Abstract：
OBJECTIVE: Language used by providers in medical documentation may reveal evidence of race-related implicit bias. We aimed to use natural language processing (NLP) to examine if prevalence of stigmatizing language in emergency medicine (EM) encounter notes differs across patient race/ethnicity.
METHODS: In a retrospective cohort of EM encounters, NLP techniques identified stigmatizing and positive themes. Logistic regression models analyzed the association of race/ethnicity and themes within notes. Outcomes were the presence (or absence) of 7 different themes: 5 stigmatizing (difficult, non-compliant, skepticism, substance abuse/seeking, and financial difficulty) and 2 positive (compliment and compliant).
RESULTS: The sample included notes from 26,363 unique patients. NH Black patient notes were less likely to contain difficult (odds ratio (OR) 0.80, 95% confidence interval (CI), 0.73-0.88), skepticism (OR 0.87, 95% CI, 0.79-0.96), and substance abuse/seeking (OR 0.62, 95% CI, 0.56-0.70) compared to NH White patient notes but more likely to contain non-compliant (OR 1.26, 95% CI, 1.17-1.36) and financial difficulty (OR 1.14, 95% CI, 1.04-1.25). Hispanic patient notes were less likely to contain difficult (OR 0.68, 95% CI, 0.58-0.80) and substance abuse/seeking (OR 0.78, 95% CI, 0.66-0.93). NH NA/AI patient notes had twice the odds as NH White patient notes to contain a stigmatizing theme (OR 2.02, 95% CI, 1.64-2.49).
CONCLUSIONS: Using an NLP model to analyze themes in EM notes across racial groups, we identified several inequities in the usage of positive and stigmatizing language. Interventions to minimize race-related implicit bias should be undertaken.
METHODS: In a retrospective cohort of EM encounters, NLP techniques identified stigmatizing and positive themes. Logistic regression models analyzed the association of race/ethnicity and themes within notes. Outcomes were the presence (or absence) of 7 different themes: 5 stigmatizing (difficult, non-compliant, skepticism, substance abuse/seeking, and financial difficulty) and 2 positive (compliment and compliant).
RESULTS: The sample included notes from 26,363 unique patients. NH Black patient notes were less likely to contain difficult (odds ratio (OR) 0.80, 95% confidence interval (CI), 0.73-0.88), skepticism (OR 0.87, 95% CI, 0.79-0.96), and substance abuse/seeking (OR 0.62, 95% CI, 0.56-0.70) compared to NH White patient notes but more likely to contain non-compliant (OR 1.26, 95% CI, 1.17-1.36) and financial difficulty (OR 1.14, 95% CI, 1.04-1.25). Hispanic patient notes were less likely to contain difficult (OR 0.68, 95% CI, 0.58-0.80) and substance abuse/seeking (OR 0.78, 95% CI, 0.66-0.93). NH NA/AI patient notes had twice the odds as NH White patient notes to contain a stigmatizing theme (OR 2.02, 95% CI, 1.64-2.49).
CONCLUSIONS: Using an NLP model to analyze themes in EM notes across racial groups, we identified several inequities in the usage of positive and stigmatizing language. Interventions to minimize race-related implicit bias should be undertaken.
摘要：
目的：医疗文件中提供者使用的语言可能揭示种族相关内隐偏见的证据。我们旨在使用自然语言处理（NLP）来检查急诊医学（EM）中污名化语言的患病率是否因患者种族/种族而异。
方法：在EM遭遇的回顾性队列中，NLP技术确定了污名化和积极的主题。Logistic回归模型分析了笔记中种族/民族和主题的关联。结果是存在（或不存在）7个不同的主题：5个污名化（困难，不合规，怀疑论,药物滥用/寻求，和财务困难)和2个积极(恭维和合规)。
结果：样本包括26,363名独特患者的注释。NHBlack患者笔记不太可能包含困难(比值比(OR)0.80，95%置信区间(CI)，0.73-0.88），怀疑论(OR0.87，95%CI，0.79-0.96)，和药物滥用/寻求(OR0.62,95%CI,0.56-0.70)与NHWhite患者相比,但更可能包含不合规(OR1.26,95%CI,1.17-1.36)和财务困难(OR1.14,95%CI,1.04-1.25)。西班牙裔患者笔记不太可能包含困难（OR0.68，95％CI，0.58-0.80）和药物滥用/寻求（OR0.78，95％CI，0.66-0.93）。NHNA/AI患者笔记中包含污名化主题的几率是NHWhite患者笔记的两倍（OR2.02，95％CI，1.64-2.49）。
结论：使用NLP模型分析跨种族群体的EM笔记中的主题，我们发现在使用正面和污名化语言方面存在一些不平等现象.应采取干预措施，以最大程度地减少与种族相关的内隐偏见。
方法：在EM遭遇的回顾性队列中，NLP技术确定了污名化和积极的主题。Logistic回归模型分析了笔记中种族/民族和主题的关联。结果是存在（或不存在）7个不同的主题：5个污名化（困难，不合规，怀疑论,药物滥用/寻求，和财务困难)和2个积极(恭维和合规)。
结果：样本包括26,363名独特患者的注释。NHBlack患者笔记不太可能包含困难(比值比(OR)0.80，95%置信区间(CI)，0.73-0.88），怀疑论(OR0.87，95%CI，0.79-0.96)，和药物滥用/寻求(OR0.62,95%CI,0.56-0.70)与NHWhite患者相比,但更可能包含不合规(OR1.26,95%CI,1.17-1.36)和财务困难(OR1.14,95%CI,1.04-1.25)。西班牙裔患者笔记不太可能包含困难（OR0.68，95％CI，0.58-0.80）和药物滥用/寻求（OR0.78，95％CI，0.66-0.93）。NHNA/AI患者笔记中包含污名化主题的几率是NHWhite患者笔记的两倍（OR2.02，95％CI，1.64-2.49）。
结论：使用NLP模型分析跨种族群体的EM笔记中的主题，我们发现在使用正面和污名化语言方面存在一些不平等现象.应采取干预措施，以最大程度地减少与种族相关的内隐偏见。
