Medicare Program; Hospital Inpatient Prospective Payment Systems for Acute Care Hospitals (IPPS) and the Long-Term Care Hospital Prospective Payment System and Policy Changes and Fiscal Year (FY) 2026 Rates; Changes to the FY 2025 IPPS Rates Due to Court Decision; Requirements for Quality Programs; and Other Policy Changes; Health Data, Technology, and Interoperability: Electronic Prescribing, Real-Time Prescription Benefit and Electronic Prior Authorization es, and the need for operational flexibility as hospitals navigate the transition to hybrid reporting methodologies. We are attentive to the concerns raised about safety net providers, recognizing that structural challenges related to patient demographics, health literacy, and language barriers could inadvertently result in penalties that reflect patient population characteristics rather than actual care quality. CMS remains committed to ongoing monitoring of measure feasibility and will continue to engage with stakeholders to ensure that quality measures accurately reflect quality of care rather than penalizing providers who serve vulnerable populations with complex healthcare needs. After consideration of the public comments, we are not finalizing a change to the Hybrid HWR measure as proposed, but instead, will maintain the policy as finalized in the FY 2025 IPPS/ LTCH PPS final rule. This finalized policy will utilize CY 2025 for the PY1 CQS baseline period and use July 1, 2024–June 30, 2025 as the measure performance period for the Hybrid HWR measure. Additionally, TEAM is maintaining alignment with the Hospital IQR Program. Given that CMS has extended the voluntary reporting e 30, 2025 as the measure performance period for the Hybrid HWR measure. Additionally, TEAM is maintaining alignment with the Hospital IQR Program. Given that CMS has extended the voluntary reporting of the core clinical data elements and linking variables for the Hybrid HWR measure through June 30, 2025, this means that for PY1, we will use the claims-only portion of the Hybrid HWR measure in the CQS calculation. In subsequent TEAM performance years, the complete Hybrid HWR Measure— incorporating both claims data and core clinical data elements—may be utilized once the core clinical data elements transition from voluntary to required reporting. Any changes or modifications, including modifications to the data used to construct the measure or the CQS baseline period, will be implemented through future notice and comment rulemaking. (3) Information Transfer Patient Reported Outcome-Based Performance Measure (Information Transfer PRO– PM) We stated in the proposed rule that the existing quality measures finalized in the FY 2025 IPPS/LTCH PPS final rule for TEAM were selected based on their relevance to episode categories tested in the model, while also considering the reporting burde quality measures finalized in the FY 2025 IPPS/LTCH PPS final rule for TEAM were selected based on their relevance to episode categories tested in the model, while also considering the reporting burden on participants. These measures focus on key domains, including hospital readmissions, patient safety, and patient reported outcomes, which we believe represents areas of quality that are particularly important to patients undergoing acute procedures. We continue to believe that quality measures used in TEAM should address one of these domains, given their importance to patient quality of care and relationship to episode care management. As stated in FY 2025 IPPS/LTCH PPS final rule (89 FR 68986), we wish to incorporate more patient-reported outcome measures (PRO–PMs) into TEAM, as these measures provide valuable insights into the patient’s perspective of care received. We indicated in the proposed rule that we also wish to incorporate quality measures that capture care in the outpatient setting, given the LEJR and Spinal Fusion episode categories initiate in the hospital outpatient department (HOPD) setting and all the measures finalized in the FY 2025 IPPS/LTCH PPS final rule (89 F setting, given the LEJR and Spinal Fusion episode categories initiate in the hospital outpatient department (HOPD) setting and all the measures finalized in the FY 2025 IPPS/LTCH PPS final rule (89 FR 68986) are measures of inpatient performance. To identify potential quality measures for episode categories initiated in the HOPD, we stated in the proposed rule that we reviewed quality measures from the CMS Hospital Outpatient Quality Reporting Program (Hospital OQR Program) that align with the domains emphasized in TEAM. To maintain a reasonable volume of quality measures in TEAM, we aimed to identify a single measure that would be clinically meaningful for both the LEJR and Spinal Fusion episode categories, rather than adding separate quality measures for each. We identified one quality measure, the Risk-Standardized Hospital Visits Within 7 Days After Hospital Outpatient Surgery, that hospitals are required to report to the Hospital OQR Program, as well as two quality measures that hospitals may voluntarily report: the Risk- Standardized PRO–PM Following Elective Primary THA and/or TKA in the HOPD Setting, and the Information Transfer PRO–PM. We stated in the proposed rule that hospitals may voluntarily report: the Risk- Standardized PRO–PM Following Elective Primary THA and/or TKA in the HOPD Setting, and the Information Transfer PRO–PM. We stated in the proposed rule that we evaluated the suitability of each quality measure for TEAM based on its pros and cons. • The Risk-Standardized Hospital Visits Within 7 Days After Hospital Outpatient Surgery is applicable to both the LEJR and Spinal Fusion episode categories, focuses on hospital readmissions, and could be included in TEAM for PY1 (CY 2026) given its current mandatory reporting status in the Hospital OQR program. However, it does not advance CMS’s or the model’s goal of increasing the number of PRO– PMs. • The Risk-Standardized PRO–PM Following Elective Primary THA and/or TKA in the HOPD Setting aligns well with the existing THA/TKA PRO–PM for inpatient LEJR episodes and would increase the number of PRO–PMs in the model; however, it is only applicable to LEJR episodes, and mandatory reporting for the Hospital OQR Program will not begin until PY3 of TEAM (CY 2028). • The Information Transfer PRO–PM is applicable to both the LEJR and Spinal Fusion episode categories and would increase the number of PR spital OQR Program will not begin until PY3 of TEAM (CY 2028). • The Information Transfer PRO–PM is applicable to both the LEJR and Spinal Fusion episode categories and would increase the number of PRO–PMs in the model; however, mandatory reporting for the Hospital OQR Program will not begin until PY2 of TEAM (CY 2027). Since our aim is to create a meaningful and efficient quality VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00552 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations 37087 measure set, we stated in the proposed rule that we did not believe it is necessary to include all three measures in TEAM. Given that the Risk- Standardized PRO–PM Following Elective Primary THA and/or TKA in the HOPD Setting measure is only applicable to the LEJR episode category, we did not consider it beneficial to propose this measure for use in TEAM. Of the remaining two measures, we recognized the value of the Risk- Standardized Hospital Visits Within 7 Days After Hospital Outpatient Surgery; however, this focuses on hospital readmis use in TEAM. Of the remaining two measures, we recognized the value of the Risk- Standardized Hospital Visits Within 7 Days After Hospital Outpatient Surgery; however, this focuses on hospital readmissions and did not provide the patient viewpoint afforded by PRO–PMs that we are prioritizing capturing in the model. As such, we proposed the addition of the Information Transfer PRO–PM for all episode categories initiated in the HOPD in TEAM. We stated in the proposed rule that the Information Transfer PRO–PM can apply to all episode categories initiated in the HOPD under TEAM as it evaluates how well information is transferred to patients after outpatient procedures, particularly in HOPDs. Additionally, we stated that this measure captures patient viewpoint afforded by PRO–PMs. To ensure alignment with the Hospital OQR Program, we proposed using the following measure specifications, as detailed and updated here: https://www.cms.gov/files/ document/patient-understanding-key- information-related-recovery-after- facility-based-outpatient-procedure- or.pdf. We indicated in the proposed rule that this document outlines key information related to the Information Transfer PRO–PM and highlig elated-recovery-after- facility-based-outpatient-procedure- or.pdf. We indicated in the proposed rule that this document outlines key information related to the Information Transfer PRO–PM and highlights the need for improved patient education for post-discharge instructions. The measure was developed by Yale New Haven Services Corporation for CMS and tested across hospital outpatient departments. We stated in the proposed rule that the goal of this measure is to enhance recovery outcomes by standardizing information transfer. We also proposed including the Information Transfer PRO–PM starting in PY3 (CY 2028) with a CY 2027 CQS baseline period and the following quality measure performance periods as displayed in Table XI.A.–03. We believed that including the Information Transfer PRO–PM in TEAM would enhance the model because it is a general measure not tied to a specific clinical diagnosis or procedure. We stated in the proposed rule that this flexibility means it could apply to current episode categories initiated in the HOPD and any future episode categories, if proposed and finalized in future rulemaking. We also emphasized the importance of increasing the number of PRO–PMs, as nt episode categories initiated in the HOPD and any future episode categories, if proposed and finalized in future rulemaking. We also emphasized the importance of increasing the number of PRO–PMs, as they offer a direct way to incorporate patient input into quality measure performance. We further believed that delaying the inclusion of the Information Transfer PRO–PM until PY3 would allow TEAM participants to gain 1 year of mandatory reporting experience before the measure is incorporated into TEAM, affecting their composite quality score (CQS) and ultimately their reconciliation amounts. Lastly, we stated in the proposed rule that similar to the other two measures that we considered but did not propose (THA/TKA PRO–PM and Hospital Visits within 7 Days after Hospital Outpatient Surgery), inclusion of the Information Transfer PRO–PM aligns with those used in ongoing models and programs (this measure aligns with already existing reporting requirements for the Hospital Outpatient Quality Reporting (OQR) Program) and therefore, would not increase TEAM participant burden. We sought comment on our proposal to include the Information Transfer PRO–PM in TEAM starting in PY 3. We also soug Reporting (OQR) Program) and therefore, would not increase TEAM participant burden. We sought comment on our proposal to include the Information Transfer PRO–PM in TEAM starting in PY 3. We also sought comment on other quality measures, including options for capturing quality of care in the outpatient setting and other PRO–PMs appropriate for TEAM quality measurement. The following is a summary of the public comments received on the proposed policy to include the Information Transfer PRO–PM in TEAM in PY3, and our responses to these comments: Comment: Many commenters provided support for the inclusion of PRO–PM based measures, highlighting their importance in capturing the patient’s voice in assessing healthcare quality, as well as provider-patient communication and the patient’s understanding of their role in recovery and outcomes. Additionally, these commenters emphasized their appreciation that these PRO–PMs align with existing reporting requirements and therefore do not increase participant reporting burden. Response: We thank the commenters for their feedback and support regarding the inclusion of the Information Transfer PRO–PM. We agree this quality measure emphasizes the i articipant reporting burden. Response: We thank the commenters for their feedback and support regarding the inclusion of the Information Transfer PRO–PM. We agree this quality measure emphasizes the importance of the ongoing collaborative relationship between the provider and patient, the need for clear and effective discharge instructions, and improving recovery outcomes. We also agree with the importance of aligning with existing reporting requirements so as not to increase participant reporting burden. Comment: A couple of commenters stated their support in the inclusion of this quality measure in TEAM, highlighting the relevance of the Information Transfer PRO–PM in outpatient settings where clear post- discharge instructions and medication adherence are crucial. A commenter raised concerns for accurate information transfer assessment and patient understanding influenced by factors like health literacy and language barriers, especially among older, complex patients. A couple of commenters made suggestions for operationalizing this measure, including the availability of technical guidance, standardized tools and learning collaboratives, especially if this measure is expanded to menters made suggestions for operationalizing this measure, including the availability of technical guidance, standardized tools and learning collaboratives, especially if this measure is expanded to other settings outside of the hospital outpatient setting. Response: We thank the commenters for their support of the Information Transfer PRO–PM in the hospital outpatient setting. We agree that the elements this measure incorporates are indeed crucial for patient care, specifically the importance of clear post-discharge instructions and medication adherence, as these commenters highlighted. We also appreciate the suggestions for technical VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00553 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k / > H P G 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00554 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations 37089 commenters shared concerns regarding the reporting threshold and a commenter suggested reducing the survey k Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations 37089 commenters shared concerns regarding the reporting threshold and a commenter suggested reducing the survey volume to 35 percent for 2 additional years or allowing documentation of two failed attempts to collect follow-up data as a pass to help participants manage data capture without significant penalties and give patients time to become familiar with the measures. A commenter noted how participants with low volume may receive a neutral quality score with no path to improvement. Many commenters shared concerns including the measure’s applicability to a broad patient population and not specific to TEAM episodes, increased burden on hospitals and patients, and operational challenges when surveying patients pre- and post-surgical events. Some commenters also noted the complexity and cost of administering PRO–PMs, the lack of comparative benchmarks, and the resource demands and technical difficulties associated with the measure. Response: We acknowledge the concerns related to the operational challenges associated with implementing the Information Transfer PRO–PM. We believe the voluntary repo ies associated with the measure. Response: We acknowledge the concerns related to the operational challenges associated with implementing the Information Transfer PRO–PM. We believe the voluntary reporting period, followed by 1 year of mandatory reporting prior to its integration into TEAM, will provide participants with the opportunity to address these challenges. We refer the commenters to the CY 2026 OPPS/ASC final rule (89 FR 94408) for data sources and measure specifications. We recognize the potential need for additional technical assistance and guidance as the model progresses and will consider this in the development of materials specifically for the inclusion in TEAM. The broad scope of the Information Transfer PRO–PM was intentionally chosen to encompass all TEAM episodes and any future episodes added to the model. Comment: Some commenters emphasized the need for episode- specific quality measures rather than general measures and did not support the inclusion of the Information Transfer PRO–PM. A commenter noted that the PRO–PM provides little insight into the quality of care for spinal fusion procedures since it is not specific to these procedures. A few commenters sugge on Transfer PRO–PM. A commenter noted that the PRO–PM provides little insight into the quality of care for spinal fusion procedures since it is not specific to these procedures. A few commenters suggested using specialty society clinical data registries relevant to each specific episode included under the model. Another commenter noted that PRO–PMs do not provide timely feedback to make improvements in patient care. A commenter suggested only including the current THA/TKA PRO–PM instead of adding another PRO–PM. Another commenter recommended developing new quality measures specifically designed for TEAM. Additionally, a commenter encouraged the use of the 3-Item Care Transition Measure (CTM–3) as an alternative measure and a commenter proposed the THA/TKA PRO–PM that will be available in the OQR in 2028. Response: We appreciate the feedback provided by the commenters. We acknowledge the preference for episode- specific measures among TEAM participants and we will consider incorporating such measures, including registries, where applicable in future rulemaking. The inclusion of general measures serves multiple goals of TEAM, including providing an indicator of overall quality of car such measures, including registries, where applicable in future rulemaking. The inclusion of general measures serves multiple goals of TEAM, including providing an indicator of overall quality of care at the hospital level. Additionally, the current measures are part of the hospital- required reporting program, which prevents duplicative reporting by participants. We will move forward with the inclusion of the Information Transfer PRO–PM as this measure encompasses a 9-question survey spanning across the three domains of applicability, medications, and daily activities, as opposed to the three questions in the CTM–31. Additionally, regarding the THA/TKA PRO–PM for outpatient reporting, this would only apply to our outpatient LEJR episodes. The Information Transfer PRO–PM applies to all outpatient episodes, and we believe it is important to use a measure that can capture quality in the outpatient setting for all episode categories rather than limiting it to a single episode category. Further, if we add other outpatient episode categories to the model through notice and comment rulemaking, we would not have to expand TEAM’s quality measure set because the Information Transfer PRO–PM urther, if we add other outpatient episode categories to the model through notice and comment rulemaking, we would not have to expand TEAM’s quality measure set because the Information Transfer PRO–PM could be applied to future outpatient episode categories. Comment: A couple of commenters discussed that the current quality measures capture all hospital patients, not just those specific to the episode being analyzed under TEAM. They expressed concerns that these measures do not provide a true picture of quality for TEAM episodes and a very small number of clinical episodes make-up the quality measure. A commenter discussed the concern that participants would not receive penalties for low- quality care with the current model structure. A commenter requested clarification on how volume constraints will be addressed in the measure calculation. Response: Previous episode-based payment models, including the BPCI Advanced model, have utilized similar hospital-level quality measures to assess participant quality performance. Therefore, we believe this approach is consistent with other CMS models. We acknowledge TEAM participants’ preference for episode-specific measures. We will consider participant quality performance. Therefore, we believe this approach is consistent with other CMS models. We acknowledge TEAM participants’ preference for episode-specific measures. We will consider incorporating episode-specific measures where applicable and may propose them in future notice and comment rulemaking. We disagree with the commenter that the Information Transfer PRO–PM does not assess quality, as it captures key information regarding the patient’s understanding of discharge instructions, which benefits many aspects of their care and progress toward recovery. This measure allows hospitals to identify their strong areas of communication and where overall improvements can be made, which is vital to improving outcomes and reducing harm. We also recognize that participants may need clarification regarding measure calculation, including specifics related to low volume. As we continue to move forward with TEAM, this information will be considered as we develop informational materials to best support the needs of participants throughout the model period. Additionally, we refer the commenters to the CY 2026 OPPS/ASC final rule (89 FR 94408) for data sources and measure specif erials to best support the needs of participants throughout the model period. Additionally, we refer the commenters to the CY 2026 OPPS/ASC final rule (89 FR 94408) for data sources and measure specifications. Comment: A commenter noted that the measure does not accurately assess quality because it evaluates the patient’s understanding of the information rather than the quality of the information provided. They also pointed out that the study CMS cited in support of the measure based its conclusions on documentation review rather than patient responses. Response: We appreciate the feedback provided by the commenter. We disagree with the commenter that the Information Transfer PRO–PM does not accurately assess quality. The survey results provide hospitals with valuable patient-reported outcome (PRO) data designed to evaluate communication efforts. This data enables hospitals to mitigate the risk of patient harm that may occur if patients do not fully understand their recovery information. Patient responses are crucial for making overall improvements in the path to recovery. For more information, we refer the commenter to the Hospital OQR Specifications Manual, Patient Understanding n. Patient responses are crucial for making overall improvements in the path to recovery. For more information, we refer the commenter to the Hospital OQR Specifications Manual, Patient Understanding of Key Information Related to Recovery After a Facility- Based Outpatient Procedure or Surgery, Patient Reported Outcome-Based Performance Measure (PRO–PM) Measure ID #: OP–46. Comment: A commenter discussed the current alignment of the Information VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00555 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k 37090 Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations Transfer PRO–PM eligibility with the criteria used for the Outpatient and Ambulatory Surgery CAHPS (OAS CAHPS) survey. The commenter stated this alignment narrows the original eligibility for the measure and requested a Current Procedural Terminology (CPT) crosswalk between TEAM episodes to ensure all of those who qualify for the Information Transfer PRO–PM are included in the data. Response: We appreciate the commenter’s suggestion and will consider it during the development episodes to ensure all of those who qualify for the Information Transfer PRO–PM are included in the data. Response: We appreciate the commenter’s suggestion and will consider it during the development of supporting documents for the model that align with the policies finalized in this rule. Comment: A commenter requested CMS clarify who should administer the survey associated with the Information Transfer PRO–PM. The commenter also requested clarification on how the measure will be used and if there will be implications if beneficiaries do not submit the survey. Response: We thank the commenter for their comment. We note that, in the CY 2025 OPPS/ASC Final Rule, the Information Transfer PRO–PM was adopted into the CMS Hospital Outpatient Quality Reporting (OQR) Program as a voluntary measure for the CY 2026 reporting period followed by mandatory reporting beginning with the CY 2027 reporting period/CY 2029 payment determination (89 FR 99406). The measure will also be used in TEAM, starting in performance year 3 (CY 2028), to assess quality performance via the composite quality score (CQS) for episodes initiated in the hospital outpatient department. We refer the commenter to the FY ting in performance year 3 (CY 2028), to assess quality performance via the composite quality score (CQS) for episodes initiated in the hospital outpatient department. We refer the commenter to the FY 2025 IPPS/LTCH PPS final rule for the methodology on how the CQS will be constructed (89 FR 69774) and the CY 2026 OPPS/ASC final rule (89 FR 94408) for data sources and measure specifications. CMS finalized that the survey should be administered 2 to 7 days post-procedure and that survey administrators should allow a 65-day window for patient response. Additionally, only fully completed surveys are included in the measure calculation. We note there is a 300 minimum random sample size of completed surveys. Hospitals that are unable to collect 300 completed surveys will not be able to perform random sampling, and would instead be required to submit data on all survey responses. While the hospital is accountable for ensuring the electronic survey is offered to all patients meeting the measure’s denominator specifications, and may administer it through their own internal means, a hospital is not precluded from using a third-party vendor to administer the survey electronically. For furthe re’s denominator specifications, and may administer it through their own internal means, a hospital is not precluded from using a third-party vendor to administer the survey electronically. For further information on the Information Transfer PRO–PM, we refer the commenter to the following resources: https:// qualitynet.cms.gov/files/6830b e4d662c6b68b52ddd2d?filename=1z_ OP46MIF_v19.0.pdf and https:// www.cms.gov/files/document/patient- understanding-key-information-related- recovery-after-facility-based-outpatient- procedure-or.pdf After consideration of the public comments, we are finalizing without modification our proposals at § 512.547(a)(3)(vi) for inclusion of the Information Transfer PRO–PM for all TEAM outpatient episodes beginning in PY3 with a CY 2027 CQS baseline period. We will continue to monitor the need for revisions, with any future updates incorporated into a subsequent notice and comment rulemaking. Given our inclusion of the Information Transfer PRO–PM in TEAM, Table XI.A.