‘‘Innovation Center model,’’ ‘‘Innovation
Center model activities,’’ ‘‘model
beneficiary,’’ and ‘‘model participant’’
and the proposed definitions of
‘‘governing documentation,’’ ‘‘standard
provisions for Innovation Center
models,’’ and ‘‘performance period.’’
Comment: We received a comment
that was supportive of our proposed
definitions.
Response: We appreciate the
commenter’s support of our proposed
definitions.
After consideration of the public
comment we received, we are finalizing
the proposed revisions to the definitions
at § 512.110 without modification.
E. Proposed Reconsideration Review
Process
We proposed to add a new § 512.190
to part 512 subpart A to codify a
reconsideration review process, based
on processes implemented under
current Innovation Center models. The
process would enable model
participants to contest determinations
made by CMS in certain Innovation
Center models, where model
participants would not otherwise have a
means to dispute determinations made
by CMS. We proposed at § 512.190(a)(1)
that such a reconsideration process
would apply only to Innovation Center
models that waive section 1869 of the
Act, which governs determinations and
appeals in Medicare, or where section
1869 would not apply because model
participants are not Medicare-enrolled.
We proposed at § 512.190(a)(2) that only
model participants may utilize the
dispute resolution process, unless the
governing documentation for the
Innovation Center model states
otherwise. Such limitations with respect
to such models are, we believe,
appropriate, because with respect to
such models, model participants do not
have another means to dispute
determinations made by CMS. We
proposed to codify a reconsideration
review process in regulation in order to
have a transparent and consistent
method of reconsideration for model
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participants participating in models that
do not utilize the standard
reconsideration process outlined in
section 1869 of the Act.
This proposed reconsideration review
process would be utilized where a
model-specific determination has been
made and the affected model participant
disagrees with, and wishes to challenge,
that determination. Each Innovation
Center model features a unique payment
and service delivery model, and, as
such, requires its own model-specific
determination process. Each Innovation
Center model’s governing
documentation details the modelspecific determinations made by CMS,
which may include, but are not limited
to, model-specific payments, beneficiary
attribution, and determinations
regarding remedial actions. Each
Innovation Center model’s governing
documentation also includes specific
details about when a determination is
final and may be disputed through the
model’s reconsideration review
processes.
We proposed at § 512.190(b) that
model participants may request
reconsideration of a determination made
by CMS in accordance with an
Innovation Center model’s governing
documentation only if such
reconsideration is not precluded by
section 1115A(d)(2) of the Act, part 512
subpart A, or the model’s governing
documentation. A model participant
may challenge, by requesting review by
a CMS reconsideration official, those
final determinations made by CMS that
are not precluded from administrative
or judicial review. We proposed at
§ 512.190(b)(i) that the CMS
reconsideration official would be
someone who is authorized to receive
such requests and was not involved in
the initial determination issued by CMS
or, if applicable, the timely error notice
review process. We proposed at
§ 512.190(b)(ii) that the reconsideration
review request would be required to
include a copy of CMS’s initial
determination and contain a detailed
written explanation of the basis for the
dispute, including supporting
documentation. We proposed at
§ 512.190(b)(iii) that the request for
reconsideration would have to be made
within 30 days of the date of CMS’
initial determination for which
reconsideration is being requested via
email to an address as specified by CMS
in the governing documentation. At
§ 512.190(b)(2), we proposed that
requests that do not meet the
requirements of paragraph (b)(1) would
be denied.
We proposed at § 512.190(b)(3) that
the reconsideration official would send
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a written acknowledgement to CMS and
to the model participant requesting
reconsideration within 10 business days
of receiving the reconsideration request.
The acknowledgement would set forth
the review procedures and a schedule
that would permit each party an
opportunity to submit position papers
and documentation in support of its
position for consideration by the
reconsideration official.
We proposed to codify at
§ 512.190(b)(4) that, to access the
reconsideration process for a
determination concerning a modelspecific payment where the Innovation
Center model’s governing
documentation specifies an initial
timely error notice process, the model
participant must first satisfy those
requirements before submitting a
reconsideration request under this
process. Should a model participant fail
to timely submit an error notice with
respect to a particular model-specific
payment, we proposed that the
reconsideration review process would
not be available to the model participant
with regard to that model-specific
payment.
We proposed to codify standards for
reconsideration at § 512.190(c). First,
during the course of the reconsideration,
we proposed that both CMS and the
party requesting the reconsideration
must continue to fulfill all
responsibilities and obligations under
the governing documentation during the
course of any dispute arising under the
governing documentation. Second, the
reconsideration would consist of a
review of documentation timely
submitted to the reconsideration official
and in accordance with the standards
specified by the reconsideration official
in the acknowledgement at
§ 512.190(b)(3). Finally, we proposed
that the model participant would bear
the burden of proof to demonstrate with
clear and convincing evidence to the
reconsideration official that the
determination made by CMS was
inconsistent with the terms of the
governing documentation.
We proposed to codify at § 512.190(d)
that the reconsideration determination
would be an on-the-record review. By
this, we mean a review that would be
conducted by a CMS reconsideration
official who is a designee of CMS who
is authorized to receive such requests
under proposed § 512.190(b)(1)(i), of the
position papers and supporting
documentation that are timely
submitted and in accordance with the
schedule specified under proposed
§ 512.190(b)(3)(ii) and that meet the
standards of submission under proposed
§ 512.190(b)(1) as well as any
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documents and data timely submitted to
CMS by the model participant in the
required format before CMS made the
initial determination that is the subject
of the reconsideration request. We
proposed at § 512.190(d)(2) that the
reconsideration official would issue to
the parties a written reconsideration
determination. Absent unusual
circumstances, in which the
reconsideration official would reserve
the right to an extension upon written
notice to the model participant, the
reconsideration determination would be
issued within 60 days of CMS’s receipt
of the timely filed position papers and
supporting documentation in
accordance with the schedule specified
under proposed § 512.190(b)(3)(ii).
Under proposed § 512.190(d)(3), the
determination made by the CMS
reconsideration official would be final
and binding 30 days after its issuance,
unless the model participant or CMS
were to timely request review of the
reconsideration determination by the
CMS Administrator in accordance with
§§ 512.190(e)(1) and (2).
We proposed to codify at § 512.190(e)
a process for the CMS Administrator to
review reconsideration determinations
made under § 512.190(d). We proposed
that either the model participant or CMS
may request that the CMS Administrator
review the reconsideration
determination. The request to the CMS
Administrator would have to be made
via email, within 30 days of the
reconsideration determination, to an
email address specified by CMS. The
request would have to include a copy of
the reconsideration determination, as
well as a detailed written explanation of
why the model participant or CMS
disagrees with the reconsideration
determination. The CMS Administrator
would promptly send the parties a
written acknowledgement of receipt of
the request for review. The CMS
Administrator would send the parties
notice of whether the request for review
was granted or denied. If the request for
review is granted, the notice would
include the review procedures and a
schedule that would permit each party
to submit a brief in support of the
party’s positions for consideration by
the CMS Administrator. If the request
for review is denied, the reconsideration
determination would be final and
binding as of the date of denial of the
request for review by the CMS
Administrator. If the request for review
by the CMS Administrator is granted,
the record for review would consist
solely of timely submitted briefs and
evidence contained in the record of the
proceedings before the reconsideration
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official and evidence as set forth in the
documents and data described in
proposed § 512.190(d)(1)(ii); the CMS
Administrator would not consider
evidence other than information set
forth in the documents and data
described in proposed
§ 512.190(d)(1)(ii). The CMS
Administrator would review the record
and issue to the parties a written
determination that would be final and
binding as of the date the written
determination is sent.
We invited public comment on the
proposed reconsideration review
process for Innovation Center models.
We received no comments on this
proposal and are finalizing this
provision as proposed with a few
technical changes for clarity.
III. Increasing Organ Transplant Access
(IOTA) Model
A. Introduction
In this final rule, we finalize the IOTA
Model, a new mandatory Medicare
alternative payment model that will be
tested under the authority of the
Innovation Center at section 1115A(b) of
the Act, that will begin on July 1, 2025,
and end on June 30, 2031. The IOTA
Model will test whether using
performance-based incentive payments
in the form of upside risk payments and
downside risk payments to and from
transplant hospitals selected to
participate in the model increases the
number of kidney transplants furnished
to patients with ESRD, thereby reducing
Medicare expenditures while preserving
or enhancing quality of care.
The goal of the performance-based
payments is to increase the number of
kidney transplants furnished to ESRD
patients placed on a kidney transplant
hospital’s waitlist; encourage
investments in value-based care and
quality improvement activities,
particularly those that promote an
equitable kidney transplant process
prior to, during, and post
transplantation for all patients;
encourage better use of the current
supply of deceased donor organs and
greater provider and community
collaborations to address the medical
and non-medical needs of patients; and
increased awareness, education, and
support for living donations. The IOTA
Model payment structure will also
promote IOTA participant
accountability by linking performancebased payments to quality. We theorize
that increasing the number of kidney
transplants furnished to ESRD patients
on the participating hospitals’ waitlists
will reduce Medicare expenditures by
reducing dialysis expenditures and
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96287
avoidable health care service utilization
and will improve the quality of life for
patients with ESRD.
As discussed in section III.B of this
final rule, studies show that kidney
transplant hospitals are underutilizing
donor kidneys and have become more
conservative in accepting organs for
transplantation, with notable variation
by region and across transplant
hospitals.10 The IOTA Model aims to
address these access and equity
problems through financial incentives
that reward IOTA participants that
improve their kidney organ offer
acceptance rate ratios over time and
hold them financially accountable for
not doing so. The IOTA Model’s
payment structure includes upside and
downside performance-based incentive
payments (‘‘upside risk payment’’ or
‘‘downside risk payment’’) for kidney
transplant hospitals selected to
participate in the IOTA Model (‘‘IOTA
participant’’) that are tied to
performance on achievement, efficiency,
and quality domains.
The achievement domain will assess
the number of kidney transplants
performed relative to a participantspecific target, with performance on this
domain being worth up to 60 points.
The efficiency domain will assess
kidney organ offer acceptance rate ratios
relative to a national rate for all kidney
transplant hospitals, including those not
selected to participate in the model, to
20 points. or to the IOTA participant’s
own past kidney organ offer acceptance
rate ratio, with performance on this
domain being worth up to 20 points.
The quality domain will assess
performance based on post-transplant
outcomes, with performance on this
domain being worth up to 20 points.
The achievement domain will be
weighted more heavily than the other
two domains because increasing the
number of transplants is a key goal of
the model and will be a primary factor
in determining the amount of the
performance-based payment.
The final performance score for each
IOTA participant will be the sum of the
points earned across the achievement
domain, efficiency domain, and quality
domain. The final performance score
will determine whether an upside risk
payment or downside risk payment
would be owed and the amount of such
payment. Specifically:
10 Mohan, S., Chiles, M.C., Patzer, R.E., Pastan,
S.O., Husain, S.A., Carpenter, D.J., Dube, G.K.,
Crew, R.J., Ratner, L.E., & Cohen, D.J. (2018).
Factors leading to the discard of deceased donor
kidneys in the United States. Kidney International,
94(1), 187–198. https://doi.org/10.1016/
j.kint.2018.02.016.
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• For PY 1, if an IOTA participant has
a final performance score between 60
and 100 points, it would qualify for the
upside risk payment in accordance with
the proposed calculation methodology
described in section III.C.6.c.(2)(a) of
this final rule (final performance score
minus 60, then divided by 40, then
multiplied by $15,000, then multiplied
by the number of kidney transplants
furnished by the IOTA participant to
beneficiaries with Medicare FFS as a
primary or secondary payer during the
PY).
• For PY 1, if an IOTA participant has
a final performance score below 60, it
would fall into a neutral zone where no
upside risk payment and no downside
risk payment would apply.
• For PY 2 and each subsequent PY
(PYs 2–6), if an IOTA participant
achieves a final performance score of 41
to 59 points, it would fall into a neutral
zone where no upside risk payment and
no downside risk payment would apply.
• For PY 2 and each subsequent PY,
if an IOTA participant achieves a final
performance score of 40 points or
below, it would be subject to the
downside risk payment in accordance
with the calculation methodology
described in section III.C.6.c.(2)(b) of
this final rule (40 minus final
performance score, then divided by 40,
then multiplied by $2,000, then
multiplied by the number of kidney
transplants furnished by the IOTA
participant to beneficiaries with
Medicare FFS as a primary or secondary
payer during the PY).
We recognize the complexity of the
transplant ecosystem, which requires
coordination between transplant
hospitals, other health care providers,
organ procurement organizations
(OPOs), patients, potential donors, and
their families. The IOTA Model does not
prescribe or require specific processes
or policy approaches that each selected
IOTA participant must implement for
purposes of the model test.
We believe the IOTA Model will
complement other efforts in relation to
the transplant ecosystem to enhance
health and safety outcomes, increase
transparency, increase the number of
transplants, and reduce disparities. We
also believe that the payment
methodology will act in concert with
efforts that are currently under
development by HRSA to increase the
numbers of both deceased and living
donor organ transplants.
This model falls within a larger
framework of activities initiated by the
Federal Government during the past
several years and planned for the
upcoming year to enhance the donation,
procurement, and transplantation of
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solid organs. This Federal collaborative,
called the Organ Transplantation
Affinity Group (OTAG), is a coordinated
group working together to strengthen
accountability, equity, and performance
in organ donation, procurement, and
transplantation.11
B. Background
A review of the literature on kidney
transplantation shows that the
increasing numbers of kidney
transplants is unable to keep pace with
the increasing need for organs and is
discussed in section III.B.3.d of this
final rule.12 While more people die
waiting for a kidney transplant, the
short- and long-term outcomes of
patients who undergo kidney
transplantation have improved, despite
both recipients and donors increasing in
age and adverse health conditions.13
Recent studies show that transplant
hospitals have become more
conservative in accepting organs for
transplantation when offered for
specific patients, avoiding the use of
less-than-ideal organs on account of
perceived risk.14 Wide variation among
geographic regions and transplant
hospitals in rates of kidney
transplantation, along with access and
equity issues, raises the need to hold
kidney transplant hospitals accountable
for performance.15 The IOTA Model
includes a two-sided performance-based
payment structure that rewards IOTA
participants for high performance in the
achievement, efficiency, and quality
domains, and imposes financial
accountability on IOTA participants that
11 Moody-Williams, J.D., & Nair, S. (2023,
September 15). Organ Transplantation Affinity
Group (OTAG): Strengthening accountability,
equity, and performance | CMS. BLOG. https://
www.cms.gov/blog/organ-transplantation-affinitygroup-otag-strengthening-accountability-equityand-performance.
12 Penn Medicine News. (2020, December 16).
Too Many Donor Kidneys Are Discarded in U.S.
Before Transplantation—Penn Medicine. (2020,
December 16). www.pennmedicine.org. https://
www.pennmedicine.org/news/news-releases/2020/
december/too-many-donor-kidneys-are-discardedin-us-before-transplantation
www.pennmedicine.org.
13 Hariharan, S., Israni, A.K., & Danovitch, G.
(2021). Long-Term Survival after Kidney
Transplantation. New England Journal of Medicine,
385(8), 729–743. https://doi.org/10.1056/
nejmra2014530.
14 Stewart, D.E., Garcia, V.C., Rosendale, J.D.,
Klassen, D.K., & Carrico, B.J. (2017). Diagnosing the
Decades-Long Rise in the Deceased Donor Kidney
Discard Rate in the United States. Transplantation,
101(3), 575–587. https://doi.org/10.1097/
tp.0000000000001539.
15 Mohan, S., Chiles, M.C., Patzer, R.E., Pastan,
S.O., Husain, S.A., Carpenter, D.J., Dube, G.K.,
Crew, R.J., Ratner, L.E., & Cohen, D.J. (2018).
Factors leading to the discard of deceased donor
kidneys in the United States. Kidney International,
94(1), 187–198. https://doi.org/10.1016/
j.kint.2018.02.016.
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perform poorly on those domains. We
proposed the IOTA Model as a
complement to wider efforts aimed at
transplant ecosystem performance and
equity improvements as discussed in
section III.B of the proposed rule.
Ultimately, we seek a set of
interventions that focus on ESRD
patients in need of a kidney transplant.
In section III.B of the proposed rule, we
summarized the transplant ecosystem
and HHS oversight within CMS and
HRSA related to kidney transplantation,
highlight related initiatives and
priorities nationally, and outlined our
rationale for the proposed IOTA Model
informed by literature, data, and
studies.
1. The Transplant Ecosystem
Kidney transplantation occurs within
an overall organ donation and
transplantation system (also known and
referred to as the transplant ecosystem)
that comprises a vast network of
institutions dedicated to ensuring that
patients are evaluated and, if
appropriate, placed onto the organ
transplant waitlist, and that those on the
organ transplant waitlist receive
lifesaving organ transplants.
Transplantation of livers, hearts, lungs,
and other organs is also well established
within the U.S. health care system. The
transplant ecosystem includes the Organ
Procurement and Transplantation
Network (OPTN); Organ Procurement
Organizations (OPOs); transplant
hospitals and providers;
histocompatibility laboratories that
provide blood, tissue, and antibody
testing for the organ matching process;
and patients, including ESRD patients
in need of a transplant, their families,
and caregivers.16 For kidney
transplantation, it also includes ESRD
facilities, commonly known as dialysis
facilities.
The National Organ Transplant Act of
1984, referred to herein as NOTA,
established the OPTN, with HHS
oversight, to manage and operate the
national organ transplantation system
(42 U.S.C. 274). The OPTN is a network
that coordinates the nation’s organ
procurement, distribution, and
transplantation systems.
Organ Procurement Organizations
(OPOs) are non-profit organizations
operating under contract with the
Federal Government that are charged,
under section 371(b) of the Public
16 Moody-Williams, J.D., & Nair, S. (2023,
September 15). Organ Transplantation Affinity
Group (OTAG): Strengthening accountability,
equity, and performance | CMS. BLOG. https://
www.cms.gov/blog/organ-transplantation-affinitygroup-otag-strengthening-accountability-equityand-performance.
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Health Service Act (PHS Act, 42 U.S.C.
273(b)) with activities including, but not
limited to, identifying potential organ
donors, providing for the acquisition
and preservation of donated organs, the
equitable allocation of donated organs,
and the transportation of donated organs
to transplant hospitals. Section 371(b) of
the Public Health Services Act requires
that an OPO must have a defined service
area, a concept that is defined at 42 CFR
part 486 subpart G as the Donation
Service Area (DSA). Section 1138(b) of
the Act states that the Secretary may not
designate more than one OPO to serve
each DSA. There are currently 56 OPOs
that serve the United States and Puerto
Rico.
Section 1138(b) of the Act lays out the
requirements that an OPO must meet for
organ acquisition costs to be payable
under Title XVIII and Title XIX.
Separately, CMS sets out the
components of allowable Medicare
organ acquisition costs at 42 CFR
413.402(b). Allowable organ acquisition
costs are those costs incurred in the
acquisition of organs intended for
transplant, and include, but are not
limited to: costs associated with special
care services, the surgeon’s fee for
excising the deceased donor organ from
the donor patient (limited to $1,250 for
kidneys), operating room and other
inpatient ancillary services provided to
the living or deceased donor, organ
preservation and perfusion costs, and
donor and beneficiary evaluation. OPOs
and transplant hospitals may incur
organ acquisition costs and include
these and some additional
administrative and general costs on the
Medicare cost report.
The CMS conditions for coverage for
OPOs at 42 CFR 486.322 require an OPO
to have written agreements with 95
percent of the Medicare and Medicaid
certified hospitals and critical access
hospitals in its DSA that have a
ventilator and an operating room and
have not been granted a waiver to work
with another OPO. These hospitals,
known as donor hospitals, are required
by the CMS conditions of participation
for hospitals at 42 CFR 482.45 to have
an agreement with an OPO under which
the donor hospital must notify the OPO
of patients who are expected to die
imminently and of patients who have
died in the hospital. (Under the hospital
conditions of participation, such an
agreement is required of all hospitals
that participate in Medicare.) Also,
under the hospital conditions of
participation, donor hospitals are
responsible for informing donor patient
families of the option to donate organs,
tissues, and eyes, or to decline to
donate; and to work collaboratively with
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the OPO to educate hospital staff on
donation, improve its identification of
potential donors, and work with the
OPO to manage the potential donor
patient while testing and placement of
the potential donor organ occurs.
At 42 CFR 482.70, CMS defines a
transplant hospital as ‘‘a hospital that
furnishes organ transplants and other
medical and surgical specialty services
required for the care of transplant
patients,’’ and a transplant program as
‘‘an organ-specific transplant program
within a transplant hospital,’’ as so
defined. In accordance with 42 CFR
482.98(b), a transplant program must
have a primary transplant surgeon and
a transplant physician with the
appropriate training and experience to
provide transplantation services, who
are immediately available to provide
transplantation services when an organ
is offered for transplantation. The
transplant surgeon is responsible for
providing surgical services related to
transplantation, and the transplant
physician is responsible for providing
and coordinating transplantation care.
In accordance with CMS’ Conditions
for Coverage (CfC) for ESRD Facilities at
42 CFR part 494, ESRD facilities are
charged with delivering safe and
adequate dialysis to ESRD patients, and,
among other requirements, informing
patients of their treatment modalities,
including dialysis and kidney
transplantation. The CfCs require ESRD
facilities to conduct a patient
assessment that includes evaluation of
suitability for referral for
transplantation, based on criteria
developed by the prospective
transplantation center and its
surgeon(s). General nephrologists refer
patients for evaluation for kidney
transplants.17 Candidates for kidney
transplant undergo a rigorous evaluation
by a transplant program prior to
placement on a waitlist, involving
evaluation by a multidisciplinary team
for conditions pertaining to the
potential success of the transplant, the
possibility of recurrence, and surgical
issues including frailty, obesity,
diabetes and other causes of ESRD,
infections, malignancies, cardiac
disease, pulmonary disease, peripheral
arterial disease, neurologic disease,
hematologic conditions, and
gastrointestinal and liver disease and an
immunological assessment; a
psychosocial assessment; assessment of
17 Virmani, S., & Asch, W.S. (2020). The Role of
the General Nephrologist in Evaluating Patients for
Kidney Transplantation: Core Curriculum 2020.
American Journal of Kidney Diseases, 76, 567–579.
https://doi.org/10.1053/j.ajkd.2020.01.001.
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adherence behaviors; and tobacco
counseling.18
Once placed on the waitlist, potential
recipients must maintain active status to
be eligible to receive a deceased donor
transplant.19 An individual may receive
a status of ‘inactive’ if they are missing
lab results, contact information, or any
of the other requirements that would be
necessary for them to receive an organ
transplant if offered. An individual may
only receive an organ offer if they have
a status of ‘active.’ 20 Each transplant
hospital has its own waitlist, and
patients can attempt to be placed on
multiple waitlists; OPTN maintains a
national transplant waiting list that
encompasses the waitlists for all kidney
transplant hospitals.21 22 Individuals
already on dialysis continue to receive
regular dialysis treatments while
waiting for an organ to become
available. After surgery, a transplant
nephrologist manages the possible
outcomes of organ rejection and
infection, and other medical
complications.23
2. HHS Oversight and Priorities
HRSA, which oversees the OPTN, and
CMS play a vital role in protecting the
health and safety of Americans as they
engage with the U.S. health care
system.24 The OPTN operates a complex
network of computerized interactions
whereby specific deceased donor organs
get matched to individual patients on
the national transplant waiting list. The
Scientific Registry of Transplant
Recipients (SRTR), operated under
18 Chadban, S.J., Ahn, C., Axelrod, D.A., Foster,
B.J., Kasiske, B.L., Kher, V., Kumar, D., Oberbauer,
R., Pascual, J., Pilmore, H.L., Rodrigue, J.R., Segev,
D.L., Sheerin, N.S., Tinckam, K.J., Wong, G., &
Knoll, G.A. (2020). KDIGO Clinical Practice
Guideline on the Evaluation and Management of
Candidates for Kidney Transplantation.
Transplantation, 104(4S1), S11. https://doi.org/
10.1097/TP.0000000000003136.
19 National kidney Foundation. (2017, February
10). The Kidney Transplant Waitlist—What You
Need to Know. National Kidney Foundation.
https://www.kidney.org/atoz/content/transplantwaitlist.
20 The kidney transplant waitlist. (n.d.).
Transplant Living. https://transplantliving.org/
kidney/the-kidney-transplant-waitlist/.
21 National kidney Foundation. (2019, June 12).
Understanding the transplant waitlist. National
Kidney Foundation. https://www.kidney.org/
content/understanding-transplant-waitlist.
22 National kidney Foundation. (2016, August 4).
Multiple Listing for Kidney Transplant. National
Kidney Foundation. https://www.kidney.org/atoz/
content/multiple-listing.
23 Transplant Nephrology Fellowship. (n.d.).
www.hopkinsmedicine.org. Retrieved May 30, 2023,
from https://www.hopkinsmedicine.org/nephrology/
education/transplant-fellowship.
24 On March 22, 2023, HRSA announced an
initiative that included several actions to strengthen
accountability and transparency in the OPTN.
These actions include modernization of the OPTN
information technology system.
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contract with HRSA, is responsible for
providing statistical and analytic
support to the OPTN. Section 373 of the
PHS Act requires the operation of the
SRTR to support ongoing evaluation of
the scientific and clinical status of solid
organ transplantation.25
CMS oversees and evaluates OPO
performance. OPOs must meet
performance measures and participate
in, and abide by certain rules of, the
OPTN.26 The PHS Act requires the
Secretary to establish outcome and
process performance measures to
recertify OPOs (Part H section 371; 42
U.S.C. 273). CMS has promulgated the
OPO CfCs at 42 CFR part 486 subpart G.
Additionally, OPTN policies specify
that OPOs whose observed organ yield
rates fall below the expected rates by
more than a specified threshold would
be reviewed by the OPTN Membership
Professional Standards Committee
(MPSC).27 CMS also conducts oversight
of transplant programs, located within
transplant hospitals, which must abide
by both the hospital and the transplant
program conditions of participation
(CoPs). CMS contracts with quality
improvement entities such as the ESRD
Networks and Quality Improvement
Organizations to provide technical
support to providers and patients
seeking improvements in the transplant
ecosystem.
Medicare covers certain transplantrelated services when provided at a
Medicare-approved facility. Medicare
Part A covers the costs associated with
a Medicare kidney transplant procedure
received in a Medicare-certified hospital
and any additional inpatient hospital
care needed following the procedure,
and kidney acquisition costs including
kidney registry fees, surgeons’ fees for
excising a kidney for transplant, and
laboratory tests associated with the
evaluation of a Medicare transplant
candidate. The evaluation or
preparation of a living kidney donor, the
living donor’s donation of the kidney,
and postoperative recovery services
directly related to the living donor’s
kidney donation are covered under
Medicare. In addition, deductible and
coinsurance requirements do not apply
to living donors for services furnished to
an individual in connection with the
donation of a kidney for transplant
surgery for a Medicare beneficiary.
25 Mission, Vision, and Values. (n.d.).
www.srtr.org. https://www.srtr.org/about-srtr/
mission-vision-and-values/.
26 U.S. Congress. (1934) United States Code:
Social Security Act, 42 U.S.C. 301–Suppl. 4 1934.
27 Bylaws–OPTN. (n.d.).
Optn.transplant.hrsa.gov. Retrieved September 13,
2024, from https://optn.transplant.hrsa.gov/media/
lgbbmahi/optn_bylaws.pdf.
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Medicare Part B coverage includes the
surgeon’s fees for performing the kidney
transplant procedure and perioperative
care. Medicare Part B also covers
physician services for the living kidney
donor without regard to whether the
service would otherwise be covered by
Medicare. Part A and Part B share
responsibility for covering blood,
including packed red blood cells, blood
components and the cost of processing
and receiving blood.
Medicare Part B covers
immunosuppressive drugs following an
organ transplant for which payment is
made under Title XVIII.
Immunosuppressive drugs following an
organ transplant are covered by Part D
when an individual did not have Part A
at the time of the transplant.
Beneficiaries who have Medicare due to
ESRD alone lose Medicare coverage 36
months following a successful kidney
transplant. Section 402(a) of the
Consolidated Appropriations Act (CAA)
of 2021 added section 1836(b) of the Act
to provide coverage for
immunosuppressive drugs beginning
January 1, 2023, for eligible individuals
whose eligibility for Medicare based on
ESRD ends by reason of section
226A(b)(2) of the Act for those threeyears post kidney transplant. Under
section 1833 of the Act, the amounts
paid by Medicare for
immunosuppressive drugs are equal to
80 percent of the applicable payment
amount; beneficiaries are thus subject to
a 20 percent coinsurance for
immunosuppressive drugs covered by
both Part B and the Medicare Part B
Immunosuppressive Drug Benefit (Part
B–ID).
3. Federal Government Initiatives To
Enhance Organ Transplantation
a. CMS Regulatory Initiatives To
Enhance Organ Transplantation
On September 30, 2019, we published
the final rule, ‘‘Medicare and Medicaid
Programs; Regulatory Provisions To
Promote Program Efficiency,
Transparency, and Burden Reduction;
Fire Safety Requirements for Certain
Dialysis Facilities; Hospital and Critical
Access Hospital (CAH) Changes To
Promote Innovation, Flexibility, and
Improvement in Patient Care’’ (84 FR
51732). The rulemaking, in part, aimed
to address the concern that too many
organs are being discarded that could be
transplanted successfully, including
hearts, lungs, livers, and kidneys. This
rule implemented changes to the
transplant program regulations,
eliminating requirements for reapproval of transplant programs
pertaining to data submission, clinical
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experience, and outcomes. We believed
that the removal of these requirements
aligned with our goal of increasing
access to kidney transplants by
increasing the utilization of organs from
deceased donors and reducing the organ
discard rate (84 FR 51732). We sought
improved organ procurement, greater
organ utilization, and reduction of
burden for transplant hospitals, while
still maintaining the importance of
safety in the transplant process.
On December 2, 2020, we issued a
final rule titled, ‘‘Medicare and
Medicaid Programs; Organ Procurement
Organizations Conditions for Coverage:
Revisions to the Outcome Measure
Requirements for Organ Procurement
Organizations’’ (85 FR 77898), which
revised the OPO CfCs by replacing the
previous outcome measures with new
transparent, reliable, and objective
outcome measures. In modifying the
metrics used for assessing OPO
performance, we sought to promote
greater utilization of organs that might
not otherwise be recovered or used due
to perceived organ quality.28
While these regulatory changes went
into effect with the goal of improving
the performance of transplant hospitals
and OPOs and to promote the procuring
of organs and delivering them to
prospective transplant recipients, we
acknowledged the need for
improvements in health, safety, and
outcomes across the transplant
ecosystem, including in transplant
programs, OPOs, and ESRD
facilities.29 30 In particular, we recognize
that further action must be taken to
address health disparities and lower
rates of transplantation for underserved
populations observed across transplant
hospitals.
We published a request for
information in the Federal Register on
28 The Organ Procurement Organizations Annual
Public Aggregated Performance Report for 2023 is
available at https://www.cms.gov/files/document/
opo-annual-public-performance-report-2023.pdf.
29 One study—Doby, B. One study—Doby, B. One
study—Doby, B. One study showed that deceased
donor organ donation increased during 2019,
during the period of public debate about regulating
OPO performance. See Doby, B.L., Ross-Driscoll, K.,
Shuck, M., Wadsworth, M., Durand, C.M., & Lynch,
R.J. (2021). Public discourse and policy change:
Absence of harm from increased oversight and
transparency in OPO Performance. American
Journal of Transplantation, 21(8), 2646–2652.
https://doi.org/10.1111/ajt.16527.
30 In addition, CMS finalized a policy in the final
rule for FY 2023 for the Medicare Physician Fee
Schedule that Medicare Part A and Part B payment
can be made for dental or oral examinations,
including necessary treatment, performed as part of
a necessary workup prior to organ transplant
surgery. In the final rule, CMS describes certain
dental services as inextricably linked and integral
to the clinical success of organ transplantation. (87
FR 69671–69675).
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December 3, 2021, titled ‘‘Request for
Information: Health and Safety
Requirements for Transplant Programs,
Organ Procurement Organizations, and
End-Stage Renal Facilities’’ (86 FR
68594) (hereafter known as the
‘‘Transplant Ecosystem RFI’’). This RFI
solicited public comments on potential
changes to the requirements that
transplant programs, OPOs, and ESRD
facilities must meet to participate in the
Medicare and Medicaid programs.
Specifically, we solicited public
comments on ways to:
• Continue to improve systems of
care for all patients in need of a
transplant;
• Increase the number of organs
available for transplant for all solid
organ types;
• Encourage the use of dialysis in
alternate settings or modalities over incenter hemodialysis where clinically
appropriate and advantageous;
• Ensure that the CMS and HHS
policies appropriately incentivize the
creation and use of future new
treatments and technologies; and
• Harmonize requirements across
government agencies to facilitate these
objectives and improve quality across
the organ donation and transplantation
ecosystem.
We also solicited information related
to opportunities, inefficiencies, and
inequities in the transplant ecosystem
and what can be done to ensure all
segments of our healthcare systems are
invested and accountable in ensuring
improvements to organ donation and
transplantation rates (86 FR 68596). The
Transplant Ecosystem RFI focused on
questions in the areas of transplantation,
kidney health and ESRD facilities, and
OPOs. For transplant programs, specific
topics included transplant program
CoPs, patient rights, and equity in organ
transplantation and organ donation (86
FR 68596). For kidney health and ESRD
facilities, topics included maintaining
and improving health of patients, ways
to identify those at risk of developing
chronic kidney disease (CKD),
improving detection rates of CKD, and
ways to close the CKD detection,
education, and care health equity gap
(86 FR 68599). Other topics included
home dialysis, dialysis in alternative
settings such as nursing homes and
mobile dialysis, and alternate models of
care (86 FR 68600). For OPOs, specific
topics included assessment and
recertification, organ transport and
tracking, the donor referral process,
organ recovery centers, organ discards,
donation after cardiac death, tissue
banks, organs for research, and vascular
composite organs. (86 FR 68601 through
68606).
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The Transplant Ecosystem RFI
followed three executive orders
addressing health equity that were
issued by President Biden on January 20
and January 21, 2021—
• Executive Order on Advancing
Racial Equity and Support for
Underserved Communities Through the
Federal Government (E.O. 13985, 86 FR
7009, January 20, 2021);
• Executive Order on Preventing and
Combating Discrimination on the Basis
of Gender Identity or Sexual Orientation
(E.O. 13988, 86 FR 7023, January 25,
2021); and
• Executive Order on Ensuring an
Equitable Pandemic Response and
Recovery (E.O. 13995, 86 FR 7193,
January 26, 2021).
The RFI was among several issued by
CMS in 2021 to request public comment
on ways to advance health equity and
reduce disparities in our policies and
programs.
CMS’s regulatory initiatives since
2018 pertaining to organ donation and
transplantation have included final
rules modifying CoPs and CfCs for
transplant programs (84 FR 51732) and
OPOs (85 FR 77898), respectively, and
our recent RFI on transplant program
CoPs, OPO CfCs, and the ESRD facility
CfCs (86 FR 68594). These regulations
and RFIs have sought to foster greater
health and safety for patients, greater
transparency for all patients, increases
in organ donation and transplantation,
and reduced disparities in organ
donation and transplantation. Through
these regulations, we are working to
attain these goals by designing and
implementing policies that improve
health for all people affected by the
transplant ecosystem.
b. CMS Innovation Center Payment
Models
The Innovation Center is currently
pursuing complementary alternative
payment model tests—the ESRD
Treatment Choices (ETC) Model and the
Kidney Care Choices (KCC) Model—
aimed at enhancing kidney
transplantation and improving healthrelated outcomes for patients with latestage CKD and ESRD, thereby reducing
costs to the Medicare program. The
impetus for the ETC and KCC Models
originated with evaluation findings for
the earlier Comprehensive ESRD Care
(CEC) Model, which ran from October
2015 through March 2021, that showed
large dialysis organizations achieving
positive clinical and financial outcomes
relating to services to Medicare
beneficiaries receiving dialysis, though
the CEC Model did not achieve net
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savings to Medicare.31 The CEC Model
focused on patients being treated in
ESRD facilities, with no explicit
incentives to encourage increases in
kidney transplantation.
The ETC and KCC Models have
engaged a broader range of health care
providers beyond ESRD facilities,
including nephrology professionals and
transplant providers, and address
transplantation. Each model includes
direct financial incentives for increasing
the number of kidney transplants.
The ETC Model, which began January
1, 2021, and which is scheduled to end
on June 30, 2027, is a mandatory model
that tests whether greater use of home
dialysis and kidney transplantation for
Medicare beneficiaries with ESRD
reduces Medicare expenditures while
preserving or enhancing the quality of
care furnished to those beneficiaries. We
established requirements for the ETC
Model in the Medicare Program;
Specialty Care Models to Improve
Quality of Care and Reduce
Expenditures final rule (85 FR 61114
through 61381). These requirements are
codified at 42 CFR subpart C. The ETC
Model tests the effects of certain
Medicare payment adjustments to
participating ESRD facilities and
Managing Clinicians (clinicians who
manage ESRD beneficiaries and bill the
Monthly Capitation Payment (MCP)).
The payment adjustments are
designed to encourage greater utilization
of home dialysis and kidney
transplantation, support beneficiary
modality choice, reduce Medicare
expenditures, and preserve or enhance
quality of care. Under the ETC Model,
CMS makes upward adjustments to
certain payments under the ESRD
Prospective Payment System (PPS) to
certain dialysis facilities on home
dialysis claims, and upward
adjustments to the MCP paid to certain
Managing Clinicians on home dialysisrelated claims (85 FR 61117). In
addition, CMS makes upward and
downward adjustments to PPS
payments to participating ESRD
facilities and to the MCP paid to
participating Managing Clinicians based
on the Participant’s home dialysis rate
and transplant waitlisting and living
31 The results of the CMS-sponsored evaluation of
the CEC Model are available at https://
innovation.cms.gov/innovation-models/
comprehensive-esrd-care. The 5-year model test
reduced Medicare expenses by $217 million, or 1.3
percent relative to the pre-CEC period. These results
do not account for shared savings payments to the
model participants. There was a 3 percent decrease
in the number of hospitalizations and a 0.4 percent
increase in the number of outpatient dialysis
sessions for Medicare beneficiaries in CEC
compared to non-CEC beneficiaries. In addition, the
CEC Model improved key quality outcomes.
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donor transplant rate (85 FR 61117). The
ETC Model’s objectives, as described in
the final rule, include supporting paired
donations and donor chains, and
reducing the likelihood that potentially
viable organs are discarded (85 FR
61128). The ETC Model was updated by
the final rule dated November 8, 2021,
titled ‘‘Medicare Program; End-Stage
Renal Disease Prospective Payment
System, Payment for Renal Dialysis
Services Furnished to Individuals With
Acute Kidney Injury, End-Stage Renal
Disease Quality Incentive Program, and
End-Stage Renal Disease Treatment
Choices Model’’ and the final rule dated
November 7, 2022, titled ‘‘Medicare
Program; End-Stage Renal Disease
Prospective Payment System, Payment
for Renal Dialysis Services Furnished to
Individuals With Acute Kidney Injury,
End-Stage Renal Disease Quality
Incentive Program, and End-Stage Renal
Disease Treatment Choices Model’’ (87
FR 67136). We finalized further
modifications to the ETC Model related
to the availability of administrative
review of an ETC Participant’s targeted
review request in the final rule issued
on November 6, 2023, titled ‘‘Medicare
Program; End-Stage Renal Disease
Prospective Payment System, Payment
for Renal Dialysis Services Furnished to
Individuals With Acute Kidney Injury,
End-Stage Renal Disease Quality
Incentive Program, and End-Stage Renal
Disease Treatment Choices Model’’ (88
FR 76345). As of the second model
evaluation report covering the first two
years of the model, the model has not
shown statistically significant results as
home dialysis grew similarly across ETC
areas and the comparison group and no
statistically significant differences in
waitlisting and living donor transplant
rates. As noted earlier, CMS will
continue to evaluate the effectiveness of
the ETC Model.
CMS is also operating the ETC
Learning Collaborative, which is
focused on increasing the availability of
deceased donor organs for
transplantation.32 The ETC Learning
Collaborative regularly convenes ETC
Participants, transplant hospitals, OPOs,
and large donor hospitals, with the goal
of using learning and quality
improvement techniques to
systematically spread the best practices
of the highest performing organizations.
CMS is employing quality improvement
approaches to improve performance by
collecting and analyzing data to identify
the highest performers, and to help
others to test, adapt and spread the best
32 Centers for Medicare & Medicaid Services.
https://innovation.cms.gov/innovation-models/
esrd-treatment-choices-model.
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practices of these high performers
throughout the entire national organ
recovery system (85 FR 61346).
The KCC Model, which began its
performance period on January 1, 2022,
and is scheduled to end on December
31, 2026, is a voluntary model that also
builds upon the CEC Model structure to
encourage health care providers to
better manage the care for Medicare
beneficiaries with CKD stages 4 and 5
and ESRD, delay the onset of dialysis,
and incentivize kidney transplantation.
Various entities are participating in the
KCC Model, including nephrologists
and nephrology practices, dialysis
facilities, and other health care
providers. The participating entities
receive a bonus payment for each
aligned beneficiary who receives a
kidney transplant, so long as the
transplant remains successful over a
certain time period. CMS plans to
continue to evaluate the effectiveness of
the ETC and KCC Models in achieving
clinical goals, improving quality of care,
and reducing Medicare costs.33
The IOTA Model will complement the
ETC and KCC Models and expand
kidney model participation to hospitals,
which are a key player in the transplant
ecosystem, to test whether two-sided
risk payments based on performance
increase access to kidney transplants for
ESRD patients placed on the waitlists of
participating transplant hospitals.
c. HRSA Initiatives Involving Kidney
Transplants
NOTA established the OPTN almost
40 years ago to coordinate and operate
the nation’s organ procurement,
allocation, and transplantation system.
There are about 400 member
organizations that comprise the OPTN.
Section 372(b)(2)(A) of the PHS Act
charges the OPTN with establishing a
national list of individuals who need
organs and a national computer system
to match organs with individuals on the
waitlist. HRSA has also undertaken
efforts in alignment with CMS efforts
and Federal Government initiatives to
improve accountability in OPTN
functions. On March 22, 2023, HRSA
launched the OPTN Modernization
Initiative to strengthen accountability,
equity, and performance in the organ
donation and transplantation system
through a focus on five key areas:
technology, data transparency,
33 The evaluation report for the first two years
(2021, 2022) of the ETC Model is available at
https://www.cms.gov/priorities/innovation/
innovation-models/esrd-treatment-choices-model
and the evaluation report for the first year (2022)
of the KCC Model is available at https://
www.cms.gov/priorities/innovation/innovationmodels/kidney-care-choices-kcc-model.
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governance, operations, and quality
improvement and innovation.34 The
OPTN Modernization Initiative was
further supported by the Securing the
U.S. Organ Procurement and
Transplantation Network Act (Pub. L.
118–14), which included several key
provisions proposed in the President’s
Fiscal Year 2024 Budget and was signed
into law on September 22, 2023.35 The
new law expressly authorizes HHS to
make multiple awards to different
entities, which could enable the OPTN
to benefit from best-in-class vendors and
provide a more efficient system that
strengthens oversight and improves
patient safety.
Effective July 14, 2022, revisions to
OPTN policies were made related to the
Transplant Program Performance to
establish new criteria for identification
of transplant programs that enter MPSC
performance review based on the
following criteria: 36
• The transplant program’s 90-day
post-transplant graft survival hazard
ratio is greater than 1.75 during the 2.5year time period; or
• The transplant program’s 1-year
post-transplant graft survival
conditional on 90-day post-transplant
graft survival hazard ratio is greater than
1.75 during a 2.5-year period.
Transplant programs that meet either
of the criteria, as reported by the SRTR,
must participate in the OPTN
Membership and Professional Standards
Committee (MPSC) performance review,
which may require the member to take
appropriate actions to determine if the
transplant program has demonstrated
sustainable improvement, including, but
not limited to—
• Providing information about the
program structure, procedures,
protocols and quality;
• Review processes;
• Adopting and implementing a plan
for improvement;
• Participating in an informal
discussion with MPSC members; and
• Participating in a peer visit.
The MPSC would continue to review
the transplant program under the
performance review until the MPSC
determines that the transplant program
has made sufficient and sustainable
improvements to avoid risk to public
34 HRSA Announces Organ Procurement and
Transplantation Network Modernization Initiative |
HRSA. (n.d.). www.hrsa.gov. Retrieved August 20,
2023, from https://www.hrsa.gov/optnmodernization/march-2023.
35 The White House. (2023, September 22). Bill
Signed: H.R. 2544. The White House. https://
www.whitehouse.gov/briefing-room/legislation/
2023/09/22/bill-signed-h-r-2544/.
36 OPTN. (n.d.). Bylaws. Retrieved September 15,
2024 from https://optn.transplant.hrsa.gov/media/
lgbbmahi/optn_bylaws.pdf.
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health or patient safety. If the MPSC’s
review determines that a risk to patient
health or public safety exists, the MPSC
may request that a member inactivate or
withdraw a designated transplant
program, or a specific component of the
program, to mitigate the risk. Transplant
programs that do not participate in the
MPSC performance review process or
fail to act to improve their performance
are subject to the policies described in
Appendix L of OPTN policies, Reviews
and Actions, including the declaration
of ‘‘Member Not in Good Standing.’’
While being designated ‘‘Member Not in
Good Standing’’ does not necessarily
lead to the closure or removal of that
program from receiving reimbursement
from Federal health insurance programs,
the Secretary can, based on a
recommendation from the OPTN Board
of Directors, revoke OPTN membership,
close an OPTN member, or remove the
ability of the member to receive Federal
funding from Medicare or Medicaid.
Additionally, numerous private payers
align with the MPSC metrics and SRTR
star rating system that evaluate
transplant hospitals on post-transplant
performance to create their Center of
Excellence (COE) programs. Therefore,
MPSC reviews and performance on the
MPSC monitoring measures are a
powerful regulatory incentive for
transplant programs.
In the final rule, dated September 22,
2020, titled ‘‘Removing Financial
Disincentives to Living Organ
Donation’’ (85 FR 59438), HRSA
expanded the scope of qualified
reimbursable expenses incurred by
living donors under the Living Organ
Donation Reimbursement Program to
include lost wages and dependent care
(childcare and elder care) expenses to
further the goal of reducing financial
barriers to living organ donation. The
program previously only allowed for
reimbursement of travel, lodging, meals,
and incidental expenses. In the final
notice, dated September 22, 2020, titled,
‘‘Reimbursement of Travel and
Subsistence Expenses Toward Living
Organ Donation Program Eligibility
Guidelines,’’ HRSA increased the
income eligibility threshold under the
Living Organ Donation Reimbursement
Program from 300 percent to 350
percent of the Federal Poverty
Guidelines (85 FR 59531).
3. Rationale for the Proposed IOTA
Model
a. Alignment With Federal Government
Initiatives and Priorities
For decades, patients and health care
providers have confronted an imbalance
in the number of transplant candidates
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and the supply of acceptable donor
organs, including kidneys and other
organs. Observed variation in access to
organ transplantation by geography,
race/ethnicity, disability status, and
socioeconomic status, as well as the
overall performance of the organ
transplantation ecosystem, raised the
need to make performance
improvements and address disparities.37
Strengthening and improving the
performance of the organ
transplantation ecosystem is a priority
for HHS. To that end, OTAG was
established in 2021 by CMS and HRSA
and has expanded interagency
coordination and collaboration to ‘‘drive
improvements in donations, clinical
outcomes, system improvement, quality
measurement, transparency, and
regulatory oversight.’’ 38 Collectively,
CMS and HRSA seek to—
• Reduce variation of pre-transplant
and referral practices; 39
• Increase availability and use of
donated organs;
• Increase accountability for organ
procurement and matching;
• Promote equitable access to
transplants; and
• Empower patients, families, and
caregivers to actively engage in the
transplant journey.
As discussed in section III.C. of the
proposed rule, we believe the IOTA
Model has the potential to substantially
increase the number of kidney
transplants in a way that enhances
fairness for all affected individuals,
regardless of socioeconomic status or
other factors that limit access to care
and negatively affect health outcomes,
thereby improving quality of care,
reducing costs to Medicare, and
prolonging lives. The IOTA Model is
complementary to the ETC and KCC
Models, and to other CMS and HRSA
initiatives, with the collective goal of
achieving improvements in processes
among transplant hospitals that would
spur an increase in both deceased donor
and living donor kidney transplantation
and reduce population health
37 Moody-Williams, J.D., & Nair, S. (2023,
December 13). Organ Transplantation Affinity
Group (OTAG): Strengthening accountability,
equity, and performance | CMS. BLOG. https://
www.cms.gov/blog/organ-transplantation-affinitygroup-otag-strengthening-accountability-equityand-performance.
38 Moody-Williams, J.D., & Nair, S. (2023,
December 13). Organ Transplantation Affinity
Group (OTAG): Strengthening accountability,
equity, and performance | CMS. BLOG. https://
www.cms.gov/blog/organ-transplantation-affinitygroup-otag-strengthening-accountability-equityand-performance.
39 Pre-transplant/referral practices are inclusive of
the referring physician’s assessment criteria, patient
education, and feedback to the referring physician
from the transplant assessment.
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disparities. The IOTA Model is targeted
to kidney transplant programs, but it
will test specific modifications for
Medicare payment and other
programmatic measures that could
establish a framework for interventions
for transplantation that could
potentially be applied to the other solid
organ types in the future.
In the following sections of this final
rule, we review scientific literature that
outlines specific ways to enhance
kidney transplantation. Our analysis is
focused on kidney transplantation, but
we also present findings pertaining to
the transplantation of other organs,
especially livers. We aim to show how
the types of interventions that we
proposed might also apply for any
future efforts to increase transplant
numbers for other organ types, and to
continue to pursue the goal of greater
equity. We also describe recent efforts
from CMS and HRSA to enhance organ
transplantation that complement to the
IOTA Model’s use of upside risk
payments and downside risk payments
as a policy lever to increase the number
of kidney transplants and achieve a
fairer distribution. of kidney
transplants.
b. End Stage Renal Disease Impact
According to the United States Renal
Data System (USRDS), in 2021 about
808,536 people in the United States
were living with ESRD, almost double
the number in 2001.40 Prevalence of
ESRD varied by Health Service Area
(HSA) and ESRD Network.41 Stratified
by age and race/ethnicity, ESRD was
consistently more prevalent among
older people (65 and older) and in Black
people.42 Diabetes and hypertension are
most often the primary cause of ESRD.43
According to the National Kidney
Foundation, these diseases
disproportionately affect minority
populations, increasing the risk of
kidney disease.44 Year-over-year,
incidence of ESRD continues to
increase, as the number of patients
newly registered increased from 97,856
in 2001 to 134,837 in 2019 and 135,972
in 2021.45 Studies show that people
40 United States Renal Data System. 2023. End
Stage Renal Disease: Chapter 1. Figure 1.5.
41 United States Renal Data System. 2023. End
Stage Renal Disease: Chapter 1. Figure 1.7.
42 United States Renal Data System. 2023. End
Stage Renal Disease: Chapter 1. Figure 1.8.
43 United States Renal Data System. 2023. End
Stage Renal Disease. Chapter 1. Table 1.3.
44 National Kidney Foundation. (2016, January 7).
Race, Ethnicity and Kidney Disease. National
Kidney Foundation. https://www.kidney.org/atoz/
content/minorities-KD.
45 United States Renal Data System. 2023. End
Stage Renal Disease. Chapter 1. Figure 1.1.
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with kidney transplants live longer than
those who remain on dialysis.46 47
Despite these positive outcomes, the
percentage of prevalent ESRD patients
with a functioning kidney transplant
remained relatively stable over the past
decade, increasing only slightly from
29.7 percent in 2011 to 30.51 percent in
2021.48 In 2021, 72,864 patients with
ESRD were on the kidney transplant
waitlist, of which 27,413 were listed
during that year.49 The IOTA Model will
partially focus on the ESRD patients
who are on the kidney transplant
waitlists of the kidney transplant
hospitals that would be required to
participate in this Model. ESRD patients
represent a small portion of the U.S.
population, but the disease burden to
the patient and to CMS is great in terms
of health outcomes, survival, quality of
life, and cost. The ESRD population
accounted for 6.1% of total Medicare
expenditures in 2020.50
Due to wide variability across eligible
kidney transplant hospitals, we are
unable to estimate the IOTA Model’s
attributed patient population until the
IOTA participants are randomly
selected.
c. Benefits of Kidney Transplantation
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ESRD, when a person’s kidney
function has declined to the point of
requiring regular dialysis or a transplant
for survival, as the person’s kidneys are
no longer able to perform life-sustaining
functions, is the final stage of CKD.
ESRD is a uniquely burdensome
condition, with uncertain survival and
poor quality of life for patients. The
higher mortality and substantially
greater expenditures and hospitalization
rates for ESRD beneficiaries compared
to the overall Medicare population
suggest the need to explore policy
interventions to enhance patients’
survival and life experience, as well as
to reduce the impact to Medicare. The
IOTA Model aims to improve patient
46 Strohmaier, S., Wallisch, C., Kammer, M.,
Geroldinger, A., Heinze, G., Oberbauer, R., & Haller,
M.C. (2022). Survival Benefit of First Single-Organ
Deceased Donor Kidney Transplantation Compared
With Long-term Dialysis Across Ages in TransplantEligible Patients With Kidney Failure. JAMA
Network Open, 5(10), e2234971. https://doi.org/
10.1001/jamanetworkopen.2022.34971.
47 Tonelli, M., Wiebe, N., Knoll, G., Bello, A.,
Browne, S., Jadhav, D., Klarenbach, S., & Gill, J.
(2011). Systematic Review: Kidney Transplantation
Compared With Dialysis in Clinically Relevant
Outcomes. American Journal of Transplantation,
11(10), 2093–2109. https://doi.org/10.1111/j.16006143.2011.03686.x.
48 United States Renal Data System. 2023. End
Stage Renal Disease: Chapter 7. Figure 7.16.
49 United States Renal Data System. 2023. End
Stage Renal Disease: Chapter 7. Figures 7.1 and 7.2.
50 United States Renal Data System. 2022. End
Stage Renal Disease: Chapter 9.
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outcomes by incentivizing increased
access to kidney transplantation across
IOTA participants. Access to this
lifesaving treatment may delay or avert
dialysis, reduce costs to the Medicare
program and to patients, and enhance
survival and quality of life.
A kidney transplant involves
surgically transplanting a kidney from a
living or deceased donor to a kidney
transplant recipient. The replacement
organ is known as a graft. Most kidneys
are transplanted alone, as kidneys
transplanted along with other organs are
very rare.51 Fewer than 1,000 patients
each year receive a simultaneous
kidney-pancreas transplant, which is
generally conducted for patients who
have kidney failure related to type 1
diabetes mellitus.52 The kidney in such
a simultaneous transplant may come
from a living or deceased donor, but
other organs mostly come from a
deceased donor.
About three-quarters of kidney
transplants in the U.S. are deceased
donor kidney transplants.53 For
deceased donor transplantation, a
patient needs to contact a transplant
hospital and arrange for an evaluation to
assess the feasibility of surgery. The
patient’s name would then be added to
a list of individuals who can receive
organ offers. This is known as the
kidney transplant hospital’s kidney
transplant waitlist. Living donation
occurs when a living person donates an
organ to a family member, friend, or
other individual. People unknown to
one another sometimes take part in
paired exchanges, which allow the
switching of recipients based on blood
type and other biological factors. The
number of deceased donor kidney
donations has increased over the past
decade, while living donor kidney
donation has remained relatively
constant, declining in 2020 with the
COVID–19 pandemic.54
Kidney transplantation is considered
the optimal treatment option for most
ESRD patients. Although not a cure for
51 According to OPTN data, in 2022, there were
389 kidney-heart transplants in the U.S. 789
kidney-liver transplants, 22 kidney-lung
transplants, and 3 kidney-intestine transplants. See
https://optn.transplant.hrsa.gov/data/view-datareports/national-data/.
52 Health Resources and Services Administration.
(2020). Scientific Registry for Transplant
Recipients. OPTN/SRTR 2020 Annual Data Report:
Pancreas. https://srtr.transplant.hrsa.gov/annual_
reports/2020/Pancreas.aspx.
53 United States Renal Data System. 2022. USRDS
Annual Data Report. Volume 2. End-stage Renal
Disease (ESRD) in the United States, Chapter 7:
Transplantation. Figure 7.10b.
54 United States Renal Data System. 2022. USRDS
Annual Data Report. Volume 2. End-stage Renal
Disease (ESRD) in the United States, Chapter 7:
Transplantation. Figure 7.10b.
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kidney disease, a transplant can help a
person live longer and improve quality
of life. On average, patients experience
14 to 16 years of function from a kidney
from a living kidney donor, while few
people survive more than a decade on
dialysis.55 According to one source, the
majority of deceased donor kidneys are
expected to function for about 9 years,
with high quality organs lasting
longer.56 A systematic review of studies
worldwide finds significantly lower
mortality and risk of cardiovascular
events associated with kidney
transplantation compared with
dialysis.57 Additionally, this review
finds that patients who receive
transplants experience a better quality
of life than treatment with dialysis.58
The average dialysis patient is admitted
to the hospital nearly twice a year, often
as a result of infection, and more than
35 percent of dialysis patients who are
discharged are re-hospitalized within 30
days of being discharged.59 Among
transplant recipients, there are lower
rates of hospitalizations, emergency
department visits, and readmissions
compared to those still on dialysis.60 In
general, from the standpoint of longterm survival and quality of life, a living
donor kidney transplant is considered
the best among all kidney transplant
options for most people with CKD.61 62
A cost advantage also arises with
kidney transplantation. Per-person per55 Get the Facts on Kidney Transplantation Before
You Start Dialysis—Penn Medicine. (2019, July 24).
www.pennmedicine.org. https://
www.pennmedicine.org/updates/blogs/transplantupdate/2019/july/kidney-transplant-facts-beforedialysis.
56 Organ Procurement and Transplantation
Network. Kidney Donor Profile Index (KDPI) Guide
for Clinicians. https://optn.transplant.hrsa.gov/
professionals/by-topic/guidance/kidney-donorprofile-index-kdpi-guide-for-clinicians/#:∼:https://
optn.transplant.hrsa.gov/professionals/by-topic/
guidance/kidney-donor-profile-index-kdpi-guidefor-clinicians/.
57 Tonelli, M., Wiebe, N., Knoll, G., Bello, A.,
Browne, S., Jadhav, D., Klarenbach, S., & Gill, J.
(2011). Systematic Review: Kidney Transplantation
Compared With Dialysis in Clinically Relevant
Outcomes. American Journal of Transplantation,
11(10), 2093–2109. https://doi.org/10.1111/j.16006143.2011.03686.x.
58 Ibid.
59 United States Renal Data System. 2022. USRDS
Annual Data Report. 2022. Volume 2. End-stage
Renal Disease (ESRD) in the United States, Chapter
5: Hospitalization. Figures 5.1a, 5.9.
60 United States Renal Data System. 2021. USRDS
Annual Data Report. Volume 2. End-Stage Renal
Disease (ESRD) in the United States. Chapter 5:
Hospitalization, Figures 5.1a, 5.6a, 5.8.
61 Nemati, E., Einollahi, B., Lesan Pezeshki, M.,
Porfarziani, V., & Fattahi, M.R. (2014). Does Kidney
Transplantation With Deceased or Living Donor
Affect Graft Survival? Nephro-Urology Monthly,
6(4). https://doi.org/10.5812/numonthly.12182.
62 United States Renal Data System. 2022. USRDS
Annual Data Report. Volume 2. End-stage Renal
Disease (ESRD) in the United States, Chapter 7:
Hospitalization. Figure 7.20.b.
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ddrumheller on DSK120RN23PROD with RULES2
year Medicare FFS spending for
beneficiaries with ESRD with a
transplant is less than half that for either
hemodialysis or peritoneal dialysis.63
While the benefits to patient survival
and quality of life from living donor
kidney transplantation are more
pronounced, a recent literature review
shows that deceased donor kidney
transplantation generally produced
better outcomes at a lower cost
compared to dialysis, although old age
and a high comorbidity load among
kidney transplant patients may mitigate
this advantage.64 An earlier study, based
on a single hospital, showed rates of
hospitalization, a substantial factor in
health care costs, to be lower among
kidney transplant patients than for those
on dialysis.65
Despite these positive outcomes
associated with kidney transplantation,
in 2020, only about 30 percent of
prevalent ESRD patients (those with
existing ESRD diagnoses) in the U.S.
had a functioning kidney transplant, or
graft.66 In 2016, only 2.8 percent of
incident ESRD patients (patients newly
diagnosed with ESRD) received a
preemptive kidney transplant, allowing
them to avoid dialysis.67 These rates are
substantially below those of other
developed nations. The U.S. was ranked
17th out of 42 reporting countries in
kidney transplants per 1,000 dialysis
patients in 2020, with 42 transplants per
1,000 dialysis patients in 2020.68 We
seek to test policy approaches aimed at
increasing the number of kidney
transplants over current levels given
these relatively low numbers and the
overall benefit to patients from
transplantation, as well as the potential
savings to Medicare.
63 United States Renal Data System. 2022. USRDS
Annual Report. Volume 2. End-stage Renal Disease
(ESRD) in the United States, Chapter 9: Healthcare
Expenditures for Persons with ESRD. Figure 9.11.
64 Fu, R., Sekercioglu, N., Berta, W., & Coyte, P.C.
(2020). Cost-effectiveness of Deceased-donor Renal
Transplant Versus Dialysis to Treat End-stage Renal
Disease. Transplantation Direct, 6(2), e522. https://
doi.org/10.1097/txd.0000000000000974.
65 Khan, S., Tighiouart, H., Kalra, A., Raman, G.,
Rohrer, R.J., & Pereira, B.J.G. (2003). Resource
utilization among kidney transplant recipients.
Kidney International, 64(2), 657–664. https://
doi.org/10.1046/j.1523-1755.2003.00102.x.
66 United States Renal Data System. 2022 Annual
Data Report. Volume 2. End Stage Renal Disease
Chapter 7 Transplantation Figure 7.16.
67 United States Renal Data System. 2018. Annual
Data Report. Volume 2. Chapter 1: Incidence,
Prevalence, Patient Characteristics, and Treatment
Modalities. Figure 1.2. Retrieved from https://
www.niddk.nih.gov/about-niddk/strategic-plansreports/usrds/prior-data-reports/2018.
68 United States Renal Data System. 2022. Annual
Data Report. Volume 2. End Stage Renal Disease.
Chapter 7. Transplantation. Figure 11.17b.
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d. Kidney Transplant Rates and Unmet
Needs
Annually, more than one hundred
thousand individuals in the U.S. begin
treatment for ESRD.69 Despite
transplantation being widely regarded
as the optimal treatment for people with
ESRD, as well as being more costeffective in the long term compared to
dialysis, only a minority of people with
ESRD (13 percent) are added to the
waitlist, and even fewer receive a
transplant. To be added to the kidney
transplant waitlist, a patient must
complete an evaluation at a transplant
hospital, and the patient must be found
to be a good candidate for a transplant.
Nearly 5,000 patients on the national
kidney transplant waiting list die each
year.70 71 72 These trends have persisted
for several decades despite increases in
the number of kidney transplants from
deceased donors and living donors.
From 1996 to 2019, the number of
kidneys made available for
transplantation from deceased donors
grew steadily, in part because of organs
that became available as a result of the
opioid epidemic.73 74 In 2018 and 2019,
the total number of kidney transplants
rose steadily as compared to previous
years.75 In 2019, almost one third of
patients received a transplant within
one year of being placed on the waitlist
(32.9 percent), and the rate reached 51.8
percent within 5 years of being placed
69 United States Renal Data System. 2022. USRDS
annual data report: Epidemiology of kidney disease
in the United States. National Institutes of Health,
National Institute of Diabetes and Digestive and
Kidney Diseases, Bethesda, MD; 2022.Volume 2:
End-stage Renal Disease (ESRD) in the United
States, Chapter 1: Incidence, Prevalence, Patient
Characteristics.
70 Scientific Registry of Transplant Recipients.
Program Specific Reports. www.srtr.org. Retrieved
June 15, 2023, from https://www.srtr.org/reports/
program-specific-reports/.
71 Penn Medicine News. (2020, December 16).
Too Many Donor Kidneys Are Discarded in U.S.
Before Transplantation—Penn Medicine.
www.pennmedicine.org. https://
www.pennmedicine.org/news/news-releases/2020/
december/too-many-donor-kidneys-are-discardedin-us-before-transplantation.
72 United States Renal Data System. 2022 Annual
Data Report. Volume 2. End Stage Renal Disease
Chapter 7 Transplantation Figure 7.4.
73 Hariharan, S., Israni, A.K., & Danovitch, G.
(2021). Long-Term Survival after Kidney
Transplantation. New England Journal of Medicine,
385(8), 729–743. https://doi.org/10.1056/
nejmra2014530.
74 Durand, C.M., Bowring, M.G., Thomas, A.G.,
Kucirka, L.M., Massie, A.B., Cameron, A., Desai,
N.M., Sulkowski, M., & Segev, D.L. (2018). The
Drug Overdose Epidemic and Deceased-Donor
Transplantation in the United States: A National
Registry Study. Annals of Internal Medicine,
168(10), 702–711. https://doi.org/10.7326/M172451.
75 United States Renal Data System. 2021. Annual
Data Report. Volume 2. End Stage Renal Disease.
Chapter 7. Transplantation. Figure 7.11.
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on the waitlist.76 The number of kidney
transplants increased by 10.2 percent
from 2018 to 2019, but fell by 2.7
percent from 2019 to 2020, from 24,511
to 23,853. The reduction was
precipitated by a 23.6 percent decline in
living donor transplants on account of
the COVID–19 pandemic.77 The overall
number of patients with a functioning
graft continued its upward trend,
reaching 245,846 in 2020, an increase of
2.7 percent from 2019.78 Nonetheless,
these gains in kidney transplantation in
the U.S. have fallen far short of the
prevailing need among individuals with
ESRD or facing the prospect of kidney
failure. The number of individuals with
ESRD added to the waitlist for a kidney
transplant reached a high of 28,533 in
2019, but dropped slightly to 25,136 in
2020, while rising to 27,413 in 2021.79
At the end of 2021, 72,864 individuals
were on the waitlist for a kidney
transplant.80
The increase in deceased donor
kidney transplantation was
accompanied by a gradual but steady
decline in the number of living donor
transplants as compared to patients
undergoing dialysis. The total number
of living donor transplants per year has
risen moderately over the past two
decades, from 5,048 in 2000 to 5,241 in
2020, and 5,971 in 2021.81 82 With the
overall dialysis population growing, the
rate of living donor transplants per 100
patient-years on dialysis declined from
1.4 to 0.8 transplants from 2010 to
2020.83 A report states the proportion of
patients undergoing living donor kidney
donation to have decreased from 37
percent in 2010 to 29 percent in 2019.84
A study in 2013 of OPTN data found
that the decline in living donation
76 United States Renal Data System. 2021. Annual
Data Report. Volume 2. End Stage Renal Disease.
Chapter 7. Transplantation. Figure 7.7.
77 United States Renal Data System. 2022. Annual
Data Report. Volume 2. End Stage Renal Disease.
Chapter 7. Transplantation. Figure 7.10b.
78 United States Renal Data System. 2022. Annual
Data Report. Volume 2. End Stage Renal Disease.
Chapter 7. Transplantation. Figure 7.16.
79 United States Renal Data System. 2023. Annual
Data Report. Volume 2. End Stage Renal Disease.
Chapter 7. Transplantation. Figure 7.1.
80 United States Renal Data System. 2023. Annual
Data Report. Volume 2. End Stage Renal Disease.
Chapter 7. Transplantation. Figure 7.2.
81 United States Renal Data System. 2012. Annual
Data Report. Atlas ESRD. Table 7.1.
82 United States Renal Data System. 2023. Annual
Data report. Volume 2. End Stage Renal Disease.
Chapter 7. Transplantation. Figure 7.10a.
83 United States Renal Data System. 2022. Annual
Data Report. Volume 2. End Stage Renal Disease.
Chapter 7. Transplantation. Figure 7.10a.
84 Charnow, J.A. (2021, June 8). Living Donor
Kidney Transplants Declined in the Last Decade.
Renal and Urology News. https://www.renaland
urologynews.com/news/living-donor-kidneytransplantation-decreased-after-2010-united-statestrends/.
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appeared most prominent among men,
Black/African Americans, and younger
and lower income adults, potentially
leading to longer waiting times for
transplantation, greater dialysis
exposure, higher death rates on the
waitlist, lower graft and patient survival
for recipients, and higher overall
healthcare costs for the care of patients
with ESRD.85
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e. Disparities
Kidney transplantation research in the
U.S. reveals disparities across a number
of different axes including geography,
race and ethnicity, disability,
socioeconomic status, neighborhood
factors, and availability of health
insurance.86 87 88 89 90 A 2020 study
showed substantial disparities in kidney
transplant rates among transplant
programs at a national level, as well as
both among and within donation service
areas (DSAs).91 92 This study examined
data from a registry that included all
85 Rodrigue, J.R., Schold, J.D., & Mandelbrot, D.A.
(2013). The Decline in Living Kidney Donation in
the United States. Transplantation Journal, 96(9),
767–773. https://doi.org/10.1097/tp.0b013e
318298fa61.
86 King, K.L., Husain, S.A., Schold, J.D., Patzer,
R.E., Reese, P.P., Jin, Z., Ratner, L.E., Cohen, D.J.,
Pastan, S.O., & Mohan, S. (2020). Major Variation
across Local Transplant Centers in Probability of
Kidney Transplant for Wait-Listed Patients. Journal
of the American Society of Nephrology, 31(12),
2900–2911. https://doi.org/10.1681/ASN.20200
30335.
87 Melanson T., Basu M., Plantiga L., Pastan S.,
Mohan S., Patzer R. (2017). Variation in Living
Donor Kidney Transplantation among U.S.
Transplant Centers. American Journal of
Transplantation, 17 (suppl 3).
88 United States Renal Data System. 2022. Annual
Data Report. Supplements: COVID–19, Racial and
Ethnic Disparities Figures 14.14 and 14.15.
89 Wesselman, H., Ford, C.G., Leyva, Y., Li, X.,
Chang, C.-C.H., Dew, M.A., Kendall, K., Croswell,
E., Pleis, J.R., Ng, Y.H., Unruh, M.L., Shapiro, R.,
& Myaskovsky, L. (2021). Social Determinants of
Health and Race Disparities in Kidney Transplant.
Clinical Journal of the American Society of
Nephrology, 16(2), 262–274. https://doi.org/
10.2215/cjn.04860420.
90 Ng, Y.-H., Pankratz, V.S., Leyva, Y., Ford, C.G.,
Pleis, J.R., Kendall, K., Croswell, E., Dew, M.A.,
Shapiro, R., Switzer, G.E., Unruh, M.L., &
Myaskovsky, L. (2019). Does Racial Disparity in
Kidney Transplant Wait-listing Persist After
Accounting for Social Determinants of Health?
Transplantation, 1. https://doi.org/10.1097/
tp.0000000000003002.
91 With the enactment of NOTA, CMS designated
DSAs; generally, each DSA includes an OPO within
its geographic area. Until March 2021, when OPTN
implemented the current policy for allocation of
deceased donor kidneys, the priority for organs
acquired by an OPO was based, among other
factors, on an individual’s residence within the
DSA extending around the OPO.
92 King, K.L., Husain, S.A., Schold, J.D., Patzer,
R.E., Reese, P.P., Jin, Z., Ratner, L.E., Cohen, D.J.,
Pastan, S.O., & Mohan, S. (2020). Major Variation
across Local Transplant Centers in Probability of
Kidney Transplant for Wait-Listed Patients. Journal
of the American Society of Nephrology, 31(12),
2900–2911. https://doi.org/10.1681/
ASN.2020030335.
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U.S. adult kidney transplant candidates
added to the waitlist in 2011 and 2015,
comprising 32,745 and 34,728
individuals, respectively.93 Among
transplant programs nationwide, in
2015, the study found that the
probability of a deceased donor
transplant within three years for the
average patient to be up to 16 times
greater in some transplant hospitals as
compared to others.94 Substantial
differences in probability of deceased
donor transplantation were found even
within DSAs, where all transplant
programs utilize the same OPO and
local organ supply. For the 2015 cohort,
there was a median 2.3-fold difference
between the highest and lowest hospital
in each DSA in the 43 of 58 DSAs with
more than one transplant hospital. The
largest absolute difference in probability
of transplant occurred in a DSA with
seven transplant programs, with a
patient on the waitlist at the transplant
program with the highest probability of
transplant being 9.8 times more likely to
receive a transplant than a patient at the
transplant program with the lowest
probability of receiving a transplant.95
Factors such as local organ supply, the
characteristics of individuals on the
waitlist of a given transplant program,
the size of the waitlist, and the
transplant program’s volume of
transplants may account for the
differences observed nationally across
DSAs. However, the variation among
transplant programs across DSAs is
significantly associated with organ offer
acceptance patterns at individual
transplant hospitals.96 This underscores
the need to address geographic
disparities and for more transparency on
how transplant programs make
decisions on organ offers for their
waitlist patients.
Living donor kidney donation also
varies widely among transplant
hospitals. A 2018 report using OPTN
data from 2015 showed that while most
transplant hospitals perform few living
donor kidney transplants, certain
transplant hospitals have substantially
higher rates for their waitlist patients
than the median rate. Differences among
transplant hospitals were correlated
with geographic region and the number
of deceased donor kidney
transplantations performed.97 This
underscores the need for initiatives and
93 King
et al. 2020. 2900.
et al. 2020. 2903.
95 King et al., 2020. 2903.
96 King et al. 2020. 2903–2904.
97 Melanson T., Basu M., Plantiga L., Pastan S.,
Mohan S., Patzer R. (2017). Variation in Living
Donor Kidney Transplantation among U.S.
Transplant Centers. American Journal of
Transplantation, 17 (suppl 3).
94 King
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processes among transplant hospitals to
encourage living donations to reduce
geographic disparities.
Disparities in kidney transplantation
rates for various populations in the U.S.
have long been documented. Literature
over the past two decades has focused
on Non-Hispanic Black patients, who
experience lower rates of deceased and
living donor kidney transplantation as
compared to Non-Hispanic White
patients, while being four times more
likely to have kidney failure. Black/
African Americans and Hispanics/
Latinos with kidney failure experience
lower rates of kidney transplantation
compared with White patients.98
Additionally, Black/African Americans
and Hispanics/Latinos, along with
Asians, American Indian/Alaskan
Natives, and other minorities, are at a
higher risk of illnesses that may
eventually lead to kidney failure, such
as diabetes and high blood pressure.99
The literature over several decades
has also addressed the effect of
differences in age, gender,
socioeconomic status (SES), and
cultural aspects.100 Recent studies have
emphasized poverty and income
differentials in analyzing the interplay
of these and other factors among
populations referred for kidney
transplantation at several large
transplant hospitals.101 102 103 104 This
98 United States Renal Data System. 2022. Annual
Data Report. Supplements: COVID–19, Racial and
Ethnic Disparities Figures 14.14 and 14.15.
99 National Kidney Foundation. (2016, January 7).
Race, Ethnicity, & Kidney Disease. National Kidney
Foundation. https://www.kidney.org/atoz/content/
minorities-KD.
100 Patzer, R.E., & Pastan, S.O. (2020). Policies to
promote timely referral for kidney transplantation.
Seminars in Dialysis, 33(1), 58–67. https://doi.org/
10.1111/sdi.12860.
101 Patzer, R. Perryman, J. Schrager, J. Pastan, S.
Amaral, S. Gazmararian, J. Klein, M. Kutner, N.
McClellan, W. 2012. Patzer, R.E., Perryman, J.P.,
Schrager, J.D., Pastan, S., Amaral, S., Gazmararian,
J.A., Klein, M., Kutner, N., & McClellan, W.M.
(2012). The Role of Race and Poverty on Steps to
Kidney Transplantation in the Southeastern United
States. American Journal of Transplantation, 12(2),
358–368. https://doi.org/10.1111/j.16006143.2011.03927.x.
102 Wesselman, H., Ford, C.G., Leyva, Y., Li, X.,
Chang, C.-C.H., Dew, M.A., Kendall, K., Croswell,
E., Pleis, J.R., Ng, Y.H., Unruh, M.L., Shapiro, R.,
& Myaskovsky, L. (2021). Social Determinants of
Health and Race Disparities in Kidney Transplant.
Clinical Journal of the American Society of
Nephrology, 16(2), 262–274. https://doi.org/
10.2215/cjn.04860420.
103 Ng, Y.-H., Pankratz, V.S., Leyva, Y., Ford, C.G.,
Pleis, J.R., Kendall, K., Croswell, E., Dew, M.A.,
Shapiro, R., Switzer, G.E., Unruh, M.L., &
Myaskovsky, L. (2019). Does Racial Disparity in
Kidney Transplant Wait-listing Persist After
Accounting for Social Determinants of Health?
Transplantation, 1. https://doi.org/10.1097/
tp.0000000000003002.
104 Schold, J.D., Gregg, J.A., Harman, J.S., Hall,
A.G., Patton, P.R., & Meier-Kriesche, H.-U. (2011).
Barriers to Evaluation and Wait Listing for Kidney
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research extends in time prior to the
Kidney Allocation System (KAS) of
2014, which aimed to lessen the impact
of racial differences on access to kidney
transplantation.
Research findings support the
proposition that a broad interpretation
of social determinants of health (SDOH)
may substantially explain racial
disparities in both deceased and living
donor kidney transplantation.105
Recently, a comprehensive survey of the
literature on disparities in
transplantation for kidneys and other
organs found that socioeconomic factors
may substantially explain
disproportionately lower transplant
rates and longer wait times.106 As
described in recent literature, a person’s
SDOH may contribute to inequities in
their prospects for waitlist registration
and receipt of transplantation.107 108 109
SDOH is defined more broadly than
socioeconomic status, to include those
conditions in the places where people
live, learn, work, and play that affect a
wide range of health and quality of life
risks and outcomes.110 More
specifically, SDOH include variations in
employment, neighborhood factors,
education, social support systems, and
healthcare coverage that impact health
outcomes.
A salient group of recent analyses
focused on a cohort of patients initially
referred for evaluation for a kidney
transplant at a large urban transplant
hospital between 2010 and 2012. These
studies showed lower waitlist
registration and transplant rates for
Black/African Americans, regardless of
SDOH.111 112 One of the studies reports
Transplantation. Clinical Journal of the American
Society of Nephrology, 6(7), 1760–1767. https://
doi.org/10.2215/cjn.08620910.
105 Reed, R.D., & Locke, J.E. (2020). Social
Determinants of Health: Going Beyond the Basics to
Explore Racial Disparities in Kidney
Transplantation. Transplantation, 104, 1324–
1325.(7), 1324–1325.–1325. https://doi.org/10.1097/
TP.0000000000003003.
106 National Academies of Sciences, Engineering,
and Medicine. (2022). Realizing the Promise of
Equity in the Organ Transplantation System (K.W.
Kizer, R.A. English, & M. Hackmann, Eds.; pp. 88–
93). National Academies Press. https://doi.org/
10.17226/26364.
107 Centers for Disease Control and Prevention.
Social Determinants of Health at CDC. Retrieved
June 13, 2023, from https://www.cdc.gov/about/
priorities/social-determinants-of-health-atcdc.html?CDC_AAref_Val=https://www.cdc.gov/
about/sdoh/.
108 Wesselman et al, 2021.
109 Ng et al, 2020.
110 Centers for Disease Control and Prevention.
Social Determinants of Health at CDC. Retrieved
June 13, 2023, from https://www.cdc.gov/about/
priorities/social-determinants-of-health-atcdc.html?CDC_AAref_Val=https://www.cdc.gov/
about/sdoh/.
111 Ng Y et al. 2020. 1453.
112 Wesselman et al, 2021. 271.
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that racial difference showed a weaker
association with the rate of waitlist
registration after the introduction of the
KAS. Another of these studies, focusing
on transplant rates as the outcome,
showed that even after accounting for
social determinants of health, Black
patients had a lower likelihood of
kidney transplant and living-donor
transplant, but not deceased-donor
transplant. Black race, older age, lower
income, public insurance, more
comorbidities, being transplanted before
changes to the KAS, greater religiosity,
less social support, less transplant
knowledge, and fewer learning activities
were each associated with a lower
probability of any kidney transplant.113
Similarly, an earlier study of a
population at a single transplant
hospital found that income and
insurance attenuated the association
between racial difference and placement
on the waitlist for a kidney
transplant.114 The findings in these
studies of the enduring influence of
cultural, socioeconomic and
demographic factors apart from racial
difference underscore the need to
consider initiatives and improvement
activities aimed at addressing SDOH for
ESRD patients to remove barriers to
access to kidney transplantations.
Living donor transplantation has
demonstrated the enduring influence of
racial disparities, but also the
importance of SES and neighborhood
factors. The cohort of patients identified
previously, initially referred for
evaluation at a large urban hospital
between 2010 and 2012, showed that for
living donor transplantation, Black/
African American race and lower
income held a stronger association with
a lower probability of living donor
transplant than with deceased donor
donation.115 These results accord with
findings nationwide that White patients
are more likely to receive a living donor
transplant, followed by Asian and
Hispanic/Latino patients. Black/African
American patients have had lower rates
of living donor transplants than other
racial or ethnic groups.116 Explanations
for these differences have included
disparate rates of diabetes, obesity, and
hypertension observed among minority
populations that may contraindicate
living donation by a relative; cultural
differences in willingness to donate or
ask for a living donation; concerns about
costs among potential donors; and lack
113 Wesselman
et al. 2021. 262.
et al, 2021.
115 Wesselman et al, 2021. 270.
116 United States Renal Data System. 2022.
Annual Data Report. End Stage Renal Disease
Chapter 7 Transplantation Figure 7.10a.
114 Schold
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of knowledge about living donor
transplantation on the part of patients,
their families, and health care
providers.117 118
Research over several decades
confirms the relation between health
care access and SES factors and
disparities in living donor kidney
transplantation receipt for Black/African
American and Hispanic/Latino patients,
and, additionally, that these disparities
have increased over time.119 120 121 122
According to one study, between 1995
and 2014, disparities in the receipt of
living donor kidney transplantation
grew more for Black/African Americans
and Hispanics/Latinos: (1) living in
poorer (versus wealthier)
neighborhoods; (2) without (versus
with) a college degree; and (3) with
Medicare (versus private insurance).123
The study suggests that delays in the
receipt of kidney care may contribute to
reported racial and ethnic differences in
the quality and timing of discussions
among patients, families, and clinicians
about living donor kidney
transplantation as a treatment option.124
One study also established
associations between rates of living
donor kidney transplantation for Black/
African Americans and transplant
hospital characteristics. While
recognizing the potential effect of
clinical factors, the study found that
hospitals with high overall rates of
living donor kidney transplantation
117 Purnell, T.S., Hall, Y.N., & Boulware, L.E.
(2012). Understanding and Overcoming Barriers to
Living Kidney Donation Among Racial and Ethnic
Minorities in the United States. Advances in
Chronic Kidney Disease, 19(4), 244–251. https://
doi.org/10.1053/j.ackd.2012.01.008.
118 Rodrigue, J.R., Kazley, A.S., Mandelbrot, D.A.,
Hays, R., LaPointe Rudow, D., & Baliga, P. (2015).
Living Donor Kidney Transplantation: Overcoming
Disparities in Live Kidney Donation in the US—
Recommendations from a Consensus Conference.
Clinical Journal of the American Society of
Nephrology, 10(9), 1687–1695. https://doi.org/
10.2215/cjn.00700115.
119 Purnell, T.S., Luo, X., Cooper, L.A., Massie,
A.B., Kucirka, L.M., Henderson, M.L., Gordon, E.J.,
Crews, D.C., Boulware, L.E., & Segev, D.L. (2018).
Association of Race and Ethnicity With Live Donor
Kidney Transplantation in the United States From
1995 to 2014. JAMA, 319(1), 49. https://doi.org/
10.1001/jama.2017.19152.
120 Hall, E.C., James, N.T., Garonzik Wang, J.M.,
Berger, J.C., Montgomery, R.A., Dagher, N.N., Desai,
N.M., & Segev, D.L. (2012). Center-Level Factors
and Racial Disparities in Living Donor Kidney
Transplantation. American Journal of Kidney
Diseases, 59(6), 849–857. https://doi.org/10.1053/
j.ajkd.2011.12.021.
121 Gore, J.L., Danovitch, G.M., Litwin, M.S.,
Pham, P–T.T., & Singer, J.S. (2009). Disparities in
the Utilization of Live Donor Renal Transplantation.
American Journal of Transplantation, 9(5), 1124–
1133. https://doi.org/10.1111/j.16006143.2009.02620.x.
122 Rodrigue et al. 2015.
123 Purnell et al. 2015. 58.
124 Purnell et al. 2015. 59.
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showed significantly decreased racial
disparities. The authors suggest that
such high rates reveal commitment to
living donor kidney transplantation,
possibly shown in better education
programs, more formalized procedures
to reduce failure to complete transplant
evaluations, increased use of medically
complex and unrelated donors, and
more success in reducing financial
barriers to living donor kidney
donation.125 The study also notes that
hospitals with higher percentages of
Black/African American candidates
experience greater racial disparities. The
authors surmise that such a high
percentage might indicate an urban
setting exhibiting greater differences in
access to health care between Black/
African Americans and other
populations.126
Studies have also shown
discrimination on the basis of disability
with regard to organ transplantation,
particularly for individuals with
intellectual and developmental
disabilities, who are often assumed by
transplant providers to be unable to
manage post-transplantation care
requirements.127 Discrimination occurs
even though individuals’ disabilities
that are not related to the need for an
organ transplant generally have little or
no impact on the likelihood that the
transplant would be successful.128 The
American Society of Transplant
Surgeons has recommended that no
patient be discriminated against or
precluded from transplant listing solely
due to the presence of a disability,
whether physical or psychological.129
CMS kept these concerns in mind
when developing the IOTA Model
proposals. The IOTA Model uses
performance-based payments that hold
transplant hospitals selected as the
IOTA participants financially
accountable for improvements in access
to both deceased and living donor
kidney transplantations. To reduce
disparities and promote health equity,
CMS proposed that the IOTA
participants would be required to
develop and submit a Health Equity
Plan to CMS. This model design feature
is aimed at encouraging IOTA
et al. 2012. 855.
et al. 2012. 855.
127 See, for example, National Council on
Disability. (2019). Organ Transplant Discrimination
Against People with Disabilities: Part of the
Bioethics and Disability Series. https://
www.ncd.gov/report/organ-transplantdiscrimination-against-people-with-disabilities.
128 Id. at 38–40.
129 Am. Soc’y of Transplant Surgeons, Statement
Concerning Eligibility for Solid Organ Transplant
Candidacy (Feb. 12, 2021), https://asts.org/
advocacy/position-statements.https://asts.org/
advocacy/position-statements.
participants to reassess their processes
and policies around living and deceased
donor kidneys and promote investments
in performance and quality
improvement activities that address
barriers to care, including SDOH. The
sequence of steps that patients need to
undertake to gain access to kidney
transplantation is complex, and the
challenge posed by this process for
potential recipients may be
compounded by racial, socioeconomic
and neighborhood factors.
undergoing dialysis, as well as a higher
proportion of recipients with a previous
kidney transplant; and (b) donors’ age
and in the percentage of donations after
circulatory death.134 Early referral of
patients for transplants, kidney
exchange programs, better diagnostic
tools to identify early acute rejection,
innovative therapies for countering
rejection and infection, and
optimization of immunosuppressive
medications may be opportunities to
enhance kidney graft survival.135
f. Post-Transplant Outcomes
While the need for kidney transplants
has grown, the rates of patient and graft
survival have increased. Between 2001
and 2020, graft survival rates at 1 and
5 years showed an increasing trend.130
Patient survival at 1 year increased from
97.5 percent in 2001 to 99.2 percent in
2018, but then declined to 98.9 percent
in 2019 and 98.4 percent in 2020;
patient survival at 5 years rose from 89.8
percent in 2001 to an all-time high of
93.6 percent in 2013, dropping slightly
to 93.2 percent in 2016.131 For living
donor kidney transplants, the rate of
graft failure at 3 years decreased from
3.0 per 100 person years in 2010 to 2.1
per 100 person years in 2018. The rate
of death at 3 years with a functioning
graft also decreased from 1.2 to 1.0 per
100 person-years.132 For deceased donor
kidney transplants, the rate of graft
failure at 3 years decreased from 2010
(6.3 per 100 patient years) to 2014 (4.9
per 100 patient years), but increased to
5.3 per 100 patient years in 2018. The
same pattern was observed for death
with a functioning graft, except that the
rate in the 2018 cohort (2.8 per 100
patient years) exceeded that of the 2010
cohort (2.6 per 100 patient years).133
A study published in the New
England Journal of Medicine in 2021
shows the advantage of transplantation
using deceased donor organs over longterm dialysis, even with an increasing
trend of adverse conditions among
recipients and donors. Notably, patient
survival improved between the 1990s
and the period from 2008 to 2011,
despite increases in both (a) recipients’
age, body-mass index (BMI), frequency
of diabetes, and length of time
g. Non-Acceptance and Discards in
Kidney Transplantation
Studies have documented the
substantial extent of deceased donor
kidney non-utilization in the U.S.
relative to other countries (although
methods of defining these rates differ
among countries), as well as a steady
increase in that trend over the past two
decades.136 137 138 139 140 A study in 2018
described donor-specific factors, such as
biopsy findings and donor history, along
with an increasing selectivity among
transplant hospitals in accepting organs
for transplant and inability to locate a
recipient as contributing to this increase
125 Hall
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130 United States Renal Data System. 2023.
Annual Data Report. Volume 2. End Stage Renal
Disease. Transplantation. Figures 7.19a and 7.19b.
131 United States Renal Data System. 2023.
Annual Data Report. Volume 2. End Stage Renal
Disease. Chapter 7. Transplantation. Figures 7.20a
and 7.20b.
132 United States Renal Data System. 2023.
Annual Data Report. Volume 2. End Stage Renal
Disease. Chapter 7. Transplantation. Figure 7.21a.
133 United States Renal Data System. 2023.
Annual Data Report Volume 2. End Stage Renal
Disease. Chapter 7. Transplantation. Figure 7.21b.
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134 Hariharan, S., Israni, A.K., & Danovitch, G.
(2021). Long-Term Survival after Kidney
Transplantation. New England Journal of Medicine,
385(8), 729–743. https://doi.org/10.1056/
nejmra2014530.
135 Hariharan, S., Israni, A.K., & Danovitch, G.
(2021). Long-Term Survival after Kidney
Transplantation. New England Journal of Medicine,
385(8), 729–743. https://doi.org/10.1056/
nejmra2014530.
136 Mohan, S., Chiles, M.C., Patzer, R.E., Pastan,
S.O., Husain, S.A., Carpenter, D.J., Dube, G.K.,
Crew, R.J., Ratner, L.E., & Cohen, D.J. (2018).
Factors leading to the discard of deceased donor
kidneys in the United States. Kidney International,
94(1), 187–198. https://doi.org/10.1016/
j.kint.2018.02.016.
137 Aubert, O. Reese. P. Audry, B. Bouatou, B.
Raynaud, M. Viglietti, D. Legendre, C. Glotz, D.
Empana, J. Jouben, X. Lefaucheur, C. Jacquelinet, C.
Loupy, A. (2019). Disparities in Acceptance of
Deceased Donor Kidneys Between the United States
and France and Estimated Effects of Increased US
Acceptance. JAMA Internal Medicine, 179(10),
1365–1374. https://doi.org/10.1001/
jamainternmed.2019.2322.
138 Ibrahim, M., Vece, G., Mehew, J., Johnson, R.,
Forsythe, J., Klassen, D., Callaghan, C., & Stewart,
D. (2019). An international comparison of deceased
donor kidney utilization: What can the United
States and the United Kingdom learn from each
other? American Journal of Transplantation, 20(5),
1309–1322. https://doi.org/10.1111/ajt.15719.
139 Stewart, D.E., Garcia, V.C., Rosendale, J.D.,
Klassen, D.K., & Carrico, B.J. (2017). Diagnosing the
Decades-Long Rise in the Deceased Donor Kidney
Discard Rate in the United States. Transplantation,
101(3), 575–587. https://doi.org/10.1097/
tp.0000000000001539.
140 Health Resources and Services
Administration. OPTN. (2017). Two year analysis
shows effects of kidney transplantation system.
Optn. transplant.hrsa.gov. Retrieved May 30, 2023,
from https://optn.transplant.hrsa.gov/news/twoyear-analysis-shows-effects-of-kidney-allocationsystem/.
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in non-utilization.141 Within the context
of the COVID–19 pandemic, the nonutilization of deceased donor kidneys in
2020 rose to the highest level up to that
time, 21.3 percent, despite the decline
in discard of organs from hepatitis Cpositive donors.142 143 According to one
analysis, the deceased donor kidney
discard rate peaked at 27 percent during
the fourth quarter of 2021.144
Since the KAS went into effect in
2014, the OPTN has aimed to address
the high rate of kidneys going unused.
The new kidney allocation system was
developed in response to higher than
necessary discard rates of kidneys,
variability in access to transplants for
candidates who are harder to match due
to biologic reasons, inequities resulting
from the way waiting time was
calculated, and a matching system that
results in unrealized life years and high
re-transplant rates.145 The KAS also
revised the system that matched
waitlisted individuals with available
organs.146 As part of the KAS, the
Kidney Donor Profile Index (KDPI) was
implemented to assess the quality of
kidneys procured for kidney
transplants. The KDPI is based on a
preliminary measurement, the Kidney
Donor Risk Index (KDRI), which
estimates the relative risk of posttransplant kidney graft failure based on
scores for the deceased donor on a set
of 10 demographic and clinic
characteristics, including age, height,
weight, ethnicity, history of
hypertension, history of diabetes, cause
of death, serum creatinine, hepatitis C
virus status, and donation after
ddrumheller on DSK120RN23PROD with RULES2
141 Mohan,
Chiles et al. (2018).
142 Lentine, K. Smith, J. Hart, A. Miller, J. Skeans,
M. Larkin, L. Robinson, A. Gauntt, K. Israni, A.
Hirose, R. Snyder, J. (2022). OPTN/SRTR 2020
Annual Data Report: Kidney. American Journal of
Transplantation 22(Suppl 2) 21–136.
143 Following the introduction of certain anti-viral
drugs, transplanting kidneys from donors infected
with Hepatitis C has shown promising outcomes in
recent studies. See Penn Medicine News ‘‘Penn
Researchers Continue to Advance Transplantation
of Hepatitis C Virus-infected kidneys into HCVNegative Recipients’’ August 31, 2020 https://
www.pennmedicine.org/news/news-releases/2020/
august/penn-researchers-advance-transplantationhepatitis-c-virus-infected-kidneys-hcv-negativerecipients.
144 Cron, D. Husain, S. Adler, J. (2022). The new
distance-based kidney allocation system:
Implications for patients, transplant centers, and
Organ Procurement Organizations. Current
Transplantation Reports, 9(4), 304. https://doi.org/
10.1007/s40472-022-00384-z.
145 OPTN Kidney Transplantation Committee.
(n.d.). The New Kidney Allocation System (KAS)
Frequently Asked Questions. Retrieved December 6,
2023, from https://optn.transplant.hrsa.gov/media/
1235/kas_faqs.pdf. p. 4.
146 OPTN. (n.d.) The New Kidney Allocation
System (KAS) Frequently Asked Questions. https://
optn.transplant.hrsa.gov/media/1235/kas_faqs.pdf.
p. 4.
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circulatory death status.147 This relative
risk is determined in relation to the
overall distribution of a grouping of
these scores across the overall deceased
donor population for the previous year.
The KDPI transforms the KDRI to a zeroto-100 scale. Lower KDPI scores are
associated with greater expected posttransplant longevity, while higher KDPI
scores are associated with a worse
expected outcome in this regard.148
According to these new allocation
rules, the KDPI of an available organ
was to be assessed, with donor kidneys
with low KDPI scores being offered to
patients scoring high in terms of
expected longevity. New revisions to the
KAS also included an individual’s time
on dialysis prior to waitlisting to assess
waiting time used for determining
priority for an available organ, and new
rules that allowed for greater access for
candidates with blood type B to donor
kidneys with other blood types.149
An OPTN data analysis from 2014 to
2016, the first two years after KAS
implementation, showed that despite
substantial increases in both deceased
kidney donor transplants and deceased
kidney donation, the kidney discard rate
increased to 19.9 percent in 2016.150
The OPTN linked the discard rates to
KDPI scores, with fewer than 3 percent
of donor kidneys with KDPI between
zero and 20 percent discarded,
compared with 60 percent of donor
kidneys with KDPI between 86 and 100
percent being discarded.151
In March 2021, OPTN finalized a
newer allocation policy, which
eliminated the use of DSAs and regions
from kidney and pancreas donor
distribution. These measures were part
of a framework announced in 2019 that
also applied to heart, lung, and liver
donor distribution, with the goal of
reducing the importance of geography in
patients’ access to organs, and, instead,
147 OPTN. (n.d.). The New Kidney Allocation
System Frequently Asked Questions. https://
optn.transplant.hrsa.gov/media/1235/kas_faqs.pdf.
pp. 8–9.
148 OPTN. (n.d.). The New Kidney Allocation
System Frequently Asked Questions . https://
optn.transplant.hrsa.gov/media/1235/kas_faqs.pdf.
p. 4.
149 OPTN. (n.d.). The New Kidney Allocation
System Frequently Asked Questions. https://
optn.transplant.hrsa.gov/media/1235/kas_faqs.pdf.
p. 4.
150 OPTN. (2017, July 9). Two Year Analysis
shows effects of Kidney Allocation System.
Retrieved June 9, 2023, from https://
optn.transplant.hrsa.gov/news/two-year-analysisshows-effects-of-kidney-allocation-system/.
151 OPTN. (2017, July 9). Two Year Analysis
shows effects of Kidney Allocation System.
Retrieved June 9, 2023, from https://
optn.transplant.hrsa.gov/news/two-year-analysisshows-effects-of-kidney-allocation-system/.
