Hidden in Plain Sight: Stigmatizing Language in Patient Medical Records

摘要 我们的医疗系统对病人不平等：一些人基于他们的种族/种族而得到质量较差的医疗服务 种族认同、社会阶层、健康素养、年龄、性别或健康状况，如肥胖、镰状细胞疾病、 和物质使用障碍。虽然造成这些差异的因素可能有很多，但隐性偏见 在临床医生中是一个重要的因素，仍然很难衡量。临床医生可能会传达和获取 在口头交流或书写/阅读病历时，彼此对病人有偏见。 尽管医疗记录是与患者沟通的一种不可或缺的方法，但很少有研究对其进行评估 病人记录作为一种将偏见从一个临床医生传递给另一个临床医生的手段。我们自己的前期工作已经 发现医疗记录中使用的语言对随后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方面，还是在他们开药的行为方面；都存在种族歧视 在使用污名化语言方面存在差异；这种语言反映了 临床医生。这是一条重要但被忽视的途径，通过它，偏见可以从一个临床医生传递到 另一种，进一步损害了个别患者的医疗质量，并加剧了总体差异 对于那些被羞辱的人。我们的首要目标是提高对以下人群的护理质量和公平性 在医疗保健领域是不受尊重和耻辱的。这项建议的具体目标是：(1)界定和发展 对病历中污名化语言的分类使用定性分析，并从临床医生和 5个不同医学专科领域的患者，(2)测量和测试使用污名化的偏见 使用自然语言处理(NLP)可以有效地检测和量化病历中的语言 使电子病历中的语言污名化，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 反馈和拥护者)，以激励和推动卫生系统变革。每一次与病人的接触都是 在医疗记录中有记录。因为语言可能对后续的影响有很大的作用 临床医生的态度和行为，对病历中使用的语言的关注可能会对 促进对受歧视群体的尊重和缩小差距。

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.