-04 represents all TEAM quality measures by performance year. (4) Approach for When TEAM Participant has No Quality Measure Performance Data As was outlined in Table X.A.–09 of the FY 2025 IPPS e XI.A.-04 represents all TEAM quality measures by performance year. (4) Approach for When TEAM Participant has No Quality Measure Performance Data As was outlined in Table X.A.–09 of the FY 2025 IPPS/LTCH PPS final rule (89 FR 69744), TEAM quality measures will be evaluated against a measure performance period. We stated in the proposed rule that the measure performance periods are consistent with those used in ongoing models and programs in which TEAM measures align, including the Hospital IQR Program and Hospital-Acquired Condition Reduction Program performance periods, so that there is no additional reporting burden on TEAM participants as a result of the quality measures used in TEAM. However, we recognized it was possible that some TEAM participants may not have a complete measure set during the performance period in which to measure their quality against. For example, in the proposed rule we stated that a newly established hospital that began seeing Medicare beneficiaries in early 2025 may have no or incomplete quality measure data given the quality measure performance periods for the VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00556 Fmt 4701 Sfmt 4700 E:\FR\ ly 2025 may have no or incomplete quality measure data given the quality measure performance periods for the VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00556 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k / > H P G 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00557 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k 37092 Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations suggested that CMS differentiate the causes of missing data in order to distinguish between hospitals unable to report due to structural factors and those that choose not to report. The commenter proposed a tiered scoring approach and encouraged transparency by publicly reporting whether a neutral score result was due to insufficient volume, new status, or non-reporting. Additionally, they suggested allowing voluntary submission of supplemental data to justify using a neutral quality score. This commenter also requested that CMS consider the impact on the CQS in areas where specialists do not have control over missing PRO or hybrid me mental data to justify using a neutral quality score. This commenter also requested that CMS consider the impact on the CQS in areas where specialists do not have control over missing PRO or hybrid measure data, noting it should not disproportionally affect the CQS in ways clinicians cannot reasonably influence. Another commenter agreed that a neutral quality score for measures with insufficient data is important for those available for self-selection in the IQR program, as facilities might choose not to report on those particular measures and should not be penalized for lack of sufficient data due to the self- selection process. Response: We thank the commenters for their thoughtful feedback. We agree that the proposed policy aims to fairly address hospitals providing high quality care but lacking sufficient quality data. We agree with the commenter that this policy does not remove a hospital’s ability to self-select measures in the Hospital IQR program. TEAM participants that do not self-select eligible measures that are also used in TEAM and have insufficient data will receive a neutral scaled quality score of 50 for that measure. We also value the suggestions for the tiered app self-select eligible measures that are also used in TEAM and have insufficient data will receive a neutral scaled quality score of 50 for that measure. We also value the suggestions for the tiered approach, public reporting and voluntary supplemental data, and will take these into consideration, and if warranted, would propose in future notice and comment rulemaking. Comment: A commenter stated the proposed policy may unfairly penalize hospitals for reasons unrelated to quality performance, such as their option to report on other voluntary measures. The commenter states this defeats the purpose of applying a standardized quality measure and urges CMS to make this model voluntary. Response: We thank the commenter for their feedback. We did consider that the Hospital IQR allows for self- selection of quality measures and considered how to approach where TEAM quality measures are included in this self-selected set. We considered requiring TEAM participants report on all TEAM quality measures, but this would remove the flexibility for their Hospital IQR self-selection. We ultimately determined that in order to stay aligned with CMS quality reporting programs, we would continue to allow res, but this would remove the flexibility for their Hospital IQR self-selection. We ultimately determined that in order to stay aligned with CMS quality reporting programs, we would continue to allow hospitals the option of self-selection, understanding that if they choose not to report on a quality measure used in TEAM, and there is insufficient data, they will receive a neutral scaled quality measure score of 50. We thank the commenter for their considerations and will monitor the impact of this policy. Any changes to TEAM’s quality measure approach will occur through future notice and comment rulemaking. Comment: Several commenters did not support the proposed policy and suggested alternative approaches for quality measures in cases where participants have insufficient data. Suggested alternatives included allowing supplemental quality data submissions, temporary exemptions or exclusion instead of a neutral score, setting the weight of these measures to zero, and using historical performance instead of a neutral value. Commenters expressed that these alternatives would avoid unfair penalties for participants facing data collection challenges while still reflecting actual perfor erformance instead of a neutral value. Commenters expressed that these alternatives would avoid unfair penalties for participants facing data collection challenges while still reflecting actual performance. Response: We thank the commenter for their insight and suggestions. We did consider other approaches for when a TEAM participant has insufficient quality data for a given quality measure. However, alternative approaches go against our effort to align with existing reporting requirements and to not increase participant reporting burden. We hear the commenters concern that alternative policies would better capture quality performance. We will monitor the impacts of this policy through the first performance year and, if needed, make adjustments through future notice and comment rulemaking. Comment: A commenter expressed concerns related to the Hospital IQR Program reporting thresholds for the THA/TKA PRO–PM being difficult to achieve, stating this affects both large and small hospitals, with smaller facilities lacking resources to carry out the PRO–PM and larger ones unable to meet reporting percentages. Further, this commenter states that the neutral quality score could unfairly r , with smaller facilities lacking resources to carry out the PRO–PM and larger ones unable to meet reporting percentages. Further, this commenter states that the neutral quality score could unfairly reduce reconciliation payments for hospitals lacking sufficient quality data, despite already having incentives to report under the Hospital IQR Program. Response: We thank the commenter for their helpful insights. Though we understand the concerns related to achieving reporting thresholds, the strategic alignment of TEAM with established quality reporting programs minimizes participant administrative burden through the utilization of quality measures with which providers are already familiar. We will continue to monitor these requirements to determine whether this alignment continues to be most beneficial for TEAM participants. Additionally, we appreciate the commenters feedback related to the neutral quality score unfairly reducing reconciliation payments. In the case of insufficient data, CMS will be unable to determine if quality performance is high or low, therefore believes the approach that is most fair is to apply a neutral score. Comment: A commenter stated that, though they ap , CMS will be unable to determine if quality performance is high or low, therefore believes the approach that is most fair is to apply a neutral score. Comment: A commenter stated that, though they appreciate the intent of this proposed policy, they are concerned it will lessen the role quality has in the model. The commenter stated that applying a neutral quality score lessens accountability and reduces the ability to apply comparisons between model participants. This commenter urged CMS to reconsider its quality strategy to better align with specific episodes and overall quality of care. Response: We appreciate the commenter’s feedback and share their desire for accountability and peer comparison. However, we disagree this approach lessens the role of quality in TEAM. We believe applying a neutral quality score will encourage TEAM participants with insufficient quality measure data to be more engaged in quality measure reporting, ultimately spurring them to improve beneficiary quality of care and allowing them to have sufficient quality measure data that results in a more accurate scaled quality measure score. After consideration of the public comments, we are finalizing without y of care and allowing them to have sufficient quality measure data that results in a more accurate scaled quality measure score. After consideration of the public comments, we are finalizing without modification our proposal at § 512.547(b)(1)(i)(D) to assign a scaled quality measure score of 50 when the TEAM participant has no or an incomplete raw quality measure score for a given quality measure. We will continue to monitor the need for updates or changes, and any future updates will be incorporated into a subsequent notice and comment rulemaking. c. Pricing Methodology (1) Background As finalized in the FY 2025 IPPS/ LTCH PPS final rule (89 FR 68986) TEAM participants will be provided with target prices for each MS–DRG/ HCPCS episode type. These target prices will be calculated using 3 years of baseline data, trended forward to the performance year, at the level of MS– DRG/HCPCS episode type and region, with updates to be made using the performance year data during the VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00558 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k Federal Register / Vol. 90, No. 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00558 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations 37093 reconciliation process. The regions are defined as the nine U.S. census divisions and the MS–DRG/HCPCS episode type is based on the episode categories that will be tested in the model: Coronary Artery Bypass Graft (CABG), Lower Extremity Joint Replacement (LEJR), Major Bowel Procedure, Surgical Hip Femur Fracture Treatment (SHFFT), and Spinal Fusion. We stated in the proposed rule that episode spending will be capped at the 99th percentile for each of the 29 MSDRG/HCPCS episode types and 9 regions, and the benchmark price will be calculated as the average capped and standardized spending in baseline year 3 dollars for each MS–DRG/HCPCS episode type in each region, resulting in 261 benchmark prices. Benchmark prices will be calculated using all hospitals in a region, regardless of TEAM participation status. CMS will apply a prospective trend factor and a discount factor to benchmark prices. During reconciliation, these prelimina ed using all hospitals in a region, regardless of TEAM participation status. CMS will apply a prospective trend factor and a discount factor to benchmark prices. During reconciliation, these preliminary target prices will be updated by updating the trend (subject to caps) and normalization factor (subject to caps) and by factoring in each participant’s realized risk adjustment factors. We stated in the proposed rule that risk adjustment factors will be calculated and made available to TEAM participants prior to the start of the performance year, so participants would be able to use them to estimate their episode-level target prices. Risk adjustment factors finalized in the FY 2025 IPPS/LTCH PPS final rule include age group, Hierarchical Condition Category (HCC) count, and beneficiary social risk as risk adjusters, as well as episode category-specific HCC adjusters and provider-level adjusters. The risk adjustment factors will be calculated at the MS–DRG/HCPCS level on baseline episodes, using a weighted linear regression where episodes are weighted differentially based on whether they belong to year 1, 2, or 3 of the baseline periods. Episodes from baseline year 1 will be weighted , using a weighted linear regression where episodes are weighted differentially based on whether they belong to year 1, 2, or 3 of the baseline periods. Episodes from baseline year 1 will be weighted at 17 percent, baseline year 2 at 33 percent, and baseline year 3 at 50 percent. The risk adjustment factors will be held fixed and applied to performance year episodes at reconciliation based on the realized case mix of the TEAM Participant in the performance year. We also stated in the proposed rule that after risk adjusting for the performance year case-mix, CMS will normalize the target prices to ensure that the average of the total risk-adjusted preliminary target price does not exceed the average of the total non-risk adjusted preliminary target price. The final normalization factor will be calculated as the national mean of the benchmark price for each MS–DRG/HCPCS episode type divided by the national mean of the risk-adjusted benchmark price for the same MS–DRG/HCPCS episode type. However, it will be capped should this ratio exceed ±5 percent of the prospective normalization factor. The final target prices will include a retrospective trend factor, which will be capped at being However, it will be capped should this ratio exceed ±5 percent of the prospective normalization factor. The final target prices will include a retrospective trend factor, which will be capped at being within 3 percent of the prospective trend. The retrospective trend factor will be calculated as the average capped performance year episode spending at the MS–DRG/ HCPCS episode type and region level divided by the capped mean baseline episode spending in baseline year 3 dollars at the MS–DRG/HCPCS episode type and region level (that is, national mean benchmark price). Table XI.A.-05 provides a few examples of the calculation of the retrospective trend factor for three MS–DRG/HCPCS regions in which the retrospective trend factor is capped at 3 percent below the prospective trend factor, not capped, and capped at 3 percent above the prospective trend factor, respectively. 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00559 Fmt 4701 Sfmt 4725 E:\FR\FM\04AUR2.SGM 04AUR2 / > H P G 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00560 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 0000 Frm 00559 Fmt 4701 Sfmt 4725 E:\FR\FM\04AUR2.SGM 04AUR2 / > H P G 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00560 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k / > H P G 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00561 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 / > H P G 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00562 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k / > H P G H P G H P G 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00563 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k 37098 Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations (3) U.S. Territories and Census Division 9 and reduce uniformity in how target prices are constructed in TEAM. In the FY 2025 IPPS/LTCH PPS final rule (89 FR 68986) that established TEAM, we noted that hospitals in the five U.S. territories (American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, and the U.S. Virgin Islands) will be grouped alongside Census Division 9 (that is, th oted that hospitals in the five U.S. territories (American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, and the U.S. Virgin Islands) will be grouped alongside Census Division 9 (that is, the Pacific region) for the purposes of construction of regional prices (89 FR 69751). In response to public inquiries asking which specific Census Division U.S. territories would be categorized into since it was not reflected in regulatory text, we proposed to revise the definition for region at § 512.505 to more clearly reflect this policy. Therefore, we proposed that hospitals located in one of the five U.S. territories (American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, and the U.S. Virgin Islands) will be grouped alongside Census Division 9. Specifically, we proposed to revise the definition for region at § 512.505 to mean one of the nine U.S. census divisions, as defined by the U.S. Census Bureau, with the U.S. territories included in Census Division 9. We stated in the proposed rule that we believed grouping the U.S. territories to Census Division 9 is the most appropriate given the majority of U.S. territories captured in this group are located in the Pacific region roposed rule that we believed grouping the U.S. territories to Census Division 9 is the most appropriate given the majority of U.S. territories captured in this group are located in the Pacific region. Mean episode spending for hospitals within the five U.S. territories is lower than hospitals in Census Division 9 for most episode types, and episode counts are significantly smaller. Therefore, including hospitals within the five U.S. territories as part of Census Division 9 will not disadvantage them since the benchmarks are expected to be higher. Moreover, any differences in spending that are due to patient case-mix between these regions should be accounted for through risk adjustment, ensuring providers are not penalized within the five U.S. territories. Further, we indicated in the proposed rule that this approach is similar to how the BPCI Advanced model grouped the U.S. territories for the Census Division peer group characteristic. This policy would address the one CBSA in Puerto Rico (10380: Aguadilla, PR) selected for participation in TEAM. TEAM participants in this CBSA would use regional target prices calculated for Census Division 9. We considered but did not propose grou Rico (10380: Aguadilla, PR) selected for participation in TEAM. TEAM participants in this CBSA would use regional target prices calculated for Census Division 9. We considered but did not propose grouping hospitals in a U.S. territory into a separate group not based on Census Division but believed that doing so would create unnecessary complexity We sought comment on our proposal at proposed § 512.505 to include U.S. territories in Census Division 9. We did not receive any comments on the proposed policy to include U.S. territories in Census Division 9 and are finalizing the proposal without modification at § 512.505. (4) Calculation and Application of Normalization Factors In the FY 2025 IPPS/LTCH PPS final rule (89 FR 68986) that established TEAM, we finalized using a normalization factor in our calculation of preliminary and reconciliation target prices. The normalization factor is the ratio of the average benchmark price divided by the average risk-adjusted benchmark price. We stated in the proposed rule that we will multiply the risk-adjusted benchmark prices by the normalization factor to ensure the average benchmark price after risk adjustment does not exceed the average ben ed in the proposed rule that we will multiply the risk-adjusted benchmark prices by the normalization factor to ensure the average benchmark price after risk adjustment does not exceed the average benchmark price prior to risk adjustment. If the average benchmark price is higher than the average risk- adjusted benchmark price, then the normalization factor will be greater than 1, and its application will increase the risk-adjusted benchmark prices. If the average benchmark price is lower than the average risk-adjusted benchmark price, then the normalization factor will be less than 1, and its application will decrease the risk-adjusted benchmark prices. In the FY 2025 IPPS/LTCH PPS final rule, we finalized a policy to calculate a prospective normalization factor during the creation of preliminary target prices, which we would then modify (by no more than +/-5 percent) for the final normalization factor when constructing reconciliation target prices. We stated in the proposed rule that under our current policy, the prospective normalization factor will be calculated as the ratio of the average total risk-adjusted preliminary target price to the average total non-risk adjusted prelim under our current policy, the prospective normalization factor will be calculated as the ratio of the average total risk-adjusted preliminary target price to the average total non-risk adjusted preliminary target price for each MS–DRG/HCPCS episode type. We also finalized in the FY 2025 IPPS/LTCH PPS final rule that the final normalization factor will be