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emphasizing medical urgency.152 153 The
new system instituted a point system
with up to 2 points (equal to 2 years on
the wait list) for patients listed at
transplant hospitals within 250 nautical
miles of the donor hospital, and the
points decreasing linearly from the
donor hospital to the circle perimeter.
The more points an individual has, the
higher their position on the waitlist and
the more likely they are to receive an
organ offer. If there is no candidate
within the designated radius, the kidney
is offered to patients listed at hospitals
outside the fixed circle, based on
separate proximity points that decrease
linearly as the location of a patient
approaches 2,500 nautical miles from
the donor hospital.154
Interested parties within the
transplant ecosystem commented that
the new policy might further contribute
to the increasing rate of donor organ
non-acceptance. According to one
review, sharing kidneys over a broader
geographic region means that OPOs
would need to work with transplant
hospitals with which there was no prior
relationship.155 Concern was also
expressed about increased
transportation time and procurement
costs, risk associated with air transport,
and a greater number of interactions
between transplant hospitals and
OPOs.156 157 158 One study notes that
policymakers would need to assess the
extent to which the new kidney
allocation policy might affect organ offer
acceptance patterns, organ recovery and
utilization rates, and wait times both for
the transplant hospital and broader
152 Potluri, V.S., & Bloom, R.D. (2021). Effect of
Policy on Geographic Inequities in Kidney
Transplantation. American Journal of Kidney
Diseases, 79(6), 897–900. https://doi.org/10.1053/
j.ajkd.2021.11.005.
153 Penn Medicine. (2021, November 17). Update:
Change in Organ Allocation Designed to Increase
Equity in US Kidney and Pancreas Transplantation.
Penn Medicine Physician Blog. https://
www.pennmedicine.org/updates/blogs/pennphysician-blog/2021/november/change-in-organallocation-designed-to-increase-equity-in-us-kidneyand-pancreas-transplantation.
154 Potluri, Bloom. (2021). 897–898.
155 Potluri, Bloom. (2021) 898.
156 Gentry, S.E., Chow, E.K.H., Wickliffe, C.E.,
Massie, A.B., Leighton, T., & Segev, D.L. (2014).
Impact of broader sharing on the transport time for
deceased donor livers. Liver Transplantation,
20(10), 1237–1243. https://doi.org/10.1002/lt.23942.
157 Chow, E.M., DiBrito, S.R., Luo, X., Wickliffe,
C., Massie, A.B., Locke, J.E., Gentry, S.E., GaronzikWang, J., & Segev, D.L. (2018). Long Cold Ischemia
Times in Same Hospital Deceased Donor
Transplants. Transplantation, 102(3), 471–477.
https://doi.org/10.1097/tp.0000000000001957.
158 Adler, J.T., Husain, S.A., King, K.L., & Mohan,
S. (2021). Greater complexity and monitoring of the
new Kidney Allocation System: Implications and
unintended consequences of concentric circle
kidney allocation on network complexity. American
Journal of Transplantation, 21(6), 2007–2013.
https://doi.org/10.1111/ajt.16441.
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geographic areas.159 Another report
cited unpublished SRTR data, saying
that preliminary results suggest an
increase in the transplant rate overall,
but a trend toward higher donor kidney
discard and increased cold ischemia
time.160
A similar study assessing deceased
donor kidney discards from 2000 to
2015 found that 17.3 percent of 212,305
procured deceased donor kidneys were
discarded, representing a 91.5 percent
increase in deceased donor kidney
discards during the same time period.
The increase in donor kidney discards
outpaced the number of organs
recovered for transplantation, adversely
impacting transplantation rates and
waitlist times. Kidneys with higher
KDPIs and from donors with more
disadvantageous characteristics were
more likely to be discarded. The
estimated 5-year graft survival for even
the lowest quality kidneys substantially
exceeds the average 5-year dialysis
survival rate, making discard patterns
concerning.161 The study indicates a
significant overlap in the quality of
discarded and transplanted deceased
donor kidneys, and substantial
geographical variation in the odds of
donor kidney discards, which, as seen
previously, would continue to be
observed in SRTR data for following
years.162 The study also found patterns
that indicate factors beyond organ
quality, including biopsy findings,
donor history and poor organ function,
and inability to locate a kidney donor
recipient, may factor into deceased
organ acceptance decisions. Other
factors may be driving the deceased
donor organ discard rates, as the study
found that ‘‘discarded organs were more
likely to come from older, heavier
donors who were Black, female,
diabetic, hypertensive, with undesirable
social behavior and higher terminal
creatinine.’’ 163 This finding accords
with observed discard patterns from
earlier studies whereby recipients of
marginal kidneys, in terms of advanced
donor age, hypertension, diabetes, or
greater cold ischemia time, showed
lower mortality and greater survival
benefit for many candidates as
159 Adler
et al., 2021. 2012.
D.C., S. Ali Husain, & Adler, J.T. (2022).
The New Distance-Based Kidney Allocation
System: Implications for Patients, Transplant
Centers, and Organ Procurement Organizations.
Current Transplantation Reports, 9(4), 302–307.
https://doi.org/10.1007/s40472-022-00384-z.
161 Mohan, Chiles et al. 2018. p. 192.
162 Mohan et al. 2018. p. 195.
163 Mohan et al. 2018. 192.
160 Cron,
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compared to staying on the transplant
wait list.164 165 166
Research at this time suggests that
CMS regulatory requirements and OPTN
policies may have been contributing to
transplant hospitals growing more
selective in choosing organs for their
waitlisted patients. A study from 2017
examined OPTN registry data for
deceased donors from 1987 to 2015,
showing that changes in the donor pool
and certain clinical practices explained
about 80 percent of the increase in nonutilization of deceased donor
kidneys.167 However, according to the
study, the remainder of kidney discards,
not accounted for by these factors,
suggests that increased risk aversion
was leading transplant hospitals to be
more selective about the kidneys they
accept, regardless of the actual risk
profile. Furthermore, increasing reliance
on the part of OPTN, CMS, and private
insurers on program-specific reports
that assessed the performance of
transplant hospitals on transplant graft
and recipient survival rates might have
been contributing to the overall trend of
organs going unused.168
The finding of high rates of non-use
of organs that could potentially be
transplanted with positive outcomes has
led to closer examination of trends
among transplant hospitals in declining
the possible use of organs for specific
patients. Information on each organ that
is recovered by an OPO is shared with
the OPTN, which runs the matching
system that determines which organ
should be offered to which recipient. If
an organ is determined to be a good
match for a particular patient, then the
OPTN would offer that organ to the
transplant hospital at which the patient
is waitlisted on the patient’s behalf.169
164 Ojo, A.O., Hanson, J.A., Herwig Ulf MeierKriesche, Chike Nathan Okechukwu, Wolfe, R.R.,
Leichtman, A.B., Agodoa, L.Y., Kaplan, B., & Port,
F.K. (2001). Survival in Recipients of Marginal
Cadaveric Donor Kidneys Compared with Other
Recipients and Wait-Listed Transplant Candidates.
Journal of the American Society of Nephrology,
12(3), 589–597. https://doi.org/10.1681/
asn.v123589.
165 Massie, A.B., Luo, X., Chow, E.K.H., Alejo,
J.L., Desai, N.M., & Segev, D.L. (2014). Survival
Benefit of Primary Deceased Donor Transplantation
With High-KDPI Kidneys. American Journal of
Transplantation, 14(10), 2310–2316. https://doi.org/
10.1111/ajt.12830.
166 Cohen, J.B., Eddinger, K.C., Locke, J.E., Forde,
K.A., Reese, P.P., & Sawinski, D. (2017). Survival
Benefit of Transplantation with a Deceased Diabetic
Donor Kidney Compared with Remaining on the
Waitlist. Clinical Journal of the American Society of
Nephrology, 12(6), 974–982. https://doi.org/
10.2215/cjn.10280916.
167 Stewart et al. (2017). 575.
168 Stewart et al. (2017). 585.
169 National kidney Foundation. (2017, February
10). The Kidney Transplant Waitlist—What You
Need to Know. National Kidney Foundation.
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A transplant hospital can decline an
offer without informing the candidate of
the offer or the reason it was
declined.170 A study in 2019 focused on
patient outcomes associated with
declines in offers of organs by transplant
hospitals. Using OPTN data, the study
identified a cohort of 280,041 adults on
the kidney transplant waitlist (out of
367,405 candidates on the waitlist from
2008 through 2015, the study period)
who received one or more offers for a
deceased donor kidney during that
period. More than 80 percent of
deceased donor kidneys were declined
on behalf of one or more candidates
before being accepted for transplant,
and a mean of 10 candidates who
previously received an offer died every
day during the study period.171 As
reported by transplant hospitals, organ
or donor quality concerns accounted for
92.6 percent of all declined offers,
whereas 2.6 percent of offers were
refused because of patient-related
factors, and an even smaller number for
logistical limitations or other concerns.
While organ or donor quality concerns
remained the primary reason for
declined offers across all KDPI ranges,
the study observed marked State-level
variability in the interval between first
offer and death or transplant and in the
likelihood of dying while having
remained on the wait list after receiving
an offer.172
The methodology and findings of this
study are notable since they draw a
correlation between the specific patterns
among transplant hospitals of organ
non-acceptance and the longevity of
patients on the wait list. The tendency
among certain hospitals to choose to not
use kidneys for specific patients is
shown apart from the distinct finding of
organs going unused and being
discarded. The study shows the
potential for a similar effect on patient
survival from organ offer nonacceptance as for organ non-use. The
authors of an earlier study commented
that low acceptance rates of organ offers
lead to inefficiency, longer ischemia
time, unequal access to donated
kidneys, and perhaps to higher rates of
discarded organs.173 The findings in the
https://www.kidney.org/atoz/content/transplantwaitlist.
170 Husain, S.A., King, K.L., Pastan, S., Patzer,
R.E., Cohen, D.J., Radhakrishnan, J., & Mohan, S.
(2019). Association Between Declined Offers of
Deceased Donor Kidney Allograft and Outcomes in
Kidney Transplant Candidates. JAMA Network
Open, 2(8), e1910312. https://doi.org/10.1001/
jamanetworkopen.2019.10312.
171 Husain et al. 2019.
172 Husain et al. 2019.
173 Wolfe, R.A., Laporte, F., Rodgers, A.M., Roys,
E., Fant, G., & Leichtman, A.B. (2007). Developing
Organ Offer and Acceptance Measures: When
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2019 study of a wide range of organ
offer acceptance rates among transplant
hospitals nationwide, as well as of the
relation between organ offer declines
and patient deaths, suggest the need for
incentives for transplant hospitals to
accept earlier offers for their patients,
which, in turn, could reduce cold
ischemia time, and, on the whole,
increase patient survival.
h. Non-Acceptance and Discards in
Transplantation for Other Solid Organ
Types
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SRTR has also tracked the non-use, or
discard rate, of other solid organ types.
In 2020, 9.5 percent of livers recovered
were not transplanted, with livers from
older donors less likely to be
transplanted.174 The discard rate for
pancreases was 23.4 percent in 2020;
organs from obese donors were highly
likely not to be transplanted.175 The
discard rate for hearts in 2020 was one
percent, having stayed similar over the
previous decade.176
Liver transplantation shows survival
benefits for individuals with chronic
liver disease, but liver transplantation
suffers from a severe shortage of donor
organs.177 178 A study from 2012 shows
organ offer non-acceptance patterns on
the part of transplant programs affect
mortality for individuals with end-stage
liver disease in a similar manner as for
ESRD patients. According to the study,
most candidates for a liver transplant
who died or were removed from the
wait list had received at least one organ
offer, suggesting that a substantial
portion of waitlist mortality results in
part from declined organ offers.179 As
the IOTA Model does for kidney
transplantation, understanding and
addressing why livers, and possibly
other organs, are not chosen for specific
‘‘Good’’ Organs Are Turned Down. American
Journal of Transplantation, 7, 1404–1411. https://
doi.org/10.1111/j.1600-6143.2007.01784.x.
174 OPTN/SRTR 2020 Annual Data Report. 2020.
Liver. Figures LI 49, 50.
175 OPTN/SRTR 2021 Annual Data Report.
Pancreas. Figures PA 39, 43.
176 OPTN/SRTR 2021 Annual Data Report. Heart.
Figure HR 52.
177 Merion, R.M., Schaubel, D.E., Dykstra, D.M.,
Freeman, R.B., Port, F.K., & Wolfe, R.A. (2005). The
Survival Benefit of Liver Transplantation. American
Journal of Transplantation, 5(2), 307–313. https://
doi.org/10.1111/j.1600-6143.2004.00703.x.
178 Ross, K., Patzer, R.E., Goldberg, D.S., & Lynch,
R.J. (2017). Sociodemographic Determinants of
Waitlist and Posttransplant Survival Among EndStage Liver Disease Patients. American Journal of
Transplantation, 17(11), 2879–2889. https://doi.org/
10.1111/ajt.14421.
179 Lai, J.C., Feng, S., & Roberts, J.P. (2012). An
Examination of Liver Offers to Candidates on the
Liver Transplant Wait-List. Gastroenterology,
143(5), 1261–1265. https://doi.org/10.1053/
j.gastro.2012.07.105.
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patients also has the potential to lead to
improved outcomes and longer lives.
team that is tasked with holistic patient
care.
i. Organ Transplant Affinity Group
On September 15, 2023, CMS
published a blog post titled ‘‘Organ
Transplantation Affinity Group (OTAG):
Strengthening accountability, equity,
and performance.’’ 180 This blog
discussed the formation of OTAG, a
Federal collaborative with staff from
CMS and HRSA working together to
strengthen accountability, equity, and
performance to improve access to organ
donation, procurement, and
transplantation for patients, donors,
families and caregivers, and providers.
The IOTA Model is a part of this
coordinated effort from the OTAG and
relies on input from across CMS and
HRSA.
a. Model Performance Period
In section III.C.1.a of the proposed
rule, we proposed a 6-year ‘‘model
performance period.’’ We proposed to
define the model performance period as
the 72-month period from the model
start date, comprised of 6 individual
PYs. The IOTA participants’
performance would be measured and
assessed during the model performance
period for purposes of determining their
performance-based payments. We
proposed to define the ‘‘performance
year’’ (PY) as a 12-month calendar year
during the model performance period.
We proposed to define the start of the
model performance period as the
‘‘model start date,’’ and we proposed a
model start date of January 1, 2025,
meaning that PY 1 would be January 1,
2025, to December 31, 2025, and the
model performance period would end
on December 31, 2030. We proposed a
6-year model performance period to
allow sufficient time for selected
transplant hospitals to invest in care
delivery transformation and realize
returns on investments.
We alternatively considered a 3- or 5year model performance period;
however, we believe that a 3-year model
performance period would be too short
to allow adequate time for selected
transplant hospitals to invest in care
delivery transformations. Additionally,
our analyses detailed in section V of this
final rule project that considerable
savings to Medicare will be achieved
after the fifth PY, which is another
reason why we proposed a 6-year model
performance period. We also considered
a 10-year model performance period
similar to some more recent Innovation
Center models; however, given that this
is a mandatory model, we felt it was
important to limit the duration of the
initial test to a shorter period.
We alternatively considered
proposing to begin the IOTA Model on
April 1, 2025, or July 1, 2025, to allow
selected transplant hospitals more time
to prepare to implement the model and
to better align the model performance
period with that of our data sources, as
detailed in section III.C.5.a of this final
rule. However, we proposed a January 1,
2025, start date because we believed
that there would be sufficient time for
IOTA participants to prepare for the
model. A proposed start date of January
1, 2025, also aligned with other CMS
calendar year rules. We separately
proposed that in the event the model
start date is delayed from the proposed
start date, the model performance
period for the entire model would be 6
C. Provisions of the Regulation
1. Implementing the IOTA Model
In this section III.C of the final rule,
we discuss our policies for the IOTA
Model, including model-specific
definitions and the general framework
for implementation of the IOTA Model.
The upside risk payments owed to the
IOTA participants and the downside
risk payments owed to CMS are
designed to increase access to kidney
transplants for patients with ESRD on
the IOTA participant’s waitlist. As
described in section I of this final rule,
access to kidney transplants varies
widely by region and across transplant
hospitals, and disparities by
demographic characteristics are
pervasive, raising the need to strengthen
and improve performance by kidney
transplant hospitals. We theorize that
the IOTA Model financial incentives
will promote improvement activities
across selected transplant hospitals that
address access barriers, including
SDOH, thereby increasing the number of
transplants, quality of care, and the
provision of cost-effective treatment.
Selected transplant hospitals may be
motivated to revisit processes and
policies around deceased and living
donor organ acceptance to identify
opportunities for improvement. The
IOTA Model payments incentivize
selected transplant hospitals to engage
in care delivery transformation to better
coordinate and manage patient care and
needs, invest in infrastructure, improve
the patient, family, and caregiver
experience, and engage a care delivery
180 Moody-Williams, J., Nair, S. Organ
Transplantation Affinity Group (OTAG):
Strengthening accountability, equity, and
performance. CMS Blog, September 15, 2023.
https://www.cms.gov/blog/organ-transplantationaffinity-group-otag-strengthening-accountabilityequity-and-performance.
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PYs, with each PY being a 12-month
period that begins on the model start
date. For example, if the IOTA Model
were to begin on April 1, 2025,
‘‘performance year’’ would be defined as
a 12-month period beginning on the
model start date, meaning April 1, 2025,
to March 31, 2026. As a result, the
model performance period would also
shift to include a 72-month period from
the model start date. In this example,
the model performance period would be
April 1, 2025, to March 31, 2031.
We sought comment on the proposed
model performance period of 6 years
and the proposed model start date. We
also sought comment on the alternative
model performance periods that we
considered of 3, 5, and 10 years. Finally,
we sought comment on the alternative
start dates of April 1, 2025, and July 1,
2025, and the subsequent adjustments to
the model performance period if the
model start date were to change.
Comment: A few commenters
supported the proposed model length of
six years, indicating that is an
appropriate length of time to be able to
evaluate a model to determine success.
Response: We thank the commenters
for the support and agree a six-year
model test should provide sufficient
evidence to determine if the IOTA
Model is achieving its goals of
improving quality of care and reducing
Medicare expenditures.
Comment: Several commenters
expressed concern around the six-year
model performance period. A few
commenters felt that a post-transplant
evaluation time horizon of six-years
contradicts the current OPTN standard
of one to three years of post-transplant
follow-up. A few commenters also felt
that six-years is too long of a model
performance period as a shorter model
performance period may allow for more
immediate assessment and refinement
and an adjustment period for
unintended consequences. Finally, a
commenter felt that the six-year model
performance period should be
suspended in the event that CMS
changes the organ acquisition
methodology as initially proposed in the
Fiscal Year 2022 Hospital Inpatient
Prospective Payment System notice of
proposed rulemaking in order to first
evaluate the unintended consequences
of that proposed change.
Response: We appreciate commenters
expressing concern about the six-year
model performance period. We believe
a six-year model performance period is
necessary to allow selected kidney
transplant hospitals enough time to
invest in care delivery changes
necessary for success under the model.
CMS research also shows that savings to
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the Medicare trust fund occur after at
least five years of a model performance
period. We disagree that a six-year
model performance period contradicts
current OPTN metrics given that the
main focus of the model is to increase
the number of transplants year over
year, and not to follow post-transplant
outcomes after six years. We believe the
composite graft survival ratio discussed
in section III.C.5.e(1) of this final rule
does not contradict the OPTN standard
of one to three years of post-transplant
follow-up, but rather expands upon
existing metrics. Furthermore, models
are constantly evaluated and modified
even during the model performance
period through subsequent rulemaking.
A shorter model performance period is
not required to make changes
responsive to IOTA participant
feedback.
We recognize that there may be other
efforts occurring simultaneously with
the implementation of the IOTA Model,
such as the OPTN Modernization efforts
and the implementation of the updated
OPO Conditions for Coverage. We
believe these efforts are synergistic
rather than antagonistic because they
broadly share the aims of increasing the
number of successful transplants and
improve quality outcomes for transplant
recipients. Therefore, we do not believe
that we need to make changes to the sixyear model performance period.
Comment: Several commenters felt
that the proposed January 1, 2025,
model start date did not provide
sufficient time for selected transplant
hospitals to authorize necessary
investments, understand updated organ
offer patterns from the updated kidney
allocation system, and understand
model performance goals. A few
commenters also noted that a January 1,
2025, start date would fall outside of the
standard hospital institutional
budgeting cycle, which would
complicate implementation
investments. In response, a few
commenters supported the alternative
model start date discussed in the
proposed rule of July 1, 2025, and a few
commenters suggested a January 1,
2026, model start date.
Response: We appreciate comments
expressing concerns around the timing
of this model. We are sensitive to
commenters’ concerns about the level of
preparation needed to implement care
redesign activities and develop
stakeholder and personnel relationships
and processes, especially for hospitals
new to value-based care. As such, we
are modifying our proposal and
finalizing a model start date of July 1,
2025, to allow the selected transplant
hospitals more time to prepare for
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model implementation, and to allow for
inclusion of any necessary investments
as a result of the IOTA Model in the
annual hospital budget cycle. As
discussed in section III.C.8 of this final
rule, several requirements are voluntary
in this first year to allow IOTA
participants a grace period to determine
how they will implement these
requirements and focus on achieving
success under the model.
Comment: A few commenters
suggested that CMS delay the start of the
model until after the request for
proposal process for the OPTN is
complete, as the possibility of new
contractors and multiple vendors could
present a risk for errors to attribution
which would inhibit beneficiary
notification and full implementation of
the program.
Response: We thank commenters for
their concern regarding the potential
overlap between the IOTA Model and
the OPTN request for proposal process.
HRSA is in the process of conducting
their solicitation as part of the OPTN
Modernization Process. They released
their first requests for proposals in May
2024 and are conducting a series of
procurements to support OPTN
operations. HRSA has committed to
ensuring smooth continued operation of
the transplant system and the OPTN,
stating that ‘‘while modernization work
is complex, the integrity of the organ
matching process is paramount and
cannot be disrupted.’’ 181 At this time,
we do not believe that this OPTN
Modernization Process would disrupt
the beneficiary attribution process of the
IOTA Model because attribution status
is based on waitlisting, which has not
been subject to any major changes
during the OPTN modernization
process. We will continue to monitor
the operation of the model to determine
if there are any unforeseen
circumstances.
After consideration of the public
comments we received, for the reasons
set forth in this rule, we are finalizing
without modification the proposed
definition of model performance period
at § 512.402. In light of the public
comments, we are also finalizing an
alternative model start date of July 1,
2025. As such, we are finalizing our
proposed definition for model start date
at § 512.402 with slight modification to
specify a July 1, 2025, model start date,
and finalizing our proposed definition
for performance year at § 512.402 with
modification to specify a 12-month
period beginning on July 1 and ending
181 https://www.hrsa.gov/about/news/pressreleases/organ-procurement-transplantationnetwork-modernization-initiative.
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the following June 30 of each year
during the model performance period.
b. Other Proposals
We are also finalizing additional
policies for the IOTA Model, including
the following: (1) the method for
selecting transplant hospitals for
participation; (2) the schedule and
methodologies for the performancebased payments, and waivers of certain
Medicare payment requirements solely
as necessary to test these payment
methodologies under the model; (3) the
performance assessment methodology
for selected transplant hospitals,
including the proposed methodologies
for patient attribution, target setting and
scoring, and calculation of performance
across the achievement domain,
efficiency domain, and quality domain;
(4) monitoring and evaluation; and (5)
overlap with other Innovation Center
models and CMS programs.
We proposed that IOTA participants
would be subject to the general
provisions for Innovation Center models
specified in 42 CFR part 512 subpart A
and in 42 CFR part 403 subpart K,
effective January 1, 2025. The general
provisions at subpart A of part 512 are
also the subject of revisions in this final
rule. As described in section II.B. of this
final rule, we proposed to expand the
applicability of the general provisions
for Innovation Center models to provide
a set of standard provisions for
Innovation Center models that are
applicable more broadly across
Innovation Center models. We believed
that this approach would promote
transparency, efficiency, and clarity in
Innovation Center models and avoid the
need to restate the provisions in each
model’s governing documentation. We
believed that applying these provisions
to the IOTA Model would also promote
these purposes.
We sought comment on our proposal
to apply the general provisions for
Innovation Center models, or the
proposed standard provisions for
Innovation Center models, to the IOTA
Model.
We received no comments on the
proposal to make IOTA Participants
subject to the general provisions for
Innovation Center models, or the
standard provisions for Innovation
Center models if they were finalized.
Therefore, we are finalizing the policy
as proposed. Since we are finalizing the
proposed revisions to the standard
provisions described in section II of this
final rule with modification, including
that the standard provisions will apply
only to the RO Model, the ETC Model,
and mandatory Innovation Center
models with performance periods that
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begin on or after January 1, 2025, we are
also finalizing our proposal to make the
standard provisions for Innovation
Center models applicable to the IOTA
Model.
2. Definitions
We proposed at § 512.402 to define
certain terms for the IOTA Model. We
describe these proposed definitions in
context throughout section III of this
final rule. We proposed to codify the
definitions and policies of the IOTA
Model at 42 CFR part 512 subpart D
(proposed §§ 512.400 through 512.470).
In addition, we proposed that the
definitions contained in the general
provision related to Innovation Center
models at subpart A of part 512, and the
revisions to those provisions proposed
in the notice of proposed rulemaking,
would also apply to the IOTA Model.
We sought comment on these proposed
definitions for the IOTA Model.
We received no comments on these
proposals and are therefore finalizing
the proposed definitions without
modification at § 512.402.
3. IOTA Participants
a. Proposed Participants
We proposed to define ‘‘IOTA
participant’’ as a kidney transplant
hospital, as defined at § 512.402, that is
required to participate in the IOTA
Model pursuant to § 512.412. In
addition, we noted that the definition of
‘‘model participant’’ contained in 42
CFR 512.110, as well as the proposed
revisions to that definition, would
include an IOTA participant.
We proposed to define ‘‘transplant
hospital’’ as a hospital that furnishes
organ transplants as defined in 42 CFR
121.2. We proposed this definition to
align with the definition used by
Medicare. We proposed to define
‘‘kidney transplant hospital’’ as a
transplant hospital with a Medicare
approved kidney transplant program. A
transplant program, as defined at 42
CFR 482.70, is ‘‘an organ-specific
transplant program within a transplant
hospital.’’ Kidney transplants are the
most common form of transplants, but
not all transplant hospitals have a
kidney transplant program. As the focus
of the IOTA Model is kidney
transplants, we proposed this definition
of kidney transplant hospital to refer
specifically to transplant hospitals that
perform kidney transplants. We
proposed to define ‘‘kidney transplant’’
as the procedure in which a kidney is
surgically transplanted from a living or
deceased donor to a transplant
recipient, either alone or in conjunction
with any other organ(s). As described in
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section III.B.3.c of this final rule, the
vast majority of kidney transplants are
performed alone. However, we believed
that it is necessary to include in the
definition of kidney transplant those
kidney transplants that occur in
conjunction with other organ
transplants to avoid creating a
disincentive for multi-organ transplants
within the IOTA Model.
Kidney transplant hospitals are the
focus of the IOTA Model because they
are the entities that furnish kidney
transplants to ESRD patients on the
waiting list and ultimately decide to
accept donor recipients as transplant
candidates. Kidney transplant hospitals
play a key role in managing transplant
waitlists and patient, family, and
caregiver readiness. They are also
responsible for the coordination and
planning of kidney transplantation with
the OPO and donor facilities, staffing
and preparation for kidney
transplantation, and oversight of posttransplant patient care, and they are
largely responsible for managing the
living donation process. The IOTA
Model is intended to promote
improvement activities across selected
kidney transplant hospitals that reduce
access barriers, including SDOH,
thereby increasing the number of
transplants, quality of care, and costeffective treatment. The IOTA Model
aims to improve quality of care for
ESRD patients on the waiting list pretransplant, during transplant, and
during post-transplant care. As
described in section III.B.2.a of this final
rule, kidney transplant access and
acceptance rates vary nationally across
kidney transplant hospitals by
geography and other demographic and
socioeconomic factors. The Innovation
Center has implemented models
targeting dialysis facilities and
nephrology providers, including in the
CEC, ETC, and KCC Models. CMS has
also implemented changes to the OPO
CfCs to strengthen performance
accountability for OPOs. However,
kidney transplant hospitals have not
been the principal focus of any
Innovation Center models to date.
Expanding accountability to kidney
transplant hospitals—key players in the
transplantation ecosystem for ESRD
patients—aligns with the larger efforts
across CMS and HRSA to improve
performance and address disparities in
kidney transplantation.
We alternatively considered having
the IOTA participants be accountable
care organizations (ACOs), such as a
kidney transplant ACOs, instead of
individual kidney transplant hospitals.
In this alternative conception, a kidney
transplant ACO would form as a
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separate legal entity, potentially
including kidney transplant hospitals,
OPOs, transplant surgeons, and other
provider types. The kidney transplant
ACO would assume accountability for
the number of kidney transplants,
equity in the distribution of transplants,
and the quality of transplant services
from the point of a patient being
waitlisted to after a transplant
recipient’s condition stabilizes
following transplantation. This
alternative would potentially carry some
advantages in the potential for improved
coordination among individual
providers and suppliers in the kidney
transplant ACO, but we believe that it
would be administratively burdensome,
as it would require the formation of an
ACO governing board distinct from the
governing boards of individual
providers. In addition, such an ACO
arrangement would potentially be
subject to additional Federal, State, and
tribal laws with respect to grievance,
licensure, solvency, and other
regulations, as well as considerable
overlap with other ACO-based
Innovation Center models. We therefore
proposed to define ‘‘IOTA participant’’
as a kidney transplant hospital, as
defined at § 512.402, that is required to
participate in the IOTA Model pursuant
to § 512.412.
We further alternatively considered
requiring OPO participation in the IOTA
Model as the entity charged with
identifying eligible donors and securing
organs from deceased donors (89 FR
43540). However, in 2020, CMS issued
a final rule that updated OPO CfC
requirements to receive Medicare and
Medicaid payment (85 FR 77898). This
final rule focuses on holding OPOs in
the transplant ecosystem accountable
for improving performance, and the
Innovation Center does not plan further
interventions regarding OPOs at this
time. Given the interactions between
OPOs and transplant hospitals
throughout the donation process,
transplant hospitals may wish to
collaborate or partner with OPOs on
strategies to increase donation and other
quality improvement activities.
We sought public comment on the
proposal that the IOTA participants
would be kidney transplant hospitals.
The following is a summary of the
comments received on our proposal that
the IOTA participants would be kidney
transplant hospitals and our responses:
Comment: Several commenters
expressed support for the proposed
definition of IOTA participants.
Response: We thank the commenters
for their support.
Comment: A commenter sought
clarification on the definition of
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‘‘kidney transplant’’ and whether safetynet kidney transplants would still be
counted as kidney transplantations in
the year following a liver, heart, and/or
lung transplant(s).
Response: We thank the commenter
for their input. As described and
finalized in this section, kidney
transplant means the procedure in
which a kidney is surgically
transplanted from a living or deceased
donor to a transplant recipient, either
alone or in conjunction with any other
organ(s).
A September 2023 OPTN proposal
established criteria for prioritizing
patients who previously received either
a heart or lung transplant, and now need
a kidney transplant. This prioritization
is referred to as a ‘‘safety net’’ for these
patients.182 As such, we clarify that
safety-net kidney transplants will be
counted as kidney transplantations in
the year following a liver, heart, or lung
transplant(s).
After careful consideration of the
public comments we received, for the
reasons set forth in this rule, we are
finalizing the definitions of IOTA
participant and kidney transplant at
§ 512.402 as proposed without
modification. We did not receive any
comments on our proposed definitions
of transplant hospital and kidney
transplant hospital and are therefore
finalizing these definitions as proposed
without modification at § 512.402.
Additionally, we note that we intend to
publicly post kidney transplant
hospitals selected to participate in the
model and information regarding the
participant selection process, as
described and finalized in section
III.C.3.d(1) of this final rule, and how it
resulted in the list of DSAs.
b. Proposed Mandatory Participation
We proposed that all kidney
transplant hospitals that meet the
eligibility requirements contained in
section III.C.3.c of the proposed rule,
and that are selected through the
participation selection process
contained in section III.C.3.d of the
proposed rule, would be required to
participate in the IOTA Model. We
182 James. (2024, January 31). FAQ: New Multiorgan polices in effect. UNOS. https://unos.org/
news/faq-safety-net-policies-for-multi-organtransplantation/ American Organ Transplant
Association. (n.d.). Establish eligibility criteria and
safety net for heart-kidney and lung-kidney
allocation. U.S. Department of Health and Human
Services. Retrieved November 9, 2024, from https://
optn.transplant.hrsa.gov/policies-bylaws/publiccomment/establish-eligibility-criteria-and-safetynet-for-heart-kidney-and-lung-kidney-allocation/
#:∼:text=At%20a%20glance&text=
The%20eligibility%20is%20based%20
on,safety%20net%E2%80%9D%20
for%20these%20patients.
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believe that a mandatory model is
necessary to ensure that a sufficient
number of kidney transplant hospitals
participate in the IOTA Model such that
CMS will be able to conduct a sound
evaluation of the model’s effects on cost
and quality of care in accordance with
section 1115A(b)(4) of the Act. A
mandatory model would also minimize
the potential for selection bias, thereby
ensuring that the model participants are
a representative sample of kidney
transplant hospitals. We believe a
mandatory model is necessary to obtain
relevant information about the effects of
the model’s proposed policies on
Medicare savings, kidney transplant
volume, kidney transplant acceptance
rates, health equity, and quality of care.
In the proposed rule we stated that,
nationally, kidney transplant hospitals
serve diverse patient populations,
operate in varied organizational and
market contexts, and differ in size,
staffing, and capability (89 FR 43541).
There is also wide variation across
kidney transplant hospitals on
performance on kidney transplant
access and organ offer acceptance rate
ratios by geography and other
demographic and socioeconomic
factors. We believed that selection bias
would be a challenge in a voluntary
model because the IOTA Model would
include financial accountability on
access to kidney transplants and quality
of care, as well as downside risk for
kidney transplant hospitals that score
poorly on the performance domains.
Voluntary participation could result in
certain kidney transplant hospitals
choosing not to participate in the model
and ultimately could inhibit the model
from testing a representative sampling
of kidney transplant hospitals. We
explained in the proposed rule that a
mandatory model would address
potential selection bias concerns that
would exist for a voluntary model by
ensuring that our model reaches ESRD
patients residing in underserved
communities and including other
safeguards against selection bias.
As described in section III.C.3.b of the
proposed rule, we alternatively
considered making participation in the
IOTA Model voluntary. However, we
were concerned that a voluntary model
would not be evaluable, would result in
insufficient numbers of kidney
transplant hospital participants, and
would not be representative of kidney
transplant hospitals and ESRD patients
nationally. These concerns reflected our
expectation that the proposed payment
approach would disproportionately
attract kidney transplant hospitals
already performing well in kidney
transplant volume, organ offer
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acceptance rate ratios, and quality of
care pre- and post-transplantation, as
they would expect to receive upside risk
payments. Kidney transplant hospitals
already positioned to score high in the
IOTA Model’s achievement, efficiency,
and quality domains may be more likely
to join the model than other kidney
transplant hospitals, as they would
expect to receive upside risk payments.
This may be especially true for kidney
transplant hospitals that would stand to
benefit the most from a model that
rewards an increase in the number of
kidney transplants. We believed that
selection bias in a voluntary model
would also limit our ability to assess
systematic differences in the IOTA
Model’s effects on kidney transplant
disparities and may further widen
disparity gaps for underserved
communities that stand to lose if the
model does not reach them. We
therefore proposed that the IOTA Model
would be mandatory for all eligible
kidney transplant hospitals selected for
participation in the model, as we
believed this would minimize the risk of
potential distortions in the model’s
effects on outcomes resulting from
hospital self-selection.
We sought public comment on our
proposal to make participation in the
IOTA Model mandatory.
The following is a summary of the
comments received on our proposal to
make participation in the IOTA Model
mandatory and our responses:
Comment: Several commenters
expressed support for requiring
mandatory participation in the IOTA
Model. Some commenters expressed
that mandatory participation would
help increase access to kidney
transplants and improve kidney
transplant outcomes.
Response: We thank the commenters
for their support.
Comment: Several commenters
expressed concern with making
participation in the IOTA Model
mandatory. Commenters shared that
mandatory participation could
negatively impact patients. A
commenter stated that CMS wrongly
presumes that all IOTA participants
have the same opportunity for success
in the model, and that careful analysis
is required to determine whether IOTA
Model participation would improve
quality of care without sacrificing
financial viability. Moreover, a
commenter suggested that the nature of
mandatory models diverts critical
resources that could be used for patient
care and instead would redirect
resources to administrative tasks,
causing administrative burden, in order
for transplant hospitals to comply with
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a mandatory model’s unproven and
experimental requirements. This
commenter also noted that mandatory
participation in the IOTA Model could
be particularly burdensome for hospitals
operating with small financial margins.
Response: We thank the commenters
for their feedback. As described in
section III.C.3.b of the proposed rule, we
believe that a mandatory model is
necessary to ensure that a sufficient
number of kidney transplant hospitals
participate in the IOTA Model such that
CMS will be able to conduct a sound
evaluation of the model’s effects on cost
and quality of care. A mandatory model
would also minimize the potential for
selection bias, thereby ensuring that the
model participants are a representative
sample of kidney transplant hospitals.
We believe a mandatory model is
necessary to obtain relevant information
about the effects of the model’s
proposed policies on Medicare savings,
kidney transplant volume, kidney
transplant acceptance rates, health
equity, and quality of care. Transplant
hospitals may have to make upfront
investments to accommodate the IOTA
Model’s requirements, but we believe
that the low volume threshold of 11
adult kidney transplants performed
during each of the baseline years, as
described and finalized in section
III.C.3.c of this final rule, will
substantially mitigate the demands
placed on smaller transplant hospitals.
Additionally, we do not believe the
IOTA Model will divert critical or
financial resources, nor do we believe
the IOTA Model will negatively impact
patient care. Rather, we believe the
incentives of the IOTA Model will
complement other efforts in relation to
the transplant ecosystem to enhance
health and safety outcomes, increase
transparency, increase the number of
transplants, and reduce disparities. For
these reasons, we are finalizing our
proposal without modification.
Comment: Multiple commenters
suggested that introducing a mandatory
payment model on top of existing
modernization initiatives would add
unnecessary disruption, risk, and
uncertainty to the transplant system. A
commenter highlighted a specific
initiative, the OPTN Modernization
Initiative launched in March 2023,
which focuses on five key areas:
technology, data transparency and
analytics, governance, operations, and
quality improvement and innovation. A
commenter also noted that,
alternatively, a voluntary model would
minimize disruption for transplant
programs whose regulatory environment
is already uncertain.
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Response: We thank the commenters
for their feedback. We recognize the
challenges kidney transplant hospitals
may face as a result of participation in
the IOTA Model. However, as described
in section III.C.3.b of the proposed rule,
we believe that a mandatory model is
necessary to ensure a sufficient number
of kidney transplant hospitals
participate in the IOTA Model such that
CMS will be able to conduct a sound
evaluation of the model’s effects on cost
and quality of care. For these reasons,
we are finalizing our proposal without
modification.
Comment: A commenter suggested
that the IOTA Model has the same goals
as the ETC Model, and the commenter
stated that the ETC Model has not
indicated any significant increase in
kidney transplants or significant
increase in patient placement on kidney
transplant waitlists or reduced Medicare
spending. The commenter stated that as
a result, CMS should not implement a
similar mandatory model.
Response: We thank the commenter
for their feedback. As described in
section III.A of the proposed rule, this
model falls within a larger framework of
activities initiated by the Federal
Government during the past several
years and forthcoming in the near future
to enhance the donation, procurement,
and transplantation of solid organs.
Relatedly, as described in section
III.B.3.b in this final rule, the IOTA
Model proposes to complement the ETC
Model and expand kidney model
participation to kidney transplant
hospitals, which are a key player in the
transplant ecosystem, to test whether
two-sided risk payments based on
performance increase access to kidney
transplants for ESRD patients placed on
the waitlists of participating transplant
hospitals. We disagree with the
suggestion that the ETC Model and the
IOTA Model have the same goals. No
prior CMS models have focused
squarely on transplant hospitals in the
way the IOTA Model does. For these
reasons, we are finalizing our proposal
without modification.
Comment: Several commenters raised
concerns about bias and disparities as a
result of mandatory model participation,
suggesting it could bias the model in
favor of underperforming transplant
hospitals or increase disparities for
underserved populations, such as dualeligible and low-income subsidy
beneficiaries, or rural transplant
hospitals already impacted by
population variability that constricts the
ease of access to transplant care.
Response: We thank the commenters
for their feedback and concern. As
described in section III.C.3.b of the
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proposed rule, we believe that a
mandatory model is necessary to ensure
that a sufficient number of kidney
transplant hospitals participate in the
IOTA Model such that CMS will be able
to conduct a sound evaluation of the
model’s effects on cost and quality of
care. A mandatory model would also
minimize the potential for selection
bias, thereby ensuring that the model
participants are a representative sample
of kidney transplant hospitals. We
believe a mandatory model is necessary
to obtain relevant information about the
effects of the model’s proposed policies
on Medicare savings, kidney transplant
volume, kidney transplant acceptance
rates, health equity, and quality of care.
We also believe the burden on smaller
kidney transplant hospitals will be
minimized as a result of the low volume
threshold of 11 adult kidney transplants
performed during each of the baseline
years, as described and finalized in
section III.C.3.c of this final rule.
Additionally, we do not believe
mandatory participation in the IOTA
Model would increase disparities for
underserved populations such as dualeligibles or low-income subsidy
beneficiaries, nor for rural transplant
hospitals. Rather, we believe the IOTA
Model will incentivize IOTA
participants to perform a greater number
of kidney transplants, including those
for underserved populations. We believe
that the IOTA Model will encourage
IOTA participants to address access
barriers low-income patients often face,
such as transportation, remaining active
on the kidney transplant waiting list,
and making their way through the living
donation process. Relatedly, while rural
transplant hospitals face additional
unique challenges, such as geographic
difficulties in accessing care, we do not
believe underserved populations will be
negatively impacted by the IOTA
Model’s mandatory nature. Rather, as
described in section III.B.3.e, differences
among transplant hospitals in living
donor kidney donation are correlated
with geographic region and the number
of deceased donor kidney
transplantations performed. This
underscores the need for initiatives and
processes among transplant hospitals,
such as the IOTA Model, to encourage
living donations to reduce geographic
disparities. For these reasons, we are
finalizing our proposal without
modification.
Comment: Several commenters
suggested that a mandatory model has
financial risks to model participants due
to high upfront costs related to
employees and IT support, and that it
places model participants at significant
financial risk regardless of their
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readiness for participation. Commenters
stated that a mandatory model
effectively cuts compensation for kidney
transplant hospitals with insufficient
resources to adequately participate,
thereby exacerbating resource
disparities and impacting the viability
of some transplant programs.
Commenters also stated that kidney
transplant hospitals selected to
participate in the model may opt out of
performing kidney transplants rather
than assume the costs of mandatory
participation.
Response: We thank the commenters
for their feedback and concern. As
described in section III.C.3.b of the
proposed rule, we believe that a
mandatory framework is essential to
ensure that a sufficient number of
kidney transplant hospitals participate
in the IOTA Model such that CMS will
be able to conduct an adequate
evaluation of the model’s effects on cost
and quality of care. Kidney transplant
hospitals selected to participate in the
model may have to make upfront
investments to accommodate the IOTA
Model’s requirements, but we believe
that the low volume threshold of 11
adult kidney transplants performed
during each of the baseline years, as
described and finalized in section
III.C.3.c of this final rule, will
substantially mitigate the demands
placed on smaller kidney transplant
hospitals. With several months of lead
time until the IOTA Model’s start date,
we believe eligible kidney transplant
hospitals selected to participate in the
IOTA Model will be sufficiently
equipped for participation and success
in the model. We do not believe
mandatory participation will cut
compensation for smaller kidney
transplant hospitals selected to
participate in the IOTA Model. Rather,
mandatory participation in the IOTA
Model offers a strong financial incentive
for those transplant hospitals chosen to
participate. Finally, we believe the twosided performance-based payment
structure, as described and finalized in
section III.C.6.a of this final rule, which
rewards IOTA participants for high
performance in the achievement,
efficiency, and quality domains—and
imposes financial accountability on
IOTA participants that perform poorly
on those domains—will encourage
maximum engagement from IOTA
participants. For these reasons, we are
finalizing our proposal without
modification.
Comment: Multiple commenters
showcased the differing opinions
regarding how the mandatory nature of
the IOTA Model may impact kidney
transplant hospitals based on size. Some
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commenters suggested that mandatory
participation could benefit lowervolume or underperforming kidney
transplant hospitals that have room to
grow, while larger-volume kidney
transplant hospitals with limited
capacity to grow would incur financial
and administrative burdens to reach
their transplant targets. Other
commenters suggested the IOTA Model
could negatively impact small kidney
transplant hospitals financially. or
increase competition for available
organs with higher-volume kidney
transplant hospitals.
Response: We thank the commenters
for their support and feedback. IOTA
participant performance on the
achievement domain in the IOTA Model
is measured based on the number of
transplants performed by the IOTA
participant in the baseline years and the
national growth rate as described and
finalized in section III.C.5.c(1) of this
final rule. As a result of this metric, we
believe kidney transplant hospitals—
including larger-volume programs in the
IOTA Model—are on equal footing to
improve their transplant rates in each
consecutive PY. IOTA participants may
have to make upfront investments to
accommodate the IOTA Model’s
requirements, but we believe that the
required low volume threshold of 11
adult kidney transplants performed for
each kidney transplant hospital in each
of the baseline years, as described and
finalized in section III.C.3.c of this final
rule, will substantially mitigate the
demands placed on smaller kidney
transplant hospitals. Additionally, we
have found that many of these kidney
transplant hospitals consistently
perform between 11 and 50 kidney
transplants annually. We direct readers
to section III.C.3.c of this final rule for
a full discussion on why we believe
provisions within the IOTA Model will
limit negative impacts to small kidney
transplant hospitals.
We recognize that IOTA participants
face varying challenges based on their
kidney transplant volumes. However,
we believe all IOTA participants,
including high-volume kidney
transplant hospitals, have opportunities
to increase the number of kidney
transplants performed. For example,
high-volume kidney transplant hospitals
could focus on improving deceased
donor organ utilization or supporting
more living donors. Regardless of each
IOTA participant’s approach or any
potential competition, we intend to
monitor the model for any unintended
consequences. For these reasons, we are
finalizing our proposal without
modification.
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Comment: A commenter suggested
that mandatory participation in the
IOTA Model may be undermined by the
absence of any meaningful adverse
consequences when an IOTA
participant is terminated from the
model.
Response: We thank the commenter
for their feedback. As described and
finalized in section III.C.16.a of this
final rule, we may take a variety of one
or more remedial actions. We believe
the remedial actions we are finalizing at
§ 512.464(b) can meaningfully
discourage noncompliance with the
IOTA Model requirements. For these
reasons, we are finalizing our proposal
without modification.
Comment: A commenter claimed that
CMS does not have the authority to
institute IOTA as a mandatory model,
while other commenters shared general
concerns about requiring mandatory
participation in the model.
Response: We thank the commenters
for their feedback and concerns. CMS’
testing of innovative payment and
service delivery models, including the
IOTA Model, complies with section
1115A of the Act and other governing
laws and regulations, including the U.S.
Constitution. Section 1115A of the Act
and the Secretary’s authority to operate
the Medicare program authorize us to
finalize mandatory participation in the
IOTA Model for the selected IOTA
participants. Section 1115A of the Act
authorizes the Secretary to test
innovative payment and service
delivery models expected to reduce
Medicare costs while preserving or
enhancing quality of care. The statute
does not require that models be
voluntary or be tested first as a
voluntary model, but rather gives the
Secretary discretion to design and test
models that meet certain requirements
as to spending and quality. Section
1115A(b)(2)(B) of the Act describes a
number of payment and service delivery
models that the Secretary may test, but
the Secretary is not limited to testing
just those models. Rather, as specified
in section 1115A(b)(2) of the Act,
models to be tested under section 1115A
of the Act must address a defined
population for which there are either
deficits in care leading to poor clinical
outcomes or potentially avoidable
expenditures. The IOTA Model
addresses a defined population (kidney
transplant waitlist patients) for which
there are potentially avoidable
expenditures arising from an inadequate
number of kidney transplants performed
each year.
We chose to make participation in the
IOTA Model mandatory for the selected
kidney transplant hospitals to avoid the
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selection bias inherent to any model in
which providers may choose whether or
not to participate. Such a design will
ensure sufficient participation of kidney
transplant hospitals, which is necessary
to obtain a diverse, representative
sample of hospitals that will allow a
statistically robust test of the model.
Moreover, the Secretary has the
authority to establish regulations to
carry out the administration of the
Medicare program. Specifically, the
Secretary has authority under sections
1102 and 1871 of the Act to implement
regulations as necessary to administer
the Medicare program, including testing
this Medicare payment and service
delivery model. We note that IOTA is
not a permanent feature of the Medicare
program. Rather, IOTA will test
innovative methods for delivering and
paying for services covered under the
Medicare program, which the Secretary
has clear legal authority to regulate. The
proposed rule went into detail about the
provisions of the proposed IOTA Model,
enabling the public to understand how
IOTA was designed and could apply to
affected kidney transplant hospitals.
and sought comment on the proposed
model design and policies. As permitted
by section 1115A of the Act, we are
testing IOTA within specified
geographic areas. If the IOTA Model test
meets the statutory requirements for
expansion, and the Secretary determines
that expansion is appropriate, we would
undertake rulemaking to implement the
expansion of the scope or duration of
the IOTA Model to additional
geographic areas or for additional time
periods, as required by section 1115A(c)
of the Act.
For these reasons, we are finalizing
our proposal without modification.
Comment: Multiple commenters
suggested the IOTA Model should begin
with a voluntary trial period or be a
purely voluntary model to minimize
negative impacts on patients. They
cautioned that an unintended
consequence of this mandatory model
could be a decrease the availability of
marginal organs for transplantation.
Several other commenters
recommended the IOTA Model allow
self-selection to encourage participation
from motivated kidney transplant
hospitals. These commenters suggested
this would incentivize voluntary
participation and enable kidney
transplant hospitals to assess if the
model is appropriate for their patients.
Response: We thank the commenters
for their feedback. As described in
section III.C.3.b of the proposed rule, we
believe that a mandatory model is
necessary to ensure that a sufficient
number of kidney transplant hospitals
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participate in the IOTA Model such that
CMS will be able to conduct a sound
evaluation of the model’s effects on cost
and quality of care as required by
section 1115A(b)(4) of the Social
Security Act. We believe a voluntary
trial period would inhibit this
evaluation.
More specifically, we are concerned
that a voluntary model would not be
evaluable, result in insufficient numbers
of IOTA participants, and not be
representative of kidney transplant
hospitals and ESRD patients nationally.
These concerns reflect our expectation
that the model’s proposed payment
approach, as described and finalized in
section III.C.6 of this final rule, would
disproportionately attract kidney
transplant hospitals already performing
well in kidney transplant volume, organ
offer acceptance rate ratios, and quality
of care pre- and post-transplantation.
Kidney transplant hospitals already
positioned to score high in the IOTA
Model’s achievement, efficiency, and
quality domains may be more likely to
join the model than other kidney
transplant hospitals, as they would
expect to receive upside risk payments.
In the context of the IOTA Model, we
believe that a voluntary model could
result in selection bias and limit our
ability to assess systematic differences
in the IOTA Model’s effects on kidney
transplant disparities.
As a mandatory model, we also
believe the IOTA Model will have
positive impacts on patients and an
increase in the availability of kidneys.
Finally, we believe the transplant
hospitals selected for mandatory
participation would be motivated to
increase the number of kidney
transplants performed due to the
financial incentives of the model. For
these reasons, we are finalizing our
proposal without modification.
Comment: Several commenters
expressed concern with making
participation in the IOTA Model
mandatory, urging CMS to consider
geographic factors or the impact of the
model on smaller kidney transplant
hospitals. For example, a commenter
argued that the IOTA Model’s
mandatory participation component
must consider geographic location. The
commenter explained that if the model
aims to address disparities in transplant
access for patients of different races,
ethnicities, socioeconomic statuses, or
from rural areas, then these factors need
to be accounted for. The commenter
stated that they see these factors directly
impacting their pool of potential living
donors, who often suffer from the same
medical and economic conditions as
their recipients and thus get ruled out.
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A commenter from a smaller, rural
kidney transplant hospital expressed
concerns about mandatory participation.
They argued that population density
varies greatly in their rural state, with
an uneven distribution. The commenter
noted this population variation impacts
both access to transplant care and the
available donor pool and would require
additional staffing and resources to
manage the model effectively.
Another commenter expressed
concerns about the impact of the IOTA
Model on small kidney transplant
hospitals if participation was made
mandatory. The commenter suggested
that a low volume threshold of 100
kidney transplants, regardless of payer
type, would be more appropriate. This,
the commenter believed, would ensure
small kidney transplant hospitals were
excluded and protect access to kidney
transplants in less populated areas.
Lastly, a commenter recognized that
the IOTA participants would be kidney
transplant hospitals. The commenter
reiterated concerns about the challenges
that mandatory payment models may
pose for physician practices. The
commenter explained that successful
participation in alternative payment
models often requires new investments
in infrastructure and technical
capabilities, such as sophisticated data
management, dedicated performance
assessment resources, and updates to
electronic medical records. They argued
that meeting these demands would be
difficult, if not impossible, for many
kidney transplant hospitals, especially
smaller ones. This could set these
kidney transplant hospitals up for
failure. The commenter recommended
that CMS apply exemptions or special
accommodations, like upside-only risk,
for small kidney transplant hospitals
that lack experience with value-based
payment arrangements, if CMS requires
future participation in new models.
Response: We took into consideration
geographic factors when proposing to
stratify the DSAs into groups based on
each DSA’s Census Division and the
total number of adult kidney transplants
performed annually across all eligible
kidney transplant hospitals in each DSA
during the baseline years for the first
PY, as described and finalized in section
III.C.3.d(1). As discussed in the
proposed rule, we believe selecting
eligible kidney transplant hospitals from
these groups of DSAs will ensure that
the IOTA participants represent eligible
kidney transplant hospitals nationwide,
both geographically and in terms of
annual adult kidney transplant volume
(89 FR 43542). Additionally, as
described and finalized in section
III.C.3.d(1) of this final rule, CMS will
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then select approximately half of all
DSAs nationwide using a stratified
sampling methodology, and all eligible
kidney transplant hospitals in the
selected DSAs will be required to
participate in the IOTA Model.
Additionally, we note that we intend
to publicly post information regarding
the selection process and how it
resulted in the list of DSAs and kidney
transplant hospitals selected to
participate in the model.
Finally, as described and finalized in
section III.C.3.c of this final rule, we
will use a low volume threshold of 11
adult kidney transplants performed
during each of the baseline years. This
low volume threshold aligns with the
minimum requirements for publishing
CMS data, ensuring the confidentiality
of Medicare and Medicaid beneficiaries
by preventing the disclosure of
information that could identify
individual beneficiaries. As described at
89 FR 43541 in the proposed rule, we
alternatively considered using a higher
threshold, such as 30 adult kidney
transplants or 50 adult kidney
transplants during each of the three
baseline years. However, we found that
many kidney transplant hospitals
consistently perform between 11 and 50
kidney transplants annually. For these
reasons, we are finalizing our proposal
without modification.
After careful consideration of the
public comments we received, for the
reasons set forth in this rule, we are
finalizing our proposal to make the
IOTA Model mandatory at § 512.412(c)
without modification.
c. Participant Eligibility
We proposed kidney transplant
hospital participant eligibility criteria
that would increase the likelihood that:
(1) individual kidney transplant
hospitals selected as IOTA participants
represent a diverse array of capabilities
across the performance domains as
discussed in section III.C.5 of this final
rule; and (2) the results of the model test
would be statistically valid, reliable,
and generalizable to kidney transplant
hospitals nationwide should the model
test be successful and considered for
expansion under section 1115A(c) of the
Act.
We proposed that eligible kidney
transplant hospitals would be those
that: (1) performed 11 or more kidney
transplants for patients aged 18 years or
older annually, regardless of payer type,
in each of the baseline years (the ‘‘low
volume threshold’’); and (2) furnished
more than 50 percent of its kidney
transplants annually to patients over the
age of 18 during each of the baseline
years. We proposed to define ‘‘baseline
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year’’ as a 12-month period within a 3year historical baseline period that
begins 48 months (or 4 years) before the
start of each model PY and ends 12
months (or 1 year) before the start of
each model PY. For example, if the
IOTA Model were to start on January 1,
2025, the 3-year historical baseline
period would begin January 1, 2021, and
end on December 31, 2023.183 We
proposed to define ‘‘non-pediatric
facility’’ as a kidney transplant hospital
that furnishes over 50 percent of their
kidney transplants annually to patients
18 years of age or older. CMS would
select approximately half of all DSAs
nationwide using a stratified sampling
methodology, and all eligible kidney
transplant hospitals in the selected
DSAs would be required to participate
in the IOTA Model.
As described in the proposed rule at
89 FR 43541, the proposed low volume
threshold of 11 or more kidney
transplants for ESRD patients aged 18
years or older during each of the three
baseline years (as described in section
I.B.2.b of the proposed rule) would
exclude low volume kidney transplant
hospitals from the IOTA Model. We
believed that these kidney transplant
hospitals should be excluded from the
model because they may not have the
capacity to comply with the model’s
policies, and because the inclusion of
this group of kidney transplant hospitals
in the model would be unlikely to
significantly alter the overall rates of
kidney transplantation. We stated that
we were also proposing a low volume
threshold of 11 adult kidney transplants
because it is consistent with the
minimum thresholds for the display of
CMS data to protect the confidentiality
of Medicare and Medicaid beneficiaries
by avoiding the release of information
that can be used to identify individual
beneficiaries. We alternatively
considered using a higher threshold,
such as 30 adult kidney transplants or
50 adult kidney transplants during each
of the three baseline years. However, we
found that many kidney transplant
hospitals consistently perform between
11 and 50 transplants per year. We
further believe that using a higher
threshold would decrease the number,
size and location of kidney transplant
hospitals eligible to be selected for
participation in the IOTA Model,
thereby limiting the generalizability of
the model test. We also recognize that
the number of kidney transplants
183 This example, which appeared in the notice of
proposed rulemaking, has been clarified to specify
that the baseline years for each PY would be each
12-month period beginning January 1, 2021, and
ending December 31, 2023.
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performed by a kidney transplant
hospital may fluctuate from year to year,
and looking back three years would help
determine if a kidney transplant
hospital has the capacity to consistently
perform 11 or more transplants per year.
We sought feedback on this approach
for determining which kidney
transplant hospitals would be eligible
for selection under the model.
We considered including pediatric
kidney transplant hospitals as eligible
participants in the IOTA Model.
However, pediatric kidney
transplantation has significantly
different characteristics, considerations,
and processes from adult kidney
transplantation. The number of
pediatric kidney transplants performed
each year is also exceedingly small,
which would present difficulties in
reliably determining the effects to the
model in the pediatric population.
Additionally, a much larger proportion
of pediatric kidney transplants are
living donor transplants than in the
adult population. As such, we do not
believe the proposed IOTA Model
would function in the same way for
both kidney transplant hospitals serving
primarily adults and those serving
primarily children, and we believe it is
necessary to include only non-pediatric
kidney transplant hospitals in the IOTA
Model.
We sought comment on our proposed
participant eligibility criteria for kidney
transplant hospitals, including the
requirement that a kidney transplant
hospital perform 11 or more kidney
transplants annually on patients aged 18
years or older during the baseline years.
We also sought comment on the
proposal to include only kidney
transplant hospitals that meet the
proposed definition for a non-pediatric
facility during the baseline years.
The following is a summary of the
comments received on our proposed
participant eligibility criteria for kidney
transplant hospitals, including the
requirement that a kidney transplant
hospital perform 11 or more kidney
transplants annually on patients aged 18
years or older during each of the
baseline years, and the proposal to
include only kidney transplant hospitals
that meet the proposed definition for a
non-pediatric facility during the
baseline years, and our responses:
Comment: Several commenters
expressed support for the IOTA
participant kidney transplant hospital
eligibility criteria, as proposed,
particularly noting the proposed
eligibility criterion by which a kidney
transplant hospital must furnish over 50
percent of their kidney transplants
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annually to patients 18 years of age or
older.
Response: We thank the commenters
for their support.
Comment: Several commenters
expressed concern with the proposed
low-volume kidney transplant threshold
for IOTA participants. A commenter
noted that there may be some
unforeseen or unintended consequences
of advantaging programs classified as
‘‘low volume,’’ where the volume is
close to the dividing line, and vice
versa. Additional commenters shared
concerns that the low volume threshold
of 11 kidney transplants performed will
disadvantage kidney transplant
hospitals that furnish a smaller number
of kidney transplants, as these
transplant programs do not meet the
requirements for COE programs and
have limited contracts with payers, and
the low volume threshold does not
ensure statistical significance. Several
commenters recommended that CMS
should increase the low volume
threshold, setting the number of kidney
transplants at a value such as 25, 50, or
100, to ensure statistical significance
and avoid burden on kidney transplant
hospitals that furnish a smaller number
of kidney transplants. Finally, a
commenter suggested CMS should only
use the number of Medicare kidney
transplants to determine eligibility,
rather than 11 kidney transplants across
all payers.
Response: We thank the commenters
for their feedback. To protect the
confidentiality of Medicare and
Medicaid beneficiaries, we proposed a
low volume threshold of 11 adult
kidney transplants. We believe this lowvolume threshold aligns with the
minimum standards for CMS data
display, preventing the release of
information that could identify
individual beneficiaries while ensuring
statistical significance (89 FR 43541).
We recognize that this could exclude
smaller kidney transplant programs,
which may not already meet COE 184
program criteria and have limited
contact with payers. However, as
described in the proposed rule, we
proposed a low volume threshold of 11
adult kidney transplants to exclude lowvolume kidney transplant hospitals that
may lack the capacity to comply with
the model’s policies, as their inclusion
would be unlikely to significantly
184 A transplant center receives Center of
Excellence (COE) designation from a private insurer
when it meets transplant volume and performance
thresholds. Without this designation, a transplant
hospital may not be approved by certain private
insurance companies to complete a transplant
procedure, which limits the transplant center where
patients may receive covered care.
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impact overall kidney transplant rates
(89 FR 43541). We considered, but did
not propose, using a higher threshold,
such as 30 adult kidney transplants or
50 adult kidney transplants during each
of the three baseline years (89 FR
43541). However, we did not propose
this, as we found that many kidney
transplant hospitals consistently
perform between 11 and 50 transplants
annually. We maintain our belief that a
higher threshold would reduce the
number, size, and geographic diversity
of kidney transplant hospitals eligible
for the IOTA Model, limiting the
model’s broader applicability.
Additionally, we recognize that kidney
transplant volumes can fluctuate yearto-year. Furthermore, we believe looking
at a 3-year historical baseline period
will help assess if a kidney transplant
hospital has the capacity to consistently
perform 11 or more kidney transplants
annually.
Relatedly, as described in section
III.C.3.d(2) of this final rule, after the
IOTA Model’s start date, we do not
anticipate making any additional
participant selections, unless 10 percent
or more of the selected participants are
terminated during the model’s
performance period. If that occurs, we
will address the selection of new IOTA
participants through future notice and
comment rulemaking, and we may
reevaluate the low volume threshold.
Finally, as described in the proposed
rule, we considered limiting IOTA
waitlist and IOTA transplant patients to
Medicare beneficiaries only, as
Medicare covers over 50 percent of
kidney transplants (89 FR 43544).
However, we ultimately did not propose
this limitation. We believe restricting
the IOTA Model assessment to Medicare
patients would reduce the sample size,
potentially hindering our ability to
detect performance changes due to
model payments. Therefore, we
proposed, and will be finalizing, that
the IOTA Model reflect both Medicare
beneficiaries and non-Medicare patients
for performance assessment, with
Medicare beneficiaries being a subset of
the patient population attributed to each
model participant. We direct readers to
section III.C.5 of this final rule for a full
discussion on the IOTA Model
performance assessment methodology.
We believe the same rationale applies
for kidney transplant hospital eligibility
criteria. For these reasons, we are
finalizing our proposal without
modification.
Comment: A commenter suggested
that IOTA participants that furnish a
smaller volume of kidney transplants
would have little incentive to engage in
the model if participant eligibility is
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based on all kidney transplants, but
financial incentives and penalties only
apply to Medicare kidney transplants.
Response: We thank the commenter
for their feedback. We considered,
limiting IOTA waitlist patients and
IOTA transplant patients to Medicare
beneficiaries only, as Medicare covers
more than 50 percent of all kidney
transplants from both deceased and
living donors (89 FR 43544). However,
we believe it’s necessary to include all
patients, regardless of payer type, in the
IOTA participant’s performance
calculations. This protects against
unintended consequences and
problematic financial incentives that
could arise if the IOTA Model only
applied to specific payer types.
Additionally, the eligible waitlist and
transplant patient population attributed
to each IOTA participant is already
relatively small, in terms of both
transplant candidates and recipients.
Limiting the IOTA Model performance
assessment, as described in section
III.C.5 of this final rule, to only
Medicare beneficiaries would further
reduce the patient sample size,
potentially affecting our ability to detect
changes in performance due to model
payments. For these reasons, we chose
not to propose limiting IOTA waitlist
patients and IOTA transplant patients to
Medicare beneficiaries only and
respectfully disagree with the
commenter.
Lastly, as described in section III.C.5
of this final rule, the IOTA Model’s
performance assessment is inclusive of
both Medicare and non-Medicare
patients. We believe this will
incentivize IOTA participants of all
sizes and patient populations to fully
engage in the model regardless of payer
type. For these reasons, we are
finalizing our proposal without
modification.
Comment: Multiple commenters
recommended that CMS exclude kidney
transplant hospitals with high volume,
high quality, and high efficiency from
the IOTA Model, and provide additional
provisions for newer kidney transplant
hospitals.
Response: We thank the commenters
for their feedback. As described in
section I.B.2.b of the proposed rule, we
proposed to select the kidney transplant
hospitals that will be required to
participate in the IOTA Model from the
group of eligible kidney transplant
hospitals using a stratified random
sampling of DSAs to ensure that there
is a fair selection process and
representative group of participating
kidney transplant hospitals. We believe
the commenter’s recommendation
would inhibit a representative sampling
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necessary to the IOTA Model. For these
reasons, we are finalizing our proposal
without modification.
Comment: Several commenters
suggested CMS should change multiple
aspects of the proposed participant
eligibility criteria. Recommendations
included excluding kidney transplant
hospitals that have had a transplant
volume growth of 30 percent or more
and expanding eligible kidney
transplant hospitals to include pediatric
kidney transplant hospitals.
Response: We thank the commenters
for their feedback and suggestions. In
section I.B.2.b of the proposed rule, we
proposed to select the kidney transplant
hospitals that will be required to
participate in the IOTA Model from the
group of eligible kidney transplant
hospitals using a stratified random
sampling of DSAs to ensure that there
is a fair selection process and
representative group of participating
kidney transplant hospitals. We believe
the commenter’s recommendation
would inhibit a representative sampling
necessary to test the proposed model.
For these reasons, we are finalizing our
proposal without modification.
Additionally, regarding the comments
that CMS consider including pediatric
kidney transplant hospitals in the IOTA
Model, we acknowledge the importance
kidney transplantation for pediatric
patients. As described at 89 FR 43541 in
the proposed rule, we considered,
including pediatric kidney transplant
hospitals in the IOTA Model. However,
for the reasons described in section
III.C.5.c of this final rule, we ultimately
decided not to propose their inclusion
as eligible kidney transplant hospitals.
pediatric kidney transplant hospitals as
eligible participants in the model. As
such, we respectfully disagree with
commenters who argued that pediatric
kidney transplant hospitals should be
eligible to participate in the model.
Finally, as described in the proposed
rule, we considered offering differential
credit for transplants by type (89 FR
43553). With this alternative
methodology, IOTA participants would
receive bonus points and score higher
for transplants that fit into categories
that lead to more savings, such as living
donor kidney transplants, high kidney
donor profile index donors, or preemptive transplants, compared to other
transplants. However, we chose not to
propose a methodology that provides
differential credit for transplants based
on type, as we believe that counting all
transplants equally will give IOTA
participants the flexibility to meet their
transplant targets. Furthermore, we
think this approach of treating all
transplants the same helps minimize the
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potential harm and unintended
consequences that could arise from a
methodology that offers differential
credit based on transplant type. We
direct readers to section III.C.5.c(2) of
this final rule for a full discussion on
alternative methodologies we
considered for calculating points in the
achievement domain. For these reasons,
we are finalizing our proposal without
modification.
Comment: A commenter
acknowledged that CMS proposed to
define a baseline year as a 12-month
period within a 3-year historical
baseline period, that begins 48 months
(or 4 years) before the start of each
model PY and ends 12 months (or 1
year) before the start of each model PY.
For PY 1 (CY 2025), as proposed, the
commenter highlighted that the
proposed 3-year historical baseline
period consists of CY 2021 through CY
2023. The commenter supported the
proposed 3-year historical baseline
period for PY 1, noting that 2020–2022
represented a low point in transplant
activity due to the Public Health
Emergency (‘‘PHE’’) declared in
response to the COVID–19 pandemic,
which reduced the number of kidneys
transplanted nationally. Additionally,
the commenter believed that starting
from this low baseline would help
ensure more attainable performance
improvement targets for model
participants, though they still had
significant reservations about the
proposed transplant targets.
Response: We thank the commenter
for their support.
Comment: Multiple commenters
expressed concern on the inclusion of
2021 in the baseline years. Specifically,
a commenter suggested that the 3-year
historical baseline period should
exclude transplant data from 2021, as
the COVID–19 public health emergency
impacted this performance year.
Response: We thank the commenters
for their feedback. As described in
section III.C.3.c of this final rule, we
proposed to define ‘‘baseline year’’ as a
12-month period within a 3-year
historical baseline period that begins 48
months (or 4 years) before the start of
each model PY and ends 12 months (or
1 year) before the start of each model
PY. For example, if the IOTA Model
were to start on July 1, 2025, the 3-year
historical baseline period would begin
July 1, 2021, and end on June 30, 2024.
In this example, the baseline years for
each PY would be 12-month periods
beginning July 1, and ending on June 30.
Relatedly, in response to commenters
requesting a later start date for the
model, we are finalizing a July 1, 2025,
model start date. This will result in the
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inclusion of only the latter six months
of 2021 into the baseline period for the
first PY. Within the context of the
COVID–19 pandemic, the nonutilization of deceased donor kidneys in
2020 rose to the highest level up to that
time, 21.3 percent. Additionally, the
number of newly added adult
candidates to the waitlist increased 11.7
percent from 2020 to 2021, recovering
from the pandemic related decline in
the prior year, and exceeding the 2015–
2019 CAGR of 9.2 percent. We do not
believe inclusion of July through
December of 2021 into the baseline year
would inhibit the overarching goal of
the IOTA Model. For these reasons, we
are finalizing our proposal without
modification.
After consideration of the public
comments we received, we are
finalizing our proposed provisions for
participant eligibility criteria for kidney
transplant hospitals at § 512.412(a)
without modification. We received no
comments for the proposed definition of
non-pediatric facility and are finalizing
the proposed definitions of nonpediatric facility, and baseline years at
§ 512.402 without modification.
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d. Participant Selection
(1) Overview and Process for Participant
Selection
In section III.C.c.3.d(1) of the
proposed rule, we proposed to select
eligible kidney transplant hospitals for
participation in the IOTA Model using
a stratified sampling of approximately
half of all DSAs nationwide. We stated
that all kidney transplant hospitals that
meet the proposed participant eligibility
criteria described in section III.C.3.c of
the proposed rule and are located in the
selected DSAs would be required to
participate in the IOTA Model. As
defined in 42 CFR 486.302, a ‘‘Donation
Service Area (DSA)’’ means a
geographical area of sufficient size to
ensure maximum effectiveness in the
procurement and equitable distribution
of organs and that either includes an
entire metropolitan statistical area
(MSA) or does not include any part of
such an area and that meets the
standards of subpart G. A DSA is
designated by CMS, is served by one
OPO, contains one or more transplant
hospitals, and one or more donor
hospitals. There were 56 DSAs as of
January 1, 2024. A map of the DSAs can
be found on the SRTR website.185 CMS
would use the list of DSAs as it appears
on January 1, 2024, to select the DSAs,
and therefore the eligible kidney
transplant hospitals that would be
185 https://www.srtr.org/reports/opo-specificreports/interactive-report.
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required to participate in the IOTA
Model.
We proposed this approach for
selecting IOTA participants to obtain a
group of eligible kidney transplant
hospitals that is representative of kidney
transplant hospitals from across the
country in terms of geography and
kidney transplant volume. We proposed
to stratify the DSAs into groups based
on each DSA’s Census Division and the
total number of adult kidney transplants
performed annually across all eligible
kidney transplant hospitals in each DSA
during the baseline years for the first
PY. Selecting eligible kidney transplant
hospitals from these groups of DSAs
would ensure that the IOTA participants
are representative of eligible kidney
transplant hospitals from across the
nation in terms of geography and the
volume of adult kidney transplants.
A second aim of our proposal to select
eligible kidney transplant hospitals from
stratified groups of DSAs is to prevent
distortions on the effects of the model’s
policies and features on outcomes. Our
analysis of kidney transplant hospital
data shows that selecting only some
eligible kidney transplant hospitals
within a selected DSA to participate in
the IOTA Model may shift the supply of
deceased donor organs from non-IOTA
participants to IOTA participants within
the same DSA. The resulting distortions
would make it difficult to attribute
changes in outcomes to the model and
would limit its evaluability.
Our proposed approach for selecting
IOTA participants would involve
stratifying DSAs into groups based on
the average number of adult kidney
transplants performed by all eligible
transplant hospitals located in the DSA
during the baseline years of PY 1. We
proposed using this variable to stratify
the DSAs into groups because increasing
the total number of adult kidney
transplants is the primary metric that
we proposed to use to evaluate the
IOTA participants’ performance in the
model.
The proposed approach for IOTA
participant selection is as follows:
• Assign all DSAs to a Census
Division.186 The Census Bureau
subdivides the United States into four
Census Regions (Northeast, Midwest,
South, and West) which are in turn
divided into nine Census Divisions.
CMS would assign each DSA to a single
Census Division. Due to the New
England region being both a DSA and a
Census Division, CMS would combine
186 A complete list of DSAs in the United States
as of 2022–2023 can be obtained using the data
reporting tool found on the SRTR website (https://
www.srtr.org/reports/opo-specific-reports/
interactive-report).
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the Middle Atlantic and New England
Census Divisions for a total of eight
Census Divisions. If CMS were to keep
the New England Census Division
separate, the New England DSA would
be guaranteed participation in the
model in subsequent steps. As such, we
proposed to combine the Middle
Atlantic and New England Census
Divisions for the purposes of this
selection methodology. Some DSAs may
span several Census Divisions, but most
DSAs will be assigned to the Census
Division where the majority of the
DSA’s population resides according to
the 2020 Census data. Puerto Rico is the
only DSA which exists outside of a
Census Division. This DSA would be
assigned to the South Atlantic Census
Division as it is the closest
geographically. This step would create
eight Census Division groups, one for
each Census Division (with the
exception of the combined Middle
Atlantic and New England Census
Divisions, which would be grouped
together to create one Census Division
group).
• Determine the kidney transplant
hospitals located within each DSA. CMS
would list out the kidney transplant
hospitals located within each DSA and
assigned Census Division group.
• Identify the eligible kidney
transplant hospitals located within each
DSA. CMS would use the criteria noted
in section III.C.3.c of the proposed rule
to identify the eligible kidney transplant
hospitals within each DSA. This step is
expected to yield approximately 180 to
200 eligible kidney transplant hospitals
total across the eight Census Division
Groups.
• For each DSA, determine the
average number of adult kidney
transplants performed annually across
all eligible kidney transplant hospitals
during the baseline years for PY 1. CMS
would use data from the baseline years
for PY 1 to determine the average
number of adult kidney transplants
performed annually across all of the
eligible transplant hospitals located in
each DSA. CMS would sum the number
of adult kidney transplants performed
by all of the eligible kidney transplant
hospitals in a DSA during each of the
baseline years for PY 1 and divide each
DSA’s sum by three to determine the
average number of adult kidney
transplants furnished annually during
the baseline years by the eligible kidney
transplant hospitals located within each
DSA.
• Within each Census Division group,
create two mutually exclusive groups of
DSAs using the average number of adult
kidney transplants performed annually
across the baseline years for PY 1. CMS
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would separate DSAs assigned to a
Census Division group into two
mutually exclusive groups of DSAs
based on the average number of adult
kidney transplants performed annually
across the baseline years for PY 1. The
two groups within each Census Division
group would be: (1) DSAs having higher
numbers of adult kidney transplants
across the baseline years; and (2) DSAs
having lower numbers of adult kidney
transplants across the baseline years.
Since the average number of adult
kidney transplants will be different
across each DSA, each Census Division
group will have a different cut off to
create these two groups. To ensure each
DSA has a 50 percent chance of being
chosen in step 7, each DSA group
within a Census Division group should
have the same number of DSAs.
However, in the event of an odd number
of DSAs within a Census Division
group, CMS would proceed to step six.
• For groups within a Census Division
group that contain an odd number of
DSAs, CMS would randomly select one
DSA from the group. Each of these
individual selected DSAs would have a
50 percent probability of being selected
for the IOTA Model. For groups within
a Census Division group that contain an
odd number of DSAs, CMS would
randomly select one DSA from the
group and determine that individual
DSA’s chance of selection for inclusion
in the IOTA Model with 50 percent
probability. Following this step, each
group within a Census Division group
would have an even number of DSAs.
• Randomly select 50 percent of
remaining DSAs in each group. CMS
would then take a random sample,
without replacement, of 50 percent of
the remaining DSAs in each group (the
groups being DSAs having higher
numbers of adult kidney transplants
across the baseline years and DSAs
having lower numbers of adult kidney
transplants across the baseline years)
within each Census Division group. All
of the eligible kidney transplant
hospitals located within the selected
DSAs would be required to participate
in the IOTA Model.
We proposed that CMS would notify
IOTA participants of their selection to
participate in the IOTA Model in a form
and manner chosen by CMS, such as
public notice and email, at least 3
months prior to the start of the model
performance period. As described in
section III.C.3.b of this final rule, we
proposed that participation in the IOTA
Model would be mandatory. As such, if
an IOTA eligible transplant hospital is
located within one of the DSAs that
CMS randomly selects for the IOTA
Model, the eligible kidney transplant
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hospital would not be able to decline
participation in this model, nor would
it be able to terminate its participation
in the model once selected. Model
termination policies are further
discussed in section III.C.16 of this final
rule.
We direct readers to section
III.C.3.d(2) of this final rule for a
summary of the comments received on
our proposed approach for selecting
IOTA participants and our responses.
(2) Consideration of Alternatives to
Proposed Participant Selection
Approach
We considered using other geographic
units for stratified random sampling to
choose IOTA participants, such as Core
Based Statistical Areas (CBSAs),
Metropolitan Statistical Areas (MSAs),
Hospital Referral Regions (HRRs), or
States (89 FR 43543). CBSAs, MSAs,
HRRs, and States are commonly known
geographic units, and have been used as
part of participant selection for other
Innovation Center models. We believe
selecting participants by DSA
significantly mitigates behavior that
would artificially inflate the model’s
effects on kidney transplant volume for
the reasons described in the preceding
section. OPOs associated with selected
DSAs would be expected to benefit from
consistency in rules across most or all
of their transplant hospitals. The
Innovation Center found that selecting
participants by DSA improved the
ability to detect changes in kidney
transplant volume to a level consistent
with the anticipated change in kidney
transplant volume associated with the
model’s payment rules. Participants
from the same DSA are, for the most
part, subject to similar levels of kidney
supply, and, with the exception of
kidneys from another DSA, the same
rules for kidney allocation apply. While
OPTN recently updated its organ
allocation methodology to allow organs
to go outside of the DSA in which an
organ was procured, many kidney
transplant hospitals still receive a
plurality of kidneys from the local OPO
in their DSA, ensuring that this is still
a meaningful method to group kidney
transplant hospitals. Using alternative
geographic units would negate these
advantages.
We also considered other random
sampling techniques, including simple
random sampling of transplant
hospitals, simple random sampling of
DSAs, and cluster sampling of DSAs (89
FR 43543). Simple random sampling of
hospitals risks oversampling regions of
the country where transplant hospitals
are concentrated and under sampling
areas with fewer eligible transplant
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hospitals. Using simple random
sampling of DSAs may result in an
unrepresentative sample of DSAs with a
greater risk of oversampling regions
where DSAs cover small geographic
areas. We considered cluster random
sampling where half of all DSAs would
be sampled in a first step and half of
eligible kidney transplant hospitals
within selected DSAs would be
sampled. However, because this
approach would retain half of eligible
kidney transplant hospitals in selected
DSAs, we expect the model’s effects on
kidney transplant volume would be
overstated because kidney supply
flowing towards non-participant
hospitals prior to the start of the model
would be redirected towards IOTA
participants. In addition, CMS’ analyses
of these alternative sampling
approaches indicated the model would
not be evaluable because these
approaches were associated with lower
precision in detecting changes in kidney
transplant volumes due to the model
compared to the increase in transplant
volume anticipated from the model’s
payment rules.
As an alternative we also considered
other variables to create DSA groups for
stratified sampling of DSAs (89 FR
43543). Specifically, after assigning each
DSA to a Census Division, we
considered stratifying DSAs using the
following DSA level variables:
• Number of eligible transplant
hospitals in DSA.
• Annual adult kidney transplants
per eligible transplant hospital in DSA.
• Average organ offer acceptance rate
ratio across eligible kidney transplant
hospitals in DSA.
• Average percent of Medicare kidney
transplant recipients dually eligible for
Medicare and Medicaid or who are LIS
recipients.
• Percent of eligible transplant
hospitals in DSA participating in the
Kidney Care Choices or ESRD Treatment
Choices Models.
• Average percent of kidney
transplants from a living donor among
eligible kidney transplant hospitals in
DSA.
These variables were given
consideration in the stratified selection
approach because their use would create
groups of DSAs whose eligible
transplant hospitals are more similar to
each other on the listed characteristics
instead of only adult kidney transplant
volume and Census Division. However,
we opted to use the simpler stratified
participant selection approach to
provide greater transparency in the
model’s participant selection approach.
We also considered stratified random
sampling of individual kidney
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transplant hospitals using similar
variables as those described in the
preceding paragraph (89 FR 43543).
Although this approach provided
representativeness of sampled
transplant hospitals along dimensions
important for the model, it would be
expected to result in a subset of eligible
kidney transplant hospitals in at least a
portion of DSAs being designated as
participants. As we have described
previously, we expect that allowing a
portion of DSA kidney transplant
hospitals to be model participants
would result in an overstatement of the
model’s effects on kidney transplant
volume and other outcomes of interest.
As with the sampling approaches
considered in the preceding paragraph,
CMS’ analyses indicated the IOTA
Model would not be evaluable if
stratified sampling of individual kidney
transplant hospitals were used in
participant selection for the reasons
described previously.
As stated at 89 FR 43544 in the
proposed rule, CMS expects that no
additional participant selections would
be made for the IOTA Model after its
start date unless 10 percent or more of
selected participants are terminated
from the model during the model
performance period. We stated that if
this were to occur, we would address
the selection of new participants in
future rulemaking.
We sought comment on our proposed
approach for selecting IOTA
participants and on the alternative
approaches considered, including
perceived advantages and disadvantages
of our proposed participant selection
approach relative to alternatives.
The following is a summary of the
comments received on our proposed
approach for selecting IOTA
participants, on the alternative
approaches considered, including
perceived advantages and disadvantages
of our proposed participant selection
approach relative to alternatives, and
our responses:
Comment: Several commenters shared
concerns about the participation
selection method, with a commenter
suggesting CMS would provide too short
a notice of selection into the IOTA
Model prior to the model start date and
that this poses a challenge to smaller
transplant programs. Additionally, a
commenter shared a concern that the
participant selection criteria highlights
the significant variance in offer
acceptance and transplant rates within
DSAs, suggesting that it would be
difficult to attribute outcome changes to
the IOTA Model as a result.
Response: As described and finalized
in section III.C.3.d(1) of this final rule,
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we proposed that CMS would notify
IOTA participants of their selection to
participate in the IOTA Model in a form
and manner chosen by CMS, such as
public notice and email, at least 3
months prior to the start of the model
performance period. We believe this is
in alignment with other Innovation
Center models and an earlier notice
would be provided if feasible. For these
reasons, we are finalizing our proposal
without modification.
Additionally, in section III.C.3.d(1) of
this final rule, we described and
finalized our approach for selecting
IOTA participants to obtain a group of
eligible kidney transplant hospitals that
is representative of kidney transplant
hospitals from across the country in
terms of geography and kidney
transplant volume. We proposed to
stratify the DSAs into groups based on
each DSA’s Census Division and the
total number of adult kidney transplants
performed annually across all eligible
kidney transplant hospitals in each DSA
during the baseline years for the first
PY. Selecting eligible kidney transplant
hospitals from these groups of DSAs
would ensure that the IOTA participants
are representative of eligible kidney
transplant hospitals from across the
nation in terms of geography and the
volume of adult kidney transplants.
A second aim of our proposal to select
eligible kidney transplant hospitals from
stratified groups of DSAs is to prevent
distortions on the effects of the model’s
policies and features on outcomes. Our
analysis of kidney transplant hospital
data showed that selecting only some
eligible kidney transplant hospitals
within a selected DSA to participate in
the IOTA Model may shift the supply of
deceased donor organs from non-IOTA
participants to IOTA participants within
the same DSA. The resulting distortions
would make it difficult to attribute
changes in outcomes to the model and
would limit its evaluability. As a result,
we do not believe this would cause
difficulty in attributing resulting
impacts to the IOTA Model. For these
reasons, we are finalizing our proposal
without modification.
Comment: CMS received several
comments and recommendations
regarding participant selection for the
IOTA Model. Specifically, commenters
suggested CMS should modify the
participant selection process in ways
such as reconsidering the DSA as a
quantifier, expanding the IOTA Model
across all transplant programs, and
providing eligible kidney transplant
hospitals selected to participate in the
IOTA Model more than a three-month
notice prior to the start of the IOTA
Model.
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Response: We thank the commenters
for their feedback and suggestions. We
direct readers to section III.C.3.d(2) of
this final rule for alternatives that we
considered.
We believe that expanding
accountability to kidney transplant
hospitals and key stakeholders in the
transplantation ecosystem for ESRD
patients, aligns with the larger efforts
across CMS and HRSA to improve
performance and address disparities in
kidney transplantation. As the most
commonly transplanted organ, and its
relationship with dialysis, of which
Medicare is the primary payer, we
believe focusing this model on kidney
transplantation is prudent. Relatedly, as
described in the proposed rule, we
believe that it is necessary to include in
the definition of kidney transplant those
kidney transplants that occur in
conjunction with other organ
transplants to avoid creating a
disincentive for multi-organ transplants
within the IOTA Model (89 FR 43540).
Finally, regarding the comments we
received about providing more than a
three-month notice to eligible kidney
transplant hospitals selected to
participate in the IOTA Model, as
described and finalized in section
III.C.3.d(1) of this final rule, we
proposed that CMS would notify IOTA
participants of their selection to
participate in the IOTA Model in a form
and manner chosen by CMS, such as
public notice and email, at least three
months prior to the start of the model
performance period. We believe this is
in alignment with other Innovation
Center models and an earlier notice
would be provided if feasible. For these
reasons, we are finalizing our proposal
without modification at § 512.412(d).
Comment: Several commenters
supported the use of stratified sampling
in selecting IOTA participants.
Specifically, several commenters
supported the proposals to use DSAs, to
group DSAs into Census Divisions, and
to randomly select 50 percent of all
eligible kidney transplant hospitals.
Response: We thank the commenters
for their support.
Comment: In the context of the ETC
Model, a commenter expressed concern
that the use of stratified DSA sampling
could penalize IOTA participants based
on the DSA boundaries. Specifically, the
commenter suggested that at times in
the ETC Model, participants were
penalized for circumstances that were
largely based on zip code and compared
to locales on the periphery of their DSA.
Response: As described and finalized
in section III.C.3.c of this final rule,
CMS will select approximately half of
all DSAs nationwide using a stratified
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sampling methodology, and all eligible
kidney transplant hospitals in the
selected DSAs will be required to
participate in the IOTA Model. We
proposed to stratify the DSAs into
groups based on each DSA’s Census
Division and the total number of adult
kidney transplants performed annually
across all eligible kidney transplant
hospitals in each DSA during the
baseline years for the first PY (89 FR
43542). Within each Census Division
group, we proposed to create two
mutually exclusive groups of DSAs
using the average number of adult
kidney transplants performed annually
across the baseline years for PY 1 (89 FR
43542). Selecting eligible kidney
transplant hospitals from these groups
of DSAs would ensure that the IOTA
participants are representative of
eligible kidney transplant hospitals from
across the nation in terms of geography
and the volume of adult kidney
transplants. We recognize that kidney
transplant hospitals in a DSA selected to
participate in the IOTA Model could be
adjacent to a DSA not selected to
participate in the IOTA Model. The
IOTA Model is looking to measure and
test whether the provisions of the IOTA
Model encourage more kidney
transplants. We do not view this as
potentially penalizing IOTA
participants in close proximity to
kidney transplant hospitals not
participating in the IOTA Model.
Rather, we believe this approach
increases the ability to monitor
performance improvements in metrics,
such as an individual IOTA
participants’ transplant target or its
organ offer acceptance rate ratio. It also
helps us distinguish between DSAs and
other similar geographical regions,
ensuring accurate comparisons. For
these reasons, we are finalizing our
proposal without modification.
Comment: Several commenters
asserted that the stratified sampling
methodology should not use DSAs, as it
could restrict organ allocation, and that
average number of kidney transplants in
a DSA does not provide a true
representation of kidney transplant
hospitals.
Response: We thank the commenters
for their feedback and suggestions. We
direct readers to section III.C.3.d(2) of
this final rule for a full discussion of the
alternatives that we considered. For
these reasons, we will be finalizing our
proposal without modification.
Comment: Several commenters
expressed their concerns with the
proposed stratified sampling
methodology, suggesting that the
proposed stratification may advantage
transplant programs close to the low-
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volume threshold. A commenter
specifically suggested CMS should
revisit this low volume threshold across
PYs, since the expectation is that the
volume of kidney transplants performed
would progressively increase for kidney
transplant hospitals selected to
participate in the IOTA Model.
Response: We thank the commenters
for their feedback and
recommendations. As described and
finalized in section III.C.5.c(1) of this
final rule, we proposed that the low
volume threshold to be 11 kidney
transplants performed for the purposes
of calculating the national growth rate.
We also proposed this approach for
calculating the national growth rate to
account for and reflect the growth in
organ procurement by OPOs that has
occurred, indicating potential growth in
the number of available organs.
Specifically, as described and
finalized in section III.C.5.c(1) of this
final rule, we will calculate the national
growth rate by determining the percent
increase or decrease of all kidney
transplants furnished to patients 18
years of age or older during the relevant
baseline years, as described and
finalized in section III.C.3.c of this final
rule. We direct readers to section
III.C.5.c(1) of this final rule for a full
discussion on the calculation of the
national growth rate.
Finally, as described in section
III.C.3.d(2) of the proposed rule, we
expect that no additional participant
selections will be made for the IOTA
Model after its start date unless 10
percent or more of selected participants
are terminated from the model during
the model performance period. If this
were to occur, we will address the
selection of new participants in future
rulemaking and we may revisit the low
volume threshold of 11 adult kidney
transplants performed annually in each
of the baseline years. We would not
extend the model performance period of
the IOTA Model. If we were to add any
new model participants, the IOTA
participants would participate in the
model until the end of model
performance period, as described and
finalized in section III.C.1.a of this final
rule. For these reasons, we will be
finalizing our proposal without
modification.
Comment: Several commenters
requested that CMS provide clarification
on the stratified sampling methodology.
Specifically, how CMS would randomly
select one DSA, the distinction between
high transplant volume or low
transplant volume groups, and the
threshold for dividing DSAs by
transplant volume.
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Response: We thank the commenters
for their feedback. As described and
finalized in section III.C.3.d(1) of this
final rule, for groups within a Census
Division group that contain an odd
number of DSAs, CMS would randomly
select one DSA from the group. Each of
these individual selected DSAs would
have a 50 percent probability of being
selected for the IOTA Model. For groups
within a Census Division group that
contain an odd number of DSAs, CMS
would randomly select one DSA from
the group and determine that individual
DSA’s chance of selection for inclusion
in the IOTA Model with 50 percent
probability. Following this step, each
group within a Census Division group
would have an even number of DSAs.
As described and finalized in section
III.C.3.d(1) of this final rule, CMS would
then randomly select 50 percent of
remaining DSAs in each group. CMS
would then take a random sample,
without replacement, of 50 percent of
the remaining DSAs in each group (the
groups being DSAs having higher
numbers of adult kidney transplants
across the baseline years and DSAs
having lower numbers of adult kidney
transplants across the baseline years)
within each Census Division group. All
of the eligible kidney transplant
hospitals located within the selected
DSAs would be required to participate
in the IOTA Model. For these reasons,
we are finalizing our proposal without
modification.
Comment: Several commenters
recommended that CMS stratify kidney
transplant hospitals based on their size
and reassess the threshold separating
low-volume and high-volume kidney
transplant hospitals.
Response: As described in section
III.C.3.d(2) of the proposed rule, we
considered alternatives to the proposed
participant selection methods. We
believe selecting model participants by
DSA significantly mitigates behavior
that would artificially inflate the
model’s effects on kidney transplant
volume for the reasons described in the
preceding section. OPOs associated with
selected DSAs would be expected to
benefit from consistency in rules across
most or all of their transplant hospitals.
We considered alternative variables to
create DSA groups for stratified
sampling of DSAs. One alternative
consideration included stratifying DSAs
by annual adult kidney transplants per
eligible transplant hospital in DSA (89
FR 43543). This and other variables
were given consideration in the
stratified selection approach, however,
we opted to use the simpler stratified
participant selection approach to
provide greater transparency in the
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model’s participant selection approach.
We direct readers to section III.C.3.d(2)
of this final rule for a full discussion of
alternative participant selection
approaches and variables that we
considered.
Additionally, as described and
finalized in section III.C.3.d(1) this final
rule, two groups within each Census
Division group would be: (1) DSAs
having higher numbers of adult kidney
transplants across the baseline years;
and (2) DSAs having lower numbers of
adult kidney transplants across the
baseline years. Since the average
number of adult kidney transplants
would be different across each DSA,
each Census Division group would have
a different cut off to create these two
groups. We believe this is an
appropriate distinction between lowvolume and high-volume kidney
transplant hospitals. For these reasons,
we will be finalizing our proposal
without modification.
Comment: Multiple commenters
suggested that CMS should establish
control groups within the same
geographical area in order to increase
the ability to monitor performance
improvements and distinguish within
DSAs to ensure accurate comparisons.
Response: We thank the commenters
for their feedback and suggestions. As
described and finalized in section
III.C.3.c of this final rule, CMS would
select approximately half of all DSAs
nationwide using a stratified sampling
methodology, and all eligible kidney
transplant hospitals in the selected
DSAs would be required to participate
in the IOTA Model. Selecting eligible
kidney transplant hospitals from these
groups of DSAs would ensure that the
IOTA participants are representative of
eligible kidney transplant hospitals from
across the nation in terms of geography
and the volume of adult kidney
transplants. As described and finalized
in section III.C.3.d(1) of this final rule,
within each Census Division group, we
would create two mutually exclusive
groups of DSAs using the average
number of adult kidney transplants
performed annually across the baseline
years for PY 1. CMS would separate
DSAs assigned to a Census Division
group into two mutually exclusive
groups of DSAs based on the average
number of adult kidney transplants
performed annually across the baseline
years for PY 1. We believe this approach
increases the ability to monitor
performance improvements and
distinguish within DSAs and similar
geographical areas to ensure accurate
comparisons. For these reasons, we will
be finalizing our proposal without
modification.
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After consideration of the public
comments we received, we are
finalizing our proposed provisions for
the sampling methodology, participant
selection process, and notifying IOTA
participants of their selection to
participate in the IOTA Model at
§§ 512.412(b), 512.412(c) and 512.412(d)
without modification. We are also
finalizing as proposed the definition of
donation service area (DSA) at
§ 512.402, with a minor technical
correction to include the complete cross
reference to subpart G.
4. Patient Population and Attribution
a. Proposed Attributed Patient
Population
We proposed that the following
patients who are alive at the time CMS
conducts attribution would be attributed
to an IOTA participant: (1) A kidney
transplant waitlist patient, as defined in
section III.C.4.a of this final rule,
regardless of payer type and waitlist
status, who is alive, 18 years of age or
older, and is registered on a waitlist, as
defined in section III.C.4.a of this final
rule, to one or more IOTA participants,
as identified by the OPTN computer
match program (‘‘IOTA waitlist
patient’’); and (2) A kidney transplant
patient who receives a kidney transplant
at the age of 18 years or older from an
IOTA participant at any time during the
model performance period (‘‘IOTA
transplant patient’’). These patients
would be referred to as IOTA waitlist
patients and IOTA transplant patients,
respectively, for purposes of assessing
each IOTA participant’s performance
across the achievement domain,
efficiency domain, and quality domain
as discussed in section III.C.5 of this
final rule. IOTA waitlist patients and
IOTA transplant patients would factor
into the model’s performance-based
payments to IOTA participants.
For the purpose of this model, we
proposed to define ‘‘waitlist’’ as a list of
transplant candidates, as defined in 42
CFR 121.2, registered to the waiting list,
as defined in § 121.2, and maintained by
a transplant hospital in accordance with
42 CFR 482.94(b). We proposed to
define ‘‘kidney transplant waitlist
patient’’ as a patient who is a transplant
candidate, as defined in § 121.2, and
who is registered to a waitlist for a
kidney at one or more kidney transplant
hospitals.
