在医疗保险受益人中,基于索赔的三个脆弱性措施的比较表现
Sara E Heins1, Denis Agniel2, Jacob Mann3
1RAND Corporation Pittsburgh, Pittsburgh, PA, USA.
概括
这项研究比较了医疗保险受益人的三种基于索赔的脆弱性算法. 金算法在预测健康结果和利用方面表现最好.
科学领域:
- 老年学是一门学科.
- 医疗保健服务研究 医疗服务研究
- 生物统计学 生物统计学
背景情况:
- 脆弱性是死亡率,医疗保健成本和健康结果的关键预测因素.
- 经过验证的脆弱性测量在临床环境中并不一致地收集,这阻碍了人口比较.
- 基于索赔的算法为使用行政数据预测脆弱提供了潜在的解决方案.
研究的目的:
- 在医疗保险受益人群中比较了三种已建立的基于索赔的脆弱性算法 (Faurot,Kim,RAND) 的性能.
- 评估这些算法的实用性,以预测各种基于索赔的结果.
主要方法:
- 利用了2014-2016年间12个月连续入学期的Medicare索赔数据.
- 使用Faurot,Kim和RAND算法计算脆弱性得分.
- 将脆弱性得分纳入回归模型,以预测随后一年的基于索赔的结果.
- 使用根平均平方误差 (RMSE) 和接收器运行特征曲线 (AUC) 下的面积来评估模型性能.
主要成果:
- 金算法在大多数结果,指标和子群体中始终优于法罗特和兰德算法.
- 对预测死亡率,医疗保健利用率和成本的表现进行了评估.
结论:
- 金脆弱算法在预测医疗保险受益者的健康结果和利用率方面表现出卓越的表现.
- 基姆脆弱性得分适用于卫生系统和研究人员的风险调整和干预向.
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