calculated as the national mean of the benchmark price for each MS–DRG/ HCPCS episode type divided by the national mean of the risk-adjusted benchmark price for the same MS–DRG/ HCPCS episode type. To ensure consistency in our approach to calculating the prospective normalization factor(s) and the final normalization factor(s), we proposed to update the language at § 512.505 to clarify that the prospective normalization factor will be calculated using the benchmark prices (that is, the average non-risk adjusted preliminary benchmark price divided by the average risk adjusted preliminary benchmark price) rather than using preliminary target prices. Specifically, we proposed to revise the definition for prospective normalization factor to mean the multiplier incorporated into the preliminary target price to ensure that the average of rget prices. Specifically, we proposed to revise the definition for prospective normalization factor to mean the multiplier incorporated into the preliminary target price to ensure that the average of the total risk-adjusted benchmark price does not exceed the average of the total non-risk adjusted benchmark price, calculated as set forth in § 512.540(b)(6). We similarly proposed revising the definition for final normalization factor at § 512.505 to mean the benchmark price for each MS– DRG/HCPCS episode type and region divided by the mean of the risk-adjusted benchmark price for the same MS–DRG/ HCPCS episode type and region. We stated in the proposed rule that benchmark prices are calculated prior to incorporating the trend factor and discount factor. Therefore, using benchmark prices rather than target prices for calculating the prospective normalization factor would preserve the effect of the trend and discount factors and would prevent the prospective normalization factor from being influenced by the trend and discount factors. The proposed policy would ensure consistency in the construction of the prospective and final normalization factors. We sought comment on our proposals ng influenced by the trend and discount factors. The proposed policy would ensure consistency in the construction of the prospective and final normalization factors. We sought comment on our proposals at § 512.505 to construct the prospective normalization factor using benchmark prices and to construct the final normalization factor to be based on MS– DRG/HCPCS episode type and region. Additionally, in the FY 2025 IPPS/ LTCH PPS final rule (89 FR 68986), we finalized a policy to calculate normalization factors at the MS–DRG/ HCPCS level—that is, to calculate normalization factors as the average national non-risk adjusted benchmark price divided by the average national risk-adjusted preliminary benchmark price for each MS–DRG/HCPCS episode type. To further ensure consistency in our approach to calculating target prices, we proposed to calculate normalization factors at the MS–DRG/ HCPCS region level. We proposed to calculate normalization factors as the average regional non-risk adjusted benchmark price divided by the average regional risk-adjusted preliminary benchmark price for each MS–DRG/ HCPCS episode type. We stated in the proposed rule that this will produce a unique normaliz d benchmark price divided by the average regional risk-adjusted preliminary benchmark price for each MS–DRG/ HCPCS episode type. We stated in the proposed rule that this will produce a unique normalization factor for each VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00564 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations 37099 region and MS–DRG/HCPCS episode type for a total of 261 normalization factors (as opposed to just 29 normalization factors, as previously proposed). We believed this approach is preferable because it will ensure that the regional average MS–DRG/HCPCS target price is equal to the regional average MS–DRG/HCPCS benchmark price. We sought comment on our proposal at §§ 512.540(b)(6) and 512.545(e)(1)(i) to construct the normalization factors for each MS–DRG/HCPCS at the region level. Table XI.A.–11 provides a few examples of the proposed calculation of the prospective and final normalization factors for three MS–DRG/HCPCS regions in which the final We also stated in the proposed rule The following is a a few examples of the proposed calculation of the prospective and final normalization factors for three MS–DRG/HCPCS regions in which the final We also stated in the proposed rule The following is a summary of the that we wished to clarify how normalization factors will be applied in the calculation of preliminary target prices and how preliminary target prices will be provided to TEAM participants. We previously finalized a policy to provide each TEAM participant within a region with the same preliminary target price for an MS–DRG/HCPCS episode type. We also stated that prospective normalization factors would be incorporated into this preliminary target price and that risk adjustment factors would be calculated and separately be made available to TEAM participants prior to the start of the performance year, so participants would be able to use them to estimate their episode-level target prices. In the proposed rule, we proposed that two separate preliminary target prices will be made available to all participants: (1) the regional average target price for each MS–DRG/HCPCS episode type, before application of the risk adjustment factors or normalization factors; and (2) a TEAM par available to all participants: (1) the regional average target price for each MS–DRG/HCPCS episode type, before application of the risk adjustment factors or normalization factors; and (2) a TEAM participant-specific preliminary target price, including the TEAM participant’s average risk adjustment factors (calculated based on the TEAM participant’s case mix in the baseline period) and the regional MS– DRG/HCPCS normalization factors. We believed that these two target prices will provide TEAM participants with the most complete information to both anticipate their final reconciliation target prices and understand their performance as compared to other participants within the same region. We sought comment on our proposal at § 512.540(b)(8) to communicate and share preliminary target prices that are region specific and TEAM participant specific. public comments received on the proposed policies to construct and apply the normalization factors, and our responses to these comments: Comment: A few commenters expressed support for the proposed changes to the normalization factor. The commenters expressed support for these methodological changes that intend to increase the accuracy of t nt: A few commenters expressed support for the proposed changes to the normalization factor. The commenters expressed support for these methodological changes that intend to increase the accuracy of the target prices, intend to make hospital spending more comparable both across and within markets, and intend to avoid unintended financial burden on providers. Response: We thank commenters for their support of the proposed changes to the normalization factor, and we agree that the changes would improve target price accuracy and lead to better assessment of TEAM participants’ performance in the model. After consideration of the public comments, we are finalizing without modification the definition of the prospective normalization factor to be calculated using benchmark prices rather than preliminary target prices at § 512.505. We are also finalizing without modification the proposal at §§ 512.540(b)(6) and 512.545(e)(1)(i) for the calculation of the prospective and final normalization factors at the MS– DRG/HCPCS episode type and region level rather than at the national level. Lastly, we are finalizing without modification our proposal at § 512.540(b)(8) to communicate and share preli tors at the MS– DRG/HCPCS episode type and region level rather than at the national level. Lastly, we are finalizing without modification our proposal at § 512.540(b)(8) to communicate and share preliminary target prices that are region specific and TEAM participant specific. (5) Calculation of the Prospective Trend Factor In the FY 2025 IPPS/LTCH PPS final rule (89 FR 68986) that established TEAM, we finalized a pricing methodology using a 3 percent capped retrospective trend factor. We stated in the proposed rule that under this methodology, reconciliation target prices are based on average regional MS–DRG spending in the contemporaneous performance year. The retrospective approach ensures that reconciliation target prices accurately account for unpredictable year-to-year fluctuations in spending, including the introduction of new technologies and medical advancements and unexpected increases or decreases to health care utilization (for example, the COVID–19 public health emergency). However, as stated in the FY 2025 IPPS/LTCH PPS final rule (89 FR 69745), we believe that providing TEAM participants with preliminary target prices before each performance year—and ensuring the accu However, as stated in the FY 2025 IPPS/LTCH PPS final rule (89 FR 69745), we believe that providing TEAM participants with preliminary target prices before each performance year—and ensuring the accuracy and reliability of preliminary target prices—is essential to participants’ success. Accurate target prices enable participants to prepare and undertake appropriate care transformation. We also believe the methodology for setting prospective target prices should be sufficiently simple so that it is transparent for participants. With our methodology, we aimed to find the balance between simplicity and predictive accuracy. The methodology finalized in the FY 2025 IPPS/LTCH PPS final rule calculates preliminary target prices by applying a trend factor to average regional MS–DRG spending in the final year of the baseline period. This trend factor is calculated as the 2-year percentage change from baseline year 1 VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00565 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k / > H P G 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00566 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2 t 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k / > H P G 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00566 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations 37101 capped. For example, if per episode spending increases year-to-year within the baseline period, episodes in more recent years will be more likely to be subject to the high-cost outlier cap than episodes in earlier years. Conversely, if the per episode spending decreases year-to-year within the baseline period, episodes in earlier years will be more likely to be capped. To ensure that the trend factor—as well as the benchmark price—are calculated in a way that treats all 3 baseline years equally, with respect to the high-cost outlier cap, we proposed to calculate the 99th percentile for a given MS–DRG/HCPCS episode type in a given region individually in each of the baseline and trend years. Although trend years are not used in the calculation of the benchmark price, we proposed to apply the high-cost outlier cap to epi a given region individually in each of the baseline and trend years. Although trend years are not used in the calculation of the benchmark price, we proposed to apply the high-cost outlier cap to episodes in the trend years as well to ensure consistency in the calculation of our trend factor. Therefore, we proposed to revise the definition for high-cost outlier cap at § 512.505 to mean the 99th percentile of regional spending for a given MS–DRG/HCPCS episode type, region, and baseline year, which is the amount at which episode spending would be capped for purposes of determining baseline and performance year episode spending. We believed this approach would improve the accuracy of our benchmark prices and trend factors and, ultimately, of target prices. We stated in the proposed rule that in proposing this revised methodology for calculating TEAM participants’ preliminary target prices, we considered multiple alternatives for each proposed change. As alternatives to the proposed regression-based approach to calculating an annual prospective trend factor, we considered retaining the approach finalized in the FY 2025 IPPS/LTCH PPS rule as well as two similar approaches. The first al ssion-based approach to calculating an annual prospective trend factor, we considered retaining the approach finalized in the FY 2025 IPPS/LTCH PPS rule as well as two similar approaches. The first alternative approach we considered would calculate the 2-year trend factor as double the average of the 1-year trend from BY1 to BY2 (that is, average regional MS–DRG episode spending in BY2 divided by average regional MS– DRG episode spending in BY1) and from BY2 to BY3. This approach would have the benefit of retaining the simplicity of the methodology previously finalized while also incorporating all 3 years of available baseline data. We also considered an approach that would use 4 years of data (3-year baseline plus 1 trend year, defined as the year prior to the start of the baseline period) to calculate the 2-year trend factor as the average of the 2-year trend from BY1 to BY3 and trend year 1 to BY2 (for example, for performance year 1, 2026, the average of the 2-year trend factor from 2022 [BY1] to 2024 [BY3] and the 2-year trend factor from 2021 [trend year 1] to 2023 [BY2]). We indicated in the proposed rule that we intended to conduct further analysis to evaluate the reliabili m 2022 [BY1] to 2024 [BY3] and the 2-year trend factor from 2021 [trend year 1] to 2023 [BY2]). We indicated in the proposed rule that we intended to conduct further analysis to evaluate the reliability of both of these approaches, as compared to the proposed approach, for historical episode spending as part of simulated reconciliation. We note the findings from this analysis in our comment responses in this section of the final rule. In the proposed rule we requested comment from stakeholders on whether either of these approaches would produce more accurate prospective trend factor estimates or meaningfully simplify our pricing methodology such that it would be easier for TEAM participants to replicate preliminary target price calculations and identify potential opportunities for spending efficiencies. Additionally, we considered proposing the use of weights for different baseline and trend years for the regression-based approach. Specifically, we considered two alternatives to our proposed approach. In the first alternative, we would weight each of the 3 baseline years at 0.25 and each of the 2 trends years at 0.125. In the second, we considered weights of: BY3 = 0.3, BY2 = 0.25, oposed approach. In the first alternative, we would weight each of the 3 baseline years at 0.25 and each of the 2 trends years at 0.125. In the second, we considered weights of: BY3 = 0.3, BY2 = 0.25, and BY1 and both trend years = 0.15. We requested comment on whether weighting more recent years used in the calculation of prospective tend factors and projection of preliminary target prices would improve the accuracy of target price calculations. Lastly, we considered alternatives to our proposal to use the average of the regional and national trend factors. Specifically, we considered using just the regional trend factor, as proposed and finalized in the FY 2025 IPPS/LTCH PPS, as well as the use of different weights on the regional and national trend factors, for example, a weight of 0.67 for the regional trend factor and 0.33 for the national trend factor. We stated in the proposed rule that we intended to conduct further analysis on whether alternative weights would provide better estimates of real FFS spending. We noted in the proposed rule that we believe our proposed revisions to our methodology for the calculation of the prospective trend factor would produce more accurate a stimates of real FFS spending. We noted in the proposed rule that we believe our proposed revisions to our methodology for the calculation of the prospective trend factor would produce more accurate and reliable preliminary target prices for TEAM participants and reduce adjustments to reconciliation target prices that are calculated during reconciliation. We will maintain the +/ - 3 percent cap on the retrospective trend factor adjustment. However, we believed that by improving the accuracy of prospective trend factor construction used in preliminary target prices, the methodological changes proposed previously will reduce the frequency with which that 3 percent cap need be applied. We sought comment on our proposals at § 512.540(b)(7) to reconstruct the prospective trend factor and at § 512.540(b)(4) to calculate the high-cost outlier cap for each baseline year in the baseline period. The following is a summary of the public comments received on the proposed policy to calculate the prospective trend factor, and our responses to these comments: Comment: Some commenters supported the proposed changes to the prospective trend methodology. They noted that including 2 additional years ospective trend factor, and our responses to these comments: Comment: Some commenters supported the proposed changes to the prospective trend methodology. They noted that including 2 additional years and using a log-linear regression to produce the prospective trend factor would result in a more reliable and accurate prospective trend factor, reducing uncertainty and large retrospective adjustments. Response: We thank the commenters for their support of the proposed changes to the prospective trend factor methodology. Comment: A commenter expressed concerns that capping the retrospective trend at 3 percent of the prospective trend factor would penalize participants and would lead to a 3 percent lower target price. Response: We disagree with the commenter who expressed concerns about the 3% cap on the retrospective trend penalizing participants and reducing their target prices significantly. Preliminary target prices contain a prospective trend factor to project baseline episode spending forward to the performance year (PY). This prospective trend factor, along with the prospective normalization factor and the risk adjustment multiplier, is updated during reconciliation. The retrosp g forward to the performance year (PY). This prospective trend factor, along with the prospective normalization factor and the risk adjustment multiplier, is updated during reconciliation. The retrospective trend is calculated as the average capped PY episode spending at the MS– DRG/HCPCS episode type and region level divided by the capped mean baseline episode spending in BY3 dollars at the MS–DRG/HCPCS episode type and region level. The retrospective trend factor is capped at +/- 3 percent of the prospective trend factor to make target price adjustment more predictable and prevent extreme, unexpected losses for both participants and CMS. As an example, if the prospective trend factor is 1.05 and the retrospective trend factor is 0.98, the capped retrospective trend factor will be 1.02 (3 points lower than the prospective trend factor as opposed to 7 points lower). VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00567 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k 37102 Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations Comment: A couple of commenters expressed concerns R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k 37102 Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations Comment: A couple of commenters expressed concerns that the 2 additional years (CYs 2020 and 2021) proposed to be used to calculate the prospective trend factor were significantly impacted by the COVID–19 public health emergency and are not representative of general patterns of care. These commenters urged CMS to revert back to the prospective trend factor calculation finalized in the FY2025 IPPS rule. Response: We thank the commenters who raised their concerns with adding data from CYs 2020 and 2021 to the prospective trend calculation. While these CYs have been significantly impacted by the COVID–19 PHE, the prospective trend factor approach finalized in the FY 2025 IPPS final rule would be more sensitive to COVID–19 and the changes in spending, as it only uses data from 2 calendar years (for TEAM PY1 it would use CY 2022 and CY 2024 data). The approach proposed in the current rule mitigates the year-to- year fluctuations by using a longer time period to establish the trends. We also conducted analysis of the proposed approach and a ta). The approach proposed in the current rule mitigates the year-to- year fluctuations by using a longer time period to establish the trends. We also conducted analysis of the proposed approach and alternative approaches to calculating the prospective trend factor, as described in the preamble of this rule. That analysis demonstrated the proposed approach to use 5 years of data was more accurate at predicting performance year spending than alternative approaches when years most affected by the COVID–19 PHE (CYs 2020 and 2021) were included in the 5-year baseline and trend period. Therefore, we anticipate this approach to not only be more accurate over the long term but also more robust to potential short-term disruptions that may affect care patterns and provider spending. Comment: A commenter requested CMS to provide more detail on the log- linear regression model, including restatements of historical trend factors using this approach. Response: The log-linear regression will be run on 5 years of trend data at the MS–DRG and region level. The dependent variable will be the natural logarithm of the episode spending. The primary dependent variable will be the calendar year of the c on 5 years of trend data at the MS–DRG and region level. The dependent variable will be the natural logarithm of the episode spending. The primary dependent variable will be the calendar year of the clinical episode based on episode end date. We intend to provide detailed methodology documents ahead of the release of the performance year 1 preliminary target prices in Fall 2025. For PY1 of TEAM the baseline period will include CYs 2022 through 2024 and CYs 2020 and 2021 will be used as additional trend years to produce the prospective trend factor. The baseline and trend years will roll forward with each PY. Comment: A few commenters expressed concerns about the ratcheting effect. A commenter suggested to avoid rebasing the benchmark each year to minimize the ratcheting effect and another commenter expressed concerns that weighing the more recent baseline years more heavily would lead to difficulties in creating financial savings. Response: We thank commenters for expressing their concerns about the ratcheting effect. In the FY 2025 IPPS/ LTCH PPS final rule (89 FR 69750), we finalized a 3-year baseline period that is rebased annually. We believe that calculating target prices at t erns about the ratcheting effect. In the FY 2025 IPPS/ LTCH PPS final rule (89 FR 69750), we finalized a 3-year baseline period that is rebased annually. We believe that calculating target prices at the MS–DRG/ HCPCS episode type and region level mitigates issues related to the provider- level ratcheting effect. Our analyses showed that the proportion of hospitals participating in similar models such as BPCI–A and CJR is low among all participants in a region, so any regional- level ratcheting effect is also minimized. We also believe that a longer baseline and trend period for calculating the prospective trend factor, with all years weighted equally, may mitigate the ratcheting effect compared to a shorter baseline period or more heavily weighting more recent years. Therefore, we are not making any change to our policy to roll the baseline period forward each performance year and will mimic this policy for the 2 trend years. However, we will take into consideration these comments as we implement and monitor TEAM participant performance, and if warranted, would propose new policies or policy modifications in subsequent notice and comment rulemaking, as appropriate. Comment: A comme e implement and monitor TEAM participant performance, and if warranted, would propose new policies or policy