We understand that many patients on
the waiting list are registered at multiple
transplant hospitals. Therefore, we
proposed attributing each of these
waitlisted patients to every IOTA
participant where they are registered on
a waitlist during a given month in the
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applicable quarter. However, ‘‘kidney
transplant patient,’’ defined as a patient
who is a transplant candidate, as
defined in § 121.2, and received a
kidney transplant furnished by a kidney
transplant hospital, regardless of payer
type, would be attributed to the IOTA
participant that furnished the kidney
transplant.
We proposed attributing kidney
transplant waitlist patients and kidney
transplant recipients to IOTA
participants for two reasons. First, we
believe that by attributing these patients
to IOTA participants it would ensure
the full population of potential and
actual kidney transplant candidates is
represented when measuring participant
performance. The waiting list captures
most candidates except some living
donor recipients. Transplant recipients
include those who received deceased or
living donor transplants. Second,
because CMS is proposing to hold IOTA
participants accountable for furnishing
kidney organ transplants; focusing on
kidney transplant waitlist patients and
kidney transplant patients, and
attributing them to IOTA participants,
aligns with the model’s goals of
improving access to, and quality of,
kidney transplantation, including posttransplant.
CMS proposed to determine an IOTA
participant’s performance across the
achievement domain, efficiency
domain, and quality domain based on
all IOTA waitlist patients and IOTA
transplant patients, regardless of payer
type, as described in section III.C.5 of
this final rule. That is, an IOTA
participant’s performance in terms of
both Medicare beneficiaries and nonMedicare patients would be used to
determine whether the IOTA participant
would receive an upside risk payment
from CMS, or owe a downside risk
payment to CMS. As described in
section III.C.6.c(2) of this final rule,
demand for kidney transplants far
exceeds supply, raising concerns that if
the IOTA Model were limited to
Medicare beneficiaries only, the model
may inadvertently incentivize
inappropriate diversion of donor organs
to Medicare beneficiaries to improve
their performance in the model, thereby
limiting access to non-Medicare
beneficiaries and potentially
disincentivizing pre-emptive kidney
transplants for patients not already
covered by Medicare because their CKD
has not progressed to ESRD. We believe
that the change in care patterns that
IOTA participants may undertake to be
successful in the IOTA Model are
unlikely to apply solely to Medicare
beneficiaries under their care.
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We considered limiting IOTA waitlist
patients and IOTA transplant patients to
Medicare beneficiaries only, as
Medicare covers more than 50 percent
of all kidney transplants from both
deceased and living donors. However,
we believe it is necessary to include all
patients, regardless of payer type, in the
IOTA participant’s performance
calculations to protect against
unintended consequences and
problematic financial incentives.
Moreover, the group of eligible waitlist
and transplant patients that would be
attributed to each IOTA participant is
already relatively small, both in terms of
transplant candidates and transplant
recipients. Limiting the IOTA Model
performance assessment, as described in
section III.C.5.b of this final rule, to
Medicare beneficiaries would further
limit the patient sample size, potentially
affecting our ability to detect changes in
performance due to model payments.
Therefore, we proposed that the IOTA
Model reflect both Medicare
beneficiaries and non-Medicare patients
for performance assessment, with
Medicare beneficiaries just being a
subset of the patient population
attributed to each model participant.
We sought public comment on our
proposals to include: (1) all kidney
transplant waitlist patients, regardless of
payer type and waitlist status, who are
alive, 18 years of age or older, and
registered on a waitlist to an IOTA
participant, as identified by the OPTN
computer match program; and (2) all
kidney transplant patients who receive
a kidney transplant, at 18 years of age
or older, from an IOTA participant at
any time during the model performance
period, in each IOTA participant’s
population of attributed patients. We
also sought public comment on our
proposal to attribute IOTA waitlist
patients and IOTA transplant patients,
respectively, to IOTA participants for
the purposes of assessing each IOTA
participant’s performance across the
achievement domain, efficiency
domain, and quality domain, and to
determine performance-based payments
to and from IOTA participants.
The following is a summary of the
comments received and our responses:
Comment: We received several
comments in support for the proposed
attributed patient population, including
the all-payer attribution approach and to
allow patients to have multiple
attributions when on the waitlist for one
or more transplant hospitals, as this
provision ensures the most patients can
benefit from the model.
Response: We thank the commenters
for their support.
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Comment: We received a comment
requesting CMS clarify if multi-organ
transplants would be counted the same
as single organ kidney transplants.
Response: We thank the commenter
for their feedback. As described in
section III.B.3.c of the proposed rule, the
vast majority of kidney transplants are
performed alone. However, we believe
that it is necessary to include in the
definition of kidney transplant those
kidney transplants that occur in
conjunction with other organ
transplants to avoid creating a
disincentive for multi-organ transplants
within the IOTA Model. As defined at
§ 512.402, kidney transplant means the
procedure in which a kidney is
surgically transplanted from a living or
deceased donor to a transplant
recipient, either alone or in conjunction
with any other organ(s).
Comment: We received a comment
suggesting CMS should monitor for
unintended consequences, such as
systemic biases, as a result of including
all payer types among attributed
patients.
Response: We thank the commenter
for their suggestion. We direct readers to
comment responses noted previously for
further discussion. For these reasons,
we are finalizing our proposal without
modification.
After consideration of the public
comments we received, for the reasons
set forth in this rule, we are finalizing
these provisions at § 512.414 with slight
modification. Specifically, we are
modifying the regulatory text at
§ 512.414(a)(1)(iii) to specify
determining performance-based
payments paid to or by IOTA
participants. We did not receive any
comments on the proposed definition of
IOTA waitlist patient, kidney transplant
waitlist patient, kidney transplant
patient or waitlist and therefore are
finalizing these definitions without
modification at § 512.402. We are also
making a minor technical correction to
the proposed definition of IOTA
transplant patient at § 512.402 to update
the cross reference. Specifically, we are
removing the cross reference to
§ 512.412(b)(2) and replacing it with
§ 512.414(b)(2). As such, we are
finalizing the definition of IOTA
transplant patient at § 512.402 to mean
a kidney transplant patient who receives
a kidney transplant at the age of 18
years of age or older from an IOTA
participant at any time during the model
performance period and meets the
criteria set forth in § 512.414(b)(2).
b. Patient Attribution Process
As described in section III.C.4.a of
this final rule, we proposed to define
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‘‘attribution’’ as the process by which
CMS identifies patients for whom each
IOTA participant is accountable during
the model performance period. CMS
would identify and assign a set of
Medicare and non-Medicare patients to
the IOTA participant through
attribution. We proposed to define
‘‘attributed patient’’ as an IOTA waitlist
patient or an IOTA transplant patient, as
described in section III.C.4.a of this final
rule. We proposed that a patient may
not opt out of attribution to an IOTA
participant under the model.
Section III.C.4.b(1) of this final rule
outlines in more detail the attribution
criteria to identify attributable kidney
transplant waitlist patients and kidney
transplant patients during initial
attribution, quarterly attribution, and at
annual attribution reconciliation using
Medicare claims data, Medicare
administrative data, and OPTN data. In
advance of the model start date, we
proposed to attribute patients to IOTA
participants through an initial
attribution process described in section
III.C.4.b(2) of this final rule; quarterly
attribution would be conducted
thereafter to update the patient
attribution list, as described in section
III.C.4.b(3) of this final rule, to include
the dates in which patient attribution
changes occur. After the fourth quarter
of each PY, we proposed to finalize each
IOTA participant’s annual attribution
reconciliation list for that PY, including
removing certain attributed patients, as
described in section III.C.4.b(4) of this
final rule. We proposed that once a
patient is attributed to an IOTA
participant, that attributed patient
would remain attributed to the IOTA
participant for the duration of the
model, unless the patient is removed
from the IOTA participant’s list of
attributed patients during the annual
attribution reconciliation process, as
described in section III.C.4.b(4) of this
final rule.
We also considered proposing that
once a patient is attributed to an IOTA
participant, either through the initial
attribution process or through quarterly
attribution, that the patient would
remain attributed only through the end
of the PY. Initial attribution would then
occur prior to the beginning of each PY.
However, we choose to align with the
attribution processes of our other kidney
models to simplify operations.
We proposed to identify kidney
waitlist patients and kidney transplant
patients using SRTR data, OPTN data,
Medicare claims data, and Medicare
administrative data.
We sought comment on our patient
attribution process proposals and
alternatives considered.
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The following is a summary of the
comments received on our proposed
patient attribution process proposals
and alternatives considered and our
responses:
Comment: We received several
comments requesting clarity from CMS
on certain categories of attributed
patients, as well as seeking clarity on
what CMS defines as an attributed
patient. Specifically, we received
comments requesting CMS to clarify if
any patients are excluded from
calculations related to the IOTA Model
in the context of kidney/pancreas
candidates and others such as those
with a high panel reactive antibody test,
re-transplanted patients, or safety-net
kidney recipients.
Response: As described and finalized
in section III.C.4.b of this final rule, we
define attributed patient as an IOTA
waitlist patient or an IOTA transplant
patient. As described and finalized in
section III.C.4.a of this final rule, an
IOTA waitlist patient is a kidney
transplant waitlist patient, as defined
and finalized in section III.C.4.a of this
final rule, regardless of payer type and
waitlist status, who is alive, 18 years of
age or older, and is registered on a
waitlist, as defined and finalized in
section III.C.4.a of this final rule, to one
or more IOTA participants, as identified
by the OPTN computer match program;
and an IOTA transplant patient is a
kidney transplant patient who receives
a kidney transplant at the age of 18
years or older from an IOTA participant
at any time during the model
performance period.
Additionally, as described and
finalized in section III.C.5.d(1)(a) of this
final rule, we proposed to use and
calculate the OPTN organ offer
acceptance rate ratio in accordance with
OPTN’s measure specifications and
SRTR’s methodology as the metrics that
would determine IOTA participants’
performance on the efficiency domain
outlined in equation 1 in paragraph
(b)(1) of § 512.426. As it pertains to
kidney/pancreas candidates, included
in this organ offer acceptance ratio are
offers to candidates on a single organ
waitlist (except for kidney/pancreas
candidates that are also listed for kidney
alone). Excluded from this measure are
offers to multi-organ candidates (except
for kidney/pancreas candidates that are
also listed for kidney alone).
In addition, paragraph (b)(1) at
§ 512.428 describes the composite graft
survival rate equation used in
determining the IOTA participant’s
quality domain score. As it pertains to
kidney/pancreas candidates and retransplant candidates, CMS excludes
them from the numerator when
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calculating the composite graft survival
rate.
As proposed, we do not exclude any
patients with high panel reactive
antibody tests or safety-net kidney
recipients from IOTA Model measures.
For these reasons, we are finalizing our
proposal without modification.
After consideration of the public
comments we received, for the reasons
set forth in this rule, we are finalizing
the patient attribution process at
§ 512.414(a) and the definitions of
attribution and attributed patient at
§ 512.402 as proposed without
modification.
(1) Attribution and De-attribution
Criteria
(i) IOTA Waitlist Patient Attribution
We proposed that kidney transplant
waitlist patients would be attributed as
IOTA waitlist patients to one or more
IOTA participants based on where the
patient is registered on a kidney
transplant waitlist, regardless of payer
type and waitlist status, as identified by
the OPTN computer match program. We
proposed that CMS would conduct
attribution on a quarterly basis, before
each quarter of the model performance
period. CMS is proposing to attribute a
kidney transplant waitlist patient as an
IOTA waitlist patient to an IOTA
participant if the patient meets all of the
following criteria:
• The patient is registered to one or
more IOTA participant’s kidney
transplant waitlist during a month in
the applicable quarter.
• The patient is 18 years or older at
the time of attribution.
• The patient is alive at the time of
attribution.
For purposes of attributing IOTA
waitlist patients to IOTA participants,
the proposed criteria must be met on the
date that CMS runs attribution, as
described in section III.C.4.b(1)(i) of this
final rule.
As described in section III.C.4.b(1) of
this final rule, a kidney transplant
waitlist patient may be registered to
more than one waitlist, which is why
we proposed to attribute kidney
transplant waitlist patients as IOTA
waitlist patients to IOTA participants in
a way that accurately reflects their
waitlist registrations. A kidney
transplant hospital should be actively
engaged in coordinating the transplant
process for kidney transplant waitlist
patients on their waitlist, as they are
responsible for accepting donor organs
and furnishing transplants. As such, if
a kidney transplant waitlist patient is
registered on the waitlist of multiple
IOTA participants, CMS would attribute
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96317
that kidney transplant waitlist patient as
an IOTA waitlist patient to all of the
IOTA participants that have the kidney
transplant waitlist patient on their
waitlists.
We alternatively considered limiting
IOTA waitlist patient attribution to only
one IOTA participant based on ‘‘active’’
waitlist status. That is, the IOTA waitlist
patient would be attributed to each
IOTA participant where the patient is
registered to a kidney transplant waitlist
with an ‘‘active’’ status in a given
quarter. A kidney transplant hospital
designates patients on its waitlist with
an ‘‘active’’ status to signal their
readiness to receive a donor kidney offer
when one becomes available. However,
we anticipate that there would be
operational challenges if CMS were to
base patient attribution on waitlist
‘‘active’’ status, as doing so would
require real-time and accurate
information regarding each patient’s
waitlist status. There may be a time
delay when changing a waitlist status
from provisionally inactive to active
once minor issues have been resolved.
A kidney transplant waitlist patient may
be made inactive or ineligible to receive
an organ offer if, for example, they have
an incomplete transplant evaluation to
assess medical readiness, their BMI
exceeds the transplant hospital’s
established threshold, due to infection
or patient choice, or because of
complications presented by other
medical issues. Additionally, due to our
inability to recognize differences in the
contributions between kidney transplant
hospitals in maintaining a patient’s
transplant readiness, we believe
attributing kidney transplant waitlist
patients as IOTA waitlist patients to all
the IOTA participants where a kidney
transplant waitlist patient is registered
is the most appropriate approach to
IOTA waitlist patient attribution,
regardless of waitlist status.
As indicated in section III.C.3.c of this
final rule, we are only proposing to
include non-pediatric facilities as
eligible participants in the IOTA Model.
In alignment with this proposal, we
proposed to exclude pediatric patients
under 18 years of age from the
population of attributed patients.
According to national data from the
OPTN, children under the age of 18
make up a small proportion of the
kidney transplant candidates registered
on the waiting list. However, pediatric
patients have greater access to both
deceased and living donor kidney
transplant relative to adults. Pediatric
patients under 18 years of age are also
infrequently the recipient of organs at
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high risk for non-use.187 Thus, CMS did
not propose to include pediatric
patients under the age of 18 as part of
the population that would be identified
and attributed to IOTA participants. We
alternatively considered including
pediatric patients under the age of 18 in
the IOTA Model patient population, but
believe focusing on adults, given their
unique challenges accessing kidney
transplants, is a priority.
The waiting list often has a delay
between when a patient’s waitlist status
changes and when that change is
reflected in the data. For example,
patients who have died are ineligible for
transplant and must be removed from
the waiting list, but there may be a time
delay between a patient’s death and
their removal. Thus, we proposed to
limit IOTA waitlist patient attribution to
patients who are alive at the time of
attribution.
We sought comment on our proposed
criteria for identifying and attributing
kidney transplant waitlist patients to
one or more IOTA participants and
alternatives considered.
The following is a summary of the
comments received on our proposed
criteria for identifying and attributing
kidney transplant waitlist patients to
one or more IOTA participants and
alternatives considered and our
responses:
Comment: A commenter
recommended CMS change the
proposed definition of a pediatric
transplant to include a transplant
performed on a patient who may be 18
years or older, but was listed on the
kidney transplant waiting list prior to
age 18. Specifically, a commenter
recommended this change in definition
because the commenter thought that its
preferred definition would satisfy
existing industry standards and better
reflect the nature of a pediatric patient
who may not receive a transplant until
after turning 18 years old, but could
remain under the care of a pediatric
transplant program.
Response: We thank the commenter
for their suggestion; however, we
disagree as we did not propose to define
a pediatric transplant. At 89 FR 43544
of the proposed rule, we proposed to
define an IOTA transplant patient as a
kidney transplant patient who receives
a kidney transplant at the age of 18
187 Lentine, K.L., Smith, J.M., Miller, J.M.,
Bradbrook, K., Larkin, L., Weiss, S., Handarova,
D.K., Temple, K., Israni, A.K., & Snyder, J.J. (2023).
OPTN/SRTR 2021 Annual Data Report: Kidney.
American journal of transplantation: official journal
of the American Society of Transplantation and the
American Society of Transplant Surgeons, 23(2
Suppl 1), S21–S120. https://doi.org/10.1016/
j.ajt.2023.02.004.
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years or older from an IOTA participant
at any time during the model
performance period. As we are
including only non-pediatric facilities
in our definition of eligible kidney
transplant hospitals, as described and
finalized in section III.C.3.c of this final
rule, we believe that those that are listed
prior to the age of 18 under the care of
a pediatric facility would not be
included in our definition of an IOTA
transplant patient. Therefore, we will be
finalizing our proposed definition of
IOTA transplant patient without
modification.
Comment: A commenter expressed
support for CMS’s proposal to attribute
kidney transplant waitlist patients to
one or more IOTA participants based on
where the patient is registered on a
kidney transplant waitlist.
Response: We thank the commenter
for their support.
Comment: We received a comment
voicing concern with the proposed
IOTA waitlist patient and patient
attribution process in that it could
create competition among transplant
hospitals due to the cross-listing of
patients.
Response: We thank the commenter
for their feedback. As described in
section I.B.2.a of this final rule, we
proposed that the IOTA Model would
test whether performance-based
incentive payments paid to or owed by
participating kidney transplant
hospitals increase access to kidney
transplants for patients with ESRD
while preserving or enhancing the
quality of care and reducing Medicare
expenditures. Specifically, we proposed
to test whether performance based
incentives (including both upside and
downside risk) for participating kidney
transplant hospitals can increase the
number of kidney transplants (including
both living donor and deceased donor
transplants) furnished to ESRD patients,
encourage investments in care processes
and patterns with respect to patients
who need kidney transplants, encourage
investments in value-based care and
improvement activities, and promote
kidney transplant hospital
accountability by tying payments to
value. We believe a cross-listing of
patients through the IOTA waitlist
patient and patient attribution process is
beneficial for patients and increases
their likelihood of receiving a
transplant. For these reasons, we are
finalizing our proposal without
modification.
After consideration of the public
comments we received, for the reasons
set forth in this rule, we are finalizing
our proposed criteria for identifying and
attributing kidney transplant waitlist
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patients as IOTA waitlist patients to one
or more IOTA participants at
§ 512.414(b)(1) without modification.
(ii) IOTA Transplant Patient Attribution
We proposed that kidney transplant
patients would be attributed as IOTA
transplant patients to the IOTA
participant that furnished a kidney
transplant during the model
performance period, if they meet the
following criteria:
• The patient was 18 years of age or
older at the time of their transplant; and
• The patient was alive at the time of
attribution.
We note that an IOTA transplant
patient who experiences transplant
failure and is then de-attributed from an
IOTA participant, as described in
section III.C.4.b(1)(iii) of this final rule,
could become attributed to an IOTA
participant again at any point during the
model performance period if they
rejoined a kidney transplant waitlist for,
or received a kidney transplant from,
any IOTA participant and satisfied all of
the criteria for attribution as described
in section III.C.4.b(1)(i) or section
III.C.4.b(1)(ii) of this final rule.
We proposed to attribute kidney
transplant patients to the IOTA
participant that furnished the transplant
to hold the IOTA participant
accountable for patient transplant and
post-transplant outcomes. We
alternatively considered attributing
kidney transplant patients based on the
plurality of post-transplant services, as
identified in Medicare claims, because it
would still result in attributing kidney
transplant patients to only one IOTA
participant and would base attribution
on where the majority of services were
furnished. We recognize that patients
may choose to receive their pre-and
post-transplant care from multiple IOTA
participants in addition to the IOTA
participant that performed their kidney
transplant. However, the model’s
incentives do not support shifting
accountability for post-transplant
outcomes away from the IOTA
participant that furnished the
transplant. We believe that the IOTA
participant that performed the
transplant should remain accountable
for any surgery related outcomes, both
successes and failures.
We proposed not to attribute patients
who are younger than 18 years of age at
the time of their kidney transplant or
who are deceased at the time of
attribution due to the same reasons
described in section III.C.4.b(1)(i) of this
final rule.
We sought comment on our proposed
criteria for identifying and attributing
kidney transplant patients as IOTA
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transplant patients to the IOTA
participant that furnished their kidney
transplant during the model
performance period. We also sought
comment on the alternative considered.
We received no comments on this
proposal and therefore are finalizing the
provisions for our proposed criteria for
identifying and attributing kidney
transplant patients as IOTA transplant
patients to the IOTA participant that
furnished their kidney transplant during
the model performance period at
§ 512.414(b)(2) as proposed without
modification.
(iii) De-Attribution Criteria
We proposed that CMS would only
de-attribute attributed patients from an
IOTA participant during annual
attribution reconciliation, as described
in section III.C.4.b(4) of this final rule.
We proposed that CMS would deattribute any attributed patient from an
IOTA participant that meets any of the
following criteria as of the last day of
the PY being reconciled, in accordance
with the annual attribution
reconciliation list as described in
section III.C.4.c of this final rule:
• The IOTA waitlist patient was not
registered on an IOTA participant’s
kidney transplant waitlist on the last
day of the PY being reconciled.
• The IOTA waitlist patient died at
any point during the PY. We proposed
that an IOTA waitlist patient who has
died during the PY would be removed
from the list of attributed IOTA waitlist
patients effective on the last day of the
PY that the death occurred.
• The IOTA transplant patient has
died at any point during the PY. We
proposed that an IOTA transplant
patient who has died during the PY
would be de-attributed from the list of
attributed IOTA transplant patients
effective on the last day of the PY that
the death occurred.
• The IOTA transplant patient’s
kidney failed during the PY, and the
patient is not included on the IOTA
participant’s waitlist. We proposed that
an IOTA transplant patient who
experiences transplant failure at any
point during the PY and does not rejoin
an IOTA participant’s kidney transplant
waitlist or receive another transplant
from an IOTA participant before the last
day of the same PY would be listed as
de-attributed in the annual attribution
reconciliation list. This IOTA transplant
patient would no longer be attributed to
the IOTA participant effective the last
day of the PY in which the IOTA
transplant patient’s kidney transplant
has failed.
We sought comment on our proposed
methodology and criteria for identifying
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and de-attributing attributed patients
from an IOTA participant.
The following is a summary of the
comments received on our proposed
methodology and criteria for identifying
and de-attributing attributed patients
from an IOTA participant and our
responses:
Comment: A commenter expressed
support for CMS’s proposed deattribution criteria.
Response: We thank the commenter
for their support.
Comment: A commenter requested
more information about the source of
the data that would be used to verify the
graft loss or death.
Response: We thank the commenter
for their feedback. As noted in section
V.C of the proposed rule, the SRTR data
source includes data on all transplant
donors, candidates, and recipients in
the U.S. As described in the proposed
rule, section III.C.4.b of the proposed
rule outlines our proposal to use of
SRTR data, OPTN data, Medicare claims
data, and Medicare administrative data
for the purposes of the IOTA Model.
Additionally, section III.C.5.e(1) of this
final rule describes and finalizes our
proposal to use of OPTN follow-up
forms to identify graft failure and retransplant dates. We acknowledge that
for the purposes of measuring graft
survival using OPTN data, use of either
concept would generate the same
outcome measurement because OPTN
data identify graft status as either
functioning or failed. However, we aim
to convey the importance of ongoing
management to preserve the health of
the transplanted graft and the health
and quality of life of the attributed
patients.
Finally, as described and finalized in
section III.C.13.a of this final rule, we
proposed that CMS, or its approved
designees, would conduct compliance
monitoring activities to ensure
compliance by the IOTA participant and
IOTA collaborators with the terms of the
IOTA Model, including to understand
IOTA participants’ use of model-specific
payments and to promote the safety of
attributed patients and the integrity of
the IOTA Model. One proposed
monitoring activity would include
audits of claims data, quality measures,
medical records, and other data from the
IOTA participant and its IOTA
collaborators. For these reasons, we are
finalizing our proposal without
modification.
After consideration of the public
comments we received, for the reasons
set forth in this rule, we are finalizing
our proposed methodology and criteria
for identifying and de-attributing
attributed patients from an IOTA
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96319
participant at § 512.414(b)(3), as
proposed without modification.
(2) Initial Attribution
We proposed that before the model
start date, CMS would conduct an
‘‘initial attribution’’ to identify and
prospectively attribute waitlist patients
to an IOTA participant pursuant to
§ 512.414. The list of IOTA waitlist
patients identified through initial
attribution, namely the initial
attribution list, would prospectively
apply to the first quarter of PY 1,
effective on the model start date. The
purpose of this initial attribution list
would be to prospectively provide IOTA
participants with a list of their IOTA
waitlist patients for the upcoming
quarter.
We considered attributing patients to
IOTA participants at different points in
time, such as the day that a kidney
transplant waitlist patient was added to
the IOTA participant’s kidney
transplant waitlist, or the day that a
kidney transplant patient received their
kidney transplant. This approach would
be more precise than considering all
attributed patients to be attributed as of
the start of the quarter. However, due to
the limitations of data sources and the
frequency with which these data are
updated, we did not see this as a viable
alternative.
We sought comment on our proposal
to conduct initial attribution before the
model start date and alternatives
considered.
We received no comments on this
proposal and therefore are finalizing the
provisions as proposed without
modification at § 512.414(c)(1) and the
definition of initial attribution at
§ 512.402, without modification.
(3) Quarterly Attribution
We proposed that CMS would
attribute patients to IOTA participants
in advance of each quarter, after initial
attribution, and distribute a ‘‘quarterly
attribution list’’ to each IOTA
participant that includes all their
attributed patients, including newly
attributed patients, on a quarterly basis
throughout the model performance
period, except in the event of
termination as described in section
III.C.16(b) of this final rule.
We considered monthly attribution
for more frequent updates to the initial
attribution list, but believe it would be
operationally burdensome. We also
considered annual attribution for less
frequent updates to the initial
attribution list, which would be less
operationally burdensome than monthly
or quarterly attribution. Annual
attribution is common in other
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Innovation Center models and CMS
programs where the participant is
managing total cost of care for a
population. The benefits of annual
attribution would include prospectively
providing participants a stable list of
patients for whom they would be held
accountable, and, as the process would
occur only once a year, would be
associated with lower administrative
burden. The downside of annual
attribution, however, is that IOTA
participants would have less frequent
updates and understanding of their
attributed population, potentially
making it hard to plan and budget
accordingly. We do not believe annual
attribution would be appropriate for the
IOTA Model’s goal of improving access
to kidney transplants and quality of care
for a patient population that changes
frequently. For example, kidney
transplant hospitals add patients to their
kidney transplant waitlist throughout
the year. Were we to limit attribution to
once a year, kidney transplant waitlist
patients added during the year would
not be attributed to an IOTA participant
until the following year, delaying our
ability to meet the minimum number of
patients required to evaluate a model
test. As such, we believe more frequent
attribution would be necessary.
We sought comment on our proposal
to conduct attribution on a quarterly
basis during the model performance
period and on the alternatives
considered.
The following is a summary of the
comments received on our proposal to
conduct attribution on a quarterly basis
during the model performance period
and on the alternatives considered and
our responses:
Comment: Several commenters voiced
their support for the proposed quarterly
attribution provisions, stating that it
would ensure accuracy and fairness.
Response: We thank the commenters
for their support.
After consideration of the public
comments we received, for the reasons
set forth in this rule, we are finalizing
our proposed quarterly attribution
provisions at § 512.414(c)(2), without
modification. We received no comments
on the proposed definition of quarterly
attribution list and there are finalizing
this definition without modification at
§ 512.402.
(4) Annual Attribution Reconciliation
We proposed that after the end of
each PY, CMS would conduct annual
attribution reconciliation. We proposed
to define ‘‘annual attribution
reconciliation’’ as the yearly process by
which CMS would: (1) create each IOTA
participant’s final list of attributed
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patients for the PY being reconciled by
retrospectively de-attributing from each
IOTA participant any attributed patients
that satisfied a criterion for deattribution pursuant to § 512.414(c); and
(2) create a final list of each IOTA
participant’s attributed patients who
would remain attributed for the PY
being reconciled, subject to the
attribution criteria in §§ 512.414(b)(1)
and (2). For the purposes of this model,
we proposed to define ‘‘annual
attribution reconciliation list’’ as the
final cumulative record of attributed
patients that would be generated
annually for whom each IOTA
participant was accountable for during
the applicable PY.
For example, after PY 1, CMS would
rerun attribution for the entire PY to
finalize the list of attributed patients
that met the criteria specified in
sections III.C.4.b(1) and (2) of this final
rule. Once the fourth quarter is
complete, CMS would use the fourth
quarter attribution list to determine and
de-attribute any attributed patients that
meet a criterion for de-attribution, as
described in section III.C.4.b(1)(iii) of
this final rule, from the IOTA
participant, as described in section
III.C.4.b(1)(iii) of this final rule, and
remove those attributed patients from
the quarterly attribution list to create the
annual attribution reconciliation list.
Before the second quarter of the
following PY, CMS would distribute the
annual attribution reconciliation list to
IOTA participants. We proposed that
these lists, at a minimum, would
identify each attributed patient, identify
reasons for de-attribution in the
previous PY, and the dates in which
attribution began, changed, or ended,
where applicable.
We sought comment on our proposal
to conduct annual attribution
reconciliation.
The following is a summary of the
comments received on our proposal to
conduct annual attribution
reconciliation and our responses:
Comment: Several commenters
expressed support for CMS’s proposal to
conduct annual attribution
reconciliation.
Response: We thank the commenters
for their support.
After consideration of public
comments, for the reasons set forth in
this rule, we are finalizing the policy for
annual attribution reconciliation as
proposed in § 512.414(c)(3), with a
minor technical correction to update the
cross references in the regulation text at
§§ 512.414(c)(3)(ii)(A) and
512.414(c)(3)(ii)(C–F). We are also
finalizing the definitions of annual
attribution reconciliation and annual
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attribution reconciliation list at
§ 512.402 without modification.
c. IOTA Patient Attribution Lists
We proposed that no later than 15
days prior to the start of the first model
performance period, CMS would
provide the IOTA participant the
‘‘initial attribution list.’’ For the
purposes of the model, we proposed to
define ‘‘days’’ as calendar days, as
defined in 42 CFR 512.110, unless
otherwise specified by CMS. On a
quarterly basis thereafter, CMS would
provide the IOTA participant the
‘‘quarterly attribution list’’ no later than
15 days prior to the start of the next
quarter. The annual attribution
reconciliation list for a given PY would
be provided to the IOTA participants
after the conclusion of the PY, before
the second quarter of the following PY.
We proposed that the initial,
quarterly, and annual attribution
reconciliation lists would be provided
in a form and manner determined by
CMS.
We sought comment on our proposed
attribution list policies.
The following is a summary of the
comments received on our proposed
attribution list policies and our
responses:
Comment: Several commenters
requested that CMS provide the patient
attribution lists be provided well in
advance of the performance period to
allow IOTA participants to prepare
accordingly and assess performance
impacts. Specifically, a commenter
suggested providing attribution lists at
least one quarter in advance of the start
of the performance period.
Response: We thank the commenters
for their feedback. As described and
finalized in section III.C.4.c of this final
rule, we proposed that 15 days prior to
the start of the first model performance
period, CMS would provide the IOTA
participant the initial attribution list. On
a quarterly basis thereafter, CMS would
provide the IOTA participant the
quarterly attribution list no later than 15
days prior to the start of the next
quarter. The annual attribution
reconciliation list for a given PY would
be provided to the IOTA participants
after the conclusion of the PY, before
the second quarter of the following PY.
This sequence for patient attribution
lists follows the same pattern as other
Innovation Center models—such as the
KCC Model—and, therefore, we are
finalizing this provision without
modification.
After consideration of public
comments, for the reasons set forth in
this rule, we are finalizing, as proposed,
our provisions at §§ 512.414(c)(1)(ii),
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512.414(c)(2)(ii), 512.414(c)(3)(ii) and
the definition of days at § 512.402
without modification.
5. Performance Assessment
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a. Goals and Proposed Data Sources
As described in section III.B. of the
proposed rule, CMS and the OPTN each
have roles in assessing the performance
of kidney transplant hospitals. CMS’
regulations in 42 CFR part 482 subpart
E require certain conditions of
participation for kidney transplant
hospitals to receive approval to perform
Medicare transplant services. Under 42
CFR part 121, the OPTN is required to
implement a peer review process by
which OPOs and transplant hospitals
are periodically reviewed for
compliance with the bylaws of the
OPTN and the OPTN final rule (63 FR
16332). The OPTN MPSC is charged
with performing these evaluations;
including the identification of threats to
patient safety and public health.188
As described in section III.C.5.a. of
the proposed rule, CMS and the OPTN
have each acknowledged the limitations
of transplant hospital performance
assessment based on the one-year
patient and transplant survival measure
alone. In 2018, CMS eliminated its
assessment of one year patient and
transplant survival for the purposes of
transplant hospital re-approval in the
final rule, ‘‘Medicare and Medicaid
Programs; Regulatory Provisions To
Promote Program Efficiency,
Transparency, and Burden Reduction;
Fire Safety Requirements for Certain
Dialysis Facilities; Hospital and Critical
Access Hospital (CAH) Changes To
Promote Innovation, Flexibility, and
Improvement in Patient Care’’ (84 FR
51732), leaving assessment of the one
year patient and transplant survival
measure only for initial Medicare
approval, due to concerns that the
measure was causing conservative
behavior in transplant hospitals.189 In
2021, the OPTN disseminated a
proposal to enhance the MPSC’s
performance monitoring process by
expanding the number of measures used
to identify transplant hospital
underperformance.190 In that proposal,
the OPTN acknowledged the potential
for transplant hospital risk aversion due
188 https://optn.transplant.hrsa.gov/about/
committees/membership-professional-standardscommittee-mpsc/.
189 https://optn.transplant.hrsa.gov/about/
committees/membership-professional-standardscommittee-mpsc/ and Burden Reduction. Federal
Register. https://www.federalregister.gov/d/201819599/p-215.
190 https://optn.transplant.hrsa.gov/media/4777/
transplant_program_performance_monitoring_
public_comment_aug2021.pdf.
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to the MPSC’s evaluations of
performance based on the one year
patient and transplant survival metric
alone and proposed transplant hospital
assessment based on a holistic set of
measures encompassing aspects of care
across the transplant journey.191
As described in section III.C.5.a. of
the proposed rule, strengthening and
improving the performance of the organ
transplantation system is a priority for
HHS, including CMS and HRSA. In
accordance with this priority and joint
efforts with HRSA, the IOTA Model
would aim to improve performance and
equity in kidney transplantation by
testing whether performance-based
payments to IOTA participants
increases access to kidney transplants
for kidney transplant waitlist and
kidney transplant patients attributed to
IOTA participants in the model, thereby
reducing Medicare program
expenditures while preserving or
enhancing quality of care. For the IOTA
Model, we proposed a broader set of
metrics which aligns with the trends
that we believe would encourage IOTA
participants to meet the model goals as
described in section III.A of this final
rule.
As described in section III.C.5.a of the
proposed rule, the IOTA Model would
assess performance on a broad set of
metrics that were selected to align with
all of the following model goals:
• Increase number of, and access to,
kidney transplants.
• Improve utilization of available
deceased donor organs.
• Support more donors through the
living donation process.
• Improve quality of care and equity.
In section III.C.5.a of the proposed
rule, we proposed using Medicare
claims and administrative data about
beneficiaries, providers, suppliers, and
data from the OPTN, which contains
comprehensive information about
transplants that occur nationally, to
measure IOTA participant performance
in the three model domains: (1)
achievement domain; (2) efficiency
domain; and (3) quality domain.
Medicare administrative data refers to
non-claims data that Medicare uses as
part of regular operations. This includes
information about beneficiaries, such as
enrollment information, eligibility
information, and demographic
information. Medicare administrative
data also refers to information about
Medicare-enrolled providers and
suppliers, including Medicare
enrollment and eligibility information,
practice and facility information, and
Medicare billing information.
191 Ibid.
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We solicited comment on our
proposal for selecting performance
metrics and performance domains. We
also solicited comment on our proposed
use of Medicare claims data, Medicare
administrative data, and OPTN data to
calculate the performance across the
three proposed domains, as described in
section III.C.5. of this final rule.
The following is a summary of
comments received on our proposal for
selecting performance metrics and
performance domains, in addition to our
proposed use of Medicare claims data,
Medicare administrative data, and
OPTN data to calculate the performance
across the three proposed domains and
our responses:
Comment: A commenter conveyed
their concern that the OPO and
transplant performance metrics are
misaligned and as a result will
minimize the impact of the IOTA
Model.
Response: We appreciate the
commenter’s feedback; however, we do
not believe that it is appropriate to
directly compare the performance
metrics of OPOs and kidney transplant
hospitals. Both OPOs and kidney
transplant hospitals have unique roles
in the transplant ecosystem, requiring
different focuses, skills sets and
responsibilities. We acknowledge the
different responsibilities of these two
parties along the continuum of care for
organ transplantation. Overall,
performance metrics, are meant to
understand current state, to set goals to
create improvement, to ensure
unintended consequences of changes
are identified, and to allow for analysis
and evaluation to pivot and modify
metrics when appropriate. With
overarching goals to improve kidney
transplant volume while maintaining
quality organs and patient care, we
believe that HRSA and CMS do not have
misaligned goals.
Comment: A few commenters stated
that they believe the three domains will
lead to a successful solution and are
acceptable.
Response: We appreciate the
commenters’ support. We believe that
including the achievement, efficiency
and quality domain are an ideal
combination to ensure that while IOTA
participants are increasing kidney
transplants, we are also monitoring
acceptance patterns and post-transplant
outcomes.
After consideration of public
comments, for the reasons set forth in
this rule, we are finalizing the proposed
provisions for selecting performance
metrics and performance domains at
§ 512.422(a), without modification. We
did not receive any comments regarding
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our proposed use of Medicare claims
data, Medicare administrative data, and
OPTN data to calculate the performance
across the three proposed domains and
therefore are finalizing this provision
without modification at § 512.422(b).
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b. Method and Scoring Overview
In accordance with our proposed
goals of the IOTA performance
assessment, as described in section
III.C.5.a of the proposed rule, we
proposed to assess performance across
three domains: (1) achievement domain;
(2) efficiency domain; and (3) quality
domain. We proposed to use one or
more metrics within each domain to
assess IOTA participant performance.
We proposed at § 512.422(a)(2) that
CMS would assign each set of metrics
within a domain a maximum point
value, with the total possible points
awarded to an IOTA participant being
100 points. We proposed to define
‘‘final performance score’’ as the sum
total of the scores earned by the IOTA
participant across the achievement
domain, efficiency domain, and quality
domain for a given PY. We also
proposed that the combined sum of total
possible points would determine
whether and how the IOTA Model
performance-based payments, as
described and finalized in section
III.C.6.c of this final rule, would apply
and be calculated. We proposed the
following point allocations for each of
these three domains:
• The achievement domain would
make up 60 of 100 maximum points.
The achievement domain would
measure the number of kidney
transplants performed relative to a
participant-specific target, as described
in section III.C.5.c of the proposed rule.
The achievement domain would
represent a large portion (60 percent) of
the maximum total performance score.
We weighted the achievement domain
performance score more than the
efficiency and quality domain because
we believe it aligns with the primary
goal of the IOTA Model, to increase the
overall number of kidney transplants.
Additionally, because increasing the
number of kidney transplants performed
is the primary goal of the model, we
believe weighing performance on this
measure more than the efficiency
domain and quality domain is necessary
to directly incentivize participants to
meet their target.
• The efficiency domain would make
up 20 of 100 maximum points. The
efficiency domain would measure
performance on a kidney organ offer
acceptance rate ratio, as described in
section III.C.5.d of the proposed rule.
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• The quality domain would make up
20 of 100 maximum points. As
described in section III.C.5.e. of the
proposed rule, the quality domain
would measure performance on a set of
quality metrics, including posttransplant outcomes, and on three
proposed quality measures—
CollaboRATE Shared Decision-Making
Score, Colorectal Cancer Screening, and
3-Item Care Transition Measure.
We believed that many prospective
IOTA participants may already be
familiar with the approach of assigning
points up to a maximum in multiple
domains. This structure is similar to
other CMS programs, including the
Merit-based Incentive Payment System
(MIPS) track of the Quality Payment
Program. For MIPS, we assess the
performance of MIPS eligible clinicians
(as defined in 42 CFR 414.1305) across
four performance categories—one of
which is quality—and then determine a
positive, neutral, or negative MIPS
payment adjustment factor that applies
to the clinician’s Medicare Part B
payments for professional services.
Similar to MIPS, we proposed that the
IOTA Model would use a performance
scoring scale from zero to 100 points
across performance domains, and apply
a specific weight for each domain. We
believed using wider scales of 0 to 100
points would allow us to calculate more
granular performance scores for IOTA
participants and provide greater
differentiation between IOTA
participants’ performance. In the future,
we believed this methodology for
assessing performance could be applied
with minimal adaptation to future IOTA
participants if CMS adds other types of
organ transplants to the model through
rulemaking. We believed that the
approach of awarding points in the
achievement, efficiency, and quality
domains for a score out of 100 points
represented the best combination of
flexibility and comparability that would
allow us to assess participant
performance in the IOTA Model.
As discussed in section III.C.5.b of the
proposed rule, the proposed
performance domains and scoring
structure would also allow us to
combine more possible metric types
within a single framework. We believed
that this approach allows for more
pathways to success than performance
measurement based on relative or
absolute quintiles, which were also
alternatively considered, as it would
reward efforts made towards achievable
targets.
As discussed in section III.C.5.b of the
proposed rule, we considered more than
three domains to assess performance,
which would potentially offer IOTA
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participants more opportunity to
succeed due to the ability to maximize
points in different combinations of
domains. The more domains there are,
the more the maximum points possible
in each domain are spread out.
However, we limited the number of
domains to three to ensure the model is
focused and goal-oriented, thus
promoting, encouraging, and driving
improvement activity and care delivery
transformation across IOTA participants
that evidence suggest may help achieve
desired outcomes. Desired outcomes
include delaying or avoiding dialysis,
improving access to kidney
transplantation by reducing barriers and
disparities, reducing unnecessary
deceased donor discards, increasing
living donors, and improving care
coordination and quality of care pre and
post transplantation. We believed that
the three domains and the proposed
performance scoring structure would
offer IOTA participants multiple paths
to succeed in the proposed IOTA Model
due to the ability to maximize points in
different combinations of domains.
In section III.C.5.b of the proposed
rule, we also considered not using the
three performance domains and scoring
structure, instead opting for alternative
methods. We considered a performance
assessment methodology in which an
IOTA participant’s performance on a
metric would be divided by an expected
value for each metric, which would
indicate whether an IOTA participant is
performing better or worse on a given
measure than expected. We would then
calculate a weighted average of all
performance scores to reach a final
score. However, we believed that setting
appropriate targets of expected
performance for each IOTA participant
for each metric would be unrealistic to
implement. The additional
methodological complexity necessary
for this approach would be difficult for
an IOTA participant to incorporate into
its operations and data systems, thereby
limiting an IOTA participant’s ability to
understand the care practice changes it
would need to make to succeed in the
IOTA Model.
As discussed in section III.C.5.b of the
proposed rule, we also considered
assessing IOTA participant performance
solely on magnitude of increased
transplants over expected transplants.
Under this approach, an IOTA
participant’s number of transplants
furnished in a given PY subtracted from
expected transplants would show a
numeric net gain or loss in total
transplants. This net value would be
multiplied by an IOTA participant’s
kidney transplant survival rate to
generate a total score for each IOTA
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participant. This option would reward
successfully completed transplants.
This methodology reflects the goals of
the IOTA Model and acknowledges that
kidney transplant failures are an
undesirable outcome. In addition, the
methodology is simple to evaluate and
understand, requiring only two inputs
and a simple calculation. However, this
approach does not account for efficiency
and quality domain metrics, as
proposed in sections III.C.5.d. and
III.C.5.e of the proposed rule, which we
believed to be important goals of the
model. Thus, we did not propose this
method to assess IOTA participant
performance.
As discussed in section III.C.5.b of the
proposed rule, we also considered
directly translating the benefits of a
kidney transplant by measuring the net
effect of increased transplants and posttransplant care at the IOTA participant
level. In a performance scoring
methodology focused on the net effect of
increased transplants and posttransplant care, the number of kidney
transplants performed in a given PY
would be compared to a benchmark year
for the IOTA participant. Each
additional kidney transplant would then
be multiplied by the expected number
of years of dialysis treatment the
transplant averted, based on organ
quality. Post-transplant care would
analyze observed versus expected
kidney transplant failures. For IOTA
participants that achieved fewer kidney
transplant failures than expected, the
difference in volumes would be
translated into life-years. Each marginal
additional year of averted dialysis care
would be used to determine the
performance-based payment. Because
calculating expected transplant failures
is a complicated calculation with
assumptions based on organ quality,
donor age, and donor health conditions,
a scoring system of this type would
require us to make multiple broad
assumptions about individual
transplants or average scores across all
transplants performed by the IOTA
participant to create an accurate
estimate of the total number of years of
dialysis treatment the kidney transplant
averted. This level of complexity would
also introduce operational risks and
burden. This approach would be aligned
with the goals of the IOTA Model as it
relates to increasing the number and
access to kidney transplants but would
still require CMS to separately assess
performance on proposed performance
measures for the IOTA Model, as
discussed in sections III.C.5.c, III.C.5.d,
and III.C.5.e of the proposed rule.
We solicited feedback from the public
on our proposal to assess IOTA
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participant performance in three
domains: (1) achievement domain; (2)
efficiency domain; and (3) quality
domain. We also sought feedback on our
proposed performance scoring approach
that would weigh the achievement
domain higher than the efficiency and
quality domain, and our proposed use of
a 0 to 100 performance scoring approach
to determine if and how performancebased payments would apply.
Additionally, we invited feedback on
the alternatives considered.
The following is a summary of the
comments received on our proposal to
assess IOTA participant performance in
three domains (achievement domain,
efficiency domain and quality domain),
our proposed performance scoring
approach, and on our proposed use of
a 0 to 100 performance scoring approach
to determine if and how performancebased payments would apply and our
responses:
Comment: A few commenters
supported the three proposed domains
for assessing an IOTA participant’s
performance. A commenter specifically
stated they supported the 100-point
structure made up of 3 domains and
another specifically stated their support
for the emphasis on the achievement
domain.
Response: We thank the commenters
for their support.
Comment: A commenter stated that
the performance metrics are conflicting
because while volume is incentivized,
achieving a high organ offer acceptance
rate ratio would require more
conservative transplants.
Response: We appreciate the
commenters feedback. We believe that
counterbalanced performance metrics
are needed to create checks and
balances within the IOTA Model. The
inclusion of the organ offer acceptance
rate ratio metric and the composite graft
survival rate discourages IOTA
participants from strictly considering
volume and encourages IOTA
participants to also prioritize long term
outcomes. We direct readers to sections
III.C.5.d(1) and III.C.5.e(1) of this final
rule for further discussion on the organ
offer acceptance rate ratio and the
composite graft survival rate. The
collection of metrics encourages IOTA
participants to understand specific
components of their transplant program
that may be optimized such as utilizing
filters, understanding what organs they
are accepting or deferring and
identifying what workflows and
resources may help them optimize their
transplant program. While IOTA
participants may believe it is
contradictory to weight achievement
higher, we believe that kidney
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transplant volume can be increased
while being mindful of post-transplant
outcomes for both living donor and
deceased donor transplant recipients.
There are a variety of ways for IOTA
participants to reach final performance
point totals that are incentivized (score
greater than 60). For example, growth of
a living donor program could increase
volume without impacting the offer
acceptance ratio entirely.
Comment: Many commenters stated
that the performance should include
other factors that could impact an IOTA
participant’s performance, such as the
IOTA participant’s history.
Response: We appreciate the
commenters’ feedback. We believe that
IOTA participant history is incorporated
into many features and performance
measurements of the IOTA Model. An
IOTA participant’s past performance is
included in the achievement domain of
the IOTA Model, by using baseline year
data to calculate kidney transplant
volume goals in the IOTA Model. While
there is not an improvement scoring
component within the achievement
domain, we intend to consider this for
future rulemaking. The organ offer
acceptance rate ratio performance
metric, which is part of the efficiency
domain, is evaluated either through
overall achievement or improvement.
Inclusion of an improvement scoring
system within the efficiency domain,
takes the IOTA participant’s history into
consideration. The quality domain
utilizes composite graft survival over a
6-year period as a performance metric.
While use of this metric in the first 1–
2 years of the model will not take IOTA
participant history into consideration,
the latter years will include earlier
model data years (IOTA participant
history) in its calculation.
Comment: Many commenters
suggested that risk adjustment should be
included in the performance measures,
with a couple of commenters stating
specifically that the lack of adjustment
incentivizes transplanting healthier
individuals and avoiding higher risk
organs. Another commenter relayed
their concern about the lack of scientific
validation for the metrics, from the
transplant community.
Response: We thank the commenters
for submitting their concerns. The data
and methodology utilized for the offer
acceptance ratio utilizes OPTN data and
SRTR methodology and is risk adjusted.
As mentioned in section III.C.5.e(1)(a) of
this final rule, we considered whether
donor demographic characteristic risk
adjustments such as race, gender, age,
disease condition and geographic
location would be significant and
clinically appropriate for our approach
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in calculating the composite graft
survival rate measure, however, we are
unsure which specific adjustments
would be most appropriate. We believe
that further analysis of the impact of the
donor’s characteristics on graft survival
is necessary prior to incorporating a risk
adjustment methodology. Additionally,
given that the IOTA Model is 6 years,
and the measure is rolling, we want to
make sure that we continue discussions
to ensure that this measure eventually
includes a robust and appropriate risk
adjustment methodology. We direct
readers to section III.C.5.e(1)(a) of this
final rule, for further discussion
regarding calculation of the composite
graft survival rate.
While the achievement domain does
not utilize risk adjustment, it assigns
points for volume of kidneys
transplanted, based on an IOTA
participant’s prior performance and
national growth rate. We did not
originally consider how volume goals
could be risk adjusted, however, we are
open to ongoing feedback as to how this
could be integrated into the
achievement domain metric.
We acknowledge the concerns raised
by a commenter about the scientific
validity of some performance measures,
but we do not believe any of the
measures are entirely novel. For
example, the OPTN has previously used
an offer acceptance rate ratio in their
metrics. Although the proposed
composite graft survival rate measure is
new, analyzing 1-year graft survival is
an established performance metric
familiar to kidney transplant hospitals.
We will consider risk-adjusting this
metric in future rulemaking. The IOTA
Model intends to closely monitor
metrics new to the transplant
community and adjust as indicated
throughout its performance years.
Comment: A couple of commenters
mentioned that performance assessment
should include a measure of additional
relevant factors, such as the donor’s risk
factors.
Response: We agree and note that the
SRTR calculation, which is used for the
organ offer acceptance rate ratio
calculation, includes numerous donor
factors that contribute to the acceptance
predictors.192 While the composite graft
survival rate metric is not risk adjusted,
we will stratify the data from the
composite graft survival rate measure
and consider public comments to
inform a risk adjustment methodology
for this measure and intend to address
192 Scientific Registry of Transplant Recipients.
(n.d.). Risk Adjustment Model: Offer Acceptance.
Offer acceptance. https://www.srtr.org/tools/offeracceptance/.
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a new or updated policy pursuant to
future rulemaking. We direct readers to
section III.C.5.e(1) of this final rule for
further discussion on the composite
graft survival metric.
Comment: Several commenters stated
that measures of transplant outcomes
should be a reliable and valid measure
and that a SRTR metric is an example
of a metric that should be used.
Response: We agree and note that the
SRTR calculation, which is used for the
organ offer acceptance rate ratio
calculation, includes numerous donor
factors that impact the acceptance
predictors.193 While the composite graft
survival rate metric is not risk adjusted,
we will stratify the data from the
composite graft survival rate measure
and consider public comments to
inform a risk adjustment methodology
for this measure and intend to address
a new or updated policy pursuant to
future rulemaking. We direct readers to
section III.C.5.e(1) of this final rule for
further discussion on the composite
graft survival metric.
Comment: Several commenters
conveyed concern that CMS should
exclude hospice patients from the oneyear mortality rate.
Response: We appreciate the
commenters’ concern. The IOTA Model
does not currently include a one-year
mortality performance measure, and
therefore discussion about hospice
patient exclusions from this metric is
not applicable. For clarification, the
IOTA Model does include a composite
graft survival rate metric, but this metric
is based on graft survival, not patient
survival. Any specifications on
exclusions for calculating the composite
graft survival rate metric would be
addressed in detail in future IOTA
Model methodology reports.
Comment: Several commenters
conveyed concern that assessment
scoring places a heavy weight on the
volume of transplants and the
subsequent possibility that this may
incentivize IOTA participants to use
‘‘sub-par’’ organs and increase
disparities.
Response: We agree that there is a
heavy focus on increasing volume of
transplants as this is one of the primary
goals of the IOTA Model. There are a
variety of ways to increase kidney
transplant volume (for example,
expanding a living donor program,
increasing volume of patients active on
the kidney transplant list, utilizing
filters to ensure appropriate offers for
risk thresholds, or using kidney
transplants from underutilized
categories, if reasonable). While some
193 Ibid.
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kidney transplant hospitals may
prioritize increasing kidney transplants
from underutilized categories such
those with a high KDPI or donation after
circulatory death (DCD) kidneys, that
decision may hinge on resources, and is
not a requirement.
The IOTA Model was designed to
create balance by requiring that IOTA
participants perform well in the
efficiency and quality domains to reach
positive performance incentives. This
ensures that kidney transplant volume
does not grow unchecked, and IOTA
participants remain responsible for long
term outcomes of patients. We believe
that increasing kidney transplants will
result in increases in patient access to
transplant along the continuum of
care—ranging from being referred for
transplant, to waitlisting, to transplant.
Given the disparities that exist in all
phases of transplant, we believe that
changes made to increase kidney
transplant volume will also help reduce
disparities. Additionally, we believe the
proposed transparency measures, which
include publishing the criteria used to
select transplant patients and reviewing
the acceptance criteria as described and
finalized in sections III.C.8.a(1) and (2)
of this final rule, complement the
performance-based metrics and will
help to reduce disparities by increasing
patient awareness and encouraging
shared decision-making. We direct
readers to section III.C.8(a) of this final
rule for a full discussion on the
transparency requirements. We intend
to monitor throughout the entirety of the
model for any unintended consequences
that would impact disparities.
Comment: Numerous commenters
expressed concern regarding the
weighting of points for each domain.
Several commenters stated that the
point allocation for each performance
domain should be spread equally across
domains or that more points should be
allocated to the quality domain (one
example specified 50 achievement
points, 30 quality points, 20 efficiency
points). A commenter suggested that
quality should have the highest weight,
while another recommended equal
weighting of achievement and quality
due to resources needed for posttransplant care, which they felt was not
reimbursed. A commenter suggested
that during PY 3 or later, CMS should
consider the point breakdown of 50, 25,
25 for the achievement, efficiency and
quality domains. There were many
specific concerns that there is too much
incentive placed on volume rather than
quality and this may incentivize poor
long-term outcomes for patients. A
commenter was specifically concerned
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about the risk of increased performance
reviews.
Response: We appreciate the
commenters’ concerns but respectfully
disagree. We believe that the domain
with the heaviest weighting, will also be
the domain that sees greatest behavioral
changes. Therefore, the achievement
domain is more heavily weighted to
increase access to transplant, a primary
goal of the IOTA Model. If an IOTA
participant prioritizes growth of their
living donor program, for example, this
would have a high likelihood of better
post-transplant outcomes, given the
longer graft lives of living donor kidney
transplants. IOTA participants that may
be restricted to expanding living
donation could consider, for example,
how to optimize their organ filters to
ensure that they receive more of the
transplant offers they are willing to
accept and transplants they can help
maintain long term. IOTA participants
can earn up to 60 points for
performance in the achievement domain
and up to 40 combined points for
performance metrics in the efficiency
and quality domains. We do not believe
this is imbalanced given the reasoning
previously mentioned. Additionally, as
described and finalized in section
III.C.5.e of this final rule, we are
modifying the metrics proposed for
inclusion in the quality domain. As
such, we do not believe that weighting
the quality domain metrics more heavily
is appropriate at this time. We direct
readers to section III.C.5.e of this final
rule for further discussion on the quality
domain. We will continue to monitor
our performance assessment strategy
across all performance domains and
may consider proposing an updated
performance scoring approach through
future rulemaking. We will be finalizing
our performance scoring approach in
section III.C.5.b of this final rule, as
proposed, which designates 3
performance domains and the
performance scoring approach as
follows: 60 points for the achievement
domain, 20 points for the efficiency
domain and 20 points for the quality
domain.
Comment: A couple of commenters
stated their concerns that prioritizing
kidney transplant volume in the
achievement domain may discourage
IOTA participants from taking on more
complex cases, because patients may
need more assistance throughout
transplant evaluation or may be at risk
of worse outcomes.
Response: We appreciate the
commenters’ feedback but believe that
kidney transplant hospitals have
different skill sets and resources. The
IOTA Model encourages IOTA
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participants to work at the top of their
scope and encourages them to identify
ways that they can optimize their
program without compromising posttransplant care. Approaches may look
very different depending on the size,
location and resources of an IOTA
participant. For example, wellestablished IOTA participants may
focus on improving outcomes for
patients receiving kidneys with a KDPI
greater than 85, whereas small IOTA
participants may decide to focus on preemptive transplant or living donation
transplant. Risk thresholds may also
vary considerably based on the
established networks between
community nephrologists and
transplant teams. Community
nephrologists are an extension of the
transplant team and can have significant
impact on helping their patients
successfully receive a transplant and
maintain graft life, after transplant. The
IOTA Model challenges the pre-existing
framework of kidney transplant
hospitals to evolve.
While we believe that increasing
access to transplant and subsequent
increase in volume is a fundamental
goal of the IOTA Model, we believe
there is also opportunity to encourage
and reward IOTA participants that excel
in the efficiency and quality domains as
they adapt their programs for growth. It
is ideal for IOTA participants to excel
across all three performance domains
throughout the model test; however, we
understand that IOTA participants may
perform better in specific performance
domains due to year-to-year variations
in available resources. The IOTA Model
scoring was designed to include posttransplant measures to prevent poor
outcomes from increased kidney
transplant volume.
Comment: A commenter
recommended that CMS include
nutritional care in their performance
metrics to address needs of patients.
Response: While we acknowledge the
importance of nutrition and nutritional
resources for patients across the CKD to
ESRD to transplant care continuum, we
do not currently believe that that
nutritional care directly aligns with the
goals of the IOTA Model or its
performance metrics. We invite ongoing
input on how nutritional care may fit
into an alternative quality metric
utilized in future iterations of the IOTA
Model.
Comment: A commenter stated that
safety net kidney transplant hospitals in
remote regions will be disadvantaged by
the three domains.
Response: We acknowledge that
remote and safety net kidney transplant
hospitals have different challenges in
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96325
their transplant programs than kidney
transplant hospitals that may be in
highly populated areas. We encourage
IOTA participants to consider the
numerous approaches that they may
take to increase kidney transplant
volume. This may be achieved by
increasing living donor kidney
transplants (LDKTs), deceased donor
kidney transplant (DDKTs) or both. If an
IOTA participant struggles to increase
their volume initially, there are
opportunities to excel in the efficiency
and quality domains. We understand
that any model can have unintended
consequences and we intend to monitor
the model impacts on IOTA
participants.
Comment: A couple of commenters
suggested that the IOTA Model should
have been weighted to encourage use of
kidneys with a KDPI greater than 85 and
improving quality of care for those
transplant recipients, rather than
prioritize increasing total number of
transplants performed.
Response: Thank you for submitting
feedback, however, we disagree. While
there is opportunity to optimize use of
kidneys with a KDPI greater than 85, we
believe this may not be the most ideal
way for all IOTA participants to
increase volume or general performance.
Prioritizing an increase in any DDKTs or
LDKTs of a specific classification allows
each IOTA participant to have flexibility
in adapting their program to meet this
goal.
While the IOTA Model is not
finalizing a performance metric
measuring utilization of kidneys with a
KDPI greater than 85, we intend to
assess and monitor the utilization of this
category of kidney transplants by IOTA
participants.
Comment: A commenter was
concerned that the IOTA Model does
not account for recovered kidneys that
are not used for transplant or for nonutilization.
Response: We thank the commenter
for their feedback. The organ offer
acceptance rate ratio is calculated by
excluding donor kidneys that are not
utilized. While no metric in the IOTA
Model specifically looks at the total
non-utilization number, this may be an
important metric to further research as
it may be impacted differently as kidney
transplant hospitals adjust their offer
acceptance filters. We believe there may
be opportunity for future collaboration
with the OPTN to ensure non-utilization
data is captured and accessible for
review.
Comment: A commenter mentioned
concern that CMS is basing kidney
transplant hospital percentile rankings
against both participating and non-
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participating kidney transplant
hospitals.
Response: We thank the commenter
for submitting their concern. IOTA
participants are awarded points in the
achievement domain based on
performance improvement relative to
historical performance for volume of
kidneys transplanted. We direct readers
to section III.C.5.c of this final rule for
a full discussion of the achievement
domain.
As described and finalized in section
III.C.5.d.(1).(b). of this final rule, the
efficiency domain applies a two-scoring
system (achievement score and
improvement score) based on its
performance on the OPTN organ offer
acceptance rate ratio; awarding points
equal to the higher of the two scores to
the IOTA participant. For achievement
scoring in the quality domain, as
described and finalized in section
III.C.5.d.(1).(b). of this final rule, points
earned will be based on the IOTA
participants’ performance on the organ
offer acceptance rate ratio relative to
national ranking, including all eligible
kidney transplant hospitals (both those
selected and not selected as IOTA
participants), and awarded based on
national quintiles. For improvement
scoring in the efficiency domain, as
described and finalized in section
III.C.5.d.(1).(b). of this final rule, points
earned will be based on the IOTA
participants’ performance on organ offer
acceptance rate ratio during a PY
relative to their performance during the
third baseline year for the PY that is
being measured. We direct readers to
section III.C.5.d of this final rule for a
full discussion on the efficiency
domain.
Lastly, as described and finalized in
section III.C.5.e(1)(b) of this final rule,
IOTA participants will earn points in
the quality domain based on its
performance on the composite graft
survival rate, as described and finalized
in section III.C.5.e(1)(a) of this final rule,
ranked nationally, inclusive of all
eligible kidney transplant hospitals.
IOTA participants will be awarded
points on the composite graft survival
rate based on the national quintiles, as
outlined in Table 1 to Paragraph (d) at
§ 512.428. We direct readers to section
III.C.5.e of this final rule for a full
discussion on the quality domain.
The IOTA Model incentivizes high
performance through a point-based
system, which we anticipate will drive
IOTA participants to outperform nonparticipating kidney transplant
hospitals, which we view as a notable
strength of the model.
Comment: A commenter stated the
IOTA Model methodology does not
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account for kidney transplant hospitals
that already perform a high-volume of
kidney transplants, and instead is based
solely on improvement.
Response: We thank the commenter
for expressing their concern. Many highvolume kidney transplant hospitals
have a combination of well-developed
living donor programs, resources such
as perfusion pumps, and the volume
that allows higher risk thresholds both
for accepting certain donors and
accepting candidates with more comorbidities. These qualities and
resources allow ongoing opportunity for
growth. We recognize that IOTA
participants with varying kidney
transplant volumes will have unique
challenges. However, we believe the
methodology’s built-in flexibility
enables IOTA participants to adapt their
kidney transplant hospital to best serve
their patient populations. We intend to
closely monitor kidney transplant
volume growth and outcomes for IOTA
participants of all kidney transplant
volume sizes and take this into
consideration in future rulemaking.
After consideration of the public
comments we received, for the reasons
set forth in this rule, we are finalizing
our proposed provisions to assess IOTA
participants in the achievement domain,
efficiency domain and quality domain
and performance scoring approach at
§ 512.422(a), without modification. We
are also codifying the proposed
definition of final performance score at
§ 512.402, without modification. We
direct readers to sections III.C.5.c,
III.C.5.d, and III.C.5.e of this final rule
for further discussion on our proposed
achievement domain, efficiency
domain, and quality domain. We also
direct commenters to section III.C.6.c of
this final rule for further discussion on
our proposed performance-based
payment methodology.
c. Achievement Domain
In section III.C.5.b of the proposed
rule, we proposed measuring IOTA
participant performance across three
domains, one of which is the
achievement domain. We proposed to
define ‘‘achievement domain’’ as the
performance assessment category in
which CMS assesses the IOTA
participant’s performance based on the
number of transplants performed on
patients 18 years of age or older, relative
to a target, subject to a health equity
performance adjustment, as described in
section III.C.5.c.(3) of this final rule,
during a PY. We proposed to use OPTN
data, regardless of payer, and Medicare
claims data to calculate the number of
kidney transplants performed during a
PY by an IOTA participant on patients
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18 years of age or older at the time of
transplant, as described in section
III.C.5.c(2) of this final rule.
In section III.C.5.c of the proposed
rule, we proposed to set the participantspecific target for the achievement
domain based on each IOTA
participant’s historic number of
transplants. A central goal of the
proposed IOTA Model test is to increase
the number of kidney transplants
furnished by IOTA participants, which
we believed would be possible via care
delivery transformation and
improvement activities, including donor
acceptance process improvements to
reduce underutilization and discards of
donor kidneys. We believed IOTA
participants may also increase the
number of kidney transplants furnished
to patients by improving or
implementing greater education and
support for living donors.
As discussed in section III.C.5.c of the
proposed rule, we considered
constructing and using a transplant
waitlisting rate measure or using SRTR’s
transplant rate 194 rather than measuring
number of transplants performed
relative to a participant-specific target
for the achievement domain. Research
has suggested that including such a
metric could demonstrate the need for
both living and deceased donor organs
for a particular transplant hospital and
be less reliant on organ availability for
a particular geographical area.195
Research also suggested that the
inclusion of a pretransplant measure,
such as waitlisting rate, may allow for
a more complete assessment of
transplant hospital performance and
provide essential information for patient
decision-making.196 However, for the
IOTA Model, we proposed to test the
effectiveness of the model’s incentives
to change outcomes, rather than on
processes. The relevant outcome for
purposes of the IOTA Model is the
receipt of a kidney transplant, not
194 For additional information on SRTR’s
transplant rate measure, please see https://
www.srtr.org/about-the-data/technical-methods-forthe-program-specific-reports#figurea2.
195 Paul, S., Melanson, T., Mohan, S., RossDriscoll, K., McPherson, L., Lynch, R., Lo, D.,
Pastan, S.O., & Patzer, R.E. (2021). Kidney
transplant program waitlisting rate as a metric to
assess transplant access. American Journal of
Transplantation: Official Journal of the American
Society of Transplantation and the American
Society of Transplant Surgeons, 21(1), 314–321.
https://doi.org/10.1111/ajt.16277.
196 Paul, S., Melanson, T., Mohan, S., RossDriscoll, K., McPherson, L., Lynch, R., Lo, D.,
Pastan, S.O., & Patzer, R.E. (2021). Kidney
transplant program waitlisting rate as a metric to
assess transplant access. American Journal of
Transplantation: Official Journal of the American
Society of Transplantation and the American
Society of Transplant Surgeons, 21(1), 314–321.
https://doi.org/10.1111/ajt.16277.
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getting on and remaining on the kidney
transplant waitlist. Additionally, the
SRTR transplant rate measure calculates
the number of those transplanted as a
share of the kidney transplant hospital’s
waitlist, which we believed does not
reflect the variety of ways that kidney
transplant hospitals construct their
waitlist practices. For example, for some
kidney transplant hospitals, the number
of kidneys transplanted as a share of
their ‘‘active’’ waitlist transplant
candidates may be a more accurate
representation of their waitlist practices.
Thus, we did not believe this was
appropriate to propose for the IOTA
Model.
We sought comment on our proposed
achievement domain performance
metric and alternative methodologies
considered for assessing transplant
rates.
The following is a summary of the
comments received on our proposed
achievement domain performance
metric and alternative methodologies
considered for assessing transplant rates
and our responses:
Comment: A couple of commenters
supported the achievement domain
performance metric. A commenter
specifically agreed with not including a
waitlisting measure.
Response: We thank the commenters
for their support of the achievement
domain.
Comment: Several commenters stated
their concern that there is a heavy
weight placed on the volume of
transplants and that this may
incentivize participants to use ‘‘subpar’’ organs and increase disparities.
Response: We agree that there is a
heavy focus on increasing volume of
transplants as this is one of the primary
goals of the IOTA Model. There are a
variety of methods that IOTA
participants may choose to increase
kidney transplant volume including, but
not limited to, expanding a living donor
program, increasing volume of patients
active on the kidney transplant list,
utilizing filters to ensure appropriate
offers for risk thresholds, or using
kidney transplants from underutilized
categories. While some kidney
transplant hospitals may prioritize
increasing kidney transplants from
underutilized categories such those
kidneys with a KDPI greater than 85 or
DCD kidneys, that decision may hinge
on resources, and is not a requirement.
We are unsure if the commenters are
defining ‘‘sub-par’’ organs as organs that
should not be offered to any candidates
or as organs that are only acceptable in
specific scenarios. We believe it will be
important for IOTA participants to
further consider what is a ‘‘sub-par’’
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kidney. While certain kidneys may not
be ideal for some waitlist candidates,
they may be a potential opportunity in
another scenario.
Comment: Commenters voiced
concerns about how the achievement
domain would impact high performing
IOTA participants. Some commenters
worried the proposed scoring system
would penalize IOTA participants who
have historically been top performers.
Another commenter suggested CMS
credit the top 20 percent of IOTA
participants to maintain their kidney
transplant volume, while using different
incentives for lower-performing IOTA
participants.
Additionally, a commenter expressed
concern that increasing kidney
transplant volume often involves
transplanting more high-risk organs.
While SRTR accounts for how this
impacts outcomes, the commenter
argued that it does not consider the
added strain on resources at high
performing kidney transplant hospitals.
Lastly, another commenter worried the
achievement domain would penalize
IOTA participants who are already
operating at full kidney transplant
capacity, unless they made substantial
new investments.
Response: We appreciate the concerns
that the commenters have submitted,
and we acknowledge the efforts exerted
by transplant hospitals to reach their
status. We believe that IOTA
participants can potentially become
‘‘high performing’’ through a variety of
practices such as utilizing kidneys of all
KDPI scores when appropriate,
adjusting filters, or expanding their
living donor program. We believe that
with the number of ways that an IOTA
participant can become more efficient
and have higher kidney transplant
volumes, that they have additional
opportunities to improve their
performance and to continue increasing
kidney transplants. We believe that the
updated methodology for setting
transplant targets, as described and
finalized in section III.C.5.c(1) of this
final rule, and the updated scoring
methodology in the achievement
domain, as described and finalized in
section III.C.5.c(2) of this final rule, will
make it more achievable for IOTA
participants of all sizes to achieve
maximum points in the achievement
domain. We direct readers to section
III.C.5.c(1) and III.C.5.c(2) of this final
rule for a full discussing on the updated
methodology for calculating the
transplant target and the updated
scoring methodology in the achievement
domain. We also note that, as described
and finalized in section III.C.6.c(2) of
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this final rule, there is no downside risk
payment in PY 1 of the IOTA Model.
Comment: A couple of commenters
stated that CMS should act to eliminate
constraints on transplant availability
due to both kidney transplant hospital
and hospital capacity and organ
availability before implementing
transplant targets in the achievement
domain.
Response: We appreciate the
commenters’ feedback; however, we do
not have control over the capacity of
kidney transplant hospitals and
hospitals or organ availability. We
encourage kidney transplant hospitals to
work with their leadership if they have
concerns about capacity limitations.
Organ availability is impacted by a
variety of factors, including, but not
limited to identification of organ
donors, allocation practices, location of
kidney transplant hospitals and donors
and utilized organs. Improving kidney
transplant volumes will require multipronged efforts. We believe the IOTA
Model will help increase the number of
kidney transplants performed.
Comment: A few commenters
suggested that CMS should engage with
stakeholders to refine goals and focus
more narrowly on certain aspects of
increasing transplant volume in the
achievement domain, especially
increasing living donation and utilizing
high-risk kidneys. Similarly, a
commenter suggested that CMS should
focus its efforts on increasing kidney
volume in categories where there is
opportunity for growth such as high
KDPI kidneys, donor kidneys with acute
kidney injury (AKI) and DCD kidneys.
Response: We thank the commenters
for their suggestions and believe that
there are a variety of practices that IOTA
participants can choose to utilize when
increasing their kidney transplant
volume. Because kidney transplant
hospitals vary significantly, we disagree
with the commenters, and do not
believe it would be appropriate to be
prescriptive about how an IOTA
participant decides to increase their
kidney transplant volume. While living
donation, for example, has had
relatively unchanged transplant rates
over the last few years, indicating
opportunity for improvement, we
acknowledge that not every kidney
transplant hospital has the same
resources or characteristics.197
Furthermore, we believe the IOTA
Model design provides flexibility that
enables IOTA participants to increase
197 United States Renal Data System. 2022.
USRDS Annual Data Report. Volume 2. End-stage
Renal Disease (ESRD) in the United States, Chapter
7: Transplantation. Figure 7.10b.
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their kidney transplant volume in a way
that best suits their transplant program
and community.
Comment: A couple of commenters
voiced concern that success in the
achievement domain is contingent upon
a multitude of uncontrollable factors,
such as limited organs and matching
challenges. Additionally, a few
commenters mentioned concern that
increasing kidney transplant volume
requires expansion of many other
resources for successful post-transplant
care, which are not reimbursed through
the Medicare cost report.
Response: We appreciate the
commenters’ feedback about their
concern about limitations of resources.
We acknowledge the multitude of
factors that can impact kidney
transplant hospital volume—from a
community level to a nationwide level,
and also acknowledge that kidney
transplant volume expansion may
require increased resources, particularly
staffing. There are intrinsic components
of the IOTA Model intended to offset
challenges of the achievement domain,
such as two other performance domains
(efficiency and quality). Additionally,
the achievement domain calculates
transplant targets for IOTA participants
based on an IOTA participant’s own
prior kidney transplant volume in their
baseline years and based on a national
growth rate that accounts for changes
year to year (as described in section
III.C.5.c.2 of this final rule). We also
note that there are not prescriptive
specifications in the achievement
domain requiring IOTA participants to
meet transplant volumes in one specific
way. This flexibility allows IOTA
participants to identify what method is
best to optimize their kidney transplant
volume. Notably, as described and
finalized in section III.C.6.c(1) of this
final rule, PY 1 does not include any
downside risk payments regardless of an
IOTA participant’s final performance
score. Furthermore, we believe the
neutral zone has a reasonable final
performance score range for PYs 2–6, as
described and finalized in section
III.C.6.c(1) of this final rule. As such, we
believe the absence of downside risk
payment in of PY 1 creates a buffer for
IOTA participants to anticipate
resources needed to succeed in PY 2.
The achievement domain scoring
methodology accounts for Medicare and
non-Medicare patients who receive a
kidney transplant. We anticipate that
since IOTA participants will aim to
increase kidney transplants for all
kidney transplant waitlist patients, this
will create opportunities to accumulate
payment through the IOTA Model
incentives and through payment by both
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Medicare and private payers for kidney
transplant related services. We believe
these payments should assist in costs
that IOTA participants may encounter
while participating in the IOTA Model.
Comment: A commenter conveyed
concern that the achievement domain,
which focuses on increasing kidney
transplant volume, is contradictory
since the Innovation Center’s goals have
traditionally been to improve value
versus volume. A few commenters were
concerned that volume does not equate
with better outcomes and even with
counterbalances in the model, will
pressure IOTA participants to complete
riskier transplants, which may have
worse outcomes.
Response: We disagree and believe
that the achievement domain
simultaneously supports increasing
kidney transplant volume and value.
There are almost 5,000 patients who die
annually while being on the kidney
transplant waitlist.198 It is well known
the life span of and lifestyle of those
patients on dialysis is drastically
different from those patients who
receive kidney transplants. Not only
does the model aim to improve access,
kidney transplant volumes and quality
of life, but also reduce spending. The
cost of yearly dialysis far exceeds the
average cost of immunosuppression and
post-transplant care. The IOTA Model is
not encouraging IOTA participants to
transplant non-viable organs or organs
where risks outweigh the benefits. The
IOTA Model design, does however,
challenge kidney transplant hospitals to
optimize all components of care from
waitlisting to transplant to posttransplant. Growth in living donor
programs is a prime example of how
increasing volume should not
compromise outcomes and should
improve overall outcomes. As for
increases in DDKT volume, we plan to
carefully monitor volume, organ offer
acceptance ratios and composite graft
survival independently and collectively
to monitor for unintended consequences
and will consider this for future
rulemaking for PY 2. We encourage
commenters to provide feedback in the
future about (1) what they define as
‘‘riskier’’ transplants from the
perspective of the donor and recipient
(2) whether this is specific to KDPI
values or qualities of the donor kidney
198 Penn Medicine News. (2020, December 16).
Too Many Donor Kidneys Are Discarded in U.S.
Before Transplantation—Penn Medicine.
www.pennmedicine.org. https://
www.pennmedicine.org/news/news-releases/2020/
december/too-many-donor-kidneys-are-discardedin-us-before-transplantation.
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and (3) if this exceeds the risk of being
on dialysis.
Comment: A few commenters believe
that the achievement domain
disadvantages smaller transplant
programs due to their lack of COE
designation and overlooks challenges to
gain this designation. Another
commenter was concerned that small
transplant programs will have to accept
higher risk kidneys. A commenter
suggested that smaller programs should
have separate performance metrics.
Response: We thank the commenters
for their feedback and acknowledge that
kidney transplant hospitals of different
sizes, will have different challenges in
increasing kidney transplant volume.
Kidney transplant hospitals that fall
below the low volume threshold would
be excluded from the IOTA Model, as
described and finalized in section
III.C.3(c) of this final rule. Based on the
updated scoring methodology in the
achievement domain, as described and
finalized in section III.C.5.c(2) of this
final rule, an IOTA participant with 20
kidney transplants during the baseline
years (with an example growth rate of 8
percent) would need a total of 27 kidney
transplants to earn maximum
achievement points (60), or
approximately 23 kidney transplants to
earn 40 points in the achievement
domain. We believe that offering wide
neutral margins for final performance
scores and offering a variety of
opportunities to gain points in the
achievement domain, efficiency domain
and quality domain creates balances for
a different size kidney transplant
hospital. We believe that increasing
kidney transplant volume will create
opportunities for smaller kidney
transplant hospitals to qualify for COE
designation in the future.
Comment: A couple of commenters
raised concerns that the achievement
domain disproportionately impacts
large transplant programs due to the
demand on resources it would require
and the general volume requirements.
Response: We thank the commenters
for submitting their concerns. As stated
in response to small kidney transplant
hospital concerns, offering wide neutral
margins for final performance scores
and offering a variety of opportunities to
gain points in the achievement,
efficiency and quality domains creates
balances for IOTA participants. Many
large kidney transplant hospitals have
significant resources, COE designation,
and paired donation opportunities that
may not be available to smaller kidney
transplant hospitals. We believe that
while volume goals may be higher, they
are proportionately similar for kidney
transplant hospitals of different sizes.
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Comment: A commenter suggested
that IOTA participant specific volume
targets should match local population
needs along economic lines, racial lines
and payer sources to increase equitable
access to underserved groups.
Response: We thank the commenter
for their feedback. While this is not a
specific requirement that we originally
proposed, we are interested to receive
more information about this suggestion,
as we consider future rulemaking. First,
we would want to consider how to do
this equitably and how kidney
transplant hospitals would identify their
local population needs.
Comment: A commenter suggested
that CMS track achievement domain
volume scores to ensure IOTA
participants do not utilize the scoring
system at the expense of patient risk.
Response: We appreciate the
commenter’s response. The IOTA Model
has thoughtfully been designed to create
counterbalances between measures. For
example, although the achievement
score is based on kidney transplant
volume, the efficiency score is based on
offer to acceptance ratios and the quality
domain includes a composite graft
calculation for a 6-year period posttransplant. While innovation models are
not perfect, and are corrected for
optimization over time, we believe that
IOTA participants that have combined
accountability for IOTA Model
requirements, OPTN metrics and
regulatory and ethical requirements,
will be mindful of avoiding
inappropriate patient risk.
Comment: A commenter
recommended that CMS differentiate
between more established kidney
transplant hospitals and newer kidney
transplant hospitals with shorter track
records and transplant volume.
Established kidney transplant hospitals
often have decades-long waitlists,
referral networks, and stable staffing of
transplant nephrologists. In contrast,
newer, smaller kidney transplant
hospitals can experience large swings in
transplant volume due to growing pains.
Furthermore, the commenter argued, the
loss of a single transplant nephrologist
can halt kidney transplants at these
newer kidney transplant hospitals while
they recruit replacements, leading to a
penalty at a time when the kidney
transplant hospital can least afford it.
Response: We thank the commenter
for their feedback. We acknowledge that
there are differences between wellestablished and, newer, smaller kidney
transplant hospitals with smaller
transplant volume. As described in
section III.C.5.c(1) of this final rule, the
updated methodology for measuring
performance in the achievement domain
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will be based on the average number of
kidney transplants performed in the
baseline years trended forward by the
national growth rate. Therefore, we
disagree with the commenter and
believe all IOTA participants can
improve their kidney transplant rates,
regardless of size. We recognize that
some IOTA participants may have to
make upfront investments, but the low
volume threshold of 11 adult kidney
transplants for each kidney transplant
hospital in every baseline year, as
described and finalized in section
III.C.3.c of this final rule, will
substantially mitigate the demands
placed on, newer, smaller kidney
transplant hospitals.
After consideration of the public
comments, for the reasons set forth in
this rule, we are finalizing, as proposed,
our provisions for setting an IOTA
participant’s transplant target based on
each IOTA participant’s historic number
of transplants at § 512.424(b)(1), as
described and finalized in section
III.C.5.c(1) of this final rule. We direct
readers to section III.C.5.c(1) of this final
rule for further discussion on the
transplant target methodology. As
described and finalized in section
III.C.5.c(2) of this final rule, we are also
finalizing our proposed provision for
identifying kidney transplants
performed by an IOTA participant using
OPTN data, regardless of payer, and
Medicare claims data at § 512.424(d),
without modification.
Furthermore, after consideration of
the public comments we received, we
will not be finalizing a health equity
performance adjustment provision, as
described in section III.C.5.c(3) of this
final rule. Therefore, we are modifying
regulatory text for the achievement
domain definition at § 512.402, to
remove references to a health equity
performance adjustment and make
minor technical corrections in
punctuation. We direct readers to
section III.C.5.c(3) of this final rule for
further discussion on our proposed
health equity performance adjustment.
While we are finalizing our provision
for setting IOTA participants’ transplant
target based on each IOTA participant’s
historic number of transplants as
mentioned in section III.C.5.c, we note
that the methodology for utilizing an
IOTA participant’s historic number of
transplants for calculating transplant
targets has changed in section
§ 512.424(b)(1) and is described in detail
and finalized in section III.C.5.c(1) of
this final rule. We direct readers to
section III.C.5.c(1) of this final rule for
further discussion on the transplant
target methodology. In addition, as
described and finalized in section
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III.C.5.c(2) of this final rule, we are
finalizing our proposed provision for
identifying kidney transplants
performed by the IOTA participant
using OPTN data, regardless of payer,
and Medicare claims data at
§ 512.424(d), without modification.
(1) Calculation of Transplant Target
In the proposed rule, we proposed
that for each model PY, CMS would
calculate a ‘‘transplant target’’ for each
IOTA participant, which would
determine performance in the
achievement domain. For the purposes
of the model, we proposed to define
‘‘transplant target’’ as the target number
of transplants set for each IOTA
participant to measure performance in
the achievement domain as described in
the proposed rule and section III.C.5.c of
this final rule. We proposed that CMS
would notify each IOTA participant of
their transplant target by the first day of
each PY, in a form and manner
determined by CMS.
For each PY, we proposed in section
III.C.5.c(1) of the proposed rule, that
CMS would calculate the transplant
target for the achievement domain by
first determining the highest number of
deceased donor kidney transplants and
living donor kidney transplants
furnished to patients 18 years of age or
older in a single year during the
baseline years, as defined and finalized
in section III.C.3.c. of this final rule.
CMS would then sum the highest
number of deceased donor kidney
transplants and living donor kidney
transplants furnished in a single year
during the baseline years calculate the
transplant target for an IOTA
participant, even if those transplant
numbers were achieved during different
baseline years. We believed that
choosing the highest transplant numbers
during the baseline years would
illustrate the capabilities and capacities
of the IOTA participant, and, when
combined, would be an appropriate
target for number of transplants
performed during the PY. We also
understood that living donation and
deceased donor donation involve
different processes by the IOTA
participant, so we chose each of those
numbers separately to recognize the
potential capacity for each IOTA
participant for both living and deceased
donor transplantation.
In section III.C.5.c(1) of the proposed
rule, we proposed that the sum of the
highest number of deceased donor and
living donor transplants across the
baseline years of the IOTA participant
would then be projected forward by the
national growth rate, as described in
section III.C.5.c(1) of this final rule, or
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zero should the national growth rate be
negative, resulting in the transplant
target for a given PY. We proposed to
define ‘‘national growth rate’’ as the
percentage increase or decrease in the
number of kidney transplants performed
over a twelve-month period by all
kidney transplant hospitals except for
pediatric kidney transplant hospitals
and kidney transplant hospitals that fall
below the low volume threshold
described and finalized in section
III.C.3. of this final rule. We proposed to
define ‘‘pediatric kidney transplant
hospitals’’ as a kidney transplant
hospital that performs 50 percent or
more of its transplants in a 12-month
period on patients under the age of 18.
We also proposed that the low volume
threshold to be 11 kidney transplants
performed for the purposes of
calculating the national growth rate. We
also proposed this approach for
calculating the national growth rate to
account for and reflect the growth in
organ procurement by OPOs that has
occurred, indicating potential growth in
the number of available organs.
In section III.C.5.c(1) of the proposed
rule, we proposed that CMS would
calculate the national growth rate by
determining the percent increase or
decrease of all kidney transplants
furnished to patients 18 years of age or
older from two years prior to the PY to
one year prior to the PY. Because the
proposed national growth rate includes
IOTA participants and non-IOTA
participant kidney transplant hospitals,
we acknowledge that it could make
achieving the transplant target number
harder. This is why, if the national
growth rate becomes negative for a PY,
we proposed treating it as zero and CMS
would not apply the national growth
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rate to project forward the sum of the
highest number of deceased and living
donor kidney transplants furnished in a
single year during the baseline years. In
other words, an IOTA participant’s
transplant target would equal the sum of
its own highest deceased and living
donor transplants furnished across the
baseline years if the national growth rate
were to be negative for a PY. We also
want to be able to share model
performance targets with IOTA
participants before the start of each PY
and are prioritizing ensuring
prospectivity over ensuring the most upto-date trend figures. We also proposed
that if the model begins on an any date
after January 1, 2025, the trend would
also be adjusted.
For example, as described in section
III.C.5.c(1) of the proposed rule, to
calculate the national growth rate for PY
1 using the proposed model start date of
January 1, 2025, CMS would first
subtract the total number of kidney
transplants furnished to patients 18
years of age or older in 2022 from the
total number of kidney transplants
furnished to patients 18 years of age or
older in 2023. Next, CMS would then
divide that number by the total number
of kidney transplants furnished to
patients 18 years of age or older in 2022
to determine national growth rate. To
create the transplant target for each
IOTA participant for PY 1 CMS would
do the following:
• If the national growth rate is
positive, CMS would trend the national
growth rate forward for an IOTA
participant by multiplying the national
growth rate by the sum of the highest
number of deceased donor and living
donor transplants furnished to patients
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Fmt 4701
Sfmt 4700
18 years of age or older across the
baseline years for the IOTA participant.
• CMS would take the product of step
1 and add it to the sum of the highest
living donor and deceased donor kidney
transplants furnished to patients 18
years of age or old across the baseline
years for an IOTA participant.
• The sum of step 2 would be the
transplant target for an IOTA
participant. However, if the national
growth rate were negative, CMS would
not trend the growth rate forward for PY
1 and the transplant target would be the
sum of the highest living donor and
deceased donor kidney transplants
across the baseline years.
In section III.C.5.c(1) of the proposed
rule, we proposed that when calculating
the national growth rate for each PY,
CMS would look to the relevant baseline
years for that PY, as depicted in Table
1. This approach would mitigate our
concern that a static baseline may
reward a one-time investment, rather
than continuous improvement. The
model PYs, as proposed in the proposed
rule, would not factor into an IOTA
participant’s transplant target
calculation until PY 3 of the model
(January 1, 2027, to December 31, 2027)
and the baseline years would not be
based exclusively on PYs until PY 5 of
the model (January 1, 2029, to December
31, 2029), which may represent an
effective phase-in approach to drive
improved performance and savings for
the Medicare trust fund. We believe that
using baseline years to calculate the
transplant targets would also account
for kidney transplant hospitals that
experience changes in strategy or
staffing that may affect their capacity to
perform transplants at the level that
they did in previous years.
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TABLE 1: EXAMPLE - PROPOSED BASELINE YEARS FOR CALCULATION OF
TRANSPLANT TARGET (FOR PROPOSED MODEL START DATE)
Jan 1, 2025December 31, 2025
2
Jan 1, 2026December 31, 2026
3
Jan 1, 2027December 31, 2027
4
Jan 1, 2028December 31, 2028
5
Jan 1, 2029December 31, 2029
6
Jan 1, 2030December 31, 2030
CY 2021:
CY 2022:
CY 2023:
CY 2022:
CY 2023:
CY 2024:
CY 2023:
CY 2024:
CY 2025:
CY 2024:
CY 2025:
CY 2026:
CY 2025:
CY 2026:
CY 2027:
CY 2026:
CY 2027:
CY 2028:
Should we finalize a model start date
other than January 1, 2025, we proposed
in section III.C.5.c(1) of the proposed
January
January
Janu
January
January
Janu
January
January
Janu
January
January
Janu
January
January
Janu
January
January
Janua
1,
1,
1,
1,
1,
2021-December 31, 2021
2022 - December 31, 2022
2023 - December 31, 2023
2022 - December 31, 2022
2023 -December 31, 2023
1, 2024 - December 31, 2024
1, 2023 -December 31, 2023
1, 2024 - December 31, 2024
1, 2025 - December 31, 2025
1, 2024 - December 31, 2024
1, 2025 - December 31, 2025
1, 2026 - December 31, 2026
1, 2025 -December 31, 2025
1, 2026 - December 31, 2026
1, 2027-December 31, 2027
1, 2026 - Decem her 31, 2026
1, 2027-December 31, 2027
1, 2028 - December 31, 2028
rule that the baseline years, as defined
and finalized in section III.B.2.c of this
CY 2023/CY 2022
CY 2024/CY 2023
CY 2025/ CY 2024
CY 2026/ CY 2025
CY 2027/ CY 2026
CY 2028/ CY 2027
final rule, would shift accordingly, as
illustrated in Table 2.
TABLE 2: EXAMPLE - PROPOSED BASELINE YEARS FOR CALCULATION OF
TRANSPLANT TARGET, FOR POTENTIAL ALTERNATIVE MODEL START DATE
July I, 2026 June 30, 2027
July I, 2027 June 30, 2028
July 1, 2028June 30, 2029
July I, 2029 June 30, 2030
July 1, 2030-
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We stated in section III.C.5.c(1) of the
proposed rule that we believe that IOTA
participants could improve on this
metric in several ways. For example,
IOTA participants could increase the
number of kidney organ offers they
accept, which would also potentially
lead to greater efficiency domain scores.
IOTA participants could also invest in
a living donation program or modify
their OR schedules to facilitate fewer
discards due to physician scheduling.
We considered basing the transplant
target on the total number of all organ
transplants performed by the IOTA
participant over the baseline years (89
FR 43518). However, we did not believe
this was appropriate because the total
would not reflect the specific
capabilities of the IOTA participant’s
kidney transplant program. We also
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I, 2021-June
I, 2022- June
1, 2023-June
I, 2022- June
I, 2023 - June
I. 2024 - June
I, 2023 - June
1, 2024- June
L 2025 - June
1, 2024 - June
1, 2025 - June
1, 2026-June
1, 2025 - June
1, 2026- June
1, 2027 - June
1, 2026- June
1, 2027 - June
1, 2028-June
30, 2022
30, 2023
30 2024
30, 2023
30, 2024
30 2025
30, 2024
30, 2025
30, 2026
30, 2025
30, 2026
30, 2027
30, 2026
30, 2027
30, 2028
30, 2027
30, 2028
30 2029
July 1, 2023 -June 30, 2024 / July I,
2022 - June 30, 2023
July I, 2024 -June 30, 2025 / July 1,
2023 - June 30, 2024
July I, 2025 -June 30, 2026 / July 1,
2024 - June 30, 2025
July 1, 2026-June 30, 2027 /July 1,
2025 - June 30, 2026
July 1, 2027 -June 30, 2028 / July I,
2026 - June 30, 2027
July 1, 2028-June30, 2029/ July 1,
2027 - June 30, 2028
considered adjusting the transplant
target by IOTA participant revenue from
hospital cost reports. In this scenario,
our consideration was to look at
historical kidney transplant data as the
best predictor, since this reveals the
demonstrated capacity for each IOTA
participant to complete kidney
transplants.
We also considered setting each IOTA
participant’s transplant target by
determining the IOTA participant’s
average total kidney transplant volume
from the three previous years instead of
using the sum of the highest living and
deceased donor kidney transplant
volumes during the baseline years (89
FR 43518). We believe that this
methodology would be simpler and
result in a transplant target that is
potentially more attainable for IOTA
PO 00000
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participants, assuming that the average
kidney transplant volume is lower than
the sum of the highest volumes of
deceased and living donor kidney
transplants. However, we do not believe
that this would reflect the potential
highest capacity for transplant that we
would otherwise like the target to
reflect.
We alternatively considered a static or
fixed baseline approach for purposes of
determining the transplant target for
each IOTA participant, as it would
minimize operational burden for CMS
due to less frequent updates to the
transplant target and ensure that the
model does not set a moving target yearover-year (89 FR 43518). However, we
believe that a fixed baseline may reward
a one-time investment, rather than
continuous improvement, and may not
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Jul
July
July
Jul
July
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Jul
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July
Jul
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account for kidney transplant hospitals
that experience changes in strategy or
staffing that may affect their capacity to
perform transplants at the level that
they did in historical years. The rolling
baseline approach we proposed uses
historical kidney transplant volumes
pre-dating the model start date through
the first two model PYs, ensuring a
phased-in approach before any
improvements made during the model
performance period are accounted for in
the baseline.
We also considered setting the
transplant target for IOTA participants
based on two baseline years, rather than
the proposed methodology of three (89
FR 43518). For the proposed model start
date of January 1, 2025, this approach
would look at the highest living and
deceased volumes from 2022 and 2023,
trended by the national growth rate from
2024, to set the transplant target for PY
1. We believe this methodology would
be more reflective of recent
transplantation volume and account for
the changes to the kidney allocation
system that were implemented in 2021.
However, we believe that using two
baseline years to set a transplant target
would be more susceptible to temporary
market disruptions or fluctuations that
may impact IOTA participants
capability or capacity to furnish kidney
transplants, such as: if the transplant
hospital experiences a shortage in
transplant surgeons or other critical
staff; if the transplant hospital is
acquired; or, the occurrence of a natural
disaster, pandemic, or other public
health emergency or other extreme and
uncontrollable circumstance that would
require the transplant hospital to
temporarily suspend operations. Any of
these disruptions or fluctuations could
result in an inaccurate transplant target
that would not accurately reflect an
IOTA participant’s volume capability.
We considered determining the
national growth rate by calculating
separately; (1) the growth rate of the
deceased donor target number by the
growth in organs procured, and (2) the
living donor target number by the
national growth rate in living donor
transplants (89 FR 43518). However,
procurement rates vary nationally
depending on variables unique to each
geography and local OPO policies.199
Because we want the model to inspire
kidney transplant hospitals to expand
199 Potluri, V.S., & Bloom, R.D. (2021). Effect of
Policy on Geographic Inequities in Kidney
Transplantation. https://doi.org/10.1053/
j.ajkd.2021.11.005; Hanaway, M.J., MacLennan,
P.A., & Locke, J.E. (2020). Exacerbating Racial
Disparities in Kidney Transplant. JAMA Surgery,
155(8), 679. https://doi.org/10.1001/
jamasurg.2020.1455.
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living donor programs, not just match
national growth rates, we did not
believe this alternative methodology
was appropriate to propose.
We also considered determining the
national growth rate using the following
information: (1) the total growth rate in
kidney transplants; (2) the change in
rate of organs procured by OPOs; (3) the
growth rate in kidney transplants in the
non-selected portions of the country;
and (4) calculating the average growth
rate across multiple baseline years (89
FR 43518). However, we believe that the
national growth rate in kidney
transplants makes the most sense to use
as the basis for the model’s growth
factor because it best reflects volume
trends in the kidney transplant
ecosystem overall, as it considers all
kidney transplant hospitals, not just
IOTA participants.
Finally, we also considered a
performance assessment methodology
for IOTA participants already achieving
higher rates of kidney transplantation by
assessing each such IOTA participant’s
total transplant volume as compared to
all IOTA participants, rather than on an
IOTA participant specific transplant
target (89 FR 43518). We believe this
methodology is both easy to understand
and simple to administer because it
rewards IOTA participants for the total
number of transplants performed.
However, we thought that this
methodology would not be fair to IOTA
participants that are smaller in size or
achieving lower rates of kidney
transplantation.
We solicited comment on our
proposal to set unique transplant targets
for each IOTA participant, the
methodology for setting transplant
targets, and any alternatives considered.
