在使用近接因果推理的诊断中检测临床医生的隐性偏见
Kara Liu1, Russ Altman2, Vasilis Syrgkanis2
1Computer Science Department, Stanford University, Stanford, CA 94305, USA, karaliu@stanford.edu.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|December 13, 2024
概括
这项研究引入了一种新的因果推断方法,以确定临床医生的隐性偏见如何使用大型数据集影响患者的健康结果. 它旨在揭示系统性歧视造成的医疗保健差异.
科学领域:
- 卫生公平研究 卫生公平研究
- 医学中的因果推理.
- 医疗保健中的大数据分析.
背景情况:
- 医疗保健专业人员的隐性偏见,源于种族主义和性别歧视等刻板印象,有助于系统歧视.
- 现有的偏差测量方法仅限于个人态度和受控设置,而不是真实世界的患者结果.
- 大规模的电子健康记录 (EHR) 和生物银行为研究健康结果差异提供了新的机会.
研究的目的:
- 开发和应用因果推理方法来检测临床医生隐性偏见对患者结果的影响.
- 利用大型的观察医学数据集,更全面地了解偏差效应.
- 为提高人们对因隐性偏见导致的不平等健康结果的认识提供一个工具.
主要方法:
- 使用因果推理框架与近邻调解分析.
- 分析大规模的现实世界观察医疗数据,特别是来自英国生物银行.
- 解开患者社会人口统计学特征对临床医生的诊断决策的途径特异性影响.
主要成果:
- 提出的方法成功地检测到临床医生隐性偏见对英国生物库数据中的患者健康结果的影响.
- 证明了因果推理能够在大型数据集中发现微妙的偏见的能力.
- 提供了关于隐性偏见如何导致患者护理和诊断方面的差异的证据.
结论:
- 应用于大数据的因果推理方法是有效的工具,用于识别医疗保健中隐性偏见的现实影响.
- 这种方法可以突出系统性歧视及其对健康结果不平等的贡献.
- 调查结果强调需要采取干预措施来缓解隐性偏见并促进健康公平.
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