modifications in subsequent notice and comment rulemaking, as appropriate. Comment: A commenter supported the inclusion of the national trend in the prospective trend factor but recommended to use a different weighting approach where the weight on the national trend is proportional to the share of episodes a hospital contributes within its region for a given MS–DRG. Response: We thank the commenter for supporting the inclusion of national trends and for providing their thoughts on alternative techniques to weighing national trends. We believe that having a different set of weights for each participant would increase administrative burden and increase the complexity of the model. We will monitor how the currently specified national trend performs in projecting target prices and may consider alternative weighing approaches in the future. Comment: A commenter requested detailed examples of the preliminary target pricing and trend factor methodology to allow participants to validate and track their targets. Response: We thank the commenter for submitting their public comment. We mples of the preliminary target pricing and trend factor methodology to allow participants to validate and track their targets. Response: We thank the commenter for submitting their public comment. We will provide technical assistance to TEAM participants throughout the duration of the model to allow TEAM participants to track their performance in TEAM. These materials will include fact sheets, FAQs, and specification documents. Many of these documents will be made publicly available on the TEAM website. Additionally, all TEAM participants will receive a target price summary report that details each component of the target price methodology including the prospective trend factor to help participants easily validate and track their targets. Comment: A commenter supported applying the high-cost outlier cap to episodes in the trend years. Response: We thank the commenter for their support. After consideration of the public comments, we are finalizing without modification our proposal at § 512.540(b)(7) to calculate the regional trend factor using a log-linear model which would fit the model to logarithmically transformed values of average regional MS–DRG spending for each of the basel 512.540(b)(7) to calculate the regional trend factor using a log-linear model which would fit the model to logarithmically transformed values of average regional MS–DRG spending for each of the baseline years and the 2 additional trend years immediately preceding the baseline years. We are also finalizing without modification at § 512.540(b)(7) the calculation of the prospective trend factor as the average (arithmetic mean) of the regional trend factor and a national trend factor. The national MS–DRG trend factor would be calculated in the same manner as regional MS–DRG trend factors using a linear regression of logarithmically transformed national average MS–DRG spending. Lastly, we are finalizing the definition of trend year and high-cost outlier cap at § 512.505 and finalizing without modification our proposal at § 512.540(b)(4) to apply the high-cost outlier cap to episodes in the trend years in addition to each baseline year in the baseline period. (6) Standardizing Area Deprivation Index (ADI) In the FY 2025 IPPS/LTCH PPS final rule (89 FR 68986) that established TEAM, we finalized a social need risk adjustment factor for beneficiary-level risk adjustment in the construction ion Index (ADI) In the FY 2025 IPPS/LTCH PPS final rule (89 FR 68986) that established TEAM, we finalized a social need risk adjustment factor for beneficiary-level risk adjustment in the construction of our preliminary and reconciliation target prices. We finalized this variable as a single binary variable with a value of yes=1 if the beneficiary—(1) was eligible for full Medicaid benefits (referred to as a dual eligible beneficiary eligible to receive both full Medicare and Medicaid benefits); (2) was eligible VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00568 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations 37103 for the Medicare Part D Low Income Subsidy (LIS); or (3) resided in a census block group with an Area Deprivation Index (ADI) above the 80th percentile of either national ranking or 8th decile of the state-level ranking. We noted that we believed that accounting for multiple potential markers of beneficiary social risk would be most appropriate to ensure accurate representation of the additional resources re ranking. We noted that we believed that accounting for multiple potential markers of beneficiary social risk would be most appropriate to ensure accurate representation of the additional resources required to treat beneficiaries with greater levels of social vulnerability and need. In the FY 2025 IPPS/LTCH PPS final rule, we also acknowledged concerns that the lack of standardization of ADI variables may make the ADI primarily a function of a subset of variables (namely income and home values) included in its calculation. Further, we further stated that we would continue to explore whether standardization of the ADI variables would be appropriate for the purposes of TEAM’s risk adjustment approach and would propose any such changes in future rulemaking. We stated in the proposed rule that as part of our preparation for TEAM and the calculation of preliminary target prices, we constructed episodes using a 2019 through 2021 baseline period to reassess the value of the social need risk adjustment factor. We calculated cross- tabulations of dual eligibility, LIS, and ADI status of episodes and beneficiaries identified as triggering a TEAM episode. We found that 99.9 percent of dual el djustment factor. We calculated cross- tabulations of dual eligibility, LIS, and ADI status of episodes and beneficiaries identified as triggering a TEAM episode. We found that 99.9 percent of dual eligible beneficiaries who triggered an episode in TEAM (as well as 99.9 percent of episodes associated with a dual eligible beneficiary) were also qualified for LIS. We indicated in the proposed rule that this is consistent with the fact that LIS has more lenient asset and income requirements than Medicaid and that dual eligible beneficiaries automatically qualify for LIS without having to apply. As previously suggested by commenters in response to the FY 2025 IPPS/LTCH PPS proposed rule, we explored options for the standardization of the ADI that would better measure deprivation in urban areas. The CMS Innovation Center’s Accountable Care Organization REACH (ACO REACH) model included an adjustment that is a blend of one-third National ADI scores, one-third State ADI scores, and one- third Dual-Eligibility or Low-Income Subsidy status. We stated in the proposed rule that in performance year 2025, CMS will remove the National/ State blended ADI from ACO REACH and replace it with an area- Dual-Eligibility or Low-Income Subsidy status. We stated in the proposed rule that in performance year 2025, CMS will remove the National/ State blended ADI from ACO REACH and replace it with an area-level deprivation measure that uses standardized variables. This will better identify deprived areas of the nation, particularly for populations in high housing cost areas where housing costs do not correlate with the other included economic variables. Specifically, we noted in the proposed rule that ACO REACH has modified the census block group deprivation index, known as the Community Deprivation Index (CDI), which updates and standardizes the variables used in the construction of the ADI. Standardization refers to the process of making the individual indicators that comprise the ADI unit to be neutral by subtracting the mean and dividing by the standard deviation before combining them to form a composite measure. The primary purpose of standardization in the ADI is to prevent any single indicator from dominating the composite score due to differences in measurement scales. Without standardization, variables with larger numerical values or greater variance would disproportionately in indicator from dominating the composite score due to differences in measurement scales. Without standardization, variables with larger numerical values or greater variance would disproportionately influence the final deprivation score. We stated in the proposed rule that given the extensive work the ACO REACH model has conducted to standardize the ADI, we believed it is important to use a similar approach to more accurately measure areas of deprivation and create alignment across CMS Innovation Center models with similar adjustments. Based on our further research and analysis, we proposed a few changes to the construction of the social need risk adjustment factor for beneficiary-level risk adjustment in TEAM. First, we proposed renaming the social needs risk adjustment factor to be the beneficiary economic risk adjustment factor and replace the use of the ADI in the construction of our beneficiary economic risk adjustment variable, with a similar but slightly modified census block group deprivation index, the Community Deprivation Index (CDI). We proposed using the same construction methodology as the ACO REACH model. Specifically, the CDI would be a factor- weighted composite mea up deprivation index, the Community Deprivation Index (CDI). We proposed using the same construction methodology as the ACO REACH model. Specifically, the CDI would be a factor- weighted composite measure of 18 variables collected from the Census Bureau. We proposed the deprivation scores would be percentile ranked relative to the Nation such that the resulting index would range from a score of 1, indicating the lowest level of relative deprivation, to 100, indicating the highest level of relative deprivation. We also proposed maintaining the use of the 80th percentile threshold for the CDI. For example, the TEAM beneficiary would be assigned a value of yes=1 on the beneficiary economic risk adjustment factor if the TEAM beneficiary’s CDI was above the 80th percentile. We believed the updated variable name better represents what the variable is risk adjusting for. We also believed the use of the CDI instead of the ADI will better represent beneficiary-level deprivation in urban areas due to the standardization of variables prior to the construction of the composite measure. Second, we proposed using only national-level CDI rankings in the construction of our beneficiary economic ri ue to the standardization of variables prior to the construction of the composite measure. Second, we proposed using only national-level CDI rankings in the construction of our beneficiary economic risk adjustment factor. In our initial proposal in the FY25 IPPS/LTCH PPS proposed rule (89 FR 36450), we stated that the use of national- and state- level ADIs would help mitigate potential concerns about the validity of the ADI as a measure of economic risk given its close correlation with home values. We believed that using a relative measure of deprivation within states, in addition to a national measure, would better identify high deprivation census block groups and beneficiaries in states with high incomes and home values. In the proposed rule, we stated that we believe that the standardization of variables in the CDI will adequately address the influence of these two variables in the aggregate measure, negating the need for the use of both national and state rankings. Furthermore, we believed that the inclusion of too many measures of beneficiary deprivation will dilute risk adjustment for TEAM participants with beneficiaries with the highest levels of economic vulnerability. Alth believed that the inclusion of too many measures of beneficiary deprivation will dilute risk adjustment for TEAM participants with beneficiaries with the highest levels of economic vulnerability. Although in the FY 2025 IPPS/LTCH PPS final rule (89 FR 69772) we confirmed that we would only make upward risk adjustments to target prices, target price increases through risk adjustment must be offset by across-the-board target price reduction with the application of the normalization factor in our target price methodology. Therefore, the more beneficiaries receive risk-adjusted target prices, the smaller those adjustments must necessarily be. As an alternative to our proposed changes to the construction of the economic risk factor for beneficiary- level risk adjustment, we considered retaining the use of the ADI, including both the national- and state-level rankings, and dual eligibility status. As previously stated, we believed that the use of the CDI as a standardized alternative to the ADI provides a more reliable measure of economic risk and negates the need for use of the state- level rankings. We further believed that minimizing the number of variables used to identify economic r ADI provides a more reliable measure of economic risk and negates the need for use of the state- level rankings. We further believed that minimizing the number of variables used to identify economic risk both keeps the methodology simpler and reduces the extent to which positive risk adjustments must be offset by normalization, therefore ensuring that VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00569 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k 37104 Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations beneficiaries with the highest levels of deprivation receive adequate risk adjustment. In the proposed rule, we also gave further consideration to additional alternatives to the ADI, including the Centers for Disease Control and Prevention’s (CDC) Social Vulnerability Index (SVI). However, as stated in the FY 2025 IPPS/LTCH PPS final rule (89 FR 69799), we continued to believe that it would not have been appropriate to use the SVI in place of the ADI or CDI, given that SVI is not as granular as the ADI (SVI uses census tracts as opposed to census block groups), and giv ieve that it would not have been appropriate to use the SVI in place of the ADI or CDI, given that SVI is not as granular as the ADI (SVI uses census tracts as opposed to census block groups), and given the limitations and timing of this source data, the American Community Survey (ACS) 5-year estimates. For these reasons, we did not propose the SVI as a potential risk adjustor in TEAM. We sought comment on our proposal at § 512.545(a) to rename the risk adjustment variable. We also sought comment on our proposal at § 512.545(a)(3)(i) to use the CDI and remove a measurement of deprivation at the State level. Finally, we considered but did not propose at this time to omit the dual eligibility (receiving both full Medicare and Medicaid benefits) variable from our construction of the single, binary economic risk adjustment factor. We stated in the proposed rule that while we continue to believe that dual eligibility is an important indicator of economic vulnerability, we believe the near complete overlap between dual eligibility and LIS status makes the use of dual eligibility status redundant. We indicated that removing the dual eligibility variable would simplify the construction of omplete overlap between dual eligibility and LIS status makes the use of dual eligibility status redundant. We indicated that removing the dual eligibility variable would simplify the construction of the economic risk adjustment factor without sacrificing the identification of beneficiaries with high economic risk. Furthermore, LIS also provides a nationally consistent measure of economic risk, as LIS eligibility is set at the national level, unlike Medicare-Medicaid dual eligibility. Lastly, the use of only LIS status, as opposed to both LIS and dual eligibility, is consistent with the specification used by CMS Innovation Center models, such as the Making Care Primary (MCP) Model. While we did not propose any change at this time to the inclusion of the dual eligibility variable in our construction of the economic risk adjustment factor, we sought comment on whether the removal of this variable to streamline construction of the economic risk adjustment factor would be preferable. The following is a summary of the public comments received on the proposed policies to rename the social needs risk adjustment factor and to replace ADI with CDI, and our responses to these comments: Comme ollowing is a summary of the public comments received on the proposed policies to rename the social needs risk adjustment factor and to replace ADI with CDI, and our responses to these comments: Comment: Many commenters supported the proposal to replace the ADI with the CDI for the purposes of constructing the beneficiary economic risk adjustment variable. Some commenters stated that the CDI would improve on the ADI by standardizing the variables used to construct the index or by correcting the weighting of the home value and income variables, which is the result of this standardization. Some commenters noted that CDI would be better at measuring deprivation in urban, high cost, or underserved areas. Another commenter noted that the ADI had been demonstrated to mask disparities in certain areas. Response: We thank the commenters for their support and agree that the fact that the CDI standardizes the variables used to construct the ADI will prevent variables with high nominal values such as income and home values from masking the other variables included in the index. As discussed in section XI.A.2.c.(6). of the preamble of this final rule and in the FY 2025 IPPS/LTCH PPS final rule h as income and home values from masking the other variables included in the index. As discussed in section XI.A.2.c.(6). of the preamble of this final rule and in the FY 2025 IPPS/LTCH PPS final rule (89 FR 69798), we have been tracking stakeholder concerns about the ADI’s lack of standardization and monitoring ways to improve the index’s ability to measure deprivation in urban areas. We believe that replacing the ADI with the CDI in the construction of the beneficiary economic risk adjustment variable will allow TEAM to better identify deprivation in areas with high cost of living and more accurately adjust TEAM target prices to account for beneficiary economic risk. Comment: A commenter appreciated that the proposal would bring TEAM into alignment with ACO REACH. Response: We thank the commenter for their support. ACO REACH used the ADI as part of its risk adjustment in the 2023 and 2024 performance year which has provided insight into the advantages and disadvantages of the index. We believe that incorporating the improvements made to ACO REACH through the replacement of the ADI with the CDI will not only increase the accuracy of risk adjustment in TEAM, but it will also increa We believe that incorporating the improvements made to ACO REACH through the replacement of the ADI with the CDI will not only increase the accuracy of risk adjustment in TEAM, but it will also increase alignment across CMS models. Comment: A commenter questioned whether the proposal would be sufficient to properly capture economic risk, particularly for safety net hospitals. The commenter noted lack of transportation and inability to afford medications as social needs that were not properly accounted for under TEAM. Response: We thank the commenter for their concern regarding properly accounting for beneficiary economic risk. We disagree that the binary economic risk variable will not sufficiently capture this risk, or that risk adjustment could be improved by adding additional economic risk variables such as lack of transportation and inability to afford medications. The binary economic risk variable was designed to capture multiple markers of beneficiary economic risk. The inability to afford medications is a measure of income, which is already captured in the variable through eligibility for the LIS and full Medicare/Medicaid dual eligibility status. The inability to access tra to afford medications is a measure of income, which is already captured in the variable through eligibility for the LIS and full Medicare/Medicaid dual eligibility status. The inability to access transportation is also related to income, and the percentage of households within a census block group that do not have access to a motor vehicle is one of the 18 variables used to construct the CDI, which is included in the binary economic risk variable. We are concerned that adding additional economic risk variables would overcomplicate risk adjustment. Additionally, adding more variables that captured economic risk would prevent CMS from enforcing sign restrictions on the binary economic risk variable. We believe that it is important for the variable to only impact preliminary or reconciliation target prices if its coefficient is positive. Furthermore, all safety net hospitals will have a provider-level safety net status risk adjuster included in the risk adjustment model to improve target price accuracy for the episode expenditure variation experienced by safety net providers. Comment: A few commenters recommended that, should this proposal be finalized, CMS monitor the impact of the price accuracy for the episode expenditure variation experienced by safety net providers. Comment: A few commenters recommended that, should this proposal be finalized, CMS monitor the impact of the CDI given how new the index is. Several of these commenters stated that CMS should specifically monitor the impact of the CDI in rural areas, with one of these commenters adding that the impact of the change on these areas must be more thoroughly examined. Another commenter requested bias testing on the measure to ensure that it did not inadvertently disadvantage high-risk patient populations or under- resourced hospitals. Response: We thank the commenters for their input. We agree that it is critical to ensure that TEAM’s risk adjustment does not disadvantage high- risk patient populations or under- resourced hospitals, such as rural hospitals. While the CDI is a new index, as discussed previously, the refinement of the ADI has been taking place for years in ACO REACH. We will continue to monitor the impact that this index has on risk adjustment in TEAM to VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00570 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i monitor the impact that this index has on risk adjustment in TEAM to VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00570 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations 37105 ensure that it accurately captures risk across all areas and populations. We would also like to reiterate that the beneficiary economic risk variable will only be used to adjust target prices if the coefficient on this variable is positive and will not be used to lower target prices for any TEAM participants. Comment: A few commenters requested additional clarity on the CDI, including the detailed methodology of how the CDI is calculated and further explanation of the 18 variables included in the index. changes to the use of dual eligibility in the economic risk adjustment variable. After consideration of the public comments, we are finalizing without modification our proposal at § 512.545(a)(3)(i) to use the CDI and remove a measurement of deprivation at the state level without modification. We did not receive any comments on our proposal to rena modification our proposal at § 512.545(a)(3)(i) to use the CDI and remove a measurement of deprivation at the state level without modification. We did not receive any comments on our proposal to rename the social risk adjustment variable the beneficiary economic risk adjustment factor, which we are finalizing without modification at § 512.545(a). Response: We refer readers to Appendix C of the ACO REACH model PY2025 Financial Operating Guide (https://www.cms.gov/files/document/ aco-reach-py25-fin-op-ovw.pdf) for additional information on the CDI methodology used for ACO REACH including the 18 variables used in the construction of the index. We also plan to release TEAM-specific technical guidance addressing the CDI’s methodology subsequent to the publication of this final rule. Comment: A commenter supported replacing the ADI but questioned why the CDI was chosen as the replacement and suggested that CMS work with stakeholders to identify the best index to measure economic