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评估从电子健康记录中提取的患者级社会风险数据的可比性:系统的范围审查

Gaia H Linfield¹, Shyam Patel¹, Hee Joo Ko¹

¹School of Medicine, University of California, San Francisco, CA, USA.

Health informatics journal

|September 7, 2023

概括

研究人员发现,从电子健康记录 (EHR) 中提取社会风险数据的方式存在显著差异. 标准化这些数据提取方法对于改善健康研究结果的概括性至关重要.

科学领域:

医疗信息学医疗信息学
健康研究的社会决定因素研究
电子健康记录 (EHR) 数据电子健康记录 (EHR) 数据

背景情况:

社会风险因素对患者的健康状况产生重大影响.
电子健康记录 (EHR) 越来越多地用于研究,提供丰富的患者数据来源.
然而,从电子健康记录中提取社会风险数据的方法尚未标准化,这可能会限制研究的概括性.

研究的目的:

系统地审查和评估用于从EHR中提取社会风险数据用于研究的方法.
在不同领域的EHR中识别社会风险数据的来源和定义.
评估数据提取的变化如何影响研究结果的概括性.

主要方法:

对同行评审的文献进行了系统的范围审查.
包括在内的研究使用了患者级EHR数据来评估六个社会风险领域:住房,交通,食品,公用事业,安全和社会支持/隔离.
分析了数据提取方法,来源和定义.

主要成果:

有111篇文章符合纳入标准.
社会支持/隔离是最常见的领域,通常由婚姻状况来定义,并从结构化的社会人口统计学领域提取出来.
住房风险主要定义为无家可归,数据来源于计费代码和选工具.
数据主要是从结构化EHR字段 (89/111) 和非结构化自由文本 (32/111) 中提取的.

结论:

在定义和从EHR中提取社会风险数据以用于研究目的方面存在显著的变化.
在运行社会风险领域中缺乏标准化,这阻碍了跨研究的洞察力和概括性.
从电子健康记录中提取社会风险数据的建立一致的方法对于推进健康研究至关重要.

关键词:

数据挖掘是数据挖掘的一个方法.电子健康记录是电子健康记录.机器学习是机器学习.健康的社会决定因素社会领域的社会领域.社会风险因素社会风险因素结构化数据是结构化数据.非结构化数据是非结构化数据.

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