使用患者健康调查数据进行风险调整,以限制医疗保险中的扭曲编码激励措施
J Michael McWilliams1, Gabe Weinreb2, Mary Beth Landrum3
1J. Michael McWilliams (mcwilliams@hcp.med.harvard.edu), Harvard University and Brigham and Women's Hospital, Boston, Massachusetts.
使用调查数据和较少的等级条件类别 (HCC) 的新混合风险评分可以改善医疗保险优势和ACO支付. 这种方法减轻了编码激励措施,并促进了公平,高效的护理服务.
科学领域:
- 卫生经济学 卫生经济学
- 医疗服务研究 医疗服务研究
- 医疗信息学医学信息学
背景情况:
- 目前的医疗保险优势和负责任的护理组织 (ACO) 风险调整依赖于等级状况类别 (HCC) 模型.
- 由于HCC模型依赖于编码诊断,因此存在风险承担实体为了经济利益而操纵的漏洞.
研究的目的:
- 评估一种混合风险调整模型,将健康状况调查数据纳入其中.
- 评估这种混合模型在减轻编码激励和改善支付分配方面的潜力.
主要方法:
- 利用现有的关于健康状况的调查数据作为不那么容易操纵的输入.
- 开发了一种混合风险评分,将调查数据与减少的一组HCC结合起来.
- 分析了对风险选择,支付效率和股权的影响.
主要成果:
- 混合模型减轻了编码激励措施,并略微减少了风险选择激励措施.
- 它加强了支付激励措施,以提供高效的护理.
- 根据人口健康标志物,在ACO之间支付分配变得更有效,支持公平目标.
结论:
- 使用调查数据的混合风险评分提供了一个有希望的策略,以提高Medicare Advantage和ACO计划中的风险调整.
- 尽管存在诸如抽样错误和不响应等挑战,但该方法是可行的,需要进一步开发.
- 这种方法可以使医疗保健支付更公平,更有效.
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