The following is a summary of the
comments received on our proposal to
set unique transplant targets for each
IOTA participant, the methodology for
setting transplant targets, any
alternatives considered and our
responses:
Comment: Commenters expressed
concern over the proposed methodology
for calculating unique transplant targets
each PY for each IOTA. Many
commenters expressed concern that the
proposed methodology is impractical as
it overestimates a transplant programs
capability to increase transplantation
throughput unilaterally, such as without
significant improvements in organ
procurement and distribution by the
OPTN and OPOs, factors beyond
hospitals’ control, does not take into
consideration year over year variability
in overall donor volume, and could not
be achieved without potentially
compromising the quality of care and
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patient safety. Many commenters stated
that the proposed transplant target
methodology was unsustainable
throughout the model, as increasing
kidney transplant volume would make
it increasingly difficult for IOTA
participants to meet ever-higher targets
in subsequent PYs, potentially leading
to penalties.
Many commenters believed that the
proposed methodology for calculating
the transplant target for each IOTA
participant would be unattainable for
high performing transplant hospitals.
For example, while a commenter
supported comparing a kidney
transplant hospital’s transplant rates to
the national average, they believed that
they would be held to an impractically
high expectation for growth. The
commenter also argued that kidney
transplant hospitals already performing
in the top 20 percent should not be
penalized for failing to reach an
unrealistically high transplant rate.
Another commenter suggested that they
would need to increase their annual
adult transplant numbers by 75 to 150
each year. They felt that the ability to
achieve this increase would rely on the
availability of a sufficient number of
viable organs and a significantly
increased waitlist. Consequently, they
believed that their kidney transplant
hospital could potentially achieve that
goal and clear their waiting list in the
first year; however, this assumption
relied on the premise that every patient
could be successfully transplanted with
an appropriate donor match, which they
considered highly unlikely. A
commenter believed that the proposed
methodology advantages smaller kidney
transplant hospitals disproportionately.
The commenter argued that it was
impractical to require a larger kidney
transplant hospital, already performing
over 400 transplants annually, to do an
additional 200 or more transplants to
earn full points and could not be done
without compromising quality of care
and patient safety. The same commenter
also noted that acquiring the necessary
staff, space, and resources to
accommodate such a rapid and
significant increase would pose a
substantial obstacle.
Commenters also raised specific
concerns over the proposal to trend the
transplant target forward by the national
growth rate, as described in section
III.C.5.c(1) of this final rule. Many
commenters indicated that the more an
IOTA participant increases its
transplant volume, the harder it will be
for them to achieve their transplant
target in the future PY because the
methodology, as proposed, also trends
the baseline transplant volume forward
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each PY. Many commenters suggested
that IOTA participants may be unfairly
penalized for responding to the model’s
goals and incentives. Specifically, that if
IOTA participants meet their transplant
target during a performance year, the
rising national growth rate could make
transplant targets harder to achieve in
future PYs. A couple commenters
suggested that the growth rate should be
regionally indexed or calculated
separately by region because regional
factors affect the potential for increased
transplantation. Lastly, a commenter
recommended that CMS determine the
national growth rate by calculating the
average growth rate across multiple
baseline years instead of the proposed
approach. This commenter believed that
this alternative approach for calculating
the national growth rate would take into
consideration the natural variability in
the annual volume of both living and
deceased donor transplants performed
at kidney transplant hospitals, resulting
in a transplant target that may be more
attainable for IOTA participants.
Response: Given the numerous
concerns from stakeholders regarding
the proposed methodology for
calculating transplant targets, we
recognized an updated methodology
may be necessary to strengthen the
model. As indicated in the proposed
rule (89 FR 43518) and discussed in the
preamble of this final rule, we
considered setting each IOTA
participant’s transplant target by
determining the IOTA participant’s
average total kidney transplant volume
from the three previous years instead of
using the sum of the highest living and
deceased donor kidney transplant
volumes during the baseline years.
Ultimately, we decided against this
approach, as we did not believe it
would accurately reflect the IOTA
participants’ full transplant capacity.
Instead, we constructed, and proposed,
a methodology to illustrate the
individual capabilities and capacities of
the IOTA participants, which when
combined, would serve as an
appropriate transplant target for the
program year. However, we recognize
that there may be a better balance in
including a simpler methodology and
result in a transplant target that is
potentially more attainable for IOTA
participants, assuming that the average
kidney transplant volume is lower than
the sum of the highest volumes of
deceased and living donor kidney
transplants while still limiting
complexity.
We conducted additional analysis that
examined one of the methodologies that
we considered for calculating the
transplant target as described in section
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III.C.5.c(1) of the proposed rule.
Specifically, based on public comment,
we reexamined setting each IOTA
participant’s transplant target by
determining the IOTA participant’s
average total kidney transplant volume
from the three previous years instead of
using the sum of the highest living and
deceased donor kidney transplant
volumes during the baseline years (89
FR 43518). Using historical transplant
data, we compared this methodology to
what we proposed, as described in this
final rule, to determine whether an
alternative methodology for setting the
transplant target would be potentially
more attainable.
Based on additional analysis and the
commenters concerns about the
proposed transplant target methodology,
we are finalizing an updated
methodology for setting transplant
targets as follows:
For each PY, CMS will calculate the
transplant target for the achievement
domain by first determining the mean of
the total number of deceased donor
kidney transplants and living donor
kidney transplants furnished to patients
18 years of age or older across the
baseline years, as defined and finalized
in § 512.402 of this final rule.
The mean number of deceased donor
and living donor transplants across the
baseline years of the IOTA participant
would then be projected forward by the
national growth rate, as described in
section III.C.5.c(1) of this final rule, or
zero should the national growth rate be
negative, resulting in the transplant
target for a given PY.
For example, to calculate the national
growth rate for PY 1 using the proposed
model start date of January 1, 2025,
CMS would first subtract the total
number of kidney transplants furnished
to patients 18 years of age or older in
2022 from the total number of kidney
transplants furnished to patients 18
years of age or older in 2023. Next, CMS
would then divide that number by the
total number of kidney transplants
furnished to patients 18 years of age or
older in 2022 to determine national
growth rate. To create the transplant
target for each IOTA participant for the
relevant PY CMS would do the
following: 1. If the national growth rate
is positive, CMS would trend the
national growth rate forward for an
IOTA participant by multiplying the
national growth rate by the mean
number of deceased donor and living
donor transplants furnished to patients
18 years of age or older across the
baseline years for the IOTA participant.
2. CMS would take the product of step
1 and add it to the mean number of the
highest living donor and deceased
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96333
donor kidney transplants furnished to
patients 18 years of age or old across the
baseline years for an IOTA participant.
3. The sum of step 2 would be the
transplant target for an IOTA
participant. However, if the national
growth rate were negative, CMS would
not trend the growth rate forward for PY
1 and the transplant target would be the
sum of the mean number of living donor
and deceased donor kidney transplants
across the baseline years. For example,
when determining individual transplant
targets for PY 1 of the model, if an IOTA
participant had a mean of 50 living
donor and deceased donor kidney
transplants furnished to patients 18
years of age or older across the relevant
baseline years, and the national growth
rate was negative, then the transplant
target for that IOTA participant would
be 50.
However, we will monitor IOTA
participant performance throughout the
model performance period and, if
warranted, will propose alternative or
updated policies in future notice and
comment rulemaking.
Comment: Commenters encouraged
CMS to reconsider how the proposed
transplant target is calculated and
suggested a variety of alternative
options. Many commenters urged CMS
to set each IOTA participant’s transplant
target by determining the IOTA
participant’s average total kidney
transplant volume from the three
previous years. Several of these
commenters urged CMS to set each
IOTA participant’s transplant target by
determining the IOTA participant’s
average total kidney transplant volume
from the three previous years across the
relevant baseline years. Specifically, a
commenter believed that using the
average number of transplants across the
relevant baseline years would ensure
that transplant programs are not
penalized for their efforts in increasing
transplant volumes prior to program
initiation. Another commenter
expressed concern that the proposed
approach does not take into account the
natural year-to-year variability in overall
and living donor and deceased donor
volume of transplants performed within
a kidney transplant hospital. Thus, they
recommended that each IOTA
participant’s transplant target be
calculated by determining the IOTA
participant’s average total kidney
transplant volume from the three
previous years. The commenter stated
that the three-year averaging approach is
frequently used by the Innovation
Center in other payment methodologies,
which could help reduce year-to-year
variability and mitigate the impact of
potential outliers for transplants from
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deceased or living donors in a given
year.
A couple commenters suggested CMS
use the average kidney transplant
volume and a fixed baseline.
Specifically, a commenter felt that using
the average kidney transplant volume
would be more reflective of an IOTA
participant’s expected performance. A
commenter also recommended that CMS
take the average of kidney transplant
volumes over a 5-year historical period,
as it would more accurately reflect past
performance. Another commenter
believed that the transplant target
should be calculated based on the
average number of kidney transplants
performed during a fixed historical
period to ensure that IOTA participants
are not penalized for their success in
increasing transplant volumes.
A commenter also suggested that CMS
select the year with the highest total
volume of living and deceased donor
kidney transplants combined in relation
to the three prior years as the historical
benchmark. The commenter felt that
this was especially crucial if the
historical benchmark is then multiplied
by a national growth rate, as proposed,
to ensure IOTA participants have a
realistic chance of meeting the target.
This same commenter also suggested
that CMS could consider identifying in
the relevant baseline years the highest
number of combined deceased donor
and living donor kidney transplants and
then measure and reward subsequent
growth in each transplant type,
deceased donor and living donor.
However, the commenter acknowledged
that this methodology would be more
complex and move away from the
simplicity originally proposed, which is
a strength of the model. Finally, a
commenter recommended that CMS use
a weighted benchmark based on the
actual number of kidney transplants for
three years, with the most recent year
being weighted the most.
Response: We appreciate the
commenters’ suggestions on alternative
methodologies for setting the transplant
target. As mentioned in comment
responses noted previously, we
recognize that there could be a more
favorable balance by adopting a simpler
methodology that could result in a
transplant target that is more feasible for
IOTA participants, assuming that the
average kidney transplant volume is
lower than the total of the highest
volumes from both deceased and living
donor kidney transplants, while still
keeping complexity to a minimum. As
such, we are finalizing an updated
methodology for setting transplant
targets at § 512.424(b). Specifically,
CMS will calculate the transplant target
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for the achievement domain by first
determining the mean of the total
number of deceased donor kidney
transplants and living donor kidney
transplants furnished to patients 18
years of age or older across the baseline
years, as defined and finalized in
section III.C.3.c of the preamble in this
final rule.
Comment: A couple commenters
suggested that CMS create a fixed
baseline year period, rather than
changing the baseline every PY. For
example, one of these commenters
stated that a permanent baseline would
be particularly beneficial for larger
institutions, for which year-over-year
growth is more difficult. Another
commenter felt that CMS should use a
fixed baseline year period of five to ten
years. The commenter noted that a
kidney transplant hospital’s annual
volume is often limited to factors
beyond their control and may vary year
to year. Thus, they believed that an
average of transplant volumes over a
five-to-ten-year period would more
accurately reflect a participant’s past
performance. The same commenter also
acknowledged that the model
performance years would not factor into
an IOTA participant’s transplant target
calculation until the third PY; however;
they argued that transplant target
methodology as proposed penalizes
IOTA participants for their earlier
successes by making it more difficult to
exceed the target in the future.
Therefore, using a fixed baseline would
ensure IOTA participants are able to
realistically meet their transplant targets
and would not be penalized for
variations in transplant volumes.
Response: We thank the commenters
for their feedback. As described at 89 FR
43552 in the proposed rule, we
considered a static or fixed baseline
approach, as it would minimize
operational burden for CMS due to less
frequent updates to the transplant target
and ensure that the model does not set
a moving target year-over-year.
However, for the reasons described in
section III.C.5.c(1) of this final rule, we
disagree with the commenters that the
baseline years should be fixed. We
maintain our belief that the proposed
rolling baseline approach, which uses
historical kidney transplant volumes
pre-dating the model start date through
the first two model PYs, ensures a
phased-in approach before any
improvements made during the model
performance period are accounted for in
the baseline. Thus, we are finalizing our
proposal to calculate the transplant
target using the relevant baseline years,
as defined and finalized in section
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III.C.3.c of the preamble in this final
rule, as proposed.
Comment: Several commenters raised
concerns about using CY 2021 when
calculating the IOTA participant
specific transplant target. Given that
transplant hospitals across the U.S. were
impacted by COVID–19 at different
points throughout the year, a couple
commenters believed that CY 2021 data
may inadvertently skew the baseline
performance, either increasing or
decreasing it, obscuring the true
performance of programs required to
participate in the IOTA Model. Another
commenter conveyed that while they
recognized the importance of analyzing
past performance over multiple years,
they suggested that CMS should
concentrate exclusively on CY’s 2022
and 2023.
A few commenters argued that CY
2021 was an outlier in various aspects
and might not reflect the usual
practices, or the current and anticipated
practices, of numerous transplant
hospitals. These aspects included the
COVID–19 pandemic and the change in
kidney allocation. These commenters
specifically noted that the COVID–19
pandemic had a profound influence on
kidney transplant volumes during 2021.
They suggested that some transplant
hospitals lowered their transplant rates,
whereas others actually ramped up their
operations. They believed that this
situation arose in part because
transplant hospitals that conducted
fewer transplants allowed for a greater
availability of high-quality kidneys for
the transplant hospitals that remained
operational. Additionally, 2021 was the
first year the new KAS250 policy took
effect, and transplant hospitals were
still adjusting to the significant increase
in organ offers.
Response: We thank commenters for
their feedback and for raising some
concerns about the proposed
methodology for setting specific
transplant targets. We acknowledge the
commenters’ concerns regarding the
inclusion of CY 2021 in the baseline
years as it pertains to setting specific
transplant targets. We considered setting
the transplant target for IOTA
participants based on two baseline
years, rather than the proposed
methodology of three, as described at 89
FR 43552 in the proposed rule. In light
of the commenters’ concerns, we
considered the potential impact of
including CY 2021 in the proposed
methodology for setting specific
transplant targets, as described in
section III.C.5.c(1) of the proposed rule.
We still believe that using two baseline
years to set a transplant target would
make the target more susceptible to
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temporary market disruptions or
fluctuations, such as those discussed at
89 FR 43552 in the proposed rule,
which could result in an inaccurate
transplant target that does not
accurately reflect the IOTA participant’s
true volume capabilities. As such, we
disagree with excluding CY 2021 from
the relevant baseline years when setting
specific transplant targets. However, as
mentioned in comment responses noted
previously in this section, we are
finalizing a modified methodology for
setting specific transplant targets.
Specifically, we are finalizing at
§ 512.424(b) that CMS would calculate
the transplant target for the achievement
domain by first determining the mean of
the total number of deceased donor
kidney transplants and living donor
kidney transplants furnished to patients
18 years of age or older across the
baseline years, as defined and finalized
in section III.C.3.c of this final rule. We
will analyze and monitor the
performance of IOTA participants to
ensure they are not unfairly
disadvantaged by the model. If our
analysis indicates the need for a new or
revised policy, we will address it
through future notice and comment
rulemaking.
Comment: A commenter requested
that CMS clarify whether the transplant
number used for the transplant target
calculation would be based on kidney
transplants performed for all payors, or
just Medicare kidney transplants.
Response: As discussed in the
proposed rule at 89 FR 43550, CMS
would calculate the transplant target for
the achievement domain by first
determining the highest number of
deceased donor kidney transplants and
living donor kidney transplants
furnished to patients 18 years of age or
older in a single year during the
baseline years, as defined in section
III.C.3.c. of the proposed rule. We clarify
that the transplant target would be
calculated based on the number of
applicable kidney transplants performed
across all payors. However, as
mentioned in comment responses noted
previously, we are finalizing an updated
methodology for setting transplant
targets. Specifically, we will be
finalizing at § 512.424(b) that CMS
would calculate the transplant target for
the achievement domain by first
determining the mean of the total
number of deceased donor kidney
transplants and living donor kidney
transplants furnished to patients 18
years of age or older across the baseline
years, as defined and finalized in
section III.C.3.c of this final rule. We
note that this would still be inclusive
across all payors and not just Medicare.
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Comment: A commenter suggested
that CMS provide each IOTA participant
with their transplant target three months
or at least one month prior to the start
of a performance year rather than by the
first day of a performance year.
Knowing the transplant target ahead of
time will allow participants to prepare
for the model.
Response: We appreciate the
commenter’s suggestion. We note that it
is our intent to provide each IOTA
participant with their transplant target
prior to the first day of each PY.
However, we acknowledge that
operational delays could occur which is
why we proposed to provide each IOTA
participant with their transplant target
by the first day of each PY. Thus, to
account for potential operational delays,
we are finalizing as proposed.
Comment: A commenter stated that
they did not agree with our proposed
definition of national growth rate.
Specifically, the commenter disagreed
with eliminating low-volume kidney
transplant hospitals when assessing the
national growth rate. Given transplant
programs can close and new transplant
programs can enter the market, the
commenter felt that the national growth
rate should be based on all adult kidney
transplants performed in the country as
this represents a true reflection of
growth in kidney transplants performed.
The commenter went on to express that
they agreed with CMS that the national
growth rate in kidney transplants makes
the most sense to use as the basis for the
model’s growth factor but felt that the
national growth rate should reflect the
total growth rate in kidney transplants
as measured across all adult transplants
performed at adult transplant programs
(with due consideration of the
definition of an IOTA transplant
patient).
Response: We appreciate the
commenter’s suggestion and
acknowledge their concerns for
excluding kidney transplant hospitals
that fall below the low volume
threshold from the proposed national
growth rate, as defined at 89 FR 43617
in the proposed rule. We note that at 89
FR 43550 we proposed that CMS would
calculate the national growth rate by
determining the percent increase or
decrease of all kidney transplants
furnished to patients 18 years of age or
older from two years prior to the PY to
one year prior to the PY. We also stated
at 89 FR 43550 that because the
proposed national growth rate includes
IOTA participants and non-IOTA
participant kidney transplant hospitals,
we acknowledged that it could make
achieving the transplant target number
harder. This is why, if the national
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growth rate becomes negative for a PY,
we proposed treating it as zero and CMS
would not apply the national growth
rate to project forward the sum of the
highest number of deceased and living
donor kidney transplants furnished in a
single year during the baseline years.
However, upon further consideration,
CMS agrees with this commenter’s
suggestion. As such, we will be
finalizing a modified definition of
national growth rate at § 512.402 to
eliminate the exclusion of kidney
transplant hospitals that fall below the
low volume threshold from the national
growth rate calculation.
Comment: A commenter indicated
that CMS proposed to calculate the
national growth rate by determining the
percent increase or decrease of all
kidney transplants furnished to patients
18 years of age or older from two years
prior to the PY to one year prior to the
PY. However, the commenter suggested
that CMS should provide clarification
around whether the national growth rate
would be rounded. Specifically, the
commenter wanted to know if, when,
and how rounding would be applied to
these calculations. Additionally, the
commenter also wanted to know if the
national growth rate would be rounded,
and if so, to what extent. The
commenter believed that this is
important for the calculation of each
IOTA participant’s transplant target.
The commenter also suggested that
providing more clarity here could help
improve understanding as the IOTA
Model is implemented.
Response: We thank the commenter
for highlighting the need for clarity
regarding whether any of the proposed
calculations for setting a transplant
target would be rounded. We clarify that
once all calculations for setting a
transplant target have been made, CMS
would do the following:
• Round the transplant target down
for decimals less than 0.500; and
• Round the transplant target up for
decimals of 0.500 or greater.
For example, if an IOTA participants
transplant target is 57.44, CMS would
round the transplant target down to 57.
Whereas, if an IOTA participants
transplant target was 57.54, CMS would
round the transplant target up to 58.
After consideration of the public
comments we received, for the reasons
set forth in this rule, we are finalizing
our proposed provisions on setting
unique transplant targets for each IOTA
participant and the methodology for
setting transplant targets, with
modification. We are codifying in our
regulation at § 512.424(b) that for each
PY, CMS will determine the transplant
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target for the achievement domain, as
proposed.
We are codifying in our regulation at
§ 512.424(b)(1) that CMS analyzes the
baseline years for the relevant PY,
without modification. In response to
comments received, we are replacing
the methodology for setting unique
transplant targets we had proposed to
use for purposes of determining
performance in the achievement
domain. Specifically, we are codifying
in our regulation in sections
§ 512.424(b)(1)(i) and (ii) that CMS
identifies the mean number of deceased
donor kidney transplants furnished by
the IOTA participant to patients 18
years of age or older across the relevant
baseline years, as defined at § 512.402
and the mean number of living donor
kidney transplants furnished by the
IOTA participant to patients 18 years of
age across the baseline years, as defined
at § 512.402.
We are finalizing our regulation at
§ 512.424(b)(2) that CMS sums the
numbers in sections §§ 512.424(b)(1)(i)
and (ii), without modification. We are
also finalizing as proposed our
provisions for calculating the national
growth rate at § 512.424(b)(3),
calculation of transplant target at
§ 512.582(b)(4), notification of
transplant target at § 512.424(c) and the
definitions of transplant target, and
pediatric kidney transplant hospitals at
§ 512.402. In response to public
comments, we are finalizing our
proposed definition of national growth
rate at § 512.402 with slight
modification to remove the exclusion of
kidney transplant hospitals that fall
below a low-volume threshold of 11.
Specifically, we are codifying at
§ 512.402 that national growth rate
means the percentage increase or
decrease in the number of kidney
transplants performed over a 12-month
period by all kidney transplant hospitals
except for pediatric kidney transplant
hospitals, as defined at § 512.402. We
note that we will analyze and monitor
IOTA participant performance
throughout the model performance
period to ensure we do not unduly
disadvantage IOTA participants. If
analysis results warrant a new or
updated policy, we will address it
pursuant to future notice and comment
rulemaking.
(2) Calculation of Points
In section III.C.5.c(2) of the proposed
rule, we proposed that the achievement
domain would be worth 60 points. We
chose this domain for the highest
number of points because we believe
that driving an increase in the number
of transplants should be the main
incentive for change in the model. We
considered allocating fewer points to
this domain, such as 50 points, but we
believe that performance in this domain
should impact the overall performance
score more than the other domains
given its centrality to the model.
In section III.C.5.c(2) of the proposed
rule, we proposed that an IOTA
participant’s performance would be
assessed relative to their transplant
target, with those performing at less
than 75 percent of the transplant target
receiving no points and those
performing at 150 percent of the
transplant target or above receiving the
maximum number of points (60 points).
That is, at the highest end of the scale,
IOTA participants performing at or
above 150 percent of the transplant
target would earn the maximum 60
points, while at the lowest end of the
scale, IOTA participants performing at
less than 75 percent of the transplant
target would earn no points for the
achievement domain; performance that
falls in between 75 percent and 150
percent of the transplant target may earn
the IOTA participant 45, 30, or 15
points in the achievement domain.
Table 3 illustrates our proposal for how
an IOTA participant’s performance
would be assessed against its transplant
target. We chose 150 percent as the
maximum performance level based on
the theoretical capability of growth in
one year and analysis in trends of
transplant over time. We recognized that
an IOTA participant might exceed 150
percent of its transplant target, but this
was not expected given the investment
needed for substantiable transplant
infrastructure to consistently support
that number of transplants over time.
TABLE 3: PROPOSED ASSESSMENT OF ACHIEVEMENT DOMAIN
We stated in the proposed rule that
we believe that a methodology based on
performance improvement relative to
historical performance is important and
would allow us to test whether the
model’s performance-based payments
drive increased behavior from IOTA
participant, as opposed to just
rewarding IOTA participants based on
the status quo (89 FR 43518). IOTA
participants that are achieving a high
rate of kidney transplantation, and
already have robust transplant programs
at the start, can more easily scale up to
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achieve the additional growth required
for excellent performance under the
model. Also, given our statutory
requirements to achieve savings, the
CMS Office of the Actuary (OACT)
estimates, as described in section VI of
the proposed rule, suggested that
savings would be driven by the effects
of increased transplants. We believed
that the model’s performance-based
payments need to be tied to a policy that
aims to create and drive Medicare
savings.
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30
15
0
We considered offering differential
credit for transplants by type (89 FR
43518). With this methodology, IOTA
participants would receive bonus points
and score higher for transplants that fit
into categories that lead to more savings,
such as living donor kidney transplants
(LDK), high KDPI donors, or preemptive transplants, compared to other
transplants. However, we believed that
counting all transplants the same,
except for transplants furnished to
underserved populations, would
maximize flexibility for IOTA
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Greater than 15 0%
Less than 150%
Less than 125%
Less than 100%
Less than 75%
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participants in meeting their targets and
minimize the potential harm and
unintended consequences the
alternative system would create.
As an alternative, we considered
including gradient points instead of
points based on bands (that is, between
X and Y) (89 FR 43518). Scoring closer
to a performance minimum would result
in increased points rather than
remaining static throughout the band.
We considered the following formula:
Percent Performance Relative to
Transplant Target * (100/2.5), not to
exceed 60 points. However, we decided
that a narrower range of results would
better differentiate performance among
IOTA participants and allow for easier
comparison across IOTA participants.
We also considered smaller point
brackets of improvement, requiring
IOTA participants to achieve a flat
number increase of kidney transplants,
such as to a 140 percent, 125 percent,
or 120 percent, to achieve the highest
performance in this category, and
asymmetric point brackets that would
make the magnitude of performance
required to achieve the highest
performance rate a flat number increase
in addition to a percentage increase (89
FR 43518). However, we wanted the
percentage of the transplant target
necessary to achieve the highest number
of points to be large enough to
incentivize behavior while still being
achievable.
We also considered improvementonly scoring, based on year-over-year
IOTA participant transplant growth,
without inclusion of national rates (89
FR 43518). In this methodology, positive
improvement rates less than 5 percent
would be scored 15 points, rates over 5
percent would be scored 30 points, rates
over 20 percent would be scored 45
points, and rates over 50 percent would
be scored 60 points. We also considered
using combinations of potential
transplant target or scoring methods,
with the final score being whichever
score was highest to ensure low-volume
IOTA participants are not penalized and
to mitigate unrealistic transplant targets.
We considered an improvement-only
scoring methodology to reflect the
historical performance of each IOTA
participant. However, because we want
a methodology that sets more of a
national standard for expected growth
rate to assess volume trends in the
transplant space overall, we chose not to
propose improvement-only scoring. As
organ supply continues to increase yearover-year, we wish to set the
expectation for IOTA participants to
grow their transplant volumes at least at
the cadence of the national growth rate.
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We solicited comment on our
proposed achievement domain scoring
methodology and alternative
methodologies considered.
The following is a summary of the
comments received on our proposed
achievement domain scoring
methodology, alternative methodologies
considered and our responses:
Comment: Numerous commenters
expressed concerns that the
achievement domain requires an
impractically significant increase in
kidney transplant volume, especially in
the later PYs of the IOTA Model. In
particular, they felt it would be virtually
impossible for IOTA participants to earn
the maximum points in this domain,
and that the proposed approach would
undermine the overall model test.
Response: We recognize the validity
of this critique from commenters and
believe in updating the achievement
domain in two key areas. The first is
that the transplant target for each IOTA
participant will be calculated based on
a rolling average of transplants, as
described and finalized in section
III.C.5.c(a) of this final rule, rather than
taking the highest number of living and
deceased transplants across the relevant
baseline years, as discussed previously.
The second is to modify our scoring
methodology for allocating points for
the achievement domain at Table 1
under § 512.424(f)(2), as illustrated in
Table 4 of this section.
Comment: Several commenters
expressed concern that the proposed
thresholds for increasing transplant
rates are aggressive such that they could
negatively impact performance score
metrics for all IOTA participants,
recommending that CMS set more
realistic performance goals by lowering
the points thresholds in the
achievement domain. For instance, a
commenter supported the proposed
methodology of awarding points based
on percentage relative to transplant
target thresholds. However, they
believed the proposed points thresholds
exceeded reasonable expectations for
eligible kidney transplant hospitals. The
commenter recommended that CMS set
the highest points threshold (60 points)
at greater than 125 percent of the
transplant target, and drop the lowest
points threshold (0 points) to less than
50 percent of the transplant target. This,
the commenter felt, would ease IOTA
participants’ ability to receive
achievement domain points, help
alleviate resource disparities between
participant hospitals, and reduce the
potential for financial considerations to
cloud clinical judgment when matching
organs to recipients.
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Another commenter recommended
that CMS use a volume growth trend
that better recognizes the potential
limits of transplant programs to expand
capacity in a more reliable, realistic, and
safe manner. The commenter felt that
having a transplant goal that is more
achievable would also incentivize the
growth the IOTA Model is trying to
achieve. Setting transplant targets too
high could discourage IOTA
participants from growing their kidney
transplant programs at all if the targets
are unrealistic and not achievable. As
such, this same commenter
recommended that CMS allow IOTA
participants to achieve the maximum 60
points for the achievement domain with
performance equal or greater than 110
percent of the transplant target.
Another commenter stated that to
achieve a 10 percent increase in kidney
transplants, a large-volume kidney
transplant hospital performing 400
transplants annually would need to do
an additional 40 per year. While the
increase would be less for smaller
kidney transplant hospitals, any
additional transplants may strain their
personnel and infrastructure. The
commenter also suggested that kidney
transplant hospitals of any size need
appropriate lead time to estimate and
accommodate the increase in transplant
volume. Expanding transplant capacity
requires significant infrastructures
investments, such as for higher-risk
candidates and donor organs, infusion
bays, access to inpatient and outpatient
dialysis for higher volumes of
recovering recipients with delayed graft
function, and additional personnel. The
commenter warned that disregarding
these infrastructure needs would put
undue stress on the healthcare system
and could prevent IOTA participants
from meeting mandated targets. For
these reasons, they recommended that
the achievement domain points
thresholds be lowered to a more realistic
performance metric (for example, 110 to
125 percent relative to transplant target).
Lastly, a commenter believed that the
proposed achievement domain points
thresholds are too aggressive and would
sharply curtail the opportunity for IOTA
participants to achieve more than 30
points in any PY. The commenter
suggested an alternative approach that
would allow IOTA participants to earn
the maximum 60 points in the
achievement domain if their
performance exceeded the transplant
target by 125 percent or more.
Response: We thank the commenters
for expressing their concerns and for
their suggestions on our proposed
methodology for awarding points for
performance in the achievement
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domain. As described in the proposed
rule at 89 FR 43553, we considered
smaller point brackets of improvement
to achieve the highest performance in
this category but chose not to propose
smaller point brackets of improvement
as we wanted the percentage of the
transplant target necessary to achieve
the highest number of points to be large
enough to incentivize behavior while
still being achievable. However, in
response to comments received, we are
updating the methodology for points
allocation in the achievement domain.
Specifically, we are finalizing, with
modification, Table 1 to Paragraph (f)(2)
at § 512.424(f)(2) to reflect the updated
points allocation, as illustrated in Table
4.
TABLE 4: ASSESSMENT OF ACHIEVEMENT DOMAIN
Greater than 125%
Less than 125%
Less than 120%
Less than 115%
Less than 105%
Less than 95%
Less than 85%
Less than 75%
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We believe that the updated scoring
system reflects our partial agreement
with commenters. Specifically, we are
lowering the maximum performance
threshold from 150 percent to 125
percent of the transplant target.
Moreover, in combination with the
updated methodology for setting
transplant targets, as described and
finalized section III.C.5.c(1) of this final
rule, we believe that this revised
standard is more achievable for IOTA
participants and strikes a balance—it
aims to incentivize performance, while
also recognizing the challenges that
IOTA participants may face in
increasing their kidney transplant
volume.
Lastly, because we are updating
achievement domain performance
thresholds and points allocation, we are
keeping the performance threshold for
earning 0 points at 75 percent of the
transplant target as proposed at 89 FR
43553. This is to ensure a minimum
level of performance from IOTA
participants and keep the focus on
ensuring that the number of kidney
transplants performed by IOTA
participants does not significantly
decrease.
Comment: A commenter suggested
that CMS adopt a more graduated
scoring scale, providing additional
opportunities for IOTA participants to
earn points in the achievement domain.
Response: We appreciate the feedback
from the commenter. As mentioned in
comment responses noted previously, in
light of the comments received, we are
updating the methodology for points
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allocation in the achievement domain,
as illustrated in Table 4 of this section.
The updated methodology for point
allocation includes additional
gradations, which we believe will
provide IOTA participants with greater
opportunities to earn points compared
to the four scoring ranges we originally
proposed at 89 FR 43553 in the
proposed rule.
Comment: A commenter expressed
concerns that the proposed
methodology for calculating transplant
targets would have compounding
negative effects on performance over
time, making it increasingly difficult for
IOTA participants to earn maximum
points in the achievement domain in
later years of the model.
Response: We thank the commenter
for raising their concern. We recognize
that the proposed methodology may
have set a standard that may have been
too difficult for IOTA participants to
meet. We believe that our updated
methodology for setting the transplant
target, as described and finalized in
section III.C.5.c(1) of this final rule, sets
a balance between trying to incentivize
improvement over time with allowing
IOTA participants to recognize the
benefits of investment in increasing
their number of kidney transplants.
Moreover, as described in the proposed
rule at 89 FR 43550, the model PYs
would not factor into an IOTA
participant’s transplant target
calculation until PY 3 of the model and
the baseline years would not be based
exclusively on PYs until PY 5 of the
model. We maintain our belief that
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40
30
20
10
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using baseline years to calculate the
transplant targets could represent an
effective phase-in approach to drive
improved performance and savings for
the Medicare trust fund, while also
accounting for kidney transplant
hospitals that experience changes in
strategy or staffing that may affect their
transplant capacity compared to
previous years.
Comment: We received a comment
that the only way that IOTA participants
can increase their supply is by using
marginal organs which would result in
increased rates of graft failure for
transplanted patients.
Response: We disagree with this
commenter and would like to provide
clarification. We did not specify how
IOTA participants should increase their
number of kidney transplants, nor do
we believe that the only way that IOTA
participants can increase their number
of transplants is by using marginal
organs. In the proposed rule at 89 FR
43551, we expressed our belief that
IOTA participants could improve on
this metric and provided several
possible ways that they might be able to.
We acknowledge that some IOTA
participants may choose to increase
their utilization of DCD kidneys or
kidneys with a KDPI greater than 85,
however, the IOTA Model does not
prescribe that they do. Additionally, the
CoPs for transplant hospitals require
that the transplanting surgeon at the
transplant program is responsible for
ensuring the medical suitability of
donor organs for transplantation into the
intended recipient (42 CFR 482.92).
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Furthermore, we believe that many
organs that are not used today have a
clinical profile similar to organs that are
ultimately transplanted. As such, we
expect that IOTA participants will
exercise their medical judgement
appropriately when determining
whether or not to accept a DCD kidney
organ offer.
Comment: We received a comment
that there is not enough available
transplant supply to increase numbers,
particularly at the thresholds that CMS
set in the proposed scoring for the
achievement domain.
Response: We believe that the
updated transplant target methodology
and scoring methodology make the
transplant targets more achievable for
IOTA participants. We also recognize
the growth in organs being procured by
OPOs since the 2020 CfC update and
believe that there is an opportunity for
transplant hospitals to take advantage of
the updated supply being procured by
OPOs. Additionally, we believe that
living donation represents an untapped
supply of potential kidney transplants
that is not dependent on procurement
practices.
Comment: A commenter expressed
their disagreement with the proposed
achievement domain performance
thresholds, as they do not take into
account the inability of transplant
programs to scale up the volume of the
number of transplants performed in a
given year. The commenter believed
that some transplant programs may have
excess capacity to perform more
transplants annually, but others would
face significant fixed costs to expand
their transplant operations beyond their
current volume. Additionally, the
commenter noted that in the current
labor market, it would be challenging to
recruit and retain the highly specialized
staff, including transplant physicians,
needed to expand the capacity of their
transplant program to meet these
transplant targets.
Response: We recognize that there
will be some need for IOTA participants
to scale up, which is why we are not
finalizing the proposed model start date
of January 1, 2025. As described and
finalized in section III.C.1.a of this final
rule, we are finalizing a model start date
of July 1, 2025. We also note that there
is no downside risk payment in PY 1,
as described and finalized in section
III.C.6.c(2)(b) of this final rule. As such,
it will be over 18 months from the
publication of this final rule until an
IOTA participant is held liable for their
number of transplants with the potential
for a downside risk payment.
Furthermore, as mentioned in comment
responses in this section, we will be
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finalizing an updated methodology for
points allocation in the achievement
domain, as illustrated in Table 4 in this
section, and our methodology for setting
transplant targets, as described and
finalized in section III.C.5.c(1) of this
final rule. For these reasons, we believe
this will give time for IOTA participants
to make investments to expand their
transplant program, resulting in a
transplant target that is potentially more
attainable for IOTA participants and
providing additional opportunities to be
awarded points.
Comment: Multiple commenters
expressed concern that the proposed
scoring methodology was too difficult
for large kidney transplant hospitals,
given that a significant percentage
increase for them represents a higher
number of additional transplants. We
also received comments pointing out
that the scoring methodology could be
punitive to IOTA participants that
already invested to increase their
number of transplants before the start of
the model.
Response: We thank the commenters
for expressing their concerns regarding
the proposed scoring methodology. We
note that, as described and finalized in
section III.C.5.c(1) of this final rule, that
we are finalizing an updated
methodology for setting transplant
targets. We direct readers to section
III.C.5.c(1) of this final rule for further
discussion on our updated methodology
for setting transplant targets. As such,
we believe that this updated
methodology for setting transplant
targets will make top performance in the
achievement domain more achievable
for all kidney transplant hospitals
participating in the model. We also
recognize that larger kidney transplant
hospitals have already invested in
additional capacity and resources to
help more patients through the
transplant process, which means that
they have experience in increasing their
transplant numbers that they can
leverage as IOTA participants.
Comment: We received comments
that the proposed scoring methodology
was too difficult for smaller kidney
transplant hospitals. Commenters
pointed out that smaller kidney
transplant hospitals may experience
fluctuations in their transplant volume.
Given their lower volume of kidney
transplants, a small numerical decrease
in the number of kidney transplants
they perform could translate to a large
percentage drop, potentially resulting in
a loss of all points in the achievement
domain.
Response: We believe that the
updated methodology for setting
transplant targets, as described and
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finalized in section III.C.5.c(1) of this
final rule, will help smaller kidney
transplant hospitals selected to
participate in the model deal with
fluctuations. We direct readers to
section III.C.5.c(1) of this final rule for
further discussion on our updated
methodology for calculating transplant
targets. The updated scoring
methodology, as shown in Table 4, will
provide more gradation in scoring. As
such, we believe that this should
prevent small kidney transplant
hospitals from being significantly
impacted if they fall short of their
transplant targets by a small margin.
The increased number of scoring
thresholds means IOTA participants
will have more opportunities to earn
points, minimizing the effect of minor
shortfalls.
Comment: Several commenters
proposed including a living donor
performance adjustment, which would
award additional points for living donor
kidney transplants. A commenter
suggested that, in the absence of
adequate risk adjustment, a performance
adjustment, similar to the proposed
health equity adjustment, with a
weighting greater than 1 should also be
considered for living donor transplants.
Another commenter suggested that CMS
should consider including an incentive
multiplier in the achievement domain
point calculation for living donor
kidney transplants, as this is the optimal
treatment for patients with end-stage
kidney disease (ESKD). Lastly, a
commenter praised CMS’s efforts to
improve the organ transplantation
system, but recommended giving greater
weight to living donor kidney
transplants over deceased donor
kidneys for several reasons. For
example, the commenter cited that
living donor kidneys typically have a
lower risk of graft failure compared to
deceased donor kidneys. This results in
longer lifespans for living donor kidney
recipients, fewer complications, better
post-transplant outcomes, and reduced
burden on the healthcare system—
ultimately enhancing overall patient
health. Additionally, they noted that
there is a reduced need for
immunosuppressive medications
because patients receiving a living
donor kidney often require less
immunosuppressive drugs. For these
reasons, the commenter proposed that
CMS either assign a larger weight to
living donor kidney transplants or apply
a multiplier akin to the proposed health
equity performance adjustment.
Response: We thank the commenters
for their suggestions to include a living
donor performance adjustment. We
recognize the benefits of living donor
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transplantation and views it as an
important part of the transplant process.
However, the IOTA Model test
prioritizes flexibility, allowing IOTA
participants to determine the best way
to perform. We also acknowledge that
IOTA participants may have varying
comfort levels with promoting living
donation. As such, we want to prioritize
flexibility for IOTA participants rather
than specifically promoting any
particular transplant type. Additionally,
we believe that the composite graft
survival rate measure, as described and
finalized in section III.C.5.e(1) of this
final rule, in the quality domain
accounts for the potential long-term
survival benefits of living donation for
patients.
Comment: A commenter suggested
that IOTA participants receive
additional points in the proposed
achievement domain scoring
methodology for preemptive kidney
transplants, as they offer considerable
survival and quality of life benefits for
patients, as well as major cost savings.
Given the substantial benefits to
patients and the substantial savings as
compared to dialysis, the commenter
recommended that CMS consider
creating a preemptive bonus or
preemptive multiplier, which could be
scaled proportionately with savings to
the Medicare program pre-emptive
transplants provide relative to
maintenance dialysis. However, the
commenter emphasized that carefully
calibrating and closely monitoring such
a bonus or multiplier would be crucial.
Ideally, this process should involve
input from the community to ensure the
incentive expands access to pre-emptive
kidney transplants rather than
exacerbating existing disparities.
Response: We thank the commenter
for their suggestion but disagree with
the commenter. We recognize the
benefits of preemptive transplantation.
However, we are unsure whether the
inclusion of a preemptive kidney
transplant performance adjustment
would be effective at incentivizing
preemptive transplantation. We plan to
monitor the effects of the model on
preemptive transplantation as part of
the evaluation process and may
consider potential changes to the model
through future notice and comment
rulemaking, depending on performance
by IOTA participants.
Comment: A couple commenters
suggested that CMS should use two
metrics to score IOTA participants in
the achievement domain: percentage
growth in kidney transplants and a flat
threshold for increased kidney
transplant volume. For instance, a
commenter proposed that IOTA
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participants earn maximum points if
they achieve 150 percent of their
transplant target or perform 25
additional kidney transplants.
Response: We thank the commenters
for their suggestions to include an
additional flat threshold scoring
methodology. We understand the merits
of this idea as it recognizes that it may
be more difficult for IOTA participants
that are already performing more
transplants to further increase their
number of transplants. As described at
89 FR 43553 in the proposed rule, we
considered a methodology based on
year-over-year IOTA participant
transplant growth, excluding national
growth rates. We also considered using
combination of potential transplant
target or scoring methodologies, taking
the highest resulting score to avoid
penalizing low-volume IOTA
participants and prevent unrealistic
transplant targets. However, for the
reasons described in section III.C.5.c(2)
of this final rule, we chose not to
propose either of the methodologies
discussed previously.
We believe that the updated
methodology for setting transplant
targets, as described and finalized in
section III.C.5.c(1) of this final rule, and
the updated scoring methodology in the
achievement domain, as illustrated in
Table 4 in comment responses noted
previously, will make it more
achievable for IOTA participants of all
sizes to achieve maximum points in this
domain.
Comment: A commenter expressed
their concern over the number of
proposed points for the achievement
domain (60 points) and quality domain
(20 points). Specifically, the commenter
was concerned that, in the context of
resource scarce kidney transplant
hospitals, resources would be pulled
from efforts to help patients succeed in
the long-term (post one-year) period in
order to deliver success on increasing
transplant rates. As such, the
commenter believed that greater
emphasis was needed to encourage
focus on, and investment in, supporting
patients’ longer-term (post-one-year and
longer) outcomes post-transplant,
recommending that CMS allocate a
maximum of 50 points for the
achievement domain instead of the
proposed 60 points.
Response: We appreciate the
commenter’s recommendation and
acknowledge their concerns. The
achievement domain performance score
was weighted more heavily than the
efficiency and quality domains because
we believe this aligns with the IOTA
Model’s primary objective of increasing
the total number of kidney transplants
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(89 FR 43548). Moreover, recognizing
that the main goal of the model is to
increase the number of kidney
transplants performed, we maintain that
weighing performance on this measure
more than the efficiency domain and
quality domain is necessary to directly
incentivize participants to meet their
target, as increasing the number of
kidney transplants performed is the
primary goal of the model. For these
reasons, we disagree with the
commenter that CMS should decrease
the number of proposed points allocated
for the achievement domain and are
finalizing our proposal to allocate 60 out
of a maximum 100 points to the
achievement domain, as described and
finalized in section III.C.5(b) of this final
rule. Regarding our proposed point
allocations across the achievement
domain, efficiency domain, and quality
domain, and alternatives we considered,
we direct readers to section III.C.4.b of
this final rule. We note that we intend
to monitor the impacts of the quality
domain and efficiency domain
throughout the model test and will
consider whether adjustments in the
maximum number of points awarded in
each domain are necessary in future
notice and comment rulemaking.
After consideration of the public
comments, for the reasons set forth in
this rule, we are finalizing our proposed
achievement domain scoring
methodology, with modification. As
described in section III.C.5.c(3) of the
preamble in this final rule, we will not
be finalizing a health equity
performance adjustment provision. As
such, we are finalizing the provisions at
§ 512.424(a) with slight modification.
Specifically, we are modifying the
regulatory text at § 512.424(a)(2) to
remove references to a health equity
performance adjustment and make
minor technical corrections in
punctuation.
We are codifying in our regulation at
§ 512.424(f) that for each PY, CMS
awards the IOTA participant zero to 60
points for its performance in the
achievement domain, as proposed. We
are also making a minor technical
correction to update the cross reference
in our regulation at § 512.424(f)(1). In
particular, we are removing the cross
reference to the health equity
performance adjustment and replacing it
to reflect § 512.424(d)(2). We direct
readers to section III.C.5.c(3) of this final
rule for further discussion on the health
equity performance adjustment.
We are also finalizing § 512.424(f)(2)
as proposed, which states that for each
PY, CMS will calculate the transplant
target for the achievement domain, as
proposed. Lastly, in response to
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comments received, we are replacing
the methodology for points allocation in
the achievement domain. Specifically,
we are finalizing, with modification,
Table 1 to Paragraph (f)(2) at
§ 512.424(f)(2) to reflect the updated
points allocation, as illustrated in Table
4 above. However, we will analyze and
monitor IOTA participant performance
through the model test to ensure we do
not unduly disadvantage kidney
transplant hospitals selected for the
model. If analysis results indicate that a
change in policy is warranted, we will
address it pursuant to future notice and
comment rulemaking.
(3) Health Equity Performance
Adjustment
Socioeconomic factors impact patient
access to kidney transplants. Patients
with limited resources or access to care
may require more assistance from
kidney transplant hospitals to overcome
barriers to transplantation. To
incentivize IOTA participants to
decrease disparities in the overall
transplant rate among patients of
various income levels, we proposed to
include a health equity performance
adjustment in the methodology for
calculating the overall number of
transplants furnished to patients
attributed to an IOTA participant during
the PY. We proposed to define the
‘‘health equity performance adjustment’’
as the multiplier applied to each kidney
transplant furnished to a low-income
population IOTA transplant patient
when calculating the transplant target
(as described in § 512.424 of the
proposed rule). For purposes of the
model, we proposed to define the ‘‘lowincome population’’ to mean an IOTA
transplant patient in one or more of the
following groups:
• The uninsured.
• Medicaid beneficiaries.
• Medicare-Medicaid dually eligible
beneficiaries.
• Recipients of the Medicare lowincome subsidy.
• Recipients of reimbursements from
the Living Organ Donation
Reimbursement Program administered
by the National Living Donor Assistance
Center (NLDAC).
In the proposed rule, we proposed to
apply a health equity performance
adjustment, a 1.2 multiplier, to each
kidney transplant furnished by an IOTA
participant to a patient, 18 years of age
or older at the time of transplant, that
meets the low-income population
definition. That is, each kidney
transplant that is furnished to a patient
who meets the low-income population
definition would be multiplied by 1.2,
thus counting that transplant as 1.2
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instead of 1. The resulting count of the
overall number of kidney transplants
performed during the PY, after the
health equity performance adjustment is
applied, would then be compared to the
transplant target. In effect, the health
equity performance adjustment would
be a reward-only adjustment to the
performance score in the achievement
domain. We also considered basing the
multiplier on the difference between
rates of transplantation for Medicare
beneficiaries with ESRD who are dual
eligible and those who are not. In 2019,
47 percent of Medicare beneficiaries
with ESRD were dually eligible for
Medicare and Medicaid. However, only
41 percent of Medicare transplants
recipients were dually eligible, which
would yield a multiplier of 1.1.200
We chose 1.2 as the health equity
performance adjustment multiplier
because, according to USRDS data, 78.6
percent of patients living with ESRD
have some form of Medicare and or
Medicaid coverage; however, only 65.1
percent of patients who received
transplants in 2020 were on Medicare,
Medicaid, or both.201 202 The 1.2
multiplier represents the ratio of those
living with ESRD and those who
received transplants. We theorized that
providing this incentive for IOTA
participants to increase their transplant
rate among low-income populations
would ultimately reduce disparities in
access to kidney transplants, as it would
encourage IOTA participants to address
access barriers low-income patients
often face, such as transportation,
remaining active on the kidney
transplant waiting list, and making their
way through the living donation
process.
We believed that the health equity
performance adjustment would be a
strong incentive to promote health
equity, as the multiplier earned would
help IOTA participants meet or exceed
their kidney transplant target, thereby
potentially resulting in upside risk
payments given the heavy weighted
scoring applied to the achievement
domain. We also believed it would
200 Gillen, E.M., Ganesan, N., Kyei-Baffour, B., &
Gooding, M. (2021, August 30). Avalere analysis of
disparities in Kidney Care Service Utilization.
Avalere Health. https://avalere.com/insights/
avalere-analysis-of-disparities-in-kidney-careservice-utilization.
201 United States Renal Data System. (2020). 2020
USRDS Annual Data Report: Epidemiology of
kidney disease in the United States. National
Institutes of Health, National Institute of Diabetes
and Digestive and Kidney Diseases. Bethesda, MD.
202 Lentine, K.L., Smith, J.M., Hart, A., Miller, J.,
Skeans, M.A., Larkin, L., Robinson, A., Gauntt, K.,
Israni, A.K., Hirose, R., & Snyder, J.J. (2022). OPTN/
SRTR 2020 Annual Data Report: Kidney. American
Journal of Transplantation, 22(S2), 21–136. https://
doi.org/10.1111/ajt.16982.
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ensure IOTA participants that serve
disproportionately high numbers of lowincome populations are not penalized in
the achievement performance scoring.
We considered not applying a health
equity performance adjustment to the
achievement performance scoring,
which would ensure all kidney
transplants, regardless of the lowincome status of individual patients, are
counted as one transplant. The concern
with the health equity performance
adjustment may be that it may
incentivize shifting of kidney
transplants from one type of patient to
another. However, we believed the
incentive is to promote improvement
activities that would increase access to
all patients while recognizing that lowincome patients may face more barriers
to care outside of the IOTA participants’
control. It also recognizes that
disparities already exist in access to
kidney transplants for low-income
patients, so, by addressing inequities,
IOTA participants would focus efforts
on tackling inequities for patients
outside the Medicare population.
For purposes of the health equity
performance adjustment, we also
considered using the area deprivation
index (ADI) to define the low-income
population. ADI ranks neighborhoods
based on socioeconomic disadvantage in
the areas of income, education,
employment, and housing quality. Areas
with greater disadvantage are ranked
higher, and they correlate with worse
health outcomes in measures such as
life expectancy.203 The areas used in the
ADI are defined by Census Block Group,
which presents a number of
challenges.204 However, because
address information for Medicare
beneficiaries may be incomplete, and
not available at all for patients who have
private insurance or the uninsured, we
opted to not use ADI to define the lowincome population. We believed that
this would leave an incomplete picture
of the transplant population for a given
IOTA participant. Furthermore, the
socioeconomic status of individuals
within a given ADI can vary greatly.
Those that are underserved in a Census
Block Group with a low ADI may be
overlooked.
We also considered including ‘‘rural
resident’’ as one of the groups that
define a low-income population in the
IOTA Model, as rural transplant patients
face numerous barriers to care,
including transportation, food, housing,
and income insecurity, and no or
203 Neighborhood Atlas—Home. (2018). Wisc.edu.
https://www.neighborhoodatlas.medicine.wisc.edu/.
204 https://www2.census.gov/geo/pdfs/reference/
GARM/Ch11GARM.pdf.
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limited access to kidney transplant
hospitals within or close to their rural
communities. We considered defining
rural beneficiaries consistent with the
criteria used for identifying a rural area
when determining CAH eligibility at 42
CFR 485.610(b)(1)(i), that is,
beneficiaries living outside an MSA.
However, we were unsure if it was
appropriate to include this group to
define a low-income population to
determine if a health equity adjustment
would apply to the achievement
performance score, particularly as the
proposed low-income definition may
already capture the majority of rural
kidney transplant patients.
We sought comment on our proposed
health equity performance adjustment,
including on the adjustment multiplier
and calculation method, the definition
of low-income population and
alternatives considered, including
consideration of ADI as an alternative
definition, or including rural resident in
the low-income population definition.
The following is a summary of the
comments received on our proposed
health equity performance adjustment,
including on the adjustment multiplier
and calculation method, the definition
of low-income population and
alternatives considered, including
consideration of ADI as an alternative
definition, or including rural resident in
the low-income population definition
and our responses:
Comment: A couple commenters
advised CMS against finalizing the
proposed HEPA provision for a variety
of reasons, arguing that it prioritizes
non-medical factors and prompts IOTA
participants to unfairly favor certain
patients over others for reasons
unrelated to clinical needs.
Response: We appreciate the feedback
from commenters, but we respectfully
disagree with their position. We
originally proposed this provision out of
concern for the existing disparities in
access to transplants. The proposed
HEPA was not intended to incentivize a
focus on any particular patient group,
but rather to encourage kidney
transplant hospitals to identify and
address the barriers faced by their
underserved patient populations, with
the goal of overcoming issues related to
SDOH. Moreover, we believe that IOTA
participants will leverage their medical
expertise to deliver the best outcomes
for patients. However, in light of all the
comments we received and about the
potential for unintended consequences,
we will not be finalizing the proposed
HEPA at this time. As part of the
evaluation process, we intend to
monitor how the model impacts lowincome individuals’ access to kidney
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transplants and may consider proposing
a new or updated policy through future
notice and comment rulemaking.
Comment: While a commenter
appreciated CMS’ focus on promoting
health equity in this model, they did not
support the proposed HEPA due to
broader concerns about using transplant
volume as a performance measure.
Specifically, the commenter noted that
although the HEPA aims to encourage
IOTA participants to provide
transplants for uninsured patients, the
bonus payments are insufficient to cover
the extensive, long-term care required
for successful transplant outcomes.
Transplant patients need a wide range
of services beyond just the surgery itself,
including preoperative testing and
monitoring, dietary counseling, and
ongoing medications. However, a lack of
insurance coverage presents a major
challenge for both patients and kidney
transplant hospitals in achieving better
kidney care outcomes. For these
reasons, the commenter argued that
CMS’ proposed health equity multiplier
approach to incentivize organ
transplantation services for underserved
patients is an inadequate solution to this
complex issue.
Response: We appreciate the feedback
and believe that the increased payment
amounts in the model could provide
additional resources for IOTA
participants to support the necessary
interventions required to overcome
barriers for underserved patients.
However, as mentioned in comment
responses noted previously in this
section, we will not be finalizing the
HEPA as we are concerned about the
potential for unintended consequences
and will keep this feedback in mind as
we consider alternatives in future notice
and comment rulemaking.
Comment: A commenter expressed
concern that the proposed HEPA
incentivizes out-of-sequence allocation
of kidneys by IOTA participants, giving
preferential treatment to low-income
candidates, in order to maximize the
number of points they receive in the
achievement domain. Given these
concerns and the pressing disparities in
access to living donor transplants, the
commenter urged CMS to consider
increasing the HEPA, but limiting the
availability of the HEPA to living donor
transplants.
Response: We appreciate this
feedback. As mentioned in commented
responses previously in this section, we
will not be finalizing the proposed
HEPA at this time. Additionally, as
described and finalized in section
III.C.13.c of this final rule, we will
monitor the rates of out-of-sequence
allocation that may result from the
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model. This is to ensure the model does
not have unintended consequences.
Accordingly, we do not anticipate any
potential impact of out-of-sequence
allocation as a way to prioritize
transplants for underserved
populations.
Comment: A commenter strongly
agreed with CMS’ intended goal of using
financial incentives to encourage IOTA
participants to improve health equity
and reduce disparities in overall
transplant rates for lower-income
patients. However, the commenter
expressed significant concerns about the
potential unintended consequences of
this design. Specifically, they believed
that financially incentivizing the use of
lower-quality kidneys for lower-income
patients, while also incentivizing more
transplants for this group, could
inadvertently link these factors and
entrench a two-tiered system. The
commenter stated that this could result
in lower-income patients being offered
lower-quality kidneys, further
exacerbating health disparities among
kidney transplant recipients.
Additionally, the commenter was
concerned that while the proposed
model would increase kidney
transplantation rates for those already
on the waitlist, it overlooked the
broader barriers in healthcare access
that prevent low-income patients from
being placed on the transplant waitlist
in the first place. As such, the
commenter recommended that CMS not
finalize the HEPA.
Response: We thank the commenter
for sharing their support and concerns.
We acknowledge potential concerns
about the proposed HEPA policy, but
also recognize the substantial benefits of
kidney transplantation over dialysis,
even for complex organs. Furthermore,
we believe IOTA participants will
exercise their medical expertise to
ensure the best possible outcomes for
patients. However, as mentioned in
comment responses noted previously in
this section, we will not be finalizing
the proposed HEPA provision at this
time due to the potential for unintended
consequences. We intend to monitor
how the model impacts low-income
individuals’ access to kidney
transplants and may consider proposing
a new or updated policy through future
notice and comment rulemaking.
Comment: A commenter suggested
that the proposed HEPA would bias
IOTA Model results toward larger
kidney transplant hospitals with the
financial resources to overcome the
challenges of serving low-income
patients. The commenter also believed
that any effort to shift transplantation
decisions away from purely clinical
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considerations would necessarily
produce adverse results, such as higher
rates of unsuccessful transplants.
Specifically, IOTA participants may
take greater risks by transplanting
kidneys into HEPA-eligible patients
rather than better clinically-matched
recipients, leading to increased failure
rates. For these reasons, the commenter
strongly recommended that CMS reduce
the multiplier for the HEA from 1.2 to
1.05 or 1.1. Additionally, the
commenter suggested lowering the
achievement domain points thresholds
commensurately, setting the highest
threshold (sixty points) at greater than
125% of target and dropping the lowest
(zero points) to less than 50% of target.
This, they believed, would help address
the resource gap between IOTA
participants. Additionally, the
commenter felt this change would also
reduce the potential adverse
consequences of clouding clinical
judgment with financial considerations
when matching organs to recipients.
Finally, the commenter noted that
making these suggested changes would
further recognize the sometimes-severe
disparity of available organs from one
PY and its relevant baseline years to the
next.
Response: We thank the commenter
for sharing their concerns; however, we
disagree that the proposed HEPA would
bias larger kidney transplant hospitals.
We believe all transplant hospitals, not
just larger ones, should focus on
overcoming barriers for underserved
populations. Moreover, many of the
interventions needed to address these
barriers are covered by organ acquisition
costs. However, in response to the
public comments we received on our
proposed HEPA, we will not be
finalizing this provision at this time.
Comment: Several commenters urged
CMS to include rural residents as a
population group in the proposed
definition of low-income population
that is eligible for the proposed HEPA;
given the limited access to transplant
services in rural areas and additional
challenges that rural residents,
regardless of income, face throughout
the transplant process. For example, a
commenter appreciated that CMS
considered including rural residents in
the proposed low-income patient
definition eligible to receive the
proposed HEPA. However, the
commenter urged CMS to reconsider
this factor, arguing that it would help
address the unique challenges rural
residents face throughout the transplant
process. Another commenter
recommended that CMS consider
including ‘‘rural resident’’ as a group in
the proposed definition of low-income
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population for the purposes of the IOTA
Model, since rural residency is
associated with significant barriers to
transplantation, a situation only made
worse by the increasingly precarious
hospital footprint in rural areas of the
country. Due to the significant barriers
to transplantation faced by rural
residents, which are exacerbated by the
increasingly limited availability of
hospitals in rural areas, a commenter
recommended that CMS should include
rural resident as a group in the proposed
low-income population definition for
the IOTA Model.
A commenter strongly supported the
proposed HEPA and applauded CMS for
recognizing that some patients require
more assistance from kidney transplant
hospitals to overcome barriers to
transplantation. This commenter felt
CMS correctly identified that rural
transplant patients face barriers to care,
some of which are income related such
as food, housing, and income insecurity.
The commenter believed that patients
facing these barriers would almost
certainly qualify for the proposed health
equity performance adjustment (HEPA)
through Medicaid eligibility or the
Medicare Low Income Subsidy (LIS).
According to the commenter, patients
confronting these barriers would likely
qualify for the proposed HEPA through
Medicaid eligibility or the Medicare
Low Income Subsidy (LIS). However,
the commenter stated that they could
attest that two of the barriers identified
by CMS—transportation issues and
‘‘limited access to kidney transplant
hospitals within or close to rural
communities’’—complicate transplant
care for patients, regardless of their
income level. The commenter argued
that by including rural residents in the
groups qualifying for the proposed
HEPA, CMS would ensure that the
additional assistance kidney transplant
hospitals must provide to help rural
patients of all income levels overcome
barriers to transplantation is properly
accounted for. Lastly, this commenter
stated their belief that the criteria used
for identifying a rural area when
determining CAH eligibility at 42 CFR
485.610(b)(1)(i) would sufficiently
capture rurality.
Lastly, a commenter greatly supported
CMS’ efforts to strengthen health equity
in value-based care, but believed CMS
should expand the proposed definition
of low-income population eligible for
the HEPA to also include rural
residents, given the limited access to
transplant services in rural areas. The
commenter argued that rural patients
face significant barriers to accessing
transplant services, as they are less
likely to be added to transplant waitlists
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or referred for transplant by dialysis
providers due to the limited availability
of transplant services in rural areas.
Therefore, the commenter felt CMS
should incentivize IOTA participants to
care for rural patients through the HEPA
for low-income populations, in order to
address the disproportionate challenges
faced by the rural population in
accessing transplant care. The
commenter suggested that if CMS is
hesitant to label all rural patients as
low-income, they could rename the
adjustment to more accurately reflect
the vulnerable populations it includes.
Response: We thank the commenters
for their support and recommendation
to include rural residents in our
proposed definition of low-income
population eligible to receive the
proposed HEPA. We recognize that rural
patients may face additional barriers
and challenges throughout the
transplant process. However, as
mentioned in comment responses noted
previously, we will not be finalizing the
proposed HEPA at this time.
Additionally, we will consider
additional adjustments to the model that
may account for the barriers faced by
patients living in rural areas in future
notice and comment rulemaking.
Comment: A commenter noted that
they have dialysis patients that get
assistance to enroll in commercial
plans. The commenter argued that these
individuals should be classified as lowincome, citing their frequent
socioeconomic barriers, and urged CMS
to revise the proposed definition of lowincome population to encompass these
individuals.
Response: We thank the commenter
for their suggestion. We chose the
specific designations in an effort to use
insurance status as a proxy for
underserved status for beneficiaries and
the statuses we proposed at 89 FR 43553
in the proposed rule (uninsured,
Medicaid beneficiaries, MedicareMedicaid dually eligible beneficiaries,
recipients of the Medicare LIS, or
recipients of reimbursements from the
Living Organ Donation Reimbursement)
only apply to lower-income
beneficiaries, whereas beneficiaries with
commercial insurance may not be lowincome. As such, we disagree with the
commenter.
Comment: A commenter expressed
strong support for reducing health
inequities but felt that the proposed
methodology for identifying low-income
populations, although clear, may not be
comprehensive in gathering the
intended information. Specifically, the
commenter cited three concerns: (1) The
commenter was unaware of transplant
hospitals that would knowingly
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transplant someone without insurance
who lacked the means to cover the costs
out-of-pocket. Therefore, the uninsured
criteria may identify patients with
significant means, unless CMS examines
people who have lost some or all
insurance after transplant.; (2)
Transplant hospitals do not know which
patients receive LIS benefits, and many
patients are unaware that they receive
this benefit, based on the commenter’s
experience.; and (3) NLDAC benefits are
attached to the donor, not the recipient,
so CMS may not have access to this
information.
Response: We thank the commenter
for their feedback. We believe that all
patients with kidney disease deserve
equitable care and access to the
transplant process. We urge transplant
hospitals to think about how to
overcome barriers for patients,
regardless of insurance status, and to
think about how to best care for
patients’ needs. Although we will not be
finalizing the proposed HEPA at this
time, we will consider the comments
that were received during the public
comment period and may make future
proposals during the course of the
model test in future notice and
comment rulemaking.
Comment: Multiple commenters
supported the proposed HEPA but urged
CMS to increase the amount of the
proposed HEPA multiplier. For
example, a commenter expressed their
strong support for the proposed HEPA
and believed that it is an appropriate
incentive to encourage IOTA
participants to address barriers that lowincome populations face in the
transplant process and to help reduce
disparities in access to transplant.
Furthermore, the commenter felt that
the proposed HEPA is also an important
tool to ensure IOTA participants are not
unfairly penalized if they serve a high
number of low-income populations. As
such, they recommend that CMS
consider increase the health equity
performance adjustment.
Additionally, a commenter
encouraged CMS to increase the
proposed HEPA multiplier to 1.25.
Another commenter supported the
precision of the IOTA Model’s
approach, which proposed to apply an
adjustor for each individual kidney
transplant furnished to a patient
meeting the proposed low-income
population definition. This
individualized method, they argued,
would more effectively address health
equity compared to the broader
approach used in the ETC Model.
However, the commenter expressed
concerns that the proposed 1.2
multiplier was insufficient to cover the
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increased costs kidney transplant
hospitals would face in expanding
transplants for low-income populations.
Therefore, the commenter believed it is
critical for CMS to consider increasing
the multiplier to at least 1.5 in order to
incentivize and enable greater
transplant access for this underserved
group.
Response: We appreciate the
commenters’ support and
recommendations. As described in
comment responses noted previously,
we will not be finalizing the proposed
HEPA. Although we are not finalizing
the proposed HEPA at this time, we will
take the comment but will consider the
appropriate magnitude of any potential
adjustment via future rulemaking, as we
are not finalizing this provision.
Comment: We received multiple
comments supporting the proposed
inclusion of a HEPA. For example,
several commenters commended CMS’s
emphasis on and approach to
implement a reward only HEPA. They
believed the proposed HEPA would be
a major stride toward promoting equity
in access to organ transplants and
motivate IOTA participants to address
the barriers faced by low-income
individuals in the transplant process. In
their comments supporting the
proposed HEPA, a couple commenters
also expressed gratitude to CMS. They
thanked CMS for acknowledging
inequities in the transplant process and
recognizing that low-income patients
may require additional resources to
receive a transplant and overcome social
barriers to health. These commenters
further appreciated CMS for recognizing
the extra challenges and burden faced
by transplant programs when treating
low-income patients, and for its
continued efforts to improve service
delivery for this population. Lastly,
another commenter strongly supported
the inclusion of a HEPA, asserting that
it serves as an important mechanism to
protect IOTA participants from being
unduly penalized for serving a high
volume of low-income populations.
Response: We appreciate the feedback
from commenters. As mentioned in
comment responses noted previously in
this section, we are not finalizing the
proposed HEPA out of the potential for
unintended consequences. We plan to
monitor the effects of the model on lowincome individuals’ access to kidney
transplants as part of the evaluation
process and may consider proposing a
new or updated policy through future
notice and comment rulemaking,
depending on performance by IOTA
participants.
Comment: Multiple commenters
suggested that CMS only apply the
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proposed HEPA to living donor
transplants. For example, a commenter
commended CMS for including the
proposed HEPA, noting its structure as
a reward-only mechanism. The
commenter further suggested that CMS
implement a similar ‘‘reward-only’’
multiplier based on donor
characteristics, which could be
integrated into IOTA participants’
transplant counts in a similar way.
Additionally, the commenter could also
envision a multiplier for living
donations from historically
disadvantaged groups, such as rural and
underserved areas. To avoid
incentivizing IOTA participants to
prioritize deceased donor transplants for
low-income candidates out-of-sequence,
a commenter suggested that CMS apply
the proposed HEPA policy only to living
donor transplants.
Response: We thank the commenters
for their suggestions. We will not be
finalizing the proposed HEPA at this
time, as described in comment
responses noted previously in this
section, but may consider this idea in
future notice and comment rulemaking
as we continue to assess ways to address
inequities in the transplant process.
Comment: A commenter expressed
their appreciation for CMS’ focus on
low-income patients but noted that
these individuals frequently arrive at
transplant hospitals with more
advanced disease, often due to delayed
referrals. Accordingly, the commenter
urged CMS to explore alternative
models that would facilitate earlier
kidney health screenings and improve
primary care access for these
underserved populations.
Response: We appreciate the
commenters’ feedback. However, we
believe that the IOTA Model works
alongside other CMS initiatives aimed at
earlier intervention for patients with
kidney disease, such as the KCC Model,
which focuses on managing care for
Medicare beneficiaries with chronic
kidney disease and end-stage renal
disease.
Comment: Multiple commenters
agreed with CMS’ decision to not use
ADI, pointing out many of the
limitations in using ADI to measure
inequity in the transplant process. For
example, a commenter argued that using
the ADI is less optimal than the
approach proposed by CMS. The
commenter stated that the ADI is a more
difficult criterion for transplant
hospitals to apply when identifying
patients who would qualify for and
benefit from interventions. This added
complexity would undermine one of the
key strengths of the IOTA Model—
simplicity. As a result, the commenter
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felt that the ADI would be less effective
than the clearly defined socioeconomic
status (SES) eligibility criteria put forth
by CMS in driving behavioral changes at
the transplant hospital level.
Additionally, the commenter noted that
while the ADI is a valuable tool,
transplant hospitals typically have a
more granular understanding of
individual patients’ SES, allowing them
to easily and immediately identify those
who should receive additional support.
While another commenter accepted the
proposed low-income population
definition for this model, recognizing
the limitations of the ADI, noting that it
fails to adequately capture low-income
populations across all regions.
Response: We thank the commenters
for their support and do not plan to use
the ADI as a way to identify
underserved populations in the IOTA
Model.
After consideration of public
comments received, for the reasons set
forth in this rule, CMS is not finalizing
the Health Equity Performance
Adjustment to the achievement domain,
due to the potential for unintended
consequences, some of which were
pointed out by commenters. We still
recognize that there are many inequities
in the transplant process and may
propose alternative approaches in future
notice and comment rulemaking that
could address some of the potential
consequences laid out by commenters.
We also plan to monitor and evaluate
the results of the IOTA Model in an
effort to see which patients receive
transplants in an effort to monitor for
any impact of the model based on
patient insurance status. However, we
are finalizing our proposed
methodology for calculating the number
of kidney transplants performed during
the PY at § 512.424(d) with slight
modification. Specifically, since we are
not finalizing the proposed health
equity performance adjustment at this
time, we are modifying our regulation at
§§ 512.424(d)(1)(i) and (2) to remove the
cross reference to the health equity
performance adjustment.
d. Efficiency Domain
At § 512.402 of the proposed rule, we
proposed to define the ‘‘efficiency
domain’’ as the performance assessment
category in which CMS assesses the
IOTA participant’s performance using
the organ offer acceptance rate ratio as
described in § 512.426. In section
III.C.5.d(1) of the proposed rule, we
stated that the efficiency domain is
focused on improving the overall
efficiency of the transplant ecosystem.
In section III.C.5.d(1) of the proposed
rule, we proposed including OPTN’s
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organ offer acceptance rate ratio
measure in the efficiency domain. The
organ offer acceptance rate ratio
measure is a ratio of observed organ
offer acceptances versus expected organ
offer acceptances, as described in
section III.C.5.d.(1) of the proposed rule.
(1) Organ Offer Acceptance Rate Ratio
As reviewed in section III.C.5.d(1) of
the proposed rule, with over 90,000
unique patients on the waiting list for a
kidney transplant, the need to
effectively use every available donor
organ is critical. However, despite the
new allocation system introduced in
2021, and more organs being offered
over a wider geographic area, the kidney
discard rate has risen to over 24.6
percent and continues to trend
upwards.205 There is a significant
shortage of organs available for
transplantation, and many patients die
waiting for a kidney transplant.
Moreover, there are large disparities in
organ offer acceptance ratio
performance. A 2020 national registry
study found that the probability of
receiving a deceased donor kidney
transplant within three years of
placement on the waiting list varied as
much as 16-fold amongst different
kidney transplant hospitals across the
U.S.206 The study also found that large
variations were still present between
kidney transplant hospitals that utilized
the same OPO and that the probability
of transplant was significantly
associated with transplant hospitals’
offer acceptance rates.207 By
incentivizing kidney organ offer
acceptance, we aimed to optimize the
use of available organs, thereby
reducing underutilization and discards
of quality donor organs.
For purposes of assessing the
performance of IOTA participants in the
achievement domain, we proposed in
section III.C.5.d(1) of the proposed rule
to include the organ offer acceptance
rate ratio as one of the two metrics of
205 MN, 1Scientific R. of T.R., Hennepin
Healthcare Research Institute, Minneapolis. (n.d.).
Kidney. Srtr.transplant.hrsa.gov. Retrieved June 19,
2023, from https://srtr.transplant.hrsa.gov/annual_
reports/2021/Kidney.aspx.
206 King, K.L., Husain, S.A., Schold, J.D., Patzer,
R.E., Reese, P.P., Jin, Z., Ratner, L.E., Cohen, D.J.,
Pastan, S.O., & Mohan, S. (2020). Major Variation
across Local Transplant Centers in Probability of
Kidney Transplant for Wait-Listed Patients. Journal
of the American Society of Nephrology, 31(12),
2900–2911. https://doi.org/10.1681/
ASN.2020030335.
207 King, K.L., Husain, S.A., Schold, J.D., Patzer,
R.E., Reese, P.P., Jin, Z., Ratner, L.E., Cohen, D.J.,
Pastan, S.O., & Mohan, S. (2020). Major Variation
across Local Transplant Centers in Probability of
Kidney Transplant for Wait-Listed Patients. Journal
of the American Society of Nephrology, 31(12),
2900–2911. https://doi.org/10.1681/
ASN.2020030335.
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performance. We believed that
including this measure in the efficiency
domain would encourage IOTA
participants to increase the utilization of
available organs. We also believed that
this measure would encourage IOTA
participants to improve efficiency in the
organ offer process, improve acceptance
practices for offers received, and allow
for maximal utilization of available
organs. We believed that the organ offer
acceptance rate ratio is an important
system-wide metric, as improved
performance by an IOTA participant
would also improve opportunities for
other kidney transplant hospitals that
would not have to wait as long for an
available donor kidney. We recognized
that all kidney transplant hospitals are
already assessed on the organ offer
acceptance rate ratio metric under the
OPTN, however, we believed that the
IOTA Model sets a higher bar for
performance, as discussed in section
III.C.5.d.(1)(a) of the proposed rule,
rather than clearing the threshold that
the OPTN sets at 0.30.208
As stated in section III.C.5.d(1) of the
proposed rule, in the United States,
kidney transplant waitlist candidates
face considerable disparities in access to
kidney transplant, such as in who is
referred and placed on the waiting list,
who remains ‘‘active’’ on the waiting
list, and how waitlisted patients are
managed by kidney transplant
hospitals.209 Additionally, kidney
208 Enhance Transplant Program Performance
Monitoring System OPTN Membership and
Professional Standards Committee. (n.d.). https://
optn.transplant.hrsa.gov/media/4777/transplant_
program_performance_monitoring_public_
comment_aug2021.pdf.
209 Schold, J.D., Gregg, J.A., Harman, J.S., Hall,
A.G., Patton, P.R., & Meier-Kriesche, H.-U. (2011).
Barriers to Evaluation and Wait Listing for Kidney
Transplantation. Clinical Journal of the American
Society of Nephrology, 6(7), 1760–1767. https://
doi.org/10.2215/cjn.08620910; Hod, T., & GoldfarbRumyantzev, A.S. (2014). The role of disparities
and socioeconomic factors in access to kidney
transplantation and its outcome. Renal Failure,
36(8), 1193–1199. https://doi.org/10.3109/
0886022x.2014.934179; Stolzmann, K.L., Bautista,
L.E., Gangnon, R.E., McElroy, J.A., Becker, B.N., &
Remington, P.L. (2007). Trends in kidney
transplantation rates and disparities. Journal of the
National Medical Association, 99(8), 923–932.
https://www.ncbi.nlm.nih.gov/pmc/articles/
PMC2574300/; Paul, S., Melanson, T., Mohan, S.,
Ross-Driscoll, K., McPherson, L., Lynch, R., Lo, D.,
Pastan, S.O., & Patzer, R.E. (2021). Kidney
transplant program waitlisting rate as a metric to
assess transplant access. American Journal of
Transplantation: Official Journal of the American
Society of Transplantation and the American
Society of Transplant Surgeons, 21(1), 314–321.
https://doi.org/10.1111/ajt.16277; Cheng, X.S.,
Busque, S., Lee, J., Discipulo, K., Hartley, C., Tulu,
Z., Scandling, J.D., & Tan, J.C. (2018). A new
approach to kidney wait-list management in the
kidney allocation system era: Pilot implementation
and evaluation. Clinical Transplantation, 32(11),
e13406. https://doi.org/10.1111/ctr.13406.
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transplant hospital performance is
commonly measured by post-transplant
outcomes. We recognized that including
pre-transplant measures could allow for
a more thorough evaluation of
transplant hospital performance and
provide insight for patient decisionmaking.
In section III.C.5.d(1) of the proposed
rule, we considered several waitlist
management metrics for assessing
performance in the efficiency domain,
such as the number of patients
registered to a waitlist, the number or
percentage of attributed patients
registered on a waitlist with an active
waitlist status, or the number or
percentage of attributed patients on a
waitlist with active waitlist status to
inactive waitlist status. Metrics focused
on the waitlist could help assess how
effectively kidney transplant hospitals
are managing their kidney transplant
waitlist patients. Organ offers to waitlist
kidney transplant patients are made
directly to the kidney transplant
hospital where they are waitlisted. Once
a kidney transplant hospital receives an
organ offer for one of their kidney
transplant waitlist patients, it is
ultimately its decision to accept or
decline an organ offer on the patient’s
behalf. Kidney transplant hospitals are
not required to inform kidney transplant
waitlist patients for whom an offer was
received when an organ offer was
received or why an organ offer was
declined. While we understood the
importance of a transplant surgeon’s
clinical decision-making and respected
the clinical judgement of transplant
surgeons, declining an offer without
involving the affected patient in the
decision-making can be detrimental to
the patient, as additional time on the
waitlist can negatively impact the
patient’s quality of life.210
As stated in section III.C.5.d(1) of the
proposed rule, we also considered
including a waitlist mortality metric for
assessing efficiency domain
performance, so as to incentivize
improvements in mortality outcomes of
attributed patients on a waitlist. On
average, as many as 20 patients on the
waitlist for a kidney transplant die each
day waiting for a kidney transplant in
the United States.211 While a waitlist
210 Husain, S.A., King, K L., Pastan, S., Patzer,
R.E., Cohen, D.J., Radhakrishnan, J., & Mohan, S.
(2019). Association Between Declined Offers of
Deceased Donor Kidney Allograft and Outcomes in
Kidney Transplant Candidates. JAMA Network
Open, 2(8), e1910312. https://doi.org/10.1001/
jamanetworkopen.2019.10312.
211 Delmonico, F.L., & McBride, M.A. (2008).
Analysis of the Wait List and Deaths Among
Candidates Waiting for a Kidney Transplant.
Transplantation, 86(12), 1678–1683. https://doi.org/
10.1097/tp.0b013e31818fe694.
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mortality metric may help assess patient
outcomes and experience while waiting
for an organ offer,212 and provide
insight into differences in waitlist
management practices across kidney
transplant hospitals, we recognize that
waitlist mortality rate is also influenced
by the insufficient supply of donor
organs available for transplantation. We
also recognized that IOTA participants
may not have a direct effect on, or
ability to improve, mortality metrics, as
nephrologists are also closer to the
direct care of waitlist patients and
would have a greater ability to affect
their care and mortality rate.
Furthermore, we believed that we are
already testing the ability of
nephrologists to manage care for
Medicare beneficiaries with ESRD or
CKD via the KCC Model.
We also considered several other
metrics for assessing efficiency domain
performance related to time to
transplant, as outlined in section
III.C.5.d(1) of the proposed rule, such
as—
• Time from initial evaluation to
transplant;
• Time from initial referral to
transplant;
• Time from initial placement on a
waitlist to transplant; and
• Time from when a patient was
initially referred to time of initial
evaluation to time of initial placement
on a waitlist to time to transplant.
As discussed in section III.C.5.d(1) of
the proposed rule, before a patient can
be considered for, and placed on, the
waiting list for a kidney transplant, they
must first be referred by either a
nephrologist or dialysis facility, at
which point they undergo a
comprehensive evaluation process by a
transplant hospital.213 Studies have
212 Shepherd, S., & Formica, R.N. (2021).
Improving Transplant Program Performance
Monitoring. 8(4), 293–300. https://doi.org/10.1007/
s40472-021-00344-z; Wey, A., Gustafson, S.K.,
Salkowski, N., Kasiske, B.L., Skeans, M.,
Schaffhausen, C.R., Israni, A.K., & Snyder, J.J.
(2019). Association of pretransplant and
posttransplant program ratings with candidate
mortality after listing. 19(2), 399–406. https://
doi.org/10.1111/ajt.15032.
213 Paul, S., Plantinga, L.C., Pastan, S.O., Gander,
J.C., Mohan, S., & Patzer, R.E. (2018). Standardized
Transplantation Referral Ratio to Assess
Performance of Transplant Referral among Dialysis
Facilities. Clinical Journal of the American Society
of Nephrology, 13(2), 282–289. https://doi.org/
10.2215/cjn.04690417; Redeker, S., Massey, E.K.,
van Merweland, R.G., Weimar, W., Ismail, S.Y., &
Busschbach, J.J.V. (2022). Induced demand in
kidney replacement therapy. Health Policy, 126(10),
1062–1068. https://doi.org/10.1016/
j.healthpol.2022.07.011; Knight, R.J., Teeter, L.D.,
Graviss, E.A., Patel, S.J., DeVos, J.M., Moore, L.W.,
& Gaber, A.O. (2015). Barriers to Preemptive Renal
Transplantation. Transplantation, 99(3), 576–579.
https://doi.org/10.1097/tp.0000000000000357;
Schold, J.D., Patzer, R.E., Pruett, T.L., & Mohan, S.
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shown long-standing barriers and
disparities to access to transplantation
by patient demographics, such as racial/
ethnic, sex, socioeconomic, and
insurance factors.214 Disparities are
driven by various factors, but we
recognized that delays or lack of
referrals for evaluation, evaluation
criteria that may unintentionally deem a
patient not eligible to be placed on a
waitlist, and organ acceptance rate
variations across kidney transplant
hospitals, may exacerbate disparities.
Thus, measuring time to transplant was
considered an appropriate potential
performance metric that could
incentivize IOTA participants to
improve. However, we chose not to
propose this type of measure due to
concerns about how to properly
measure start and end points and
unintended consequences that may
harm patients, as it may create
opportunities for kidney transplant
hospitals to manipulate average times
by only adding patients to the waitlist
when they are certain of imminent
transplant, which could exacerbate
waitlist inequities.
We also considered including a
transplantation referral to evaluation
conversion rate measure, as discussed in
section III.C.5.d(1) of the proposed rule.
For patients with ESRD, access to
transplantation is influenced by both
referral patterns of pre-transplantation
providers and transplant hospital
processes of care and evaluation
criteria.215 Additionally, some studies
found considerable variation in referral
rates to transplantation by dialysis
facilities, proposing significant regional
and facility-level variation in care.216
(2019). Quality Metrics in Kidney Transplantation:
Current Landscape, Trials and Tribulations, Lessons
Learned, and a Call for Reform. American Journal
of Kidney Diseases, 74(3), 382–389. https://doi.org/
10.1053/j.ajkd.2019.02.020.
214 Shepherd, S., & Formica, R.N. (2021).
Improving Transplant Program Performance
Monitoring. 8(4), 293–300. https://doi.org/10.1007/
s40472-021-00344-z; Ernst, Z., Wilson, A., Peña, A.,
Love, M., Moore, T., & Vassar, M. (2023). Factors
associated with health inequities in access to
kidney transplantation in the USA: A scoping
review. Transplantation Reviews, 100751. https://
doi.org/10.1016/j.trre.2023.100751.
215 Schold, J.D., Patzer, R.E., Pruett, T.L., &
Mohan, S. (2019). Quality Metrics in Kidney
Transplantation: Current Landscape, Trials and
Tribulations, Lessons Learned, and a Call for
Reform. American Journal of Kidney Diseases,
74(3), 382–389. https://doi.org/10.1053/
j.ajkd.2019.02.020.
216 Ibid; Alexander, G. Caleb., & Sehgal, A.R.
(2002). Variation in access to kidney transplantation
across dialysis facilities: Using process of care
measures for quality improvement. American
Journal of Kidney Diseases, 40(4), 824–831. https://
doi.org/10.1053/ajkd.2002.35695; Patzer, R.E.,
Plantinga, L.C., Paul, S., Gander, J., Krisher, J.,
Sauls, L., Gibney, E.M., Mulloy, L., & Pastan, S.O.
(2015). Variation in Dialysis Facility Referral for
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However, because dialysis facilities are
often the primary referrer and are not
IOTA participants, we did not propose
this measure. We also had concerns
about how this data would be collected.
Finally, we also considered a living
donor rate as one of the metrics used to
assess performance in the efficiency
domain to measure percentage of
potential living donors who are
evaluated to donate a kidney and that
actually donated a kidney. This metric
could help assess success towards
addressing living donor concerns and
improvements in education on the
living donor process. However, we did
not propose this metric because we have
concerns about our ability to access data
needed for measurement.
Ultimately, as discussed in section
III.C.5.d(1) of the proposed rule, we
chose not to propose to include waitlist
management metrics when assessing
IOTA participant performance in the
efficiency domain because we believed
that waitlist costs are already accounted
for in the Medicare cost report.
Transplant waitlist measures also do not
capture living donation, which is an
additional path to a successful kidney
transplant that CMS already
incentivizes living donations in the ETC
Model. Moreover, studies have shown
that organ acquisition costs have been
rising and were not solely attributable to
the cost of procurement, suggesting that
an increased focus on the waiting list
could further increase Medicare
expenditures.217 Also, for some of the
measures considered (that is, waitlist
mortality, transplantation referral to
evaluation rate), nephrologists and
dialysis facilities play large roles in
maintaining the patient’s health, and we
did not believe it is appropriate to
include a measure that would depend
largely upon the behavior and actions of
physicians and facilities other than the
IOTA participant. We also thought this
type of measure could distract from
increasing rates of transplant and
provide false expectations for time to
transplant for kidney transplant waitlist
patients. We are also concerned that a
waitlist measure could have unintended
consequences and potentially lead to
those most in need of transplant not
being listed to receive a transplant.
Kidney Transplantation Among Patients With EndStage Renal Disease in Georgia. JAMA, 314(6), 582.
https://doi.org/10.1001/jama.2015.8897.
217 Cheng, X.S., Han, J., Braggs-Gresham, J.L.,
Held, P.J., Busque, S., Roberts, J.P., Tan, J.C.,
Scandling, J.D., Chertow, G.M., & Dor, A. (2022).
Trends in Cost Attributable to Kidney
Transplantation Evaluation and Waitlist
Management in the United States, 2012–2017.
JAMA Network Open, 5(3), e221847. https://doi.org/
10.1001/jamanetworkopen.2022.1847.
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We solicited comment on our
proposed organ offer acceptance rate
ratio metric for purposes of assessing
performance in the efficiency domain,
and the alternatives considered.
The following is a summary of the
comments received on our proposed use
of the organ offer acceptance rate ratio
in the efficiency domain and our
responses:
Comment: A commenter was
specifically opposed to including the
OPTN organ offer acceptance rate
measure in the efficiency domain. A few
other commenters were concerned with
using the organ offer acceptance rate
ratio because it may be inflated by a
high use of out-of-sequence kidneys, or
it may promote kidney transplant
hospitals to perform more DDKTs.
Response: We thank the commenters
for their feedback. While there is no
downside risk for out-of-sequence
allocation we acknowledge commenters’
concerns that an unintended
consequence of using the organ offer
acceptance rate ratio performance
metric could be a rise in out-of-sequence
allocation. We encourage the transplant
community to continue providing
feedback about appropriately capturing
out-of-sequence organ offers, as we will
consider this for future rulemaking and
performance years. While we agree with
the commenter who stated that the
organ offer acceptance rate ratio metric
may increase DDKTs, we do not believe
that this automatically means that
transplants will be of lesser quality.
There are currently underutilized
subsets of deceased donor kidneys and
high rates of organ non-use 218 due to a
number of reasons including, but not
limited to, systematic inefficiencies 219
and lack of organ filters.220 We refer
readers to sections III.B and III.C.5.d(1)
of this final rule for further discussion
on organ acceptance patterns. Preexisting OPTN mortality metrics and the
new composite graft survival metric that
we mention in section III.C.5.e of this
final rule discourage transplant
programs from transplanting kidneys
that are very obviously not viable.
Comment: A few commenters
suggested that if CMS is using the organ
218 Mohan, S., Yu, M., King, K.L, & Husain, S.A.
(2023) Increasing discards as an unintended
consequence of recent changes in United States
kidney allocation policy. Kidney International
Reports, 8(5): 1109–1111. https://doi.org/10.1016/
j.ekir.2023.02.108.
219 Wood, N.L., VanDerwerken, D.N., Segev, D.L.,
& Gentry, SE (2022). Increased logistical burden in
circle-based kidney allocation. Transplantation,
106(10): 1885–1887. https://doi.org/10.1097/
tp.0000000000004127.
220 UNOS. (2023, September 5). Research in focus:
Examining organ offers. Retrieved October 11, 2024
from https://unos.org/news/in-focus/organ-offers/
#Impact.
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offer acceptance rate ratio measure, that
they need to address out-of-sequence
allocation, should utilize SRTRs risk
adjustment, and should modify OPTN
codes to develop more targeted
responses to the discard rate.
Response: We thank the commenters
for their suggestions. As previously
mentioned in comment responses in
this section, while there is no downside
risk for out-of-sequence allocation in the
IOTA Model, we acknowledge
commenters concern that an unintended
consequence of using the organ offer
acceptance rate ratio performance could
be an increase in out-of-sequence
allocation. We encourage the transplant
community to continue providing
feedback about appropriately capturing
out-of-sequence organ offers, as we will
consider this for future rulemaking and
performance years.
We intend to use the SRTR risk
adjustment model for the offer
acceptance metric; see section III.C.5.e
of this final rule for more details.
Comment: A commenter stated that
offers should be analyzed via validated
metrics.
Response: We thank the commenter
for their response. The organ offer
acceptance rate ratio has been utilized
by SRTR since 2023 and while lacking
formal validation, is not unknown to the
transplant community.221 With the use
of this measure by SRTR and CMS by
way of the IOTA Model, we believe this
creates opportunity to better understand
its validity and adapt risk-adjustment.
Comment: A commenter requested
clarification around what is considered
an ‘‘unsuitable kidney’’ in the list of
exclusions for the expected organ offer
acceptance.
Response: We recommend the
commenter review Table 6 in section
III.C.5.d(1)(a) of this final rule, for a full
list of exclusions from the measure.
While an ‘‘unsuitable kidney’’ is not
specifically listed in the exclusion list,
we believe that the exclusion criteria of
a kidney having a ‘‘match run with no
acceptances’’ would apply.
Comment: A commenter was
concerned that the organ offer
acceptance metric was too broad and
should be calculated based on offers
within and outside of a 250-mile radius
given the variation in regional importing
of organs and the variation in kidney
transplant hospital wait times.
Response: We appreciate the
commenter’s feedback. This was not a
221 OPTN. (2023, September 14). New pretransplant performance metric now in effect, offer
acceptance rate ratio. Retrieved August 15, 2024
from https://optn.transplant.hrsa.gov/news/newpre-transplant-performance-metric-now-in-effectoffer-acceptance-rate-ratio/.
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consideration made during proposed
rulemaking in order to align our metric
with the pre-existing SRTR
methodology. We are, however,
interested in considering this for future
rulemaking.
Comment: Many commenters stated
their support for the use of OPTN’s
organ offer acceptance rate measure in
the efficiency domain.
Response: We thank the commenters
for their support.
Comment: A commenter conveyed
concern that listing practices could
penalize them in the efficiency domain.
For example, if a transplant program
listed all patients for high KDPI kidneys,
resulting in passing on kidneys for
offers sometimes, their organ offer
acceptance rate ratio could be impacted.
Response: We appreciate the
commenter’s feedback. While IOTA
participants may choose to encourage
all their patients to enroll for kidneys
with a KDPI greater than 85 to increase
their offer opportunity, as the
commenter points out, this may have
risks. The purpose of selecting organ
offer acceptance rate ratio as a metric is
to increase utilization of available
organs. If frequent ‘‘passing’’ is
occurring for patients listed for kidneys
with a KDPI greater than 85, there may
be additional opportunities for utilizing
filters. We also acknowledge that no
transplant program will accept every
offer they receive due to outliers and
offers that may not be ideal due to
comorbidities/risks of the donor kidney
and recipient or both. Results in PY 1
will be monitored closely, to help
identify reasonable and achievable
organ offer acceptance ratio goals for
future performance years and
rulemaking.
Comment: A commenter was
concerned that organ offer acceptance
rate metric will encourage more
conservative choices, which contradicts
increasing overall kidney transplant
volume, another goal of the IOTA
Model.
Response: We appreciate your
feedback; however, we believe the three
performance domains counterbalance
each other. The three performance
domains challenge IOTA participants to
consider if there is opportunity for
growth in their kidney transplant
hospital and how to navigate the task of
increasing volume while offering a good
quality of life for patients and
appropriate long-term outcomes while
minimizing non utilization of organs
when possible. We would argue that the
organ offer acceptance rate ratio
measure does not encourage
conservative choices but rather choices
that better align with organs they will
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accept, to prevent overall organ non-use.
We are asking IOTA participants to
consider fine-tuning their organ offer
filters and general processes.
Comment: A commenter suggested
that the performance measures include
a metric assessing performance in
excluded communities, awarding IOTA
participants more points who have
organ offer acceptance rate ratios
matching population needs and
showing evidence of improved access to
underserved populations. Similarly, a
commenter suggested stratifying organ
offer acceptance rate ratios by the
beneficiary’s payer, race, ethnicity and
the income of the local population.
Response: Thank you for your
responses. We did not consider further
stratifying organ offer acceptance rate
ratio goals. This approach could aid in
identifying disparities across kidney
transplant waitlist patients and organ
offer acceptance patterns; however, it
may be challenging to create adjusted
metrics specific to each IOTA
participant and their local population
needs. This would also require IOTA
participants to annually identify their
local population to formulate baselines.
These calculations would then need to
be utilized to determine how to award
points to IOTA participants who exceed
expectations for underserved
populations. We are interested in
considering how the organ offer
acceptance rate ratio could be tailored to
local populations and underserved
communities during future rulemaking.
Comment: A couple of commenters
suggested CMS use a living donor
metric. They had specific concerns that
a domain dependent on DDKTs may not
help to increase LDKTs. A commenter
stated that CMS should include a living
donor metric such as converting
potential to actual living donors, and
another stated CMS should implement a
living donor and pre-emptive transplant
measure given the significant benefits
with living donation.
Response: We thank the commenters
for their suggestions. We intend to
further consider how living donor
metrics could be included in future
rulemaking. Setting a target number for
the number of living donor evaluations
versus the actual number of living donor
evaluations who proceed with a surgery
creates numerous risks. This could
inadvertently cause kidney transplant
hospitals to change their practices for
those patients they accept for
evaluations (potentially lowering
criteria thresholds) or who they approve
to be donors. Either result could cause
reduced access to donation and create
ethical concerns or both. While we do
not believe that this would be an
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appropriate metric for the IOTA Model,
we do however, encourage ongoing
feedback about other opportunities for
metrics specific to living donation.
Comment: A commenter requested
use of a measure that does not
incentivize acceptance of organ offers
for the sole purpose of reaching a target
number.
Response: We appreciate the
commenter’s feedback. We acknowledge
that almost all metrics are imperfect.
The purpose of including organ offer
acceptance rate as a metric is to increase
utilization of available organs in the
system. The efficiency domain, as
proposed, is not dependent on volume
of kidney transplants performed but
how well kidney transplant hospitals
can prevent receiving offers they will
knowingly decline, how kidney
transplant hospitals can optimize filters
to meet their individual needs and
minimize organ non-use. We believe
that performing well in the efficiency
domain will result in more efficient
utilization of organs, which can impact
the number of organs transplanted.
Comment: A commenter
recommended following adjusted nonuse rates to account for different donor
pools year-to-year.
Response: We appreciate the
commenter’s response. In the context of
organ offer acceptance rate ratio, as
described and finalized in III.C.5.d (1)(a)
of this final rule, we are utilizing a risk
adjustment model from year-to-year to
account for consistent measurement
between PYs.
Comment: A commenter suggested
adding a metric that specifically follows
non-utilization, particularly for kidneys
with a KDPI greater than 85.
Response: We thank the commenter
for their suggestion. While kidneys with
a KDPI greater than 85 have high nonuse rates, we recognize that there is
underutilization of kidneys in all
categories. Furthermore, in PY 1 we
believe it is ideal to improve utilization
broadly, which allows IOTA
participants the flexibility to focus on
improving access to groups of donors
and recipients that may vary between
regions and IOTA participants.
Comment: A commenter suggested
routine reviews of the organ offer
acceptance rate ratio metric to guarantee
high quality outcomes.
Response: We thank the commenter
for their feedback and agree that the
organ offer acceptance rate ratio
calculations and goals will need to be
monitored closely to ensure their use
improves the performance of IOTA
participants without unintended
consequences. If analysis results
warrant a new or updated policy, we
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point allocation and calculation
methodology for the organ offer
acceptance rate ratio metric.
