对检测电子健康记录偏差的方法论方法进行全面审查
Patrick J A Kelly1,2, Andrew M Snyder3, Madina Agénor1,2,4,5
1Department of Behavioral and Social Sciences, Brown University School of Public Health.
Stigma and health
|September 29, 2025
概括
电子健康记录 (EHR) 中存在偏见,不成比例地影响黑人患者. 机器学习方法在检测和减少患者数据中的这种偏差方面表现有前途.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 健康 公平 卫生 公平
背景情况:
- 电子健康记录 (EHR) 越来越容易被患者访问,因此需要检查其中的潜在偏差.
- 现有的关于在电子健康记录中检测患者偏见的文献需要总结以告知未来的研究和干预措施.
研究的目的:
- 审查检测电子健康记录中的偏见语言的方法.
- 识别检测到的偏见的性质和目标.
- 总结美国电子健康登记的偏见调查结果.
主要方法:
- 在五个主要数据库 (PubMed,CINAHL,Web of Science,APA PsycInfo,SOCIndex) 进行了全面的文献搜索.
- 包括的研究是同行评审的,英语,并在美国于2022年12月22日之前发表.
- 确定了七项研究,采用自然语言处理 (NLP) 和手动内容分析等方法.
主要成果:
- 确定了四种主要方法:NLP (基于机器学习,语言调查和词汇计数,探索性词汇分析) 和手动内容分析.
- 四项研究发现,与白人患者相比,黑人患者的EHR偏差明显更大.
- 关于健康状况 (糖尿病,物质使用障碍,慢性疼痛),女性和暴露前预防 (PrEP) 的偏见被检测出来.
结论:
- 基于机器学习的NLP方法有利于分析大型数据集,检测像偏差这样的罕见结果,并促进推断分析.
- 调查结果强调了需要方法来分析EHR的偏见,以告知干预和政策.
- 减少医疗保健提供者文件中的偏见对于改善健康公平至关重要.
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