对现实世界医疗保健证据中偏见来源的多维层次结构框架:范围审查
Haeun Lee1, Christelle Xiong1, Derek Baughman1
1Biomedical Informatics and Data Science, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
Journal of biomedical informatics
|January 22, 2026
概括
这项研究从电子健康记录中确定了现实世界证据生成中的偏见来源. 一个新的框架有助于理解如何在使用真实世界的数据的大型跨国研究中产生偏见.
科学领域:
- 生物医学信息学 生物医学信息学
- 现实世界的证据研究研究.
- 数据科学数据科学数据科学
背景情况:
- 来自电子健康记录 (EHR) 的现实世界证据 (RWE) 对于大规模的观察性研究至关重要.
- 了解和减轻RWE偏差对于可靠的研究结果至关重要.
- 现有的框架往往缺乏在整个RWE生成过程中对偏差的全面了解.
研究的目的:
- 在使用EHR数据的RWE生成过程中识别和分类偏差来源.
- 开发一个多维的概念框架来描述多国联合网络研究中的偏见.
- 为了解大规模观测研究中的偏见起源提供一个结构化的方法.
主要方法:
- 开发了一个三阶段偏见框架,整合了现有框架,文献评论和专家评估.
- 在PubMed和Web of Science中对220篇论文 (2016-2025) 进行了范围审查,重点关注现实世界数据研究中的偏见.
- 使用定向内容分析,根据其在RWE生成过程中的阶段对偏差源进行分类.
主要成果:
- 209个不同的偏见来源被确定并分为七个层次:获得护理,提供护理,数据采集,文档/编码,数据提取,建模和分析.
- 在医疗保健阶段偏见最为普遍 (108个来源),其次是数据管理 (54) 和研究 (47).
- 已识别的偏见来源在患者,提供者,行政,IT,信息学和分析领域之间相互连接.
结论:
- 开发的多维框架为理解RWE产生的偏差提供了一个全面的结构.
- 这一框架突出了RWE生命周期中各个领域的偏差源的相互联系.
- 它为研究人员提供了一个基础工具,以识别和解决大规模观测研究中的潜在偏见.
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