用AI分析病历文本，发现特定人群病历中歧视性语言更多
Application of CARE-SD text classifier tools to assess distribution of stigmatizing and doubt-marking language features in EHR
- 通过词典匹配和分类器识别病历中的质疑性与歧视性表述
- 黑人患者、低收入群体病历中歧视语言使用率高出1.16至2.46倍
- 护士和社会工作者记录中歧视性语言更突出，适合医疗公平研究者阅读
电子健康记录（EHR）是医疗团队中患者污名化传播的关键媒介。本研究通过扩展词典匹配与监督学习分类器，在MIMIC-III数据集中识别出怀疑标记和歧视性标签的语言特征。利用泊松回归模型评估各语言特征的预测因子。结果显示，非裔患者（相对风险RR: 1.16）、拥有医疗保险或政府保险的患者（RR: 2.46）、自费患者（RR: 2.12）以及患有多种被污名化疾病或精神健康问题的患者，其病历中歧视性标签出现频率更高；怀疑性语言模式类似，男性患者使用更多（RR: 1.25）。护士（RR: 1.40）和社工（RR: 2.25）使用的歧视性语言也显著偏高。结论表明，历史上受污名化的患者群体面临更高频率的歧视性语言，且由多类医务人员共同加剧。
原文摘要 · Abstract (English)
Introduction: Electronic health records (EHR) are a critical medium through which patient stigmatization is perpetuated among healthcare teams. Methods: We identified linguistic features of doubt markers and stigmatizing labels in MIMIC-III EHR via expanded lexicon matching and supervised learning classifiers. Predictors of rates of linguistic features were assessed using Poisson regression models. Results: We found higher rates of stigmatizing labels per chart among patients who were Black or African American (RR: 1.16), patients with Medicare/Medicaid or government-run insurance (RR: 2.46), self-pay (RR: 2.12), and patients with a variety of stigmatizing disease and mental health conditions. Patterns among doubt markers were similar, though male patients had higher rates of doubt markers (RR: 1.25). We found increased stigmatizing labels used by nurses (RR: 1.40), and social workers (RR: 2.25), with similar patterns of doubt markers. Discussion: Stigmatizing language occurred at higher rates among historically stigmatized patients, perpetuated by multiple provider types.
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