We are also codifying at § 512.402 the
definition of efficiency domain as the
performance assessment category in
which CMS assesses the IOTA
participant’s performance using the
organ offer acceptance rate ratio as
described in § 512.426. We intend to
analyze and monitor IOTA participant
performance to ensure we do not
unduly disadvantage IOTA participants
selected for the IOTA Model. If analysis
warrants a new or updated policy, we
will address it pursuant to future
rulemaking.
(a) Calculation of Metric
In section III.C.5.d(1)(a) of the
proposed rule, we proposed calculating
organ offer acceptance rates for an IOTA
participant using OPTN’s offer
acceptance rate ratio performance
ddrumheller on DSK120RN23PROD with RULES2
Organ Offer Acceptance Rate Ratio
metric (see Equation 1). Per OPTN’s new
offer acceptance rate ratio, a rate ratio
for a kidney transplant hospital that is
greater than 1 indicates that the kidney
transplant hospital usually accepts more
offers than expected. A rate ratio that is
less than 1 conveys a kidney transplant
hospital’s tendency to accept fewer
offers than expected compared to
national offer acceptance practices.222
The OPTN MPSC has reported that this
metric assesses kidney transplant
hospitals’ rate of observed organ offer
acceptances to expected acceptances
and is intended to answer the following
question: Given the types of offers
received to the specific candidates, does
this program accept offers at a rate
higher/lower than national experience
for similar offers to similar
candidates.223
Equation 1: Organ Offer Acceptance
Rate Ratio 224
Number of Acceptances + 2
Number of Expected Acceptances + 2
As discussed in section III.C.5.d(1)(a)
of the proposed rule, expected
acceptances are based solely on kidneys
that are accepted and transplanted by a
kidney transplant hospital, so
unsuitable kidneys are excluded from
this measure, and are calculated using
logistic regression models to determine
the probability that a given organ offer
will be accepted. The measure, as
specified by SRTR methodology, is
inherently risk adjusted as it only
counts organs that are ultimately
accepted by a kidney transplant
hospital.225 We proposed to use SRTR
data to calculate the OPTN organ offer
acceptance rate ratio, as described in
section III.C.5.d.(1)(a) of the proposed
rule.
Per the SRTR measure, we proposed
in section III.C.5.d(1)(a) of the proposed
rule, dividing the number of kidney
transplant organs accepted by each
IOTA participant (numerator) by the
risk-adjusted number of expected organ
offer acceptances (denominator).226 This
measure utilizes a logistic regression
and risk adjusts for the following: donor
quality and recipient characteristics;
donor-candidate interactions, such as
size and age differences; number of
previous offers; and, distance of
potential recipient from the donor.227
We proposed to use SRTR’s adult
kidney model strata risk adjustment
methodology and most recently
available set of coefficients to calculate
the number of expected organ offer
acceptances.
For example, suppose we have a
model for predicting the probability a
kidney offer will be accepted, and this
model adjusts for the number of years
the candidate has been on dialysis,
whether the kidney was biopsied, and
the distance between the donor hospital
and the candidate’s transplant center
(89 FR 43557). Consider the offer of a
biopsied kidney 150 nautical miles
(NM) away to a candidate who has been
on dialysis for 2 years. As described in
section III.C.5.d(1)(a) of the proposed
rule, to calculate the probability of
acceptance, we would first multiply
these values by their respective model
coefficients and then sum up those
products with the model’s intercept, as
illustrated in Table 5.228
222 OPTN. (2022). OPTN Enhanced Transplant
Program Performance Metrics. https://
optn.transplant.hrsa.gov/media/r5lmmgcl/mpsc_
performancemetrics_3242022b.pdf.
223 Mpsc-enhance-transplant-programperformance-monitoring-system_srtr-metrics.pdf.
(n.d.). Retrieved December 28, 2022, from https://
optn.transplant.hrsa.gov/media/qfuj3osi/mpscenhance-transplant-program-performancemonitoring-system_srtr-metrics.pdf.
224 Ibid.
225 Scientific Registry of Transplant Recipients.
(n.d.). Risk Adjustment Model: Offer Acceptance.
Offer acceptance. https://www.srtr.org/tools/offeracceptance/.
226 Ibid.
227 SRTR. (2023). Srtr.org. https://tools.srtr.org/
OAModelApp_2205/; Ibid.
228 CMS notes that some risk adjustment factors
in the SRTR models may only apply in certain
ranges of a continuous variable. For example, a term
that applies if the patient’s age at the time of listing
is >35 may be named ‘‘can_age_at_listing_right_
spline_knot_35’’. In these cases, obtain the product
using this formula if the patient’s age at listing was
>35: product = (Age¥35)*(model coefficient).
Others may apply if the value is less than (<) a
specified value. For example, for a term like ‘‘can_
age_at_listing_left_spline_knot_18’’, obtain the
product for a patient younger than 18 as: product
= (18¥Age)*(model coefficient).
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ER04DE24.004</GPH>
will address it pursuant to future
rulemaking.
After consideration of public
comments, for the reasons set forth in
this rule, we are finalizing, as proposed,
our provisions to assess performance in
the efficiency domain using the organ
offer acceptance rate ratio metric at
§§ 512.426(a) and (b), as described and
finalized in section III.C.5.d(1) of this
final rule. We direct readers to section
III.C.5.d(1)(a) of this final rule for a full
discussion on the organ offer acceptance
rate ratio methodology. As described
and finalized in section III.C.5.d(1)(c) of
this final rule, we are finalizing the
proposed provisions for the point
allocation and calculation methodology
for the efficiency domain scoring and
scoring for organ offer acceptance rate
ratio for the IOTA Model at § 512.426(c),
with slight modifications. We direct
readers to section III.C.5.d.(1).(b). of this
final rule for a full discussion on the
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TABLE 5: EXAMPLE OF SUMMING UP COEFFICIENTS
-0.525
-0.225
-2
use 1 for interce t
Total
We would then plug that total into the
following equation (see Equation 2) to
get that the probability of acceptance is
approximately 0.119 (that is., 11.9
percent chance of acceptance).
Equation 2: Probability of Organ Offer
Acceptance
not appear on the match run, usually to
avoid discard if the intended recipient
is unable to undergo transplant. If the
eventual recipient was not a multi-organ
transplant candidate and was blood type
compatible per kidney allocation policy,
then these transplants would be
included in the organ offer acceptance
rate. For purposes of the IOTA Model,
we proposed to define ‘‘match run’’ as
a computerized ranking of transplant
candidates based upon donor and
candidate medical compatibility and
criteria defined in OPTN policies.
Per OPTN’s new organ offer
acceptance rate ratio, as described in
section III.C.5.d(1)(a) of the proposed
rule, Table 6 summarizes the types of
organ offers that we proposed be
included and excluded in the
calculation of this metric. For the
purposes of organ offers excluded from
the organ offer acceptance rate ratio, we
proposed to define ‘‘missing responses’’
as organ offers that the kidney
transplant hospital received from the
OPO but did not submit a response
(accepting or rejecting) in the allotted
time frame from the time the offer was
made per OPTN policy 5.6.B.229 For
purposes of organ offers excluded from
the organ offer acceptance rate ratio
measure, we proposed to define
‘‘bypassed response’’ as an organ offer
not received due to expedited
placement 230 or a decision by a kidney
transplant hospital to have all of its
waitlisted candidates skipped during
the organ allocation process based on a
set of pre-defined filters matching the
characteristics of the potential organ to
be transplanted.231
229 OPTN. (2023). OPTN Policies. https://
optn.transplant.hrsa.gov/media/eavh5bf3/optn_
policies.pdf.
230 Expedited placement has the potential to
minimize delays in organ allocation by directing
organs that may not be ideal to transplant centers
that have demonstrated a willingness to utilize such
organs. Currently, expedited placement, also known
as ‘‘accelerated placement’’ or ‘‘out-of-sequence’’
allocation, permits OPOs to deviate from the
standard match run, which determines the priority
of patients on the waiting list for organ offers, under
exceptional circumstances. This discretionary tool
of expedited placement is employed by OPOs when
there are suboptimal donor characteristics
associated with donor disease or recovery-related
issues, in order to prevent the organ from going
unused. For numerous years, expedited organ
placement has played a crucial role in organ
allocation, enabling OPOs to promptly allocate
organs that they believe are at risk of not being
utilized for transplantation.
231 King, K.L., S Ali Husain, Cohen, D.J., Schold,
J.D., & Mohan, S. (2022). The role of bypass filters
in deceased donor kidney allocation in the United
States. American Journal of Transplantation, 22(6),
1593–1602. https://doi.org/10.1111/ajt.16967;
Transplant Quality Corner | The New MPSC Metric.
(n.d.). The Organ Donation and Transplantation
Alliance. Retrieved February 23, 2024, from https://
www.organdonationalliance.org/insights/qualitycorner/new-mpsc-metric/.
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To determine the number of offers a
transplant program was expected to
accept, we would add up the probability
of acceptance for every offer that
transplant program received (89 FR
43557). The final organ offer acceptance
rate ratio (OAR) is then constructed
from the observed (O) number of
acceptances and the expected (E)
number of acceptances using Equation 1
as described in section III.C.5.d(1)(a) of
this final rule. In this example we
showed a simple logistic regression
model that only included three riskadjusters. The actual models used by the
SRTR adjust for many more variables,
but the process demonstrated here is the
same.
As discussed in section III.C.5.d(1)(a)
of the proposed rule, a kidney may be
transplanted into a candidate who did
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Probability of Organ Offer Acceptance
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TABLE 6: ORGAN OFFERS INCLUDED AND
EXCLUDED FROM MEASURE232
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•
Organ offers that are ultimately accepted
and transplanted.
Offers to candidates on a single organ
waitlist (except for Kidney/Pancreas
candidates that are also listed for kidney
alone).
As discussed in section III.C.5.d(1)(a)
of the proposed rule, we believed that
IOTA participants could improve on the
organ offer acceptance rate ratio metric
in at least two ways. First, IOTA
participants could increase the number
of organ offers they accept, which
would also potentially lead to greater
performance scores in the achievement
domain. Second, IOTA participants
could also decrease the number of
expected acceptances by adding better
filters so that they are only receiving
offers that they are likely to accept.
Stricter filters may help ensure that an
IOTA participant is not delaying the
allocation of organs that they are
uninterested in that could otherwise be
accepted by another kidney transplant
hospital. Since there are multiple ways
to improve the offer acceptance ratio,
the IOTA Model is not requiring
increased utilization of higher KDPI
kidneys that some IOTA participants
may not want to use due to their clinical
protocols. Additionally, the IOTA
Model is not prescribing or requiring
specific care delivery transformation or
improvement activities of IOTA
participants, so as to allow for flexibility
and innovation.
In section III.C.5.d(1)(a) of the
proposed rule, we considered
calculating the organ offer acceptance
rate by dividing the number of organs
each IOTA participant accepts by the
number offered to that transplant
hospital’s patients that are ultimately
accepted elsewhere; however, the lack
of risk adjustment in this metric may be
232 OPTN. (2022). OPTN Enhanced Transplant
Program Performance Metrics. https://
optn.transplant.hrsa.gov/media/r5lmmgcl/mpsc_
performancemetrics_3242022b.pdf; For Transplant
Center Professionals. (n.d.). www.srtr.org. Retrieved
February 22, 2023, from https://www.srtr.org/faqs/
for-transplant-center-professionals/
#oaconsideration.
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•
•
•
•
•
Multiple match runs from same donor
combined and duplicate offers.
Match run had no acceptances.
Offer occurred after last acceptance in a
match run.
Missing or bypassed response.
Offers to multi-organ candidates (except
for Kidney/Pancreas candidates that are
also listed for kidne alone .
unfair to some IOTA participants (89 FR
43558).
As mentioned in section III.C.5.d(1)(a)
of the proposed rule, we also considered
updating the calculation for organ offer
acceptance rate ratio to account for the
benefits of living donation by increasing
the number of organs in the system
because the proposed organ offer
acceptance rate ratio only shows
improvement in deceased donor
utilization. This modification would
add a single 1 in the numerator and a
single 1 in the denominator for each
living donation a transplant hospital
completes. However, we did not
propose updating the organ offer
acceptance rate ratio because we
decided to focus on deceased donor
acceptance to remain aligned with the
SRTR calculation. We also did not
believe this was appropriate to propose
because we believe that IOTA
participants with an established or high
performing living donation program
would be able to gain points more easily
in the achievement domain, which has
a larger percent of overall points, which
we thought may be unfair to IOTA
participants that do not.
We sought comment on our proposal
to use and calculate the OPTN organ
offer acceptance rate ratio in accordance
with OPTN’s measure specifications and
SRTR’s methodology as the metrics that
would determine IOTA participants’
performance on the efficiency domain.
We also sought comments on the
alternatives we considered.
Additionally, we sought comment on
our proposed definitions.
The following is a summary of the
comments received on our proposed
utilization of the organ offer acceptance
rate ratio using OPTN measure
specifications and SRTR metrics for the
efficiency domain and our responses:
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Comment: Several commenters
requested clarification for how organ
offer filters will be used when
calculating the organ offer acceptance
rate ratio. They were concerned that
using filters may create conflicts
between kidney transplant volume and
offer acceptances.
Response: We appreciate the
commenters’ feedback. Organ offer
filters allow kidney transplant hospitals
to specify characteristics of donors or
donor-recipient matches they would not
transplant at their transplant program,
to prevent unnecessary organ offers and
to allow the organ to go to another
kidney transplant hospital who may
accept the offer, more expeditiously.
Organ filter use does not directly
contribute to the organ offer acceptance
rate ratio calculation. Use of filters,
however, can impact the calculation
result. Kidney transplant hospitals may
choose to use less filters, allowing
increased offers; or they may choose to
use more strict filters to ensure that they
are very likely to accept the offers they
receive. We acknowledge that kidney
transplant hospitals will not accept
every organ offer and that they must
maintain some flexibility to keep some
filter criteria liberal to meet the needs of
some of their beneficiaries, however, we
believe these practices will be relatively
consistent between kidney transplant
hospitals to create comparable results.
We also agree that it may take kidney
transplant hospitals time to optimize
their organ offer filters and their
increase in kidney transplants, which is
one of the reasons that we ensured that
PY 1 does not have any downside risk,
regardless of final performance score.
Comment: A few commenters
requested clarification as to whether
CMS would create a new organ
acceptance rate measure, stating it must
be validated, if so.
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Response: As outlined in section
III.C.5.d(1)(a) of this final rule, we
proposed OPTN’s measure
specifications and SRTR’s methodology.
Comment: Several commenters stated
their concerns about the CMS
calculations for organ offer acceptance
rate ratio. Each commenter within this
group had a different concern, including
the lack of risk adjustment, unfair
comparison of large and small kidney
transplant hospitals, how calculations
are applied to beneficiaries that are
toward the bottom of the waitlist and if
the methodology will make a kidney
transplant hospital’s waitlist criteria
more strict.
Response: We thank the commenters
for their feedback. The SRTR
methodology outlined in section
III.C.5.d.(1).(a). of this final rule
includes a risk-adjusted number of
expected organ offer acceptances in its
calculation.
While we acknowledge the different
challenges of IOTA participants with
variable volumes of kidney transplants,
we also believe that each category of
IOTA participants has different
opportunities to impact their organ offer
acceptance rate ratio. An IOTA
participant with high volume of kidney
transplants may focus on accepting
higher score kidneys, whereas an IOTA
participant with low volume of kidney
transplants may be able to have more
strict filter criteria to ensure the organ
offers they receive are those that they
will accept.
The SRTR methodology is based on a
match run, if the IOTA participant
accepts an organ offer and whether the
IOTA participant was expected to
accept the offer, based on the
methodology and risk adjustment as
described in section III.C.5.d.(1).(a). of
this final rule. If a kidney transplant
waitlist patient is not at the top of the
waiting list and does not match, this
calculation would not be applicable.
Finally, we agree that if a kidney
transplant hospital uses very strict filter
criteria this could impact their waitlist,
however, we also believe it is important
to consider having organ offer filter
criteria reflect the organ offers that their
transplant programs actually accept.
The organ offer acceptance rate ratio
methodology and subsequent use of
organ offer filters encourages IOTA
participants to minimize non-use of
organs and minimize cold ischemic
times.
Comment: A commenter requested
clarification around what is considered
an ‘‘unsuitable kidney’’ in the list of
exclusions for the expected organ offer
acceptance rate ratio.
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Response: We recommend the
commenter review III.C.5.d.(1)(a) Table
6 for a full list of exclusions from the
measure. If a kidney transplant organ is
not used by any kidney transplant
hospital, that kidney is excluded from
the organ offer acceptance rate ratio
calculation.
Comment: A commenter stated they
agreed with the inclusion and exclusion
criteria for organ offers included in the
calculation of the organ offer acceptance
rate ratio.
Response: We thank the commenter
for their support.
Comment: A commenter was
concerned that out-of-sequence kidney
offers are included in the measurement
of success. Similarly, another
commenter suggested CMS monitor the
rate of out-of-sequence allocation that
occurs.
Response: We appreciate the
commenter’s feedback. The commenter
is correct that the SRTR methodology
does not account for out-of-sequence
kidney offers. Given the historic rise of
out-of-sequence allocation over the last
few years, we intend to monitor this
closely.233 If analysis results warrant a
new or updated policy, we will address
it pursuant to future rulemaking.
Comment: A commenter asked that
CMS clarify filter use and how it would
impact those patients that remain after
filtering.
Response: We appreciate the
commenter’s feedback. Organ offer
filters allow kidney transplant hospitals
to specify characteristics of donors or
donor-recipient matches they would not
transplant at their transplant program,
to prevent unnecessary organ offers and
to allow the organ to go to another
kidney transplant hospital who may
accept the offer, more expeditiously. By
utilizing filters that more closely match
what offers a kidney transplant hospital
is likely to accept for their waitlisted
patients, the kidney transplant hospital
will have a higher likelihood of organ
offer acceptance. Furthermore, this
would increase their organ offer
acceptance rate ratio.
Comment: A commenter was
concerned that the SRTR methodology
does not account for non-viable kidneys.
Response: We appreciate the
commenters concern and agree that not
all offers are viable and acknowledges
this in section III.C.5.d.(1).(a). of this
final rule, Table 6, where exclusions for
the organ offer acceptance rate ratio
233 Liyanage, L.N., Akizhanov, D., Patel, S.S.,
Segev, D.L., Massie, A.B., Stewart, D.E., & Gentry,
S.E. (in press). Contemporary prevalence and
practice patterns of out-of-sequence kidney
allocation. American Journal of Transplantation.
https://doi.org/10.1016/j.ajt.2024.08.016.
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metric are included. Kidney match runs
that have no acceptances are excluded
in this metric. The calculation leaves
‘‘viability’’ judgment to the kidney
transplant hospitals. If the commenter is
concerned that there are too many nonviable kidney organ offers occurring,
this would be a matter that may need to
be discussed with OPOs and is outside
the scope of the IOTA Model.
Comment: A commenter disagreed
with use of the SRTR data because the
c-statistic of their tool has not been
published.
Response: We thank the commenter
for their feedback. Availability of the
published c-statistic of the SRTR data is
not something we took into
consideration, however, we believe that
the SRTR methodology and OPTN data
is appropriate for use in the IOTA
Model given its risk adjustment, as
outlined in section III.C.5.d.(1).(a). of
this final rule. If analysis results warrant
a new or updated policy, we will
address it pursuant to future
rulemaking.
Comment: A commenter suggested
CMS modify its organ offer acceptance
rate ratio calculation methodology by
dividing accepted organs by organs
offered elsewhere that are accepted.
Response: We thank the commenter
for their suggestion. We previously
considered this as an option for the
efficiency domain performance metric;
however, we were concerned that the
lack of risk adjustment would be unfair
to IOTA participants.
Comment: A few commenters
suggested that CMS not use the SRTR
methodology. Each individual
commenter had a different concern,
including that this methodology follows
unproven outcomes, that the UNOS data
is more up to date than SRTR data, and
that using SRTR methodology conflicts
with the achievement domain.
Response: We appreciate the
commenters’ suggestions and concerns
and hope to provide some clarification.
We are not using SRTR data and note
that there is not ‘‘UNOS data’’. The
SRTR methodology is calculated with
OPTN data. By using the same
methodology and data as the OPTN’s
organ offer acceptance rate ratio metric,
the IOTA Model results will align with
those tested by OPTN/UNOS, as
recommended by the MPSC. As
previously mentioned, the organ offer
acceptance rate ratio has been utilized
by SRTR since 2023 and while lacking
formal validation, is not unknown to the
transplant community.234 If analysis
234 OPTN. (2023, September 14). New pretransplant performance metric now in effect, offer
acceptance rate ratio. Retrieved August 15, 2024
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results warrant a new or updated policy,
we will address it pursuant to future
rulemaking.
Additionally, we believe SRTR
methodology, or more generally the
organ offer acceptance rate ratio,
ensures balance in the model. While the
achievement domain focuses on
increasing kidney transplant volume,
the efficiency domain metrics focuses
on efficient utilization of kidney
transplants to reduce organ non-use. By
optimizing filters, IOTA participants are
ensuring that their kidney transplant
waitlist patients that are active on the
transplant waitlist will actually be
transplanted. Additionally, we believe
organ filters allow kidneys to be
directed to the appropriate kidney
transplant hospital to improve quality of
organs (lesser cold ischemic time) and
potentially increase volume of
transplants due to a more efficient
process.
After consideration of public
comments, for the reasons set forth in
this rule, we are finalizing, without
modification, at § 512.426(b)(1) our
proposals to use and calculate the OPTN
organ offer acceptance rate ratio in
accordance with OPTN’s measure
specifications and SRTR’s methodology
as the metrics that would determine
IOTA participants’ performance on the
efficiency domain. Additionally, we are
finalizing as proposed the definitions of
match run, missing responses, and
bypassed response at § 512.402.
(b) Calculation of Points
As described in section III.C.5.b. of
the proposed rule, we proposed that
performance on the efficiency domain
would be worth up to 20 points of 100
maximum points. As indicated in
section III.C.5.c.(2). of this final rule, the
efficiency domain is weighted lower
than the achievement domain but equal
to the quality domain to ensure
performance measurement is primarily
focused on increasing number of kidney
transplants, while still incentivizing
efficiency and quality. Within the
efficiency domain, we proposed that the
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OPTN organ offer acceptance rate ratio
would account for the entirety of the 20
allocated points in that domain.
In section III.C.5.d.(1).(b) of the
proposed rule, we proposed applying a
two-scoring system to award up to 20
points to the IOTA participant based on
its performance on the OPTN organ offer
acceptance rate ratio. Under this twoscoring system, we would determine
two separate scores for an IOTA
participant: an ‘‘achievement score’’
reflecting its current level of
performance, and an ‘‘improvement
score’’ reflecting changes in its
performance over time. We proposed
that the IOTA participant would be
awarded points equal to the higher of
the two scores, up to a maximum of 20
points. We believed that this approach
would recognize both high achievement
among high performing IOTA
participants as well as IOTA
participants that make marked
improvement in their performance. We
believe that average or low-performing
IOTA participants would likely require
multiple years of transformation to
catch up with those who have a high
organ offer acceptance rate ratio.
In section III.C.5.d.(1).(b). of the
proposed rule, for achievement scoring,
we proposed that points earned would
be based on the IOTA participants’
performance on the organ offer
acceptance rate ratio ranked against a
national target,235 inclusive of all
eligible kidney transplant hospitals,
both those selected and not selected as
IOTA participants. Currently, there is a
large disparity in organ offer acceptance
ratio performance. As previously noted,
a 2020 national registry study found that
the probability of receiving a deceased
donor kidney transplant within 3 years
of waiting list placement varied 16-fold
between different kidney transplant
hospitals across the U.S.236 Large
235 Subsequent to the publication of the proposed
rule, we found that.
236 King, K.L., Husain, S.A., Schold, J.D., Patzer,
R.E., Reese, P.P., Jin, Z., Ratner, L.E., Cohen, D.J.,
Pastan, S.O., & Mohan, S. (2020). Major Variation
across Local Transplant Centers in Probability of
Kidney Transplant for Wait-Listed Patients. Journal
of the American Society of Nephrology, 31(12),
2900–2911. https://doi.org/10.1681/
ASN.2020030335.
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variations were still present between
kidney transplant hospitals that utilized
the same OPO.237 The probability of
transplant was significantly associated
with transplant hospitals’ offer
acceptance rates.238
We proposed that achievement
scoring points be awarded based on the
national quintiles, as outlined in Table
7 of section III.C.5.d.(1).(b). of this final
rule. Utilizing quintiles aligns with the
calculation of the upside and downside
risk payments in relation to the final
performance score, as detailed in
section III.C.6.c.(2). of this final rule,
where average performance yields half
the number of points. The scoring is
normalized, meaning an average
performing IOTA participant earns 10
points out of 20, 50 percent of the total
possible points. We recognized that
there was an upper limit to the benefits
of efficiency, and quintiles combine the
highest 20 percent of performers in a
point band. Due to the current disparity
among kidney transplant hospitals on
this metric, we did not expect every
IOTA participant to reach top-level
performance.
In the proposed rule, we proposed the
following Organ Offer Acceptance Rate
Achievement point allocation for IOTA
participants, as illustrated in Table 7 of
section III.C.5.d.(1).(b). of this final rule:
• IOTA participants in the 80th
percentile and above, 20 points.
• IOTA participants in the 60th to
below the 80th percentile of performers,
15 points.
• IOTA participants in the 40th to the
60th percentile of performers, 10 points.
• IOTA participants in the 20th to
below the 40th percentile of performers,
6 points.
• IOTA participants who are below
the 20th percentile of performers, 0
points.
237 King, K.L., Husain, S.A., Schold, J.D., Patzer,
R.E., Reese, P.P., Jin, Z., Ratner, L.E., Cohen, D.J.,
Pastan, S.O., & Mohan, S. (2020). Major Variation
across Local Transplant Centers in Probability of
Kidney Transplant for Wait-Listed Patients. Journal
of the American Society of Nephrology, 31(12),
2900–2911. https://doi.org/10.1681/
ASN.2020030335.
238 Ibid.
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TABLE 7: ORGAN OFFER ACCEPTANCE RATE ACHIEVEMENT SCORING
60th Percentile
40th Percentile
20th Percentile
20th Percentile
Less than 80 th
Less than 60 th
Less than 40 th
Less than 20 th
As discussed in section
III.C.5.d.(1).(b). of the proposed rule, we
considered the approach used by the
MPSC, that would yield maximum
points if transplant hospitals have at
least a .35 organ offer acceptance rate
ratio. However, we do not believe that
this approach fits with the IOTA
Model’s goals. MPSC metrics are more
focused on highlighting and improving
performance for the lowest performers,
whereas the model seeks to improve
performance across the board, not just
avoid poor performance.
For improvement scoring, we
proposed in section III.C.5.d.(1).(b). of
the proposed rule, that points earned
would be based on the IOTA
participants’ performance on organ offer
acceptance rate ratio during a PY
relative to their performance during the
third baseline year for the PY that is
being measured. We proposed to use the
same baseline year definition used for
participant eligibility, as described in
section III.C.3. of the proposed rule,
including the rationale for doing so. We
separately proposed to calculate an
‘‘improvement benchmark rate,’’
defined as 120 percent of the IOTA
participants’ performance on the organ
offer acceptance rate ratio during the
third baseline year for each PY. We
would award points by comparing the
IOTA participant’s organ offer
acceptance rate ratio during the PY to
the IOTA participant’s improvement
benchmark rate to determine the
improvement scoring points earned.
Specifically:
• IOTA participants whose organ
offer acceptance rate ratio during a PY
Equation 3: Proposed Improvement
Scoring for Organ Offer Acceptance Rate
Ratio
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the IOTA participant and comparing the
scores. However, given the variation
that is present amongst kidney
transplant hospitals, we thought it
might be difficult for some IOTA
participants to achieve top tier points
for the first two model PYs. Thus,
incorporating an improvement scoring
method would ensure that IOTA
participants are still rewarded for
improvements made towards the
efficiency domain goal.
We considered using the scoring
method proposed for the post-transplant
outcomes metric within the quality
domain, as described in section
III.C.5.e.(1)(b) of the proposed rule, as it
would award full points if the hazard
ratio or confidence interval of the metric
includes the number one or higher. We
believed this scoring method would
honor the intent of the organ offer
acceptance rate ratio metric, which is to
determine if an IOTA participant is
accepting more organs than expected.
However, given the variation in
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two values as the performance score or
points earned (of 20 possible points) for
the organ offer acceptance rate ratio
metric within the efficiency domain.
In section III.C.5.d.(1).(b). of the
proposed rule, we considered setting the
improvement benchmark rate to be 200
percent of the IOTA participant’s third
baseline year for a given PY to measure
performance on the organ offer
acceptance rate ratio. The scoring
structure would be the same, with 12 or
0 points to be awarded depending on
whether the benchmark is met.
However, we believed this would be too
strict and risk penalizing already highachieving IOTA participants.
In section III.C.5.d.(1).(b). of the
proposed rule, we considered
simplifying the performance scoring for
the organ offer acceptance rate ratio
metric within the efficiency domain by
only awarding performance points
based on the proposed achievement
scoring methodology, rather than also
calculating an improvement score for
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=
Rate Earned in Performance Year - Rate Earned in Third Baseline Year
.
.
Benchmark Rate - Third Baseline Year Rate
As discussed in section
III.C.5.d.(1).(b). of the proposed rule, we
proposed using Equation 3 to mirror the
methodology used in the Hospital Value
Based Purchasing (VBP) Program, with
the only modification being the number
of points available for this metric.
Equation 3 would also allow for a
maximum of 12 points to be earned by
IOTA participants whose organ offer
acceptance rate ratio during the PY is
greater than the baseline year organ offer
acceptance rate ratio but less than the
improvement benchmark rate. We did
not want the improvement score to be
worth more than, or equal to, the
achievement score, as proposed for the
organ offer acceptance rate ratio
performance scoring, so as to reserve the
highest number of points (15 points) for
top performers in the metric.
Once both the achievement score and
the improvement score were calculated,
we proposed, in section III.C.5.d.(1).(b).
of the proposed rule, comparing the two
scores and applying the higher of the
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0
is at or above the improvement
benchmark rate would receive 12
points.
• IOTA participants whose organ
offer acceptance rate ratio during a PY
is at or below the organ offer acceptance
rate ratio during the third baseline year
for that respective PY would receive no
points.
• IOTA participants whose organ
offer acceptance rate ratio during a PY
is greater than the organ offer
acceptance rate ratio during the third
baseline year for that respective PY, but
less than the improvement benchmark
rate, would earn a maximum of 12
points in accordance with Equation 3.
Organ Offer Acceptance Rate Ratio Improvement Scoring
12 X
15
Federal Register / Vol. 89, No. 233 / Wednesday, December 4, 2024 / Rules and Regulations
performance on this metric across all
kidney transplant hospitals, we believe
improvement opportunities exist in this
metric. We also believe that our
proposed approach rewards both
achievement and improvements and is a
more rigorous scoring methodology.
As discussed in section
III.C.5.d.(1).(b). of the proposed rule, we
considered a continuous scoring range
from zero to 20, where IOTA
participants may earn a score of any
point value instead of bands. We
thought that a continuous scoring range
could provide more flexibility for IOTA
participants and greater variety of
scores. However, we believe grading
using bands provides a more favorable
scoring system for IOTA participants by
grouping performance. We also
recognize there is diminishing marginal
efficiency for higher and higher organ
offer acceptance rate ratios.
We considered using the lower and
upper bounds of the offer acceptance
odds ratio within a confidence interval,
like we proposed in the quality domain
for post-transplant outcomes, as
described in section III.C.5.e.(1).(b). of
the proposed rule. However, the organ
offer acceptance rate ratio metric, unlike
post-transplant outcomes, had wider
disparity in performance than in posttransplant outcomes. We believe that
there is a clear benefit to patients and
the transplantation ecosystem overall by
continuing to increase performance on
this metric and promoting better
performance than the national average.
Under this alternative, IOTA
participants would be evaluated based
on whether the lower bound, acceptance
ratio, and upper bound all crossed 1.
Doing so would indicate the IOTA
participant’s true offer acceptance ratio
with 95 percent probability. We did not
propose this approach, however, as our
analyses using SRTR data indicated that
the majority of kidney transplant
hospitals had either all three bounds
cross 1 or all three never cross 1. Thus,
scoring would largely not have differed
from utilizing the offer acceptance ratio
alone.
Finally, in section III.C.5.d.(1).(b). of
the proposed rule, we also considered
stratifying offer acceptance by KDRI
status, with different score targets based
on KDRI status ranges, such as KDRI of
less than 1.05, between 1.05 and 1.75,
and more than 1.75. We thought that
this scoring method may potentially
prevent IOTA participants from
narrowing their criteria to only receive
selected offers. However, we believed
that it was already risk adjusted for
organ status inherently in the measure
because only organs that are ultimately
transplanted are counted in the
denominator.
We sought comment on our proposed
organ offer acceptance rate ratio
performance scoring methodology for
purposes of assessing efficiency domain
performance for each IOTA participant,
including on the achievement and
improvement score calculation and
point allocation method. We also seek
comments on alternatives considered.
The following is a summary of
comments received on our proposed
scoring methodology for the organ offer
acceptance rate ratio performance in the
efficiency domain and our responses:
Comment: Several commenters
relayed concern that there may be a typo
in the proposed rule, which stated the
highest amount of points for the
efficiency domain is 15.
Response: We thank the commenters
for identifying a typo in the proposed
rule. The highest amount of points
available for IOTA participants to earn
is 20 points if they are in the highest
quintile of the organ offer acceptance
rate ratio achievement score.
Comment: There were numerous
comments about scoring methodology.
Several commenters requested
clarification as to why the improvement
component of the efficiency domain
does not provide more than 12 points.
A couple of commenters had specific
concerns that quintile methodology is
not ideal and creates uncertainty. A
commenter was concerned that
improvement score of the efficiency
domain does not account for high
performers who may have challenges
improving every year.
Response: Thank you for seeking
clarification. An improvement goal was
selected in addition to an achievement
goal to account for the variation among
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kidney transplant hospitals and in
acknowledgement that it may be
challenging for some kidney transplant
hospitals to reach high performance
levels in the achievement component of
the efficiency domain. In the proposed
rule, we chose not to provide maximum
points in the improvement domain, in
order to reward the top-tiered programs
in efficiency performance. Additionally,
if some kidney transplant hospitals
newly utilize filters, while others have
already been utilizing filters, this will
increase their improvement score
significantly. By limiting improvement
points, this prevents mismatch in
recognizing those who newly and
previously utilize filters.
We note that we are finalizing these
policies as proposed but with a minor
technical correction to update the
maximum number of points awarded for
improvement scoring from 12 points to
15 points. In the proposed rule at 89 FR
43560, we proposed to award IOTA
participants whose organ offer
acceptance rate ratio during a PY is at
or above the improvement benchmark
rate would receive 12 points. We also
proposed at 89 FR 43560 that IOTA
participants whose organ offer
acceptance rate ratio during a PY is
greater than the organ offer acceptance
rate ratio during the third baseline year
for that respective PY, but less than the
improvement benchmark rate, would
earn a maximum of 12 points in
accordance with equation 1 to
paragraph (c)(1)(ii)(B)(1) of § 512.426.
However, we also stated at 89 FR 43560
that we did not want the improvement
score to be worth more than, or equal to,
the achievement score, as proposed for
the organ offer acceptance rate ratio
performance scoring, so as to reserve the
highest number of points (15 points) for
top performers in the metric. Thus, we
are updating the regulation text at
§ 512.426(c)(1)(ii)(B)(1) to reflect 15
points instead of 12 points and equation
1 to paragraph (c)(1)(ii)(B)(1) of
§ 512.426, as illustrated in equation 4
below, to reflect a multiplier of 15
instead of 12.
Equation 4: Improvement Scoring for
Organ Offer Acceptance Rate Ratio
Rate Earned in Performance Year - Third Baseline Year Rate
Improvement Benchmark Rate - Third Baseline Year Rate
15x------------------------Additionally, the commenters are
correct that the methodology creates a
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moving target for rankings within the
scoring quintiles, year to year. This
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targets reflect current practices and
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trends across kidney transplant
hospitals.
We also note that we are finalizing
this policy as proposed but with a minor
technical correction to update the
terminology used to provide points for
achievement scoring in the efficiency
domain. In the proposed rule at 89 FR
43559, we proposed that achievement
scoring, would be based on the IOTA
participant’s performance on the organ
offer acceptance rate ratio ranked
against a national target, inclusive of all
eligible kidney transplant hospitals,
both those selected and not selected as
IOTA participants. However, we also
stated at 89 FR 43559 that achievement
scoring points be awarded based on the
national quintiles, as outlined in Table
6 of section III.C.5.d.(1).(b). of the
proposed rule. Thus, we are updating
our regulation text at § 512.426(c)(2)(i)
to remove the reference to performance
being measured against a national target
and instead based on national ranking.
Based on PY 1 and ongoing feedback,
we will consider in future rulemaking if
there should be alternative point
opportunities for the efficiency
improvement scoring scale in later
performance years. If analysis results
warrant a new or updated policy, we
will address it pursuant to future
rulemaking.
Comment: A couple of commenters
were concerned that IOTA participants
may accept deceased donor organs more
aggressively or make their waitlist
criteria more stringent, to have a high
score in the efficiency domain due to
the percentile scoring.
Response: We agree that some IOTA
participants with higher risk thresholds
may accept deceased donor organs more
aggressively, if they believe they have
the resources and support for their
patients post-transplant. While this may
apply to some kidney transplant
hospitals, however, we do not believe
that this will be a common approach.
IOTA participants have the opportunity
to consider utilizing filters that more
closely match their risk threshold and
waitlist patient population. While we do
not believe that the efficiency domain
will make waitlist criteria more
stringent, we do believe that paired with
the transparency notification
requirement in section III.C.8.a(2), IOTA
participants may be more inclined to
remove patients from their active
waitlist who are not potential kidney
transplant candidates. Should we notice
an adverse effect of the efficiency
domain, such as reduction in access to
waitlisting or being active on the
waitlist, we will take this into
consideration for future rulemaking.
Additionally, as mentioned in the
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comments noted previously in this
section, we are we are updating our
regulation text at § 512.426(c)(2)(i) and
in Table 1 to Paragraph (c)(1)(i) at our
regulation at § 512.426 to remove
reference to performance being
measured against a national target and
is instead based on national ranking.
Comment: A few commenters
suggested considerations for the
efficiency domain scoring. These
considerations included ensuring not to
penalize IOTA participants that are
already accepting more organs than
expected, moderating the proposed
expectations for performance in the
achievement and improvement scores,
and aligning the efficiency domain
point system to SRTR’s upcoming
method of creating performance tiers.
Several commenters also provided
suggestions for alternative criteria for
kidney transplant hospitals to receive
the full 20 points in the efficiency
domain. The suggestions included
awarding full points for meeting the
OPTN’s minimum ratio, having an organ
offer acceptance ratio of 1.0, and
meeting organ acceptance expectations.
There were also a few suggestions that
kidney transplant hospitals that meet
the improvement component criteria
should be awarded the full 20 points as
well; this could potentially be
accomplished by having programs opt
in to either an achievement or
improvement track. Finally, a
commenter pointed out that because the
organ offer acceptance rate ratio is
compared to national performance for
the achievement component of the
efficiency domain, a program may
improve its rate but not its ratio
depending on the national rate. They
same commenter suggested considering
relative acceptance rate. Similarly, a
commenter stated the scoring system, as
proposed, is too harsh.
Response: We thank the commenters
for their feedback. As mentioned in the
proposed rule, we do not expect every
IOTA participant to reach top-level
performance. If an IOTA participant is
already accepting more organs than
expected, they will likely have a high
scoring ratio as well. An IOTA
participant that scores in the 50th
percentile of performance for the organ
offer acceptance rate achievement score
would receive 10 out of 20 points.
Alternatively, if an IOTA participant
improves their organ offer acceptance
rate ratio by 120 percent of their
benchmark rate, as proposed, they can
earn 15 points. As mentioned in the
comments noted previously in this
section, we are finalizing our proposed
organ offer acceptance rate ratio
improvement scoring methodology to
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reflect that the maximum number of
points awarded for improvement
scoring is 15 points, rather than 12
points.
For PY 1, we believe it is appropriate
to carve out more points for those IOTA
participants who have the highest
performance. We do not believe the
OPTNs minimum ratio is high enough
to nudge transplant programs to
continue to improve on this
performance metric. As mentioned in
the comments noted previously in this
section, we are finalizing our proposed
organ offer acceptance rate ratio
achievement scoring methodology with
slight modifications to reflect that
points earned will be based on national
ranking rather than a national target.
Although we did not consider the
SRTRs performance tier assessment in
the proposed rule, we are interested to
learn more about this methodology once
implemented and to further consider
this for future rulemaking. We will also
continue to consider if the improvement
maximum score should be equivalent to
the achievement maximum score and if
achieving upper quintile ranks is too
challenging. This, in addition to
ongoing feedback and performance
during PY 1 will help guide us in future
rulemaking.
Comment: A commenter was
concerned that the organ offer
acceptance rate ratio would be impacted
by transplant programs completing dual
organ transplants, who may receive
priority offers.
Response: We thank the commenter
for their feedback and recommends
reviewing Table 6 of section
III.C.5.d(1)(a) of this final rule, which
includes organ offers included and
excluded from the organ offer
acceptance rate ratio metric. This
specifically identifies that offers to
multi-organ candidates (except kidney
pancreas candidates that are also listed
for kidney alone) are excluded from the
measure.
Comment: A few commenters were
concerned about overall impact of risks
and costs of the organ offer acceptance
ratio methodology. A couple of
commenters were concerned that point
allocation for the organ offer acceptance
rate ratio and kidney transplant volume
will increase marginal kidney use and
have higher financial costs and risks to
patients. A commenter specifically
asked whether there will be subsequent
increase in reimbursement and SRTR
adjustments. Similarly, another
commenter stated that the organ offer
acceptance ratio incentivizes IOTA
participants to accept offers they may
not ordinarily accept and is concerned
that the IOTA Model needs to minimize
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the risk of adverse outcomes when
evaluating participating hospitals fairly.
Response: We thank the commenters
for sharing their concerns. We agree that
some IOTA participants may choose to
increase their utilization of DCD
kidneys or kidneys with a KDPI greater
than 85, however, this is a choice for
each IOTA participant based on their
comfort level and resources and is not
the only way for an IOTA participant to
perform well in the IOTA Model.
Regardless of the approach of each
IOTA participant, we intend to monitor
for unintended consequences that my
occur with the model. We bring
attention to the fact that while IOTA
participants who achieve a final
performance score of 60 or more points
will receive an upside risk payment, as
described and finalized in section
III.C.6.c(1) of this final rule, there is also
a neutral zone for IOTA participants
who achieve a final performance score
between 0 and 59 points in PY 1 and a
final performance score of 40–59 points
in PY 2 through PY 6, as described and
finalized in section III.C.6.c(1) of this
final rule. We direct readers to sections
III.C.6 of this final rule for a full
discussion on payment. With increasing
resources and knowledge such as access
to timely donor biopsies and research
on what factors prompt kidneys to be
designated as high KDPI kidneys, there
are growing opportunities in the
transplant ecosystem to identify kidneys
that may or may not be ideal to
transplant.
As for as modifications to SRTR
adjustments and reimbursement, we
will continue to collaborate with other
groups in OTAG to work on aligning
goals across the transplant ecosystem.
Comment: A few commenters had
concerns that IOTA participants may
change their habits or manipulate their
listing or transplant practices to
improve their organ offer acceptance
rate. Specifically, a couple of
commenters conveyed their concern
that kidney transplant hospitals will use
organ offer filters to have a better offer
acceptance rate ratio, whereas kidney
transplant hospitals that utilize
marginal kidneys and try to have higher
volumes will have worse performance
for this ratio. They requested
clarification on how CMS will prevent
IOTA participants from being rewarded
if they choose to use filters for this
metric. Another commenter stated their
concern that to achieve a better organ
offer acceptance ratio, IOTA
participants may inactivate patients,
causing subsequent disadvantages.
Additionally, a commenter was
concerned that OPOs may start
bypassing IOTA participants if they
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scrutinize whether the organ is an
optimal match for a recipient.
Response: We appreciate the
commenters’ feedback and believe that
organ offer filters are often an
underutilized resource that help
minimize organ non-use, out-ofsequence allocation, and prolonged cold
ischemic times. Therefore, we disagree
with the commenter views and
encourage kidney transplant hospitals to
use filters to reduce unnecessary offers
to their transplant programs, when
appropriate, for categories of offers that
the transplant program will definitively
not accept. We recognize this may be
challenging due to high thresholds for
marginal kidneys or different risk
thresholds for different rotating
surgeons in the same transplant
program. However, we believe that
given the rise in organ offers made by
OPOs, there is opportunity to reduce
administrative burden and organ nonuse, by way of using filters and
impacting their organ offer acceptance
rate.
We acknowledge that there are some
unique cases that are very high risk and
require specific donor and recipient
criteria, which may impact acceptance
practices. We also acknowledge that it is
unrealistic for kidney transplant
hospitals to accept every offer they
receive.
If OPOs start bypassing IOTA
participants due to in depth analysis of
whether an organ is optimal for their
patients, we believe this would be
important model feedback for IOTA
participants to relay to us. If analysis
results warrant a new or updated policy,
we will address it pursuant to future
rulemaking.
Comment: A commenter suggested
CMS mandate the use of organ offer
filters by a certain date.
Response: We appreciate the
commenter’s suggestion. Currently, we
do not believe mandating organ filters is
appropriate for the IOTA Model. While
the performance domains and
performance metrics in the IOTA Model
do indirectly encourage use of organ
offer filters, we believe IOTA
participants should have the
opportunity to identify what organ offer
filters are appropriate for their
transplant program and the populations
they serve, as they participate. This is a
topic for the entire transplant ecosystem
to collectively consider in the future.
Comment: A few commenters
conveyed concerns that unique
situations may impact post-transplant
outcomes and impact acceptance rates.
For example, a commenter stated that
CMS should consider patient
characteristics and how they impact a
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successful transplant. Another
commenter is concerned that not all
offers are viable. A commenter
conveyed concern that filter settings for
distance may conflict with allocation
registered distance. For example, a
kidney available in Alaska may show as
local per UNOS assignment but will
show as 2500 miles away from a kidney
transplant hospital in Washington per
filters, which would require liberal
filters for distance, to capture donors in
that region.
Response: We appreciate the
commenters bringing these concerns to
our attention. We acknowledge that
there are unique donor and recipient
characteristics that may impact offer
acceptances. We do not expect that any
IOTA participant will accept every
organ offer it receives since there are
scenarios that are difficult to predict.
We agree with the second commenter
who stated that not all offers are viable
and acknowledges this in section
III.C.5.d.(1)(a), Table 6, where
exclusions for the organ offer
acceptance rate ratio metric are
included. Kidney match runs that have
no acceptances are excluded in this
metric.
We appreciate the commenter
bringing UNOS and offer filter distance
criteria mismatch to our attention. This
was not considered at the time of the
proposal of the IOTA Model. We plan to
further discuss this internally and
analyze how this can appropriately be
accounted for in future performance
years.
Comment: A commenter requested
that CMS consider how IOTA
participants using organ offer filters
prior to the model will be compared to
IOTA participants that newly utilize
organ offer filters and receive higher
scores in the efficiency domain.
Response: We appreciate the
commenter’s feedback. The proposed
organ offer acceptance rate ratio
achievement scoring methodology is
independent of pre-existing or new filter
use and is strictly dependent on a ratio
compared to national ranking. As
mentioned in the comments noted
previously in this section, we are
finalizing our proposed achievement
scoring methodology with slight
modifications to reflect that points
earned will be based on national
ranking rather than a national target.
Additionally, the organ offer acceptance
rate ratio improvement scoring
methodology has a ceiling of 15 points,
which prevents IOTA participants that
are new to using filters from having an
unfair advantage over IOTA participants
who previously utilized this resource.
As mentioned in the comments noted
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previously in this section, we are
finalizing our proposed improvement
scoring methodology to reflect that the
maximum number of points awarded for
improvement scoring is 15 points, rather
than 12 points.
Comment: A commenter encouraged
CMS to consider that not all kidney
transplant hospitals have the same
capabilities, and this contradicts the
achievement component of the
efficiency domain since kidney
transplant hospitals are not uniform.
Response: We appreciate the
commenter’s concerns and acknowledge
the differences between kidney
transplant hospitals but also believe that
these unique variations create flexibility
in how an IOTA participant may choose
to adapt practice to impact their organ
offer acceptance rate ratio. For those
IOTA participants who prioritize
improving their own score year-to year,
the organ offer acceptance rate ratio
improvement scoring methodology, as
described in section III.C.5.d.(1).(b). of
this final rule, allows them to earn
points independent of comparison to
other IOTA participants.
Comment: A commenter relayed
concern that keeping track of potential
offers and acceptances is burdensome.
Response: We appreciate the
commenter’s feedback and will take this
into consideration when planning for
and implementing the IOTA Model in
addition to identifying appropriate
intervals for IOTA participants to have
access to interim results. The IOTA
Model does not mandate that IOTA
participants keep track of their potential
organ offers and acceptances but
understands that IOTA participants may
want to have access to this information
for personal tracking purposes.
Comment: A couple commenters
expressed their support for CMS’
proposal to include the organ offer
acceptance rate ratio as a performance
measure in the efficiency domain. They
contended that the organ offer
acceptance rate ratio metric motivates
kidney transplant hospitals to utilize
filters that reflect their acceptance
practices, while also providing the
flexibility to modify these filters.
Furthermore, they suggested that this
metric would encourage increased
acceptance rates.
Response: We appreciate the support
received from commenters for our
proposal to include the organ offer
acceptance rate ratio metric as a
performance measure in the efficiency
domain.
After consideration of the public
comments, for the reasons set forth in
this rule, we are finalizing the proposed
provisions for the point allocation and
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calculation methodology for efficiency
domain scoring and scoring for organ
offer acceptance rate ratio for the IOTA
Model at § 512.426(c), with slight
modifications. In the proposed rule at
89 FR 43559, we proposed that
achievement scoring points be awarded
based on the national quintiles, as
outlined in Table 6 of section
III.C.5.d.(1).(b). of the proposed rule. As
such, we are updating our regulation
text at § 512.426(c)(2)(i) and in Table 1
to Paragraph (c)(1)(i) at our regulation at
§ 512.426 to remove reference to
performance being measured against a
national target and is instead based on
national ranking. Additionally, we are
updating the regulation text at
§ 512.426(c)(1)(ii)(B)(1) to reflect 15
points instead of 12 points and updating
the multiplier in equation 1 to
paragraph (c)(1)(ii)(B)(1) at § 512.426, as
illustrated in Equation 4 in this section,
to reflect 15 instead of 12. Lastly, we are
updating our regulation text language at
§ 512.402 to clarify our definition for
improvement benchmark rate, which we
modified to 120 percent of the IOTA
participants’ performance on the organ
offer acceptance rate ratio, as specified
under § 512.426(c)(1)(ii)(A) rather than
120 percent of the IOTA participants’
performance on organ offer acceptance
rate ratio, as specified under
§ 512.426(c)(1)(ii)(A).
e. Quality Domain
In the proposed rule, we proposed to
define ‘‘quality domain’’ as the
performance assessment category in
which CMS assesses the IOTA
participant’s performance using a
performance measure and quality
measure set focused on improving the
quality of transplant care, as described
in section III.C.5.e of the proposed rule
and section III.C.5.e. of this final rule.
We proposed that performance on the
quality domain would be worth up to 20
points out of the proposed 100 points.
The quality domain is focused on
monitoring post-transplant care and
quality of life for IOTA transplant
patients.
In section III.C.5.e of the proposed
rule, we stated that our goal for the
quality domain within the IOTA Model
is to achieve acceptable post-transplant
outcomes while incentivizing increased
kidney transplant volume. We believed
that transplant hospital accountability
for patient-centricity and clinical
outcomes continues posttransplantation. While transplant
outcomes have historically received the
most attention, often at the exclusion of
other factors, we sought to encourage a
better balance in the system to offer the
benefits of transplant to more patients.
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Therefore, we proposed to include one
post-transplant outcome measure, as
described in section III.C.5.e(1) of this
final rule, and a quality measure set that
includes two patient-reported outcomebased performance measures (PRO–PM)
and one process measure, as described
in section III.C.5.e(2) of this final rule.
We sought comment on the proposed
definition of the quality domain.
We did not receive any comments on
the proposed definition of the quality
domain and are finalizing the proposed
definition for quality domain at
§ 512.402, with slight modification to
remove the following words from the
definition: and quality measure set.
Since we are not finalizing our proposal
to include our proposed quality measure
set that includes two patient-reported
outcome-based performance measures
(PRO–PM) and one process measure, as
described in the section III.C.5.e(2) of
this final rule, we modified the quality
domain definition and removed
reference to the quality measure set. As
such, we are also finalizing the general
provisions for the quality domain as
proposed, with a minor technical
correction to update the cross reference
in the regulation text at § 512.424(a).
Specifically, we are removing the cross
reference to the proposed quality
measure set at § 512.424(a). We direct
readers to section III.C.5.e(2) of this final
rule for further discussion on our
proposed quality measure set
methodology. We are also finalizing our
regulation as proposed without
modification at § 512.424(b) that for
each PY, CMS assesses each IOTA
participant using the specified quality
metrics. Lastly, we direct readers to
section III.C.5.e(1) of this final rule for
further discussion on our proposed
post-transplant outcome measure.
(1) Post-Transplant Outcomes
In the proposed rule, we proposed
using an unadjusted rolling ‘‘composite
graft survival rate,’’ defined as the total
number of functioning grafts relative to
the total number of adult kidney
transplants performed, as described in
the proposed rule (89 FR 43518) and
section III.C.5.e(1)(a) of this final rule, to
assess IOTA participant performance on
post-transplant outcomes. In this
measure, the numerator (observed
functioning grafts) and denominator
(number of kidney transplants
completed) would increase each PY of
the IOTA Model to include a cumulative
total.
In section III.C.5.e(1) of the proposed
rule, we stated that over the past few
decades, advances in
immunosuppressive therapies, surgical
techniques, and organ preservation
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methods have resulted in significant
improvements in kidney transplantation
outcomes.239 According to the OPTN,
the overall 1-year survival rate for
kidney transplantation recipients in the
United States is over 90 percent, and the
5-year survival rate is around 75
percent. However, even with the
advances that have been made to
improve kidney outcomes, the success
of kidney transplantation is still
dependent upon factors such as the age
and health of the donor and recipient,
the presence of comorbidities (for
example, diabetes), and the
effectiveness of the immunosuppressive
regimen. Kidney transplant outcomes
can also be affected by possible posttransplant complications, including
infection, cardiovascular disease, and
kidney failure.240
More recently, CMS received feedback
from transplant hospitals, patient
advocacy groups, and transplant
societies, including on the recent rule
making (‘‘Medicare and Medicaid
Programs; Regulatory Provisions To
Promote Program Efficiency,
Transparency, and Burden Reduction’’
(83 FR 47686)), that the 1-year measure
was causing transplant centers to be risk
averse about the patients and organs
they would transplant while being
simultaneously topped out (83 FR
47706).241 Notably, even the lowest
239 Stewart, D.E., Garcia, V.C., Rosendale, J.D.,
Klassen, D.K., & Carrico, B.J. (2017). Diagnosing the
Decades-Long Rise in the Deceased Donor Kidney
Discard Rate in the United States. Transplantation,
101(3), 575–587. https://doi.org/10.1097/
tp.0000000000001539; Vinson, A., Kiberd, B.A., &
Karthik Tennankore. (2021). In Search of a Better
Outcome: Opting Into the Live Donor Paired Kidney
Exchange Program. 8, 205435812110174–
205435812110174. https://doi.org/10.1177/
20543581211017412; Shepherd, S., & Formica, R.N.
(2021). Improving Transplant Program Performance
Monitoring. 8(4), 293–300. https://doi.org/10.1007/
s40472-021-00344-z.
240 Gioco, R., Sanfilippo, C., Veroux, P., Corona,
D., Privitera, F., Brolese, A., Ciarleglio, F.,
Volpicelli, A., & Veroux, M. (2021). Abdominal wall
complications after kidney transplantation: A
clinical review. Clinical Transplantation, 35(12),
e14506. https://doi.org/10.1111/ctr.14506; Wei, H.,
Guan, Z., Zhao, J., Zhang, W., Shi, H., Wang, W.,
Wang, J., Xiao, X., Niu, Y., & Shi, B. (2016). Physical
Symptoms and Associated Factors in Chinese Renal
Transplant Recipients. Transplantation
Proceedings, 48(8), 2644–2649. https://doi.org/
10.1016/j.transproceed.2016.06.052; Mehrabi, A.,
Fonouni, H., Wente, M., Sadeghi, M., Eisenbach, C.,
Encke, J., Schmied, B.M., Libicher, M., Zeier, M.,
Weitz, J., Büchler, M.W., & Schmidt, J. (2006).
Wound complications following kidney and liver
transplantation. Clinical Transplantation, 20(s17),
97–110. https://doi.org/10.1111/j.13990012.2006.00608.x.
241 Medicare and Medicaid Programs; Regulatory
Provisions To Promote Program Efficiency,
Transparency, and Burden Reduction (September,
20, 2018) https://www.federalregister.gov/
documents/2018/09/20/2018-19599/medicare-andmedicaid-programs-regulatory-provisions-topromote-program-efficiency-transparency-and.
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ranked programs, as measured by the
SRTR, achieved a result of 90 percent of
transplanted patients have a functioning
graft at one year.242
To safeguard patient outcomes under
the IOTA Model, we proposed to
include this measure as a checkpoint
(89 FR 43518). Because there is
significant variation in post-transplant
outcomes across kidney transplant
hospitals, we believed the IOTA Model
should promote improvement in
outcomes for the benefit of attributed
patients. We also believed that this
measure would build upon, and
complement, existing OPTN and SRTR
measures to the maximum extent
possible. Additionally, we believed that
this approach could be applied with
minimal adaptation to other organs were
they to be added to the model through
future rulemaking. Furthermore, we
believed that this measure would
enhance patient understanding of
clinically important post-transplant
outcomes beyond existing 90-day, 1year and 3-year post transplant
outcomes.
We considered measuring posttransplant outcomes using SRTR’s
methodology at 90 days,243 and
constructing 5-year and 10-year posttransplant measures (89 FR 43518).
However, we did not select these
measures because post-transplant
outcomes are already measured at 90days by SRTR. Additionally, because
the IOTA Model as proposed spans only
6 years, we did not believe we could
appropriately measure post-transplant
outcomes at 5 or 10 years.
We considered constructing an
ongoing post-transplant outcome
measure that would continuously
evaluate post-transplant outcomes at 1year throughout the model performance
period of the IOTA Model. In this
measure the numerator (observed graft
failures) and denominator (number of
transplants completed) would increase
each PY of the model to a cumulative
total (89 FR 43518). For example, in PY
1 of the model an IOTA participant
could have five 1-year observed graft
failures and complete 20 transplants,
resulting in a graft failure rate of 0.25.
In PY 2 of the model, the same IOTA
participant could have eight 1-year
observed graft failures and complete 30
transplants. To calculate the IOTA
242 Scientific Registry of Transplant Recipients.
Request for Information. Requested on 05/02/2023.
https://www.srtr.org/.
243 Mpsc-enhance-transplant-programperformance-monitoring-system_srtr-metrics.pdf.
(n.d.). Retrieved December 28, 2022, from https://
optn.transplant.hrsa.gov/media/qfuj3osi/mpscenhance-transplant-program-performancemonitoring-system_srtr-metrics.pdf.
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participant’s graft failure rate for PY 2
of the model, we would divide the
cumulative total of 13 1-year observed
graft failures by the cumulative total of
50 completed transplants. However, we
felt it was important to measure posttransplant outcomes in terms of graft
survival rather than in terms of graft
failure. We acknowledged that for the
purposes of measuring graft survival
using OPTN data, use of either concept
would generate the same outcome
measurement because OPTN data
identify graft status as either functioning
or failed. However, we aim to convey
the importance of ongoing management
to preserve the health of the
transplanted graft and the health and
quality of life of the attributed patients.
We considered constructing a
continuous patient survival measure
that would evaluate patient survival
throughout the entirety of the IOTA
Model (89 FR 43518). Similar to the
considered measure mentioned in the
previous paragraph, the numerator
(number of patients alive) and
denominator (number of received
kidney organ offers) would increase
each PY of the model to a cumulative
total. For the denominator, we
considered only including organ offers
where the sequence number was less
than 100 or less than 50. In other words,
under that rationale we would only
include offers that came within a certain
point of time that could have potentially
benefited the patient or should not have
been turned down. We believed that this
type of measure would not
disincentivize waitlisting and could
potentially increase equity within this
population. Additionally, we believed
that this type of measure would
indirectly encourage living donor
transplants because those would only
hit the numerator (number of people
alive) but not the denominator (number
of kidney organ offers received).
However, we felt that this measure
would be somewhat duplicative of other
parts of the model where we are already
evaluating organ offer acceptance. We
also chose not to propose this measure
due to logistical concerns, and felt that
it could be difficult to determine how
many people were offered a specific
organ and determining what an
appropriate sequence number cutoff
should be.
We considered measuring estimated
glomerular filtration rate (eGFR) at the
1-year anniversary of the date of
transplant (89 FR 43518). Glomerular
filtration rate (GFR) is a way to assess
renal function, and eGFR is the test used
to assess renal function in primary
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clinical care.244 Despite the fact that
studies indicate eGFR’s potential as a
reliable predictor of long-term posttransplant prognosis, our goal is to
adopt a measure that resonates more
with the transplant community’s
evaluation of post-transplant
outcomes.245 We recognized that the
equation for calculating eGFR was
revised in 2021 to not include race, but
we still have some concerns over the
potential for bias and inaccurate results
and the limitations that still exist with
the updated equation and did not
believe it was appropriate to propose.246
We considered constructing several
hospital-based post-transplant outcome
measures such as those that measure:
the number of days spent out of the
hospital post-transplant, how many
days spent at home post-transplant
before returning to work, and number of
hospital readmissions post-transplant
(89 FR 43518). However, we do not
want to penalize the use of moderate-tohigh KDPI kidneys, as we recognize that
utilizing these organs carries an
increased risk of transplant recipient
hospitalizations. Additionally, we had
concerns over how we would assess and
measure this type of metric.
We considered proposing a phased-in
approach to measuring post-transplant
outcomes, in which no post-transplant
outcome metrics would be included
until PY 3 of the model (89 FR 43518).
In this alternative methodology, the
quality domain for the first two PYs
would only include our proposed
quality measure set, as described in
section III.C.5.e(2) of the proposed rule
and this final rule. Starting PY 3 of the
model, IOTA participants would be
244 Mayne, T.J., Nordyke, R.J., Schold, J.D., Weir,
M.R., & Mohan, S. (2021). Defining a minimal
clinically meaningful difference in 12-month
estimated glomerular filtration rate for clinical trials
in deceased donor kidney transplantation. Clinical
Transplantation, 35(7), e14326. https://doi.org/
10.1111/ctr.14326.
245 Ibid; Wu, J., Li, H., Huang, H., Wang, R., Wang,
Y., He, Q., & Chen, J. (2010). Slope of changes in
renal function in the first year post-transplantation
and one-yr estimated glomerular filtration rate
together predict long-term renal allograft survival.
Clinical Transplantation, 24(6), 862–868. https://
doi.org/10.1111/j.1399-0012.2009.01186.x; Schold,
J.D., Nordyke, R.J., Wu, Z., Corvino, F., Wang, W.,
& Mohan, S. (2022). Clinical events and renal
function in the first year predict long-term kidney
transplant survival. Kidney360, 10.34067/
KID.0007342021. https://doi.org/10.34067/
kid.0007342021; Hariharan, S., Mcbride, M.A.,
Cherikh, W.S., Tolleris, C.B., Bresnahan, B.A., &
Johnson, C.P. (2002). Post-transplant renal function
in the first year predicts long-term kidney
transplant survival. Kidney International, 62(1),
311–318. https://doi.org/10.1046/j.15231755.2002.00424.x.
246 Majerol, M., & Hughes, D.L. (2022, July 5).
CMS Innovation Center Tackles Implicit Bias.
Health Affairs. Retrieved January 16, 2024, from
https://www.healthaffairs.org/content/forefront/
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evaluated on two post-transplant
outcome measures (SRTR’s 1-year posttransplant outcome conditional on 90day survival measure and 3-year posttransplant outcome measure) in
addition to our proposed quality
measure set. This approach incorporates
a time delay, allowing us to assess the
post-transplant outcomes of IOTA
participants using SRTR’s measures.
Because we felt that it was critical to
include a post-transplant measure from
the onset of the model to check for
unintended consequences throughout
the entirety of the model performance
period, we did not believe that this
alternative was appropriate to propose.
We also considered using SRTR’s new
‘‘1-year post-transplant outcome
conditional on 90-day graft survival’’
measure and including a 3-year posttransplant outcome measure, such as the
one currently used by SRTR (89 FR
43518). We also considered constructing
our own 3-year post-transplant outcome
measure conditional on 1-year survival.
However we chose not to propose
SRTR’s conditional 1-year or 3-year
post-transplant outcome measures or
our own measure for the following
reasons: (1) because SRTR’s conditional
1-year metric has a 2.5 year lookback
period, it would require us to evaluate
IOTA participants on post-transplant
outcomes prior to starting the model for
at least the first two PYs; (2) because
SRTR does not currently have a 3-year
conditional post-transplant outcome
measure, we would not be in alignment
with SRTR if we constructed our own;
(3) including SRTR’s 3-year posttransplant outcome measure would
include time outside of the model for at
least the first three PYs and we want to
evaluate IOTA participants based on
their performance within the model;
and (4) we recognize there may be some
logistical issues and difficulty in
measuring performance in that time. We
may consider incorporating a 3-year
post-transplant outcome measure into
the model in the future, through
rulemaking.
We sought public comment on our
proposal to evaluate IOTA participants
on post-transplant outcomes using our
new composite graft survival rate
metric, as well as on the alternatives we
considered. We were also interested in
public comment on how we may be able
to use OPTN data to characterize
different clinical manifestations of graft
survival, as we understand that not all
surviving grafts are clinically equivalent
or have the same impact on the patient
and graft health. We were further
interested to hear from the public on
which factors involved in graft survival
are modifiable by the care team.
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The following is a summary of the
comments received on our proposal to
evaluate IOTA participants on posttransplant outcomes using our new
composite graft survival rate metric, as
well as on the alternatives we
considered and our responses:
Comment: There were many
commenters requesting CMS use
alternative metrics for graft survival rate
that include risk adjustment
methodologies in place of the proposed
composite graft survival rate. For
example, a commenter suggests that
CMS develop additional post-transplant
outcome measures that could be utilized
to measure the quality of care provided,
surrogates for long term allograft
function, in addition to early indicators
for allograft function. This commenter
additionally recommended measures of
kidney function at 12 months or new
onset albuminuria (for example, urine
albumin to creatinine ratio [ACR]). A
couple commenters that suggested that
CMS reconsider using eGFR at 12
months. Specifically, a commenter
stated that, on a population level, the
data suggests that eGFR at 12 months is
predictive of long-term outcomes.
Taking into consideration the dual goals
of increasing organ utilization and
patient outcomes, as well as outcomes
that are superior to the dialysis, the
same commenter recommended that an
appropriate gauge of success in such a
measure could be an eGFR superior to
dialysis initiation or listing for retransplant (for example, greater than 20
mL/min) such as 25 or 30 mL/min.
Another commenter suggested that
eGFR more accurately conveys longterm patient outcomes and
incorporating granular measures of
allograft function into performance
metrics instead of using a binary
(functioning/failed) indicator could
improve patient care by prioritizing
allograft function as a measure of
program quality.
Several commenters urged CMS to
reconsider current SRTR outcome
measures. For example, although a
commenter agreed with CMS that it may
not be possible to use SRTR’s 1-year
graft survival conditional on 90-day
survival or 3-year survival for short term
evaluations of transplant program
outcomes, they noted that SRTR has
available models to assess 90-day
outcomes along with the first full year
posttransplant. The same commenter
suggested that the 90-day models could
be used to assess near-term success of
the transplants in a risk-adjusted
framework, and the full 1-year models
could be used as the model develops
and more performance years are
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included to also incorporate risk
adjustment into the evaluations.
This commenter also stated that the
90-day and 1-year models conditional
on 90-day survival are currently used by
the MPSC to evaluate transplant
program outcomes. Therefore, they
believed that not only is it feasible to
use the 90-day and 1-year adjusted
evaluations following the SRTR
methodology, but it was also imperative
to achieve the goals of the IOTA Model.
Several commenters also urged CMS to
use the outcomes already available from
the SRTR, as it is well-established.
Although the data is delayed, these
commenters argued for CMS to include
SRTR outcome measures citing reasons
such as that it is well-established,
accepted, and tested nationally and
offers a comprehensive evaluation of
graft survival that accounts for the
complexities of both donors and
recipients. A commenter believed CMS
should remove the proposed measure
and instead continue to use the existing
SRTR post-transplant survival measures
if CMS wants to increase the number of
kidney transplants in part by
encouraging kidney transplant hospitals
to accept higher risk organs. This would
also reduce the additional reporting
burden associated with a new quality
measure. Alternatively, a commenter
suggested that CMS could utilize
SRTR’s CUSUM data as it could provide
more real-time measurements.
Response: We thank the commenters
for their suggestions on additional riskadjusted measures that could be
considered for measuring posttransplant outcomes in the model. As
described at 89 FR 43562 in the
proposed rule, we considered measuring
eGFR at the 1-year anniversary of the
date of transplant. However, our goal is
to adopt a measure that better resonates
with the transplant community’s
evaluation of post-transplant outcomes.
As a result, we did not propose
including eGFR at the 1-year
anniversary. Additionally, we have
ongoing concerns about potential bias,
inaccurate results, and limitations with
the updated eGFR equation. Given these
issues, we did not believe it was
appropriate to propose using eGFR at
the 1-year mark.247
We also considered using SRTR’s 1year graft survival conditional on 90-day
survival or 3-year post-transplant
outcome measure. However, for the
reasons stated at 89 FR 43562 in the
proposed rule, we chose not propose
using SRTR’s 1-year graft survival
conditional on 90-day survival or 3-year
post-transplant outcome measure. As
such, we will be finalizing our proposed
composite graft survival rate metric to
measure post-transplant outcomes in the
IOTA Model. We will take into
consideration the suggested posttransplant outcome metrics for IOTA
and, if we determine that a new measure
post-transplant outcome measure
should be included, we would do so
through future notice and comment
rulemaking.
Comment: A commenter opposed the
proposed graft survival rate measure
given that the transplant community
already has statistically valid
measurements for outcomes utilizing a
rolling 2.5-year cohort. Thus, the
commenter felt relying on a raw
calculation was not a reasonable
replacement.
Response: We appreciate commenters
recommendation to use an existing posttransplant outcome measure in place of
the proposed composite graft survival
rate. We will take the recommendation
into consideration for future rulemaking
and direct the commenter to comment
responses noted previously in this
section for further discussion on
alternative metrics considered.
Comment: Several commenters
expressed support for using the
unadjusted Composite Graft Survival
Rate as proposed—notably, that the
proposed unadjusted composite graft
survival rate is simple and would be
easy for the patients to understand. For
example, a commenter reported that
their kidney patients frequently
expressed confusion about transplant
data metrics and appreciated CMS’s
efforts to establish a clearer measure for
assessing graft survival. Furthermore,
the commenter voiced support for using
a graft survival metric rather than a graft
failure metric, citing the reasons
outlined in the proposed rule. A
commenter also agreed with using this
measure as a checkpoint to help ensure
patient safety and improve
understanding of post-transplant
outcomes for patients. Another
commenter concurred with CMS’s
proposal to calculate post-transplant
outcomes using a rolling, unadjusted,
composite graft survival measure.
Although they believed that many
commenters would argue for an urgent
need to add ‘‘risk adjustment’’ to the
measure, they felt that the proposed
measure had the virtues of being
straightforward, unambiguous, easy to
understand, and easy to explain to
patients and their families. This same
commenter also stated their belief that
these virtues are, too often,
underemphasized.
Response: We thank the commenters
for their support.
After consideration of the public
comments received, for the reasons set
forth in this rule, we are finalizing our
proposed provision to assess IOTA
participant performance on posttransplant outcomes using the
composite graft survival rate at
§ 512.428(b)(1), without modification.
We are also finalizing without
modification the definition of composite
graft survival rate at § 512.402.
247 Majerol, M., & Hughes, D.L. (2022, July 5).
CMS Innovation Center Tackles Implicit Bias.
Health Affairs. Retrieved January 16, 2024, from
https://www.healthaffairs.org/content/forefront/
cms-innovation-center-tackles-implicit-bias.
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(a) Calculation of Metric
In section III.C.5.e(1)(a) of the
proposed rule, we proposed that for
each model PY, CMS would calculate a
composite graft survival rate for each
IOTA participant, as defined and
finalized in section III.C.5.e(1) of this
final rule, to measure performance in
the quality domain as described in
section III.C.5.e. of this final rule.
In section III.C.5.e(1)(a) of the
proposed rule, we proposed to use our
own unadjusted composite graft
survival rate equation to evaluate posttransplant outcomes. We proposed to
calculate the composite graft survival
rate by taking the total number of
functioning grafts an IOTA participant
has and dividing that by the total
number of kidney transplants furnished
to patients 18 years of age or older at the
time of the transplant in PY 1 and all
subsequent PYs (see Equation 4) to
evaluate post-transplant outcomes
during the IOTA Model performance
period.
For example, as described in section
III.C.5.e(1)(a) of the proposed rule, if in
PY 1 of the model, an IOTA participant
had 20 observed functioning grafts and
furnished 25 kidney transplants to
patients 18 years of age or older at the
time of transplant, the composite graft
survival rate for that IOTA participant
would be 0.8 (20 from PY 1 divided by
25 from PY 1). Continuing this example,
for PY 2 of the model if the same IOTA
participant had 30 observed functioning
grafts and furnished 35 kidney
transplants to patients 18 years of age or
older at the time of transplant, and two
functioning kidney grafts failed from PY
1, CMS would calculate its composite
graft survival rate for PY 2 as follows.
CMS would divide the cumulative total
of 48 observed functioning grafts (30
from PY 2 + 20 from PY 1—2 from PY
1) by the cumulative total of 60
completed kidney transplants (35 from
PY 2 + 25 from PY 1), resulting in a
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composite graft survival rate of 0.8 (48
divided by 60).
Equation 4: Composite Graft Survival
Rate
Composite Graft Survival Rate
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In the proposed equation, the
numerator (number of functioning
grafts) is defined as the total number of
living adult kidney transplant patients
with a functioning graft. The numerator,
functioning grafts, would exclude grafts
that have failed, as defined by SRTR.
SRTR counts a graft as failed when
follow-up information indicates that one
of the following occurred before the
reporting time point: (1) graft failure
(except for heart and liver, when retransplant dates are used instead); (2) retransplant (for all transplants except
heart-lung and lung); or 3) death.248
OPTN follow-up forms are used to
identify graft failure and re-transplant
dates.249 We also proposed to use OPTN
adult kidney transplant recipient
follow-up forms 250 to identify graft
failure and re-transplant dates for all
transplants furnished to kidney
transplant patients 18 years of age or
older at the time of the transplant. In the
proposed equation, we noted that the
numerator and denominator would not
be limited to the attributed IOTA
transplant patients. By this, we meant
that it could include IOTA transplant
patients who have been de-attributed
from an IOTA participant due to
transplant failure. We believed that
IOTA participants could improve on
this metric by working with IOTA
collaborators to coordinate posttransplant care.
We considered incorporating a risk
adjustment methodology to our
proposed composite graft survival
equation, such as the one used by SRTR
for 1-year post-transplant outcomes
conditional on 90-day survival or
constructing our own (89 FR 43518).
While we recognized that risk
adjustment methodologies may help
account for patient and donor traits, we
could not find a risk adjustment
248 Technical Methods for the Program-Specific
Reports. (n.d.). www.srtr.org. Retrieved December 3,
2022, from https://www.srtr.org/about-the-data/
technical-methods-for-the-program-specificreports/; OPTN. (2022). OPTN Enhanced
Transplant Program Performance Metrics. https://
optn.transplant.hrsa.gov/media/r5lmmgcl/mpsc_
performancemetrics_3242022b.pdf.
249 Technical Methods for the Program-Specific
Reports. (n.d.). www.srtr.org. Retrieved December 3,
2022, from https://www.srtr.org/about-the-data/
technical-methods-for-the-program-specificreports/.
250 https://unos.org/wp-content/uploads/AdultTRF-Kidney.pdf.
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# of Functioning Grafts
# of Completed Kidney Transplants
approach that has consensus agreement
within the kidney transplant
community. We also believed that our
proposed measure is inherently risk
adjusted as it only counts organs that
are ultimately transplanted to patients
18 years of age or older by a kidney
transplant hospital.
We invited public comment on our
proposed methodology to calculate posttransplant outcomes in the IOTA Model,
and on alternatives considered.
Although we proposed an unadjusted
composite graft survival rate to measure
post-transplant outcomes, we were
interested in comments on whether risk
risk-adjustments are necessary, and
which ones, such as donor demographic
characteristics (i.e., race, gender, age,
disease condition, geographic location),
would be significant and clinically
appropriate in the context of our
proposed approach.
The following is a summary of the
comments received on our proposed
methodology to calculate posttransplant outcomes in the IOTA Model,
on whether risk risk-adjustments are
necessary, and which ones, such as
donor demographic characteristics (i.e.,
race, gender, age, disease condition,
geographic location), would be
significant and clinically appropriate in
the context of our proposed approach,
alternatives considered and our
responses:
Comment: Commenters expressed
concern that the lack of risk adjustment
in the proposed composite graft survival
rate metric could have adverse
consequences and would add additional
administrative burden. Many
commenters expressed concern that the
unadjusted composite graft survival rate
does not account for the clinical risk
factors of the recipient or the donor,
therefore, it may inadvertently lead to
disparities in transplant by
incentivizing participants to select
healthier patients. For example, a
commenter felt that the absence of risk
adjustment in the IOTA Model was
problematic and could be detrimental to
patient care; stating that without
accounting for the varying complexities
of patients’ health conditions, hospitals
might avoid referring higher-risk
patients who could benefit most from
transplants. Another commenter
suggested that the lack of risk
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adjustment to the composite graft
survival measure would incentivize
IOTA participants to choose the
healthiest patients to transplant and
would reject those who are sensitized.
Highly sensitized patients have high
levels of anti-HLA antibodies, making
them more likely to reject a kidney from
a donor. These highly sensitized
patients are more likely to be African
American. This same commenter cited a
study published in the Nephrology
Dialysis Transplantation journal that
found that highly sensitized kidney
transplant recipients were more
frequently African American compared
to non-sensitized patients.251 Thus, the
commenter believed that failure to riskadjust this measure could lead to
outcomes that run counter to CMS’s
stated desire to reduce disparities. A
commenter believed that the inclusion
of a post-transplant graft survival metric
is innate and relevant to the IOTA
Model. However, the commenter stated
that one of the longstanding frustrations
of transplant programs is that various
regulatory bodies use different
definitions and standards for graft
survival. As proposed, this would
represent another new definition and
benchmarking system for kidney graft
survival. The same commenter also
found the lack of risk-adjustment
concerning, as they would be taking on
donor organs and recipients of
progressively higher complexity,
particularly for those programs that
wish to pursue the greater-than-150
percent volume target.
Several commenters felt that the
proposed measure misaligns with the
model’s goal of increasing kidney
transplants in a more complex
population without risk adjusting for
allograft and recipient factors. Without
proper risk adjustment, these
commenters suggested it could cause
IOTA participants to be more risk averse
with the types of organs they accept or
disincentivizing IOTA participants from
transplanting candidates who have a
higher likelihood of graft failure, such as
older candidates or those with more
comorbid conditions.
251 Zhang, R. (2017). Donor-Specific Antibodies in
Kidney Transplant Recipients. Clinical Journal of
the American Society of Nephrology, 13(1), 182–
192. https://doi.org/10.2215/cjn.00700117.
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Some commenters suggested specific
donor and recipient characteristics that
CMS should risk adjust for when
calculating the proposed composite graft
survival rate. For example, a commenter
recommended that CMS risk adjust for
how sick the patient is or the health of
the kidney. Another commenter urged
CMS to use SRTR’s risk adjustment
methodology, as it undergoes regular
testing and is updated annually. This
commenter also stated that the current
SRTR model recommends adjusting for
both donor and recipient characteristics,
including (1) donor and recipient
demographic characteristics such as age,
gender, and race, (2) donor and
recipient clinical characteristics such as
BMI, past behavior, medication history,
and (3) history of certain conditions. A
commenter suggested CMS consider
risk-adjusting the composite graft rate
using age, sex, major comorbidities, and
neighborhood disadvantage index or
similar (for example, CDC Social
Vulnerability Index 252). Lastly, a
commenter appreciated CMS’s emphasis
on encouraging focus on post-transplant
outcomes beyond the one- (and three-)
year time horizon that currently receive
the most focus. The commenter also
broadly supported the proposed rolling
composite graft survival metric as a
mechanism to do so, and in particular,
appreciated the simplicity of the
proposed approach. However, they
believed that CMS should risk-adjust for
at least a small number of variables that
would allow for a simple model that is
understandable by including the biggest
drivers for variation in outcomes and
thereby disincentivize the creation of
additional hurdles for more complex
patients. For example, a model that
includes age, ESRD vintage, and
diabetes mellitus (y/n) the same
commenter felt would leverage
currently available data and remain
easily measurable and understood.
Response: We appreciate the concerns
and suggestions from the commenters.
We recognize the importance of
providing a risk adjustment
methodology, but we disagree with
modifying how the composite graft
survival rate, as proposed, is calculated
for PY 1. As discussed in section
III.C.5.e(1)(a) of this final rule, we
proposed to include this measure as a
checkpoint to safeguard patient
outcomes under the IOTA Model and
sought to convey the importance of
ongoing management to preserve the
health of the transplanted graft and the
health and quality of life of the
252 CDC/ATSDR Social Vulnerability Index (CDC/
ATSDR SVI). (2024, June 14). cdc.gov. https://
www.atsdr.cdc.gov/placeandhealth/svi/.
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attributed patients. As discussed at 89
FR 43536 in the proposed rule, 1-year
post-transplant outcomes are markedly
stable while long term post-transplant
outcomes have historically been
unchanged. In addition, research has
shown that kidney transplant recipients,
on average, experience one-year graft
and patient survival rates above 95
percent.253 As such, we believe the
composite graft survival rate measure, as
proposed, will reflect that for PY 1. We
also maintain our belief that this
measure would build upon, and
complement, existing OPTN and SRTR
measures to the maximum extent
possible and enhance patient
understanding of clinically important
post-transplant outcomes beyond
existing 90-day, 1-year and 3-year post
transplant outcomes.
In light of commenters suggestions,
we considered finalizing a risk
adjustment methodology that adjusted
for donor age, recipient age and
recipient diabetes. However, we do not
believe that adjusting for these three
alone are appropriate. Organ availability
is affecting the kidney transplantation in
its entirety, leading to transplant teams
expanding the criteria for accepting
organ donors. In these circumstances,
we believe that analysis of the impact of
the donor’s characteristics on graft
survival becomes mandatory before
incorporating a risk adjustment
methodology. Additionally, given that
the IOTA Model is 6 years, and the
measure is rolling, we want to make
sure that we continue discussions to
ensure that this measure eventually
includes a robust and appropriate risk
adjustment methodology. Furthermore,
we believe that the lack of risk
adjustment for PY 1 will be minimal in
terms of impacting IOTA participants
scores and note that IOTA participants
would not owe a downside risk
payment in PY 1, as described and
finalized in section III.C.6 of this final
rule.
Therefore, we will be finalizing our
composite graft survival methodology,
as proposed, to calculate post-transplant
outcomes in the IOTA Model. However,
in light of comments received, we will
be stratifying the data from the
composite graft survival rate measure
253 Poggio, E.D., Augustine, J.J., Arrigain, S.,
Brennan, D.C., & Schold, J.D. (2021). Long-term
kidney transplant graft survival—Making progress
when most needed. American Journal of
Transplantation, 21(8). https://doi.org/10.1111/
ajt.16463; Meier-Kriesche, H.U., Schold, J.D., &
Kaplan, B. (2004). Long-Term Renal Allograft
Survival: Have we Made Significant Progress or is
it Time to Rethink our Analytic and Therapeutic
Strategies? American Journal of Transplantation,
4(8), 1289–1295. https://doi.org/10.1111/j.16006143.2004.00515.x.
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and will work with stakeholders to
inform a risk adjustment methodology
for this measure and intend to address
a new or updated policy pursuant to
future notice and comment rule making.
We also note that since we are not
finalizing our proposed quality measure
set or quality measure set scoring
methodology, as described in sections
III.C.5.e(2) and III.C.5.e(2)(e) of this final
rule, and based on public comment, we
will be modifying our proposed points
allocation. We direct readers to section
III.C.5.e(1)(b) for further discussion on
the points allocation for the composite
graft survival rate measure.
Comment: Several commenters
expressed concern over the proposed
composite graft survival rate outcome
measure. In particular, some
commenters felt that the measure
contradicts the primary objective of the
IOTA Model, which is to increase the
number of kidney transplants
performed. For instance, a commenter
believed that because this proposed
measure would evaluate post-transplant
outcomes during the IOTA Model
performance period that the added
requirement to provide six-year data
detracts from what should be an
unerring and resolute focus on
increasing transplant volumes. A
commenter also urged CMS to modify or
remove this measure from the model in
order for the model to succeed in
achieving its primary objective. A
couple commenters argued that this
proposed measure would deter IOTA
participants from transplanting lowerquality organs, which are significantly
less likely to maintain function for six
years post-transplant. Therefore, the
commenters felt that the proposed
outcome measure is inconsistent with
the main objectives of the IOTA Model.
Some commenters also shared that
they felt collecting the data required for
the proposed composite graft survival
rate metric would add additional
administrative burden for IOTA
participants. Specifically, a commenter
suggested that finalizing this measure as
proposed would significantly increase
the data collection burden on
participating transplant programs, as no
existing database contains six-year posttransplant graft function data. A
commenter also argued that the
proposed six-year outcome measure
conflicts with the existing monitoring
and reporting framework, and
introducing a significant unfunded
change would be illogical, as it is
incongruent with the model’s strategic
goals. A few commenters felt that this
measure, as proposed, increases the
time horizon for post-transplant graft
survival accountability for transplant
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programs that participate. They noted
that after the first-year post-transplant,
the recipient’s nephrologist, rather than
the transplant facility, is primarily
responsible for the patient’s ongoing
care. Thus, they felt the six-year
timeline was unreasonable, as it would
hold transplant programs accountable
for ensuring graft function long after the
period for which they can be held
responsible.
Response: We thank commenters for
their input and acknowledge their
recommendations and concerns around
the proposed composite graft survival
rate. As mentioned in comment
responses noted previously in this
section, we will be finalizing the
composite graft survival rate as
proposed. However, we will take these
insights and recommendations into
consideration as we continue to assess
our composite graft survival rate
measure methodology and, if warranted,
will propose a new or updated policy
through future notice and comment
rulemaking. We also note that in light of
comments received, we intend to
incorporate a risk adjustment
methodology into our proposed
approach for calculating post-transplant
outcomes in the IOTA Model in future
notice and comment rulemaking.
Comment: Several commenters
expressed support for using the
unadjusted composite graft survival rate
as proposed.
Response: We thank the commenters
for their support. We direct readers to
section III.C.5.e(1)(a) of this final rule
for the full discussion of the comments
received in support of our proposed
composite graft survival rate measure.
After consideration of the public
comments received, for the reasons set
forth in this rule, we are finalizing the
proposed provisions for calculating the
composite graft survival rate as
proposed at § 512.428(b)(1), without
modification. While we are finalizing
our provision for calculating the
composite graft survival rate as
proposed, we will be stratifying the data
from the composite graft survival rate
measure to inform a risk adjustment
methodology for this measure and may
consider future notice and comment
rulemaking on this topic.
(b) Calculation of Points
As described in section III.C.5.e of the
proposed rule, performance on the
quality domain would be worth up to 20
points. Within the quality domain, we
proposed that the composite graft
survival rate would account for 10 of the
20 allocated points. We proposed that
the points earned would be based on the
IOTA participants’ performance on the
composite graft survival rate metric
ranked against a national target,
inclusive of all eligible kidney
transplant hospitals, both those selected
and not selected as IOTA participants.
We believe that using percentiles would
create even buckets of scores among the
continuum of IOTA participants.
We proposed that points would be
awarded based on the national quintiles,
as outlined in Table 8, such that IOTA
participants that perform—
• At or above the 80th percentile
would earn 10 points;
• In the 60th percentile to below the
80th percentile would earn 8 points;
• In the 40th to below the 60th
percentile would earn 5 points;
• In the 20th percentile to below the
40th percentile would earn 3 points;
and
• Below the 20th percentile would
receive no points for the composite graft
survival rate.
TABLE 8: COMPOSITE GRAFT SURVIVAL RATE SCORING
goth Percentile :S
60th :S and < goth Percentile
g
5
:S and <
Percentile
20th :S and < 40th Percentile
< 20th Percentile
60th
Utilizing quintiles aligns with the
calculation of the upside and downside
risk payments in relation to the final
performance score as detailed and
finalized in section III.C.6.c(2) of this
final rule, where average performance
yields half the number of points. The
scoring is normalized, meaning an
average performing IOTA participant
earns 5 points out of 10, or about 50
percent of possible points. We recognize
that there is an upper limit to the
benefits of efficiency, and quintiles
combine the highest 20 percent of
performers in a point band. Due to the
current disparity among kidney
transplant hospitals, we do not expect
every IOTA participant to reach toplevel performance on this metric.
We considered a strategy similar to
the proposed organ offer acceptance
methodology which would apply a two-
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0
scoring system in which we would
determine an achievement score and
improvement score and award the point
equivalent to the higher value between
the two scores. We also considered
proposing just an improvement score, in
which we would evaluate IOTA
participants’ performance on composite
graft survival during a PY relative to
their performance the previous CY. We
considered both approaches because we
recognize that if an IOTA participant
does not do well one year in our
proposed methodology, that it may be
difficult for it to improve during the
model performance period. However,
we chose not to propose either of these
other methodologies (achievement and
improvement or just improvement
scoring) because we had concerns over
our ability to measure improvement
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year over year due to potentially small
numbers.
We sought public comment on the
proposed point allocation and
calculation methodology for posttransplant outcomes within the quality
domain for the IOTA Model and
alternatives considered.
The following is a summary of the
comments received on our proposed
point allocation and calculation
methodology for post-transplant
outcomes within the quality domain for
the IOTA Model and our responses:
Comment: A few commenters
expressed concern over the proposed
points allocation. Specifically, a
commenter indicated that, despite
performing as expected on one-year
outcomes, they would receive zero
points based on the proposed points
allocation, as the observed survival is
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ranked low. The commenter attributed
this to the transplant hospitals
willingness to take on riskier waitlist
patients and accept donors that other
transplant hospitals may otherwise not.
A commenter expressed concern that a
small number of adverse scores could
significantly skew a transplant
hospital’s data. They argued that with
the relatively low volume of transplants,
just a few outlier scores could make it
challenging to draw meaningful
conclusions or implement impactful
changes. As a result, the commenter
believed these widely used quality
metrics were better suited for evaluating
large patient populations, such as in
primary care settings. Lastly, a
commenter also recommended that CMS
adjust the eligibility to obtain maximum
points downward in the composite graft
survival rate points allocation.
Specifically, the commenter suggested
that full points be awarded to IOTA
participants at the 60th percentile and
above instead of the proposed 80th
percentile and above.
Response: We thank the commenters
for raising concerns around the
potential difficulties IOTA participants
may face in achieving a top score on the
composite graft survival rate metric.
Regarding the concerns that a small
number of adverse scores could
significantly skew a transplant
hospital’s data, we believe that is
difficult for us to approach with so little
data. However, we recognize there have
been significant improvements in
kidney transplantation outcomes over
time due to advances in
immunosuppressive therapies, surgical
techniques, and organ preservation
methods. We also recognize that posttransplant outcomes are already
incentivized through private payers’
COE programs and OPTN metrics.
Additionally, we acknowledge that
IOTA participants will need time to
establish relationships with IOTA
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collaborators, as described and finalized
in section III.C.11.c of this final rule,
and we want to allow time for those to
be established.
Thus, given this myriad of issues, and
in light of public comment, we are
finalizing an alternate scoring system for
PY 1. Points will be awarded based on
the national quintiles, as outlined in
Table 9, such that IOTA participants
that perform:
• At or above the 80th percentile
would earn 20 points;
• In the 60th percentile to below the
80th percentile would earn 18 points;
• In the 40th percentile to below the
60th percentile would earn 16 points;
• In the 20th to below the 40th
percentile would earn 14 points;
• In the 10th to below the 20th
percentile would earn 12 points; and
• Below the 10th percentile would
receive 10 points for the composite graft
survival rate.
TABLE 9: COMPOSITE GRAFT SURVIVAL RA TE SCORING
We recognize that for PY 2 and future
PYs there will be more events and a
longer time horizon and plan to
implement a more robust methodology
that can account for both the likelihood
of graft failure based on the donor and
the recipient and can account for
relative benefits of transplantation over
remaining on dialysis. We will continue
to assess our quality domain
methodology and how to best balance
incentives in the efficiency domain and
quality domain and address a new or
updated policy pursuant to future notice
and comment rule making.
Comment: A commenter expressed
support for the proposed point
allocation and calculation methodology
for post-transplant outcomes within the
quality domain for the IOTA Model.
Response: We thank the commenter
for their support. As mentioned in
comment responses noted previously,
since we are not finalizing our proposed
quality measure set or quality measure
set scoring methodology, as described in
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16
14
12
10
sections III.C.5.e(2) and III.C.5.e(2)(e) of
this final rule, and based on public
comment, we will be modifying our
proposed points allocation, as
illustrated in Table 9 in this section. We
will continue to assess our quality
domain methodology and how to best
balance incentives in the efficiency
domain and quality domain and address
a new or updated policy pursuant to
future notice and comment rule making
and provide further specification based
on commenters suggestions, if
warranted.
After consideration of the public
comments we received, for the reasons
set forth in this rule, we are finalizing
our proposed composite graft survival
rate scoring methodology within the
quality domain at § 512.428(d), as
proposed with minor technical
corrections to update language to reflect
what we proposed at 89 FR 43518 of the
proposed rule. Specifically, at
§ 512.428(d) we are updating the
language to reflect that CMS awards
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points to the IOTA participant based on
the IOTA participant’s performance on
the composite graft survival rate, as
described in paragraph (b)(1) of this
section, ranked nationally, inclusive of
all eligible kidney transplant hospitals.
We are also finalizing our proposal for
the proposed point allocation for posttransplant outcomes within the quality
domain for the IOTA Model with slight
modifications. In section III.C.5.e(2)(e)
of the proposed rule, we proposed that
the IOTA participant would receive up
to 10 points for performance on our
three proposed measures within the
quality domain while also noting in the
proposed rule at 89 FR 43564, that if we
finalized fewer measures, then we
proposed to allocate the points
accordingly within the remaining
measures. We acknowledge that by not
finalizing any of the proposed quality
measures for inclusion in the quality
measure set of the quality domain, as
described in section III.C.5.e(2) of this
final rule, there is a need to account for
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80th Percentile :S:
60th :s; and < 80th Percentile
40th :S: and < 60th Percentile
20th :s; and < 40th Percentile
10th :s; and < 20th Percentile
< 10th Percentile
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Federal Register / Vol. 89, No. 233 / Wednesday, December 4, 2024 / Rules and Regulations
the points that we proposed to allocate
to them, as described in section
III.C.5.e(2)(e) of the preamble in this
final rule. Therefore, we are finalizing
our proposal with slight modification in
Table 1 to paragraph (d) at our
regulation at § 512.428(d) to allot a
maximum of 20 points for performance
on the composite graft survival rate
measure.
Additionally, after consideration of
the public comments we received, we
are also finalizing, with modification,
Table 1 to paragraph (d) at § 512.428(d)
to reflect the updated points allocation,
such that IOTA participants that
perform—
• At or above the 80th percentile
would earn 20 points;
• In the 60th percentile to below the
80th percentile would earn 18 points;
• In the 40th percentile to below the
60th percentile would earn 16 points;
• In the 20th to below the 40th
percentile would earn 14 points;
• In the 10th to below the 20th
percentile would earn 12 points; and
• Below the 10th percentile would
receive 10 points for the composite graft
survival rate.
ddrumheller on DSK120RN23PROD with RULES2
(2) Quality Measure Set
In section III.C.5.e(2) of the proposed
rule, we proposed to select and use
quality measures to assess IOTA
participant performance in the quality
domain. Performance on the proposed
IOTA Model quality measure set would
be used to assess the performance of an
IOTA participant on aspects of care that
we believe contribute to a holistic and
patient-centered journey to receiving a
kidney transplant.
In section III.C.5.e(2) of the proposed
rule, we proposed the following three
measures for inclusion in the IOTA
Model quality measure set: (1)
CollaboRATE Shared Decision-Making
Score (CBE ID:3327), (2) Colorectal
Cancer Screening (COL) (CBE ID: 0034),
and (3) the 3-Item Care Transition
Measure (CTM–3) (CBE ID:
0228).254 255 256 The quality measures
that we proposed share common
features. We proposed measures that
have been or are currently endorsed by
the CMS Consensus-Entity (CBE)
through the CMS Consensus-Based
Process. This ensures that the measures
254 collaboRATE. (2019). Glyn Elwyn. https://
www.glynelwyn.com/collaborate.html.
255 Colorectal Cancer Screening—NCQA. (2018).
NCQA. https://www.ncqa.org/hedis/measures/
colorectal-cancer-screening/ https://www.ncqa.org/
hedis/measures/colorectal-cancer-screening/.
