在行政医疗索赔数据集中扣除的归算
Betsy Q Cliff1, Julia C P Eddelbuettel2, Mark K Meiselbach3
1Department of Public Health Sciences, University of Chicago, Chicago, Illinois, USA.
Health services research
|January 17, 2024
概括
将计划特定的可扣除支出模式归因于计划数据缺失时,可以准确地识别高可扣除健康计划 (HDHP) 的入学者. 这种方法在行政索赔数据集中提供了与真假扣除率的最佳相关性.
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
- 在医疗保健中的数据科学.
背景情况:
- 行政索赔数据对于医疗服务研究至关重要,但往往缺乏详细的计划结构信息.
- 识别高扣除率健康计划 (HDHP) 的入学者对于了解医疗保健利用率和成本至关重要.
- 需要准确的归算方法来从索赔数据中推断计划级的扣除额.
研究的目的:
- 验证从行政索赔中推断计划级扣除额的归算方法.
- 通过使用索赔数据,确定确定HDHP注册人员的最准确方法.
- 评估计算和实际可扣除水平之间的相关性.
主要方法:
- 利用OptumLabs数据仓库的2017年医疗和制药索赔,用于65岁以下持续注册的个人.
- 我们比较了四种归算方法:参数预测 (个人支出,有/没有计划特征),最高计划特定模式和第80百分位支出.
- 基于正预测值 (PPV),负预测值 (NPV) 和与实际可扣除水平的相关性进行评估的方法.
主要成果:
- 所有的归算方法都实现了PPV≥87%的PPV,以区分高额免税计划和低额免税计划.
- 使用最高计划特定模式的个人年度可扣除支出的方法显示了最高的准确性 (PPV:95%;NPV:91%),并且计算密集度最低.
- 这种最佳方法与实际的扣除额度有很强的相关性,69%的归算扣除额度在真实扣除额度的250美元以内.
结论:
- 将计划特定模式的个人年度可扣除支出归因于缺乏计划结构数据的行政索赔中推断可扣除金额的最准确方法.
- 这种归算技术有效地预测了HDHP的入学人数.
- 这些发现提供了一种可靠的方法,用于使用索赔数据分析HDHP入学情况.
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