risk at the geographic level. The commenter questioned whether the CDI was validated across a wide range of geographic areas, such as rural areas. The commenter suggested that CMS consider other indices, includi geographic level. The commenter questioned whether the CDI was validated across a wide range of geographic areas, such as rural areas. The commenter suggested that CMS consider other indices, including the Vizient® Vulnerability IndexTM. Response: We thank the commenter for their suggestions. We finalized the use of the ADI for the purposes of economic risk adjustment in the FY 2025 IPPS/LTCH PPS final rule. While we stated in that rule that we would assess the use of standardization in calculating the ADI, we had no intentions of utilizing an entirely new index. CMS is familiar with the strengths and weaknesses of the ADI, and accordingly, the updated CDI, through the use of the index in other value-based programs such as ACO REACH. We believe that refining this index, as opposed to a larger overhaul of economic risk adjustment, will both provide more accurate risk adjustment and minimize changes to the model. Comment: A few commenters supported keeping dual eligibility in the economic risk variable, noting that this variable is consistent, accessible, and easy for hospitals to understand. Response: We appreciate the commenters pointing out the value of the familiarity hospitals ic risk variable, noting that this variable is consistent, accessible, and easy for hospitals to understand. Response: We appreciate the commenters pointing out the value of the familiarity hospitals have with this variable. We will not be finalizing any (7) Hierarchical Condition Categories (HCC) in Risk Adjustment (a) Lookback Period In the FY 2025 IPPS/LTCH PPS final rule (89 FR 68986) that established TEAM, we recognized the need to account for beneficiary acuity in setting target prices for episode categories tested in TEAM. We finalized the use of beneficiary level variables that are episode category specific. These beneficiary level variables are drawn from the HCCs used in the CMS–HCC risk adjustment model that informs the Medicare Advantage (MA) capitation rates and Part C and Part D Payment Policies. While the specific HCCs were finalized for each episode category in TEAM, we did not finalize the lookback period duration to capture the HCCs. Specifically, we did not specify how far back from the episode start date CMS would look to capture HCC data to determine the total count of HCCs and the episode-specific HCC variables. We stated in the proposed rule that in the early ow far back from the episode start date CMS would look to capture HCC data to determine the total count of HCCs and the episode-specific HCC variables. We stated in the proposed rule that in the early years of BPCI Advanced, we used a 90-day lookback for each beneficiary, beginning with the day prior to the anchor hospitalization or anchor procedure. We would use the beneficiary’s Medicare FFS claims from that 90-day lookback period to determine which HCC flags the beneficiary is assigned and create a count of those HCC flags. During the COVID–19 public health emergency (PHE), BPCI Advanced participants urged CMS to reconsider the 90-day lookback period because beneficiaries were hesitant to interface with providers during this time, which directly affected the risk adjustment and target price methodology. Given those concerns, BPCI Advanced began using a 180-day lookback period. Since the COVID–19 PHE has ended and utilization is now once again similar to pre-PHE levels, we proposed in the FY 2025 IPPS/LTCH PPS proposed rule (89 FR 35934) that we would conduct a 90-day lookback for each beneficiary, beginning with the day prior to the anchor hospitalization or anchor procedure. We n the FY 2025 IPPS/LTCH PPS proposed rule (89 FR 35934) that we would conduct a 90-day lookback for each beneficiary, beginning with the day prior to the anchor hospitalization or anchor procedure. We would use the beneficiary’s Medicare FFS claims from that 90-day lookback period to determine which HCC flags the beneficiary is assigned and create a count of those HCC flags. This methodology would have been consistent with the earlier years of BPCI Advanced and would represent a more uniform way of measuring clinical complexity across beneficiaries. It would also reduce the incentive for increased coding intensity at the time of the initiating procedure. However, following feedback from public comments, we held off on finalizing a lookback period to take more time to consider alternatives, such as a longer lookback period. We proposed in the proposed rule to conduct a 180-day lookback for each beneficiary, beginning with the day prior to the anchor hospitalization or anchor procedure. We proposed to use the beneficiary’s Medicare FFS claims from that 180-day lookback period to determine which HCC variables (or flags) the beneficiary is assigned and determine the HCC episode specifi We proposed to use the beneficiary’s Medicare FFS claims from that 180-day lookback period to determine which HCC variables (or flags) the beneficiary is assigned and determine the HCC episode specific flags as well as the TEAM HCC count flag. We also proposed that the TEAM beneficiary would need to meet beneficiary inclusion criteria, as described in § 512.535, during the entire 180-day lookback period. We stated in the proposed rule that we believe a 180- day lookback period would sufficiently capture beneficiary acuity and ultimately improve the risk adjustment methodology to better reflect the level of spending outside of the hospital’s control. This methodology would be consistent with the current BPCI Advanced methodology and would continue to represent a more uniform way of measuring clinical complexity across beneficiaries. We noted in the proposed rule that in past internal analyses, CMS has found that a 180-day lookback period may improve model fit in a risk adjustment model but may reduce episode volume. Internal analysis demonstrated that using a 180- day lookback period in BPCI Advanced reduced total episodes from 12,473,202 to 12,451,784 when looking at a period from but may reduce episode volume. Internal analysis demonstrated that using a 180- day lookback period in BPCI Advanced reduced total episodes from 12,473,202 to 12,451,784 when looking at a period from October 1, 2015, through September 30, 2019. We further state that our analysis further found that extending the lookback period from 90 days to 180 days resulted in an average increase in regional MS–DRG benchmark prices of just 0.04 percent. The average change in regional MS– DRG benchmark prices was just +/¥0.2 percent and only 16 of the 261 benchmark prices changed by more than VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00571 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k 37106 Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations +/¥0.5 percent. Use of 270-day and 365-day lookback periods produced only marginally different results. lookback period to determine which HCC flags the beneficiary is assigned, and our responses to these comments: However, because of the importance Comment: A few commenters of accurate and complete data when risk-adjusting for TEAM, we state lags the beneficiary is assigned, and our responses to these comments: However, because of the importance Comment: A few commenters of accurate and complete data when risk-adjusting for TEAM, we stated in the proposed rule that we believe 180- days is the most appropriate duration as opposed to lookback periods longer than 180 days. The 180-day lookback period allows for improvements in model fit and modest adjustments in target price accuracy, relative to a 90-day lookback period, without a large drop in episode volume in the lookback period. Additionally, we believed a 180-day lookback period would address public commenters’ concerns that the 90-day lookback period did not adequately account for past spending associated with beneficiary health status. It would also reduce the incentive for increased coding intensity at the time of the initiating procedure. Using a lookback period, rather than including diagnoses from the episode initiating admission/ procedure, will minimize the opportunities for participants to change coding intensity among their patients relative to non-participants. In the proposed rule we stated that we recognize other CMS initiatives may use different lookba rtunities for participants to change coding intensity among their patients relative to non-participants. In the proposed rule we stated that we recognize other CMS initiatives may use different lookback periods. For example, the Enhancing Oncology Model uses HCCs from the previous calendar year, and some of the episode-based cost measures in the Merit-based Incentive Payment Systems that align with similar episode categories tested in TEAM use a 120-day lookback period. Therefore, we considered, but did not propose, a 90-day, 120-day, 270-day, or 365-day lookback period to determine which HCC flags the beneficiary is assigned. We did not consider lookback periods longer than 1 year as we believed that it would capture beneficiary acuity that may be unrelated to their episodic care in TEAM, and thus arbitrarily adjusting target prices. There is limited research into the most appropriate lookback period duration for risk adjustment; however, there are some findings that suggest that incorporating clinical information beyond 1 year does not improve risk adjustment. Although we did not propose this alternative, we sought comment on whether these alternative lookback periods would be ap rporating clinical information beyond 1 year does not improve risk adjustment. Although we did not propose this alternative, we sought comment on whether these alternative lookback periods would be appropriate for TEAM or if there are other lookback period options we should consider. We sought comment on our proposal at proposed § 512.545(a) to use a 180- day lookback period to determine which HCC flags the beneficiary is assigned. The following is a summary of the public comments received on the proposed policy to use a 180-day supported the 180-day lookback period, noting this would lead to improved data collection, quality improvement and a more comprehensive assessment of patient risk. A couple commenters also supported the proposal since this would align with the lookback period implemented in BPCI Advanced. Response: We thank the commenters for their support. Comment: A few commenters opposed the proposed lookback period. These commenters also noted that 180 days is not sufficient to document patient risk. A few commenters noted that a lookback period of only 180 days would create burden for providers to capture all risk factors during pre- operation visits within this period t to document patient risk. A few commenters noted that a lookback period of only 180 days would create burden for providers to capture all risk factors during pre- operation visits within this period. Commenters were concerned that this may be especially challenging for unplanned surgical episodes and may unintentionally drive additional utilization if providers are concerned patient acuity may not otherwise be captured. Response: We appreciate the commenters’ concerns with the 180-day lookback period. We disagree that the 180-day lookback period would create additional reporting burden for providers. We believe that 180 days will significantly reduce any burden put on providers to capture risk factors during pre-operation visits relative to the 90- day lookback period, or even in-episode HCC risk adjustment. Additionally, the 180-day lookback period was used in BPCI Advanced. In that model, we believe there were minimal concerns on reporting burden put on providers with respect to the lookback period. We believe the issue with additional utilization may be more of a concern in the 90-day lookback period, and that a 180-day lookback period will mitigate this concern. We believe 18 e lookback period. We believe the issue with additional utilization may be more of a concern in the 90-day lookback period, and that a 180-day lookback period will mitigate this concern. We believe 180 days is long enough to capture relevant patient risk information without increasing reporting burden for providers. Based on internal analyses, there were insignificant differences in the risk- adjusted benchmark prices across the different lookback periods assessed (90, 180, 270, and 365 days). Specifically, only 2 out of 29 MS–DRG/HCPCS episode type risk-adjusted benchmark prices changed by more than 2 percent when extending the lookback period from 180 to 365 days. Thus, we believe 180 days is sufficient to document risk, since extending the lookback period beyond that did not significantly affect risk adjustment’s impact on these benchmark prices. We acknowledge that it may be possible for less data to be captured in the lookback period for unplanned episodes compared to planned procedures. However, based on the results of the internal analyses, extending the lookback longer than 180 days did not capture data significant enough to change the risk-adjusted benchmark prices. Commen edures. However, based on the results of the internal analyses, extending the lookback longer than 180 days did not capture data significant enough to change the risk-adjusted benchmark prices. Comment: A commenter was concerned that a 180-day lookback period may disadvantage smaller, rural providers or those treating underserved populations, since they may not be able to capture as much clinical data in the 180-day lookback. Additionally, FFS hospitals may not have been previously incentivized to record diagnoses and may be disadvantaged by this lookback compared to providers who participated in the CJR model. Response: We acknowledge the commenter’s concern for smaller, rural providers, those treating underserved populations, and those who have not previously participated in CJR or other programs. We believe our risk adjustment model is robust enough to capture spending accurately, even for these provider types. Specifically, we include provider-specific risk adjusters, such as safety net status, bed size, and a beneficiary economic index variable to appropriately capture risk for these provider types. Further, we believe that extending the lookback period longer than 180 days wi y net status, bed size, and a beneficiary economic index variable to appropriately capture risk for these provider types. Further, we believe that extending the lookback period longer than 180 days will decrease episode volume since the lookback period will also apply to relevant episode-level exclusions, which could negatively impact the reach of the model, particularly for smaller providers. Comment: Many commenters suggested a one-year lookback period for TEAM since it will capture more annual visits, and thus, more diagnoses recorded in the annual visits, compared to the 180-day lookback period. A few commenters proposed that a 365-day lookback would lead to better predictions in spending and a more comprehensive picture of patient health. Many commenters also noted that a 365- day lookback period also aligns with Medicare Advantage risk adjustment methodology and other CMS programs, such as the CJR model. Response: We acknowledge the commenter’s concerns that it is possible for fewer annual wellness visits to be captured in the 180-day lookback period relative to a 365-day lookback period. However, as stated in the proposed rule and in the previous responses, we do not believe wer annual wellness visits to be captured in the 180-day lookback period relative to a 365-day lookback period. However, as stated in the proposed rule and in the previous responses, we do not believe that extending the lookback period from 180 days to 365 days would provide significantly more patient risk information or significantly impact pricing. As stated previously, only 2 of VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00572 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations 37107 29 risk-adjusted benchmark prices changed by more than 2 percent when extending the lookback period from 180 to 365 days. On the other hand, in these same analyses, extending the lookback decreased the episode volume by about 100,000 among all episode types across a 3-year baseline period. Since the lookback period for episode-level exclusions and risk adjustment must be consistent, we believe extending the lookback to a full year will decrease episode volume and negatively impact the reach of the model. Additionally, we thank the comment s and risk adjustment must be consistent, we believe extending the lookback to a full year will decrease episode volume and negatively impact the reach of the model. Additionally, we thank the commenters for acknowledging that a 365-day lookback period would align with Medicare Advantage and the CJR model. We believe our risk adjustment model and list of specific risk adjusters is more robust, especially compared to CJR. Thus, we do not believe we need to align the lookback period with that of other models or programs if the risk adjusters included in TEAM risk adjustment methodology is not consistent with those other programs. Comment: A few commenters suggested to extend the lookback period to also include in-episode HCCs, since not including them may have a negative effect for unplanned procedures, which have fewer pre-episode visits. Response: We appreciate the commenters’ suggestion to include HCCs on the claims incurred during the anchor hospital encounter or during the episode. However, as stated in the proposed rule, we believe that using Medicare FFS claims from the lookback period, as opposed to the anchoring claim, is beneficial since it will reduce the incentive for inc However, as stated in the proposed rule, we believe that using Medicare FFS claims from the lookback period, as opposed to the anchoring claim, is beneficial since it will reduce the incentive for increased coding intensity at the time of the initiating procedure or elsewhere in the episode window. We are only including HCC risk adjustment data prior to the anchor hospitalization or procedure. ICD–9 diagnosis codes, leading to more granular HCCs. After consideration of the public comments, we are finalizing without modification our proposal at § 512.545(a)(1) to use a 180-day lookback period to determine which HCC flags the beneficiary is assigned. The 180-day lookback period will also be applicable to episode-level exclusions which are dependent on the lookback period. (b) HCC Version In the FY 2025 IPPS/LTCH PPS final rule we finalized TEAM’s approach to risk adjustment for target prices, which included episode category risk adjusters linked to specific HCCs that aimed to improve target price accuracy by accounting for beneficiary-driven episode expenditure variation (89 FR 69763). As indicated in the final rule, a Lasso regression analysis with additional input from a Technical get price accuracy by accounting for beneficiary-driven episode expenditure variation (89 FR 69763). As indicated in the final rule, a Lasso regression analysis with additional input from a Technical Expert Panel (TEP) of clinicians was performed to identify the finalized risk adjusters, including the specific HCCs. The analysis used HCCs from version 22 (v22) of the CMS–HCC risk adjustment model as this version is the version used in the BPCI Advanced model which TEAM predicated its risk adjustment approach on. However, we stated in the proposed rule that we are aware that v22 is not the most updated version used in the CMS–HCC risk adjustment model. Currently, version 28 (v28), as finalized in the Risk Adjustment Data Validation (RADV) final rule (88 FR 6643), is used in Medicare Part C and other CMS initiatives. Given there is a more recent HCC version and its adoption across CMS and its initiatives, we believed it was important for TEAM to use a more recent HCC version that relies on ICD–10 diagnosis codes, rather than previous versions that include We stated in the proposed rule that given HCC v28 results in more granular HCCs, there is not a one-to-one mapping of the HCCs use on ICD–10 diagnosis codes, rather than previous versions that include We stated in the proposed rule that given HCC v28 results in more granular HCCs, there is not a one-to-one mapping of the HCCs used in v22 to v28. As there is not a one-to-one match between HCCs in v22 and v28, a Lasso regression analysis with additional clinician input was repeated to identify the specific HCCs in v28 that would be used to risk adjust target prices in TEAM. We noted in the proposed rule that lasso regression analysis is a statistical modeling method used to identify a subset of risk adjusters which are most relevant for prediction of the natural log difference between clinical episode spending and the benchmark price. The objective of Lasso regression is to find the risk adjusters that minimize the residual sum of squares. In other words, the Lasso regression analysis identifies the risk adjusters that minimize the difference between the predicted and the actual values. We also noted in the proposed rule that clinician input helps to identify risk adjusters relevant to clinical practice and predicting target prices. Clinician input was informed by a literature review of perioperative comorbiditi rule that clinician input helps to identify risk adjusters relevant to clinical practice and predicting target prices. Clinician input was informed by a literature review of perioperative comorbidities that would affect outcome and Lasso covariate estimates to support their recommendations. Based on the Lasso analysis and clinician input, we proposed to use HCC v28 to identify the episode category specific HCC risk adjusters used in TEAM’s risk adjustment methodology. Specifically, we proposed replacing the HCC episode category specific risk adjusters finalized in FY 2025 IPPS/ LTCH PPS final rule with the following HCC episode category specific risk adjusters as demonstrated in Table XI.A.