将患者调查数据与诊断代码相结合 改善了医疗保险优势 风险调整准确性
Meghan Bellerose1, Hannah O James2, Jay Shroff3
1Meghan Bellerose (meghan_bellerose@brown.edu), Brown University, Providence, Rhode Island.
Health affairs (Project Hope)
|January 6, 2025
概括
医疗保险优势风险调整可以通过将患者调查答案与诊断代码相结合来改进. 与当前方法相比,这种组合可以更好地预测医疗保健利用率.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 卫生经济学 卫生经济学
- 公共卫生 公共卫生
背景情况:
- 目前的医疗保险优势 (MA) 风险调整激励计划过度报告诊断以获得更高的支付.
- 该系统可能会损害风险调整模型的准确性和完整性.
- 为了减轻这些问题,已经提出了替代风险评分构建方法.
研究的目的:
- 将四种替代风险调整策略的预测准确度与标准的等级条件类别 (HCC) 方法进行比较.
- 评估将患者报告的数据纳入风险调整模型的有用性.
主要方法:
- 利用了2016-2019年医疗和制药索赔数据,与消费者评估卫生保健提供者和系统 (CAHPS) 调查答案相关联.
- 分析了来自151,432名医疗保险优势计划入学者的数据.
- 将标准HCC得分的预测精度与使用修改的HCC得分和/或调查数据预测医疗保健利用率的模型进行比较.
主要成果:
- 将HCC分数与调查答案结合在一起的模型显示,对医疗保健使用的预测准确度更高.
- 这些增强模型解释了MA利用率的5.8-6.0%的变化,而标准HCC模型的5.1%.
- 排除某些诊断代码或仅使用药物声明显示改善较小或预测能力下降.
结论:
- 患者调查反应,当与诊断代码相结合时,可以显著加强医疗保险优势风险调整.
- 整合患者报告的结果提供了一个有希望的途径,以提高MA支付的准确性和公平性.
- 对患者报告的风险调整数据进行进一步研究是有必要的.
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