现实世界EHR数据中的地理空间划分:分析工作流程来评估区域偏差和对健康公平的潜在影响
Serena Jinchen Xie1, Flavia P Kapos1, Stephen J Mooney1
1University of Washington, Seattle, WA.
概括
电子健康记录 (EHR) 显示了地理空间偏差,影响了研究的概括性. 一个新的空间分析工作流量量化了这种偏见,确定了代表性不足的地区,以公平地部署医疗保健资源和生成现实世界的证据.
科学领域:
- 医疗信息学 医疗信息学
- 地理空间分析的研究.
- 公共卫生 公共卫生
背景情况:
- 现实世界数据 (RWD),包括电子健康记录 (EHR),为二次研究提供了巨大的潜力.
- 然而,用于临床护理收集的EHR数据可能会表现出地理空间偏差,从而限制了研究结果对不同人口和地区的概括性.
- 评估和解决这种偏见对于公平的研究和医疗保健服务至关重要.
研究的目的:
- 开发和应用空间分析工作流程,以量化大型卫生系统电子健康记录数据中的地理空间代表性.
- 在EHR系统中识别数据表示的区域差异.
- 调查超重/肥胖和抑郁的地理空间模式,以告知有针对性的干预措施.
主要方法:
- 开发了一个空间分析工作流程,以定量评估EHR数据的地理空间代表性.
- 该工作流应用于一个大型卫生系统的电子健康记录数据.
- 分析了超重/肥胖和抑郁的地理空间模式,并对已识别的代表性偏见进行了调整.
主要成果:
- 该研究在EHR数据中发现了显著的地理空间偏差,表明不同地区的代表性不均.
- 工作流程成功量化了区域代表性,并确定了数据覆盖不足的领域.
- 确定了超重/肥胖和抑郁症的地理空间集群,并根据代表性偏差调整了分析.
结论:
- 评估地理空间代表性对于RWD的有效解释和应用至关重要.
- 开发的工作流提供了一个数据驱动的方法来识别和解决EHR数据中的地理空间偏差.
- 解决这些偏见对于指导医疗保健资源分配和产生公平的现实世界的证据至关重要.
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