¥12. BILLING CODE 4120–01–P 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00573 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 37108 Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00574 Fmt 4701 Sfmt 4725 E:\FR\FM\04AUR2.SGM 04AUR2 / > H P G 2014 00:36 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00574 Fmt 4701 Sfmt 4725 E:\FR\FM\04AUR2.SGM 04AUR2 / > H P G 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00575 Fmt 4701 Sfmt 4725 E:\FR\FM\04AUR2.SGM 04AUR2 / > H P G 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00576 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 / > H P G 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00577 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k 37112 Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations • HCC 63: Chronic Liver Failure/End- • HCC 211: Respirator Dependence/ Stage Liver Disorders • HCC 93: Rheumatoid Arthritis and Other Specified Inflammatory Rheumatic Disorders • HCC 109: Acquired Hemolytic, Aplastic, and Sideroblastic Anemias • HCC 125: Dementia, Severe • HCC 126: Dementia, Moderate • HCC 127: Dementia, Mild or Unspecified • HCC 180: Quadriplegia • HCC 181: Paraplegia • HCC 191: Quadriplegic Cerebral Palsy • HCC 198: Multiple Sclerosis • HCC 199: Parkinson and Other Degenerative Disease of Basal Ganglia • HCC nspecified • HCC 180: Quadriplegia • HCC 181: Paraplegia • HCC 191: Quadriplegic Cerebral Palsy • HCC 198: Multiple Sclerosis • HCC 199: Parkinson and Other Degenerative Disease of Basal Ganglia • HCC 211: Respirator Dependence/ Tracheostomy Status/Complications • HCC 213: Cardio-Respiratory Failure and Shock • HCC 226: Heart Failure, Except End- Stage and Acute • HCC 238: Specified Heart Arrhythmias • HCC 249: Ischemic or Unspecified Stroke • HCC 253: Hemiplegia/Hemiparesis • HCC 280: Chronic Obstructive Pulmonary Disease, Interstitial Lung Disorders, and Other Chronic Lung Disorders • HCC 326: Chronic Kidney Disease, Stage 5 • HCC 383: Chronic Ulcer of Skin, Except Pressure, Not Specified as Through to Bone or Muscle • HCC 402: Hip Fracture/Dislocation For Major Bowel Procedure episodes, the following 30 risk adjustment variables are included: age bracket variable, HCC count variable, beneficiary economic risk adjustment variable, long-term institutional care use variable, hospital bed size variable, safety net hospital status variable, and the following 24 HCCs: • HCC 17: Cancer Metastatic to Lung, Liver, Brain, and Other Organs; Acute Myeloid Leukemia Except Promyelocytic • HCC ed size variable, safety net hospital status variable, and the following 24 HCCs: • HCC 17: Cancer Metastatic to Lung, Liver, Brain, and Other Organs; Acute Myeloid Leukemia Except Promyelocytic • HCC 22: Bladder, Colorectal, and Other Cancers • HCC 37: Diabetes with Chronic Complications • HCC 48: Morbid Obesity • HCC 78: Intestinal Obstruction/ Perforation • HCC 125: Dementia, Severe • HCC 126: Dementia, Moderate • HCC 127: Dementia, Mild or Unspecified • HCC 151: Schizophrenia • HCC 155: Major Depression, Moderate or Severe, without Psychosis • HCC 199: Parkinson and Other Tracheostomy Status/Complications • HCC 213: Cardio-Respiratory Failure and Shock • HCC 224: Acute on Chronic Heart Failure • HCC 226: Heart Failure, Except End- Stage and Acute • HCC 238: Specified Heart Arrhythmias • HCC 253: Hemiplegia/Hemiparesis • HCC 267: Deep Vein Thrombosis and Pulmonary Embolism • HCC 280: Chronic Obstructive Pulmonary Disease, Interstitial Lung Disorders, and Other Chronic Lung Disorders • HCC 326: Chronic Kidney Disease, Stage 5 • HCC 327: Chronic Kidney Disease, Severe (Stage 4) • HCC 383: Chronic Ulcer of Skin, Except Pressure, Not Specified as Through to Bone or Muscle • HCC 463: • HCC 326: Chronic Kidney Disease, Stage 5 • HCC 327: Chronic Kidney Disease, Severe (Stage 4) • HCC 383: Chronic Ulcer of Skin, Except Pressure, Not Specified as Through to Bone or Muscle • HCC 463: Artificial Openings for Feeding or Elimination For LEJR episodes, the following 29 risk adjustment variables are included: age bracket variable, HCC count variable, procedure-related variable (ankle procedure or reattachment, partial hip procedure, partial knee arthroplasty, total hip arthroplasty or hip resurfacing procedure, and total knee arthroplasty), variable for disability as the original reason for Medicare enrollment, beneficiary economic risk adjustment variable, prior post-acute care use variable, hospital bed size variable, safety net hospital status variable, and the following 21 HCCs: • HCC 17: Cancer Metastatic to Lung, Liver, Brain, and Other Organs; Acute Myeloid Leukemia Except Promyelocytic • HCC 36: Diabetes with Severe Acute Complications • HCC 37: Diabetes with Chronic Complications • HCC 48: Morbid Obesity • HCC 125: Dementia, Severe • HCC 126: Dementia, Moderate • HCC 127: Dementia, Mild or Unspecified • HCC 151: Schizophrenia • HCC 155: Major Depression, Moder ic Complications • HCC 48: Morbid Obesity • HCC 125: Dementia, Severe • HCC 126: Dementia, Moderate • HCC 127: Dementia, Mild or Unspecified • HCC 151: Schizophrenia • HCC 155: Major Depression, Moderate or Severe, without Psychosis • HCC 199: Parkinson and Other Degenerative Disease of Basal Ganglia • HCC 224: Acute on Chronic Heart Failure • HCC 225: Acute Heart Failure (Excludes Acute on Chronic) • HCC 226: Heart Failure, Except End- Degenerative Disease of Basal Ganglia • HCC 201: Seizure Disorders and Stage and Acute • HCC 238: Specified Heart Convulsions Arrhythmias • HCC 253: Hemiplegia/Hemiparesis • HCC 267: Deep Vein Thrombosis and Pulmonary Embolism • HCC 280: Chronic Obstructive Pulmonary Disease, Interstitial Lung Disorders, and Other Chronic Lung Disorders • HCC 326: Chronic Kidney Disease, Stage 5 • HCC 327: Chronic Kidney Disease, Severe (Stage 4) • HCC 383: Chronic Ulcer of Skin, Except Pressure, Not Specified as Through to Bone or Muscle • HCC 402: Hip Fracture/Dislocation For Spinal fusion episodes, the following 31 risk adjustment variables are included in the TEAM risk adjustment methodology: age bracket variable, HCC count variable, prior post- acute care use v tion For Spinal fusion episodes, the following 31 risk adjustment variables are included in the TEAM risk adjustment methodology: age bracket variable, HCC count variable, prior post- acute care use variable, beneficiary economic risk adjustment variable, hospital bed size variable, safety net hospital status variable, and the following 25 HCCs: • HCC 17: Cancer Metastatic to Lung, Liver, Brain, and Other Organs; Acute Myeloid Leukemia Except Promyelocytic • HCC 18: Cancer Metastatic to Bone, Other and Unspecified Metastatic Cancer; Acute Leukemia Except Myeloid • HCC 37: Diabetes with Chronic Complications • HCC 48: Morbid Obesity • HCC 93: Rheumatoid Arthritis and Other Specified Inflammatory Rheumatic Disorders • HCC 125: Dementia, Severe • HCC 126: Dementia, Moderate • HCC 127: Dementia, Mild or Unspecified • HCC 155: Major Depression, Moderate or Severe, without Psychosis • HCC 180: Quadriplegia • HCC 181: Paraplegia • HCC 182: Spinal Cord Disorders/ Injuries • HCC 192: Cerebral Palsy, Except Quadriplegic • HCC 193: Chronic Inflammatory Demyelinating Polyneuritis and Multifocal Motor Neuropathy • HCC 199: Parkinson and Other Degenerative Disease of Basal Ganglia • HCC 224: Acu alsy, Except Quadriplegic • HCC 193: Chronic Inflammatory Demyelinating Polyneuritis and Multifocal Motor Neuropathy • HCC 199: Parkinson and Other Degenerative Disease of Basal Ganglia • HCC 224: Acute on Chronic Heart Failure • HCC 226: Heart Failure, Except End- Stage and Acute • HCC 238: Specified Heart Arrhythmias • HCC 249: Ischemic or Unspecified Stroke • HCC 253: Hemiplegia/Hemiparesis • HCC 254: Monoplegia, Other Paralytic Syndromes • HCC 267: Deep Vein Thrombosis and Pulmonary Embolism VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00578 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations 37113 • HCC 326: Chronic Kidney Disease, Stage 5 • HCC 383: Chronic Ulcer of Skin, Except Pressure, Not Specified as Through to Bone or Muscle • HCC 401: Vertebral Fractures without Spinal Cord Injury Comment: Some commenters urged CMS to include additional risk adjusters in TEAM. A couple commenters suggested risk adjusters to stratify by elective status to ensure target prices are equitable for hospitals more likely to provide n S to include additional risk adjusters in TEAM. A couple commenters suggested risk adjusters to stratify by elective status to ensure target prices are equitable for hospitals more likely to provide non-elective procedures. A few commenters urged CMS to include more risk adjusters to account for clinical complexity within episode categories, such as inpatient versus outpatient setting, frailty and procedure-specific factors. A commenter recommended CMS include a risk adjuster for swing bed utilization, limited to rural participants or specific participation tracks. A couple commenters suggested to use the disability risk adjustment factor across all episode types. Response: As stated in the proposed rule, we did not propose or consider changes to the non-HCC episode category risk adjusters. However, we will take into consideration these public comments as we implement the model and monitor TEAM’s risk adjustment methodology. With respect to emergent versus elective procedures, as noted in the FY 2025 IPPS/LTCH PPS final rule (89 FR 69769), we believe that grouping emergent and elective procedures together, rather than stratifying them, reduces the incentive for increasing coding in in the FY 2025 IPPS/LTCH PPS final rule (89 FR 69769), we believe that grouping emergent and elective procedures together, rather than stratifying them, reduces the incentive for increasing coding intensity. Similarly, we believe that setting separate target prices for inpatient versus outpatient settings may create incentives for adverse selection and gaming. We believe that the risk adjustment model, which includes clinical risk adjusters, should be sufficient in accounting for pricing differences and clinical complexities among emergent procedures. Comment: Some comments requested hospital-specific risk adjusters in TEAM. Another couple commenters recommended HCC weights and HCC counts. A few commenters urged CMS to include more risk adjusters to account for clinical complexity within episode categories, such as fracture versus non-fracture, as well as demographic-specific factors. Response: We thank the commenters for their recommendations. Hospital- specific (bed size and safety net status) and demographic-specific risk adjusters, as well as those for HCC counts are already included in TEAM. SHFFT and LEJR episode types also include a risk adjuster specific to Hip Fracture/ D net status) and demographic-specific risk adjusters, as well as those for HCC counts are already included in TEAM. SHFFT and LEJR episode types also include a risk adjuster specific to Hip Fracture/ Dislocation (HCC 402) and LEJR procedure-specific factors (for example, ankle procedures or reattachments, partial hip procedure, partial knee arthroplasty, total hip arthroplasty or hip resurfacing procedure, and total knee arthroplasty). We refer the readers to the comprehensive list of risk adjusters included in TEAM summarized within this section. After consideration of the public comments, we are finalizing without modification our proposal to use HCC version 28 to construct our episode category specific HCC risk adjusters in TEAM. We are also finalizing our proposal without modification at § 512.545(a)(6)(i) through (v) to use the updated list of HCC risk adjusters for each episode as a result of using HCC version 28. (8) Low Volume Hospitals In both CJR and BPCI Advanced, we recognized that hospitals that perform a number of episodes below a certain volume threshold may have challenges taking on two-sided financial risk. As noted in the Episode-Based Payment Model Request for Inf ed that hospitals that perform a number of episodes below a certain volume threshold may have challenges taking on two-sided financial risk. As noted in the Episode-Based Payment Model Request for Information (88 FR 45872), episode volume is an important feature in an episode-based payment model because episode categories with sufficient volume help to reduce pricing volatility and spread financial risk. In the 2015 CJR final rule (80 FR 73285), we acknowledged that such hospitals might not find it in their financial interests to make systemic care redesigns or engage in an active way with the CJR model. At 80 FR 73292, we acknowledged commenter concerns about low volume providers, including but not limited to observations that low volume providers could be: less proficient in taking care of LEJR patients in an efficient and cost-effective manner, more financially vulnerable with fewer resources to respond to the financial incentives of the model, and disproportionately impacted by high- cost outlier cases. In spite of these potential challenges, we stated that the inclusion of low volume hospitals in CJR was consistent with the goal of evaluating the impact of bundled payment and h- cost outlier cases. In spite of these potential challenges, we stated that the inclusion of low volume hospitals in CJR was consistent with the goal of evaluating the impact of bundled payment and care redesign across a broad spectrum of hospitals with varying levels of infrastructure, care redesign experience, market position, and other considerations and circumstances (80 FR 73292). In the proposed rule for TEAM, we stated that in CJR, we set the low volume threshold as fewer than 20 CJR episodes across the 3-year baseline years of 2012 through 2014. Low volume hospitals received target prices based on 100 percent regional data, rather than a blended target price that incorporated their participant-specific data, because a target price based on limited data is less likely to be accurate and reliable. These hospitals were also subject to the lower stop-loss limits that we offered to rural hospitals, in recognition of the fact that they might be less prepared to take on downside risk than hospitals with higher episode volume. In the CJR 2017 final rule that reduced the number of mandatory MSAs, low volume hospitals were among the types of hospitals that were required to opt in i k than hospitals with higher episode volume. In the CJR 2017 final rule that reduced the number of mandatory MSAs, low volume hospitals were among the types of hospitals that were required to opt in if they wanted to remain in the model (82 FR 57072). In the CJR 2020 final rule, we removed the remaining low volume hospitals from the CJR extension when we limited the CJR participant hospital definition to those hospitals that had been mandatory participants throughout the model (86 FR 23497). We stated in the proposed rule that in BPCI Advanced, our low volume threshold policy was to not provide a target price for a given clinical episode category if performed at a hospital that did not meet the 41 clinical episode minimum volume threshold during the 4-year baseline period. This meant that no BPCI Advanced episodes would be triggered for that particular clinical episode category during the applicable performance period at that hospital. However, participants could continue to trigger other clinical episode categories for which they had enrolled and for which there was sufficient baseline volume. Additionally, clinical episodes that occurred at the hospital during the performance per her clinical episode categories for which they had enrolled and for which there was sufficient baseline volume. Additionally, clinical episodes that occurred at the hospital during the performance period, though not triggering a BPCI Advanced episode, would count toward the low volume threshold when that year became part of a subsequent baseline period. Therefore, as the baseline shifted forward each year, bringing a more recent year into the baseline and dropping the oldest year, a hospital could potentially meet the volume threshold and receive a target price for the clinical episode category for a subsequent performance period. Last year, in the FY 2025 IPPS/LTCH PPS proposed rule (89 FR 35934) that established TEAM, we proposed that TEAM would include a low volume threshold. We proposed that if a TEAM participant did not meet the proposed low volume threshold of at least 31 total episodes across all episode categories in the baseline period for PY1, CMS would still reconcile their episodes, but the TEAM participant would be subject to the Track 1 stop-loss and stop-gain limits for PY1. If a TEAM participant did not meet the proposed low volume threshold of at least 31 total epi episodes, but the TEAM participant would be subject to the Track 1 stop-loss and stop-gain limits for PY1. If a TEAM participant did not meet the proposed low volume threshold of at least 31 total episodes in the applicable 3-year baseline periods for PYs 2 through 5, the TEAM VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00579 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k 37114 Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations participant would be subject to the Track 2 stop-loss and stop-gain limits for PY 2 through 5. However, after many comments that this policy was insufficient for low volume hospitals, in the FY 2025 IPPS/LTCH PPS final rule (89 FR 68986), we determined we would not finalize a policy for low volume hospitals and instead would propose a new policy in future notice and comment rulemaking. In the proposed rule, rather than offering a specific proposal, we proposed to maintain our current policy of having no low volume episode policy, given that Track 1 of the model has no downside risk and we expect most TEAM participants to select Track 1 for the , we proposed to maintain our current policy of having no low volume episode policy, given that Track 1 of the model has no downside risk and we expect most TEAM participants to select Track 1 for the first performance year. Rather, we sought comment on several potential policies to address prior commenters’ concerns about low volume providers participating in TEAM. First, we considered, but did not propose, that a low volume threshold would apply to specific episode categories in the baseline period for a given PY, similar to BPCI Advanced. If a TEAM participant did not meet the considered low volume threshold of at least 31 episodes in a given baseline period for a given episode category, CMS would still reconcile their episodes, but the TEAM participant would not be held accountable for any performance year episode spending that exceeded the reconciliation target price for each of the MS–DRG/HCPCS episode types in that given episode category during the applicable performance year. We stated in the proposed rule that this policy would effectively waive downside financial risk for the TEAM participant for episode categories in which they did not meet the considered low volume thre e stated in the proposed rule that this policy would effectively waive downside financial risk for the TEAM participant for episode categories in which they did not meet the considered low volume threshold. For example, in PY1, if a TEAM participant only initiated 30 episodes in the baseline period for the major bowel procedure episode category, and initiated 31 or more episodes in the baseline period for each of the other episode categories tested in TEAM, then the TEAM participant would not be held accountable for performance year episode spending that exceeded the reconciliation target price for the major bowel procedure episode category but would be accountable for performance year episode spending that exceeded the reconciliation target price for all the other episode categories for PY1. We noted that the baseline period for a given performance year in TEAM rolls forward each year. Therefore, we acknowledged in the proposed rule that it is possible for a TEAM participant to not meet the low volume threshold for a given episode category in one performance year and then meet the low volume threshold the next performance year because the baseline period rolled forward and capture et the low volume threshold for a given episode category in one performance year and then meet the low volume threshold the next performance year because the baseline period rolled forward and captured a different volume of baseline period episodes. We stated in the proposed rule that we did not anticipate there would be a significant number of hospitals meeting the threshold one performance year and not the next (and vice versa), because procedure volumes tend to remain consistent across performance years. We noted in the proposed rule that this considered policy may address commenters’ concerns, by placing the low volume threshold at the episode category level rather than across all episode categories and acknowledge commenters’ concerns regarding the level of financial risk that is tolerable for low volume hospitals, especially hospitals that are safety net hospitals or rural hospitals. We stated in the proposed rule that TEAM participants with low volume may not have enough episode volume to spread the risk or create efficient care pathways sufficient for downside risk. Further, and as compared to the BPCI Advanced model, this considered policy would allow TEAM participants to volume to spread the risk or create efficient care pathways sufficient for downside risk. Further, and as compared to the BPCI Advanced model, this considered policy would allow TEAM participants to still initiate episodes and earn a reconciliation payment amount if they can reduce spending and provide quality care. However, we were concerned that waiving downside risk for low volume hospitals may affect potential TEAM savings for CMS. Additionally, we stated in the proposed rule that the 31- episode category threshold may not be the optimal threshold to ensure a low volume policy adequately addresses the concerns of TEAM participants and stakeholders affected by a potential low volume policy. A 31-episode is a similar approach to capturing the per baseline year threshold in BPCI Advanced, but this threshold could theoretically be too low to capture all TEAM participant hardship caused by episode volatility. It could also be too high and exclude too many episodes from the model and thus deprive TEAM participants an opportunity to enhance patient quality of care or provider efficiency and earn associated reconciliation payments. We also considered, but did not propose, different lo us deprive TEAM participants an opportunity to enhance patient quality of care or provider efficiency and earn associated reconciliation payments. We also considered, but did not propose, different low volume thresholds for the previously considered policy in the baseline period for a given episode category, including 91, 61, 51, 41, 21, and 11 episodes. In the proposed rule we stated that in an internal analysis of hospitals that were potentially eligible for TEAM using claims data from calendar year 2023, we found that 30 percent of acute care hospital (ACH)-clinical episode category (CEC) combinations had 10 or fewer episodes and were not flagged as a low volume hospital using the baseline period methodology of fewer than 31 episodes in a given CEC. Presumably, these could be seen as false negative results for low volume status or indications that the fewer than 31- episode threshold was set too high. Among these ACH–CEC combinations, the average episode count was seven. Additionally, 14 percent of these ACH– CEC combinations had five episodes or fewer. We noted that it could be the case that the 31 or fewer episode threshold could include hospitals that are not truly so low vol y, 14 percent of these ACH– CEC combinations had five episodes or fewer. We noted that it could be the case that the 31 or fewer episode threshold could include hospitals that are not truly so low volume as to justify waiving downside risk. Alternatively, hospitals may just barely cross the 31 or fewer episode threshold and thus be subject to downside risk and may still be fundamentally similar to identified low volume TEAM participants experiencing hardship from the natural volatility involved in having fewer qualifying episodes. Though this is true of any threshold, the likelihood of this increases at lower thresholds than larger thresholds. Therefore, we considered alternative thresholds such as fewer than 91 episodes (approximately 3 times the fewer than 31 episode threshold), fewer than 61 episodes (approximately 2 times the fewer than 31 episode threshold), fewer than 51 episodes (the fewer than 31 episode threshold plus 3 times the average count of episodes for ACH–CEC combinations in our mock reconciliation not cited as low volume), fewer than 41 episodes (the fewer than 31 episode threshold plus one-third the threshold), fewer than 21 episodes (3 times the average count of ons in our mock reconciliation not cited as low volume), fewer than 41 episodes (the fewer than 31 episode threshold plus one-third the threshold), fewer than 21 episodes (3 times the average count of episodes for ACH–CEC combinations in our mock reconciliation not cited as low volume), and fewer than 11 episodes (a threshold that should only flag ACH–CEC combinations at the lowest threshold found in our analysis). We considered, but did not propose, limiting the scope of a potential low volume policy to safety net and rural hospitals only, since these hospital types are more likely to initiate lower volumes of episodes. However, we were concerned that this restriction would unfairly hinder other low-volume providers (which are not safety net or rural) from gaining efficiency in care coordination, since they would still bear the same financial risk as higher volume hospitals. We stated in the proposed rule that in an internal analysis, approximately 343 acute care hospitals are not designated as safety net hospitals or rural hospitals. Of these hospitals, approximately 109 acute care hospitals would have at least one episode category that had fewer than 31 VerDate Sep 2014 00:36 Au ignated as safety net hospitals or rural hospitals. Of these hospitals, approximately 109 acute care hospitals would have at least one episode category that had fewer than 31 VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00580 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations 37115 episodes in the baseline period and would meet the definition of low volume if safety net hospital status or rural hospital status was not required for a low volume qualification. We stated that excluding non-safety net hospitals and non-rural hospitals from a low volume status could unfairly hinder nearly one-third of non-safety net hospitals or non-rural hospitals. We also considered, but did not propose, including alternative approaches to a low episode volume threshold in TEAM, including an approach similar to BPCI Advanced, where if a TEAM participant did not meet the 31 episode low volume threshold for a given episode category in the baseline period, the TEAM participant would not be held accountable for that episode category for the perf ticipant did not meet the 31 episode low volume threshold for a given episode category in the baseline period, the TEAM participant would not be held accountable for that episode category for the performance year that aligned with the baseline period. In other words, they would not be eligible to initiate episodes in that episode category during the performance year and would not be eligible to earn any reconciliation payment amount or repayment amount for that given episode category during the performance year. However, we stated in the proposed rule that we were concerned that imposing a minimum volume threshold that removes TEAM participant accountability may restrict the number of hospitals eligible to participate in TEAM and limit beneficiary access to the benefits of value-based, coordinated care. We also considered allowing low- volume episode types to be subject to a stop-loss/stop-gain limit of 5 percent, similar to Track 2, or a lower stop-loss/ stop-gain limit of 3 percent, 2 percent, and 1 percent, such that TEAM participants are subject to a lower level of financial risk and gain, but still held accountable for the care provided under these episode categories. We noted 2 percent, and 1 percent, such that TEAM participants are subject to a lower level of financial risk and gain, but still held accountable for the care provided under these episode categories. We noted in the proposed rule that under this approach, after creating the quality- adjusted reconciliation amount based on the TEAM participant’s track selection, CMS would calculate the proportion of the quality-adjusted reconciliation amount that each episode category contributes to based on the PY episode weight. For example, Table XI.A.