McWilliams J Michael, Weinreb Gabe, Landrum Mary Beth, Chernew Michael E
J. Michael McWilliams (
Gabe Weinreb, Harvard University.
Health Aff (Millwood). 2025 Jan;44(1):48-57. doi: 10.1377/hlthaff.2023.01351.
A core problem with the current risk-adjustment system in Medicare Advantage and accountable care organization (ACO) programs-the Hierarchical Condition Categories (HCC) model-is that the inputs (coded diagnoses) can be influenced for gain by risk-bearing plans or providers. Using existing survey data on health status (which provide less manipulable inputs), we found that the use of a hybrid risk score drawing from survey data and a scaled-back set of HCCs would, in addition to mitigating coding incentives, modestly lessen risk-selection incentives, strengthen payment incentives to deliver efficient care, allocate payment across ACOs more efficiently according to markers of population health that are not as affected by practice patterns or coding efforts, and redistribute payment in a manner that supports equity goals. Although sampling error and survey nonresponse present challenges, analyses suggest that these should not be prohibitive. Overall, our proof-of-concept analysis suggests that using survey data to improve risk-adjustment performance is a promising strategy that merits further development.
医疗保险优势计划和 accountable care organization（ACO）项目中当前风险调整系统的一个核心问题——分层条件类别（HCC）模型——是其输入（编码诊断）可能会受到承担风险的计划或提供者出于利益目的的影响。利用现有的关于健康状况的调查数据（这些数据提供的输入更不易被操纵），我们发现，采用从调查数据和一套精简后的 HCC 中得出的混合风险评分，除了能减轻编码动机外，还能适度减少风险选择动机，强化提供高效医疗服务的支付动机，根据不太受实践模式或编码工作影响的人群健康指标更有效地在各 ACO 之间分配支付，并以支持公平目标的方式重新分配支付。尽管抽样误差和调查无应答带来了挑战，但分析表明这些不应成为阻碍。总体而言，我们的概念验证分析表明，利用调查数据来改善风险调整绩效是一个有前景的策略，值得进一步发展。
