用因果推断分析医生隐性偏见如何影响诊断结果
Detecting clinician implicit biases in diagnoses using proximal causal inference
- 通过近端中介分析分离社会属性对诊断的影响路径
- 在英国生物银行数据中发现种族与性别相关诊断差异
- 为揭示医疗不平等提供可量化的工具，适合政策研究者
临床诊疗决策受种族、能力歧视、性别偏见等隐性偏见影响，反映医疗系统性不公，加剧弱势群体边缘化。现有测量方法依赖受控随机实验，仅捕捉个体态度而非实际后果。随着电子健康记录（EHRs）和生物银行等大规模观察数据的兴起，本研究提出一种因果推断方法，用于检测医生隐性偏见对患者结局的影响。具体采用近端中介分析，分离患者社会人口学特征对诊断决策的特定路径效应。方法在英国生物银行（UK Biobank）真实数据上验证，可作为揭示隐性偏见导致健康不平等的量化工具，推动对医疗公平性的讨论。
原文摘要 · Abstract (English)
Clinical decisions to treat and diagnose patients are affected by implicit biases formed by racism, ableism, sexism, and other stereotypes. These biases reflect broader systemic discrimination in healthcare and risk marginalizing already disadvantaged groups. Existing methods for measuring implicit biases require controlled randomized testing and only capture individual attitudes rather than outcomes. However, the "big-data" revolution has led to the availability of large observational medical datasets, like EHRs and biobanks, that provide the opportunity to investigate discrepancies in patient health outcomes. In this work, we propose a causal inference approach to detect the effect of clinician implicit biases on patient outcomes in large-scale medical data. Specifically, our method uses proximal mediation to disentangle pathway-specific effects of a patient's sociodemographic attribute on a clinician's diagnosis decision. We test our method on real-world data from the UK Biobank. Our work can serve as a tool that initiates conversation and brings awareness to unequal health outcomes caused by implicit biases.
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