–13 demonstrates a TEAM participant, assuming Track 3 participation, meeting the low-volume threshold for the LEJR episode category but not for the SHFFT episode category. Table XI.A.¥14 continues the example by showing the stop-loss/stop- gain cap would then be applied to each episode category where the low-volume episode-type is subject to a 5 percent stop-loss/stop-gain cap while any other non-low volume episode types are subject to the stop-loss/stop-gain cap based on the TEAM participant’s Track 3 selection. However, as demonstrated by Tables XI.A.–13 and XI.A.–14, we were concerned that this approach adds complexity to the reconciliation calculations ap based on the TEAM participant’s Track 3 selection. However, as demonstrated by Tables XI.A.–13 and XI.A.–14, we were concerned that this approach adds complexity to the reconciliation calculations by adding additional steps. Further, we stated that we were also concerned that lower stop-loss/stop-gain limits would still not sufficiently protect low-volume episode TEAM participants from undue financial risk in the model. We also considered implementing low episode volume thresholds during the performance year. Specifically, we considered not holding TEAM participants accountable for a given episode category if they initiated less than 11 or 6 episodes in a given episode category or less than 31 or 21 total episodes across episode categories in a performance year. However, we indicated in the proposed rule that we were concerned that including minimum episode volume thresholds during the performance year may introduce program integrity issues. We sought comment on our considered policies. We also sought comment on low volume policy alternatives we have not considered. The following is a summary of the public comments received on the considerations for low volume hospitals, and our ies. We also sought comment on low volume policy alternatives we have not considered. The following is a summary of the public comments received on the considerations for low volume hospitals, and our responses to these comments: Comment: Many commenters expressed concerns about the lack of a low volume policy in TEAM and urged CMS to establish one. Many commenters stated that a low volume policy was necessary to protect hospitals with low volumes from the volatility caused by small sample sizes. This volatility could result in hospitals facing large losses due to random variation over a small number of episodes. A couple of commenters also noted that, because of this variation, VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00581 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k / > H P G H P G 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00582 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations 37117 placed on low volume hospitals, the need to fairly evalua i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations 37117 placed on low volume hospitals, the need to fairly evaluate participants, the goal of maximizing participants’ incentives to invest in health care infrastructure, redesigned care processes, and provide higher value care, and potential savings to Medicare. We believe that a threshold of 31 episodes per episode category over three baseline years sufficiently protects low volume hospitals and provides a high enough volume for CMS to fairly assess their performance and for TEAM participants to fairly assess care transformation efforts made in these episode categories. When assessing what protections should be provided to hospitals that fell below the low volume threshold, we determined that, in order to maximize incentives to transform care, it was important to continue to reconcile episodes for low volume hospitals who earn reconciliation payments from CMS. This approach, which exposes CMS to downside risk on low volume episodes, reduces savings for CMS, and that savings reduction increases as the low volume threshold rises. Regarding the sugge from CMS. This approach, which exposes CMS to downside risk on low volume episodes, reduces savings for CMS, and that savings reduction increases as the low volume threshold rises. Regarding the suggestion to set the low volume threshold at the MS–DRG level, while we recognize that pricing is done at this level, we believe that setting this granular of a low volume threshold would make the low volume threshold susceptible to increased coding intensity. While costs across MS–DRGs within an episode category may be different, we believe that care transformation efforts made within these categories will be largely transferable. Finally, we believe that annual thresholds, if assessed during the performance year, would also create gaming opportunities. Comment: A few of commenters supported a threshold of 41 episodes per episode category across a 4-year baseline period, similar to BPCI Advanced. Response: We thank the commenters for their suggestion. While we recognize that BPCI Advanced utilized a 4-year baseline period, TEAM has a 3-year baseline period. A threshold of 31- episode over 3 baseline years is the same per baseline year threshold as 41 episodes over 4 baseline years. Commen lized a 4-year baseline period, TEAM has a 3-year baseline period. A threshold of 31- episode over 3 baseline years is the same per baseline year threshold as 41 episodes over 4 baseline years. Comment: A commenter suggested that CMS adopt a tiered threshold or set a low volume threshold that is based on the percentage of total episodes within a category to account for the fact that some episode categories inherently have higher volumes than others. Response: We thank the commenter for their suggestion. While we recognize that episode counts are inherently different across different episode categories, we believe that varying the low volume threshold by episode category would add unnecessary complexity to the model at this time. Comment: A commenter expressed concern that Track 1 being optional in PY 1 meant that this track did not guarantee protection against financial vulnerability for low volume hospitals. Response: We would like to clarify that, while Track 1 is optional for participants in PY 1, participants will be assigned this track unless they notify CMS that they would like to participate in Track 3. Therefore, the only TEAM participants that face downside risk under TEAM ipants in PY 1, participants will be assigned this track unless they notify CMS that they would like to participate in Track 3. Therefore, the only TEAM participants that face downside risk under TEAM in PY 1 will be those that voluntarily elect to do so. We believe that this does guarantee protection against financial vulnerability for TEAM participants in PY 1. Comment: A couple of commenters urged CMS to finalize a low volume policy prior to PY 1, stating that participants must know the key elements of TEAM prior to the start of the model for planning purposes. The commenters also noted that CMS should minimize mid-model changes. Another commenter stated that a low volume policy was one of several key features of TEAM that had not been determined, and that CMS should delay the model until these features were finalized so that hospitals had time to prepare for participation. Response: We thank the commenters for their concerns regarding hospitals’ ability to plan for TEAM and the importance of minimizing mid-model changes. We disagree that TEAM participants do not have adequate time to prepare for the model. There are approximately 17 months between the start date of TEAM and whe ortance of minimizing mid-model changes. We disagree that TEAM participants do not have adequate time to prepare for the model. There are approximately 17 months between the start date of TEAM and when hospitals were notified of their participation through the 2025 IPPS/LTCH PPS proposed rule. TEAM participants that elect to participate in Track 1 will have 12 additional months to prepare before they take on downside risk in PY 2, and TEAM participants that qualify as safety net will have up to 24 additional months to prepare before taking on downside risk in PY 4. We understand that the low volume policy is an important aspect of the model, and TEAM participants will have approximately 17 months between the low volume policy being finalized in this rule and when they are exposed to downside risk in PY 2. Furthermore, all TEAM participants will be notified whether or not they qualify as low volume in a given episode category based on the baseline period of that performance year ahead of the due dates for participation track selection for that performance year. TEAM participants will have the necessary information to decide what level of financial risk/ reward they want to opt-into f the due dates for participation track selection for that performance year. TEAM participants will have the necessary information to decide what level of financial risk/ reward they want to opt-into for a given performance year. We recognize the importance of minimizing mid-model changes and will take the instability caused by such changes into account in future notice and comment rulemaking. However, we also recognize the importance of being responsive to TEAM participants. We will continue to monitor the low volume policy throughout the model test and may propose changes through future notice and comment rulemaking if we identify an approach that is more responsive to TEAM participants and spurs care improvements for beneficiaries at low volume hospitals. Comment: A commenter expressed concerns that low volume providers would struggle to establish pre- and post-acute partnerships, causing them to struggle in TEAM. Response: We thank the commenter for their feedback. We agree that these partnerships could significantly impact hospitals’ ability to succeed in TEAM. We also recognize that factors outside of a hospital’s control, such as geographic location and market dynamics, will ese partnerships could significantly impact hospitals’ ability to succeed in TEAM. We also recognize that factors outside of a hospital’s control, such as geographic location and market dynamics, will impact their ability to form these partnerships. We believe that the low volume policy finalized in this rule, as well as other policies included in TEAM, such as the ability for rural hospitals to participate in Track 2, will help protect the TEAM participants most likely to struggle to form partnerships with providers for reasons outside of their control. Comment: Some commenters noted that, for low volume providers, the upfront costs of participation in the model, such as updating analytics infrastructure or staffing, would outweigh the benefits of participating in the model, or that investing in care delivery improvements for specific episode categories would not be financially viable if hospitals performed low volumes of those procedures. A commenter added that low volume providers are not incentivized to make meaningful changes in care delivery under the model. A few commenters expressed concerns about the level of financial investment in infrastructure, analytics, and system re re not incentivized to make meaningful changes in care delivery under the model. A few commenters expressed concerns about the level of financial investment in infrastructure, analytics, and system redesigns hospitals would need to succeed under the model and some stated that the low volume threshold should be high enough that hospitals that met the threshold accrued enough episodes to determine if care delivery changes had a meaningful impact. Response: We thank the commenters for their concern regarding low volume hospitals’ ability to afford the upfront VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00583 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k 37118 Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations costs needed to make meaningful changes in care delivery, their incentives to make these changes, and their ability to evaluate the impact of these changes. TEAM is intended to incentivize investments and changes in care processes that improve both the quality and efficiency of care. We understand that many of these improvements include fixed costs, and that it ma ntended to incentivize investments and changes in care processes that improve both the quality and efficiency of care. We understand that many of these improvements include fixed costs, and that it may be difficult for hospitals to justify these costs in areas where they have low volume of cases, as the hospital could provide greater quality and efficiency improvements by investing resources elsewhere. We acknowledge that it is difficult to justify major care changes without a high enough case volume to accurately measure the impact of these changes. We believe that the finalized low volume threshold of 31 episodes per episode category in a given baseline period represents a high enough volume for hospitals to evaluate the impact of changes made while participating in TEAM. Additionally, we believe that this policy will prevent hospitals from owing repayment amounts for episode categories in which they lack the incentive to invest in care transformation efforts. Comment: Some commenters stated that the lack of a low volume policy would hurt rural, small, or safety net providers. A few commenters stated that safety net hospitals, which display low margins and depend on public payors s stated that the lack of a low volume policy would hurt rural, small, or safety net providers. A few commenters stated that safety net hospitals, which display low margins and depend on public payors, could not afford to invest in care teams for low volume procedures. Response: We thank the commenters for their concerns for rural and safety net hospitals. While we are finalizing a low volume policy, we want to emphasize that TEAM provides significant protection for these hospitals outside of a low volume policy. Safety net hospitals, as defined at § 512.505, will be able to participate in Track 1 with no downside risk for the first 3 performance years of the model. Additionally, we will incorporate a variable in the TEAM risk adjustment model that accounts for safety net status when calculating target prices, providing more accurate targets for hospitals whose spending may exceed the averages in their region for reasons outside of their control. Both safety net hospitals and rural hospitals, as defined at § 512.505, will be able to participate in Track 2 for performance years 2 through 5 of the model with a 5 percent stop-gain and stop-loss limit. We believe that these protections pitals, as defined at § 512.505, will be able to participate in Track 2 for performance years 2 through 5 of the model with a 5 percent stop-gain and stop-loss limit. We believe that these protections, in conjunction with the low volume policy finalized in this rule, will allow rural and safety net hospitals the opportunity to succeed in TEAM. Comment: Some commenters stated that CMS should apply the low volume threshold to all hospitals, as opposed to just rural and safety net hospitals. Response: We thank the commenters for their suggestion. We agree that the difficulties that low volume hospitals face are not specific to rural and safety net hospitals. The low volume policy finalized in this rule will apply to all TEAM participants regardless of their status as rural or safety net hospitals. Comment: Some commenters suggested that CMS maintain data sharing with hospitals that fell below the low volume threshold, as this data could help them prepare for future performance years in which they might exceed the threshold. Response: We thank the commenters for their suggestion. Falling below the low volume threshold will have no impact on data sharing for TEAM participants. We agree ich they might exceed the threshold. Response: We thank the commenters for their suggestion. Falling below the low volume threshold will have no impact on data sharing for TEAM participants. We agree with the commenters that continuing to share data with hospitals that fall below the low volume threshold is critical, as these hospitals are still TEAM participants and could exceed the threshold for future performance years. Given that hospitals who fall below the threshold for a given episode category will still have their episodes reconciled, just without facing downside risk, it would not make sense to withhold data from these hospitals. Comment: A commenter stated that the low volume threshold should also apply to benchmarking calculations. Response: We thank the commenter for their suggestion. While we recognize that low volume hospitals may generate outlier episodes, we believe that our current benchmarking methodology fairly accounts for those outliers. Additionally, because preliminary target prices are at the MS–DRG/HCPCS- region level, a regional low-volume policy would not be practical to implement as CMS will need to create target prices for all MS–DRG/HCPCS in all region minary target prices are at the MS–DRG/HCPCS- region level, a regional low-volume policy would not be practical to implement as CMS will need to create target prices for all MS–DRG/HCPCS in all regions. After consideration of the public comments, we are finalizing at § 512.550(c)(1) through (7) a low volume threshold policy that received the majority of public comment support in TEAM such that if a TEAM participant did not meet the low volume threshold of at least 31 episodes in a given baseline period for a given episode category, CMS would still reconcile their episodes, but the TEAM participant would not be held accountable for any performance year episode spending that exceeded the reconciliation target price for each of the MS–DRG/HCPCS episode types in that given episode category during the applicable performance year. (9) Aligning Date Range in the Baseline and Performance Years and Timing of Reconciliation In the FY 2025 IPPS/LTCH PPS final rule (89 FR 68986) that established TEAM, we finalized the policy that we would calculate preliminary target prices using a 3-year rolling baseline period as described in § 512.540(b)(2). For example, for PY 1, covering the period from J EAM, we finalized the policy that we would calculate preliminary target prices using a 3-year rolling baseline period as described in § 512.540(b)(2). For example, for PY 1, covering the period from January 1, 2026, to December 31, 2026, we would use a baseline period from January 1, 2022, to December 31, 2024. We noted that we would attribute episodes to the baseline period based on the episode start date. An episode with an anchor hospitalization beginning in December 2022 and an anchor hospitalization discharge date in January 2023 would have an episode start date in 2022 and would be included in the baseline for PY 1 but not for PY 2, for which the baseline period is January 1, 2023, to December 31, 2025. However, as indicated in § 512.540(a)(3), we finalized our proposal to attribute episodes to performance years based on the date of discharge from the anchor hospitalization or the date of the anchor procedure for the purpose of assigning target prices. We further clarified this approach in section X.A.3.d.(3).(d) of the FY 2025 IPPS/LTCH PPS final rule and gave the following example: If an episode has an anchor hospitalization or anchor procedure end date in December 2026 but pproach in section X.A.3.d.(3).(d) of the FY 2025 IPPS/LTCH PPS final rule and gave the following example: If an episode has an anchor hospitalization or anchor procedure end date in December 2026 but an episode end date in January 2027, the episode is assigned to PY 1 and will have the PY 1 target price applied to it. However, if the episode starts in 2026 but both the anchor hospitalization discharge and episode end dates are in 2027, the episode is assigned to PY 2 and will have the PY 2 target price applied to it. To better align our episode attribution and pricing methodologies across the baseline and performance periods, we proposed to modify our approach to attribution of episodes to baseline years for the purpose of calculating preliminary target prices. Specifically, we proposed adopting the same approach that we finalized for attribution of performance year episodes, as described previously. Therefore, we proposed that an episode with an anchor hospitalization beginning in a given baseline year and an anchor hospitalization discharge date in the subsequent baseline year would be attributed to the baseline year when the anchor hospitalization discharge date occurred. For e given baseline year and an anchor hospitalization discharge date in the subsequent baseline year would be attributed to the baseline year when the anchor hospitalization discharge date occurred. For example, an episode VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00584 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations 37119 with an anchor hospitalization beginning in December 2022 with an anchor hospitalization discharge date in January 2023 would be included in the baseline for both PY 1 (as baseline year 2 of a baseline period from January 1, 2022, to December 31, 2024) and PY 2 (as baseline year 1 of a baseline period from January 1, 2023, to December 31, 2025). We stated in the proposed rule that this modification does not make any change to the methodology for attribution of episodes to the performance year. We believed this approach simplifies the construction of baseline and performance year episodes and maintains consistent application of episode assignment between baseline and performance years. We also indicate this approach simplifies the construction of baseline and performance year episodes and maintains consistent application of episode assignment between baseline and performance years. We also indicated in FY 2025 IPPS/ LTCH PPS final rule (89 FR 68986) that for episodes that begin in one performance year and end in a subsequent performance year we would reconcile episodes based on the episode end date. However, we recognized that reconciling an episode based on the episode’s end date may unnecessarily increase operational burden when trying to manage when an episode would be reconciled, especially when comparing the target price to the performance year. For example, if an episode starts in one performance year and ends in a subsequent performance year, then a TEAM participant would have to wait an additional year before that episode would be reconciled even though its target price was aligned with the performance year of the anchor hospitalization discharge date. Table XI.A.