256 THE NATIONAL QUALITY FORUM
Specifications for the Three-Item Care Transition
Measure—CTM–3. (n.d.). Retrieved May 28, 2023,
from https://mhdo.maine.gov/_pdf/NQF_CTM_3_
%20Specs_FINAL.pdf.
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proposed have been assessed against
established evaluation criteria of
importance, acceptability of measure
properties, feasibility, usability, and
competing measures.257 Our proposed
measure set is patient-centered,
reflecting areas that we have heard from
patients are important and for which
there is significant variation in
performance among transplant
hospitals. We proposed measures that
would incentivize improvements in care
that we would otherwise not expect to
improve based on the financial
incentives in the model alone. We are
also proposing a measure set that would
allow us to make a comprehensive
assessment of post-transplant outcomes.
The composite graft survival rate that
we proposed in section III.C.5.e(1) of the
proposed rule and this final rule would
provide an essential, albeit limited,
assessment of the success of a kidney
transplant. Finally, we proposed
measures that we believe would
incentivize improvement in aspects of
post-transplant care that are important
to patients and modifiable by IOTA
participants.
We stated in the proposed rule at
section III.C.5.e(2) that on March 2,
2023, Jacobs et al. published Aligning
Quality Measures across CMS—The
Universal Foundation, which describes
CMS leadership’s vision for a set of
foundational quality measures known as
the Universal Foundation. This measure
set would be used by as many CMS
value-based and quality programs as
possible, with other measures added
based on the population or healthcare
setting.258 CMS selected measures for
the Universal Foundation that are
meaningful to a broad population,
reduce burden by aligning measures,
advance equity, support automatic and
digital reporting, and have minimal
unintended consequences.259
We considered only including two
measures in the initial quality measure
set and pre-measure development
because we were concerned about the
potential added reporting burden placed
on IOTA participants (89 FR 43518).
257 Supplemental Material to the CMS Measures
Management System (MMS) Hub CMS ConsensusBased Entity (CBE) Endorsement and Maintenance.
(2022). https://www.cms.gov/files/document/
blueprint-nqf-endorsement-maintenance.pdf.
258 Jacobs, D.B., Schreiber, M., Seshamani, M.,
Tsai, D., Fowler, E., & Fleisher, L.A. (2023).
Aligning quality measures across CMS—the
Universal Foundation. New England Journal of
Medicine, 388(9), 776–779. https://doi.org/10.1056/
nejmp2215539.
259 Jacobs, D.B., Schreiber, M., Seshamani, M.,
Tsai, D., Fowler, E., & Fleisher, L.A. (2023).
Aligning quality measures across CMS—the
Universal Foundation. New England Journal of
Medicine, 388(9), 776–779. https://doi.org/10.1056/
nejmp2215539.
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However, we chose to propose three
measures and pre-measure development
because we want to use them to
incentivize and improve patient care.
We sought additional feedback on
which of the proposed measures have
the highest potential to impact changes
in behavior, while minimizing provider
burden.
We also considered only including
COL in the quality measure set and
allotting this measure 4 points, with the
remaining 16 points allotted to the
composite graft survival rate (89 FR
43518). It is worth noting that if we
choose fewer measures, then we
proposed allocating the points
accordingly within the remaining
measures.
We considered several alternative
measures for the quality domain
performance assessment (89 FR 43518).
We considered the Hospital Consumer
Assessment of Healthcare Providers and
Systems (HCAHPS) survey because
hospitals are already required to report
that survey in the Hospital VBP
Program, thereby reducing or limiting
burden to IOTA participants burden
since it is already in use. We did not
propose the HCAHPS measure for the
IOTA Model because HCAHPS data is
based on survey results from a random
sample of adult patients across medical
conditions. We believe that the
HCAHPS would present sample size
issues for purposes of calculation.
We considered the Gains in Patient
Activation Measure (PAM®) (CBE ID:
2483) (89 FR 43518). The PAM®
measure is being used in the voluntary
KCC Model and was included on the
2022 Measures Under Consideration
(MUC) List for the ESRD Quality
Incentive Program (QIP) and MIPS.260
We considered whether the PAM®
Measure could encourage IOTA
participants and IOTA Collaborators, as
defined and finalized in section
III.C.11.d of this final rule, to activate
IOTA waitlist patients to work in
collaboration with IOTA participants to
complete requirements to maintain
active waitlist status; however, we were
unable to locate any peer-reviewed
literature to support this hypothesis.
As described in section III.C.5.e(2) of
the proposed rule, we also considered
the Depression Remission at 12 Months
measure (CBE ID: 0710e). Studies have
shown that depression and anxiety are
common amongst people on dialysis
and suggested that incorporating patient
reported outcome measures (PROs) that
260 Pre-Rulemaking | The Measures Management
System. (n.d.). Mmshub.cms.gov. Retrieved May 12,
2023, from https://mmshub.cms.gov/measurelifecycle/measure-implementation/pre-rulemaking/
overview.
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ddrumheller on DSK120RN23PROD with RULES2
focus on depression can improve healthrelated quality of life in patients with
ESRD.261 One study found that, at the
time of kidney evaluation, over 85
percent of patients exhibited at least
minimal depressive symptoms and that
patients with depressive symptoms
were less likely to gain access to the
waitlist.262 Although the waitlist offers
some hope to patients, being waitlisted
for a kidney transplant is also
psychologically distressing, with
patients reporting disillusionment,
moral distress, unmet expectations,
increasing vulnerability, and
deprivation.263 These factors are likely
contributors to high rates of stress and
anxiety observed among waitlisted
patients.264 The conditions of
participation (CoPs) for transplant
hospitals require that prospective
transplant candidates receive a
psychosocial evaluation prior to
placement on a waitlist (42 CFR
482.90(a)(1)), if possible, and OPTN
bylaws specify that transplant hospitals
must include team members to
coordinate a transplant candidate’s
psychosocial needs; however, neither
the CoP nor the OPTN bylaws require
specific assessment of, or intervention
into, patients’ behavioral health. The
ESRD QIP measure set includes the
Clinical Depression Screening and
Follow-Up measure; however,
performance on the measure requires
only documentation that an attempt at
screening and follow up was made.265
261 Feroze, U., Martin, D., Kalantar-Zadeh, K.,
Kim, J.C., Reina-Patton, A., & Kopple, J.D. (2012).
Anxiety and depression in maintenance dialysis
patients: Preliminary data of a cross-sectional study
and brief literature review. Journal of Renal
Nutrition, 22(1), 207–210. https://doi.org/10.1053/
j.jrn.2011.10.009; Mclaren, S., Jhamb, M., & Unruh,
M. (2021). Using Patient-Reported Measures to
Improve Outcomes in Kidney Disease. Blood
Purification, 1–6. https://doi.org/10.1159/
000515640; Cukor, D., Donahue, S., Tummalapalli,
S.L., Bohmart, A., & Silberzweig, J. (2022). Anxiety,
comorbid depression, and dialysis symptom
burden. Clinical Journal of the American Society of
Nephrology, 17(8), 1216–1217. https://doi.org/
10.2215/cjn.01210122.
262 Chen, X., Chu, N.M., Basyal, P.S., Vihokrut,
W., Crews, D., Brennan, D.C., Andrews, S.R.,
Vannorsdall, T.D., Segev, D.L., & McAdamsDeMarco, M.A. (2022). Depressive symptoms at
kidney transplant evaluation and access to the
kidney transplant waitlist. Kidney International
Reports, 7(6), 1306–1317. https://doi.org/10.1016/
j.ekir.2022.03.008.
263 Tong, A., Hanson, C.S., Chapman, J.R.,
Halleck, F., Budde, K., Josephson, M.A., & Craig,
J.C. (2015). ‘suspended in a paradox’—patient
attitudes to wait-listing for Kidney Transplantation:
Systematic review and thematic synthesis of
qualitative studies. Transplant International, 28(7),
771–787. https://doi.org/10.1111/tri.12575.
264 Ibid.
265 CMS ESRD Measures Manual for the 2023
Performance Period. (2022). https://www.cms.gov/
files/document/esrd-measures-manual-v81.pdf.
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Additionally, this measure is already
being used in the KCC Model.
We stated in the proposed rule that
while we understand the importance of
including measures focused on
depression, we believe that IOTA
participants may have limited
experience diagnosing and treating
depression and may struggle to make
referrals due to limited behavioral
health providers (89 FR 43518). We also
believe that this measure may be
duplicative with other policies in this
model that strive to improve the health
and post-transplant outcomes of
attributed patients. Additionally, based
on the KCC Model experience, the
Depression Remission measure is
operationally complex due to the 10month reporting period and novel
collection and reporting processes. We
believe that IOTA participants would
experience similar challenges due to the
mandatory nature of the model and
unfamiliarity with reporting quality
measure data to the Innovation Center.
In section III.C.5.e(2) of the proposed
rule, we considered the Depression
Remission at 12 Months measure (CBE
ID: 0710e) because major depression is
prevalent in the dialysis population and
most kidney transplant recipients spend
some time on a dialysis modality.266
Depression measures are included in the
Universal Foundation because
successfully treating depression can
improve physical health outcomes, in
addition to behavioral health
outcomes.267 A depression measure
would align with the behavioral health
domain of Meaningful Measures 2.0. We
considered a depression remission
measure over a depression screening
measure because we believed a
depression remission measure would
incentivize IOTA participants to work
with the other clinicians and providers
involved in the care of attributed
patients to resolve or improve the
depressive symptoms rather than only
identifying them. Our review of the
literature found that presence of
behavioral health symptoms affected the
ability of patients to get on the kidney
transplant waiting list, but did not affect
likelihood of receiving a kidney
266 Cukor, D., Donahue, S., Tummalapalli, S.L.,
Bohmart, A., & Silberzweig, J. (2022). Anxiety,
comorbid depression, and dialysis symptom
burden. Clinical Journal of the American Society of
Nephrology, 17(8), 1216–1217. https://doi.org/
10.2215/cjn.01210122.
267 Jacobs, D.B., Schreiber, M., Seshamani, M.,
Tsai, D., Fowler, E., & Fleisher, L.A. (2023).
Aligning quality measures across CMS—the
Universal Foundation. New England Journal of
Medicine, 388(9), 776–779. https://doi.org/10.1056/
nejmp2215539.
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transplant.268 We did not propose the
Depression Remission at 12 Months
Measure because we were unable to
locate any publications that found
depression remission affected access to
a kidney transplant. We also chose not
to propose this type of measure because
the IOTA Model does not target prewaitlist patients for attribution to model
participants. We also believe that IOTA
participants may have limited
experience in diagnosis and treating
depression and may struggle to make
referrals due to limited behavioral
health providers. Additionally,
behavioral health management is not
under the purview of a kidney
transplant hospital that might see a
kidney transplant waitlist patient
perhaps only a handful of times, but
may be more appropriate for the
patient’s nephrologist or dialysis center.
We sought comment on our proposed
quality measure set that includes two
PRO–PMs (CollaboRATE Shared
Decision-Making Score and 3-Item Care
Transition Measure) and one process
measure (Colorectal Cancer Screening)
for purposes of measuring performance
in the quality domain. We also sought
comment on alternative quality
measures considered.
The following is a summary of the
comments received on our proposed
quality measure set that includes two
PRO–PMs (CollaboRATE Shared
Decision-Making Score and 3-Item Care
Transition Measure) and one process
measure (Colorectal Cancer Screening)
for purposes of measuring performance
in the quality domain and alternative
quality measures considered and our
responses:
Comment: We received many
responses from commenters who did
not agree with the proposed quality
measure set that includes two PRO–PMs
(CollaboRATE Shared Decision-Making
Score and 3-Item Care Transition
Measure) and one process measure
(Colorectal Cancer Screening), as
described in the preamble of this final
rule, in the IOTA Model and highlight
several reasons. Commenters stated that
the proposed measures have not been
268 Szeifert, L., Bragg-Gresham, J.L., Thumma, J.,
Gillespie, B.W., Mucsi, I., Robinson, B.M., Pisoni,
R.L., Disney, A., Combe, C., & Port, F.K. (2011).
Psychosocial variables are associated with being
wait-listed, but not with receiving a kidney
transplant in the dialysis outcomes and Practice
Patterns Study (dopps). Nephrology Dialysis
Transplantation, 27(5), 2107–2113. https://doi.org/
10.1093/ndt/gfr568; Chen, X., Chu, N.M., Basyal,
P.S., Vihokrut, W., Crews, D., Brennan, D.C.,
Andrews, S.R., Vannorsdall, T.D., Segev, D.L., &
McAdams-DeMarco, M.A. (2022). Depressive
symptoms at kidney transplant evaluation and
access to the kidney transplant waitlist. Kidney
International Reports, 7(6), 1306–1317. https://
doi.org/10.1016/j.ekir.2022.03.008.
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developed, validated, or evaluated for
use in this patient population and
expressed uncertainty to how effective
they would be in the model. A few
commenters noted that the
CollaboRATE Shared Decision-Making
measure and CTM–3 are not currently
being utilized by transplant hospitals
and lack any evidence base for use in
kidney transplantation or in patients
with CKD and ESRD. Thus, including
PRO–PMs without any convincing
evidence base for efficacy could be
counterproductive and discourage
support for PRO measurements
generally. Additionally, because the
proposed quality measures are not
currently used in any CMS program, a
commenter anticipated that IOTA
participants would face additional costs
to implement these new requirements.
Response: We thank commenters for
expressing their concerns with the
proposed quality measures. While we
recognize that the CollaboRATE
measure, COL and CTM–3 are not
specific to transplantation, we believe
they are helpful measures for assessing
hospital quality and performance for the
reasons set forth in sections
III.C.5.e(2)(b), (c), and (d) of this final
rule. However, in response to public
comments, we will not be finalizing our
proposed quality measure set that
includes two PRO–PMs (CollaboRATE
Shared Decision-Making Score and 3Item Care Transition Measure) and one
process measure (Colorectal Cancer
Screening) at this time.
Comment: A commenter agreed with
the importance of assessing both
patient’s level of SDM and readiness for
self-care at the time of discharge but did
not support the use of patient report
survey-based measures. The commenter
suspected that adding another survey
would likely result in low response
rates and survey fatigue. Patients are
already overwhelmed by the numerous
surveys from hospitals, doctors, dialysis
centers, and post-acute care providers.
Additionally, the commenter argued
that transplant patients, who already
face significant demands on their time
and energy, would likely not prioritize
completing survey measures.
Response: We appreciate the
commenters’ concerns regarding the use
of patient-report survey measures, but
we disagree. Chronic kidney disease is
complex and demands thorough
medical management, even after
transplantation. Thus, when taking into
consideration the lasting impact of CKD,
symptom burden and its correlation to
mental health and psychosocial
difficulties, we believe it is essential
that we understand the entirety of the
patient experience and take steps to
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improve it using the policy levers
available in the IOTA Model. We
maintain that failure to address what is
important to patients could result in
continued, or the development of,
decreased quality of life in addition to
psychosocial distress, increased
symptom burden and new physical
problems or both to arise and be left
untreated. We also acknowledge that it
is equally important that any PROM
included be relevant to the population
being measured. To date, there are not
only no kidney transplant specific PROs
that are endorsed by NQF but there also
remains a shortage of kidney transplant
specific validated measures. However,
given commenters concerns, we are
persuaded not to finalize our proposed
quality measure set that includes two
PRO–PMs (CollaboRATE Shared
Decision-Making Score and 3-Item Care
Transition Measure) and one process
measure (Colorectal Cancer Screening)
at this time. We still believe in the
importance of using validated, personcentered, measures of quality of care to
support a holistic and patient-centered
kidney transplant process, but
acknowledge the challenges presented
by commenters in the proposed quality
measures set. We intend to propose
additional quality measures which may
include a focus on health-related quality
of life (HRQoL) for kidney transplant
recipients or address pre-transplant
processes of care through future notice
and comment rule making. We believe
these measures will support the goals of
the IOTA Model to improve quality and
equity of care. In the interim, we have
been convinced the other requirements
that enforce SDM in the pre-transplant
process (for example, Transplant
Hospitals’ CoP) are adequate and
mitigate the challenges posed by the
proposed measures. Although we are
not finalizing any of the proposed
measures in our quality measure set, we
think that the IOTA Model promotes
SDM through some of our other policies,
such as the proposed transparency
requirements as described and finalized
in section III.C.8(a) of the preamble in
this final rule.
Comment: Some commenters
encouraged CMS to include the PAM®
in the IOTA Model. A couple
commenters noted that while the PAM®
is not validated for use in
transplantation it would serve as
continuity with other models. A few
commenters acknowledged that we
considered whether the PAM® Measure
could encourage IOTA participants and
IOTA Collaborators, as defined at
§ 512.402 of the proposed rule, to
activate IOTA waitlist patients to work
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in collaboration with IOTA participants
to complete requirements to maintain
active waitlist status; however, we were
unable to locate any peer-reviewed
literature to support this hypothesis.
One of these commenters recommended
that CMS reevaluate possible inclusion
of the PAM in the IOTA Model quality
measure set after the public release of
data on the PAM® use in the voluntary
KCC Model. While a couple commenters
disagreed with CMS, suggesting that
there was ample evidence to support the
inclusion of PAM® in the IOTA Model.
Specifically, they asserted that the
PAM® is well established, in use, valid
and reliable across the kidney care
journey, including specific peer
reviewed studies on the proposed IOTA
population. Moreover, they asserted that
the evidence demonstrates the crucial
importance of patient activation for
patients diagnosed with CKD,
particularly within the transplant
population. Furthermore, the findings
suggest that clinical teams could have a
profound impact on supporting the
main objectives of the IOTA Model.
Response: We appreciate the
suggestion from commenters to include
the PAM® in the IOTA Model and will
consider the suggestion for future
rulemaking, where appropriate. Given
the concerns raised by commenters
about participant burden associated
with PRO–PMs, including PAM®, we
are not proposing to add it at this time.
Rather, as mentioned in comment
responses noted previously, we will
consider future PRO–PMs use in the
model.
Comment: Many commenters
suggested alternative measures that the
IOTA Model should include in place of
those proposed quality measure set. For
example, a commenter recommended
that CMS consider implementing
stronger quality protections during the
first two years of the model; suggesting
that this could include assessing
performance on additional process
measures that reflect appropriate care
delivery, rather than relying solely on
pay-for-reporting. To align with the
updates to the Hospital Consumer
Assessment of Healthcare Providers and
Systems (HCAHPS) survey, a
commenter suggested that CMS should
replace the retired CTM–3 measure with
the proposed ‘‘Care Coordination’’ SubMeasure.
Several commenters suggested that
CMS include more specific health
screening measures in place of the COL.
For example, a commenter stated that
colon cancer rates are similar between
kidney transplant and non-transplant
patients. Whereas skin cancer has a
much higher prevalence in transplant
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patients compared to non-transplant
patients. Thus, they suggested that there
would be more value in creating a skin
cancer measure. The commenter also
mentioned that they contemplated
suggesting that CMS consider using a
vaccination rate measure in place of the
COL, since being current on
vaccinations is more directly relevant to
transplant candidate readiness and
transplant recipient well-being
regardless of age than colorectal cancer
screening. However, they suggested that
vaccination rates could present an
evolving challenge for IOTA
participants to achieve given the
growing skepticism of vaccinations in
the post-COVID–19 pandemic era. The
same commenter also believed that
many programs exclude individuals
who refuse vaccinations who would
otherwise be good transplant
candidates, and such a metric could
further encourage the exclusion of these
patients. A couple of commenters
suggested that addressing posttransplant cardiovascular risk factors
could lead to better long-term outcomes.
This is because multiple adverse cardiac
events are more common causes of
death than cancer or infection after
transplant, noting that nearly 25 percent
of deaths in the first-year posttransplant are related to cardiovascular
reasons. Therefore, the commenters
recommended that CMS include
measures to screen for post-transplant
diabetes mellitus and manage
hyperlipidemia.
A few commenters mentioned that
CMS should include the Hemoglobin
A1c poor control (≤9%) (CBE #0559)
and Advance Care Plan (CBE #0326)
measures to the quality domain to align
with the Universal Measures. A
commenter suggested that the Advance
Care Plan and CollaboRATE score align
with the program’s other measures,
collectively upholding a high standard
of care for transplant patients.
Specifically, the commenter proposed
that the Advance Care Plan and
CollaboRATE score could work together
to facilitate a comprehensive, patientinformed decision-making process.
Another commenter encouraged CMS to
consider the 15-item Care Transition
Measure (CTM–15), proposing that it
could facilitate a better understanding of
post-transplant expectations for patients
due to its incorporation of components
like a written care plan and a list of
scheduled appointments.
Response: We would like to thank all
commenters that closely reviewed and
shared their suggestions for with the
IOTA Model proposed quality measures,
and recognize the efforts made by
commenters to align measures relevant
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to the target population and to align to
the Universal Foundation, a key CMS
priority. We are committed to including
quality measures in the IOTA quality
domain to further the model goals for
improving quality of care and
supporting a holistic, patient-centered
kidney transplant process. Responsive
to comments, we will not be finalizing
our proposed quality measure set that
includes two PRO–PMs (CollaboRATE
Shared Decision-Making Score and 3Item Care Transition Measure) and one
process measure (Colorectal Cancer
Screening). We will consider future
measures aligned to the priority areas of
the kidney transplant process and will
align, where possible, with CMS
priorities and other CMS programs.
Comment: Numerous commenters
expressed concerns about the proposed
quality measure reporting requirements.
They cited challenges with data
collection, administrative burden, and
unfamiliarity with the measures;
ultimately suggesting that the data
collected would not justify the added
administrative burden. For example, a
commenter stated that if patients are
attributed to multiple transplant
hospitals, collecting quality measures
data on the entire attributed population
could be duplicative and burdensome.
The same commenter also believed that
allowing for quality measures to change
each PY that it would cause confusion
and lost revenue, and that more
consideration should be put into the
process for data collections so that it
does not unduly burden programs in a
way that compromises clinical
outcomes and organ transplant access. A
commenter stated that SDM and patient
involvement in transplant care, as well
as patient autonomy, are respected and
assessed in the evaluation process but
do not directly support the goal of
improving patient outcomes. Thus, they
felt that the that administering the
CollaboRATE Shared Decision-Making
Score and CTM–3 would cause
unnecessary administrative burden.
Another commenter expressed their
belief that administering and
documenting the CollaboRATE Shared
Decision-Making Score and CTM–3
would be laborious due to the volume
of patients on the waitlist and
questioned how this would be
accomplished in a consistent manner.
Response: We appreciate and
acknowledge the commenters’ concern
and challenges with the proposed
quality measures. We recognize the
difficulties associated with patient
reported outcome measures and the
underlying data collection tools used in
a clinical domain. At this point, as
mentioned in comment responses noted
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previously, we are not finalizing any of
the three quality measures that we
proposed. In the future we plan to
propose additional quality measures
which may include a focus on HRQoL
for kidney transplant recipients or
address pre-transplant processes of care.
We suggest these measures would
support the goals of the IOTA Model to
improve quality and equity of care and
acknowledge the burden of data
collection in measures using EHR or
survey data. However, it is a CMS
priority to incorporate person-centered
measures, including patient-reported
measures, where possible. We will
continue to consider EHR reporting
challenges when selecting quality
measures to account for future
performance and intends to propose
new quality measures for inclusion in
the IOTA Model through future notice
and comment rulemaking.
Comment: A few commenters
supported the inclusion of patientreported outcome (PRO) measures in the
IOTA Model. For example, a commenter
believed that including PROs is
essential for evaluating the quality of
care and patient satisfaction but
believed that the quality measure set
scoring methodology, as described at
§ 512.428(e) of the proposed rule could
inaccurately reflect the quality of care or
patient satisfaction and lacked
transparency and consistency;
suggesting that it could cause
discrepancies in evaluating IOTA
participant performance. A commenter
strongly supported the use of quality
measures to evaluate transparency and
SDM. This commenter also voiced their
belief that the proposed quality measure
was good because it did not
significantly increase administrative
burden but thought the measures’
simplicity might limit their ability to
provide meaningful insights into the
quality of care these patients receive.
Another commenter voiced their
appreciation for CMS’s inclusion of
PROMs in the IOTA Model. The same
commenter agreed that increasing
patient involvement in the kidney
transplant process is a critical objective
but expressed concern over the
inclusion of CollaboRATE and CTM–3.
Specifically, the commenter felt that
administering and documenting these
measures, which have not been
validated for this specific patient
population, would increase burden on
both IOTA participants and its
attributed patients, without improving
quality of care.
Response: We thank the commenters
for expressing their support. We agree
that when taking into consideration the
lasting impact of CKD, symptom
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burden, and its correlation to mental
health and psychosocial difficulties, it is
important that the patient perspective
and voice be included through the use
of patient-reported outcome measures
(PROMs) to truly grasp how CKD
impacts their lives.269 As described at
89 FR 43603 in the proposed rule, we
also recognize that in spite of the
growing recognition over the past two
decades that this is paramount to
advancing the quality of care at both the
patient and policy levels, there remains
significant information gaps in
understanding how PROMs are, and can
be utilized across different domains,
especially within nephrology to enrich
patient-centered care, and measure
other important quality components,
such as access to transplantation,
shared-decision making and quality of
life post-transplantation, to provide a
comprehensive understanding.270
However, given commenters concerns,
we are persuaded not to finalize the
three quality measures proposed for
inclusion in the IOTA Model at this
time. It is a CMS priority to incorporate
person-centered measures, including
patient-reported measures, where
possible and CMS believes in the
importance of elevating patient’s voice
in their care. We plan, in future notice
and comment rulemaking, to propose
additional quality measures which may
include a focus on HRQoL for kidney
transplant recipients or address pretransplant processes of care. We suggest
these measures will support the goals of
269 Schick-Makaroff, K., Thummapol, O.,
Thompson, S., Flynn, R., Karimi-Dehkordi, M.,
Klarenbach, S., Sawatzky, R., & Greenhalgh, J.
(2019). Strategies for incorporating patient-reported
outcomes in the care of people with chronic kidney
disease (PRO kidney): a protocol for a realist
synthesis. Systematic Reviews, 8(1). https://doi.org/
10.1186/s13643-018-0911-6; Brett, K.E., Ritchie,
L.J., Ertel, E., Bennett, A., & Knoll, G.A. (2018).
Quality Metrics in Solid Organ Transplantation.
Transplantation, 102(7), e308–e330. https://doi.org/
10.1097/tp.0000000000002149; Mendu, M.L.,
Tummalapalli, S.L., Lentine, K.L., Erickson, K.F.,
Lew, S.Q., Liu, F., Gould, E., Somers, M., Garimella,
P.S., O’Neil, T., White, D.L., Meyer, R., Bieber, S.D.,
& Weiner, D.E. (2020). Measuring Quality in Kidney
Care: An Evaluation of Existing Quality Metrics and
Approach to Facilitating Improvements in Care
Delivery. Journal of the American Society of
Nephrology, 31(3), 602–614. https://doi.org/
10.1681/ASN.2019090869; Tang, E., Bansal, A.,
Novak, M., & Mucsi, I. (2018). Patient-Reported
Outcomes in Patients with Chronic Kidney Disease
and Kidney Transplant—Part 1. Frontiers in
Medicine, 4. https://doi.org/10.3389/
fmed.2017.00254; Anderson, N.E., Calvert, M.,
Cockwell, P., Dutton, M., Aiyegbusi, O.L., & Kyte,
D. (2018). Using patient-reported outcome measures
(PROMs) to promote quality of care in the
management of patients with established kidney
disease requiring treatment with haemodialysis in
the UK (PROM–HD): a qualitative study protocol.
BMJ Open, 8(10), e021532. https://doi.org/10.1136/
bmjopen-2018-021532.
270 Ibid.
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the IOTA Model to improve quality and
equity of care.
Comment: Lastly, many commenters
urged CMS to focus on new measure
development and collaborate with
stakeholders, clinicians, and patients to
develop meaningful quality measures in
this space that can be validated in this
setting. For example, many commenters
encouraged CMS to eliminate the
proposed quality measure and pursue
new measure development. These
commenters also stated that it is critical
that CMS include all relevant
stakeholders when developing new
measures to ensure that any new
measure is appropriate, reliable, and
representative of the diverse patient
population. A commenter appreciated
CMS’s interest in developing a PROM
pertaining to HRQoL in the context of
kidney transplant especially given the
relative paucity of measures of quality
of care for kidney transplant; nothing
that no validated PROMs of quality of
life currently exist, much less any
PROMs that are appropriate for use in
the IOTA Model. A commenter strongly
supported the development of a HRQoL
PROM and suggested CMS invest in
developing a measure(s) along these
lines for inclusion in the IOTA Model
as soon as possible. Some commenters
voiced their belief that CMS should
work with relevant stakeholders and
focus on, and invest, in new measure
development, provided it is rigorously
tested and developed using the highest
standards. One of these commenters
suggested that it be used as a reporting
measure initially before rewarding
performance against quality
performance benchmarks and should
assess SDM about patient-focused risk
tolerance regarding organ offer quality.
Response: We acknowledge
commenters suggestions for CMS to
focus on new measure development for
use in the IOTA Model, including
support for a future PROM related to
HRQoL for kidney transplant recipients.
Appropriately evaluating the change in
quality of care is an essential goal of the
IOTA Model and we will consider
future measure development,
potentially in the areas of HRQoL and
pre-transplant processes of care.
After considering public comments,
for the reasons set forth in this rule, we
are not finalizing our proposed quality
measure set that includes two PRO–PMs
(CollaboRATE Shared Decision-Making
Score and 3-Item Care Transition
Measure) and one process measure
(Colorectal Cancer Screening) for
purposes of measuring performance in
the quality domain at this time. We
continue to note that quality of care is
an important element of the IOTA
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Model, and we will be monitoring
quality through other care delivery
requirements and through the required
independent evaluation of the model.
We also will continue to evaluate the
changing inventory of quality measures,
considering public input, and have
already begun developing new measures
more clinically and setting appropriate.
Because of the uncertain nature of
timing of developing new quality
measures we will not specify a timeline
for incorporation but may in future
rulemaking.
(a) Quality Measure Set Selection,
Reporting and Changes
In section III.C.5.e(2) of the proposed
rule, we proposed that CMS select and
use quality measures to assess IOTA
participant performance in the quality
domain. We proposed that each PY,
IOTA participants would be required to
report quality measure data during
survey and reporting windows to CMS
in a form and manner, and at times,
established by CMS. We also proposed
that, where applicable, IOTA
participants would be required to
administer any surveys or screenings
relevant to the quality measures selected
for inclusion in the IOTA Model to
attributed patients. We proposed to
define ‘‘survey and reporting windows’’
as two distinct periods where IOTA
participants would be required to
administer a quality measure-related
survey or screening to attributed
patients or submit attributed patient
responses to CMS pursuant to
§ 512.48(b)(2)(ii). We proposed that
CMS would notify, in a form and
manner as determined by CMS, IOTA
participants of the survey and reporting
window for applicable quality measures
by the first day of each PY.
In section III.C.5.e(2)(a) of the
proposed rule, we proposed that CMS
would use future rulemaking to make
substantiative updates to the
specifications of any of the quality
measures in the IOTA Model.
Additionally, we proposed that the
quality measures finalized for inclusion
in the IOTA Model would remain in the
quality measure set unless CMS,
through future rulemaking, removed or
replaced them.
In section III.C.5.e(2)(a) of the
proposed rule, we proposed that CMS
could remove or replace a quality
measure based on one of the following
factors:
• A quality measure does not align
with current clinical guidelines or
practice.
• Performance on a quality measure
among IOTA participants is so high and
unvarying that meaningful distinctions
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and improvement in performance can
no longer be made (‘‘topped out’’
measure), as defined in 42 CFR
412.140(g)(3)(i)(A).
• Performance or improvement on a
quality measure does not result in better
patient outcomes.
• The availability of a more broadly
applicable quality measure (across
settings or populations) or the
availability of a quality measure that is
more proximal in time to desired patient
outcomes for the particular topic.
• The availability of a quality
measure that is more strongly associated
with desired patient outcomes for the
particular topic.
• Collection or public reporting of a
quality measure leads to negative
unintended consequences other than
patient harm.
• It is not feasible to implement the
quality measure specifications.
• The costs associated with a quality
measure outweigh the benefit of its
continued use in the IOTA Model.
In section III.C.5.e(2)(a) of the
proposed rule, we proposed that CMS
would assess the benefits of removing or
replacing a quality measure from the
IOTA Model on a case-by-case basis. We
proposed that CMS would use the future
rulemaking process to add, remove,
suspend, or replace quality measures in
the IOTA Model to allow for public
comment, unless a quality measure
raises specific safety concerns. We
proposed that if CMS determines that
the continued requirement for IOTA
participants to submit data on a quality
measure raises specific patient safety
concerns, CMS could elect to
immediately remove the quality
measure from the IOTA Model quality
measure set. Finally, we proposed that
CMS would, upon removal of a quality
measure, and in a form and manner
determined by CMS, do the following:
• Provide notice to IOTA participants
and the public at the time CMS removes
the quality measure, along with a
statement of the specific patient safety
concerns that would be raised if IOTA
participants continued to submit data
on the quality measure.
• Provide notice of the removal in the
Federal Register.
We sought comment on the
requirement that IOTA participants
report quality measure data to CMS. We
additionally sought comment on our
proposed process for adding, removing,
or replacing quality measures in the
IOTA Model.
The following is a summary of the
comments received on our proposal to
require that IOTA participants report
quality measure data to CMS and our
proposed process for adding, removing,
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or replacing quality measure in the
IOTA Model and our responses:
Comment: Several commenters felt
that more consideration should be put
into the process for data collection and
reporting requirements so that it does
not unduly burden IOTA participants in
a way that could compromise clinical
outcomes and transplant access. A
commenter felt that CMS’s proposed
rule lacked key logistical details
necessary to understand how IOTA
participants would collect the required
quality measures and how CMS would
evaluate them. Specifically, the
proposed rule did not specify what
patient information IOTA participants
must collect and report alongside the
measure results, nor whether hospitals
should provide patient-level or
aggregate data.
Response: We understand the need for
IOTA participants understand any
quality measure set survey and
reporting requirements finalized for
inclusion in the IOTA Model.
Additionally, we acknowledge the
importance of, are committed to,
providing key logistical details, where
warranted, to mitigate administrative
burdens for IOTA participants. As
discussed in section III.C.5.e(2) of this
final rule, we are not finalizing our
proposed quality measure set. We
intend to propose new quality measures
for inclusion in the IOTA Model in
future notice and comment rule making.
As such, we will not be finalizing our
proposed quality measure set survey
and reporting requirements at
§ 512.428(b)(2)(ii), our proposed process
for adding, removing or replacing a
quality measure at § 512.428(b)(3) or the
definition of survey and reporting
windows at § 512.402 as described in
the proposed rule. While we are not
finalizing any of the aforementioned
provisions, we will continue to assess
our quality measure data reporting
requirement and policy for adding,
removing, or replacing quality measures
in the IOTA Model and intend to
address a new or updated policy
pursuant to future notice and comment
rule making.
Comment: A commenter urged CMS
to allow greater flexibility in the
proposed survey and reporting
timelines, as discussed in section
III.C.5.e(2)(a) of this final rule, for IOTA
participants and recommended that
CMS allow IOTA participants to adjust
data collection processes to align with
clinical schedules and patient
preference. They noted that by allowing
for greater flexibility that this would
enable them to collect patient data in
alignment with clinical practice for preand post-transplant appointments and
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prevent potential operational challenges
if or when a survey and reporting
window misaligns.
Response: We appreciate the
commenters’ suggestion. As discussed
in section III.C.5.e(2) of this final rule,
we are not finalizing our proposed
quality measure set. We intend to
propose new quality measures for
inclusion in the IOTA Model in future
notice and comment rule making. As
such, we will not be finalizing our
proposed quality measure set survey
and reporting requirements at
§ 512.428(b)(2)(ii), our proposed process
for adding, removing or replacing a
quality measure at § 512.428(b)(3) or the
definition of survey and reporting
windows at § 512.402 as described in
the proposed rule. While we are not
finalizing any of the provisions
proposed in section III.C.5.e(2) of the
proposed rule, we will take into
consideration the commenter’s
recommendation to allow for greater
flexibility during survey and reporting
windows and continue to assess our
quality measure data reporting
requirement and policy for adding,
removing, or replacing quality measures
in the IOTA Model. We note that we
will continue to assess our quality
measure data reporting requirement and
policy for adding, removing, or
replacing quality measures in the IOTA
Model and intend to address a new or
updated policy pursuant to future notice
and comment rule making.
Comment: A commenter noted that in
the proposed rule we proposed that if
performance on a quality measure
among IOTA participants is so high and
unvarying that meaningful distinctions
and improvement in performance can
no longer be made (‘‘topped out’’
measure), as defined in 42 CFR
412.140(g)(3)(i)(A) that CMS could
remove or replace that quality measure
(89 FR 43518). They requested that CMS
provide further detail on the proposed
CMS review process and timeline for
evaluating if ‘‘topping out’’ or other
criteria has occurred. They also felt that
while case-by-case adjustments may be
appropriate when specific concerns
arise, an ad hoc evaluation process risks
overlooking instances where quality
measures fall short of the established
criteria.
Response: We appreciate the
commenters’ suggestion. As discussed
in section III.C.5.e(2) of this final rule,
we are not finalizing our proposed
quality measure set. We intend to
propose new quality measures for
inclusion in the IOTA Model in future
notice and comment rule making. As
such, we will not be finalizing any of
the provisions proposed in section
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III.C.5.e(2)(a) of the proposed rule.
While we are not finalizing any of these
proposed provisions, we will take into
consideration the commenter’s request
to provide further specificity to our
application of measure removal factors
and continue to assess our quality
measure data reporting requirement and
policy for adding, removing, or
replacing quality measures in the IOTA
Model.
After consideration of the public
comments we received, for the reasons
set forth in this rule, we are not
finalizing our policy for adding,
removing, or replacing quality measures
in the IOTA Model, as proposed at
§ 512.428(b)(2) of the proposed rule.
Additionally, because we are not
finalizing any of the quality measures
we proposed, as described and finalized
in section III.C.5.e(2) of this final rule,
we are not finalizing our proposed
provision requiring IOTA participants to
report quality measure data to CMS at
§ 512.428(b)(2)(ii) or the definition of
survey and reporting windows at
§ 512.402 as described in the proposed
rule. While we are not finalizing any of
these proposed provisions, we will
continue to assess our quality measure
data reporting requirement and policy
for adding, removing, or replacing
quality measures in the IOTA Model
and address a new or updated policy
pursuant to future notice and comment
rule making.
ddrumheller on DSK120RN23PROD with RULES2
(b) CollaboRATE Shared DecisionMaking Score
In section III.C.5.e(2)(b) of the
proposed rule, we stated that the
CollaboRATE Shared Decision-Making
Score is a patient-reported measure of
shared decision-making. The measure
provides a performance score
representing the percentage of adults 18
years of age and older who experience
a high degree of shared decision
making. The CollaboRATE Shared
Decision-Making Score is based on three
questions that assess the degree to
which effort was made to inform the
patient of his or her health issues, to
listen to the patient’s priorities, and the
extent to which the patient’s priorities
were included in determining next
steps. The measure is generic and
applies to all clinical encounters,
irrespective of the condition or the
patient group. We proposed that IOTA
participants would be required to
administer the CollaboRATE Shared
Decision-Making Score to attributed
patients once per PY, at minimum, and
report quality measure data to CMS
during survey and reporting windows,
as defined in section III.C.5.e(2)(a) of the
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proposed rule, that would be
established by CMS.
In section III.C.5.e(2)(b) of the
proposed, we stated that we believed
incentivizing shared decision-making is
critical to ensuring the model centers
the patient experience and treatment
choice to meet the IOTA desired goals
of improving equity, increasing the
number of kidney transplants, and
reducing kidney non-utilization.
Patients needing a kidney transplant
often face many challenges when
making healthcare decisions, as they
must first decide between treatment
options (such as dialysis versus
transplantation, living donor versus
deceased-donor transplantation) and
where they wish to be evaluated for
transplantation. Research findings
demonstrate the importance and impact
of shared decision-making throughout
the entire transplant process for patients
because of the types of complex
decisions they must make, and the
dynamic factors involved in each
patient’s decision.271 Research studies
271 Jones, E.L., Shakespeare, K., McLaughlin, L., &
Noyes, J. (2023). Understanding people’s decisions
when choosing or declining a kidney transplant: a
qualitative evidence synthesis. BMJ Open, 13(8),
e071348. https://doi.org/10.1136/bmjopen-2022071348; Stephenson, M.D., & Bradshaw, W. (2018).
Shared decision making in chronic kidney disease.
Renal Society of Australasia Journal, 14(1), 26.
https://www.proquest.com/scholarly-journals/
shared-decision-making-chronic-kidney-disease/
docview/2283078287/se-2; Gordon, E.J., Butt, Z.,
Jensen, S.E., Lok-Ming Lehr, A., Franklin, J., Becker,
Y., Sherman, L., Chon, W.J., Beauvais, N.,
Hanneman, J., Penrod, D., Ison, M.G., & Abecassis,
M.M. (2013). Opportunities for Shared Decision
Making in Kidney Transplantation. American
Journal of Transplantation, 13(5), 1149–1158.
https://doi.org/10.1111/ajt.12195; Salter, M.L.,
Babak Orandi, McAdams-DeMarco, M.A., Law, A.,
Meoni, L.A., Jaar, B.G., Sozio, S.M., Hong, W.,
Parekh, R.S., & Segev, D.L. (2014). Patient- and
Provider-Reported Information about
Transplantation and Subsequent Waitlisting.
Journal of the American Society of Nephrology,
25(12), 2871–2877. https://doi.org/10.1681/
asn.2013121298; Schold, J.D., Huml, A.M., Poggio,
E.D., Reese, P.P., & Mohan, S. (2022). A tool for
decision-making in kidney transplant candidates
with poor prognosis to receive deceased donor
transplantation in the United States. Kidney
International. https://doi.org/10.1016/
j.kint.2022.05.025; Schaffhausen, C.R., Bruin, M.J.,
McKinney, W.T., Snyder, J.J., Matas, A.J., Kasiske,
B.L., & Israni, A.K. (2019). How patients choose
kidney transplant centers: A qualitative study of
patient experiences. 33(5), e13523–e13523. https://
doi.org/10.1111/ctr.13523; Hart, A., Bruin, M., Chu,
S., Matas, A., Partin, M.R., & Israni, A.K. (2019).
Decision support needs of kidney transplant
candidates regarding the deceased donor waiting
list: A qualitative study and conceptual framework.
Clinical Transplantation, 33(5), e13530. https://
doi.org/10.1111/ctr.13530; S. Ali Husain, Brennan,
C., Michelson, A., Tsapepas, D., Patzer, R.E.,
Schold, J.D., & Mohan, S. (2018). Patients prioritize
waitlist over posttransplant outcomes when
evaluating kidney transplant centers. 18(11), 2781–
2790. https://doi.org/10.1111/ajt.14985; Patzer,
R.E., McPherson, L., Basu, M., Mohan, S., Wolf, M.,
Chiles, M., Russell, A., Gander, J.C., Friedewald, J.J.,
Ladner, D., Larsen, C.P., Pearson, T., & Pastan, S.
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have found that shared decision-making
shifts the patient-physician relationship
past traditional practices and
contributes to better health outcomes,
increased quality of life, increased
patient knowledge and medication
adherence, and lower healthcare
expenditures.272 Furthermore, research
findings support that shared decisionmaking with the patient could reduce
kidney non-utilization, improve equity,
and increase the number of kidney
transplants.273
(2018). Effect of the iChoose Kidney decision aid in
improving knowledge about treatment options
among transplant candidates: A randomized
controlled trial. American Journal of
Transplantation: Official Journal of the American
Society of Transplantation and the American
Society of Transplant Surgeons, 18(8), 1954–1965.
https://doi.org/10.1111/ajt.14693.
272 Stephenson, M. Stephenson, M. Stephenson,
M. Stephenson, M.D., & Bradshaw, W. (2018).
Shared decision making in chronic kidney disease.
Renal Society of Australasia Journal, 14(1), 26.
https://www.proquest.com/scholarly-journals/
shared-decision-making-chronic-kidney-disease/
docview/2283078287/se-2; Gordon, E. J., Butt, Z.,
Jensen, S.E., Lok-Ming Lehr, A., Franklin, J., Becker,
Y., Sherman, L., Chon, W.J., Beauvais, N.,
Hanneman, J., Penrod, D., Ison, M.G., & Abecassis,
M.M. (2013). Opportunities for Shared Decision
Making in Kidney Transplantation. American
Journal of Transplantation, 13(5), 1149–1158.
https://doi.org/10.1111/ajt.12195; Schold, J.D.,
Huml, A.M., Poggio, E.D., Reese, P.P., & Mohan, S.
(2022). A tool for decision-making in kidney
transplant candidates with poor prognosis to
receive deceased donor transplantation in the
United States. Kidney International. https://doi.org/
10.1016/j.kint.2022.05.025; Schaffhausen, C.R.,
Bruin, M.J., McKinney, W.T., Snyder, J.J., Matas,
A.J., Kasiske, B.L., & Israni, A.K. (2019). How
patients choose kidney transplant centers: A
qualitative study of patient experiences. 33(5),
e13523–e13523. https://doi.org/10.1111/ctr.13523;
Hart, A., Bruin, M., Chu, S., Matas, A., Partin, M.R.,
& Israni, A.K. (2019). Decision support needs of
kidney transplant candidates regarding the
deceased donor waiting list: A qualitative study and
conceptual framework. Clinical Transplantation,
33(5), e13530. https://doi.org/10.1111/ctr.13530;
Patzer, R.E., McPherson, L., Basu, M., Mohan, S.,
Wolf, M., Chiles, M., Russell, A., Gander, J.C.,
Friedewald, J.J., Ladner, D., Larsen, C.P., Pearson,
T., & Pastan, S. (2018). Effect of the iChoose Kidney
decision aid in improving knowledge about
treatment options among transplant candidates: A
randomized controlled trial. American Journal of
Transplantation: Official Journal of the American
Society of Transplantation and the American
Society of Transplant Surgeons, 18(8), 1954–1965.
https://doi.org/10.1111/ajt.14693.
273 Kucirka, L.M., Grams, M.E., Balhara, K.S., Jaar,
B.G., & Segev, D.L. (2011). Disparities in Provision
of Transplant Information Affect Access to Kidney
Transplantation. American Journal of
Transplantation, 12(2), 351–357. https://doi.org/
10.1111/j.1600-6143.2011.03865.x; Patzer, R.E.,
Retzloff, S., Buford, J., Gander, J., Browne, T., Jones,
H., Ellis, M., Canavan, K., Berlin, A., Mulloy, L.,
Gibney, E., Sauls, L., Muench, D., Reeves-Daniel,
A., Zayas, C., DuBay, D., Mutell, R., & Pastan, S.O.
(2021). Community Engagement to Improve Equity
in Kidney Transplantation from the Ground Up: the
Southeastern Kidney Transplant Coalition. Current
Transplantation Reports, 8(4), 324–332. https://
doi.org/10.1007/s40472-021-00346-x; Schold, J.D.,
Huml, A.M., Poggio, E.D., Reese, P.P., & Mohan, S.
(2022). A tool for decision-making in kidney
transplant candidates with poor prognosis to
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ddrumheller on DSK120RN23PROD with RULES2
By pairing the CollaboRATE Shared
Decision-Making Score measure with
the proposed achievement domain
number of kidney transplants metric, as
described in section III.C.5.c. of the
proposed rule, and the proposed quality
domain post-transplant outcomes
metrics, as described in section
III.C.5.e.(1). of the proposed rule, we
aimed to incentivize care delivery
transformation and improvement
activity across IOTA participants that
would center attributed patients and
their family and caregiver as a critical
decision-maker in treatment choices
that align with their preferences and
values. This may include greater
transparency on donor organ offers and
reasons for non-acceptance, and
increased education and support on the
living donor process. We also believed
that this would support attributed
patients in receiving a kidney that may
be at higher risk of non-use, but that
may offer a survival and quality of life
advantage over remaining on dialysis,
dying while waitlisted, or being delisted.274
In section III.C.5.e(2)(b) of the
proposed rule, we acknowledged that
the instrument used for the
CollaboRATE Shared Decision-Making
Score is generic; however, we were
unable to identify alternative measures
of shared decision-making that are
specific to kidney transplant that have
been endorsed by the CBE. Similarly,
while there may be value in an
instrument that measures shared
decision-making regarding the types of
kidney organ offers attributed patients
are willing to accept, no such measure
exists. We believed the CollaboRATE
Shared Decision-Making Score would
capture variation in the presence and
quality of shared decision-making
among IOTA participants and that the
instrument need not be specific to
kidney transplant to incentivize
meaningful improvements in patientcentricity and the patient experience,
equity, and reducing kidney non-use.
receive deceased donor transplantation in the
United States. Kidney International. https://doi.org/
10.1016/j.kint.2022.05.025; Patzer, R.E., McPherson,
L., Basu, M., Mohan, S., Wolf, M., Chiles, M.,
Russell, A., Gander, J.C., Friedewald, J.J., Ladner,
D., Larsen, C.P., Pearson, T., & Pastan, S. (2018).
Effect of the iChoose Kidney decision aid in
improving knowledge about treatment options
among transplant candidates: A randomized
controlled trial. American Journal of
Transplantation: Official Journal of the American
Society of Transplantation and the American
Society of Transplant Surgeons, 18(8), 1954–1965.
https://doi.org/10.1111/ajt.14693.
274 Massie, A.B., Luo, X., Chow, E.K.H., Alejo,
J.L., Desai, N.M., & Segev, D.L. (2014). Survival
benefit of primary deceased donor transplantation
with high-KDPI kidneys. American Journal of
Transplantation, 14(10), 2310–2316. https://doi.org/
10.1111/ajt.12830.
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We sought comment on our proposal
to include the CollaboRATE Shared
Decision-Making Score as a quality
measure for purposes of quality domain
performance assessment.
The following is a summary of the
comments received on our proposal to
include the CollaboRATE Shared
Decision-Making Score as a quality
measure for purposes of quality domain
performance assessment and our
responses:
Comment: Commenters expressed
concern over the proposed inclusion of
CollaboRATE Shared Decision-Making
Score as a quality measure within the
quality measure set to assess IOTA
participant performance in the quality
domain. Many commenters noted its
lack of validation for use with hospitals
and data to support the use of this
measure in this population. Many
commenters expressed concerns that the
CollaboRATE measure is for use in the
outpatient setting and has not been
designed for hospitals or transplant
patients. Many commenters questioned
the inclusion of CollaboRATE Shared
Decision-Making Score because it does
not require transplant-related
discussions and its applicability for
inclusion in the model is unclear. Some
commenters were concerned that the
CollaboRATE Shared Decision-Making
Score might not impact the specific
issues of organ offers when used to
capture all kidney transplant care, but
pilot work and a trail funded by the NIH
are specifically studying shared
decision making for kidney transplant
organ offers with a focus on materials
and interventions to support SDM in a
specific decision or encounter. Several
commenters expressed concern over
whether survey responses would
provide relevant data for care under the
IOTA Model and suggested that
responses might need to be adjusted to
factor in patient demographic
characteristics. A couple commenters
noted that it was unclear when the
survey would be completed, and
questioned whether administering the
survey once per year, as proposed,
would result in each survey covering
multiple visits, making it difficult to
observe quality differences or determine
how to intervene. Several commenters
had concerns about the amount of
burden placed on transplant hospitals to
implement the CollaboRATE Shared
Decision-Making Score. A couple
commenters indicated that this would
be especially burdensome for small
transplant hospitals without access to
electronic sampling methods and that a
focus on high response rates may limit
resources for SDM.
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Response: In response to these
comments, we will not be finalizing our
proposal to include the CollaboRATE
Shared Decision-Making Score as a
quality measure for purposes of
assessing performance within the
quality domain, as described in section
III.C.5.e(2) of this final rule. We believe
incentivizing SDM is critical to
centering the patient experience and
treatment choices in the IOTA Model.
This aligns with the model’s goals of
improving equity, increasing kidney
transplants, and reducing nonutilization, as discussed in section
III.C.5.e(2)(b) of the preamble in this
final rule. While we are not finalizing
this SDM measure, the IOTA Model
promotes it through other policies, such
as the transparency requirements as
described and finalized in section
III.C.8(a) of the preamble in this final
rule.
Comment: A few commenters
expressed support for CMS’s proposal to
include CollaboRATE Shared DecisionMaking Score as a quality measure
within the quality measure set to assess
IOTA participant performance in the
quality domain. A commenter indicated
that the CollaboRATE Shared DecisionMaking Score would capture how well
providers engage with patients before
and after surgery and help promote
patient-centered care. A commenter also
expressed belief that incorporating a
SDM patient-reported measure
requirement is critical for transplant
patients. They also suggested that
incentivizing SDM between patients and
healthcare providers would foster
patient-centered care and promote
informed choices.
Response: We thank commenters for
their support and for their comments in
support of our proposal to include
CollaboRATE Shared Decision-Making
Score as a quality measure for purposes
of assessing performance within the
quality domain, as described in section
III.C.5.e(2) of this final rule. However, in
response to public comment, we will
not be finalizing the CollaboRATE
Shared Decision-Making Score as a
quality measure, as described in section
III.C.5.e(2) of this final rule. We still
believe that incentivizing shared
decision-making is critical to ensuring
the model centers the patient experience
and treatment choice to meet the IOTA
desired goals of improving equity,
increasing the number of kidney
transplants, and reducing kidney nonutilization, as discussed in section
III.C.5.e(2)(b) of this final rule. Although
we are not finalizing this measure at
this, we think that the IOTA Model
promotes SDM through some of our
other policies, such as the proposed
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transparency requirements as described
and finalized in section III.C.8(a) of the
preamble in this final rule.
After consideration of the public
comments we received, for the reasons
set forth in this rule, we are not
finalizing our proposal to include the
CollaboRATE Shared Decision-Making
Score as a measure within the quality
measure set to assess IOTA participant
performance in the quality domain.
(c) Colorectal Cancer Screening
In section III.C.5.e(2)(C) of the
proposed rule, we stated that the
Colorectal Cancer Screening (COL)
measure identifies the percentage of
patients 50–75 years of age who had
guideline concordant screening for
colorectal cancer. Kidney transplant
recipients are at higher risk for cancer
than the general population, due in part
to long-term immunosuppression.275
Kidney transplant recipients have a
higher incidence of colorectal cancer
and advanced adenomas and may have
worse prognoses than the general
population, both of which support
improved screening and prophylactic
care for kidney transplant
recipients.276 277 278
The COL measure is a Universal
Foundation measure in the CMS
Meaningful Measures 2.0 Wellness and
Prevention Domain. By nature of its
inclusion in the Universal Foundation
measure set, the COL measure addresses
a condition associated with significant
morbidity and mortality and
incentivizes action on high-value
preventive care.279 The COL measure is
also aligned with the goals of the
President’s Cancer Moonshot to reduce
the death rate from cancer by 50 percent
ddrumheller on DSK120RN23PROD with RULES2
275 Rama,
I., & Grinyó, J.M. (2010). Malignancy
after renal transplantation: The role of
immunosuppression. Nature Reviews Nephrology,
6(9), 511–519. https://doi.org/10.1038/
nrneph.2010.102.
276 Komaki, Y., Komaki, F., Micic, D., Ido, A., &
Sakuraba, A. (2018). Risk of colorectal cancer in
chronic kidney disease. Journal of Clinical
Gastroenterology, 52(9), 796–804. https://doi.org/
10.1097/mcg.0000000000000880.
277 Privitera, F., Gioco, R., Civit, A.I., Corona, D.,
Cremona, S., Puzzo, L., Costa, S., Trama, G.,
Mauceri, F., Cardella, A., Sangiorgio, G., Nania, R.,
Veroux, P., & Veroux, M. (2021). Colorectal cancer
after Kidney Transplantation: A screening
colonoscopy case-control study. Biomedicines, 9(8),
937. https://doi.org/10.3390/biomedicines9080937.
278 Farrugia, D., Mahboob, S., Cheshire, J., Begaj,
I., Khosla, S., Ray, D., & Sharif, A. (2014).
Malignancy-related mortality following kidney
transplantation is common. Kidney International,
85(6), 1395–1403. https://doi.org/10.1038/
ki.2013.458.
279 Jacobs, D.B., Schreiber, M., Seshamani, M.,
Tsai, D., Fowler, E., & Fleisher, L.A. (2023).
Aligning quality measures across CMS—the
Universal Foundation. New England Journal of
Medicine, 388(9), 776–779. https://doi.org/10.1056/
nejmp2215539.
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over the next 25 years and improve the
experience of people living with cancer
and those who have survived it.280
As described in section III.C.5.e(2)(c)
of the proposed rule, we proposed the
COL measure for inclusion in our
assessment of quality domain
performance in the model because we
believed it would provide a signal of the
importance of ongoing post-transplant
care and reduce variation in the
screening and prophylactic care of
kidney transplant recipients by
transplant hospital. We proposed that
IOTA participants would be required to
administer the COL measure yearly to
all attributed IOTA transplant patients
who are Medicare beneficiaries. The
COL measure would work in concert
with the proposed composite graft
survival metric to increase the
likelihood that attributed patients in the
IOTA Model would receive
comprehensive post-transplant care that
would account not only for the
attributed patient and graft survival, but
also complications and comorbidities
associated with receiving a kidney
transplant.
We sought comment on our proposal
to include the COL measure as a quality
measure for purposes of quality domain
performance assessment.
The following is a summary of the
comments received on our proposal to
include the COL measure as a quality
measure for purposes of quality domain
performance assessment and our
responses:
Comment: Many commenters
expressed concerns about our proposal
to include the COL measure as a quality
measure for purposes of quality domain
performance assessment. Specifically,
some commenters noted that many
transplant recipients return to
community providers after their
transplant, making it challenging for
transplant hospitals to ensure
appropriate post-transplant screenings
after they are no longer responsible for
overseeing their care. As described in
section III.C.5.e.(2).(c). of this final rule,
we proposed that IOTA participants
would be required to administer the
COL measure yearly to all attributed
IOTA transplant patients who are
Medicare beneficiaries. A couple
commenters suggested that the COL
measure, as proposed, would more
accurately reflect the care provided by
patients’ primary care physicians, since
many transplant hospitals transfer the
patients’ care back to their local primary
care physicians. A few commenters
noted that transplant hospitals are
280 Cancer Moonshot. (n.d.). The White House.
https://www.whitehouse.gov/cancermoonshot/.
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already required to administer the COL
to patients prior to waitlisting;
suggesting that its inclusion in the IOTA
Model would be redundant and
unnecessarily increase costs without
improving patient care. Many
commenters urged CMS to remove COL
from inclusion in the IOTA Model;
citing that this measure is unrelated to
transplant outcomes, cancers other than
colorectal cancer are much more
common in transplant recipients, the
measure was not designed to identify
the quality of care, is not a transplantspecific quality measure and shifts
primary care responsibilities to
transplant hospitals as reasons for its
removal. Some commenters felt that the
inclusion of COL in the IOTA Model is
redundant and not directly relevant to
kidney transplant care and suggested
removing COL or replacing it with
quality measures more closely aligned
to kidney transplant outcomes, such as
a more comprehensive cancer screening
protocol.
Response: We thank commenters for
sharing their concerns. In response to
these comments, we will not be
finalizing our proposal to include the
COL measure as a quality measure for
purposes of assessing performance
within the quality domain as described
in section III.C.5.e.(2). of this final rule.
After consideration of the public
comments we received, for the reasons
set forth in this rule, we are not
finalizing our proposal to include the
COL as a measure within the quality
measure set to assess IOTA participant
performance in the quality domain, as
discussed in section III.C.5.e.(2). of this
final rule.
(d) 3-Item Care Transition Measure
(CTM–3)
As described in section
III.C.5.e.(2).(d). of the proposed rule, the
3-Item Care Transition Measure (CTM–
3) is a hospital-level, patient-reported
measure of readiness for self-care at
time of discharge from an acute care
hospital. The CTM–3 is based on data
from a three-question instrument that
assesses whether the patient and
family’s preferences were accounted for
in the care plan; whether patients
understood their role in selfmanagement; and, whether appropriate
medication education was provided. A
higher score on the CTM–3 reflects a
higher quality transition of care. We
proposed that IOTA participants would
be required to administer the CTM–3 to
attributed patients once per PY, at
minimum, and report quality measure
data to CMS during survey and
reporting windows, as defined and
finalized in section III.C.5.e(2)(a) of this
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final rule, that would be established by
CMS.
Transitions of care after kidney
transplant are common and indicate
elements of modifiable transplant
hospital quality. One study found that
30-day hospital readmissions after an
organ transplant were significantly
associated with graft loss and death.281
Poor understanding of and adherence to
immunosuppressive drugs were
identified as key elements associated
with an increased risk for early hospital
readmission.282 Mitigating readmission
risk may be of special importance given
that IOTA participants may choose to
increase their number of transplants by
transplanting more kidneys that may
have clinical value to patients.
Simultaneously, there may also be
increased healthcare utilization needs
due to delayed graft function (DGF),
which could require longer hospital
stays, readmissions, and more complex
care coordination.283 We have also
heard from interested parties about the
need for patient-reported measures to
contribute to the assessment of posttransplant outcomes.
The CTM–3 is a patient-reported
measure and would measure transplant
hospital performance on an aspect of
care that we understand to be important
to the patient experience, modifiable by
transplant hospitals, and that may not
otherwise improve based on the
financial incentives in the model
targeted towards one- and three-year
outcomes, but not directly at
perioperative transitions of care and
readmission risk. The CTM–3 is a
domain of the HCAHPS (CBE ID: 0166).
We believe that IOTA participants
would have some familiarity with the
HCAHPS survey and that the hospital
systems of which IOTA participants
would be a part would have an
infrastructure in place for the
administration of HCAHPS that could
be leveraged to support administration
of the CTM–3.
281 Covert, K.L., Fleming, J.N., Staino, C., Casale,
J.P., Boyle, K.M., Pilch, N.A., Meadows, H.B.,
Mardis, C.R., McGillicuddy, J.W., Nadig, S., Bratton,
C.F., Chavin, K.D., Baliga, P.K., & Taber, D.J. (2016).
Predicting and preventing readmissions in Kidney
Transplant Recipients. Clinical Transplantation,
30(7), 779–786. https://doi.org/10.1111/ctr.12748.
282 Covert, K.L., Fleming, J.N., Staino, C., Casale,
J.P., Boyle, K.M., Pilch, N.A., Meadows, H.B.,
Mardis, C.R., McGillicuddy, J.W., Nadig, S., Bratton,
C.F., Chavin, K.D., Baliga, P.K., & Taber, D.J. (2016).
Predicting and preventing readmissions in Kidney
Transplant Recipients. Clinical Transplantation,
30(7), 779–786. https://doi.org/10.1111/ctr.12748.
283 Jadlowiec, C.C., Frasco, P., Macdonough, E.,
Wagler, J., Das, D., Budhiraja, P., Mathur, A.K.,
Katariya, N., Reddy, K., Khamash, H., & Heilman,
R. (2022). Association of DGF and early
readmissions on outcomes following Kidney
Transplantation. Transplant International, 35.
https://doi.org/10.3389/ti.2022.10849.
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We sought comment on our proposal
to include the CTM–3 measure as a
quality measure as a quality measure for
purposes of quality domain performance
assessment.
The following is a summary of the
comments received on our proposal to
include the CTM–3 measure as a quality
measure for purposes of quality domain
performance assessment and our
responses:
Comment: Many commenters urged
CMS not to finalize CTM–3 as a quality
measure within the quality measure set
to assess IOTA participant performance
in the quality domain, noting that it
would add additional burden to patients
and IOTA participants and unnecessary
complexity, and cost to IOTA
participants. A couple commenters
urged CMS not to include the CTM–3
measure, indicating that the association
between CTM–3 and readmissions is
inconsistent in that it does not predict
30-day outcomes and only weakly
predicts 3- and 12-month outcomes.
Several commenters also noted that
participants would be required to report
the CTM–3 separately from their
HCAHPS surveys, as this measure will
soon be removed from the revised
Inpatient Quality Reporting Program
(IQR). A commenter also noted that
collecting CTM–3 data could be
redundant, as it will soon be removed
from the hospital IQR in favor of an
updated set of HCAHPS care
coordination items. Finally, a
commenter stated that they opposed the
inclusion of CTM–3 as a quality
measure within the quality measure set
to assess IOTA participant performance
in the quality domain.
Response: We thank the commenters
for their comment and appreciate these
commenters concerns to our proposal to
include the CTM–3 measure as a quality
measure for purposes of assessing
performance in the quality domain. In
response to these comments, we will not
be finalizing our proposal to include the
CTM–3 measure as a quality measure for
purposes of assessing performance
within the quality domain, as described
in section III.C.5.e(2) of this final rule.
Comment: Some commenters
recommended alternative measures that
CMS should consider replacing the
CTM–3 with. For example, several
commenters suggested that CMS should
replace the retired CTM–3 measure with
the proposed ‘‘Care Coordination’’ SubMeasure to align with the updates to the
HCAHPS survey. A commenter
suggested that CMS should consider
only looking at readmission rates as a
proxy for sound care transition planning
or using HCAPS data instead of the
CTM–3 measure.
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Response: We thank the commenters
for their comment and appreciate these
commenters suggested alternatives to
our proposal to include the CTM–3
measure as a quality measure for
purposes of assessing performance in
the quality domain. In response to the
public comments we received, we will
not be finalizing our proposal to include
the CTM–3 measure as a quality
measure for purposes of assessing
performance within the quality domain
as described in section III.C.5.e(2) of this
final rule.
Comment: A couple commenters
expressed support for the inclusion of
CTM–3 as a quality measure within the
quality measure set to assess IOTA
participant performance in the quality
domain. A commenter urged CMS to
finalize this measure suggesting that it
would encourage providers to actively
engage patients before and after surgery
to ensure they can make an informed
decision about their treatment options
and are prepared to manage their care
afterwards.
Response: We thank commenters for
their support and for their comments in
support of our proposal to include the
CTM–3 measure as a quality measure for
purposes of quality domain performance
assessment. We believe that transitions
of care after kidney transplant are
common and indicate elements of
modifiable transplant hospital quality,
as discussed in section III.C.5.e(2)(d) of
this final rule. However, as described in
comment responses noted previously,
due to concerns raised by commenters
we will not be finalizing CTM–3 as a
quality measure, as described in section
III.C.5.e(2) of this final rule. We will
continue to evaluate the changing
inventory of quality measures,
considering public input, and intend to
propose alternative quality measures
through future notice and comment
rulemaking
After considering public comments,
for the reasons set forth in this rule, we
are not finalizing our proposal to
include the CTM–3 as a measure within
the quality measure set to assess IOTA
participant performance in the quality
domain, as described and finalized in
section III.C.5.e(2) of this final rule.
(e) Calculation of Points
In section III.C.5.e(2)(e) of the
proposed rule, we proposed that the
IOTA participant would receive up to
10 points for performance on our three
proposed measures within the quality
domain—the CollaboRATE Shared
Decision-Making Score, COL, and CTM–
3 measures. For purposes of quality
measure set performance scoring, we
proposed that IOTA participants may
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receive up to 4 points for performance
on the CollaboRATE Shared DecisionMaking Score measure, up to 2 points
on the COL measure, and up to 4 points
on the CTM–3 measure. Lower weight
in terms of scoring points were given to
the COL measure because it is a claimsbased measure that does not require
reporting from IOTA participants.
Because the CTM–3 and CollaboRATE
are PRO–PMs we believed it was
important to allot more points to them,
to recognize the additional operational
activities necessary for IOTA
participants.
‘‘pay-for-reporting’’ method for PY 1—
PY 2, while performance would be
measured against quality performance
benchmarks calculated by CMS
applicable to our proposed ‘‘pay-forperformance’’ method for PY 3—PY 6.
Table 10 illustrates our proposed payfor-reporting and pay-for-performance
timeline. We noted that we anticipated
establishing a quality performance
benchmarks and minimum attainment
levels for quality measures in future rule
making.
In section III.C.5.e(2)(e) of the
proposed rule, we proposed to phase-in
quality performance benchmarks for the
three quality measures selected for the
IOTA quality measure set, such that we
would reward reporting for the first two
years of the model performance period
(‘‘pay-for-reporting’’), at minimum,
before we reward performance against
quality performance benchmarks for
each measure (‘‘pay-for-performance’’).
Thus, performance for each of these
three quality measures would be
measured against a ‘‘response rate
threshold’’ applicable to our proposed
TABLE 10: MEASURE PAYMENT TYPE BY PERFORMANCE YEAR
Measure
CollaboRATE Shared DecisionMaking Score
Colorectal Cancer Screening (COL)
CTM-3
PYl
Pay for Reporting (P4R)
P4R
P4R
P4R
P4R
P4R
In section III.C.5.e(2)(e) of the
proposed rule, we proposed that CMS
would determine and share with IOTA
participants the response rate threshold
by the first day of each PY in a form and
manner chosen by CMS. We stated that
this approach to assessing IOTA
participant quality performance would
serve four key purposes. First, it would
promote measure implementation,
uptake, and data collection by IOTA
participants through a rewards-only
scoring system. Second, it would build
experience over the first two model PYs,
giving IOTA participants more time to
prepare and build capacity to meet
performance benchmarks. Third, it
would allow CMS to collect data needed
to develop measure benchmarks.
Finally, it would focus model incentives
on care delivery transformation and
improvement activity directly aimed at
PY2
PY3
Pay for
Performance (P4P)
P4P
P4P
meeting quality performance goals, as to
ensure the patient is centered in this
approach. Ultimately, we considered the
pay-for-reporting approach to be a
reasonable approach. We also believed
that some IOTA participants may be
familiar with this as it is similar to the
format within the KCC Model. We
recognized that these measures already
exist, but, because they are used in a
much broader population, there are no
benchmarks that are applicable for the
model.
In section III.C.5.e(2)(e) of the
proposed rule, we proposed to define
the ‘‘response rate threshold’’ as the
level of complete and accurate reporting
for each quality measure, within the
quality measure set of the quality
domain, that the IOTA participant must
meet to earn points on the quality
domain during a performance year as
PY4
P4P
PYS
P4P
PY6
P4P
P4P
P4P
P4P
P4P
P4P
P4P
described in § 512.428(c) and (e) of the
proposed rule. For the CTM–3 and
CollaboRATE measures, we proposed
that points be awarded based on
response rate thresholds, as illustrated
in Table 11, such that IOTA participants
with a response rate threshold of—
• 90–100 percent of attributed
patients would receive 4 points;
• 50–89 percent of attributed patients
would receive 2 points; or
• Under 50 percent of attributed
patients would receive 0 points.
In section III.C.5.e(2)(e) of the
proposed rule, we proposed for the COL
measure that a completion rate of 50
percent or greater would result in the
IOTA participant receiving two points,
and a completion rate of less than 50
percent would result in the IOTA
participant receiving zero points, as
illustrated in Table 11.
TABLE 11- IOTA MODEL QUALITY MEASURE SET SCORING
As described in section III.C.5.e(2)(e)
of the proposed rule, we recognized that
the proposed response rate thresholds
are high, but we want to make sure that
we have enough data to set appropriate
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Lower Bound
Condition
Equals 90%
Equals 50%
NIA
Equals 50%
NIA
and meaningful benchmarks in PY 3
through PY 6. We considered setting a
higher maximum measure completion
rate; however, given that each IOTA
participant may have different levels of
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Upper Bound
Condition
Greater than 90%
Less than 90%
Less than 50%
Greater than 50%
Less than 50%
Points
Earned
4
2
0
2
0
engagement with kidney transplant
waitlist patients, we felt a higher
threshold may be difficult for IOTA
participants to achieve. We also
believed that a higher response rate
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ER04DE24.014</GPH>
CollaboRATEICTM-3
CollaboRATE I CTM-3
CollaboRATE I CTM-3
COL
COL
Performance
Relative to Tar2et
90% Response Rate
50% Response Rate
50% Response Rate
50% Response Rate
50% Response Rate
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would incentivize IOTA participants to
collect the data. We considered the
following variations to the response rate
threshold for each of the proposed
quality measure:
• Response rate threshold of 100
percent would receive 10 points, if not
100 percent 0 points would be awarded.
• Response rate threshold of 80–100
percent would receive 10 points, 50–79
percent would receive 5 points, and 49–
0 percent would receive 0 points.
• 50–100 percent would receive 10
points; under 50 percent would receive
0 points.
As described in section III.C.5.e(2)(e)
of the proposed rule, we considered
mirroring the point structure under
which an IOTA participant would
receive either all possible points, or, if
data was not collected from all their
attributed patients, none of the possible
points. We thought that this could
incentivize IOTA participants to
administer the surveys associated with
the proposed quality measures, which
would allow us to create meaningful
benchmarks for future model years.
However, because there would be some
additional burden placed onto IOTA
participants to administer the surveys
associated with the proposed quality
measures, we believed this point
structure would be difficult for some
and wanted to provide more attainable
response rate thresholds. We also
considered lowering the response rate
thresholds for the same reasons
mentioned earlier, but, because there are
currently no benchmarks for these
measures in this specific population, we
felt that the response rate threshold
needed to be higher but still attainable.
We also considered achievement and
improvement scoring for the proposed
quality measures. However, because
none of the measures included in the
proposed quality measure set, as
described in section III.C.5.e(2) of this
final rule, currently have benchmarks,
we did not believe it was appropriate to
propose achievement and improvement
scoring for the proposed quality
measures at this time.
We sought comment on our proposed
calculation of points for the quality
measure set, as well as the proposal to
reward IOTA participant reporting for
the first two PYs (‘‘pay-for-reporting’’),
before rewarding IOTA participant
performance against quality
performance benchmarks. We sought
comment on the proposed response rate
thresholds and point allocations for
measures included in the proposed
quality measure set within the quality
domain.
The following is a summary of the
comments received on our proposed
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calculation of points for the quality
measure set, as well as the proposal to
reward IOTA participant reporting for
the first two PYs (‘‘pay-for-reporting’’),
before rewarding IOTA participant
performance against quality
performance benchmarks and the
proposed response rate thresholds,
point allocations for measures included
in the proposed quality measure set
within the quality domain and our
responses:
Comment: Some commenters
expressed concern about the proposed
response rate thresholds and point
allocations and requested that CMS
lower the proposed response rate
threshold for the proposed quality
measures. For example, a commenter
expressed their belief that how well
IOTA participants do getting their
patients to respond to specific surveys
is not an accurate reflection of quality.
A few of commenters indicated that
transplant hospitals currently struggle to
achieve patient experience survey
response rates above 30 percent. Given
this challenge, they felt that the
proposed 90 percent response rate
threshold for quality measures is
unrealistic. To achieve a 90 percent
response rate for two new quality
measures, a commenter suggested this
would require that the surveys be
administered in person; noting that this
approach could create an administrative
burden by requiring staff to distribute
and collect the surveys, as well as
necessitate patients making extra clinic
visits solely for the purpose of
completing the surveys. Several
commenters urged CMS to adjust the
response rate thresholds to mitigate this
challenge. Specifically, a commenter
recommended that CMS adopt a similar
minimum response rate threshold like
what is proposed for awarding domain
points; suggesting 4 points awarded for
response rate thresholds above 50
percent, 2 points awarded for response
rate thresholds of 25 percent to 50
percent, and 0 points awarded for
response rates below 25 percent.
Response: We thank these
commenters for sharing their concerns.
We acknowledge the concerns related to
the high response rate thresholds
proposed for the CollaboRATE Shared
Decision-Making Score and CTM–3. As
we stated in the proposed rule, we
acknowledge that the proposed response
rate thresholds are quite high and that
these measures are already in use,
though applied to a much wider
population. As a result, there are no
benchmarks that can be utilized for the
IOTA Model, and we sought to ensure
that we had enough data to set
appropriate and meaningful quality
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performance benchmarks in PY 3
through PY 6.
We also thank the commenters for
their recommendations to lower the
response rate thresholds given the
number of surveys requests and
obligations transplant patients are
already asked to complete and the
additional burden that could be placed
onto IOTA participants to administer
the surveys associated with the
proposed quality measures and lower
the response rate thresholds. We also
appreciate the commenters suggestion
for an alternative scoring methodology.
As we stated in the proposed rule, we
did consider lowering the response rate
thresholds for the same reasons
mentioned earlier, but, because there are
currently no benchmarks for these
measures in this specific population, we
felt that the response rate threshold
needed to be higher but still attainable.
We direct readers to section
III.C.5.e.(2)(e) for further discussion on
the alternative scoring methodologies
that were considered for inclusion in
the IOTA Model. We also note that we
considered the added reporting burden
on IOTA participants when evaluating
potential quality measures for inclusion
in the IOTA Model, and direct
commenters to section III.C.5.e(2) of this
final rule for further discussion.
Lastly, because we are not finalizing
our proposed quality measure set, as
described in section III.C.5.e(2) of this
final rule, and in consideration on
public comment received, we will not
be finalizing our proposed quality
measure set scoring methodology. In
section III.C.5.e(e) of the proposed rule,
we proposed that the IOTA participant
would receive up to 10 points for
performance on our three proposed
measures within the quality domain
while also noting in the proposed rule
at 89 FR 43564, that if we finalize fewer
measures, then we proposed to allocate
the points accordingly within the
remaining measures. Given that we are
not finalizing any of the proposed
measures within the quality measure set
or quality measure set scoring
methodology, the 10 points we
proposed to award IOTA participants
for performance on our three proposed
measures within the quality domain
will be allocated to the composite graft
survival rate within the quality domain,
as described and finalized in section
III.C.5.e(1)(b) of this final rule.
Although we are not finalizing our
quality measure set scoring
methodology at this time, CMS will take
into consideration the commenters
concerns and suggestions and intends to
propose an alternative or updated policy
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proposal in future notice and comment
rulemaking.
Comment: A couple commenters
expressed support for the proposed
response rate thresholds, but they felt
that a 90% response rate would be
extremely unlikely to be achieved.
Response: We thank commenters for
their support and for their comments in
support of our proposed response rate
thresholds and concern over the
achievability of a 90% response rate. As
mentioned in comment responses noted
previously, we acknowledge that the
response rate thresholds we proposed
were high. As discussed in the preamble
of this final rule, we proposed the
response rates for the proposed quality
measures, as illustrated in Table 11
noted previously, to allow CMS to
collect enough data to develop
meaningful and appropriate measure
benchmarks in PYs 3–6.
However, because we are not
finalizing our proposed quality measure
set, as described in section III.C.5.e(2) of
this final rule, and based on public
comment, we will not be finalizing our
proposed quality measure set scoring
methodology, as described in section
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Response Rate
III.C.5.e(2)(e) of this final rule, at this
time, and intend to propose a new or
updated policy in future notice and
comment rulemaking that will address
concerns with respect to response rate
thresholds IOTA participants may have.
Comment: A couple commenters
requested that CMS provide additional
clarity about the proposed response rate
thresholds and point allocations. For
example, a commenter urged CMS to
not only propose response rate
thresholds, but also define what
constitutes ‘‘complete and accurate
reporting’’ and provide specifics on how
the response rate threshold would be
calculated for CollaboRATE; stating that
until CMS did so, they could not
support the inclusion of this measure in
the IOTA Model. Another commenter
cited that the Healthcare Effectiveness
Data and Information Set (HEDIS)
specifications for the COL measure
indicate that COL is an administrative
measure,284 noting that CMS proposed
response rate thresholds for it during
the pay-for-reporting years of the model.
This commenter asked CMS to clarify
two key points: (1) How the response
commenters request that CMS clarify
how our proposals for calculating
response rate thresholds differs from
calculating performance benchmarks in
later PYs, we note that, as discussed in
the proposed rule at 89 FR 43658, we
anticipated establishing quality
performance benchmarks and minimum
attainment levels for quality measures
in future rule making.
Finally, as mentioned in comment
responses noted previously in this
section, since we are not finalizing our
proposed quality measure set, as
described in section III.C.5.e(2) of this
final rule, and based on public
comment, we will not be finalizing our
proposed quality measure set scoring
methodology at this time and the 10
points we proposed to award IOTA
participants for performance on our
three proposed measures within the
quality domain will be allocated to the
composite graft survival rate within the
quality domain, as described and
finalized in section III.C.5.e(1)(b) of this
final rule. We also note that we intend
to propose a new or updated policy in
future notice and comment rulemaking.
284 National Committee for Quality Assurance.
‘‘Colorectal Cancer Screening—NCQA.’’ NCQA,
2024, www.ncqa.org/hedis/measures/colorectalcancer-screening/.
18:30 Dec 03, 2024
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Equation 5: Response Rate Threshold
# of complete and accurate responses submitted
# of eligible attributed patients surveyed
XlOO
For example, if in PY 1 of the model,
an IOTA participant was required to
administer the CollaboRATE to 30 of
their attributed patients and submitted
28 complete and accurate responses, the
response rate for that IOTA participant
on the CollaboRATE would be 93% (28
complete and accurate responses
submitted divided by 30 and then
multiplied by 100). Based on our
proposed quality measure set scoring
methodology, as described in the
preamble of this final rule, that IOTA
participant would be awarded four
points for their response rate threshold
on the CollaboRATE.
In accordance with the Share Savings
Program Final Rule as outlined in 76 FR
67873, we are clarifying that ‘‘complete
and accurate reporting’’ signifies that
that the quality data submitted to CMS
is accurate, complete, and truthful.
However, we disagree with the
commenters’ belief that CMS needs to
define what is meant by ‘‘complete and
accurate reporting,’’ as this is language
that has been used in other models,
such as the Shared Savings Program at
42 CFR 425.502. Regarding the
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rate would be calculated for an
administrative measure, and (2) How
this calculation differs from the quality
performance benchmarks that would
need to be met once the measure
transitions to pay-for-performance in
future program years.
Response: We appreciate the
commenters comments and clarifying
questions. In the proposed rule at 89 FR
43658, we proposed to define response
rate threshold as the level of complete
and accurate reporting for each quality
measure, within the quality measure set
of the quality domain, that the IOTA
participant must meet to earn points on
the quality domain during a
performance year as described in
§§ 512.428(c) and 512.428(e). In
response to the commenters request that
CMS further explain how the response
rate threshold would be calculated for
CollaboRATE and COL, we clarify here
that, based on our proposed definition
and industry standards, the response
rate for each of the proposed quality
measures would be calculated as
follows:
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After consideration of the public
comments we received, for the reasons
set forth in this rule, we are not
finalizing our proposed quality measure
set scoring methodology, as described at
§ 512.428(e) of the proposed rule, or our
proposed definition of response rate
threshold, as described at § 512.402 of
the proposed rule. Although we are not
finalizing any of the measures that we
proposed for inclusion in our proposed
quality measure set, as described in
section III.C.5.e(2) of this final rule, we
intend to propose alternatives in future
notice and comment rulemaking.
Additionally, in section III.C.5.e(e) of
the proposed rule, we proposed that the
IOTA participant would receive up to
10 points for performance on our three
proposed measures within the quality
domain while also noting in the
proposed rule at 89 FR 43564, that if we
finalize fewer measures, then we
proposed to allocate the points
accordingly within the remaining
measures. Given that we are not
finalizing the proposed quality measure
set within the quality domain or quality
measure set scoring methodology, the 10
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points we proposed to award IOTA
participants for performance on our
three proposed measures within the
quality domain will be allocated to the
composite graft survival rate within the
quality domain, as described and
finalized in section III.C.5.e(1)(b) of this
final rule. We will continue to assess
our quality domain methodology and
how to best balance incentives in the
efficiency domain and quality domain
and will address a new or updated
policy pursuant to future notice and
comment rulemaking.
■
6. Payment
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a. Purpose and Goals
We believe that risk-based payment
arrangements in Innovation Center
models drive healthcare innovation and
transform the healthcare payment
system by rewarding value over volume.
Risk-based payment models hold
participants financially accountable, as
these payments are structured to
incentivize value-based care that
improves quality and reduces total cost
of care for beneficiaries. Risk-based
payment models may be upside-risk
only, or have two-sided, upside and
downside, risk. Under these risk-based
arrangements, IOTA participants may
receive a payment from CMS if
performance goals are met or exceeded,
and, if the model features downside
risk, may owe a payment to CMS for
failing to meet performance goals.285
For the IOTA Model, we proposed an
alternative payment model (APM)
structure that incorporates both upside
and downside risk to existing Medicare
fee-for-service (FFS) payments for
kidney transplantations as described in
section III.C.6.b. of the proposed rule.
The IOTA Model will test whether
performance-based payments, including
an upside risk payment and downside
risk payment, to IOTA participants
increases access to kidney transplants
for attributed patients while preserving
or enhancing quality of care and
reducing kidney transplant hospital
expenditures. As described in section
III.C.5. of this final rule, IOTA
participants will be assessed against
proposed metrics to assess performance
for each PY relative to specified targets,
thresholds, or benchmarks proposed
and determined by CMS. The final
performance score, not to exceed a
maximum of 100 points, will determine
if and how upside and downside risk
payments are applied, as described in
section III.C.6.c. of this final rule. We
believe this upside and downside risk
285 https://www.cms.gov/priorities/innovation/
key-concepts/risk-arrangements-health-care.
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approach will be a strong incentive to
promote performance improvement.
We sought comment on our proposed
two-sided risk payment design to
incentivize model performance goals.
The following is a summary of the
comments received on our proposed
two-sided risk payment design and our
responses:
Comment: We received multiple
comments pointing out that kidney
transplant hospitals do not make their
decisions for transplants based on
financial incentives and that it is
inappropriate to incentivize IOTA
participants to do more transplants
through a pay-for-performance model.
Response: We understand that the
decision to transplant a specific
beneficiary is not made for financial
reasons. However, we recognize that
resource allocation decisions for a
kidney transplant hospital are made at
an administrative level that will allocate
resources in part based on CMS
reimbursement policies, which is why
we are testing the IOTA Model using
this framework.
Comment: We received a comment
saying that CMS should consider the
impact on private payer COE programs
for transplant based on the incentives in
the model.
Response: We recognize the
importance of COE programs to kidney
transplant hospitals and recognizes that
being in a COE for a payer is a key
source of revenue for many kidney
transplant hospitals. The model was
designed to align with many of the
metrics used for a COE, which generally
include a minimum volume
requirement and some minimum level
of performance on post-transplant
outcomes. Though their metrics do not
generally include a major requirement
to increase volume like those of the
IOTA Model, transplants represent a
major source of potential savings on the
plan side, just as it does for CMS. CMS
is hopeful that with the finalization of
the IOTA Model that other payers will
more closely harmonize their measures
to create a unified regulatory framework
that reduces burden for kidney
transplant hospitals and improves
overall quality.
Comment: We received a comment
saying that the model should not focus
on accountability at the kidney
transplant hospital level, but instead
direct resources directly to the most
vulnerable patients to assist them
through the transplant process.
Response: We understand the
comment, but ultimately disagree with
the commenter. The IOTA Model is
based on the idea that the kidney
transplant hospital is the key locus for
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the transplant process, given the role of
the kidney transplant hospital in getting
candidates onto the waitlist, deciding
which organs to accept, performing
transplant surgeries, managing the
living donor process, and overseeing
post-transplant care. Given that role, we
believe that the kidney transplant
hospitals are closer to their patients and
will be better able to determine their
exact needs to help get them through the
transplant process.
Comment: We received a comment
saying that downside risk in the IOTA
Model was inappropriate because organ
supply is out of the control of kidney
transplant hospitals.
Response: We recognize that kidney
transplant hospitals are not the entities
responsible for recovering organs.
However, research has shown
significant variance in organ-offer
acceptance practices, even among
kidney transplant hospitals that are
geographically proximate, as discussed
in the background section. Additionally,
kidney transplant hospitals are in
complete control of the living donor
kidney process, which is not dependent
upon the procurement process.
Comment: We received multiple
comments saying that downside risk in
the model was inappropriate because
kidney transplant hospitals are new to
value-based care.
Response: We understand the need for
IOTA participants to ramp up their
value-based care operations, which is
why there is no downside risk for IOTA
participants in PY 1. Additionally, in
this final rule, we removed many
requirements that may have been
perceived as burdensome by kidney
transplant hospitals, such as reporting
on multiple quality measures and on
declined organ offers and we believe
that this will make it more achievable
for IOTA participants to devote the
necessary resources required to succeed
in the IOTA Model. The IOTA Model
also focuses on major functions and
activities that kidney transplant
hospitals are already doing, rather than
changing the focus to a more population
health perspective as is done in many
other Innovation Center models. Given
these circumstances, we then believe
that downside risk can be fairly applied
in PY 2 to help further incentivize
performance in the model.
Comment: We received a comment
saying that many kidney transplant
hospitals face structural barriers that
prevent them from increasing their
numbers of transplants, making
downside risk inappropriate for the
model.
Response: We recognize that different
kidney transplant hospitals face
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different limitations in how they
manage the transplant process. This is
why the IOTA Model includes a flexible
scoring system that gives IOTA
participants different areas to focus on
to achieve an upside risk payment
under the model. Every IOTA
participant can adjust their organ offer
filters to be more efficient and remove
offers that they are unlikely to use.
Additionally, the model is not
prescriptive on how IOTA participants
can transplant more organs, meaning
that IOTA participants could invest in
their living donor program or could
focus on using deceased donor organs
that they may not have utilized in the
baseline years. Finally, each IOTA
participant is judged against scored
based on their own historic number of
transplants, or historic organ offer
acceptance rate, for the achievement
and efficiency domains. This approach
demonstrates CMS’s effort to recognize
that kidney transplant hospitals are
starting at different places before the
IOTA Model and to provide an
opportunity to fostering innovation by
competing against their own historic
performance.
Comment: We received a comment
saying that many smaller or essential
kidney transplant hospitals lack the
resources to effectively participate in
the IOTA Model and should have no
downside risk.
Response: We understand that smaller
kidney transplant hospitals may have
fewer overall resources and we do not
want any kidney transplant hospitals to
stop offering kidney transplant services
because of the IOTA Model. To address
this issue, we proposed a low-volume
threshold of 11 or more kidney
transplants performed annually to
exclude the kidney transplant hospitals
with the lowest volumes, as described
and finalized in section III.C.3.c of this
final rule. Additionally, benchmarks for
the achievement domain and efficiency
domain in the IOTA Model are based on
improvement relative to the IOTA
participant’s own historic number of
transplants, or historic organ offer
acceptance rate, meaning that for 80 of
100 possible points that an IOTA
participant can earn for the model, they
are evaluated against their own historic
performance. Finally, the payment
methodology for the IOTA Model is
based on the number of transplants
performed and includes asymmetrically
less downside risk, minimizing the
potential downside for smaller kidney
transplant hospitals. We will monitor
the effects of these different
mechanisms within the IOTA Model to
see if they are successful in helping
smaller kidney transplant hospitals and
will consider further efforts in future
rulemaking based on the results of those
monitoring efforts.
Comment: We received a comment
supporting the two-sided risk structure
for the IOTA Model, supporting the
inclusion of downside risk in order to
help change behavior of IOTA
participants.
Response: We appreciate the feedback
and believe that downside risk is
ultimately necessary to help incentivize
IOTA participants to achieve the goals
of the IOTA Model.
Comment: A commenter questioned
whether payment adjustments
effectively drive physician behavior,
and instead urged CMS to prioritize
upstream investments as a means of
promoting increased organ
transplantation.
Response: We appreciate the feedback
but disagree with the commenter. We
recognize some of the limitations of
payment adjustments to move physician
behavior. However, we recognize that
they have never been tried in this area.
There is significant variation kidney
transplant hospitals among their use of
organ offer filters, organ offer
acceptance rate, and investment in the
living donation process, and the IOTA
Model will test whether IOTA
participants can learn from other IOTA
participants that may be higher
performing in these areas. We also
recognize that organ transplant, as
opposed to many other areas covered in
other Innovation Center models,
contains a cost-based reimbursement
model for organ acquisition costs that
provides a significant source of funding
to support IOTA participants’
investments in performance.
After consideration of the public
comments we received, for the reasons
set forth in this rule, we are finalizing
this two-sided payment framework as
originally designed. We believe that the
two-sided framework best creates a clear
incentive for improved performance by
IOTA participants, with sufficient
upside to reward IOTA participants for
excellent performance. Furthermore, as
described and finalized in section
III.C.6.c(1) of this final rule, we are
finalizing at § 512.430(b)(3)(i) that for
PY 1, the IOTA participant does not owe
a downside risk payment to CMS. We
direct readers to sections III.C.6.C(2)(ac) for a full discussion on our proposed
286 https://hcp-lan.org/workproducts/apmrefresh-whitepaper-final.pdf.
287 https://hcp-lan.org/workproducts/apmrefresh-whitepaper-final.pdf.
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upside risk payment, downside risk
payment, and neutral zone provisions.
b. Alternative Payment Design Overview
There are two payment components
in the current Medicare FFS program for
organ transplantation. Under the
Medicare Inpatient Prospective Payment
System (IPPS), kidney transplant
hospitals are paid a prospective
payment system rate based on the MS–
DRG for the organ transplant. Payment
for organ acquisition costs as described
at 42 CFR 413.402, which include costs
associated with beneficiary and donor
evaluation, is made on a reasonable cost
basis. To remain active on the transplant
waitlist, candidates must meet a variety
of criteria, including annual screenings
for cardiovascular diseases and cancers.
In the IOTA Model, CMS proposed
two-sided performance-based payments
for ‘‘Medicare kidney transplants,’’
defined as kidney transplants furnished
to attributed patients whose primary or
secondary insurance is Medicare FFS, as
identified in Medicare FFS claims with
MS–DRGs 008, 019, 650, 651 and 652,
and as illustrated in Table 12. We stated
that this APM design aligns with the
Health Care Payment Learning & Action
Network (LAN) Category 3 APM
framework in which IOTA participants
continue to be paid on the basis of
Medicare FFS, but a retrospective
annual attribution reconciliation and
performance assessment after the end of
each model PY is conducted to
determine performance-based
payments.286 287
The IOTA Model’s performance-based
payments are linked to existing
Medicare Part A and Part B services for
kidney transplants, and align with other
Innovation Center models’ payment
structure, including the ETC Model
where upward and downward
adjustments are made to certain
Medicare payments under the ESRD
Prospective Payment System and
Physician Fee Schedule depending on
an ETC Participant’s performance at the
aggregation group level under the
model. The difference between ETC and
the IOTA Model, for example, is how
these retrospective adjustments would
be paid or recouped by CMS. CMS did
not propose to adjust existing Medicare
IPPS payments for kidney transplants
furnished to Medicare beneficiaries.
Instead, CMS proposed to make
performance-based payments to IOTA
participants separate from claims-based
payments.
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TABLE 12: MS-DRGs PROPOSED FOR INCLUSION IN DEFINITION OF
MEDICARE KIDNEY TRANSPLANTS
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Description
SIMULTANEOUS PANCREAS AND KIDNEY TRANSPLANT
SIMULTANEOUS PANCREAS AND KIDNEY TRANSPLANT WITH HEMODIALYSIS
KIDNEY TRANSPLANT WITH HEMODIALYSIS WITH MCC
KIDNEY TRANSPLANT WITH HEMODIAL YSIS WITHOUT MCC
KIDNEY TRANSPLANT
We proposed to base performancebased payments on increasing the
number of transplants and other metrics
of efficiency and quality because we
believe this approach: (1) would be a
strong proxy for total cost; (2) directly
aligns with the model’s goal of
increasing access to and the volume of
kidney transplantations; (3)
acknowledges kidney waitlist and
transplant patients are high-cost and
high-need, making performance based
on total cost of care unfair for IOTA
participants with lower volume and
fewer capabilities and resources given
the increased opportunity for outliers;
and (4) may safeguard against
unintended consequences introduced by
defining value based on cost for an
attributed patient population already at
high-risk, such as inappropriate cost
shifting and widening access to care
disparities. We theorize that increasing
the number of, and access to, kidney
transplants alone would result in better
quality. As indicated in our estimates
presented in section IV of this final rule,
it would also result in savings to
Medicare.
While we proposed to assess model
performance for each IOTA participant
for all attributed patients regardless of
payer type, as described in section
III.C.6.c of this final rule, we proposed
model performance-based payments that
would only be based on kidney
transplants furnished to attributed
patients with Medicare FFS as their
primary or secondary insurance.
As described in section III.C.6.b of the
proposed rule, we considered also
basing the model performance-based
payments on kidney transplants
furnished to attributed patients enrolled
in Medicare Advantage (MA), as kidney
transplants are a Medicare-covered
service that MA plans must also cover.
As these payments would be made to
kidney transplant hospitals, a potential
waiver of section 1851(i)(2) of the Act,
which provides that only the MA plan
shall be entitled to payments for
services furnished to the beneficiary,
may have been necessary to apply the
payments to attributed patients enrolled
in MA. Because further consideration
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was needed for the implications of such
a potential waiver, we did not propose
to apply model performance-based
payments performed on attributed
patients enrolled in MA.
We believed that the benefits of
applying model performance-based
payments to transplants furnished to
attributed patients enrolled in MA
would be recognizing the growth in MA
enrollment relative to Medicare FFS
enrollment, strengthening the model test
through aligned payment incentives
across payers, and protecting against
unintended consequences of
incentivizing inappropriate organ offer
acceptance based on payer type.
However, we did not propose to base
payments on attributed patients
enrolled in MA because of concerns
about potentially waiving section
1851(i)(2) of the Act. This provision
states that only the MA plan is entitled
to payments for services provided to the
beneficiary. We noted that waiving this
requirement would be unprecedented
and the effects are unknown. We
recognized that the proposed incentives
in the IOTA Model would have a larger
effect if kidney transplant hospitals
were receiving performance-based
payments based on their entire panel of
attributed beneficiaries who receive
transplants, and not just based on
transplants for attributed beneficiaries
with Medicare FFS as their primary or
secondary insurance. To that end, we
proposed that the IOTA Model would
encourage multi-payer alignment with
the goal of aligning on goals, incentives,
and quality. We noted in the proposed
rule that CMS intended to engage with
the payer community, including MA,
Medicaid, and commercial payers, in
future years to discuss opportunities
and approaches for alignment.
We requested comment and feedback,
especially from MA plans, on our
decision not to calculate model
performance-based payments to
transplants furnished to attributed
patients enrolled in MA. We were
especially interested in comments that
address how the Innovation Center
should generally approach the growing
MA population with the design of its
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models, which have traditionally been
focused on the fee-for-service Medicare
population.
