电子健康记录算法的验证,用于在安全网卫生系统中识别与住房相关的需求
Nicole C McCann1, Stephanie Ettinger de Cuba1,2,3, Melissa Hofman4,5
1Department of Health Law, Policy, and Management, Boston University School of Public Health, Boston, Massachusetts, USA.
Health services research
|December 17, 2025
概括
一个新的电子健康记录 (EHR) 算法有效地识别住房不稳定的患者. 这种务实的工具在安全网设置中显示了适度的灵敏度和高的特异性,改进了传统方法.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
- 医疗保健服务研究 医疗服务研究
背景情况:
- 识别住房相关需求的患者对于卫生系统解决与健康相关的社会需求 (HRSN) 至关重要.
- 电子健康记录 (EHR) 为有效识别这些需求提供了一个潜在的途径.
- 现有的方法在不同的医疗保健环境中可能缺乏必要的效率或准确性.
研究的目的:
- 开发和验证一种基于EHR的新,务实的算法,用于识别有住房相关需求的患者.
- 在安全网医院环境中评估算法的灵敏度和特异性.
- 将算法的性能与单个数据元素和传统方法进行比较.
主要方法:
- 波士顿医疗中心 (BMC) 的一个安全网医院对EHR算法的回顾性评估.
- 该算法包含了六个EHR结构化数据元素,包括HRSN查结果.
- 与两个参考组验证:住房计划入学者和初级保健患者样本与手动图表审查.
主要成果:
- 该算法在住房计划入学者中实现了62%的灵敏度,在初级保健患者中达到81%.
- 在初级保健组中,特异性高达97%.
- 虽然HRSN查具有最高的单元敏感性,但包括多个元素的算法超过了单个组件和诊断代码.
结论:
- 使用本地定制结构化数据的实用EHR算法是识别住房相关需求的患者的可访问和有效方法.
- 这项研究是第一个在安全网设置中验证此类算法的研究,证明了中度灵敏度和高特异性.
- 进一步的改进需要加强HRSN查和EHR文档实践.
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