Analysis of Race, Sex, and Language Proficiency Disparities in Documented Medical Decisions

Hadi Amiri, Nidhi Vakil, Mohamed Elgaar, Jiali Cheng, Mitra Mohtarami, Adrian Wong, Mehrnaz Sadrolashrafi, Leo Anthony G. Celi
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Abstract

Abstract Importance: Detecting potential disparities in documented medical decisions is a crucial step toward achieving more equitable practices and care, informing healthcare policy making, and preventing computational models from learning and perpetuating such biases. Objective: To identify disparities associated with race, sex and language proficiency of patients in the documentation of medical decisions. Design: This cross-sectional study included 451 discharge summaries from MIMIC-III, with all medical decisions annotated by domain experts according to the 10 medical decision categories defined in the Decision Identification and Classification Taxonomy for Use in Medicine. Annotated discharge summaries were stratified by race, sex, language proficiency, diagnosis codes, type of ICU, patient status code, and patient comorbidities (quantified by Elixhauser Comorbidity Index) to account for potential confounding factors. Welch's t-test with Bonferroni correction was used to identify significant disparities in the frequency of medical decisions. Setting: The study used the MIMIC-III data set, which contains de-identified health data for patients admitted to the critical care units at the Beth Israel Deaconess Medical Center. Participants: The population reflects the race, sex, and clinical conditions of patients in a data set developed by previous work for patient phenotyping. Main Outcomes and Measures: The primary outcomes were different types of disparities associated with language proficiency of patients in documented medical decisions within discharge summaries, and the secondary outcome was the prevalence of medical decisions documented in discharge summaries. The data set will be made available at https://physionet.org/ Results: This study analyzed 56,759 medical decision text segments documented in 451 discharge summaries. Analysis across demographic groups revealed a higher documentation frequency for English proficient patients compared to non-English proficient patients in several categories, suggesting potential disparities in documentation or care. Specifically, English proficient patients consistently had more documented decisions in critical decision categories such as "Defining Problem" in conditions related to circulatory system and endocrine, nutritional and metabolic diseases. However, this study found no significant disparities in medical decision documentation based on sex or race. Conclusions and Relevance: This study illustrates disparities in the documentation of medical decisions, with English proficient patients receiving more comprehensive documentation compared to non-English proficient patients. Conversely, no significant disparity was identified in terms of sex or race. These findings suggest a potential need for targeted interventions to improve the equity of medical documentation practices so that all patients receive the same level of detailed care documentation and prevent computational models from learning and perpetuating such biases.
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有据可查的医疗决定中的种族、性别和语言能力差异分析
摘要重要性:检测记录在案的医疗决策中可能存在的差异是实现更公平的医疗实践和护理、为医疗政策制定提供信息以及防止计算模型学习和延续此类偏见的关键一步。目标:确定在医疗决策记录中与患者的种族、性别和语言能力相关的差异。设计:这项横断面研究纳入了来自 MIMIC-III 的 451 份出院摘要,所有医疗决策均由领域专家根据《医学决策识别与分类标准》中定义的 10 个医疗决策类别进行注释。注释后的出院摘要按种族、性别、语言能力、诊断代码、重症监护室类型、患者状态代码和患者合并症(以Elixhauser合并症指数量化)进行分层,以考虑潜在的混杂因素。采用 Welch's t 检验和 Bonferroni 校正来确定医疗决策频率的显著差异。环境:研究使用了 MIMIC-III 数据集,该数据集包含贝斯以色列女执事医疗中心重症监护病房住院患者的去标识化健康数据。参与者:该数据集反映了患者的种族、性别和临床状况,该数据集是由以前的患者表型分析工作开发的。主要结果和测量指标:主要结果是出院摘要中记录的医疗决定中与患者语言能力相关的不同类型的差异,次要结果是出院摘要中记录的医疗决定的普遍性。数据集将在 https://physionet.org/ 网站上公布:本研究分析了 451 份出院摘要中记录的 56,759 个医疗决定文本片段。对不同人群的分析显示,与非英语熟练的患者相比,英语熟练的患者在多个类别中记录的频率更高,这表明在记录或护理方面可能存在差异。具体来说,在与循环系统、内分泌、营养和代谢疾病相关的 "定义问题 "等关键决策类别中,英语熟练的患者记录的决策一直较多。不过,本研究并未发现基于性别或种族的医疗决策记录存在明显差异。结论和相关性:本研究说明了医疗决策记录方面的差异,与非英语熟练的患者相比,英语熟练的患者获得的医疗决策记录更全面。相反,在性别或种族方面没有发现明显的差异。这些发现表明,可能需要采取有针对性的干预措施来改善医疗记录的公平性,从而使所有患者都能获得同样详细的护理记录,并防止计算模型学习和延续这种偏见。
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