While kidney transplant hospitals are
subject to value-based payment
programs, some IOTA participants may
have limited APM experience,
resources, and capacity to meet model
goals. We considered an upside-risk
payment only framework that would
still base model payments on kidney
transplant utilization and other metrics
of efficiency and quality. However, we
believed that two-sided risk payments
would be stronger incentives to achieve
the desired goals. We also recognized
this in the model design by proposing
a phased-in approach to two-sided risk,
with only upside-risk applied to the first
model PY. We also considered other
APM frameworks that would link
performance to quality, such as pay-forreporting on the measures. We did not
propose these frameworks, as they did
not align with our goals of establishing
two-sided risk accountability for IOTA
participants. We recognized the benefits
of a rewards-focused approach,
particularly as it relates to quality
performance, and we therefore did
incorporate a rewards-focused
performance scoring structure designed
as pay-for-reporting and pay-forperformance within the quality domain
performance assessment. (89 FR 43571).
Another alternative we considered
was a flat positive adjustment to the
Medicare FFS payment for a kidney
transplant based on the number of
completed kidney transplants that an
IOTA participant performs. Increasing
the amount paid for completed kidney
transplants through a FFS adjustment is
the simplest policy and aligns with the
IOTA Model’s focus on increasing the
number of kidney transplants.
Additionally, adjusting the FFS
payment would directly incentivize an
increase in the number of kidney
transplants performed by IOTA
participants. Under this approach,
eligible claims would be identified
utilizing Medicare claims data with
Medicare Severity Diagnosis Related
Groups (MS–DRGs) 008 (simultaneous
pancreas-kidney transplant) and 652
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(kidney transplant); and claims with
ICD–10 procedure codes 0TY00Z0
(transplantation of right kidney,
allogeneic, open approach), 0TY00Z1
(transplantation of right kidney,
syngeneic, open approach), 0TY00Z2
(transplantation of right kidney,
zooplastic, open approach) 0TY10Z0
(transplantation of left kidney,
allogeneic, open approach), 0TY10Z1
(transplantation of left kidney,
syngeneic, open approach), and
0TY10Z2 (transplantation of left kidney,
zooplastic, open approach).
We did not propose a performance
methodology based solely on adjusting
the DRG payment for a kidney
transplant, because this option would
not encourage IOTA participants to
focus on issues other than transplant
volume, including equity, increased
utilization of donor kidneys, quality of
care, and patient outcomes, all of which
are important parts of the transplant
process where we believe performance
is variable and can be improved. We
further believe that the claims-only
approach would not be as effective in
incentivizing a continuous increase in
transplants because IOTA participants
that already have high kidney transplant
volumes would be rewarded through
increased reimbursements whether they
improved year-over-year or not. Finally,
we do not believe that this approach
would provide any additional
encouragement for IOTA participants to
manage post-transplant care.
We also considered establishing a
payment for transplant waitlist
management to encourage additional
investment in the transplant process,
but decided to focus more on the
outcomes described in section III.C.5 of
the proposed rule. Additionally, given
that IOTA participants are already
reimbursed at cost for efforts to manage
beneficiaries on the waitlist, we did not
believe an explicit additional payment
would be necessary in this area.
We sought feedback on our proposed
alternative payment model design, data
source to identify kidney transplants,
and proposal to only apply model
performance-based payments, both
upside and downside, to Medicare FFS
kidney transplants. We also sought
feedback on alternative approaches
considered, such as the alternative
approach of including MA transplants.
We welcomed input on how CMS may
be able to work with multiple payers to
ensure alignment with the IOTA Model.
The following is a summary of the
comments we received regarding our
proposed alternative payment model
design, data source to identify kidney
transplants, our proposal to apply
model performance-based payments and
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our alternative approach of including
MA transplants, and our responses:
Comment: We received over twenty
comments urging CMS to apply the
payment adjustments in the IOTA
Model to transplants performed for
beneficiaries with Medicare Advantage
as a primary or secondary payer, and
not just beneficiaries with Medicare FFS
as a primary or secondary payer.
Commenters pointed out the limited
reach of the proposed incentives by
focusing the incentives solely on a small
portion of a kidney transplant hospital’s
overall patient panel. They were
worried that the model may be
ineffective without the incentive effects
provided by applying the payment
adjustments in the IOTA Model to more
than just Medicare FFS transplants.
Many commenters also pointed out that
there is a rising number of beneficiaries
enrolling in Medicare Advantage
relative to Medicare FFS, which would
decrease the effects of the model’s
proposed incentives over time.
Commenters also pointed out that
kidney transplant hospitals are paid
directly through FFS Medicare for
Organ Acquisition Costs for kidney
transplants as defined in 42 CFR
413.402, even for beneficiaries with
Medicare Advantage, due to their
statutory exclusion in § 1853(k)(5) of the
Act. Another commenter pointed out
that in other Medicare APMs operated
by the Innovation Center, when a
beneficiary has transitioned from FFS
Medicare to Medicare Advantage, it has
made them become ineligible for
payments from the APM and
discouraged potential investment in
those beneficiaries.
Response: We appreciate the feedback
from commenters. However, we plan to
finalize the policy as proposed as we do
not believe that the additional incentive
effects from including Medicare
Advantage in the calculation for upside
and downside payments are necessary
at this point to provide sufficient
incentive to test the model. We plan to
further engage with Medicare Advantage
plans to think about the incentives in
the IOTA Model and those set up by
Medicare Advantage plans. We also
plan to monitor relative enrollment of
beneficiaries who receive kidney
transplants in Medicare FFS as opposed
to Medicare Advantage to see if further
policy changes will be necessary for
future years of the IOTA Model.
Comment: Multiple commenters
expressed concern that the proposed
payment structure for the IOTA Model,
which would make payments based
only on Medicare FFS kidney
transplants, could lead to IOTA
participants preferring to transplant
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Medicare FFS patients at the expense of
patients with Medicare Advantage.
Response: We appreciate the feedback
from commenters as this is an outcome
that we do not want. We recognize that
the achievement domain is based on
transplants performed across all payers
and is worth the greatest number of
points, which we believe will help to
prevent this behavior. Additionally, we
plan to monitor for potential shifts by
payer as an unintended side effect of the
model to ensure that this outcome does
not occur, and we may consider taking
additional action in future rulemaking if
we see significant evidence that this is
occurring.
Comment: A commenter supported
our proposed policy to exclude
payments for beneficiaries with
Medicare Advantage from the positive
and negative payment adjustments in
the Model.
Response: We plan to monitor relative
enrollment of beneficiaries who receive
kidney transplants in Medicare FFS as
opposed to Medicare Advantage to see
if further policy changes will be
necessary for future years of the IOTA
Model.
Comment: We received a comment
urging CMS to align the payments in the
IOTA Model with those from Medicare
Advantage plans.
Response: We recognize the
importance of multi-payer alignment
and has engaged in numerous
conversations with Medicare Advantage
plans about their transplant strategies. It
is our understanding from discussions
with MAOs that most MAOs use their
COE programs to evaluate kidney
transplant hospitals for network
inclusion often provide them special
contracting rates. Many plans use a
variety of criteria to determine COE,
including a minimum transplant
volume, and minimum performance on
certain outcomes metrics.288 We believe
that IOTA participants’ quality
improvement activities as a result of the
model’s performance metrics and
payment methodology may help them
reach and maintain COE status.
Comment: We received multiple
comments urging CMS to include
kidney transplants covered by other
payers in the model’s payment
methodology, particularly the Medicaid
program.
Response: Medicare is the dominant
payer in the marketplace for transplants,
accounting for 57 percent of adult
transplants, relative to only 7 percent
288 For instance, Aetna’s criteria is here: https://
www.aetna.com/content/dam/aetna/pdfs/
aetnacom/healthcare-professionals/documentsforms/Aetna-Institutes-of-Excellence.pdf.
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for patients with Medicaid. As such, we
believe that testing the model payment
incentives based on just those
transplants for beneficiaries with
Medicare will provide sufficient
incentive to drive the increases in
transplants that CMS is hoping will
occur from the Model. Additionally,
transplants provide additional savings
for the Medicare program given that
patients may become entitled to
Medicare based on ESRD, and given that
Medicare is the primary payer for
services for the majority of patients with
ESRD across the country.
However, we urge other payers,
including private plans, to follow the
lead of CMS and learn from the lessons
we glean from this Model to evaluate
how they pay kidney transplant
hospitals to incentivize quality care and
better outcomes.
As a result, we believe that applying
these payments in the IOTA Model to
all Medicare FFS transplants will apply
a strong incentive for IOTA participants
to increase access to kidney
transplantation given Medicare’s
dominant role in the marketplace.
After consideration of the public
comments we received, for the reasons
set forth in this rule, we are finalizing
our proposed definition of Medicare
kidney transplants at § 512.402 without
modification.
c. Performance-Based Payment Method
We proposed that the final
performance score as described in
section III.C.5. of this final rule would
determine if and how an IOTA
participant qualifies for an upside risk
payment, falls in the neutral zone, or
qualifies for a downside risk payment,
proposed using a two-step process.
First, we would determine if an IOTA
participant’s final performance score
qualifies the IOTA participant for
upside risk payments, downside risk
payments, or the neutral zone, as
described in section III.C.6.c.(1). of this
final rule. Second, we would apply the
proposed calculation formula for each of
type of payment, as described in section
III.C.6.c.(2). of this final rule.
Ultimately, we proposed a performancebased payment method that prioritizes
the following principles:
• Significant weight should be given
to performance in the achievement
domain, representing up to 60 points
relative to a 100 maximum performance
score, in alignment with the primary
goals of the model to increase number
of kidney transplants.
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• The magnitude of performancebased payments should be tied to
relative number of kidney transplants,
given significant differentials across
kidney transplant hospitals nationally.
• The largest performance-based
payments amount in total dollars should
go to IOTA participants that perform the
most transplants because they are
removing the most people from dialysis
and creating the largest quality
improvement and cost savings for the
Medicare Trust Fund.
• The payments need to be calibrated
to provide an incentive to IOTA
participants, but still ensure net savings
to Medicare based on the analysis
performed by OACT in section IV of this
final rule.
• The mechanisms should recognize
that CMS has not previously offered
kidney transplant hospitals a valuebased care payment model around
transplantation and should provide a
transition to any form of downside risk
to allow for an opportunity to become
familiar with the value-based care
process.
• Limit operational complexity for
both IOTA participants and CMS to
avoid any potential for errors.
(1) Determine Final Performance Score
Range Category
We proposed to establish three final
performance score range categories, as
illustrated in Table 13, that dictate
which type of performance-based
payment would apply to an IOTA
participant for a given PY.
We proposed at § 512.402 to define
‘‘upside risk payment’’ as a lump sum
payment that CMS would make to an
IOTA participant if the IOTA
participant’s final performance score for
a PY falls within the payment range
specified in section III.C.6.c(2)(a) of this
final rule. As proposed and indicated in
Table 13, if in PY 1–6, an IOTA
participant’s final performance score is
greater than or equal to 60 points, the
IOTA participant would qualify for an
upside risk payment.
We proposed at § 512.402 to define
‘‘neutral zone’’ as the final performance
score range in which the IOTA
participant would not owe a downside
risk payment to CMS or receive an
upside-risk payment from CMS if the
IOTA participant’s final performance
score falls within the ranges specified in
section III.C.6.c.(2).(c). of this final rule.
In the first year of the model, we
proposed that the neutral zone would
apply for final performance scores
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96383
below 60. As such, only upside
payments and the neutral zone would
exist in PY 1. We also proposed that the
neutral zone in PYs 2–6 would apply for
final performance scores of 41–59
(inclusive). We believe that average
performance should yield no upside or
downside risk payment.
We proposed at § 512.402 to define
‘‘downside risk payment’’ as a lump
sum payment the IOTA participant
would be required to pay to CMS after
a PY if the IOTA participant’s final
performance score falls within the
ranges specified in section
III.C.6.c.(2).(b). of this final rule. We
proposed that there will be no downside
risk payment in the PY 1. We proposed
no downside risk payment in the first
PY to allow IOTA participants time to
implement changes to improve
performance prior to facing downside
risk. In PYs 2–6, we proposed to
introduce downside risk payments. We
proposed that an IOTA participant’s
final performance score of 40 or below
in PYs 2–6, would result in a downside
risk payment. We believe that below
average performance should yield a
downside risk payment.
The performance assessment scoring
method, as described in section III.C.5.
of this final rule, was designed such that
IOTA participants with limited
experience in APMs would still be
likely to achieve a sufficient final
performance score that would result in
no downside risk payment. For
example, it is expected that most IOTA
participants would earn around 30 of 60
possible points in the achievement
domain. We believe that average
performance should be neither
rewarded nor penalized. We also
considered eliminating the neutral zone
and only applying upside and downside
performance payments, narrowing the
neutral zone score range (that is, 44–55),
or applying a wider-to-narrower phasedin approach over the model
performance period. We believed these
alternative options would be less
flexible and more penalty-focused, with
some IOTA participants more likely to
be penalized due to varying degrees of
capabilities and capacity that would
limit their ability to achieve
performance targets as they progress and
evolve over the model performance
period. Thus, we proposed a neutral
zone that would allow for more
opportunities and incentives to achieve
improvements over time without a large
probability of downside risk.
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TABLE 13. PROPOSED PERFORMANCE-BASED PAYMENTS BY FINAL
PERFORMANCE SCORE
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PYl
Upside Risk Payment
Neutral Zone
Neutral Zone
We sought feedback on the use of the
final performance scores to determine
the upside risk payment, the downside
risk payment, and the neutral zone.
The following is a summary of the
comments received on our proposal to
use the final performance scores to
determine the upside risk payment, the
downside risk payment, the neutral
zone and our responses:
Comment: We received multiple
comments urging a delay of downside
payments until PY 3 or PY 4 of the
model.
Response: We believe that downside
risk is an important part of testing
models. We recognize the importance of
transition into the model, but our
thought is that the six-month starting
delay, along with no downside risk in
PY 1 allows for times for IOTA
Participants to invest and transition into
the accountability of the model, while
still allowing for increased
accountability in future years of the
model.
Comment: A commenter noted that
IOTA participants would not receive
their PY 1 results until PY 2,
diminishing the impact of the initial
year’s lack of downside risk.
Response: We understand that IOTA
participants will not receive final results
until into PY 2, but we know that IOTA
participants are able to track their
number of transplants done and their
post-transplant outcomes. To help IOTA
participants to better project their
potential results, CMS will also share
interim data reports with IOTA
participants.
Comment: We received comments
urging that we lower the top of the
neutral zone from 60 to 50 points.
Response: In designing the scoring
system, CMS wanted to make sure that
performance was evaluated
symmetrically, such that it would take
excellent performance or performance
far below what was expected to be able
to get a positive or negative payment
adjustment. Additionally, given the
breakdown of quality points for PY 1,
we believe that reaching a positive
payment adjustment will be more
achievable for IOTA participants to be
able to earn a positive payment
adjustment.
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PY2-6
Upside Risk Payment
Neutral Zone
Downside Risk Payment
Comment: We received multiple
comments recommending that we lower
the points required for a downside risk
adjustment, including one
recommending lowering the threshold
to 20 points.
Response: We considered this
recommendation but decided to keep it
at 40 points to balance all the different
goals on the model. Given that an IOTA
participant performing as expected on
the achievement and efficiency domains
would receive 40 points, the proposed
scoring methodology is our attempt to
balance the goals of being fair to IOTA
participants, while also attempting to
incentivize improvement on the IOTA
performance metrics.
After consideration of the public
comments we received, we are
finalizing our proposal to use the final
performance scores to determine the
upside risk payment, the downside risk
payment, and the neutral zone as
proposed without modification at
§ 512.430(a). Additionally, we are
finalizing as proposed the definitions of
upside risk payment, and neutral zone
at § 512.402 without modification.
Finally, we are finalizing as proposed
the definition of downside risk payment
§ 512.402, with a minor technical
correction to include the complete cross
reference to § 512.430.
(2) Apply Payment Calculation Formula
to Final Performance Score
In the proposed rule at § 512.430(a),
we proposed that after determining if an
IOTA participant’s final performance
score qualifies the IOTA participant for
an upside risk payment, downside risk
payment, or the neutral zone, as
described in section III.C.6.c(1) of this
final rule, we would apply a calculation
formula unique to each PY to the final
performance score, as specified in
sections III.C.6.c(2)(a) through (c) of this
final rule.
We are finalizing this provision
without modification at § 512.430(a) and
direct commenters to section III.C.6.c(1)
of this final rule for discussion of the
methodology for determining the final
performance score and the use of the
final performance scores to determine
the upside risk payment, the downside
risk payment, and the neutral zone.
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(a) Upside Risk Payment
If, in PYs 1–6, an IOTA participant’s
final performance score is greater than
or equal to 60 points, we proposed that
the IOTA participant would qualify for
an upside risk payment. If an IOTA
participant’s final performance score
would qualify them for the upside risk
payment, we proposed a methodology to
calculate their upside risk payment
using the formula in Equation 6 below,
where:
• $8,000 is a fixed, risk-based
payment amount within the calculation
formula, estimated to be about 33
percent of the average Medicare FFS
kidney transplant MS–DRG cost. We
aimed to create a strong financial
incentive with significant earning
opportunity for IOTA participants that
meet or exceed model performance
expectations. We believe this amount or
proportion of the MS–DRG to be a large
financial incentive to promote behavior
changes while maintaining expectations
of net savings to Medicare. We
calibrated this based on projection of
the incentive effects that would
encourage the necessary support and
infrastructure investment needed to
achieve high performance and produce
overall model savings and have the
effects that we are looking for.
• The final performance score is the
sum of points earned from the
achievement domain, efficiency
domain, and quality domain in a PY, as
described in section III.C.5 of this final
rule.
• Medicare kidney transplants is the
number of Medicare kidney transplants
furnished by the IOTA participant in a
PY.
Equation 6: Proposed Upside Risk
Payment Calculation Formula
Upside Risk Payment = $8,000 * (( Final
Performance Score¥60)/40) *
Medicare Kidney Transplants
We also considered calculating the
maximum positive multiplier per
Medicare kidney transplant claim based
on the Kidney Transplant Bonus in the
KCC Model. In 2019, the Kidney
Transplant Bonus for entities
participating in the KCC Model was set
to $15,000. Adjusted for inflation, this is
roughly $18,000, which would be the
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Final Performance Score
60-100
41-59
0 - 40
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maximum allowable positive bonus
payment per transplant. The Kidney
Transplant Bonus was originally
calculated based on the difference in
spending between a beneficiary who
went on to get a transplant and the
average ESRD beneficiary cost.
However, we believed that the
maximum positive adjustment may be
too large in relation to current Medicare
payments for kidney transplants for the
model to yield net savings.
We also considered using a system
similar to the Hospital VBP Program
under which CMS withholds 2 percent
of participating’s hospitals Medicare
payments and uses the sum of these
reductions to fund value-based
incentive payments to hospitals based
on their performance under the
program. However, we wished to have
the opportunity for both upside and
downside across IOTA participants to
most effectively incentivize
performance in the model.
We also considered adjusting the
maximum upside multiplier in PYs 2–
6; however, we felt making that decision
prior to the start of the model would be
premature and wish to understand
IOTA participant performance before
making such a decision.
We sought comment on our proposed
methodology to calculate the upside risk
payment and alternatives considered.
The following is a summary of the
comments received on our proposed
methodology to calculate the upside risk
payment, alternatives considered and
our responses:
Comment: We received many
comments saying that the proposed
payment amount was not high enough
to incentivize performance in the
model. Commenters pointed out a
concern that they lose money on kidney
transplants, based on the difference
between their cost and the Medicare
FFS DRG payments and that an
increased number of transplants would
be more likely to come from using more
complex organs, which would be more
expensive for the IOTA participants.
Many commenters also believed that the
proposed maximum upside amount of
$8,000 would not be sufficient to
incentivize investment by hospital
leadership, particularly given that the
payment amount was only proposed to
be applied to Medicare FFS kidney
transplants.
Response: We appreciate the feedback
from commenters and recognize the
validity of the concerns expressed. The
IOTA Model is designed to save money
for CMS, improve care for beneficiaries,
to save money for Medicare, and to
increase payments to IOTA participants
who do more transplants. To effectively
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accomplish those goals, the incentives
must be effectively calibrated high
enough to incentivize improved
performance, while still ensuring
sufficient savings for CMS. We believe
that applying the payment adjustments
to all Medicare kidney transplants, as
discussed previously will help to
increase the incentives in the model and
account for the changing nature of the
Medicare program. Additionally, the
CMS Office of the Actuary conducted
additional analyses and determined that
CMS would still be able to see projected
savings of $22 million if the maximum
upward adjustment were raised to
$15,000. We considered this alternative
based on the Kidney Transplant Bonus
in the KCC Model, which was designed
to reflect the net savings to the Medicare
Trust Fund from a patient who is
transplanted. Our analyses also show an
average cost in 2023 of approximately
$40,000 for performing MS–DRG 650,
which is billed for Kidney transplants
that then require hemodialysis
afterwards. We recognize that many of
the kidney transplants that will be
performed under the IOTA Model may
be for more complex organs that require
hemodialysis after being transplanted
and wants to recognize the increased
costs to the IOTA participants for the
transplant surgery and recovery when
that occurs. Given that costs will grow
over the course of the model period
until 2030, we believe that it is
appropriate to take approximately 1⁄3 of
those costs to calculate the maximum
upward adjustment, as we did for the
average payment in the proposed rule,
to also come up with the $15,000 figure.
We proposed to keep this figure flat over
the course of the model, given that it
already accounts for some level of cost
growth over the six-year period of the
model. We will also evaluate the effects
of this maximum upward adjustment
and consider updating the amount
based on the incentive effects and CMS
savings.
Comment: We received multiple
comments arguing that higher risk
candidates are more expensive and are
the ones who are likely to receive
transplants based on the incentives in
the model. Commenters urged CMS to
base payment amounts on DRGs for
more complex transplant surgeries given
this concern.
Response: We recognize this concern
from commenters and, as described in
comment responses in this section, are
finalizing an increased maximum
upside risk payment amount of $15,000,
based on the increased costs of DRG–
650, which CMS projects may be
necessary to be billed for the use of
more complex organs.
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96385
Comment: Multiple commenters
suggested that CMS should base the
upward risk payment amount on the
Kidney Transplant Bonus from the
Kidney Care Choices Model.
Response: We recognize the validity
of these comments and adjusted the
amount upwards to be similar to the
amount that the Innovation Center paid
out in the KCC Model.
Comment: We received a comment
expressing concern that the maximum
upward payment amounts would not be
sufficient to support IOTA collaborators,
given that they would only be used by
IOTA participants.
Response: We recognize the
commenter’s concern and believe that
the increased payment amounts and
increased overall payments by
accounting for all Medicare kidney
transplants gives the opportunity for
IOTA participants to earn enough
upward payments through the model to
be able to support collaboration with
IOTA collaborators.
Comment: We received a comment
from commenters that the maximum
upward adjustment should increase
over the years of the model.
Response: We recognize that costs
have historically risen over time and
CMS payments have gone up. As a
result, the updated payment amount is
based on a projected rise in costs from
the 2023 costs of MS–DRG 650 of
$40,151. We are taking slightly more
than 1⁄3 of that amount and keeping it
as a flat rate for all six years of the
model to help account for a potential
rise in costs in the future. We may also
re-evaluate the effects of the maximum
adjustment over time based on any
potential future rise in payments and
the effects on the Medicare Trust Fund.
After consideration of the public
comments we received, for the reasons
set forth in this rule, we are finalizing
our proposed methodology to calculate
the upside risk payment upside risk
payment at § 512.430(b)(1), with slight
modifications. Specifically, we are
making a technical correction at
§ 512.430(b)(1)(i) to remove the
following verbiage: from 100. In the
proposed rule at 89 FR 43572, we
proposed that the upside risk payment
would be calculated by subtracting 60
from the IOTA participant’s final
performance score, as outlined in
Equation 2 of section III.C.6.c(2)(a) of
the proposed rule. As such, we are
finalizing at § 512.430(b)(1)(i) that CMS
subtracts 60 from the IOTA participant’s
final performance score. We are also
modifying our regulation at
§ 512.430(b)(1)(iii) to reflect a maximum
upside risk payment multiplier amount
of $15,000 (see Equation 7).
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Lastly, we are finalizing our proposed
definition of Medicare kidney
transplants at § 512.402 without
modification, as described and finalized
in section III.C.6(b) of this final rule.
Equation 7: Upside Risk Payment
Calculation Formula
Upside Risk Payment
_
- $15,000
* (Final Performance Score
40
- 60)
(b) Downside Risk Payment
If an IOTA participant’s final
performance score is at or below 40
points in PYs 2–6, the IOTA participant
would qualify for a downside risk
payment. If an IOTA participant
qualifies for a downside risk payment,
we describe the methodology to
calculate their downside risk payment
risk using the formula in Equation 8:
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Equation 8: Proposed Downside Risk
Payment Calculation Formula
Downside Risk Payment= $2,000 *
((40¥Final Performance Score)/40)
* Medicare Kidney Transplants
• $2,000 is a fixed, risk-based
payment amount within the calculation
formula, estimated to be about onetwelfth, or 8 percent, of the average
Medicare FFS kidney transplant MS–
DRG cost. We proposed a lower
downside-risk value relative to the
upside-risk value proposed for the
upside risk payments (about one-fourth
lower) because we wanted to maintain
a greater rewards approach, while still
holding IOTA participants accountable
for poor performance. We also believe
that this approach is more flexible and
accommodating to IOTA participants
with no, or limited, APM experience, or
that are more limited in terms of
resources and capabilities.
• The final performance score is the
sum of points earned from the
achievement domain, efficiency
domain, and quality domain, as
described in section III.C.5. of this final
rule.
• Medicare kidney transplants is the
count of furnished Medicare kidney
transplants during the PY.
We also considered applying the same
fixed amount to both the upside and
downside risk payment ($8,000 or
$2,000 in both) or having the downside
risk payment be 50 percent of the fixed
amount of the upside risk payment
($4,000) but opted against it to maintain
lower levels of risk given the fact that
this model would be mandatory for
eligible kidney hospitals. As discussed
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in section III.C.6.b of this final rule, we
considered an upside-risk only payment
framework, thus eliminating the
application of downside-risk payments.
Recognizing the potential for volatility
in performance year-over-year, we also
considered requiring IOTA participants
to owe downside-risk payments to CMS
if their final performance score was at
or below 40 for more than one PY,
starting from PY 1, potentially giving
IOTA participants a similar phased-in,
or, rather, ramp-up, opportunity to
adjust and improve before downsiderisk payments kick in. We considered
this option to be unnecessary and
operationally complex, particularly as it
would function in a similar way as our
proposed approach from a phasing-in
standpoint. We also considered
adjusting the $2,000 fixed, risk-based
payment amount for PYs 2–6; however,
we believe a fixed amount would
provide greater transparency to IOTA
participants on financial risk and model
implementation experience would
better inform if this approach would be
necessary.
We sought comment on our proposed
downside risk payment calculation
formula, and alternatives considered.
The following is a summary of the
comments received on our proposed
downside risk payment calculation
formula, alternatives considered, and
our responses:
Comment: A couple commenters
suggested that we should increase the
maximum downside risk payment. To
encourage greater engagement from
IOTA participants who are likely to
struggle, a commenter recommended
two changes: (1) Lowering the proposed
final performance score threshold for
the downside risk payment zone in PY
2 from less than 40 points to less than
20 points, and (2) Increasing the
maximum downside risk payment
amount to ¥$4000 per Medicare kidney
transplant. The commenter believed that
by decreasing the likelihood of failure
but increasing its consequences, CMS
would ensure that only IOTA
participants who actively choose not to
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engage would face negative
repercussions. Another commenter
proposed increasing the maximum
downside risk payment for each
Medicare kidney transplant from the
proposed $2,000 to $3,750. They
believed the IOTA Model incentives
must be substantial enough to capture
the attention of transplant hospital and
health system administrators, while the
downside risk payment should be high
enough to motivate IOTA participants to
avoid incurring it entirely.
Another commenter pointed out that
IOTA participants who abstain from
participating risk termination from the
model and may face penalties.
Specifically, under the proposed rule,
terminated IOTA participants could be
liable for a penalty in the PY of their
termination and may have to refund any
upside risk payments from previous
PYs. The commenter further noted that
IOTA participants could view the
penalty as a low-cost way to avoid
accountability in the model through
2031. The commenter also pointed out
that the shrinking pool of Medicare FFS
patients, has the same effect of reducing
both upside risk payments and
downside risk payments. Based on these
concerns, the commenter urged CMS to
reconsider how it calculates downside
risk payments, and at minimum, to
apply the same $8,000 fixed amount
used in the upside risk payment
calculation to the downside risk
payment calculation.
Response: We thank the commenters
for their suggestions. In putting
downside risk in the model, we are
attempting to incentivize improved
performance on the IOTA metrics, while
also attempting to not make the model
too punitive for IOTA participants. As
such, we will be finalizing the
maximum downside risk payment as
proposed. We will evaluate the effects of
our payment methodology and may
propose raising the maximum downside
risk payment if we are not seeing the
level of change that we are hoping for
in future notice and comment rule
making
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Comment: A commenter urged that
CMS make the proposed maximum
downside risk payment proportional to
the proposed maximum upside risk
payment.
Response: The model was designed
with asymmetric upside and downside
risk in recognition of the benefits
provided by transplant to the Medicare
Trust Fund and the desire of CMS to not
be overly punitive in a mandatory
model. We plan to test out the effects of
a $2,000 maximum downside risk
payment to assess its effects on the
metrics in the IOTA Model. Based on
the results, we may consider increasing
the maximum downward amount in
future notice and comment rule making.
After consideration of the public
comments we received, for the reasons
set forth in this rule, we are finalizing
the proposed provision for calculating
the downside risk payment at
§ 512.430(b)(3), without modification..
We also note that we are finalizing, as
proposed, the definition of Medicare
kidney transplants at § 512.402 without
modification, as described and finalized
in section III.C.6(b) of this final rule.
(c) Neutral Zone
If, in PY 1, an IOTA participant’s final
performance score was below 60 points,
or if, in PYs 2–6, an IOTA participant’s
final performance score was between 41
and 59 (inclusive), we proposed that the
final performance score, as described in
section III.C.6.c.(1). of this final rule,
would qualify the IOTA participant for
the neutral zone, where no upside risk
payment or downside risk payment
would apply. As such, in a PY where an
IOTA participant’s final performance
score falls in the neutral zone, no money
would be paid to the IOTA participant
by CMS, nor would money be owed by
the IOTA participant to CMS.
We sought comment on our proposed
neutral zone.
Comment: Multiple comments urge
constricting the neutral zone to make it
more likely that an IOTA participant
would receive a positive or negative
payment adjustment.
Response: To begin the model, we
plan to keep the neutral zone as
designed. Our goal is to recognize both
excellent performers and those that fall
far below expectations and ensure that
only those IOTA participants receive a
positive or negative payment
adjustment. We will evaluate how many
IOTA participants fall into the neutral
zone and consider constriction in the
future.
After consideration of the public
comments we received, for the reasons
set forth in this rule, we are finalizing
the neutral zone provisions at
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§ 512.430(b)(2) as proposed without
modification.
(3) Payments Operations and Timelines
After the end of each PY, CMS would
assess each IOTA participant’s
performance in accordance with section
III.C.5. of this final rule and calculate
performance-based payments in
accordance with the methodology
specified in section III.C.6.c. of this final
rule. We proposed to define this process
as ‘‘preliminary performance assessment
and payment calculations.’’
We proposed that CMS would
conduct and calculate preliminary
performance assessment and payment
calculations at least 3 to 6 months after
the end of each PY to allow for
sufficient Medicare kidney transplant
claims runout. We proposed that CMS
would notify IOTA participants of their
preliminary model performance
assessment, including the IOTA
participant’s score for each metric
within the achievement domain,
efficiency domain, and quality domain
and the final performance score, and
payment calculations with respect to
any applicable upside risk payment or
downside risk payment, at least 5 to 9
months after the end of each PY,
allowing for a two-to-three month
period for CMS to conduct calculations
after the claims runout period. We
proposed that a 30-day notification
period between preliminary and final
calculations would apply, giving IOTA
participants 30 days to review
preliminary data and calculations and
request targeted reviews, as described in
section III.C.6.c.(4). of this final rule.
This 30-day notification period would
also be intended to provide IOTA
participants with advance notice of
forthcoming performance-based
payments before upside risk payments
or demand letters for downside risk
payments would be issued by CMS. We
also proposed that CMS would notify
IOTA participants of their model
performance assessment and payment
calculations in a form and manner
determined by CMS, such as letters,
email, or model dashboard. We
proposed that CMS would notify the
IOTA participant of their final
performance score and any associated
upside risk payment or downside risk
payment at least 30 days after notifying
the IOTA participant of their
preliminary model performance
assessment and payment calculations.
We proposed that after CMS notifies
the IOTA participant of their final
performance score and any associated
upside risk payment and by a date
determined by CMS, CMS would issue
the upside risk payment to the tax
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identification number (TIN) on file for
the IOTA participant in the Medicare
Provider Enrollment, Chain, and
Ownership System (PECOS).
We proposed that after CMS notifies
the IOTA participant of their final
performance score and any associated
downside risk payment and by a date
determined by CMS, CMS would issue
a demand letter to the TIN on file in
PECOS for the IOTA participant for
downside risk payments owed to CMS,
with a payment due date of at least 60
days after the date on which the
demand letter is issued. We proposed
that the demand letter would include
details on model performance, the
downside risk payment, and how
payments would be made to CMS.
Rather than the proposed lump-sum
payment and demand letter approach,
we also considered making the upside
risk payments and downside risk
payments to IOTA participants in the
form of Medicare FFS claim
adjustments. The benefit of this
approach would be that upside risk
payments and downside risk payments,
which are retrospective, would be
applied prospectively and spread out
over a 12-month period, so that a
transplant hospital would not need to
pay back to CMS a large sum of monies
owed all at once. However, we believe
that this approach would delay model
payments and collection of monies
owed to CMS. We also consider this
approach to be disruptive to standard
claims processing systems and
operationally complex, with more
opportunities for error and less
flexibility to correct errors in a timely
manner.
We sought comment on our proposed
payment operations and timeline and
alternative considered.
The following is a summary of the
comments received on our proposed
payment operations and timeline,
alternative considered and our
responses:
Comment: We received a comment
approving of the payment operations
timeline process.
Response: We appreciate that
comment and plan to finalize as
proposed.
Comment: We received a comment
urging an alternative methodology for
potential repayments that would allow
an IOTA participant to mitigate the
downside risk payments owed to CMS
through an agreed upon strategy of
process and performance improvement
across various metrics.
Response: We see this as an
interesting idea, but ultimately decided
to go with the proposed strategy of
repayment to recognize the large
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behavioral incentives of wanting to
avoid writing a check to repay CMS. We
also see that this process is inherently
present in the model, given that
performance on model measures resets
each year. We also recognize that there
is no downside risk in PY 1, and we
hope that any IOTA participants with a
final performance score below 40 who
would otherwise have had to pay
downside risk payments to CMS can use
that as an opportunity for process
improvement to avoid having to make
downside risk payments for PY 2.
After consideration of the public
comments we received, for the reasons
set forth in this rule, we are finalizing
these provisions without modification at
§ 512.430(d). We are also finalizing the
definition of preliminary performance
assessment and payment calculations at
§ 512.402, without modification.
(4) Targeted Review
We believe that CMS calculation
errors are possible, and therefore IOTA
participants should be able to dispute
the results of calculations.
Thus, upon receipt of CMS issued
notifications of preliminary
performance assessment and payment
calculations, as described in section
III.C.6.c(3) of this final rule, we
proposed at § 512.434 that IOTA
participants may appeal via a ‘‘targeted
review process,’’ defined as the process
in which an IOTA participant could
dispute performance assessment and
payment calculations made, and issued,
by CMS.
We proposed at § 512.434(a) that an
IOTA participant would be able to
request a targeted review for one or
more calculations made and issued by
CMS within the preliminary
performance assessment and payment
calculations. We proposed at
§§ 512.434(a)(1) and (2) that an IOTA
participant would be able to request a
targeted review for CMS consideration
if—
• The IOTA participant believes an
error occurred in calculations due to
data quality or other issues; or
• The IOTA participant believes an
error occurred in calculations due to
misapplication of methodology.
We proposed at § 512.434(b)(1) that an
IOTA participant would be required to
submit a targeted review request within
30 days, or another time period as
specified by CMS, of receiving its
preliminary performance assessment
and payment calculations from CMS.
We also proposed at § 512.434(b)(2) that
the request would require supporting
information from the IOTA participant,
in a form and manner specified by CMS.
The 30-day window to appeal generally
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aligns with the length of time we have
finalized for submitting appeals in other
CMS models, such as the ETC Model, as
well as under the Hospital VBP
Program, and we believed would allow
ample time for IOTA participants to
separately review CMS calculations.
We proposed at § 512.434(c) that the
targeted review process would not
provide IOTA participants the ability to
dispute policy and methodology, as it
would be limited to the dispute of
calculations. Specifically, we proposed
at § 512.434(c)(1) that CMS would not
consider targeted review requests
regarding, without limitation, the
following:
• The selection of the kidney
transplant hospital to be an IOTA
participant.
• The attribution of IOTA waitlist
patients and the attribution of IOTA
transplant patients to the IOTA
participant, or to any other kidney
transplant hospital selected for
participation in the IOTA Model, or to
any kidney transplant hospital not
selected for participation in the IOTA
Model.
• The methodology used for
determining the achievement domain,
efficiency domain, and quality domain.
• The methodology used for
calculating and assigning points for
each metric within the achievement
domain, efficiency domain, and quality
domain.
• The methodology used for
calculating the payment amount per
Medicare kidney transplant paid to an
IOTA participant.
We proposed § 512.434(c)(2) that a
targeted review request that includes
one or more of the exclusions under
§ 512.434(c)(1) could still be reviewed
by CMS, given that all remaining
considerations of the request meet all
other criteria for consideration by CMS.
Upon receipt of a targeted review
request from an IOTA participant, we
proposed at § 512.434(d)(1) that CMS
would conduct an initial assessment
and final assessment of the targeted
review. We believed that this proposal
would be in line with other CMS
models.
The CMS targeted review initial
assessment would determine if the
targeted review request met the targeted
review requirements and contained
sufficient information to substantiate
the request. If the request was not
compliant with the requirements or
required additional information, CMS
would follow up with IOTA participants
to request additional information in a
form and manner determined by CMS.
Any additional information that CMS
requests from an IOTA participant
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would be due to CMS within 30 days of
CMS’s request, also in a form and
manner determined by CMS. An IOTA
participant’s non-responsiveness to the
request for additional information from
CMS could result in the closure of the
targeted review request.
In a final assessment, CMS would
determine whether it erred in a
calculation, as disputed by the IOTA
participant.
CMS’s correction of an error may
delay the date of payment of an IOTA
participant’s upside risk payments or
downside risk payments.
We stated in the proposed rule that
were a calculation error to be found as
a result of an IOTA participant’s
targeted review request, we would
notify the IOTA participant within 30
days of any findings in a form and
manner determined by CMS and resolve
and correct the error and discrepancy in
the amount of the upside risk payment
or downside risk payment in a time and
manner as determined by CMS.
We proposed at § 512.434(d)(2) that
targeted review decisions made by CMS
would be final, unless submitted by the
IOTA participant or CMS for a CMS
Administrator review. We also proposed
to include the reconsideration
determination process as outlined in
proposed § 512.190 in the IOTA Model.
We noted that if an IOTA participant
has regular Medicare FFS claims issues
or decisions that it wishes to appeal
(that is, issues during the model
performance period with Medicare FFS
that are unrelated to the model
performance and payment calculations
and payments), then the IOTA
participant should continue to use the
standard CMS procedures. Section 1869
of the Act provides for a process for
Medicare beneficiaries, providers, and
suppliers to appeal certain claims and
decisions made by CMS.
We sought comment on our proposals
regarding the process by which an IOTA
participant could request a targeted
review of CMS calculations.
The following is a summary of the
comments received on our proposals
regarding the process by which an IOTA
participant could request a targeted
review of CMS calculations and our
responses:
Comment: We received a comment
approving of the proposed targeted
review process.
Response: We that the commenter for
their support and plan to finalize these
provisions as proposed.
After consideration of the public
comments we received, for the reasons
set forth in this rule, we are finalizing
the provisions for the proposed targeted
review process at 512.434(d) without
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modification. We are also finalizing the
definition of targeted review process at
§ 512.402, with a minor technical
correction to update the cross reference.
(5) Extreme and Uncontrollable
Circumstances
As we stated in the proposed rule,
events may occur outside the purview
and control of the IOTA participant that
may affect their performance in the
model (89 FR 43518). In the event of
extreme and uncontrollable
circumstances, such as a public health
emergency, we proposed that CMS may
reduce the downside risk payment, if
any, prior to recoupment by an amount
determined by multiplying the
downside risk payment by the
percentage of total months during the
PY affected by an extreme and
uncontrollable circumstance, by the
percentage of attributed patients who
reside in an area affected by the extreme
and uncontrollable circumstance. We
proposed to address only the downside
risk payment under this policy, as we
wish to mitigate the harm to entities due
to extreme and uncontrollable
circumstances. We considered applying
this policy to upside risk payments and
final performance scores in the neutral
zone, but we believe that IOTA
participants that have been able to
achieve model success do not need to be
made whole by this policy.
We proposed at § 512.436(a)(1) to
apply determinations made under the
Quality Payment Program with respect
to whether an extreme and
uncontrollable circumstance has
occurred, and the affected areas, during
the PY. We chose the Quality Payment
Program to align across Innovation
Center models and CMS policy. We
proposed at § 512.436(a)(2) that CMS
has the sole discretion to determine the
time period during which an extreme
and uncontrollable circumstance
occurred and the percentage of
attributed patients residing in affected
areas for the IOTA participant.
We requested comment on our
extreme and uncontrollable
circumstances policy and whether the
determinations by the Quality Payment
Program that an extreme and
uncontrollable circumstance have
occurred should apply to IOTA
participants.
We did not receive any comments on
this policy and therefore are finalizing
these provisions without modification at
§ 512.436.
7. Data Sharing
a. General
As discussed in the proposed rule, we
expect that IOTA participants would
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work toward independently identifying
and producing their own data, through
electronic health records, health
information exchanges, or other means
that they believe are necessary to best
evaluate the health needs of their
patients, improve health outcomes, and
produce efficiencies in the provision
and use of services.
To assist IOTA participants in this
process, we proposed to provide IOTA
participants with certain beneficiaryidentifiable data for their Medicare
beneficiaries who are attributed
patients, upon request. We anticipated
that IOTA participants would use this
data to better assess transplant readiness
and post-transplant outcomes. We also
proposed to provide certain aggregate
data that has been de-identified in
accordance with the HIPAA Privacy
Rule, 45 CFR 164.514(b), as discussed
later in this section, for the purposes of
helping IOTA participants understand
their progress towards the model’s
performance metrics.
Specifically, subject to the limitations
discussed in this final rule, and in
accordance with applicable law,
including the HIPAA Privacy Rule, we
proposed that CMS may offer an IOTA
participant an opportunity to request
certain Medicare beneficiaryidentifiable data and reports as
discussed in section III.C.7.b of this
final rule. We proposed that CMS would
share this beneficiary-identifiable data
with IOTA participants on the condition
that the IOTA participants, their IOTA
collaborators, and other individuals or
entities performing functions or services
related to the IOTA participant’s
activities observe all relevant statutory
and regulatory provisions regarding the
appropriate use of data and the
confidentiality and privacy of
individually identifiable health
information, and comply with the terms
of the data sharing agreement described
in this section of the final rule.
We proposed that the beneficiaryidentifiable claims data described in
section III.C.7.b of this final rule would
omit individually identifiable data for
Medicare beneficiaries who have opted
out of data sharing with the IOTA
participant, as described in section
III.C.7.c of this final rule. We also noted
that, for the beneficiary-identifiable
claims data, we would exclude
information that is subject to the
regulations governing the
confidentiality of substance use disorder
patient records (42 CFR part 2) from the
data shared with an IOTA participant.
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b. Beneficiary-Identifiable Data
(1) Legal Authority To Share
Beneficiary-Identifiable Data
As discussed in the proposed rule, we
believe that an IOTA participant may
need access to certain Medicare
beneficiary-identifiable data for the
purposes of evaluating its performance,
conducting quality assessment and
improvement activities, conducting
population-based activities relating to
improving health or reducing health
care costs, or conducting other health
care operations listed in the first or
second paragraph of the definition of
‘‘health care operations’’ under the
HIPAA Privacy Rule, 45 CFR 164.501.
We proposed that, subject to
providing the beneficiary with the
opportunity to decline data sharing as
described in section III.C.10.a of this
final rule, and subject to having a valid
data sharing agreement in place, an
IOTA participant may request from CMS
certain beneficiary identifiable claims
for attributed patients who are Medicare
beneficiaries.
As stated in section III.C.7(b)(1) of the
proposed rule, we recognized there are
sensitivities surrounding the disclosure
of individually identifiable (beneficiaryspecific) health information, and several
laws place constraints on the sharing of
individually identifiable health
information. For example, section 1106
of the Act generally bars the disclosure
of information collected under the Act
unless a law (statute or regulation)
permits the disclosure. Here, we noted
that, in this circumstance, the HIPAA
Privacy Rule would allow for the
proposed disclosure of individually
identifiable health information by CMS.
We noted in the proposed rule that
under the HIPAA Privacy Rule, covered
entities (defined in 45 CFR 160.103 as
health care plans, health care providers
that submit certain transactions
electronically, and health care
clearinghouses) are barred from using or
disclosing individually identifiable
health information (called ‘‘protected
health information’’ or PHI) in a manner
that is not explicitly permitted or
required under the HIPAA Privacy Rule,
without the individual’s authorization
(89 FR 43518). The Medicare FFS
program, a ‘‘health plan’’ function of the
Department, is subject to the HIPAA
Privacy Rule limitations on the
disclosure of PHI without an
individual’s authorization. IOTA
participants are also covered entities,
provided they are health care providers
as defined by 45 CFR 160.103 and they
or their agents electronically engage in
one or more HIPAA standard
transactions, such as for claims,
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eligibility or enrollment transactions. In
light of these relationships, as discussed
in the proposed rule, we believe that the
proposed disclosure of the beneficiaryidentifiable data under the IOTA Model
would be permitted by the HIPAA
Privacy Rule under the provisions that
permit disclosures of PHI for ‘‘health
care operations’’ purposes. Under those
provisions, a covered entity is permitted
to disclose PHI to another covered entity
for the recipient’s health care operations
purposes if both covered entities have or
had a relationship with the subject of
the PHI to be disclosed, the PHI pertains
to that relationship, and the recipient
will use the PHI for a ‘‘health care
operations’’ function that falls within
the first two paragraphs of the definition
of ‘‘health care operations’’ in the
HIPAA Privacy Rule (45 CFR
164.506(c)(4)).
The first paragraph of the definition of
health care operations includes
‘‘conducting quality assessment and
improvement activities, including
outcomes evaluation and development
of clinical guidelines,’’ and
‘‘population-based activities relating to
improving health or reducing health
costs, protocol development, case
management and care coordination.’’
The second paragraph of the definition
of health care operations includes
‘‘evaluating practitioner and provider
performance’’ (45 CFR 164.501).
Under our proposal, IOTA
participants would be using the data on
their patients to evaluate the
performance of the IOTA participant
and other providers and suppliers that
furnished services to the patient,
conduct quality assessment and
improvement activities, and conduct
population-based activities relating to
improved health for their patients.
When done by or on behalf of a covered
entity, these are covered functions and
activities that would qualify as ‘‘health
care operations’’ under the first and
second paragraphs of the definition of
health care operations at 45 CFR
164.501. Hence, as discussed in the
proposed rule, we believe that this
provision is extensive enough to cover
the uses we would expect an IOTA
participant to make of the beneficiaryidentifiable data and would be
permissible under the HIPAA Privacy
Rule. Moreover, our proposed
disclosures would be made only to
HIPAA covered entities that have (or
had) a relationship with the subject of
the information, the information we
would disclose would pertain to such
relationship, and those disclosures
would be for purposes listed in the first
two paragraphs of the definition of
‘‘health care operations.’’ Finally, the
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proposed disclosures would be limited
to beneficiary-identifiable data that we
believe would meet HIPAA
requirements in 45 CFR 164.502(b) to
limit PHI to the minimum necessary to
accomplish the intended purpose of the
use, disclosure, or request.
The Privacy Act of 1974 also places
limits on agency data disclosures. The
Privacy Act applies when Federal
agencies maintain systems of records by
which information about an individual
is retrieved by use of one of the
individual’s personal identifiers (names,
Social Security numbers, or any other
codes or identifiers that are assigned to
the individual). The Privacy Act
generally prohibits disclosure of
information from a system of records to
any third party without the prior written
consent of the individual to whom the
records apply (5 U.S.C. 552a(b)).
As described in the proposed rule,
‘‘routine uses’’ are an exception to this
general principle (89 FR 43576). A
routine use is a disclosure outside of the
agency that is compatible with the
purpose for which the data was
collected. Routine uses are established
by means of a publication in the Federal
Register about the applicable system of
records describing to whom the
disclosure will be made and the purpose
for the disclosure. As we stated in the
proposed rule, we believe that the
proposed data disclosures are consistent
with the purposes for which the data
discussed in this rule was collected,
and, thus, would not run afoul of the
Privacy Act, provided we ensure that an
appropriate Privacy Act system of
records ‘‘routine use’’ is in place prior
to making any disclosures. The systems
of records from which CMS would share
data are the Medicare Integrated Data
Repository (IDR) and the Health
Resources and Services Administration
(HRSA) Organ Procurement and
Transplantation Network (OPTN)/
Scientific Registry of Transplant
Recipients (SRTR) Data System. We
stated in the proposed rule that we
believe that the proposed data
disclosures are consistent with the
purposes for which the data were
collected and may be disclosed in
accordance with the routine uses
applicable to those records.
We proposed that CMS would share
the following beneficiary-identifiable
lists and data with IOTA participants
that have submitted a formal request for
the data. Under our proposal, the
request must be submitted on an annual
basis in a manner and form and by a
date specified by CMS. The request also
would need to identify the data being
requested and include an attestation
that (A) the IOTA participant is
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requesting this beneficiary-identifiable
data as a HIPAA covered entity or as a
business associate, as those terms are
defined at 45 CFR 160.103, to the IOTA
participant’s providers and suppliers
who are HIPAA covered entities; and (B)
the IOTA participant’s request reflects
the minimum data necessary for the
IOTA participant to conduct health care
operations work that falls within the
first or second paragraph of the
definition of health care operations at 45
CFR 164.501. In addition, we proposed
that IOTA participants who request this
data must have a valid and signed data
sharing agreement in place, as described
in more detail later in this section. We
proposed that we would make available
beneficiary-identifiable data as
described in section III.C.8.b. of this
final rule for IOTA participants to
request for purposes of conducting
health care operations that fall within
the first or second paragraph of the
definition of health care operations at 45
CFR 164.501 on behalf of their
attributed patients who are Medicare
beneficiaries. We explained that we
believe that access to beneficiaryidentifiable claims data would improve
care coordination between IOTA
participants and other health care
providers. Patients can spend months in
between their visits to the kidney
transplant hospital at which they are
listed, and the post-transplant period is
critical to transplant success. We stated
that we believe that improved care
coordination would improve outcomes
and keep patients engaged in their care.
We also proposed that IOTA
participants limit the request for
beneficiary-identifiable claims data to
Medicare beneficiaries whose name
appears on the quarterly attribution list
who have been notified in compliance
with section III.C.10.a. of the proposed
rule, and who did not decline having
their claims data shared with the IOTA
participant, as proposed in section
III.C.7.d. of the proposed rule. Finally,
we proposed that CMS would share
beneficiary identifiable data with an
IOTA participant on the condition that
the IOTA participant, its IOTA
collaborators, and other individuals or
entities performing functions or services
related to the IOTA participant’s
activities, observe all relevant statutory
and regulatory provisions regarding the
appropriate use of data and the
confidentiality and privacy of
individually identifiable health
information and comply with the terms
of the data sharing agreement described
in section III.C.7.f. of the proposed rule.
The following is a summary of the
public comments we received on the
proposal to share certain beneficiary-
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identifiable data with IOTA participants
and our responses:
Comment: A couple of commenters
expressed support for the proposal to
share certain beneficiary-identifiable
data with IOTA participants. The
commenters indicated that these data
would enable IOTA participants to
identify their patient populations, plan
and improve care, and gauge the quality
of post-acute care providers.
Response: We thank the commenters
for their support for the proposal to
share certain beneficiary-identifiable
data under this model and concur with
the stated benefits for IOTA participants
in receiving such data.
After consideration of the comments
received, we are finalizing at § 512.440
our proposals to share certain
beneficiary-identifiable claims data with
IOTA participants as proposed with
minor technical corrections.
Specifically, we made a minor technical
correction at § 512.440(a) to clarify that,
as stated in this section and in the
proposed rule, CMS shares certain
beneficiary-identifiable data as
described in § 512.440(b) and certain
aggregate data as described in
§ 512.440(c) with IOTA participants
regarding attributed patients who are
Medicare beneficiaries and performance
under the model. We also made a minor
technical correction at § 512.440(b)(3) to
correct a grammatical error.
(2) Quarterly Attribution Lists
We proposed that this beneficiaryidentifiable data would include, for the
relevant PY, a beneficiary attribution
report, shared quarterly, that would
include a list of attributed patients and
patients who have been de-attributed
from the IOTA participant. We proposed
that the report would include at least
the following information for each
attributed patient: the attribution year
the attributed patient became attributed
to the IOTA participant; the effective
date of the attributed patient’s
attribution to the IOTA participant; the
effective date of the patient’s deattribution from the IOTA participant
and the reason for such removal (if
applicable); and the attributed patient’s
data sharing preferences made pursuant
to section III.C.7.d. of this final rule. We
proposed that CMS may include
additional information at its discretion
in any of the quarterly attribution
reports as data becomes available. Such
data may include information from the
SRTR or OPTN on waitlist status or
transplant status.
We requested comment on whether
such additional information would be
beneficial to IOTA participants or
whether this information is best
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accessed by the IOTA participant
through other means.
We received no public comments on
these proposals and therefore are
finalizing this provision as proposed to
provide quarterly attribution lists to
IOTA participants at § 512.440(b)(5)(i),
without modification.
(3) Beneficiary-Identifiable Claims Data
In section III.C.7(b)(3) of the proposed
rule, we proposed to offer certain
beneficiary-identifiable claims data to
IOTA participants no later than one
month after the start of each PY, in a
form and manner specified by CMS. We
proposed that IOTA participants may
retrieve this data at any point during the
relevant PY and that it would include,
at a minimum—
• Three years of historical Parts A, B,
and D claims data files for attributed
patients who are Medicare beneficiaries
for 36 months immediately preceding
the effective date of the Medicare
beneficiary’s attribution to the IOTA
participant;
• Monthly Parts A, B, and D claims
data files specified for attributed
patients who are Medicare beneficiaries;
and
• Monthly Parts A, B, and D claims
data files for Medicare beneficiaries who
have been de-attributed from the IOTA
participant for claims with a date of
service prior to the date the Medicare
beneficiary was removed from
attribution to the IOTA participant.
We proposed that CMS would omit
from the beneficiary-identifiable claims
data any substance use disorder patient
records subject to 42 U.S.C. 290dd–2
and the implementing regulations at 42
CFR part 2.
We stated that we believe these data
elements would consist of the minimum
data element necessary for IOTA
participants to effectively manage the
care of Medicare beneficiaries who are
attributed patients. Specifically, this
data would allow IOTA participants to
coordinate care across the continuum as
Medicare beneficiaries who are
attributed patients transition from IOTA
waitlist patients to IOTA transplant
patients.
We requested comments on this
proposal to share beneficiaryidentifiable claims data with IOTA
participants at § 512.440(b)(5)(ii).
The following is a summary of the
public comments we received on the
proposal to share beneficiaryidentifiable claims data with IOTA
participants and our responses:
Comment: A few commenters
expressed support for the proposal to
share certain beneficiary-identifiable
claims data with IOTA participants. A
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96391
commenter indicated that more data
delivered more frequently to ensure
timely opportunity to influence
performance would be more beneficial.
Response: We thank the commenters
for their support for the proposal to
share certain beneficiary-level data
under this model and will strive to
deliver data to IOTA participants in a
timely manner to assist in their
performance under the model. We have
committed to a minimum data set and
this specific frequency to allow for
potential operational challenges or
delays.
After consideration of the comments
received, we are finalizing our
regulation at § 512.440 (b)(5)(ii) to share
certain beneficiary-identifiable claims
data with IOTA participants, without
modification.
c. Minimum Necessary Data
We proposed IOTA participants must
limit their beneficiary-identifiable data
requests to the minimum necessary to
accomplish a permitted use of the data.
We proposed the minimum necessary
Parts A and B data elements may
include, but are not limited to, the
following data elements:
• Medicare beneficiary identifier (ID).
• Procedure code.
• Gender.
• Diagnosis code.
• Claim ID.
• The from and through dates of
service.
• The provider or supplier ID.
• The claim payment type.
• Date of birth and death, if
applicable.
• Tax Identification Number (TIN).
• National Provider Identification
(NPI).
We proposed the minimum necessary
Part D data elements may include, but
are not limited to, the following data
elements:
• Beneficiary ID.
• Prescriber ID.
• Drug service date.
• Drug product service ID.
• Quantity dispensed.
• Days supplied.
• Brand name.
• Generic name.
• Drug strength.
• TIN.
• NPI.
• Indication if on formulary.
• Gross drug cost.
We requested comment and feedback
on the minimum beneficiaryidentifiable claims data necessary for
IOTA participants to request for
purposes of conducting permissible
health care operations purposes under
this model.
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We received no public comments on
our proposed provisions regarding the
minimum beneficiary-identifiable
claims data necessary for IOTA
participants to request for purposes of
conducting permissible health care
operations under this model. Thus, we
are finalizing the proposed provisions at
§ 512.440(b)(ii)(6), without
modification.
d. Medicare Beneficiary Opportunity To
Decline Data Sharing
As described in section III.C.10.a. of
this final rule, we proposed that
Medicare beneficiaries must receive
notification about the IOTA Model. We
also proposed that Medicare
beneficiaries must be given the
opportunity to decline claims data
sharing, and instructions on how to
inform CMS directly of their preference.
We proposed that Medicare
beneficiaries would be notified about
the opportunity to decline claims data
sharing through the proposed
notifications discussed in section
III.C.10.a. of this final rule. We proposed
that these notifications must state that
the IOTA participant may have
requested beneficiary identifiable claims
data about the Medicare beneficiary for
purposes of its care coordination and
quality improvement work and/or
population-based activities relating to
improving health or reducing health
care costs, and inform the Medicare
beneficiary how to decline having his or
her claims information shared with the
IOTA participant in the form and
manner specified by CMS. We proposed
that Medicare beneficiary requests to
decline claims data sharing would
remain in effect unless and until a
beneficiary subsequently contacts CMS
to amend that request to permit claims
data sharing with IOTA participants.
As discussed in the proposed rule (89
FR 43577), we proposed that Medicare
beneficiaries may not decline to have
the aggregate, de-identified data
proposed in section III.C.7.f. of the
proposed rule shared with IOTA
participants. We also proposed that
Medicare beneficiaries may not decline
to have the initial attribution lists,
quarterly attribution lists, or annual
attribution reconciliation list as
proposed in section III.C.4.b.(2)., b.(3).
and b.(4). of this final rule shared with
IOTA participants. We noted that, in
accordance with 42 U.S.C. 290dd–2 and
its implementing regulations at 42 CFR
part 2, CMS would not share beneficiary
identifiable claims data relating to the
diagnosis and treatment of substance
use disorders under this model.
In section III.C.7(d) of the proposed
rule, we noted that the proposed opt out
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provisions discussed in this section
would relate only to the proposed
sharing of beneficiary-identifiable data
between the Medicare program and the
IOTA participant under the IOTA
Model, and were in no way intended to
impede existing or future data sharing
under other authorities or models.
We requested comment and feedback
on our proposed policies to enable
Medicare beneficiaries to decline data
sharing under the model.
We received no comments on this
proposal and therefore are finalizing the
proposed provisions to allow Medicare
beneficiaries to decline data sharing at
§ 512.440(b)(ii)(7), without
modification.
e. Data Sharing Agreement
(1) General
As noted in section III.C.7.a. of this
final rule, we proposed that, prior to
receiving any beneficiary-identifiable
data, IOTA participants would be
required to first complete, sign, and
submit—and thereby agree to the terms
of—a data sharing agreement with CMS.
We proposed that under the data
sharing agreement, the IOTA participant
would be required to comply with the
limitations on use and disclosure that
are imposed by HIPAA, the applicable
data sharing agreement, and the
statutory and regulatory requirements of
the IOTA Model. We also proposed that
the data sharing agreement would
include certain protections and
limitations on the IOTA participant’s
use and further disclosure of the
beneficiary-identifiable data and would
be provided in a form and manner
specified by CMS. Additionally, we
proposed that an IOTA participant that
wishes to retrieve the beneficiary
identifiable-data would be required to
complete, sign, and submit to CMS a
signed data sharing agreement at least
annually. We stated that we believe that
it is important for the IOTA participant
to complete and submit a signed data
sharing agreement at least annually so
that CMS has up-to-date information
that the IOTA participant wishes to
retrieve the beneficiary-identifiable data
and information on the designated data
custodian(s). As described in greater
detail later in this section, we proposed
that a designated data custodian would
be the individual(s) that an IOTA
participant would identify as
responsible for ensuring compliance
with all privacy and security
requirements and for notifying CMS of
any incidents relating to unauthorized
disclosures of beneficiary-identifiable
data.
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As described in section III.C.7.e(1) of
the proposed rule, CMS believes it is
important for the IOTA participant to
first complete and submit a signed data
sharing agreement before it retrieves any
beneficiary-identifiable data to help
protect the privacy and security of any
beneficiary-identifiable data shared by
CMS with the IOTA participant. We
noted that there are important
sensitivities surrounding the sharing of
this type of individually identifiable
health information, and CMS must
ensure to the best of its ability that any
beneficiary-identifiable data that it
shares with IOTA participants would be
further protected in an appropriate
fashion.
We solicited public comment on our
proposal to require that the IOTA
participant agree to comply with all
applicable laws and terms of the data
sharing agreement as a condition of
retrieving beneficiary-identifiable data,
and on our proposal that the IOTA
participant would need to submit the
signed data sharing agreement at least
annually if the IOTA participant wishes
to retrieve the beneficiary-identifiable
data.
The following is a summary of the
public comments we received on the
proposals to define the IOTA data
sharing agreement, to require
compliance with the terms of the IOTA
data sharing agreement as a condition of
retrieving the beneficiary-identifiable
data, and to require submission of the
IOTA data sharing agreement at least
annually, and our responses to these
comments:
Comment: A couple commenters
expressed support and appreciation for
the proposed protections surrounding
the sharing of beneficiary-identifiable
data with IOTA participants. A
commenter reiterated that any data
sharing should be conducted in a
manner that protects patient privacy
and allows all points of care to
maximize lessons learned and
implement quality improvement
activities. A commenter expressed
concern with prohibiting disclosures to
an individual practitioner in a treatment
relationship with the attributed patient
who is a Medicare beneficiary, or that
practitioner’s business associates.
Response: We thank the commenters
for their support and agree that
appropriate protections must be ensured
in the sharing of beneficiary-identifiable
data. We are finalizing that the data
sharing agreement will include a
provision prohibiting any further
disclosure, not otherwise required by
law, of the beneficiary-identifiable data
to anyone who is not a HIPAA covered
entity or business associate, as defined
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in 45 CFR 160.103, or who is not an
individual practitioner in a treatment
relationship with the attributed patient
who is a Medicare beneficiary, or that
practitioner’s business associates.
Therefore, this provision would not
prohibit data sharing with a covered
entity or its business associate for
treatment purposes. Such a prohibition
would be similar to that imposed by
CMS in other models tested under
section 1115A of the Act, such as the
KCC Model, in which CMS shares
certain beneficiary-identifiable data
with model participants for their health
care operations.
CMS will include this prohibition in
the data sharing agreement because
there exist important legal and policy
limitations on the sharing of the
beneficiary-identifiable data and must
carefully consider the ways in which
and reasons for which CMS would
provide access to this data for purposes
of the IOTA Model.
After consideration of the comments
received, for the reasons set forth in this
rule, we are finalizing at § 512.440(b)(8)
the provisions of the data sharing
agreement as an agreement entered into
between the IOTA participant and CMS
that includes the terms and conditions
for any beneficiary-identifiable data
shared with the IOTA participant under
§ 512.440, without modification. In
addition, we are finalizing at
§ 512.440(b)(8)(i) the proposal that the
IOTA participant would need to submit
the signed IOTA data sharing agreement
at least annually if the IOTA participant
wishes to retrieve the beneficiaryidentifiable data from CMS.
We are also finalizing at
§ 512.440(b)(8)(ii) the proposed
requirement that the IOTA participant
agree to comply with all applicable laws
and the terms of the IOTA data sharing
agreement as a condition of retrieving
the beneficiary-identifiable data.
(2) Content of the Data Sharing
Agreement
We proposed that CMS would share
the following beneficiary-identifiable
data with IOTA participants that have
requested the data and have a valid data
sharing agreement in place, as described
in more detail later in this section. We
proposed that an IOTA participant that
wishes to receive beneficiaryidentifiable data for its attributed
patients who are Medicare beneficiaries
must also agree to certain terms,
namely: (1) to comply with the
requirements for use and disclosure of
this beneficiary-identifiable data that are
imposed on covered entities by the
HIPAA regulations at 45 CFR part 160
and part 164, subparts A and E, and the
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requirements of the proposed IOTA
Model; (2) to comply with additional
privacy, security, breach notification,
and data retention requirements
specified by CMS in the data sharing
agreement; (3) to contractually bind
each downstream recipient of the
beneficiary-identifiable data that is a
business associate of the IOTA
participant, including all IOTA
collaborators, to the same terms and
conditions with the IOTA participant is
itself bound in its data sharing
agreement with CMS as a condition of
the business associate’s receipt of the
beneficiary-identifiable data retrieved
by the IOTA participant under the IOTA
Model; and (4) that if the IOTA
participant misuses or discloses the
beneficiary-identifiable data in a
manner that violates any applicable
statutory or regulatory requirements or
that is otherwise non-compliant with
the provisions of the data sharing
agreement, CMS may: (A) deem the
IOTA participant ineligible to retrieve
the beneficiary-identifiable data under
paragraph (b) of this section for any
amount of time; (B) terminate the IOTA
participant’s participation in the IOTA
Model under § 512.466; and (C) subject
the IOTA participant to additional
sanctions and penalties available under
the law.
We stated in the proposed rule that
CMS believes these proposed terms for
sharing beneficiary-identifiable data
with IOTA participants are appropriate
and important, as CMS must ensure to
the best of its ability that any
beneficiary-identifiable data that it
shares with IOTA participants would be
further protected by the IOTA
participant, and any business associates
of the IOTA participant, in an
appropriate fashion.
CMS sought public comment on the
additional privacy, security, breach
notification, and other requirements that
we would include in the data sharing
agreement. CMS has these types of
agreements in place as part of the
governing documents of other models
tested under section 1115A of the Act
and in the Medicare Shared Savings
Program. In these agreements, CMS
typically requires the identification of
data custodian(s) and imposes certain
requirements related to administrative,
physical, and technical safeguards
relating to data storage and
transmission; limitations on further use
and disclosure of the data; procedures
for responding to data incidents and
breaches; and data destruction and
retention. These provisions would be
imposed in addition to any restrictions
required by law, such as those provided
in the HIPAA privacy, security, and
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96393
breach notification regulations. We
noted that these data sharing agreement
provisions would not prohibit the IOTA
participant from making any disclosures
of the data otherwise required by law.
CMS also sought public comment on
what specific disclosures of the
beneficiary identifiable data might be
appropriate to permit or prohibit under
the data sharing agreement. For
example, we stated that CMS was
considering prohibiting, in the data
sharing agreement, any further
disclosure, not otherwise required by
law, of the beneficiary-identifiable data
to anyone who is not a HIPAA covered
entity or business associate, as defined
in 45 CFR 160.103, or to an individual
practitioner in a treatment relationship
with the attributed patient who is a
Medicare beneficiary, or that
practitioner’s business associates. Such
a prohibition would be similar to that
imposed by CMS in other models tested
under section 1115A of the Act in
which CMS shares certain beneficiaryidentifiable data with model
participants for their health care
operations.
We noted in the proposed rule that
CMS is considering these possibilities
because there exist important legal and
policy limitations on the sharing of the
beneficiary-identifiable data and CMS
must carefully consider the ways in
which and reasons for which we would
provide access to this data for purposes
of the IOTA Model. We stated that CMS
believes that some IOTA participants
may require the assistance of business
associates, such as contractors, to
perform data analytics or other
functions using this beneficiaryidentifiable data to support the IOTA
participant’s review of their care
management and coordination, quality
improvement activities, or clinical
treatment of IOTA beneficiaries. CMS
also believes that this beneficiaryidentifiable data may be helpful for any
HIPAA covered entities who are in a
treatment relationship with the IOTA
beneficiary.
We sought public comment on how
an IOTA participant might need to, and
want to, disclose the beneficiaryidentifiable data to other individuals
and entities to accomplish the goals of
the IOTA Model, in accordance with
applicable law.
Under our proposal, the data sharing
agreement would include other
provisions, including requirements
regarding data security, retention,
destruction, and breach notification. For
example, as stated in section III.C.7 of
the proposed rule, we were considering
including, in the data sharing
agreement, a requirement that the IOTA
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participant designate one or more data
custodians who would be responsible
for ensuring compliance with the
privacy, security and breach notification
requirements for the data set forth in the
data sharing agreement; various security
requirements like those found in
participation agreements for other
models tested under section 1115A of
the Act, but no less restrictive than
those provided in the relevant Privacy
Act system of records notices; how and
when beneficiary-identifiable data could
be retained by the IOTA participant or
its downstream recipients of the
beneficiary-identifiable data; procedures
for notifying CMS of any breach or other
incident relating to the unauthorized
disclosure of beneficiary-identifiable
data; and provisions relating to
destruction of the data. We stated that
these are only examples and are not the
only terms CMS would potentially
include in the data sharing agreement.
We solicited public comment on this
proposal to impose certain additional
requirements in the IOTA data sharing
agreement related to privacy, security,
data retention, breach notification, and
data destruction.
We received no comments on this
proposal and therefore are finalizing
these proposed provisions at
§ 512.440(b)(8), without modification.
f. Aggregate Data
We proposed that CMS would share
certain aggregate performance data with
IOTA participants in a form and manner
to be specified by CMS. This aggregate
data would be de-identified in
accordance with HIPAA requirements at
45 CFR 164.514(b) and would include,
when available, transplant target data.
We proposed that, for the relevant PY,
CMS would provide aggregate data to
the IOTA participant detailing the IOTA
participant’s performance against the
transplant target, as described in section
III.C.5.c.(2). of this final rule.
We sought comment and feedback on
our proposal to share aggregate data
with IOTA participants.
We received no comments on this
proposal and therefore are finalizing the
proposed provisions at § 512.440(c)
without modification.
8. Other Requirements
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a. Transparency Requirements
(1) Publication of Patient Selection
Criteria for Kidney Transplant
Evaluations
Transplant hospitals are currently
required to use written patient selection
criteria in determining a patient’s
suitability for placement on the waitlist
or a patient’s suitability for
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transplantation per the CoP (see 42 CFR
482.90). If the transplant hospital
performs living donor transplants, the
transplant hospital must use written
donor selection criteria to determine the
suitability of candidates for donation.289
The patient selection criteria must
ensure fair and non-discriminatory
distribution of organs, and the program
must document in the patient’s medical
record the patient selection criteria
used.290 Prior to placement on the
transplant hospital’s waitlist, a
prospective transplant candidate must
receive a psychosocial evaluation, if
possible.291 Before a transplant hospital
places a transplant candidate on its
waitlist, the candidate’s medical record
must contain documentation that the
candidate’s blood type has been
determined.292 In addition, when a
patient is placed on a hospital’s waitlist
or is selected to receive a transplant, the
transplant hospital must document in
the patient’s medical record the patient
selection criteria used.293 Currently, the
transplant hospital must also provide a
copy of its patient selection criteria to
a transplant patient, or a dialysis
facility, as requested by the patient or a
dialysis facility. For living donor
selection, the transplant hospital’s
living donor selection criteria must be
consistent with the general principles of
medical ethics.294 295 Transplant
hospitals must also ensure that a
prospective living donor receives a
medical and psychosocial evaluation,
document in the living donor’s medical
records the living donor’s suitability for
donation, and document that the living
donor has given informed consent.296
Available data and studies
demonstrate that disparities exist for
patients in underserved communities
who seek or are referred for, or are
evaluated for a transplant and who
eventually are placed on a transplant
waitlist and receive an organ transplant
(89 FR 43579).297 For instance, the data
289 https://www.ecfr.gov/current/title-42/section482.90.
290 Ibid.
291 Ibid.
292 Ibid.
293 Ibid.
294 OPTN. (n.d.). OPTN Policies—Living
Donation, Chapter 14. https://
optn.transplant.hrsa.gov/media/eavh5bf3/optn_
policies.pdf.
295 AMA Council on Ethical and Judicial Affairs.
(2019). AMA Code of Medical Ethics’ Opinions on
Organ Transplantation. AMA Journal of Ethics,
14(3), 204–214. https://doi.org/10.1001/
virtualmentor.2012.14.3.coet1-1203.
296 https://www.ecfr.gov/current/title-42/section482.90.
297 Park, C., Jones, M.-M., Kaplan, S., Koller, F.L.,
Wilder, J.M., Boulware, L.E., & McElroy, L.M.
(2022). A scoping review of inequities in access to
organ transplant in the United States. International
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has shown that White patients are more
likely than Black patients to be referred
for organ transplant, while Black
patients are less likely than White
patients to be referred for transplant
evaluation.298 Racial disparities also
exist in transplant wait listing, even
after correcting for SDOH.299 In
addition, there are sex and gender
disparities in access to the kidney
transplant waitlist, with men more
likely to have access compared to
women.300 Finally, a recent article in
the Journal of the American Medical
Association considers how transplant
programs factor patient financial
resources into waitlist decisions.301 The
authors’ review of several studies
suggested that socioeconomically
deprived patients were proportionally
less likely to be selected for placement
on a waitlist for an organ transplant.
They suggested, based on the strong and
consistent associations between race
and poverty, that ‘‘withholding
transplants from those with inadequate
financial resources equates to an
example of structural racism in the
health care system.’’ We refer readers to
the numerous additional studies
regarding disparities in organ
transplantation and organ donation that
are cited throughout the final rule.
In section III.C.8.a(1) of the proposed
rule, to improve transparency for those
looking to gain access to a transplant
waitlist in the transplant program
evaluation processes, we proposed to
require IOTA participants to publicly
post, on a website, their patient
selection criteria for evaluating patients
for addition to their kidney transplant
waitlist by the end of PY 1. We
proposed to finalize this requirement
only if it is not redundant with other
Journal for Equity in Health, 21(1). https://doi.org/
10.1186/s12939-021-01616-x.
298 Epstein, A.M., Ayanian, J.Z., Keogh, J.H.,
Noonan, S.J., Armistead, N., Cleary, P.D.,
Weissman, J.S., David-Kasdan, J.A., Carlson, D.,
Fuller, J., Marsh, D., & Conti, R.M. (2000). Racial
Disparities in Access to Renal Transplantation—
Clinically Appropriate or Due to Underuse or
Overuse? New England Journal of Medicine,
343(21), 1537–1544. https://doi.org/10.1056/
nejm200011233432106.
299 Ng, Y.-H., Pankratz, V.S., Leyva, Y., Ford, C.G.,
Pleis, J.R., Kendall, K., Croswell, E., Dew, M.A.,
Shapiro, R., Switzer, G.E., Unruh, M.L., &
Myaskovsky, L. (2019). Does Racial Disparity in
Kidney Transplant Wait-listing Persist After
Accounting for Social Determinants of Health?
Transplantation, 1. https://doi.org/10.1097/
tp.0000000000003002.
300 Ahern, Patrick et al. Sex Disparity in
Deceased-Donor Kidney Transplant Access by
Cause of Kidney Disease. 2021. Clinical Journal of
the American Society of Nephrology. 16 (2) 241–
250, https://cjasn.asnjournals.org/content/16/2/241.
301 Wadhwani, S.I., Lai, J.C., & Gottlieb, L.M.
(2022). Medical Need, Financial Resources, and
Transplant Accessibility. JAMA, 327(15), 1445.
https://doi.org/10.1001/jama.2022.5283.
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HHS guidance. We also considered
requiring that IOTA participants update
their selection criteria at a certain
frequency to ensure that attributed
patients have the most up to date
information. However, we are unsure
what cadence of update would be most
appropriate.
We solicited public comments on this
proposal and on how often the selection
criteria should be updated by the IOTA
participant.
The following is a summary of the
comments received on our proposal to
require IOTA participants to publicly
post their patient selection criteria for
kidney transplant waitlist candidates on
a website and the frequency at which
updating this information should occur
and our responses:
Comment: Many commenters stated
they support the publication of patient
selection criteria for kidney transplant
evaluations. A commenter specified that
it could help reduce distrust around
organ transplant decisions.
Response: We thank the commenters
for their support. We agree that posting
patient selection criteria for evaluating
patients for addition to a waitlist will
help reduce distrust about organ
transplant decisions.
Comment: A commenter suggested
that patient selection criteria should be
posted in common languages of the
local community and that any written
materials be delivered in patients’
preferred language.
Response: We thank the commenter
for their suggestion. We agree that
public facing patient selection criteria
for evaluating patients for addition to a
waitlist should be made available in
local languages and should be
compliant with regulations requiring
patients to have written information in
their preferred language.
Comment: Numerous commenters
were concerned about the impact of
publicly posted patient selection criteria
on their patients. A commenter was
concerned that overwhelming patients
with selection criteria published on a
public-facing website is not patientcentered, does not promote autonomy
and impacts the patient-provider
relationship. Similarly, a commenter
conveyed their concern that there is a
significant risk of misinterpretation of
the selection criteria by referring
providers in the community and
patients, which may decrease referrals.
Additionally, a commenter was
concerned that public disclosure of
waitlist selection criteria that only
applies to IOTA participants, does not
help patients who may live in a region
with access to more than one kidney
transplant hospital.
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Response: We thank the commenters
for their responses and concerns. We
believe that providing patient selection
criteria for evaluating patients for
addition to a waitlist publicly creates
transparency for both patients and for
their referring nephrologists. Referring
nephrologists have more patient contact
than a transplant nephrologist at time of
referral, and therefore are key in
referring patients for kidney transplant
evaluation and in having the ability to
guide the patient to the kidney
transplant hospital that may be most
ideal for the patient. With the
overwhelming amount of information
that a kidney transplant patient learns
during their multi-hour initial
transplant evaluation, we believe that
resources to encourage early transplant
discussions between a referring
nephrologist and patient can create
opportunities for a more fruitful
evaluation experience for the patient.
This may also open communication
between transplant nephrologists and
referring nephrologists. We agree that
potential transplant candidates and
selection criteria can be extremely
complex and vary on a case-by-case
basis; however, we believe that
providing general expectations for
kidney transplant candidacy is by no
means unreasonable and can make the
evaluation process more efficient. For
example, if a kidney transplant hospital
will definitively not transplant a patient
with a certain co-morbidity, whereas
another kidney transplant hospital may,
this can be extremely helpful for a
patient to know before taking off from
work or a dialysis session and
organizing transportation or both for a
kidney transplant hospital that is
hundreds of miles away. Sometimes it
may take months to schedule specialist
visits or preventative health screenings,
needed for transplant waitlisting.
Listing selection waitlist criteria can
help patients anticipate what
appointments they may need to
schedule. We understand there are
‘‘gray’’ areas of candidacy and
subsequently have not created
prescriptive requirements for patient
selection lists.
Public-facing patient selection criteria
for evaluating patients for addition to a
waitlist allows patients to understand
general expectations earlier in their
transplant evaluation journey, ensures
keeping criteria up to date, and provides
greater access and autonomy to patients.
While non-participants of the IOTA
Model are not mandated by this
requirement, we suggest that other
kidney transplant hospitals follow suit.
Comment: A commenter was
concerned that public posting of kidney
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transplant waitlist selection criteria
policy is redundant since it is already
available publicly through groups such
as CMS, HRSA, UNOS and OPTN.
Response: We thank the commenter
for their concern. While 42 CFR 482.90
already requires documentation of
selection criteria within the patient’s
medical record upon placement on the
waiting list, it does not specify the need
for publicly posting patient selection
criteria decisions.302 Currently, there is
not a centralized site listing all
transplant programs’ selection criteria.
Patients have access to their medical
records through patient portals or can
alternatively access a hard copy of their
records by request. We believe it is also
important that the patient has access to
this information before the visit. We
also believe that public facing listing
criteria provides greater access to
patients who may not be able to easily
access their patient portal, reducing
disparities.
Comment: A commenter suggested
that CMS would need to closely monitor
this transparency requirement and
penalize IOTA participants that do not
comply.
Response: Thank you for your
responses regarding monitoring for
compliance. We agree that long term
there will need to be monitoring and
auditing to ensure that IOTA
participants are compliant with listing
their selection criteria. We are hopeful
to receive further feedback throughout
and after PY 1 to modify this
requirement to be as specific as is
reasonable to ensure compliance.
Additionally, we are hopeful that there
is opportunity to have a collective site,
which would feature all IOTA
participants’ selection criteria on one
website.
Comment: A couple of commenters
were concerned by the differences in
self-reported listing criteria versus
characteristics of patients that are
ultimately listed. One of these
commenters recommended that CMS
focus on the data of waitlist patients. A
commenter stated that CMS should also
consider the differences in the criteria
for accepting a referral, evaluating the
patient, and listing the patient.
Response: We thank the commenters
for their feedback. We recognize there
are limitations in mandating public
posting of selection criteria and that
there is discordance between selfreported kidney transplant hospital
listing criteria and the actual
characteristics of their listed patients for
transplant. While we acknowledge that
302 https://www.ecfr.gov/current/title-42/section482.90.
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it may be challenging to package
numerous patient co-morbidities into an
easily digestible and reasonable list of
selection criteria, we believe that
exercising a requirement to bring
transparency to selection criteria will
also assist kidney transplant hospitals in
tailoring those criteria and be as specific
as possible. To avoid deterring referrals
of possible transplants, we have not
considered posting referral requirements
at this time and will not do so without
further consideration and input from the
transplant community. We do, however,
believe it would be greatly beneficial for
kidney transplant hospitals to outline
the difference between referral,
evaluation and listing on their website
and additionally review this
information during every patient’s
transplant evaluation visit.
Comment: A couple of commenters
included their support for the
development of a centralized,
standardized way to present information
about transparency requirements such
as selection criteria and bypass filters. A
commenter further recommended that
patient education surrounding this
transparency information should be
created by a centralized group (such as
OPTN or SRTR) to reduce kidney
transplant hospital burdens.
Response: We agree that a centralized
location for waitlist selection criteria
and organ offer acceptance criteria
would be ideal and are hopeful that the
transplant community can move toward
a database that is accessible to patients
and providers or both that will provide
this information; however, we do not
believe that this is necessary for PY 1 for
IOTA participants. We believe it is
reasonable and not overly burdensome
to request IOTA participants to post
their selection criteria on their website.
We intend to continue discussions
about a centralized database for patient
waitlist selection criteria and will
consider this for future rulemaking,
Comment: A commenter suggested
that IOTA participants should be
required to conduct targeted outreach to
non-citizens and other underserved
communities to provide clarifications
and education on transplant.
Response: We appreciate the
commenter’s feedback. We believe it is
in the purview of individual IOTA
participants to have outreach events to
serve their community. Currently the
IOTA Model does not outline the topic
of educational outreach; however, we
will take this comment into
consideration for future rulemaking
since patient education is extremely
important throughout the continuum of
kidney care and is needed to expand
equal access to transplant. Additionally,
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please note that community outreach
would be a potential opportunity for
IOTA participant to consider as part of
the voluntary health equity plans in the
IOTA Model, as reviewed in section
III.C.8.c of this final rule.
Comment: A commenter requested
that CMS provide flexibility regarding
the frequency of updating waitlist
selection criteria. A couple of
commenters were concerned with
balancing accurate information with
resource burden.
Response: We appreciate the
commenter’s response regarding
frequency of waitlist criteria updates
and type of information included.
Beyond requirements previously
outlined in 42 CFR 482.90, we have not
provided specific requirements that
IOTA participants must include
regarding listing practices.303 We do,
however, expect and trust that IOTA
participants are acting in good faith to
provide accurate waitlisting criteria and
specific details, when possible. While
we did not propose a specific cadence
as to how frequently IOTA participants
should be required to update their
selection criteria after PY 1, we will take
these comments into consideration
during future rulemaking. We do not
believe that requesting a public online
posting about patient waitlist selection
criteria by the end of PY 1, is overly
burdensome to IOTA participants, as
IOTA participants are already expected
to provide these criteria in patient
waitlist documentation. We are
finalizing this requirement as originally
proposed in section III.C.8.a(1) of the
proposed rule, for PY 1, without
modification.
Comment: A commenter suggested
that waitlist selection criteria should
include specific details such as absolute
contraindications of IOTA participants
(for example, BMI limits), whether there
are financial reserve requirements, and
if other factors such as psychiatric or
psychosocial factors impact listing.
Response: We thank the commenters
for their recommendations. Beyond
requirements previously outlined in 42
CFR 482.90, CMS has not provided
specific requirements that IOTA
participants must include regarding
listing practices.304 We do believe,
though, that if IOTA participants have a
list of absolute versus relative
contraindications for their patients, it
would be beneficial to make patients
303 https://www.ecfr.gov/current/title-42/section482.90.
304 https://www.ecfr.gov/current/title-42/section482.90.
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and referring nephrologists aware of
these concerns.
While we agree that it could be
helpful for patients to understand
specific psychosocial and psychiatric
requirements, we believe that this could
be challenging given the
multidimensional evaluation that is
completed during transplant evaluation
and the complexity of understanding
each individual’s situation.
Additionally, psychiatric and
psychosocial diagnoses can be fluid,
and we would not want to discourage
patients from transplant evaluation,
particularly since they may learn about
helpful resources during the evaluation.
A goal of the IOTA Model is to reduce
disparities in kidney transplant, and we
believe that listing granular
psychosocial or psychiatric
requirements could be contradictory to
these goals.
Listing specific financial requirements
could be helpful if transplant programs
have absolute cutoffs for transplant
recipients; however, if patients do not
initially meet financial requirements,
transplant program resources (financial
counselor, social workers) may be able
to help that patient create a financial
plan to meet that requirement. We will
take this comment into consideration for
future iterations of the IOTA Model and
encourage additional feedback from
kidney transplant hospitals during PY 1.
Comment: A commenter suggested it
may be easier if CMS created a list of
criteria that each IOTA participant
needs to address in the selection
criteria.
Response: We thank you for your
comment. As previously mentioned in
section III.C.8.a.(1) of this final rule, 42
CFR part 428.90 does outlines basic
requirements for kidney transplant
evaluation.305 Currently, we believe that
being prescriptive beyond these
requirements prevents kidney transplant
providers and kidney transplant
hospitals from creating selection criteria
applicable to risk level they believe is
appropriate based on their resources
and their community. We believe that
including referring nephrologists in
conversations regarding specific listing
criteria could be helpful, however, we
are not mandating this.
After consideration of public
comments, for the reasons set forth in
this rule, we are finalizing the
requirement that IOTA participants
must publicly post their patient
selection waitlist criteria on a website
by the end of PY 1 at § 512.442(a),
without modification. We intend to use
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future rulemaking to determine the
cadence of updating this website and
patient selection criteria. For IOTA
participants who choose to post their
patient selection criteria for evaluating
patients for addition to their kidney
transplant waitlist early in the PY 1, we
also encourage them to update their
criteria again, should it change
throughout the year.
(2) Transparency Into Kidney
Transplant Organ Offers
As discussed in section III.C.8.a(2) of
the proposed rule, those active on a
kidney transplant waitlist may receive
organ offers at any time. However, there
is currently no requirement for
providers to discuss organ offers with
their patients. A provider may decline
an organ offer for any number of
reasons; however, declining without
disclosing the rationale with the patient
may miss an important opportunity for
shared decision-making.
In section III.C.8.a(2) of the proposed
rule, we proposed to add requirements
to increase transparency for IOTA
waitlist patients who are Medicare
beneficiaries regarding the volume of
organ offers received on their behalf
while on the waitlist. Specifically, we
proposed that for each month an organ
is offered for an IOTA waitlist patient
who is a Medicare beneficiary, an IOTA
participant must inform the Medicare
beneficiary, on a monthly basis, of the
number of times an organ is declined on
the Medicare beneficiary’s behalf and
the reason(s) for the decline. We are not
proposing to prescribe the method of
this notification but would require that
the medical record reflect that the
patient received this information and
the method by which it was delivered
(for example, mail, email, medical
appointment, internet portal/dashboard,
etc.). We proposed that this information
must be shared with the IOTA waitlist
patient who is a Medicare beneficiary,
and should be shared, where deemed
appropriate, with their nephrologist or
nephrology professional, to provide the
opportunity for questions and
clarification of information.
Organ offer filters are a tool that
transplant programs can use to bypass
organ offers they would not accept.
Offer filters were tested during two pilot
programs and released nationally in
January 2022.306 In section III.C.8.a(2) of
the proposed rule, we proposed that
IOTA participants would be required to
review transplant acceptance criteria
306 Optimizing Usage of Kidney Offer Filters—
OPTN. (n.d.). Optn.transplant.hrsa.gov. Retrieved
March 11, 2023, from https://
optn.transplant.hrsa.gov/policies-bylaws/publiccomment/optimizing-usage-of-kidney-offer-filters/.
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and organ offer filters with their IOTA
waitlist patients who are Medicare
beneficiaries at least once every 6
months that the Medicare beneficiary is
on their waitlist. We proposed that this
review may be done on an individual
basis in a patient visit, via phone, email,
or mail. We believed that sharing this
information with the patient would offer
an opportunity for shared decisionmaking between the patient and IOTA
participants and may increase the
patient’s quality of care. We proposed
that Medicare beneficiaries would be
able to decline this review with the
IOTA participant, as some may not wish
to have this information. We anticipated
that the Medicare beneficiary may
decline this review during their next
provider visit or over the phone.
We solicited public comment on
whether an alternative frequency of
sharing of organ offers with the
Medicare beneficiary is more
appropriate. We also solicited comment
on whether there is a more suitable
timeframe and frequency for addressing
acceptance criteria with attributed
patients. Per 42 CFR 482.94(c), and
482.102(a) and (c), kidney transplant
hospitals currently review these criteria
with patients upon patient request. Our
goal was to provide a balance of
transparency and patient engagement in
this process without being overly
prescriptive or burdensome. We also
recognized that there are beneficiaries
on the waitlist who may not be eligible
to receive an organ offer for multiple
years, so we sought feedback on
whether this requirement should be
limited to beneficiaries who have
received or are likely to receive an organ
offer in the next year.
The following is a summary of
comments we received on our proposal
to (1) require monthly notifications to
Medicare beneficiaries receiving organ
offers who are IOTA waitlist patients
about number of organs declined and
the rationale for the decline and to (2)
require review of transplant acceptance
criteria and organ offer filters with their
IOTA waitlist patients who are
Medicare beneficiaries at least once
every 6 months that the Medicare
beneficiary is on their waitlist and our
responses:
Comment: Commenters expressed
concern about the proposed
transparency into kidney transplant
organ offers provision, which would
require IOTA participants to inform, on
a monthly basis, IOTA waitlist patients
who are Medicare beneficiaries of the
number of times an organ is declined on
the patient’s behalf and the reason(s) for
the decline. Specifically, commenters
felt this would impose a significant
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administrative burden on IOTA
participants. Some of these commenters
were concerned that notifying waitlisted
Medicare patients of organ offer
declines and the reasons for those
declines would be burdensome, costly,
and of questionable value. This was
seen as at odds with the IOTA Model’s
quality and efficiency domain goals and
was seen as disproportionately
burdensome to smaller transplant
hospitals. Commenters also noted that
the provision does not account for the
clinical and administrative resources
needed to review the high volume of
organ declines across all waitlisted
individuals. This could divert resources
away from patient care. Furthermore, a
commenter stated that patient care
groups are more interested in data on
time-to-transplant and likelihood of
receiving a transplant, which are
already publicly available.
Response: We thank the commenters
for their concerns. Due to the many
concerns received, we recognize that
monthly notification to Medicare
beneficiaries regarding volume and
reason for organ decline could be very
burdensome to IOTA participants and
their staff in PY 1 since this is a new
initiative and there is not current
infrastructure or database resources to
aid in minimizing burden on IOTA
participants. We believe we need more
time to better identify how we can
increase transparency of the organ offer
process for transplant recipients with
the help of the transplant community.
Minimizing administrative burden for
kidney transplant hospitals while
maximizing meaningful communication
with beneficiaries will be key in these
discussions as the transplant
community participates in this dialogue.
Subsequently, we will not be finalizing
our regulation at proposed § 512.442(b),
which required that Medicare
beneficiaries on the IOTA participant’s
waitlist be notified monthly about organ
offers. We look forward to engaging in
conversation with transplant
stakeholders to understand additional
transparency opportunities to mutually
meet patient and provider goals, prior to
potentially revisiting this in future
rulemaking.
Comment: A commenter expressed
concern that discussions about organ
offer filters, while allowing patients to
influence decisions, may not provide
providers with enough data to fully
inform and engage patients. For
example, providers may lack
information on how these filters impact
wait times. The commenter suggested
this could prevent patients from
believeing they can meaningfully
contribute to shared decision-making.
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Response: We appreciate the
commenter’s feedback and subsequently
recognize that our proposal to require
IOTA participants to review transplant
acceptance criteria and organ offer
filters with their IOTA waitlist patients
who are Medicare beneficiaries requires
clarification. We also acknowledge that
explaining the organ offer filter itself
may not promote the same outcome as
sharing the impact of organ offer
acceptance criteria. In light of this, we
are finalizing our review of selection
criteria and organ offer filters provisions
with slight modifications. Specifically,
we are finalizing at § 512.442(c) that
IOTA participants must review
transplant organ offer acceptance
criteria (rather than acceptance criteria
and organ offer filters) with their IOTA
waitlist patients who are Medicare
beneficiaries at least once every 6
months that the Medicare beneficiary is
on their waitlist. Additionally, we are
removing all references to organ offer
filters.
Regarding the commenter’s concern
that they may not have enough
information to share with patients
regarding organ offer filters, we believe
that generally discussing organ offer
acceptance criteria is a first step in
increasing patient’s awareness about
why certain organs may or may not be
accepted at a particular transplant
program. As IOTA participants may
choose to analyze data to better
understand ideal organ offer filters,
these findings can be used as supporting
evidence when explaining to
beneficiaries why their transplant
program for example, may not accept
kidney transplant with a particular cold
ischemic time.
Comment: A commenter agreed that
organ offer filters should be reviewed
with patients at least every 6 months to
strengthen their original education.
Response: We thank the commenter
for their support. We recognize that
explaining the organ offer filter itself
may not promote the same outcome as
sharing the organ offer acceptance
criteria. Subsequently, we are finalizing
and clarifying that reviewing organ offer
acceptance criteria (rather than the filter
itself), with IOTA waitlist patients who
are Medicare beneficiaries at least every
6 months, will meet this requirement.
We suspect that IOTA participants will
have more frequent changes in their
organ offer filters during the first few
years of the IOTA Model as kidney
transplant hospitals optimize their
practices.
Comment: A commenter expressed
support for reviewing transplant organ
offer acceptance criteria with IOTA
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waitlist patients who are Medicare
beneficiaries every six months.
Response: We thank the commenter
for their support.
Comment: A commenter argued that
operationalizing the proposed
transparency into kidney transplant
organ offers would be more efficiently
achieved by directing the OPTN to
develop a patient portal. This portal
would allow patients to view their own
organ offer filters and organ decline
statistics online, rather than requiring
each IOTA participant to develop their
own reporting system. The commenter
emphasized that this approach would
promote patient engagement, education,
and accountability at kidney transplant
hospitals, as patients would be able to
access the information themselves.
Overall, the commenter felt this would
be both more efficient and more
effective in achieving the desired result
of increased transparency.
Response: We thank the commenter
for their valuable suggestions. We
recognize the importance of delivering
consistent messages about patient
education and matters such as organ
offer filters, organ offer acceptance
criteria, and declined organ offers. As
we continue our collaborative work
with OTAG, we will carefully consider
these recommendations. Additionally,
we encourage IOTA participants to
discuss this proposal within the IOTA
Model learning system. We direct
readers to section III.C.15 of this final
rule for a full discussion on the IOTA
Model learning system.
Comment: A few commenters
suggested reviewing acceptance criteria
and declined organ offers during key
timeframes, such as transplant
evaluation, annual waitlist visits, or
when first listed on the waiting list. For
example, a commenter, while
supporting transparency, encouraged
upfront communication with patients
about organ offer practices during
evaluation and annual visits. As an
alternative, this commenter
recommended that IOTA participants be
required to educate patients on the
organ offer process, declines, and
patients’ right to information—with
IOTA participants providing specific
details upon patient request.
Another commenter expressed
support for sharing organ offer filters
and transplant acceptance criteria with
patients. However, the commenter
recommended IOTA participants review
these details with patients when they
are first listed on the waiting list, and
update patients if any changes are made.
For patients who want information
about declined offers, the commenter
suggested discussing their transplant
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acceptance criteria periodically as they
receive that information. For patients
who opt out of declined offer details or
do not discuss them with the IOTA
participant, the commenter
recommended an annual review of their
organ offer filters and transplant
acceptance criteria (or at the time of reevaluation, whichever comes first).
