Fang He, Ariella Hirsch, Chris Beadles, Yan Tang, Bridget Hagerty, Sarah Irie
{"title":"Highly Stable Beneficiary Attribution in Medicare's Comprehensive Primary Care Plus Model.","authors":"Fang He, Ariella Hirsch, Chris Beadles, Yan Tang, Bridget Hagerty, Sarah Irie","doi":"10.1097/MLR.0000000000002027","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Advanced primary care models are key in moving primary care practices toward greater accountability for the quality and cost of a beneficiary's care. One critical but often overlooked detail in model design is the beneficiary attribution methodology. Attribution results are key inputs in calculating practice payments. Stable attribution yields predictable practice payments, fostering longer-term investments in advanced primary care.</p><p><strong>Objective: </strong>We examine attribution stability for Medicare fee-for-service beneficiaries in Medicare's Comprehensive Primary Care Plus (CPC+) Model.</p><p><strong>Design: </strong>To measure attribution stability, we calculate churn rates, which we define as the percentage of beneficiaries eligible for CPC+ who were not attributed to the same practice in a later period. Using 2017-2021 CPC+ program data and Medicare administrative data, we calculate churn rates for CPC+ overall and for beneficiary subgroups. To assess whether CPC+ attribution was responsive enough to changes in a beneficiary's practice, we calculate how long before attribution changes following a beneficiary's long-distance move.</p><p><strong>Results: </strong>We find that for every 100 beneficiaries attributed to a CPC+ practice, 88 were still attributed to the same practice a year later (ie, churn rate of 12%), 79 were attributed 2 years later, 74 three years later, and 70 four years later. However, some vulnerable subgroups, such as disabled beneficiaries, had higher churn rates. Our analysis of long-distance movers reveals that only after 5 quarters did attribution change for more than half of these movers.</p><p><strong>Conclusions: </strong>Overall, high attribution stability may have encouraged CPC+ practices to make longer-term investments in advanced primary care.</p>","PeriodicalId":3,"journal":{"name":"ACS Applied Electronic Materials","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Electronic Materials","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/MLR.0000000000002027","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/6/11 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Advanced primary care models are key in moving primary care practices toward greater accountability for the quality and cost of a beneficiary's care. One critical but often overlooked detail in model design is the beneficiary attribution methodology. Attribution results are key inputs in calculating practice payments. Stable attribution yields predictable practice payments, fostering longer-term investments in advanced primary care.
Objective: We examine attribution stability for Medicare fee-for-service beneficiaries in Medicare's Comprehensive Primary Care Plus (CPC+) Model.
Design: To measure attribution stability, we calculate churn rates, which we define as the percentage of beneficiaries eligible for CPC+ who were not attributed to the same practice in a later period. Using 2017-2021 CPC+ program data and Medicare administrative data, we calculate churn rates for CPC+ overall and for beneficiary subgroups. To assess whether CPC+ attribution was responsive enough to changes in a beneficiary's practice, we calculate how long before attribution changes following a beneficiary's long-distance move.
Results: We find that for every 100 beneficiaries attributed to a CPC+ practice, 88 were still attributed to the same practice a year later (ie, churn rate of 12%), 79 were attributed 2 years later, 74 three years later, and 70 four years later. However, some vulnerable subgroups, such as disabled beneficiaries, had higher churn rates. Our analysis of long-distance movers reveals that only after 5 quarters did attribution change for more than half of these movers.
Conclusions: Overall, high attribution stability may have encouraged CPC+ practices to make longer-term investments in advanced primary care.