Hidden in Plain Sight: Stigmatizing Language in Patient Medical Records

摘要 我们的医疗保健系统并没有平等对待病人：有些人根据种族/性别接受质量较差的护理。 种族认同、社会阶层、健康素养、年龄、性别或医疗状况，如肥胖、镰状细胞病， 和物质使用障碍。虽然可能有许多因素导致这些差异，但内隐偏见 是一个仍然难以衡量的重要因素。临床医生可以传达和获取 在口头交流或书写/阅读病历时，彼此对患者的偏见。 虽然医疗记录是沟通患者的一种不可或缺的方法，但很少有研究评估 患者记录作为将偏见从一个临床医生传递到另一个临床医生的手段。我们的初步工作 发现医疗记录中使用的语言对后来阅读笔记的临床医生有直接影响， 无论是在他们对病人的态度和他们的药物处方行为方面;有种族 在使用侮辱性语言方面的差异;这种语言反映了 临床医生这是一个重要的和被忽视的途径，通过它，偏见可以从一个临床医生传递到 另一方面，进一步损害了个别患者的医疗保健质量，并加剧了整体差异 对于那些被污名化的人。我们的首要目标是提高以下人员的护理质量和公平性： 在医疗保健方面不受尊重和歧视。本建议的具体目标是：（1）界定和发展 使用临床医生输入的定性分析对医疗记录中的污名化语言进行分类， 5个不同医学专业领域的患者，（2）测量和测试使用污名化的偏倚 使用自然语言处理（NLP）的医疗记录中的语言，可以有效地检测和量化 电子病历中的污名化语言，然后（3）减少医疗记录中的污名化语言 使用经过验证的实施科学（IS）战略（利益相关者参与、教育、审计和 反馈和倡导者），以激励和促进卫生系统的变革。每次遇到病人 记录在医疗记录中。因为语言可能在影响随后的 临床医生的态度和行为，注意医疗记录中使用的语言可能有很大的影响， 促进对遭鄙视贬斥群体的尊重，减少他们之间的差距。

ABSTRACT Our healthcare system does not treat patients equally: some receive poorer quality care based on their racial/ ethnic identity, social class, health literacy, age, gender or medical conditions such as obesity, sickle cell disease, and substance use disorders. While there may be many factors that contribute to these disparities, implicit bias among clinicians is one important factor that remains difficult to measure. Clinicians may convey and acquire bias towards patients from each other when communicating orally or when writing/reading medical records. Although medical records are an integral method of communicating about patients, few studies have evaluated patient records as a means of transmitting bias from one clinician to another. Our own preliminary work has found that language used in medical records has a direct influence on subsequent clinicians who read the notes, both in terms of their attitudes towards the patient and their medication prescribing behavior; that there are racial disparities in the use of stigmatizing language; and that this language reflects actual attitudes held by the clinicians. This is an important and overlooked pathway by which bias can be passed from one clinician to another, further impairing healthcare quality for the individual patient as well as exacerbating disparities overall for those who are stigmatized. Our overarching goal is to improve the quality and equity of care for persons who are disrespected and stigmatized in healthcare. The specific aims of this proposal are: (1) to define and develop a taxonomy of stigmatizing language in medical records using qualitative analyses with input from clinicians and patients across 5 different medical specialty areas, (2) to measure and test for bias in the use of stigmatizing language in medical records using natural language processing (NLP) that can efficiently detect and quantify stigmatizing language in electronic patient records, and then (3) to reduce stigmatizing language in medical records using proven implementation science (IS) strategies (stakeholder engagement, education, audit and feedback, and champions) to motivate and enable health system change. Every encounter with a patient is documented in the medical record. Because language may have a powerful role in influencing subsequent clinician attitudes and behavior, attention to the language used in medical records could have a large impact on the promotion of respect and reduction of disparities for stigmatized groups.