Additionally, the commenter supported
CMS’s proposal to allow patients to
decline this review altogether. Lastly, a
commenter suggested that IOTA
participants review organ offers
received with their waitlisted patients
during annual or biannual waitlist
visits. The commenter asserted that this
would give patients the chance to
discuss any changes to their organ offer
acceptance criteria and ask their
provider questions directly.
Response: We appreciate the valuable
feedback from commenters. Although
many kidney transplant hospitals see
their waitlisted patients at least
annually, this practice is inconsistent.
Waitlist patient visit frequency can also
vary depending on the patient’s active
or inactive waitlist status. To better
inform patients about organ offers and
the reasons for declining them, beyond
the initial evaluation and waitlist clinic
visits, we proposed more frequent
patient notifications, as described in
section III.C.8.a(2) of this final rule. In
light of the comments received, we
recognize that successfully
implementing an organ offer notification
process will require more extensive
planning. Therefore, we will not be
finalizing the transparency into kidney
transplant organ offer provisions at
proposed § 512.442(b). However, we
remain committed to increasing
communication and engagement with
patients on the kidney transplant
waitlist.
Regarding the proposed review of
acceptance criteria and organ offer
filters transparency requirement, as
described in section III.C.8.a(2) of this
final rule, we believe it is important to
finalize this provision for several key
reasons: (1) it should not create a
significant administrative burden; (2) it
provides the building blocks of
education for IOTA waitlist patients;
and (3) due to other themes of the IOTA
Model that may impact organ offer filter
use, we believe reviewing organ offer
acceptance criteria with patients every 6
months is appropriate. As mentioned in
comment responses in this section, we
also recognize that explaining organ
offer filters with waitlisted patients may
not promote the same outcome as
reviewing organ offer acceptance
criteria. As such, we will be finalizing
our proposed review of acceptance
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criteria provision at § 512.442(c) with
minor technical corrections.
Specifically, we added ‘‘organ offer’’ to
transplant acceptance criteria that must
be disclosed and removed all references
to ‘‘organ offer filters’’. Additionally, we
will provide further sub-regulatory
guidance on how IOTA waitlist patients
who are Medicare beneficiaries can
choose to decline the review of their
transplant organ offer acceptance
criteria.
Comment: Several commenters
recommended organ offer inclusion or
exclusion criteria for the proposed
transparency into kidney transplant
organ offer provision. The commenters
believed the proposed notification
requirement should be limited to
minimize administrative burden. Their
suggested inclusion criteria were: (1) if
the patient is the primary recipient, or
(2) if the kidney offer is declined by one
hospital but used by another. Their
suggested exclusion criteria included:
(1) kidneys outside a 250-mile radius,
(2) discarded kidneys, (3) kidney organ
offers that were declined by all kidney
transplant hospitals on the match run,
or (4) patients removed from a waitlist
before a monthly reporting period
concluded. Several commenters replied
about the inclusions and exclusions
from notification requirements.
Response: We appreciate the
commenters’ feedback. We reiterate that,
as mentioned in comment responses in
this section, we are not finalizing the
proposed transparency organ offer
notification provision at proposed
§ 512.442(b). We aim to engage with the
transplant community to identify
conditions that should be captured in
exclusion criteria, to inform future
rulemaking pertaining to transparency
into kidney transplant organ offers.
Comment: Some commenters
expressed concerns about the proposed
transparency into kidney organ offers
provision. In particular, they worried it
may require IOTA participants to
carefully manage how information is
shared. The commenters also mentioned
that additional security controls may be
needed to prevent donor information
from being shared with recipients.
Another commenter stated the
transparency into kidney transplant
organ offers provision should include
specific details on donor kidney offers,
to protect patient privacy and prevent
increased use of suboptimal kidneys.
Additionally, a commenter cited
safeguarding patients’ legal and ethical
rights to informed consent and
autonomy as paramount. Lastly, a
couple commenters suggested
alternatives, such as only discussing
declined organ offer review at the
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programmatic level among transplant
program providers, or using a
collaborative model with some privacy
walls while sharing select information
with patients or the public.
Response: We thank the commenters
for sharing their concerns and
suggestions about patient privacy. We
agree that patient privacy of donors and
potential recipients is paramount and
believe that safeguarding patients’ rights
to informed consent and autonomy is
imperative. However, in response to the
comments we received, as mentioned in
comment responses in this section, we
are not finalizing the proposed
transparency into kidney transplant
organ offers provision, requiring IOTA
participants to inform IOTA waitlist
patients who are Medicare beneficiaries
of the number of times an organ is
declined on the patient’s behalf, at
proposed § 512.442(b).
Comment: A few commenters
expressed concerns that the
transparency into kidney transplant
organ offers provisions are overly
complex and unnecessary. Moreover, a
commenter felt these requirements are
redundant, as transplant programs must
already provide patients access to SRTR
data resources that publicly disclose
information about their organ offer
acceptance rates.
Response: We thank the commenters
for expressing their concerns. While we
acknowledge that the new processes
needed to meet the proposed
transparency into kidney transplant
organ offer provisions (89 FR 43580)
would initially be labor-intensive or
technologically challenging, we
maintain that these requirements are
important and increase patient
awareness.
Additionally, we disagree that the
proposed transparency into kidney
transplant organ offers requirements are
redundant programmatic requirements
of providing SRTR data; providing
generalized organ offer acceptance rate
ratio data is very different from
providing direct notification to a patient
about an organ offer that was declined
on their behalf. However, based on
commenter feedback, we recognize the
complexities of notifying patients about
declined organ offers. While we are not
finalizing the proposed transparency
into kidney transplant organ offers
provisions at proposed § 512.442(b), we
remain interested in exploring
alternative ways to promote
transparency for kidney transplant
waitlist patients.
Comment: A couple commenters
urged CMS to consider how the
proposed transparency into kidney
transplant organ offers provision could
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inadvertently impact the behavior of
kidney transplant hospitals. For
example, a commenter noted that the
proposed organ offers notification
requirement emphasizes the importance
of discussing organ offer declines with
patients, which is crucial for informed
decision-making. However, the
commenter expressed concern that the
focus on organ offer declines could
deter the use of higher-risk organs,
ultimately reducing the number of
viable transplants, or kidney transplant
hospitals might potentially offer the
organ despite it not being the best fit for
the recipient.
Response: We appreciate the
commenters concerns regarding the
proposed transparency into kidney
transplant organ offers provision, as
outlined at § 512.442(b) in the proposed
rule. We agree that this provision may
impact provider and staff awareness of
consistent kidney transplant offers that
are being declined, which could affect
filtering practices. Increasing patientstaff conversations not only creates
opportunities for patients to stay better
informed about their care, but also
allows transplant staff to stay up to date
on a patient’s waitlist status and recent
medical changes. We view more
frequent patient interactions as a
positive behavioral change. As
previously discussed in comment
responses in this section, we are not
finalizing the transparency into kidney
transplant organ offers provision at
proposed § 512.442(b), however, we
continue to be committed to working
with the transplant community to
identify alternative transparency
opportunities for kidney transplant
waitlist patients.
Comment: A couple of commenters
stated that CMS should consider
alternate ways to promote transparency,
including incorporating the voices of
consumers, including patients in
community councils, inviting
community members to serve on boards
and equipping patients with data about
kidney transplant hospitals so they can
make informed decisions.
Response: We appreciate the
commenters’ feedback. We believe
direct dialogue and advocacy between
patients and kidney transplant hospitals
can enhance communication, helping
these hospitals better understand areas
needing improvement, such as
information gaps and lack of
transparency. HHS intends to make
organ offer information more easily
accessible for patients who are on the
waiting list, to minimize administrative
burden. While these concepts are not
incorporated into the IOTA Model, we
believe they are concepts that kidney
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transplant hospitals should further
consider.
Comment: A commenter expressed
concern that the proposed organ offer
notification requirement would create
disparities, as it would only apply to
Medicare patients and IOTA
participants.
Response: We thank the commenter
for sharing their concern that the
transparency into kidney transplant
organ offers provision, as proposed,
would create disparities because only
Medicare patients and IOTA patients
would be subject to the requirement.
The Innovation Center’s authority in
this proposed rule only extends to
Medicare beneficiaries, which is why
we only proposed that it apply to IOTA
waitlist patients who are Medicare
beneficiaries. However, as mentioned in
comment responses in this section, we
are not finalizing the proposed
transparency into kidney transplant
organ offers provision, requiring IOTA
participants to inform IOTA waitlist
patients who are Medicare beneficiaries
of the number of times an organ is
declined on the patient’s behalf, at
proposed § 512.442(b).
Comment: A few commenters urged
CMS to reduce the administrative
burden on IOTA participants imposed
by the proposed transparency
requirements. Suggestions included
leveraging existing technology and data,
evaluating the administrative and
financial impacts, and providing IOTA
participants with the necessary
resources to successfully implement the
proposed transparency requirements.
Several commenters supported a
centralized process to achieve
transparency, facilitated by CMS or
UNOS/OPTN, which could include
standardized patient-specific reports
using existing OPTN information, an
application programming interface, or a
patient portal.
Response: We agree that a future
centralized online resource could
improve patient access and reduce
administrative burdens for kidney
transplant hospitals by providing
patient organ offer notifications. HHS
intends to make organ offer information
more easily accessible in the future, to
minimize administrative burden for
transplant programs. As previously
mentioned in this section, we will not
be finalizing the proposed transparency
into kidney transplant organ offers
provision at proposed § 512.442(b). We
aim to examine the administrative and
financial challenges involved in
notifying patients of organ offers, and
explore how technology can be used to
reduce this administrative burden.
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Comment: A commenter expressed
support for informing patients on the
transplant waitlist, if a patient is active
on the transplant waiting list and
eligible to receive organ offers, when
those organ offers have been declined
on their behalf. The commenter argued
that transparency should not be
compromised for these patients.
Additionally, the commenter urged
CMS to hold IOTA participants
accountable for communicating a
patient’s waitlisting status when: (1) a
patient becomes inactive, including
explaining the reasons why and possible
solutions to regaining active status, if
feasible; and, (2) a patient regains active
waitlisting status after being inactivated.
Response: We thank the commenter
for their support of the proposed
transparency into kidney transplant
organ offers provision. However, as
mentioned in comment responses in
this section, we will not be finalizing
this provision at this time. We still
believe that it is important to increase
transparency for kidney transplant
waitlist patients regarding the volume of
organ offers received and declined on
their behalf while on the waiting list.
We also value the commenter’s
recommendation to hold IOTA
participants accountable for
communicating a patient’s waitlisting
status. We acknowledge the importance
of patient awareness regarding their
waitlist status, an aspect that is often
overlooked. Additionally, we recognize
the significant number of inactive
patients on the waiting list, many of
whom may be unaware of their inactive
status or the reasons behind it. This
aligns with our goal of promoting
transparency and SDM between the
patient and IOTA participants. We will
consider the commenter’s suggestion
along with the public comments on the
proposed transparency requirements
and may make future proposals during
the course of the model test.
Comment: A commenter asserted that
CMS could achieve the goals of the
proposed transparency into kidney
transplant organ offers requirements
without significantly increasing the
administrative burden on participating
kidney transplant hospitals. Instead of
the proposed requirements, the
commenter recommended that CMS
mandate a discussion about offer
screening during the patient consent
process. Additionally, the commenter
suggested that participating kidney
transplant hospitals be required to
document these discussions, include
them in their records, or address them
with patients during evaluations or once
they are placed on the waitlist.
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Response: We thank the commenters
for their suggestions. However, we are
concerned that organ offer discussions
at the time of initial evaluation for
transplant candidacy, while a good start,
is insufficient for patient education.
Patients often feel overwhelmed by the
extensive transplant education they
receive when first considering a kidney
transplant. This can be especially
challenging for those who have recently
been diagnosed with kidney disease,
making the prospect of transplant seem
particularly daunting. While
comprehensive education at the time of
evaluation and waitlist is important, we
believe patients would benefit from
more frequent, ongoing guidance about
organ offers, acceptance criteria, and
deferral tendencies throughout the
listing process. As previously
mentioned in comment responses in
this section, we will not be finalizing
the transparency into kidney transplant
organ offers provisions at proposed
§ 512.442(b) at this time due to the
aforementioned concerns. We are
committed to exploring new ways to
increase transparency in collaboration
with the transplant community.
Comment: A commenter highlighted
that they previously urged CMS to
mandate greater transparency about the
risk aversion of transplant hospitals and
surgeons. This transparency, the
commenter argued, would allow
patients to find a transplant hospital
that aligns with their personal risk
tolerance. While the commenter
welcomed the IOTA Model’s proposal to
include two such transparency policies,
they strongly disagreed with the policies
being part of a demonstration rather
than a nationwide requirement.
Response: We thank the commenter
for their support. The Innovation Center
is limited in exercising authority
specific to Medicare beneficiaries and is
unable to create nationwide mandates
for patients with all types of insurance
coverage. However, successful
Innovation Center models are often
reviewed and discussed as
opportunities to expand to the nation
through other policies. While we are not
finalizing the proposed transparency
into kidney transplant organ offers
requirements at § 512.442(b) of the
proposed rule, we hope that transplant
hospitals who are not selected to
participate in the IOTA Model will
consider integrating IOTA Model
concepts into their kidney transplant
hospital.
Comment: A few commenters
mentioned that modifications to the
transparency requirements were needed
or that the transparency into kidney
transplant offers provision should be
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eliminated entirely but did not provide
further suggestions or justification.
Response: We thank the commenters
for the feedback. We are interested in
understanding the commenters’ specific
modification suggestions and invite
them to provide further details in the
future.
Comment: Several commenters
supported the provision requiring
transparency into kidney transplant
organ offers, with some of them
specifying that providing Medicare
beneficiaries the option to be informed
about organs that were declined on their
behalf supports increased
communication and shared decision
making between patients and providers.
One of these commenters also believed
that increasing transparency would hold
kidney transplant hospitals accountable,
drive ongoing improvements across the
transplant system and help eliminate
health disparities.
Response: We greatly appreciate the
commenters’ words of support;
however, we are not finalizing this
provision. We look forward to future
feedback as we work to create
transparency requirements that are not
unduly burdensome. We remain
invested in evaluating alternative
transparency opportunities with the
transplant community.
Comment: A couple of commenters
conveyed concerns with barriers to
patient receipt of transparency
notifications, stating that IOTA
participants may use automated
notifications in place of the meaningful
communication that would be required
to provide quality care. A commenter
was specifically concerned by technical
barriers reaching patients, such as
outdated contact information.
Response: We agree these are valid
challenges with all types of patient
communications. While automated
notifications may be preferred by some
patients, it may further worsen
disparities in already vulnerable
populations. We recognize that
disparities in access to technology can
limit certain patients, making phone
calls or other methods of contact
necessary. Patient portals may provide a
source of quick, easy access to
information; however, this can prevent
real-time discussions. This concern is
one of the reasons that we will not be
finalizing the proposed transparency
into kidney transplant organ offers
provision as proposed at § 512.442(b).
We look forward to engaging with
kidney transplant hospitals to identify
and share efficient yet appropriate
methods for equitably notifying and
making patients aware of declined
kidney transplant organ offers, without
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creating disparities for those who may
not have access to technology.
Comment: Several commenters
suggested CMS modify the transparency
into kidney transplant organ offers
provision, which would require IOTA
participants to inform, on a monthly
basis, IOTA waitlist patients who are
Medicare beneficiaries of the number of
times an organ is declined on the
patient’s behalf and the reason(s) for the
decline. Specifically, they suggested
that organ offer declines should be
shared only to a certain sequence
number in the match run, keeping the
information to a manageable amount
and focusing on organs that the patient
had a reasonable likelihood of receiving.
Suggested notification thresholds
included the top 5, 100, 150, or 200
matches of the match run, or only when
the organ was used for a transplant
candidate positioned further down on
the waiting list. For example, a
commenter suggested that since a
quarter of organ offers are accepted at or
after having been offered to 73
transplant candidates, organ offer
declines should be shared with
transplant candidates up to match run
sequence 150, which is about 73
doubled. Alternatively, the commenter
suggested that CMS could mirror the
SRTR definition of a hard-to-place
kidney (100) and cap sharing the organ
offer decline information at transplant
candidates who were lower than 100 in
the match run sequence.
Response: We thank the commenters
for their suggestion to only share organ
offer declines to a certain sequence
number in the match run and modify
the provision requiring transparency
into kidney transplant organ offers.
Since we are not currently finalizing
this provision, as mentioned in
comment responses in this section, we
will keep this feedback in mind as we
consider alternatives in future
rulemaking.
Comment: Many commenters
requested clarification on the proposed
transparency into kidney transplant
organ offer provision requiring IOTA
participants, for months in which an
organ offer is made, to inform IOTA
waitlist patients who are Medicare
beneficiaries of the number of times an
organ is declined on the patient’s behalf.
For example, a commenter wanted to
know what deliverable(s) CMS expects
in order to validate compliance with
this requirement. Another commenter
asked CMS to clarify what constitutes
an organ offer decline. The commenter
stated that due to the complexity of the
organ offer system and variability in
OPO behavior, a transplant hospital may
receive an organ offer before many
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transplant hospitals ahead of them have
reviewed and declined it. As a result,
the commenter was concerned that a
transplant hospital may review an offer
when they do not actually have the
opportunity to transplant the organ, as
they are not the ‘‘primary’’ recipient.
The commenter also noted a recent
significant increase in expedited organ
placement, where an OPO can send an
organ to a hospital that is not next in
line. Additionally, the commenter
pointed out that an IOTA waitlist
patient may have a declined offer but
then be removed from the waitlist due
to transplant or other reasons before the
monthly report period ends; potentially
creating uncertainty for IOTA
participants on whether to notify the
IOTA waitlist patient in such scenarios.
Furthermore, the commenter suggested
that different IOTA participants may
define the required reporting differently,
and that some declined offers may be
more relevant to IOTA waitlist patients
than others.
A few commenters sought clarity on
which organ offers and declines would
be included in this requirement. For
instance, a commenter asked if the
requirement would cover only primary
offers, which occur sporadically, or all
offers regardless of match quality—
potentially numbering in the hundreds
per month. This same commenter also
raised questions about whether hospital
representatives or physicians (who may
be unaffiliated private practitioners)
should have discussions about organ
offers with IOTA waitlist patients, and
how IOTA participants could effectively
communicate complex clinical
information to non-clinical patients
without causing strife or animosity, as
patients and families often
misunderstand or underestimate the
risks of poorly matched organs and
recipients.
Response: We thank the commenters
for their questions and feedback. As
mentioned in comment responses in
this section, we are not finalizing the
proposed transparency into kidney
transplant organ offers provision,
requiring IOTA participants to inform
IOTA waitlist patients who are
Medicare beneficiaries of the number of
times an organ is declined on the
patient’s behalf. However, as we
continue to consider ways to increase
transparency, we will consider this
feedback in future rulemaking.
Comment: A few commenters
expressed concerns that the new
transparency requirements into kidney
transplant organ offers may have
unintended consequences. They
worried the requirements could
encourage IOTA participants to accept
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lower-quality kidneys, offer kidneys that
are not the best fit for recipients, or
deter the use of higher-risk organs.
Additionally, a commenter noted that
monthly reporting on declined kidney
offers does not account for the
increasing reliance on out-of-sequence
allocation for high-risk kidneys that may
otherwise be discarded.
Many commenters emphasized the
importance of allowing transplant
surgeons, who are knowledgeable about
each patient’s unique circumstances, to
exercise discretion in making clinical
decisions without facing pressure to
accept suboptimal organs or penalties
for denying them. They warned that
restricting this discretion could
undermine trust between the transplant
program and patients. One of these
commenters also expressed concern that
transplant programs are worried about
patient dissatisfaction and potential
legal actions due to declinations. This is
because patients might falsely be given
the sense that they would have had the
option of accepting a kidney that is not
clinically acceptable.
Response: We thank the commenters
for their feedback. The proposed
provisions for transparency into
declined kidney transplant offers is not
intended to question a provider’s
medical judgment or expertise. Rather,
it aims to better inform patients about
whether they are receiving offers and
the reasons behind any declines. For
instance, if a size mismatch between the
recipient and donor kidney prompts
deferring the transplant to an alternative
recipient, the transparency requirement
should not impact that clinical decision.
However, we proposed that IOTA
waitlist patients who are Medicare
beneficiaries be made aware of any
declined offers and the rationale,
allowing them the opportunity to ask
questions and understand the process.
The goal of this proposed transparency
requirement is to facilitate more open
patient-provider discussions about the
kidney transplant process before
undergoing the major, life-altering
procedure—not to erode trust or
encourage litigation. Although we are
not finalizing the proposed transparency
into kidney transplant organ offers
provisions at proposed § 512.442(b), we
continue to support increasing
transparency for patients on the waiting
list and will consider alternative
pathways with the transplant
community to fulfill this important
need.
Comment: Numerous commenters
voiced concerns about the transparency
into kidney transplant organ offers
requirements. Specifically, they worried
that notifying patients about declined
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organ offers could undermine patient
trust, evoke strong emotions, and
negatively impact mental health.
Commenters also expressed concern
that patients and families may not fully
grasp complex medical factors like
organ quality and suitability, potentially
leading to confusion over the clinical
decisions made.
Response: We appreciate the
commenters’ feedback and agree that
monthly notifications of declined organ
offers may not be the right option for
every patient. We believe this is an
important topic to consider as we
evaluate future opportunities for
transparency requirements. At this time,
we will not be finalizing the proposed
transparency into kidney transplant
organ offers provisions; however, we
will take this feedback into
consideration for future notice and
comment rulemaking.
Comment: Several commenters
mentioned that patient-centered and
secure reporting is important stating
that CMS should consider beneficiaries’
preferences to ensure that the
transparency requirements are practical
for IOTA participants to implement and
meaningful to kidney transplant
patients and should ensure that data
reported is meaningful. A commenter
specified the information should be
culturally and linguistically
appropriate. Several commenters stated
that information should be processed in
a way that safeguards patients and their
families, and authentication measures
should be implemented to verify that
patients’ contact information.
Commenters added that mechanisms for
sharing information should be
developed carefully and with input
from the donation and transplant
community. Some of these commenters
also felt patients should be able to opt
in and out of receiving notifications.
Response: We appreciate the
commenters’ feedback. We agree that
organ offer notifications in addition to
organ offer acceptance criteria need to
be practical and consider linguistic and
cultural modifications. Although we are
not finalizing the proposed transparency
into kidney transplant organ offers
provisions, as mentioned in comment
responses in this section, we will
consider these important patientcentered provision details in future
notice and comment rulemaking.
Comment: A commenter
recommended that rather than report
monthly on kidney transplant offers,
CMS should require IOTA participants
to report their quartile rank for their
organ offer acceptance rate ratio to all
wait-listed patients on a semiannual or
annual basis.
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Response: Thank you for your
recommendation. As described in
section III.C.5.d of this final rule, we are
finalizing the inclusion of the organ
offer acceptance rate ratio performance
measure in the efficiency domain.
Section 1115A(b)(4)(B) of the Act
requires CMS to the public, and we plan
to do so annually. This report would
include the organ offer acceptance rate
ratio results. Despite making organ offer
acceptance rate ratio results available to
patients, we believe that this does not
negate the need for other transparency
requirements as one data point focuses
on kidney transplant hospital level data
while the other focuses on patient level
data. Although we are not finalizing the
proposed transparency into kidney
transplant organ offers provisions, as
mentioned in comment responses in
this section, this remains an important
topic requiring ongoing discussion.
Comment: A couple of commenters
recommended that organ offer declines
be shared with both the patient and
their referring nephrologist.
Response: We appreciate the
commenters’ feedback and agree that
referring nephrologists are an important
individual in the care continuum for
patients with kidney disease. As
described in comment responses in this
section, we are not finalizing our
proposed transparency into kidney
transplant organ offers provisions at this
time. However, we believe this is an
important consideration and will take
this comment into consideration in
future notice and rulemaking. After
consideration of public comment, for
the reasons set forth in this rule, we are
not finalizing our proposed provision
for transparency into kidney transplant
organ offers at § 512.442(b).
We are, however, finalizing the
provisions as proposed at § 512.442(c),
with minor technical corrections.
Specifically, we added ‘‘organ offer’’ to
transplant acceptance criteria that must
be disclosed and removed all references
to ‘‘organ offer filter’’ from the provision
at § 512.442(c). Additionally, at
§ 512.442(c) we replaced ‘‘selection
criteria’’ to now say ‘‘acceptance
criteria’’. These changes were made in
order to clarify the specific provisions
regarding the review of transplant organ
offer acceptance criteria, as described in
section III.C.8(a)(2) of the preamble in
this final rule. We will provide further
sub-regulatory guidance on the specifics
of how IOTA waitlist patients who are
Medicare beneficiaries can decline
reviewing their transplant organ offer
acceptance criteria.
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(3) Publication of IOTA Participant
Results
In the Specialty Care Models final
rule (85 FR 61114), CMS established
certain general provisions in 42 CFR
part 512 subpart A that apply to all
Innovation Center models. One such
general provision pertains to rights in
data. Specifically, in the Specialty Care
Models final rule, we stated that to
enable CMS to evaluate the Innovation
Center models as required by section
1115A(b)(4) of the Act and to monitor
the Innovation Center models pursuant
to § 512.150, in § 512.140(a) we would
use any data obtained in accordance
with § 512.130 and 512.135 to evaluate
and monitor the Innovation Center
models (85 FR 61124). We also stated
that, consistent with section
1115A(b)(4)(B) of the Act, CMS would
disseminate quantitative and qualitative
results and successful care management
techniques, including factors associated
with performance, to other providers
and suppliers and to the public. We
stated that the data to be disseminated
would include, but would not be
limited to, patient de-identified results
of patient experience of care and quality
of life surveys, as well as patient deidentified measure results calculated
based upon claims, medical records,
and other data sources. We finalized
these policies in 42 CFR part 512.140(a).
Consistent with these provisions, we
proposed in section III. C.8.a(3) of the
proposed rule, to publish results from
all PYs of the IOTA Model. Specifically,
for each PY, we intend to post
performance across the achievement
domain, efficiency domain, and quality
domain for each IOTA participant. We
would also identify each IOTA
participant for the PY. The results
would be published on the IOTA Model
website. Given that we have proposed
that the IOTA Model would include a
process for IOTA participants to request
a targeted review of the calculation of
performance score which is calculated
based on the various rates we intend to
publish, CMS anticipates that it would
publish these rates only after they have
been finalized and CMS has resolved
any targeted review requests timely
received from IOTA participants under
section II.E. of this final rule. We
believed that the release of this
information would inform the public
about the cost and quality of care and
about IOTA participants’ performance
in the IOTA Model. This would
supplement, not replace, the annual
evaluation reports that CMS is required
to conduct and release to the public
under section 1115A(b)(4) of the Act.
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In section III.C.8.a(3) of the proposed
rule, we considered requiring IOTA
participants to publish their
performance results on their own
websites as well to increase
transparency; however, we did not want
to place additional reporting burden on
IOTA participants, particularly because
we proposed that CMS would publish
the performance results, which should
be adequate.
We sought comment on our intent to
post this information to our website, as
well as the information we intend to
post and the manner and timing of the
posting.
The following is a summary of
comments received on our intent to
publish this information to our website,
as well as the information we intend to
post and the manner and timing of the
posting and our responses:
Comment: A commenter urged CMS
to ensure that any data shared on the
CMS website is easily understandable
for the public.
Response: We thank the commenter
for their feedback. We agree that it is
important for patients to have
information that is presented in a format
that is easily reviewed and understood.
We will review the results to be
published and further consider how to
best present information to both the
public and kidney transplant hospitals
in a meaningful manner, while abiding
by the requirements of section
1115A(b)(4) of the Act.
Comment: A commenter stated that
sharing results during the test phase
should be limited to enrolled IOTA
participants to avoid confusion and
inequities.
Response: We thank the commenter
for their recommendation and sharing
their concerns, however, section
1115A(b)(4)(B) of the Act requires that
model evaluation results be made
available to the public. We believe it is
important for patients to have model
information available to them as they
review IOTA participants. Additionally,
access to these reports by all patients
invites further research and evaluation
by the transplant community to identify
model requirements that should be
applied to all kidney transplant
hospitals and to identify areas of
necessary changes in future iterations of
the IOTA Model and transplant policy.
Comment: A commenter suggested
that CMS should develop charts or other
tools that track and communicate
performance to IOTA participants in
real-time. The commenter also
suggested that performance-related
information should be made available to
providers in addition to IOTA
participants so they can better identify
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96403
areas for improvement and change
behaviors as necessary before each
performance year ends.
Response: We appreciate the
commenter’s feedback. We suggest
referring to section III.C.7 of this final
rule, on data sharing, for more detailed
comment and will consider this request
for timely performance reports as we
develop implementation methodology
for data collection and data reporting to
IOTA participants.
Comment: A few commenters relayed
their support for the publication of
IOTA participant results. A commenter
stated that they are eager to evaluate the
model after its conclusion to determine
whether the three domains were
effective and whether the IOTA Model
goals have been achieved, but also want
to reevaluate further future
improvements, encouraging CMS to
publish annual interim reporting to
assess the model’s progress.
Response: We thank the commenters
for their support and we reiterate the
importance of transparency of
performance results of IOTA
participants to understand the pros and
cons of the IOTA Model, what to modify
in future iterations of the IOTA Model,
and what components should be part of
routine care for all kidney transplant
hospitals in the future. Additionally,
these performance results give patients,
the transplant community and IOTA
participants the opportunity to compare
kidney transplant hospitals and identify
where there is room for improvement
year over year.
After consideration of public
comments, for the reasons set forth in
this rule, we are finalizing our proposals
to publish results from all PYs of the
IOTA Model, without modification, as
outlined in section III.C.8.a(3) of this
final rule. Specifically, for each PY, we
intend to identify each IOTA participant
for the PY and to post performance
across the achievement domain,
efficiency domain, and quality domain
for each IOTA participant on the IOTA
Model website annually, as they become
available. Not only does this meet CMS
requirements, as previously discussed,
but also demonstrates transparency for
the transplant community. We will
further consider the frequency and
availability of interim performance
results in future rulemaking. We direct
readers to section III.C.7 of this final
rule, for further details on data sharing.
b. Health Equity Data Reporting
(1) Demographic Data Reporting
As previously discussed in section
III.B. of this final rule, and throughout
this final rule, disparities exist
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throughout the transplant process.
These circumstances highlight the
importance of data collection and
analysis that includes race, ethnicity,
language, disability, sexual orientation,
gender identity, and sex characteristics
or other demographics by health care
facilities. Such data are necessary for
integration of health equity in quality
programs, because the data permits
stratification by patient
subpopulation.307 308 Stratified data can
produce meaningful measures that can
be used to expose health disparities,
develop focused interventions to reduce
them, and monitor performance to
ensure interventions to improve care do
not have unintended consequences for
certain patients.309 Furthermore, quality
programs are carried out with wellknown and widely used standardized
procedures, including but not limited
to, root cause analysis, plan-do-studyact (PDSA) cycles, health care failure
mode effects analysis, and fish bone
diagrams. These are common
approaches in the health care industry
to uncover the causes of problems, show
the potential causes of a specific event,
test a change that is being implemented,
prevent failure by correcting a process
proactively, and identify possible causes
of a problem and sort ideas into useful
categories, respectively.310 311 312 313
Adding a health equity prompt to these
standardized procedures integrates a
health equity lens within the quality
structure and cues considerations of the
patient subpopulations who receive care
307 IOM (Institute of Medicine). 2009. Race,
Ethnicity, and Language Data: Standardization for
Health Care Quality Improvement (p.287). The
National Academies Press https://www.ahrq.gov/
sites/default/files/publications/files/
iomracereport.pdf.
308 Sivashanker, K., & Gandhi, T.K. (2020).
Advancing Safety and Equity Together. New
England Journal of Medicine, 382(4), 301–303.
https://doi.org/10.1056/nejmp1911700.
309 Weinick, R.M., & Hasnain-Wynia, R. (2011).
Quality Improvement Efforts Under Health Reform:
How To Ensure That They Help Reduce
Disparities—Not Increase Them. Health Affairs,
30(10), 1837–1843. https://doi.org/10.1377/
hlthaff.2011.0617.
310 American Society for Quality. (2019). What is
root cause analysis (RCA)? Asq.org. https://asq.org/
quality-resources/root-cause-analysis.
311 Agency for Healthcare Research and Quality.
(2020). Plan-Do-Study-Act (PDSA) directions and
examples. www.ahrq.gov. https://www.ahrq.gov/
health-literacy/improve/precautions/tool2b.html.
312 Failure Modes and Effects Analysis (FMEA)
Tool | IHI—Institute for Healthcare Improvement.
(2017). www.ihi.org. https://www.ihi.org/resources/
Pages/Tools/
FailureModesandEffectsAnalysisTool.aspx.
313 Kane, R. (2014). How to Use the Fishbone Tool
for Root Cause Analysis. https://www.cms.gov/
medicare/provider-enrollment-and-certification/
qapi/downloads/fishbonerevised.pdf.
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and services from a transplant
hospital.314
To align with other Innovation Center
efforts, we considered proposing that,
beginning with the first PY and each PY
thereafter, each IOTA participant would
be required to collect and report to CMS
demographic and SDOH data pursuant
to 42 CFR part 403.1110(b) for the
purposes of monitoring and evaluating
the model. We considered proposing
that, in conducting the collection
required under this section, the IOTA
participant would make a reasonable
effort to collect demographic and social
determinants of health data from all
attributed patients but, in the case the
IOTA participant attributed patient
elects not to provide such data to the
IOTA participant, the IOTA participant
would indicate such election by the
attributed patient in its report to CMS.
We decided not to propose the
collection of demographic data as this
data is already collected by OPOs and
the SRTR, thereby making such a
requirement for purposes of this model
potentially duplicative and
unnecessarily burdensome. We wish to
minimize reporting burden on IOTA
participants where possible to ensure
sufficient time and effort is spent
adjusting to the requirements of a
mandatory model.
We solicited public comment on the
decision not to propose the collection of
this data and potential applications.
The following is a summary of the
comments received and our responses:
Comment: A few commenters agreed
with CMS’ decision not to propose the
collection of demographic data as this
data is already collected, thereby
making such a requirement for purposes
of this model potentially duplicative
and unnecessarily burdensome.
Response: We thank commenters for
their support in our decision to not
include demographic data reporting in
the IOTA Model.
After consideration of the public
comments we received, we are not
finalizing any requirements to include
demographic data reporting in the IOTA
Model.
(2) Health Related Social Needs (HRSN)
Data Reporting
The Innovation Center is charged with
testing innovations that improve quality
and reduce the cost of health care. There
is strong evidence that non-clinical
drivers of health are the largest
contributor to health outcomes and are
314 Sivashanker, K., & Gandhi, T.K. (2020).
Advancing Safety and Equity Together. New
England Journal of Medicine, 382(4), 301–303.
https://doi.org/10.1056/nejmp1911700.
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associated with increased health care
utilization and costs.315 316 These
individual-level, adverse social
conditions that negatively impact a
person’s health or healthcare are
referred to as ‘‘health-related social
needs’’ or HRSNs.317 CMS aims to
expand the collection, reporting, and
analysis of standardized HRSNs data in
its efforts to drive quality improvement,
reduce health disparities, and better
understand and address the unmet
social needs of patients. Standardizing
HRSN Screening and Referral as a
practice can inform larger, communitywide efforts to ensure the availability of
and access to community services that
are responsive to the needs of Medicare
beneficiaries.
HRSN screening is becoming
increasingly common nationally, but
implementation is not uniform across
geography or health care setting. A
literature review of national surveys
measuring prevalence of social
screening found that almost half of State
Medicaid agencies have established
managed care contracting requirements
for HRSN screening in Medicaid.318 It
also found that health care payers and
delivery organizations or both reported
a screening prevalence of 55–77 percent,
with ‘‘the highest estimate reported
among American Hospital Association
member hospitals.’’ 319 Despite
screening proliferation and generally
positive views toward screening among
both patients and health care providers,
implementation of screening and
referral policies for beneficiaries of CMS
programs with similar health—and even
demographic—profiles may be
inconsistent, potentially exacerbating
315 Booske, B.C., Athens, J.K., Kindig, D.A., Park,
H., & Remington, P.L. (2010). County Health
Rankings (Working Paper). https://
www.countyhealthrankings.org/sites/default/files/
differentPerspectivesForAssigning
WeightsToDeterminantsOfHealth.pdf.
316 ROI Calculator for Partnerships to Address the
Social Determinants of Health Review of Evidence
for Health-Related Social Needs Interventions.
(2019). https://www.commonwealthfund.org/sites/
default/files/2019-07/COMBINED-ROI-EVIDENCEREVIEW-7-1-19.pdf.
317 Medicare Program; End-Stage Renal Disease
Prospective Payment System, Payment for Renal
Dialysis Services Furnished to Individuals with
Acute Kidney Injury, End-Stage Renal Disease
Quality Incentive Program, and End- Stage Renal
Disease Treatment Choices model NPRM (citing A
Guide to Using the Accountable Health
Communities Health-Related Social Needs
Screening Tool) 87 FR 38554 (Jun. 28, 2022).
318 De Marchis, E., Brown, E., Aceves, B., Loomba,
V., Molina, M., Cartier, Y., Wing, H., Ma, L., &
Gottlieb. (n.d.). State of the Science of Screening in
Healthcare Settings siren State of the Science on
Social Screening in Healthcare Settings Summer
2022. https://sirenetwork.ucsf.edu/sites/default/
files/2022-06/final%20SCREEN%20State-ofScience-Report%5B55%5D.pdf.
319 Ibid.
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disparities in the comprehensiveness
and quality of care.
One of the goals stated in the
Innovation Center Strategy Refresh for
advancing system transformation is to
require all new models to collect and
report demographic and SDOH data.
Thus, in addition to the proposed health
equity requirements in section III.C.8.b.
of this final rule, we considered
proposing a requirement that IOTA
participants conduct HRSN screening
for at least four core areas—food
security, housing, transportation, and
utilities. We recognize these areas as
some of the most common barriers to
kidney transplantation and the most
pertinent for the IOTA participant
patient population. However, given the
need for a psychosocial evaluation prior
to addition to the waitlist, we
understand that such a requirement may
be redundant given current clinical
practices, we have refrained from
making such a proposal.
We sought comment on whether we
should include a requirement for IOTA
participants to conduct HRSN screening
and report HRSN data in a form and
manner specified by CMS each PY for
their attributed patients. We sought
input on following the questions in this
section, and comment on any aspect of
the psychosocial evaluation of
waitlisted patients and how this
compares to HRSN screenings for the
four domains—food security, housing,
transportation, and utilities. Even if
CMS were to adopt an HRSN screening
and reporting requirement in the final
rule, CMS might consider delaying the
implementation of such a requirement.
• When evaluating a patient for
potential addition to the kidney
transplant waitlist, what questions are
asked as part of the psychosocial
evaluation?
• How might a psychosocial
evaluation compare to an HRSN
screening? What HRSNs are identified
as part of a psychosocial evaluation?
• What data is collected from the
psychosocial evaluation on HRSNs?
• If HRSNs are identified as part of
the evaluation process, what, if any,
steps are taken to assist the patient in
addressing these needs and improving
their transplant readiness?
• If HRSNs are identified of a patient
already on the transplant waitlist, how
might this affect their status on the
transplant waitlist? Could a patient be
removed from the transplant waitlist if
HRSNs are identified that may impact
transplant readiness?
• What, if any, follow-up is
conducted with waitlist patients that
have identified HRSNs?
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• Are there any concerns with HRSN
screening and data collection
requirements?
We received 33 submissions on this
RFI. We thank commenters for their
comments. While we will not be
responding to specific comments
submitted in response to this RFI, we
have shared all the comments received
with the appropriate agencies and
offices for consideration in subsequent
rulemaking for the inclusion of
demographic data reporting.
c. Health Equity Plans
To further align with other Innovation
Center models and promote health
equity across the transplant process, we
proposed that, for PY 2 through PY 6,
each IOTA participant must submit to
CMS, in a form and manner and by the
date(s) specified by CMS, a health
equity plan. Given that this would be a
mandatory model, we proposed that the
health equity plan be voluntary in the
first PY of the model to allow IOTA
participants time to adjust to model
requirements. We proposed that the
health equity plan must:
• Identify target health disparities.
We proposed to define ‘‘target health
disparities’’ as health disparities
experienced by one or more
communities within the IOTA
participant’s population of attributed
patients that the IOTA participant
would aim to reduce.
• Identify the data sources used to
inform the identification of target health
disparities.
• Describe the health equity plan
intervention. We proposed to define
‘‘health equity plan intervention’’ as the
initiative(s) the IOTA participant would
create and implement to reduce target
health disparities.
• Include a resource gap analysis. We
proposed to define ‘‘resource gap
analysis’’ as the resources needed to
implement the health equity plan
interventions and identifies any gaps in
the IOTA participant’s current resources
and the additional resources that would
be needed.
• Include a health equity project plan.
We proposed to define ‘‘health equity
project plan’’ as the timeline for the
IOTA participant to implement the
IOTA participant’s the health equity
plan.
• Identify health equity plan
performance measure(s). We proposed
to define ‘‘health equity performance
plan measure(s)’’ as one or more
quantitative metrics that the IOTA
participant would use to measure the
reductions in target health disparities
arising from the health equity plan
interventions.
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• Identify health equity goals and
describes how the IOTA participant
would use the health equity goals to
monitor and evaluate progress in
reducing targeted health disparities. We
proposed to define ‘‘health equity goals’’
as targeted outcomes relative to the
health equity plan performance
measures for the first PY and all
subsequent PYs.
In the proposed rule, we proposed
that once an IOTA participant submits
their health equity plan to CMS, CMS
would use reasonable efforts to approve
or reject the health equity plan within
60 business days (89 FR 43582). We
proposed that if CMS approves the
IOTA participant’s health equity plan,
the IOTA participant must engage in
activities related to the execution of the
IOTA participant’s health equity plan,
including implementing health equity
plan interventions and monitoring and
evaluating progress in reducing target
health disparities. Discrimination on the
basis of race, ethnicity, national origin,
religion, or gender in activities related
to the execution of the IOTA
participant’s health equity plan would
be prohibited.
Should CMS determine that the IOTA
participant’s health equity plan does not
satisfy the proposed requirements and is
inconsistent with the applicable CMS
Health Equity Plan guidance, does not
provide sufficient evidence or
documentation to demonstrate that the
health equity plan is likely to
accomplish the IOTA participant’s
intended health equity goals, or is likely
to result in program integrity concerns
or negatively impact beneficiaries’
access to quality care, we proposed that
CMS may reject the health equity plan
or require amendment of the health
equity plan at any time, including after
its initial submission and approval (89
FR 43582).
We proposed that if CMS rejects the
IOTA participant’s health equity plan,
in whole or in part, the IOTA
participant must not, and must require
its IOTA collaborators to not, conduct
health equity activities identified in the
health equity plan that have been
rejected by CMS (89 FR 43582).
We proposed that in PY 3, and each
subsequent PY, in a form and manner
and by the date(s) specified by CMS,
each IOTA participant would be
required to submit to CMS an update on
its progress in implementing its health
equity plan (89 FR 43582). We stated
that this update would be required to
include all of the following:
• Updated outcomes data for the
health equity plan performance
measure(s).
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• Updates to the resource gap
analysis.
• Updates to the health equity project
plan.
We proposed that if an IOTA
participant fails to meet the
requirements of the heath equity plan
described in this section of the proposed
rule, the IOTA participant would be
subject to remedial action as specified
in section III.C.16. of this final rule.
Such remedial actions could include
requesting a corrective action plan,
recoupment of any upside risk
payments; or termination from the
model (89 FR 43582).
We solicited feedback on these
proposals. We also solicited comment
on the potential impact of creation of a
health equity plan, whether such plans
should be voluntary, and whether
health equity plans should only be a
requirement in later PYs of the IOTA
Model.
The following is a summary of the
comments received on our proposed
health equity plan provisions, whether
such plans should be voluntary, and
whether health equity plans should be
a requirement in later PYs of the IOTA
Model and our responses:
Comment: Several commenters
applauded CMS’ proposed requirement
to integrate health equity plans into the
model framework. Commenters
expressed support stating the health
equity plans provide a context-specific
system-level approach to addressing the
social determinants of health and the
health equity plan provision will
encourage IOTA participants to identify
health equity gaps and to develop and
implement targeted strategies to address
those gaps.
Response: We appreciate the
commenters’ support of the IOTA health
equity plan. We acknowledge
commenters’ support for CMS’ and the
IOTA model’s goal to promote health
equity across the transplant process.
Comment: A few commenters
suggested that CMS should not pursue
the health equity plan provision.
Several commenters supported the
proposed requirements to delay the
submission of the heath equity plans
until performance year two, however,
other commenters recommended CMS
reconsider requiring each IOTA
participant to submit to CMS an update
on its progress in implementing its
health equity plan (in PY 3, and each
subsequent PY). Some commenters
expressed the health equity plan
requirement would be burdensome and
inhibit IOTA participants resources and
their ability to successfully implement
and operationalize the model
requirements. For example, commenters
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stated the health equity plans would be
an unfair requirement and burdensome
for transplant hospitals that have a
larger low-income patient population
and would penalize model participants’
efforts to address health equity issues.
Other commenters suggested that to
reduce burden, CMS should provide
clarity on the health equity plan criteria.
For example, commenters stated CMS
should consider providing IOTA
participants examples of a
comprehensive health equity plan that
describes the health equity plan
inclusion criteria, and clear and
measurable endpoints on which CMS
would deem suitable for approval.
Response: We thank the commenters
for their feedback. However, we disagree
with the suggestion to remove the health
equity plan provision from the model.
We believe health equity plans are vital
to incentivize meaningful changes and
promote health equity across the
transplant process. However, we
recognize that the IOTA health equity
plan requirement may be burdensome
for some model participants, and CMS
solicited comment on whether such
plans should be voluntary. With respect
to comments received, we are modifying
our proposal to allow health equity
plans to be a voluntary provision for all
performance years.
Comment: Several commenters
recommended that CMS provide upfront
investment funding to support the
development and implementation of the
IOTA participants’ health equity plans.
Several commenters stated the health
equity plan requirements would be
burdensome to model participants and
would require significant resources and
investments involving administrative,
human and operational capital from
model participants to be successful. In
addition, some commenters stated that
the health equity plan requirement fails
to consider or address patients’ barriers
such as high out-of-pocket costs, or
patients living in rural areas.
Other commenters expressed their
support of the health equity plan policy
but expressed concerns that the lack of
upfront investments of resources and
the design rigor would make the health
equity plan requirements unlikely to
yield meaningful results for patients.
For example, these commenters
suggested CMS should include upfront
financial support to help empower
participating hospitals to fully engage in
the IOTA Model without compromising
their financial stability or the quality of
care they provide to their communities
and patients. A commenter stated that
tasking transplant hospitals to address
patient’s social risk factors and the
social determinants of health via the
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health equity plan is beyond the
purview or expertise of transplant
hospitals. The commenter stated that
the social determinants of health issues
among transplant hospital patients are
generally managed by social workers
(and/or non-clinical staff) within the
patients’ communities, and therefore,
supplemental funding would be needed
to hire appropriate staff and support the
resources needed to design and
implement the IOTA health equity plan.
Other commenters suggested CMS
should consider issuing waivers to
allow for broader financial assistance
programs for underserved communities
who may be facing additional barriers
and social risk factors such as food
insecurity, housing insecurity,
inaccessible transportation and high
childcare costs. A commenter suggested
CMS should include additional
incentives or supplemental funding for
local healthcare providers and dialysis
units to screen patients for social
determinants of health metrics and link
patients to community-based services.
Response: We appreciate the
commenters’ suggestions for CMS to
include supplemental funding for the
health equity plan provision. We believe
it is important that IOTA participants
receive the necessary support to
successfully implement their health
equity plan. We sought comment on the
potential impact of creation of a health
equity plan, and we will consider
including health equity plan
supplemental funding opportunities in
future rulemaking.
Comment: Some commenters
expressed concern that the health equity
plan provision may promote
discriminatory practices on the basis of
race. Specifically, commenters stated
the health equity plan requirement
incentivizes model participants to
prioritize certain group(s) over others in
a discriminatory manner. A commenter
suggested that the IOTA health equity
plan ‘‘target health disparities’’
requirement should be defined in raceneutral terms, and CMS should prohibit
IOTA participants’ health equity plans
from being implemented in a
discriminatory manner.
Response: We acknowledge the
commenters concerns. Our proposal
states that ‘‘discrimination on the basis
of race, ethnicity, national origin,
religion, or gender in activities related
to the execution of the IOTA
participant’s health equity plan would
be prohibited.’’ We believe there are
significant safeguards in place to assure
health equity plans will not be designed
or implemented in a discriminatory
manner.
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Comment: Some commenters
recommended CMS implement the
IOTA health equity plans through the
CMS Hospital Inpatient Quality
Reporting (IQR) Program. For example,
commenters stated they do not agree
that the IOTA model is an appropriate
venue to promote health equity and the
health equity plan provision would be
better served within the IQR program
given transplant hospitals already
participant in IQR. Commenters
suggested the IOTA health equity plan
requirements would be duplicative,
create additional administrative burden,
and be confusing for hospitals given
CMS has already introduced the
Hospital Commitment to Health Equity
via the IQR program. Other commenters
suggested CMS should implement the
model’s health equity plans through The
Joint Commission instead of an IOTAspecific plan. Another commenter
recommended dialysis centers would be
a more suited environment to
implement health equity plans rather
than via transplant hospitals.
Response: We disagree with
implanting IOTA health equity plans
within other CMS or hospital programs.
The IOTA Model structure is designed
to promote improvement activities
across selected transplant hospitals,
including the social determinants of
health, and health equity. The IOTA
health equity plans are designed
specifically for the selected transplant
hospital participants.
After consideration of the public
comments we received, for the reasons
set forth in this rule, we are finalizing
our proposed provisions on health
equity plans at § 512.444(a)(1–7) with
slight modifications. Specifically, we
are redesignating what was proposed at
§ 512.444 to be § 512.446. Additionally,
we proposed at § 512.444(a) that the
health equity plan be voluntary for
IOTA participants for PY 1 and
mandatory for PY 2 through PY 6. We
are instead finalizing at § 512.446(a) that
a health equity plan shall be voluntarily
submitted by an IOTA participant for all
performance years (PY 1 through PY 6)
in a form and manner and by the date(s)
specified by CMS. We are also finalizing
that a health equity plan voluntarily
submitted by an IOTA participant must
include all elements as proposed at
§ 512.446(a)(1–7), without modification.
Additionally, we are finalizing as
proposed without modification the
definitions of target health disparities,
health equity plan intervention,
resource gap analysis, health equity
project plan, health equity performance
plan measure(s) and health equity goals
at § 512.402. We also note that we are
finalizing the proposed definition of
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health equity performance plan
measure(s) with a slight modification to
correct the defined term to read as
follows: health equity plan performance
measure(s). In the proposed rule at 89
FR 43582, we proposed to define health
equity performance plan measure(s) as
one or more quantitative metrics that
the IOTA participant would use to
measure the reductions in target health
disparities arising from the health
equity plan interventions. However, in
the proposed rule at 89 FR 43582, we
proposed that health equity plans must
identify health equity plan performance
measure(s). Additionally, in the
proposed rule at 89 FR 43582, we
proposed to define health equity goals
as targeted outcomes relative to the
health equity plan performance
measures for the first PY and all
subsequent PYs. As such, we are
finalizing the definition of health equity
plan performance measure(s) at
§ 512.402 as one or more quantitative
metrics that the IOTA participant would
uses to measure the reductions in target
health disparities arising from the
health equity plan interventions.
9. Overlap With Other Innovation
Center Models, CMS Programs, and
Federal Initiatives
a. Other Innovation Center Models and
CMS Programs
We proposed that IOTA participants
would be allowed to simultaneously
participate in IOTA and other CMS
programs and models. The IOTA Model
would overlap with several other CMS
programs and models and Departmental
regulatory efforts, and we sought
comment on our proposals to account
for overlap.
KCC Model—The KCC Model is a
voluntary Innovation Center model for
nephrologists, dialysis facilities,
transplant providers, and other
providers and suppliers that are focused
on beneficiaries with CKD and
beneficiaries with ESRD. The KCC
Model performance period began on
January 1, 2022, and is scheduled to end
December 31, 2026. As such, the KCC
Model would run concurrently for 2
years with the IOTA Model, which
would have a proposed start date of
January 1, 2025. The KCC Model
includes a payment incentive called the
Kidney Transplant Bonus (KTB). KCC
participants are eligible for up to
$15,000 for every aligned beneficiary
with CKD or ESRD who receives a
kidney transplant, whether from a living
or deceased donor, provided the
transplant remains successful. Kidney
Contracting Entities (KCEs) participating
in the KCC Model are also required to
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include a transplant provider, defined
as a transplant program that provides
kidney transplants, a transplant hospital
that provides kidney transplants, a
transplant surgeon who provides kidney
transplants, a transplant nephrologist, a
transplant nephrology practice, an OPO,
or another Medicare-enrolled provider
or supplier that provides kidney
transplant related covered services to
Medicare beneficiaries.
Though transplant hospitals are one
of the types of health care provider
eligible to serve as a transplant provider,
CMS has found relatively low
participation by transplant hospitals in
the KCC Model. Across the 100 KCEs
participating in the model in 2023, there
were only 10 kidney transplant
hospitals participating in the model and
serving as the transplant provider for
the relevant KCE. In discussions with
participants and with kidney transplant
hospitals, CMS heard a few reasons for
this relatively low rate of participation.
CMS heard that it was difficult
administratively for kidney transplant
hospitals to participate as they are part
of corporate entities that may have a
larger organizational focus on broader
shared savings efforts, rather than just
for the kidney population.
We proposed that any providers or
suppliers participating in the KCC
Model that meet the proposed IOTA
participant eligibility requirements
would still be required to participate in
the IOTA Model. We believed that
granting an exemption to the IOTA
Model for these providers or suppliers
could disrupt the patterns of care being
tested in the KCC Model. We also
believed that a prohibition on dual
participation could prevent enough
KCEs from having a transplant provider
and meeting model requirements, which
could undermine participation in the
KCC model.
We considered proposing that any
transplant hospitals participating in the
IOTA Model would not be able to
participate in the KCC Model and be
able to receive any portion of a Kidney
Transplant Bonus payment. However,
we did not believe that this was
necessary given that there are currently
only 10 transplant hospitals
participating in the KCC Model,
meaning that dual participation should
not substantially affect the evaluation of
either model. We also considered
proposing that any kidney transplant for
an aligned beneficiary that results in a
Kidney Transplant Bonus being paid out
in the KCC Model would not be counted
for calculating an upside risk payment
or downside risk payment in the IOTA
Model. We decided not to propose this
policy because of potential disruption to
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the KCC Model, which would be in its
fourth performance year when the
proposed IOTA Model would likely
begin in 2025. Additionally, the Kidney
Transplant Bonus payment in the KCC
Model serves multiple functions within
that model, as it also incentivizes posttransplant care for up to three years
post-transplant.
We believed that it is important to test
both the IOTA Model and the KCC
Model, to test the effectiveness of
payment incentives for kidney
transplants at different points of the care
coordination process. The IOTA Model
would test the effect of upside and
downside risk payments for kidney
transplant hospitals, while the KCC
Model tests how nephrologists and
other providers and suppliers can
support transplantation in the overall
care coordination process. Upside risk
payment and downside risk payment
from the IOTA Model would not be
counted as expenditures for purposes of
the KCC Model, as they would not be
adjustments to claims for individual
beneficiaries, but would be paid out in
a lump sum based on aggregate
performance directly tied to individual
beneficiary level claims. Additionally,
we do not want to potentially hurt KCC
participants that have beneficiaries who
could benefit from the KCC participant’s
potential high performance in the IOTA
Model.
Both the KCC Model and the IOTA
Model would include explicit
incentives for participants when aligned
beneficiaries receive kidney transplants;
and a transplant hospital participating
in both models would be eligible to
receive a portion of a Kidney Transplant
Bonus from a KCE under the KCC Model
and an upside risk payment or
downside risk payment under the IOTA
Model. Kidney transplants represent the
most desired and cost-effective
treatment for most beneficiaries with
ESRD, but providers and suppliers may
currently have insufficient financial
incentives to assist beneficiaries through
the transplant process because dialysis
generally results in higher
reimbursement over a more extended
period of time than a transplant. As a
result, CMS believed it would be
appropriate to allow a transplant
hospital to receive both an upside risk
payment or downside risk payment
from the IOTA Model and portion of a
Kidney Transplant Bonus from the KCC
Model and the IOTA Model
simultaneously to assess their effects on
the transplant rate.
ETC Model—The ETC Model is a
mandatory Innovation Center model
that includes as participants certain
clinicians who manage dialysis patients
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(referred to as Managing Clinicians) and
ESRD facilities and provides incentives
for increasing rates of home dialysis,
transplant waitlisting, and living donor
transplantation. The ETC Model began
on January 1, 2021, and the model
performance period is scheduled to end
December 31, 2025, and it would have
one year of overlap with the proposed
model performance period of the IOTA
Model beginning January 1, 2025. The
ETC Model includes an upward or
downward payment adjustment called
the Performance Payment Adjustment
(PPA) that is calculated in part based on
the rates of transplant waitlisting and
living donor transplants for the
population of beneficiaries aligned to a
participating Managing Clinician or
ESRD facility.
We believed that the goals of the ETC
Model and the goals of the proposed
IOTA Model are aligned. As CMS
described in the 2020 rule finalizing the
ETC Model (85 FR 61114), ‘‘[t]he ETC
Model [is] a mandatory payment model
focused on encouraging greater use of
home dialysis and kidney transplants.’’
We believe that the IOTA Model would
then test a corresponding incentive on
the transplant hospital side to further
assist beneficiaries in moving through
the transplant process to get a
transplant. CMS believed it is
appropriate to test both models as the
ETC Model does not include direct
incentives for transplant hospitals and
we believe that transplant hospitals play
a very important role in the transplant
process.
We note for the ETC Model,
participants are selected based on their
location in a Selected Geographic Area,
which are randomly selected Hospital
Referral Regions (HRR), stratified by
census region, representing
approximately one third of the country,
as well as HRRs predominately
comprised of ZIP codes in Maryland.
This is a different randomization
strategy than is being proposed for the
IOTA Model. It is our intent to look at
the effects of each model and its
randomization strategy on the transplant
rate as part of our model evaluation,
which is discussed in section III.C.12 of
this final rule.
Additionally, we note that the ETC
Model includes the ETC Learning
Collaborative as part of its model test.
This is further discussed in section
III.C.13. of this final rule, where we
sought feedback about the experience of
kidney transplant hospitals, OPOs, ETC
Participants, and other interested parties
engaged in the ETC Learning
Collaborative, as we consider how to
best promote shared learning in the
IOTA Model.
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Other Medicare Alternative Payment
Models (APMs)—For the Medicare
Shared Savings Program (the Shared
Savings Program) and the ACO
Realizing Equity, Access, and
Community Health (ACO REACH)
Model, which focus on total cost of care,
payment adjustments made under the
IOTA Model would not be counted as
program expenditures. The Medicare
Shared Savings Program regulations
address payments under a model,
demonstration, or other time-limited
program when defining program
expenditures. Specifically, when
calculating Shared Savings and Shared
Losses for an ACO in the Shared
Savings Program, CMS considers only
‘‘individually beneficiary identifiable
final payments made under a
demonstration, pilot, or time limited
program’’ to be a part of the ACO’s
Medicare Parts A and B fee-for-service
expenditures (see, for example, 42 CFR
425.605(a)(5)(ii)). Similarly, in the ACO
REACH Model, an ACO’s performance
year expenditure is defined to include
the total payment that has been made by
Medicare fee-for-service for services
furnished to REACH Beneficiaries (see
ACO REACH Model First Amended and
Restated Participation Agreement (Dec.
1, 2023)). Payments under the IOTA
Model are not directly tied to any
specific beneficiary. Instead, they are
made on a lump sum basis based on
aggregate performance across transplant
patients seen by the center during the
performance year. IOTA Model
payments, therefore, would not be
considered by the Shared Savings
Program as an amount included in Part
A or B fee-for-service expenditures or by
the ACO REACH Model as an amount
included in payment for REACH
Beneficiaries’ Medicare fee-for-service
services.
Hospital VBP Program—CMS adjusts
payments to hospitals under the
Inpatient Prospective Payment System
(IPPS) based on their performance under
the Hospital VBP Program. However, the
Hospital VBP Program does not
currently include any measures related
to transplant services. In addition,
transplant services are only offered by a
subset of hospitals. Given the different
focuses between the Hospital VBP
Program and the IOTA Model, we are
not proposing any changes to the
Hospital VBP Program and believe it is
appropriate to test the IOTA Model
alongside the existing Hospital VBP
Program.
b. Overlap With Departmental
Regulatory Efforts
December 2020 OPO Conditions for
Coverage—In December 2020, CMS
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issued a final rule titled ‘‘Organ
Procurement Organizations Conditions
for Coverage: Revisions to the Outcome
Measure Requirements for Organ
Procurement Organizations; Final Rule’’
(85 FR 77898). The final rule revised the
OPO CfCs and was intended to increase
donation rates and organ transplantation
rates by replacing the previous outcome
measures. In general, the new outcome
measures improve on the prior measures
by using objective, transparent, and
reliable data, rather than OPO selfreported data, to establish the donor
potential in the OPO’s DSA. The rule
also permits CMS to begin decertifying
underperforming OPOs beginning in
2026.
We believed that the proposed IOTA
Model supports the policies set out in
that final rule. We noted that we have
received feedback from OPOs and other
interested parties that OPOs are
required to procure more organs, while
there is not a corresponding incentive
on the transplant hospital side to
transplant more organs into
beneficiaries. We also noted that the
number of discarded organs has risen
from 21 percent to 25 percent from 2018
to 2022.320 Though there have been
other changes during that time,
including the updated organ allocation
system and the effects of the COVID–19
pandemic, this rise in discarded organs
is highly concerning, and we believed
that the IOTA Model can help to
mitigate this troubling rise by giving
transplant hospitals an incentive to
accept more offers that they may not
have accepted without that incentive.
In September 2019, CMS finalized a
rule titled ‘‘Medicare and Medicaid
Programs; Regulatory Provisions to
Promote Program Efficiency,
Transparency, and Burden Reduction;
Fire Safety Requirements for Certain
Dialysis Facilities; Hospital and Critical
Access Hospital (CAH) Changes To
Promote Innovation, Flexibility, and
Improvement in Patient Care’’ (84 FR
51732). This rule was in part motivated
by a commitment across CMS and HHS
to ‘‘the vision of creating an
environment where agencies
incorporate and integrate the ongoing
retrospective review of regulations into
Department operations to achieve a
more streamlined and effective
regulatory framework.’’
One of the major provisions finalized
in this rule was the removal of data
320 Sumit Mohan, Miko Yu, Kristen L. King, S. Ali
Husain, Increasing Discards as an Unintended
Consequence of Recent Changes in United States
Kidney Allocation Policy, Kidney International
Reports, Volume 8, Issue 5, 2023, Pages 1109–1111,
ISSN 2468–0249, https://doi.org/10.1016/
j.ekir.2023.02.1081.
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submission, clinical experience, and
outcomes requirements for Medicare reapproval that were previously required
of transplant hospitals participating in
the Medicare program. As described in
the rule, CMS had put in place
additional CoPs in the March 2007 final
rule (72 FR 15198) in an effort to
increase the quality of care by
specifying minimal health and safety
standards for transplant hospitals. In
addition, outcome metrics (1 year graft
and patient survival) were included in
the regulation and mirrored the OPTN
outcomes metrics as calculated by the
SRTR.
CMS removed the outcomes
requirements for a few key reasons.
First, the concern was that transplant
centers were also subject to OPTN
policies, so parallel regulation on the
CMS side was duplicative. Additionally,
the concern was that ‘‘increased
emphasis on organ and patient survival
rates, as key metrics of transplant
performance, created incentives for
transplant programs to select organs
most likely to survive after transplant
without rejection, and to select
recipients most likely to survive after
the transplant.’’ This focus had the
effect of creating ‘‘performance
standards that focused only on organ
and patient survival rates for those who
received a transplant, not on survival
rates of patients awaiting
transplant.’’ 321
In December 2021, CMS published an
RFI titled ‘‘Health and Safety
Requirements for Transplant Programs,
Organ Procurement Organizations, and
End-Stage Renal Disease Facilities’’ (86
FR 68594).322 In this RFI, CMS asked
questions about the overall transplant
ecosystem, with goal of helping ‘‘to
inform potential changes that would
create system-wide improvements,
which would further lead to improved
organ donation, organ transplantation,
quality of care in dialysis facilities, and
improved access to dialysis services.’’
We noted that we were seeking ways
to harmonize policies across the
primary HHS agencies (CMS, HRSA,
and the Food and Drug Administration
(FDA)) that are involved in regulating
stakeholders in the transplant ecosystem
so that our requirements are not
duplicative, conflicting, or overly
burdensome. We asked if there any
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p-87.
current requirements for transplant
programs, ESRD facilities, or OPOs that
are unnecessarily duplicative of, or in
conflict with, OPTN policies or policies
that are covered by other government
agencies. We also asked about the
impacts of these duplicative
requirements on organ utilization and
transplant program/ESRD facility/OPO
quality and efficiency (86 FR 68596).
Given the concerns described in these
past efforts, the OPTN has been in part
responsive to concerns from interested
parties about their metrics and effects
and has expanded which metrics they
are evaluating transplant centers for
their performance. In December 2021,
the OPTN approved four new riskadjusted metrics to be used to monitor
transplant program performance,
including 90-day graft survival hazard
ratio, 1-year conditional graft survival
hazard ratio, pre-transplant mortality
rate ratio, and offer acceptance ratio.323
This added two new metrics for areas
beyond simply looking at transplant
survival, and looked at a more holistic
view of patient care for beneficiaries on
the transplant list. There is a critical
role for both the Department and the
OPTN with regard to the transplant
ecosystem. The final rule governing the
operation of the OPTN from 1996 (63 FR
16296) stated the following:
The Department believes that the
transplantation network must be
operated by professionals in the
transplant community, and that both
allocation and other policies of the
OPTN should be developed by
transplant professionals, in an open
environment that includes the public,
particularly transplant patients and
donor families. It is not the desire or
intention of the Department to interfere
in the practice of medicine. This rule
does not alter the role of the OPTN to
use its judgment regarding appropriate
medical criteria for organ allocation nor
is it intended to circumscribe the
discretion afforded to doctors who must
make the difficult judgments that affect
individual patients. At the same time,
the Department has an important and
constructive role to play, particularly on
behalf of patients. Human organs that
are given to save lives are a public
resource and a public trust.
We believed that the proposed IOTA
Model recognizes the goals of the
Department on behalf of the public and
the medical judgment exhibited by the
OPTN. We believed that constructing
322 Request for Information; Health and Safety
Requirements for Transplant Programs, Organ
Procurement Organizations, and End-Stage Renal
Disease Facilities. https://www.federalregister.gov/
documents/2021/12/03/2021-26146/request-forinformation-health-and-safety-requirements-fortransplant-programs-organ-procurement.
323 OPTN Board adopts new transplant program
performance metrics—OPTN. (2021, December 16).
Optn.transplant.hrsa.gov. Retrieved May 30, 2023,
from https://optn.transplant.hrsa.gov/news/optnboard-adopts-new-transplant-programperformance-metrics/.
321 https://www.federalregister.gov/d/2019-20736/
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this as a model test would enable the
Department to test out a different
approach to incentivize certain behavior
for transplant centers, while also
acknowledging the role of the OPTN
and transplant professionals in this area.
We noted the concern put forward by
kidney transplant hospitals that they
would not be able to increase their
number of transplants without
potentially affecting their performance
90 day and 1-year graft survival rate
metrics used by the MPSC. However, we
believed that there are several different
ways that IOTA participants would
ultimately be able to succeed under the
IOTA Model and OPTN policies:
• The MPSC standard represents a
standard far below the national average
of performance that should be able to be
met by member transplant centers. The
MPSC describes this as meaning that to
be identified for outcomes review in a
document describing their Performance
Reviews,324 ‘‘[t]he adult criteria is based
on the likelihood that the program’s
performance was at least 75 percent
worse than an average program,
accounting for differences in the types
of recipients and donor organs
transplanted. The pediatric criterion is
based on the likelihood that the
program’s performance was at least 60
percent worse than an average program,
accounting for differences in the types
of recipients and donor organs
transplanted. Even if a program meets
one or both of the criteria for graft
survival, the MPSC may not send the
program an inquiry based on various
situations, such as recent release from
review for outcomes or program
membership status.’’ This represents a
minimum standard of care and only a
small percentage were flagged for not
meeting those standards.
• The IOTA Model incentivizes
investment in both living and deceased
donor transplants. Living donor
transplantation has rates that have been
relatively flat for 20 years and has
recipients of those organs with better
post-transplant outcomes.
• MPSC outcomes metrics are risk
adjusted based on organ quality and can
account for the use of organs that are
currently being discarded.
• Many organs currently being
discarded are quality organs. Though
the median KDRI of discarded kidneys
was higher for discarded kidneys than
transplanted kidneys, there is a large
overlap in the quality of discarded and
transplanted kidneys.325
324 https://optn.transplant.hrsa.gov/media/
5j5dov5s/what_to_expect_performance_reviews.pdf.
325 Mohan, S., Chiles, M.C., Patzer, R.E., Pastan,
S.O., Husain, S.A., Carpenter, D.J., Dube, G.K.,
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• Per 42 CFR 121.10(c)(1), the reviews
conducted by the OPTN result in an
advisory opinion to the Secretary of a
recommended course of action. The
Secretary then has the option under 42
CFR 121.10(c)(2) of requesting
additional information, declining to
accept the recommendation, accepting
the recommendation, or taking such
other action as the Secretary deems
necessary. Given the enforcement
discretion given to the Secretary, the
Secretary may take into account
performance on the metrics evaluated in
the IOTA Model as part of a holistic
evaluation of transplant hospital
performance.
Additionally, CMS also considered,
but did not propose, a limited waiver of
section 1138(a)(1)(B) of the Act as part
of the IOTA Model, which requires that
a hospital be a member and abide by the
rules and requirements of the OPTN. We
considered retaining transplant
hospitals’ membership obligations to the
OPTN with the exception of their
required responsiveness to MPSC
transplant hospital performance reviews
and the potential for adverse actions
that may risk a transplant hospital’s
operations and reimbursement by
Federal health insurance programs.
However, we do not believe that this
waiver is necessary for testing the
model, and that a transplant hospital
can perform on both the metrics put
forward by the MPSC and demonstrate
successful performance in the IOTA
Model.
We invited public comments on our
proposals to account for overlaps with
other CMS programs and models.
The following is a summary of the
comments received on our proposals to
account for overlaps with other CMS
programs and models and our
responses:
Comment: We received multiple
comments about the OPTN
Modernization and concerns that the
OPTN Modernization process is
happening right now, as the IOTA
Model is being implemented, which
would potentially be too disruptive to
the transplant system. We also received
comments concerned about the
solicitation for a new OPTN contractor
and concerns that any potential
transition that could happen from a new
contract could lead to disruption that
could impact ability to perform in the
IOTA Model.
Response: We disagree with the
commenters as we believe that the
Crew, R.J., Ratner, L.E., & Cohen, D.J. (2018).
Factors leading to the discard of deceased donor
kidneys in the United States. Kidney International,
94(1), 187–198. https://doi.org/10.1016/
j.kint.2018.02.016.
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OPTN Modernization process will
improve the system overall and includes
a series of improvements in technology,
governance, and organ tracking that will
benefit IOTA participants as they
participate in this model. At a high
level, the IOTA Model was proposed
and developed in coordination with
CMS and HRSA in an effort to create a
series of coordinated initiatives across
the transplant ecosystem, using a variety
of different levers to improve
performance and equity in the United
States transplant system. Through the
OTAG, CMS and HRSA have
collaborated and produced the IOTA
Model, the OPTN Modernization
Process, and further efforts to come
including around the HIV Organ Policy
Equity Act in an effort to increase
accountability in the transplant system
and improve it for patients.
Additionally, HRSA and the OPTN
are committed that the Modernization
Process will not disrupt existing
procurement and allocation practices.
HHS also believes that this
modernization process will improve
accountability and performance for the
OPTN and accelerate progress in
technology, data transparency and
analytics, governance, operations, and
quality improvement and innovation.
Some key steps that have already been
taken include in August 2024 separating
the OPTN Board of Directors from the
OPTN contractor so it may better serve
the interests of patients and their
families, which HHS believes will
strengthen governance and prevent
conflicts of interest within the Network.
Other major steps include issuing a
Request for Proposals for a multi-vendor
contract solicitation for critical OPTN
functions and a transition to an
upgraded IT system that leverages
industry-leading standards. The net
result of these efforts will be a more
functional and accountable system that
will better be able to get and share data
than in the status quo. We also believe
that the delayed start date for model
accountability to July 1, 2025, will
enable the OPTN Modernization to
progress further and allow for the
awarding of these contracts and
onboarding of new contractors before
accountability begins. We also note that
the randomized design of the model
means that major national changes, like
this OPTN Modernization effort, will
apply equally to both the selected IOTA
DSAs and the DSAs that are not selected
and are in the comparison group,
meaning that CMS will still be able to
fully evaluate the impacts of the
interventions in the IOTA Model.
Comment: We received multiple
comments about the OPTN’s
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Expeditious Task Force, with some
concerns about the implications of
overlapping initiatives both designed to
increase number of transplants. A
commenter specifically pointed out that
a part of the Expeditious effort includes
a proposed allocation variance to allow
for the study of out-of-sequence
allocation.
Response: We appreciate the
comments and believe that overall, there
is a great deal of synergy between the
efforts being promoted as part of the
Expeditious Task Force and the IOTA
Model, starting with their initial aim of
greatly increasing the number of
transplants completed across the
country. The Expeditious efforts include
many different components, many of
which will help selected IOTA
participants perform better in the
model. This includes efforts like
analyzing patterns of non-use of
kidneys, conducting data analysis to
improve organ offer filters, and working
on how to best secure commitments
from hospital leadership to secure
investment for the IOTA participant to
be able to build up infrastructure to
support a growth in kidney transplants.
We believe that these efforts are
incredibly helpful and will support
improved performance in the
achievement and efficiency domains in
the IOTA Model.
We saw multiple comments about
out-of-sequence allocation and the
proposed limited trials being proposed
by the Expeditious Task Force that are
designed to test a proposed variance to
the allocation methodology for certain
OPOs. We note that these proposed
trials are meant to last for only a few
months and are meant to be limited in
scope and do not believe that they
would impact the ability to evaluate the
IOTA Model. We also note that we
described in the monitoring section of
this rule that we plan to monitor out-ofsequence allocation in the context of the
IOTA Model to see if that is a strategy
used by IOTA participants to utilize
more kidneys.
Comment: We received a comment
saying that this model is being proposed
to be implemented amidst too many
other initiatives, including the proposed
new OPTN data collection initiative.
Response: HHS believes that
collecting the proposed data from OPOs
and kidney transplant hospitals will be
beneficial for patients, improve the
overall transplant process, and help
IOTA participants succeed in the IOTA
Model. The transplant hospital forms
will help to track sources of waitlist
referral, the results of referrals, and the
results of transplant evaluations to see
who makes it onto the transplant
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waiting list. We believe that this data
driven approach will help transplant
hospitals better understand their
sources of referral and potential areas of
improvement in the waitlisting process
that may allow for better waitlist
management. The organ procurement
forms will require OPOs to track how
effective they are at responding to
referrals from donor hospitals and how
effective they are at procuring organs
from potential donor candidates. We
believe that this data driven approach
will help OPOs with quality
improvement to understand their
success at different stages in the
procurement process and will therefore
help to increase the supply of organs for
IOTA participants.
At the same time, HHS understands
this potential criticism and will work to
coordinate once the waitlist referral and
evaluation forms are established,
recognizing that it would be the same
staff at transplant hospitals who would
be likely to fill these out as those who
would be working to increase the
number of transplants under the IOTA
Model. We also note that the proposed
forms for transplant hospitals and OPOs
will undergo a thorough public review
process that began via Federal Register
Notice on November 4, 2024.326
Comment: We received multiple
comments about the metrics used by the
MPSC, some pointing out the
duplicative nature with the metrics that
are a part of the IOTA Model and some
worried that their performance on
MPSC metrics may be hurt by their
performance under the IOTA Model.
Response: As discussed previously in
this section, we anticipated this concern
and believe that there are several
different ways that IOTA participants
would ultimately be able to succeed
under the IOTA Model and OPTN
policies. Given the relatively low bar for
the different metrics for the MPSC, the
risk adjusted nature of their metrics, and
the potential for increasing transplants
with the quality organs that are
currently going unused and the
opportunity to increase living donation
rates, we see many ways that
participants will be able to be successful
under both sets of metrics. Additionally,
we constructed the IOTA Model in the
context of the regulatory efforts through
the OPTN and the CMS Transplant
Center CfCs, recognizing that CMS is
326 U.S. Department of Health and Human
Services. (2024, November 4). Agency information
collection activities; proposed collection; public
comment request [Docket No. HHS–2024–25522].
Federal Register. https://www.federalregister.gov/
documents/2024/11/04/2024-25522/agencyinformation-collection-activities-proposedcollection-public-comment-request-information.
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incentivizing more transplants for
patients, but that we want to make sure
they are done in a way that still ensures
an appropriate level of patient safety.
Comment: We received a comment
about the potential that OPTN will
move to continuous allocation for
kidneys, which could disrupt their
operations.
Response: HHS recognizes that the
OPTN is considering further
adjustments to the organ allocation
system. We believe that the randomly
selection methodology in the IOTA
Model will help to account for any
changes to the allocation system, given
the national focus of any of these
changes. We also believe that the focus
on organ offer acceptance rate in the
model will encourage participating
kidney transplant hospitals to carefully
consider their organ offer filters, which
will help to limit potential disruption to
transplant operations.
We also received comments about the
potential overlap between initiatives
and regulations elsewhere within CMS
and the IOTA Model.
Comment: We received multiple
comments worried about
implementation of the 2020 update to
the OPO CfCs and their potential impact
on OPO decertification, with worries
about the potential effects of new OPOs
coming in on organ allocation. We also
received comments about the OPO CfC
methodology that were out of scope.
Response: We recognize that
implementation of accountability in the
IOTA Model will intersect with the
recertification period for OPOs in 2026.
However, we believe that though there
is a hypothetical potential for some
disruption as a new OPO takes over a
DSA, we believe that the interaction
between the IOTA Model and the
updated CfCs will ultimately be positive
for both OPOs and transplant hospitals
and will better allow both to perform
better on their respective metrics. Since
the updated CfCs were finalized in
2020, OPOs have been procuring more
organs and have complained that there
was not a corresponding incentive on
the transplant hospital side to use more
of the organs that are procured.
Additionally, the number of organ offers
and turndowns has grown since the
updated CfCs and allocation system
were finalized. We believe that the
incentives in the IOTA Model will help
to better ensure more judicious use of
organ offer filters to better reflect
potential for utilization, which will
make it easier for OPOs to place the
organs that they procure. CMS also
commits to recognizing the potential for
disruption with the decertification of
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any OPO and will work to make this
process as smooth as possible.
Comment: We received a comment
asking CMS to prioritize waiver requests
from hospitals seeking to work with a
different OPO before taking action on
creating a new transplant model.
Response: We appreciate this
commenters suggestion and the
importance of this issue; however, this
comment is beyond the scope of this
rule.
Comment: We received a comment
from a hospital pointing out that they
are already subject to the CMS Survey
and Certification process, making the
IOTA Model unnecessary.
Response: As discussed previously, in
2019, CMS removed any outcomes
requirements from its Survey and
Certification requirements. The IOTA
Model focuses on increasing numbers of
transplants and improving organ offer
acceptance rate, neither of which were
addressed in the previous version of the
Survey and Certification requirements
and includes financial incentives for
performance that are not included in the
CMS Survey and Certification process.
We believe that this model test can
complement existing Survey and
Certification requirements as those will
help to ensure a baseline level of patient
care in the transplant process, while
still enabling CMS to test out a new
method to pay for care, without
compromising care for patients.
Comment: We received some concern
about the potential implications on the
IOTA Model if CMS implements some
previously proposed changes to the way
that organ acquisition costs are
calculated.
Response: In the FY 2022 IPPS Final
Rule (CMS 1752–FC3), We decided not
to finalize a proposed change to the way
that Medicare’s share of organ
acquisition costs are calculated for
centers. Based on the consideration of
concerns received from commenters,
CMS decided not to finalize the
proposed policy with respect to
counting organs at this time, but stated
that we may consider it in future
rulemaking.
We also received comments from the
public about interaction with multiple
efforts at the Innovation Center.
Comment: We received a comment
from a dialysis company pointing out
the potential for cooperation between
selected IOTA transplant hospitals and
participants in the existing ETC and
KCC Models.
Response: We appreciate the
comment, as these models were
designed to fit together. Participating
entities in the KCC Model have the
opportunity to partner with selected
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IOTA participants and to even add them
to their participant lists for an upcoming
performance year. CMS encourages
greater collaboration throughout the
entire spectrum of transplant care and
believes that alignment for patients from
the first detection of CKD, through the
need for dialysis, and all the way
through the delivery of a transplant
results in the best outcomes for most
beneficiaries.
Comment: We received a comment
from a hospital association urging that
transplant hospitals participating in any
Innovation Center Advanced APM
model be able to opt out of the proposed
IOTA Model.
Response: We disagree with the
comment as CMS decided to make the
model mandatory for reasons discussed
previously in the relevant section. We
recognize that many kidney transplant
hospitals have made decisions to be
involved in many other different valuebased purchasing programs like the
Shared Savings Program or another
Innovation Center Model and allowing
those involved in those other models to
opt out could hurt the ability to evaluate
the IOTA Model. We also recognize that
none of these models, outside of the
KCC Model which has seen a relatively
low level of participation from kidney
transplant hospitals, are particularly
focused on transplantation, which we
believe helps to show the need for a
transplant-focused value-based care
model.
Comment: We received a comment
from one hospital expressing concern
about being opted into both the IOTA
Model and the TEAM Model, recently
finalized by the Innovation Center, and
were concerned about their ability to
conduct change management at their
hospital if they are selected into both
models.
Response: The TEAM Model was
finalized in the 2025 IPPS Rule in July
2024 (CMS–1808–F). We recognize the
potential complications as CMS and
particularly the Innovation Center tests
multiple models at the same time.
However, we believe that this model has
very different goals than the TEAM
Model, which is focused on surgical
bundles for five procedures and postacute care spending, rather than the
transplant process. We also note that
both models include a period of time
before implementation, creating an
opportunity for hospitals that are
required to participate in both models’
time to enact necessary changes in
practice.
After consideration of the public
comments we received, we are
finalizing the overlaps policy in the
model as proposed. The Innovation
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Center will continue to monitor
developments in the transplant
ecosystem to see if changes are needed
to the model for unintended
consequences. The Innovation Center is
committed to continuing to work and
coordinate with other components of
CMS and HRSA as they continue to
implement the updated OPO CfCs and
the OPTN Modernization process in
order to see if any actions do end up
affecting the ability of selected IOTA
transplant hospitals to perform in the
model. These coordination efforts
through the Organ Transplant Affinity
Group are part of a larger HHS effort to
ensure policy coordination and ensure
input across HHS as we consider and
implement reforms to the transplant
system.
10. Beneficiary Protections
a. Beneficiary Notifications
At § 512.450 of the proposed rule, we
proposed to require IOTA participants
to provide notice to attributed patients
that the IOTA participant is
participating in the IOTA Model. We
believed it would be important for IOTA
participants to provide attributed
patients with a standardized, CMSdeveloped, beneficiary notice to limit
the potential for fraud and abuse,
including patient steering. We intended
to provide a notification template that
IOTA participants would be required to
use. This template would, at minimum,
indicate content that the IOTA
participant would not be permitted to
change and would indicate where the
IOTA participant could insert its own
content. It would also include
information regarding the attributed
patient’s ability to opt-out of data
sharing with IOTA participants and how
they may opt out if they choose to do
so (89 FR 43518).
At § 512.450 of the proposed rule, we
proposed requiring IOTA participants to
display a notice containing these rights
and protections prominently at each
office or facility location where an
attributed patient may receive
treatment, in a clear manner on its
public facing website, and to each
attributed patient in a paper format.
This would increase the probability that
the attributed patients would receive
and take note of this information.
We sought comment on the proposed
requirements for beneficiary
notifications.
The following is a summary of the
public comments received on these
proposals and our response:
Comment: Several commenters
expressed support for requiring
hospitals and providers to notify
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patients about their participation in the
IOTA Model.
Response: We thank the commenters
for their support.
Comment: A commenter expressed
concern that CMS should provide more
information about the required notice of
attribution, including expectations for
hospitals and patients.
Response: We thank the commenter
for its feedback. We will provide a
template for the beneficiary notification
that will have additional information
concerning the notice of attribution. We
will take the commenter’s feedback into
consideration as we draft the template.
Comment: Several commenters
suggested that the beneficiary
notifications should require an IOTA
participant to notify patients of
participation in IOTA in multiple
languages and that CMS limit the
requirement for beneficiary notifications
to be provided only upon patient
request and only at the main transplant
hospital.
Response: We thank the commenters
for their feedback. Although the IOTA
Model does not require IOTA
participants to provide beneficiary
notifications in multiple languages,
other federal laws and regulations that
apply to language services will still
apply to IOTA participants.
Accordingly, we decline to include such
requirements in the IOTA Model
regulations at this time.
We also disagree with the suggestion
that the notice only be required upon
patient request. Many patients may not
be aware of their rights and not know
that such a request should be made.
Additionally, we disagree with the
suggestion that the notice only be
required at the main location of the
IOTA participant. It is possible that a
beneficiary would not be seen at the
main location of the IOTA participant
and therefore not be properly informed.
After consideration of the public
comments received, for the reasons set
forth in this rule, we are finalizing our
proposed provision to require IOTA
participants to provide notice to
attributed patients that the IOTA
participant is participating in the IOTA
Model, including the requirement to
display a notice containing these rights
and protections prominently at each
office or facility location, at § 512.450,
with minor technical corrections to
update the spacing in the regulation and
provide clarification, including the
removal of duplicative text, at
§ 512.450(a)(3)(ii).
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b. Availability of Services and
Beneficiary Freedom of Choice
In section II.B of the proposed rule,
we proposed the Standard Provisions
for Innovation Center Models relating to
availability of services and beneficiary
freedom of choice would apply to the
IOTA Model. These provisions were
originally finalized as general
provisions in the Code of Federal
Regulations (42 CFR part 512 subpart A)
that applied to specific Innovation
Center models, but are finalized
separately in section II.B of this final
rule for expansion to all mandatory
Innovation Center Models with
performance periods that begin on or
after January 1, 2025. Consistent with
this final rule, IOTA participants will
need to preserve beneficiary freedom of
choice and continue to make medically
necessary covered services available to
beneficiaries to the extent required by
applicable law.
We received no comments on these
proposals and therefore are finalizing
these proposals without modification.
11. Financial Arrangements and
Attributed Patient Engagement
Incentives
a. Background
We believe it is necessary to provide
IOTA participants with flexibilities that
could support their performance in the
IOTA Model and allow for greater
support for the needs of attributed
patients. These flexibilities are outlined
in this section and include the ability to
engage in financial arrangements to
share IOTA upside risk payments and
responsibility for paying Medicare for
IOTA downside risk payments with
providers and suppliers making
contributions to the IOTA participants’
performance against model metrics, and
the availability of the provision of
attributed patient engagement
incentives. Such flexibilities would
allow IOTA participants to share all or
some of the payments they may be
eligible to receive from CMS and to
share the responsibility for the funds
needed to pay CMS providers and
suppliers engaged in caring for
attributed patients, if those providers
and suppliers have a role in the IOTA
participant’s spending or quality
performance. Additionally, we believe
that IOTA participants caring for
attributed patients may want to offer
attributed patient engagement
incentives to encourage adherence to
recommended treatment and active
patient engagement in recovery. These
incentives may help an IOTA
participant reach their quality and
efficiency goals for the model, while
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also benefitting beneficiaries’ health and
the Medicare Trust Fund if the IOTA
participant improves the quality and
efficiency of care that results in the
Medicare beneficiary’s reductions in
hospital readmissions, complications,
days in acute care, and mortality, while
recovery continues uninterrupted or
accelerates.
b. Overview of IOTA Model Financial
Arrangements
We believe that IOTA participants
may wish to enter into financial
arrangements with providers and
suppliers caring for attributed patients
to share model upside risk payments or
downside risk payments, to align the
financial incentives of those providers
and suppliers with the IOTA Model
goals of increasing the number of kidney
transplants furnished to attributed
patients to lower costs and to improve
their quality of life. To do so, we expect
that IOTA participants would identify
key providers and suppliers caring for
attributed patients in their communities
and DSAs. The IOTA participants could
establish partnerships with these
providers and suppliers to promote
accountability for the quality, cost, and
overall care for attributed patients,
including managing and coordinating
care; encouraging investment in
infrastructure, enabling technologies,
and redesigning care processes for high
quality and efficient service delivery;
and carrying out other obligations or
duties under the IOTA Model. These
providers and suppliers may invest
substantial time and other resources in
these activities, yet they would neither
be the direct recipients of any model
upside risk payments from Medicare,
nor directly responsible for paying to
CMS any downside risk payments
incurred. Therefore, we believe it is
possible that an IOTA participant that
may receive an upside risk payment
from Medicare or may need to pay a
downside risk payment to Medicare
may want to enter into financial
arrangements with other providers or
suppliers to share these performance
adjustments with the IOTA participant.
We require that all financial
relationships established between IOTA
participants and providers or suppliers
for purposes of the IOTA Model would
only be those permitted under
applicable law and regulations,
including the applicable fraud and
abuse laws and all applicable payment
and coverage requirements. As
discussed in section III.C.3 of this final
rule, CMS determined that the Federal
anti-kickback statute safe harbor for
CMS-sponsored model arrangements (42
CFR 1001.952(ii)(1)) is available to
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protect the financial arrangements
proposed in this section when
arrangements with eligible providers
and suppliers are in compliance with
this policy and the conditions for use of
the Federal anti-kickback statute safe
harbor set out at § 1001.952(ii)(1).
We recognize that there are numerous
arrangements that IOTA participants
may wish to enter other than the
financial arrangements described in the
proposed regulations for which safe
harbor protection may be extended that
could be beneficial to the IOTA
participants. For example, IOTA
participants may choose to engage with
organizations that are neither providers
nor suppliers to assist with matters such
as data analysis; local provider and
supplier engagement; care redesign
planning and implementation;
beneficiary outreach; beneficiary care
coordination and management;
monitoring IOTA participants’
compliance with the model’s terms and
conditions; or other model-related
activities. Such organizations may play
important roles in an IOTA participant’s
plans to implement the model based on
the experience these organizations may
bring, such as prior experience with
living donation initiatives, care
coordination expertise, familiarity with
a particular local community, or
knowledge of SRTR data. We require
that all relationships established
between IOTA participants and these
organizations for purposes of the model
would be those permitted only under
existing law and regulation, including
any relationships that would include
the IOTA participant’s sharing of model
upside risk payments or downside risk
payments with such organizations., and
must comply with all applicable laws
and regulations We require these
relationships to be solely based on the
level of engagement of the
organization’s resources to directly
support the participants’ model
implementation.
c. IOTA Collaborators
Given the financial incentives of the
IOTA performance-based payments, as
described in section III.C.6.c of this final
rule, an IOTA participant may want to
engage in financial arrangements with
providers and suppliers making
contributions to the IOTA participant’s
performance across the achievement
domain, efficiency domain, and quality
domain. Such arrangements would
allow the IOTA participant to share
monies earned from the upside risk
payments. Likewise, such arrangements
could allow the IOTA participant to
share the responsibility for the funds
needed to repay CMS the downside risk
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payments. We proposed to use the term
‘‘IOTA collaborator’’ to refer to these
providers and suppliers.
Because attributed patients include
both those on the kidney transplant
waitlist and those who have received a
kidney transplant, as described in
section III.C.4.a of this final rule, many
providers and suppliers other than the
IOTA participant would furnish related
services to attributed patients during the
model performance period. As such, for
purposes of the Federal anti-kickback
statute safe harbor for CMS-sponsored
model arrangements (42 CFR
1001.952(ii)), we proposed that the
following types of providers and
suppliers that are Medicare-enrolled
and eligible to participate in Medicare
may be IOTA collaborators:
• Nephrologist.
• ESRD Facility.
• Skilled Nursing Facility (SNF).
• Home Health Agency (HHA).
• Long-Term Care Hospital (LTCH).
• Inpatient Rehabilitation Facility
(IRF).
• Physician.
• Nonphysician practitioner.
• Therapist in a private practice.
• Comprehensive Outpatient
Rehabilitation Facility (CORF).
• Provider or supplier of outpatient
therapy services.
• Physician Group Practice (PGP).
• Hospital.
• Critical Access Hospital (CAH).
• Non-physician provider group
practice (NPPGP).
• Therapy Group Practice (TGP).
We sought comment on the proposed
definition of IOTA collaborators and
any additional Medicare-enrolled
providers or suppliers that should be
included in this definition.
The following is a summary of the
public comments received on this
proposal and our responses:
Comment: Several commenters
supported the inclusion of IOTA
collaborators in the model and
encouraged expanding the types of
entities allowed as IOTA collaborators
to include other provider types.
Commenters recommended including
in the list of IOTA collaborators:
audiologists, registered dietitian
nutritionists (RDNs), and rural
emergency hospitals.
Response: We thank commenters for
their recommendations and support of
this initiative. We appreciate your
insights on expanding the types of
entities allowed as IOTA collaborators.
We will take them into consideration in
future rulemaking.
After consideration of the public
comments received, for the reasons set
forth in this rule, we are finalizing the
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proposal for the model definition of
IOTA collaborators as proposed at
§ 512.402. We are also finalizing as
proposed the definitions for the types of
IOTA collaborator Medicare-enrolled
providers or suppliers at § 512.402 with
minor technical corrections to update
cross references. Specifically, we are
finalizing our proposed definition of
nonphysician practitioner at § 512.402
with a minor technical correction to
include the full cross reference.
Additionally, we are finalizing our
proposed definition of therapist at
§ 512.402 with a minor technical
correction to include the correct cross
reference to the regulatory definition for
that term. Lastly, we are finalizing our
proposed definition of hospital at
§ 512.402 with a technical correction to
specify that hospital has the meaning set
forth in § 1861(e) of the Act.327
d. Sharing Arrangements
(1) General
Similar to the Comprehensive Care for
Joint Replacement Payment Model (CJR)
(42 CFR part 510), we proposed that
certain financial arrangements between
an IOTA participant and an IOTA
collaborator be termed ‘‘sharing
arrangements.’’ For purposes of the
Federal anti-kickback statute safe harbor
for CMS-sponsored model arrangements
(§ 1001.952(ii)(1)), we proposed that a
sharing arrangement would be a
financial arrangement to share only—(1)
the upside risk payment; and (2) the
downside risk payment.
Where a payment from an IOTA
participant to an IOTA collaborator is
made pursuant to a sharing
arrangement, we proposed to define that
payment as a ‘‘gainsharing payment,’’
which is discussed in section
III.C.11.d.(3). of this final rule. Where a
payment from an IOTA collaborator to
an IOTA participant is made pursuant to
a sharing arrangement, we proposed to
define that payment as an ‘‘alignment
payment,’’ which is discussed in section
III.C.11.d.(3). of this final rule.
We sought comment about all
provisions described in the preceding
discussion.
We received no comments on these
proposals and therefore are finalizing
these proposals as proposed in our
regulation at § 512.452. We are also
finalizing without modification the
proposed definitions of sharing
327 Subsequent to the publication of the proposed
rule, we found that the proposed definition of
‘‘hospital’’ included an incorrect citation to the
Social Security Act. Section 1861(u) of the Act
defines ‘‘provider of services,’’ which includes
more than just hospitals. We clarify that, for the
purposes of the IOTA Model, the term ‘‘hospital’’
has the meaning set forth in § 1861(e) of the Act.
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alignment payment at § 512.402.
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(2) Requirements
We proposed several requirements for
sharing arrangements to help ensure
that their sole purpose is to create
financial alignment between IOTA
participants and IOTA collaborators
toward the goals of the model while
maintaining adequate program integrity
safeguards. An IOTA participant must
not make a gainsharing payment or
receive an alignment payment except in
accordance with a sharing arrangement.
We proposed that a sharing arrangement
must comply with the provisions of
§ 512.452 and all other applicable laws
and regulations, including the
applicable fraud and abuse laws and all
applicable payment and coverage
requirements.
We proposed that the IOTA
participant must develop, maintain, and
use a set of written policies for selecting
providers and suppliers to be IOTA
collaborators. To safeguard against
potentially fraudulent or abusive
practices, we proposed that the
selection criteria must include the
quality of care delivered by the potential
IOTA collaborator. We also proposed
that the selection criteria cannot be
based directly or indirectly on the
volume or value of referrals or business
otherwise generated by, between, or
among the IOTA participant, any IOTA
collaborator, any collaboration agent, or
any individual or entity affiliated with
an IOTA participant, IOTA collaborator,
or collaboration agent. Additionally, we
proposed that IOTA participants must
consider the selection of IOTA
collaborators based on criteria related
to, and inclusive of, the anticipated
contribution to the performance of the
IOTA participant across the
achievement domain, efficiency
domain, and quality domain by the
potential IOTA collaborator to ensure
that the selection of IOTA collaborators
takes into consideration the likelihood
of their future performance.
It is necessary that IOTA participants
have adequate oversight over sharing
arrangements to ensure that all
arrangements meet the requirements of
the model. Therefore, we proposed that
the board or other governing body of the
IOTA participant have responsibility for
overseeing the IOTA participant’s
participation in the model, including,
but not limited to, its arrangements with
IOTA collaborators, its payment of
gainsharing payments, its receipt of
alignment payments, and its use of
beneficiary incentives (as discussed in
III.C.11.g of this final rule).
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Finally, we proposed that if an IOTA
participant enters a sharing
arrangement, its compliance program
must include oversight of sharing
arrangements and compliance with the
applicable requirements of the model.
Requiring oversight of sharing
arrangements to be included in the
compliance progra
