为了将机器学习驱动的多社会风险评分集成到电子健康记录工作流中
Xing He1,2, Yu Huang1,2, Yu Hu3
1Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, Indiana, USA.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
概括
健康的社会决定因素 (SDoH) 影响健康差异. 这项研究整合了基于EHR的工具,使用多社会风险评分来识别和解决患者的社会需求,改善医疗保健服务.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
- 医疗保健中的机器学习
背景情况:
- 健康的社会决定因素 (SDoH) 显著影响健康差异,80%的可改变因素源于社会需求.
- 医疗保健系统对于解决这些未满足的社会需求,以改善患者健康结果至关重要.
- 在临床环境中管理社会风险的现有工具需要进一步整合到医疗保健工作流程中.
研究的目的:
- 开发和启动基于电子健康记录 (EHR) 的机器学习管道的集成,以识别和管理患者的社会需求.
- 使用多社会风险评分量化社会风险,以帮助医疗保健提供者优先考虑干预措施.
- 为以用户为中心的设计过程奠定基础,以加强在临床工作流程中的社会风险评估和干预实施.
主要方法:
- 开发了一个基于EHR的机器学习管道,以确定与住院风险相关的社会需求.
- 创建了一个多社会风险评分来量化患者的社会风险.
- 根据以用户为中心的设计 (UCD) 原则,与医疗保健提供者和社会工作者进行参与式设计会议,以将该模型集成到EHR工作流程中.
主要成果:
- 成功开始将社会风险评估预测模型集成到EHR工作流程中.
- 通过定量多社会风险评分建立了加强社会风险识别和干预优先级的基础.
- 展示了使用参与式设计的可行性,以弥合预测建模和临床应用之间的差距.
结论:
- 将用于社会风险评估的机器学习驱动工具集成到EHR中,对于解决SDoH至关重要.
- 以用户为中心的设计方法对于在临床实践中成功实施预测模型至关重要.
- 这项初步工作支持制定综合战略,以管理社会需求和减少健康差异.
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