开发和应用BE-FAIR公平框架到人口健康预测模型:一个回顾性观察队列研究
Reshma Gupta1,2, Mayu Sasaki3, Sandra L Taylor4
1Office of Population Health and Accountable Care, University of California (UC) Davis Health, Sacramento, CA, USA. R44gupta@gmail.com.
Journal of general internal medicine
|March 15, 2025
概括
医疗预测模型可以延续偏见. 评估,实施和重新设计的偏见减少和公平框架 (BE-FAIR) 框架是为了最大限度地减少偏见和提高这些模型的公平性而开发的.
科学领域:
- 医疗信息学 医疗信息学
- 健康 公平 卫生 公平
- 预测建模预测建模
背景情况:
- 人口健康计划利用预测模型来分配资源.
- 现有的模型有风险延续偏见,加剧边缘化社区的健康差异.
研究的目的:
- 在医疗预测模型中开发和测试评估,实施和重新设计 (BE-FAIR) 的偏见减少和公平框架.
- 为了最大限度地减少偏见,并在一个大型卫生系统内的人口健康预测模型中提高公平性.
主要方法:
- 从2020年9月到2022年10月,在一个学术医疗中心进行了一项回顾性队列研究.
- 电子健康记录和索赔数据被用于识别患者,按种族,种族,性别和社会脆弱性分层 (健康地点指数).
- 实施了BE-FAIR框架,结合了反种族主义镜头,脱离分离的团队,历史干预审查,分类数据分析和校准评估.
主要成果:
- 预测模型的校准在各个人口群体之间差异很大.
- 与白人/白人相比,非洲裔美国人,西班牙裔,AAPI和多种族个体的预测住院概率明显较低.
- 较低的健康地点指数得分与预测的住院概率明显较低相关,表明偏差.
结论:
- BE-FAIR框架提供了一种实用的方法来识别和减轻医疗预测模型中的偏差.
- 实施BE-FAIR可以提高医疗保健的公平性和公平性,特别是对于少数群体.
- 该框架支持制定更具包容性和有效性的人口健康战略.
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