–15 demonstrates how episodes starting in a one performance year and ending in a subsequent performance year are reconciled. Therefore, we proposed to reconcile an episode based on the episode’s anchor hospitali rates how episodes starting in a one performance year and ending in a subsequent performance year are reconciled. Therefore, we proposed to reconcile an episode based on the episode’s anchor hospitalization or anchor procedure discharge date. We believed this approach would simplify tracking episodes and their reconciliation timing for TEAM participants. Additionally, we stated in the proposed rule that it would keep all episodes aligned to a given performance year based on target price construction to the same reconciliation time period. Table XI.A.–16 demonstrates the proposed approach to reconciling episodes based on anchor hospitalization or anchor procedure discharge date. We sought comment on our proposal at § 512.540(b)(2)(i) through (v) to construct baseline year episodes based on the anchor hospitalization or anchor procedure discharge date. We also sought comment on our proposal at § 512.540(a)(3) to reconcile episodes based on anchor hospitalization or anchor procedure discharge date. The following is a summary of the public comments received on the proposed policies to construct baseline year episodes and to reconcile episodes based on the anchor hospitalization or anch arge date. The following is a summary of the public comments received on the proposed policies to construct baseline year episodes and to reconcile episodes based on the anchor hospitalization or anchor procedure discharge date, and our responses to these comments: Comment: A few commenters expressed support for our proposal to assign baseline year episodes to corresponding baseline years based on the anchor hospitalization or anchor procedure discharge date and also to reconcile episodes based on anchor hospitalization or anchor procedure discharge date. Response: We thank the commenters for sharing their support. Comment: Some commenters expressed concerns that the proposed change would delay reconciliation. Response: We disagree that the proposed change would delay reconciliation. Rather, the policy has been proposed to avoid delays in reconciliation. For example, per the policy finalized in the FY2025 IPPS/ LTCH final rule, an episode in PY1 that has an anchor hospitalization or anchor procedure discharge date in 2026 and an episode end date in 2026 will be reconciled in Fall 2027 whereas a PY1 episode with anchor hospitalization or anchor procedure discharge date in 2026 but a chor procedure discharge date in 2026 and an episode end date in 2026 will be reconciled in Fall 2027 whereas a PY1 episode with anchor hospitalization or anchor procedure discharge date in 2026 but an episode end date in 2027 will be reconciled in Fall 2028. Even though both episodes fall in the same performance year and have the same final target price applicable to them, they would be reconciled in different time periods due to the end dates falling in different calendar years. As noted in the proposed rule, this may unnecessarily increase operational burden and delay the timely delivery of reconciliation reports. Therefore, we had proposed to reconcile episodes based on anchor hospitalization or VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00585 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k / > H P G H P G 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00586 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations 37121 applying the IPPS-derived wage facto AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations 37121 applying the IPPS-derived wage factor to the entire episode (that is, all claims grouped to it, including those in the post-discharge period) may not accurately reflect differences in real costs across participants and settings of care. Second, we stated that using only the wage factor fails to take into account non-wage differences in Medicare payment amounts such as outlier payments and provider-specific adjustments from other Medicare initiatives. Given all of these considerations, we did not propose any methodology for converting standardized target prices and reconciliation amounts to real dollars at this time. Instead, we kept target prices and reconciliation amounts in standardized dollars, while requesting comment on whether we should convert to real dollars and the preferred methodology for doing so, including but not limited to all the approaches discussed herein. We sought comment on whether and how to convert target prices and reconciliation amounts from standardized dollars to real dollars in a consistent ma ot limited to all the approaches discussed herein. We sought comment on whether and how to convert target prices and reconciliation amounts from standardized dollars to real dollars in a consistent manner. The following is a summary of the public comments received on the considerations for converting target prices and reconciliation amounts to real dollars, and our responses to these comments: Comment: A few commenters suggested CMS should convert standardized dollars to real dollars for both target prices and reconciliation amounts. A couple commenters urged CMS to establish a methodology to convert standardized dollars to real dollars to issue performance-based payments and recoupments from participating TEAM hospitals only during the reconciliation calculation for each performance year. Among commenters suggesting converting standardized dollars to real dollars for either the reconciliation amounts only or for both target prices and reconciliation amounts, a few commenters requested that CMS consider the wage factor conversion method used in the CJR model to best account for differences in hospitals’ episode expenditures in relation to the target price. A commenter suggested rec that CMS consider the wage factor conversion method used in the CJR model to best account for differences in hospitals’ episode expenditures in relation to the target price. A commenter suggested reconciliation amounts should have a real-to-standardized dollar ratio applied, similar to the calculation in BPCI Advanced. However, this commenter suggested to exclude inpatient indirect medical education (IME) payments and disproportionate share hospital (DSH) payments. A couple commenters supported keeping target prices and reconciliation amounts in standardized dollars since it will better allow peer participants to compare themselves to a fixed reference point. Response: We believe that keeping the reconciliation amounts in standardized dollars minimizes the risk of creating unfair rewards/penalties for hospitals. Specifically, we believe it may negatively impact participants, who receive additional payments like IME and DSH payments leading to higher real to standardized ratio causing an amplification of TEAM repayment amounts. On the other hand, hospitals that receive deductions through various hospital programs may lead to lower rewards through lower real to standardized ratio. C an amplification of TEAM repayment amounts. On the other hand, hospitals that receive deductions through various hospital programs may lead to lower rewards through lower real to standardized ratio. Comment: A commenter noted that the conversion methodologies used in BPCI Advanced and CJR are imperfect and suggested a six-step conversion methodology which applied different wage factors based on the payment system (that is, IPPS, OPPS, Physician Fee Schedule) used in each of the clinical episodes. Response: We appreciate the detailed six-step methodology outlined by the commenter but have concerns regarding the complexity and feasibility of such an approach. Given the multiple payment systems throughout the various anchor and post-discharge period claim settings, the ability to calculate wage factors and apply them to target prices which are established using baseline data adds considerably to the operational complexity of the model. Comment: A commenter stated that the use of standardized dollars removes any elements that would make one provider’s Medicare payment different from another provider’s payment for the same claim. Response: We disagree that the use of standardized dollar zed dollars removes any elements that would make one provider’s Medicare payment different from another provider’s payment for the same claim. Response: We disagree that the use of standardized dollars removes the ability to distinguish the cost of care between two claims. Standardized dollars allow fair comparisons by removing the impact of geographical factor like wage index for the same services between two different providers. Cost differences based on variations in practice patterns and health care delivery choices continue to exist and are avenues for potential additional savings. We did not propose, and after consideration of public comments, we are not convinced that we should make any changes to the calculation of target prices and reconciliation amounts. Therefore, the calculation of target prices and reconciliation amounts will remain in standardized dollars. We believe the standardized dollar payments will continue to remove differences in wage indexes and hospital-level payment adjustments across target prices and reconciliation amounts for TEAM participants. (b) Converting Post-Episode Spending Amounts to Real Dollars In the FY 2025 IPPS/LTCH PPS final rule (89 FR 689 yment adjustments across target prices and reconciliation amounts for TEAM participants. (b) Converting Post-Episode Spending Amounts to Real Dollars In the FY 2025 IPPS/LTCH PPS final rule (89 FR 68986) that established TEAM, we noted that some hospitals may have an incentive to withhold or delay medically necessary care until after an episode ends to reduce their actual episode payments. In order to identify and address such inappropriate shifting of care, we finalized a post- episode spending calculation methodology. In this approach, we would identify whether the average 30- day post-episode spending for a TEAM participant in any given performance year is greater than 3 standard deviations above the regional average 30-day post-episode spending, based on the 30-day post-episode spending for episodes attributed to all TEAM regional hospitals in the same region as the TEAM participant. We finalized that beginning with PY 1 for Track 3 TEAM participants, and PY 2 for Track 2 TEAM participants, if the TEAM participant’s average post-episode spending exceeds this threshold, the amount above the threshold would be subtracted from the reconciliation amount or added to the repayment am icipants, if the TEAM participant’s average post-episode spending exceeds this threshold, the amount above the threshold would be subtracted from the reconciliation amount or added to the repayment amount for that performance year. In the proposed rule, we stated that we recognize it is important to remain consistent across our calculations when converting to real dollars. Therefore, we also sought comment on whether and how to convert the post-episode spending amounts from standardized dollars to real dollars. Specifically, we requested comment on whether, if a TEAM participant’s average post- episode spending in the MS–DRG/ HCPCS episode type exceeds the region’s threshold in that MS–DRG/ HCPCS episode type, the amount above the threshold should be converted from standardized to real dollars using a hospital-level real-to-standardized spending ratio. Additionally, we considered that the post-episode spending amounts would be determined at a MS–DRG-hospital level rather than an episode level like our target price and reconciliation amount consideration because— • Average post-episode spending is more representative of consistent patterns in the delay of medically necessary service e level like our target price and reconciliation amount consideration because— • Average post-episode spending is more representative of consistent patterns in the delay of medically necessary services in the post discharge period by a hospital; and • Hospitals do not have the same incentives to not exceed the expected post-episode spending that they have with in-episode spending. Hence, TEAM participants may be subject to VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00587 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k 37122 Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations higher penalties if the post-episode calculation is at an episode level compared to an aggregate hospital-level. Therefore, we stated in the proposed rule that were we to propose to convert from standardized dollars to real dollars, we would propose to do so at the hospital level to align with the hospital-level post-episode spending amounts. The hospital level real-to- standardized ratios would be determined as the ratio of sum of total post-episode spending in real dollars to sum of total post-e pital-level post-episode spending amounts. The hospital level real-to- standardized ratios would be determined as the ratio of sum of total post-episode spending in real dollars to sum of total post-episode spending in standardized dollars using the set of reconciled episodes in the corresponding MS–DRG/HCPCS episode type. We sought comment on our consideration to determine post-episode spending amounts at the MS–DRG- hospital level rather than an episode level. We also sought comment on whether and how to convert post- episode spending amounts from standardized dollars to real dollars in a consistent manner. The following is a summary of the public comments received on the considerations for post-episode spending amounts at the hospital rather than episode level and to convert post- episode spending amounts to real dollars, and our responses to these comments: Comment: A commenter noted that CMS should carefully evaluate whether post-episode spending should be converted to real dollars as the conversion may improve transparency but could also introduce unintended financial risks for hospitals. This commenter requested that CMS further engage with stakeholders to determine whether s as the conversion may improve transparency but could also introduce unintended financial risks for hospitals. This commenter requested that CMS further engage with stakeholders to determine whether real-dollar conversions would enhance financial predictability or create additional burdens for hospitals. Another commenter supported the use of real-dollar conversion for post- episode spending calculations as they believe the conversion will provide more meaningful insight into actual costs. Response: We thank the commenters for sharing their thoughts regarding the conversion of post-episode spending amounts from standardized dollars to real dollars. We agree that hospitals in high wage index may incur high penalty amounts compared to low wage index areas, however it is not the primary factor of consideration for keeping the penalty amounts in standardized amounts since Medicare payments received by the providers are reflective of these wage-indexes. CMS believes provider-specific add-on payments like IME and DSH payments may lead to higher real to standardized ratio which in turn may unduly penalize the TEAM participants. We did not propose, and after consideration of the public co n payments like IME and DSH payments may lead to higher real to standardized ratio which in turn may unduly penalize the TEAM participants. We did not propose, and after consideration of the public comments, we are not convinced that any changes should be made to the calculation of post-episode spending amounts. Therefore, the calculation of post- episode spending amounts will remain in standardized dollars and at the episode level. d. Health Data Reporting As described in the FY 2025 IPPS/ LTCH PPS final rule (89 FR 69800), we finalized voluntary reporting of three elements that aims to address reducing health disparities for TEAM beneficiaries. The elements include health equity plans, demographic data, and health related social needs data. We stated in the proposed rule that we continue to believe that it is important to understand and address health needs of all TEAM beneficiaries so that they can benefit from the care redesign interventions implemented by TEAM participants. However, due to the new Administration’s priorities and concern over placing additional burdens on TEAM participants in a mandatory model, we recognized the need to remove the voluntary health equity plan a r, due to the new Administration’s priorities and concern over placing additional burdens on TEAM participants in a mandatory model, we recognized the need to remove the voluntary health equity plan and the health-related social needs data to reduce burden on TEAM participants. We noted in the proposed rule that we recognized that asking TEAM participants to submit health equity plans or report health related social needs data, even on a voluntary basis, could add an additional burden that CMS does not intend to add in the model. Even if TEAM participants choose to not voluntarily submit a health equity plan or report health related social needs data, we believed it would be a better use of TEAM participant resources to focus on care redesign activities that would help improve their performance in the model and improve the quality of care and care experience for the beneficiary, rather than spend resources on collecting and reporting health equity plan information or health related social needs data. Therefore, we proposed to completely remove the health equity plan and health related social needs data policies from TEAM, including all references to health equity plans. We stated i ial needs data. Therefore, we proposed to completely remove the health equity plan and health related social needs data policies from TEAM, including all references to health equity plans. We stated in the proposed rule that though currently there is no replacement for these policies, CMS will consider adding elements that are consistent with the new Administration’s focus on Making America Healthy Again. We believe there is opportunity through TEAM to encourage healthy habits among TEAM beneficiaries to drive improvements in overall health. We noted in the proposed rule that changes to TEAM that would incorporate the Administration’s focus on prevention and healthy living would be proposed in future notice and comment rulemaking. Given our desire to remove health equity plans, we also proposed to remove the ‘‘Health equity reporting’’ title to § 512.563 and replace it with ‘‘Health data reporting’’. Lastly, we also proposed removing the definition for ‘‘Health equity goal’’, ‘‘Health equity plan’’, ‘‘Health equity plan intervention strategy’’, ‘‘Health equity plan performance measure’’, and ‘‘Underserved community’’ from the definitions at § 512.505. Additionally, we proposed remo equity plan’’, ‘‘Health equity plan intervention strategy’’, ‘‘Health equity plan performance measure’’, and ‘‘Underserved community’’ from the definitions at § 512.505. Additionally, we proposed removing the voluntary collection of health- related social needs screening and reporting. This included removing voluntary reporting of the Screening for Social Drivers of Health measure, adopted at § 512.563(b); and the Screen Positive Rate for Social Drivers of Health measure, adopted at § 512.563(b). In the proposed rule we stated that we also continue to believe voluntarily collecting demographic data is important to better understand TEAM beneficiaries. Therefore, we did not propose any changes to this element. We noted that we did discuss in the FY 2025 IPPS/LTCH PPS final rule (89 FR 69802) potential demographic data variables that CMS would voluntarily collect from TEAM participants such as race, ethnicity, language, disability, sexual orientation, gender identity, sex characteristics, and other demographics. While we have not specified the exact variables TEAM participants will report and will notify TEAM participants through sub-regulatory guidance of the demographic variables w , and other demographics. While we have not specified the exact variables TEAM participants will report and will notify TEAM participants through sub-regulatory guidance of the demographic variables we wish to collect, as indicated in the final rule (89 FR 69804), we clarified in the proposed rule that we will not be collecting variables such as sexual orientation, race, ethnicity, or gender identity to align with the Administration’s priorities and to reduce reporting burden on TEAM participants. Finally, to align with the Administration’s executive order to identify an individual’s immutable biological classification as either male or female, we proposed to update the name of a beneficiary-identifiable data variable, that is not used for pricing or payment purposes, that we would share with TEAM participants, pursuant to a data request and executed TEAM data sharing agreement.407 Specifically, we 407 Executive Order No. 14168 of January 20, 2025, Defending Women from Gender Ideology VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00588 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k Federal Register / V VerDate Sep 2014 00:36 Aug 02, 2025 Jkt 265001 PO 00000 Frm 00588 Fmt 4701 Sfmt 4700 E:\FR\FM\04AUR2.SGM 04AUR2 2 S E L U R h t i w D O R P 4 4 1 S 7 W 9 K S D n o d n o m m a h k Federal Register / Vol. 90, No. 147 / Monday, August 4, 2025 / Rules and Regulations 37123 proposed the ‘‘gender’’ variable identified at § 512.562(c)(3) to be renamed ‘‘sex’’. We believe sex better represents the binary variable that we would be sharing with TEAM participants and allows for consistent interpretation of the term across Federal programs and initiatives. We sought comment on our proposal at § 512.505 to remove from the definitions section health equity goal, health equity plan, health equity plan intervention strategy, health equity plan performance measure, and underserved community. We sought comment on our proposal at § 512.563 to retitle the header and remove the health equity plan and health related social needs data elements. We also sought comment on our proposal at § 512.562(c)(3) to rename the ‘‘gender’’ variable to ‘‘sex’’. The following is a summary of the public comments received on the proposed policies to remove health equity terms, remove the health equity plan and health rel name the ‘‘gender’’ variable to ‘‘sex’’. The following is a summary of the public comments received on the proposed policies to remove health equity terms, remove the health equity plan and health related social needs data elements, and rename the gender variable, and our responses to these comments: Comment: Some commenters supported CMS’s decision to remove health equity plans from TEAM. Response: We thank these commenters for their support. Comment: A number of commenters did not support CMS’s proposal to remove health equity plans from TEAM. These commenters recognized the plans were voluntary components of TEAM but noted environmental and social factors are important to understanding the context and health of patients fully. These commenters noted that this additional data on patients allows them to better understand and care for vulnerable patients. Response: We thank these commenters for their feedback. We believe it is important for TEAM participants to have the autonomy and flexibility to determine how they can best meet the needs of their patient population. TEAM participants are permitted to collect additional information on their patients in the model, while remaining c