Blind/Disability and Intersectional Biases in E-Health Records (EHRs) of Diabetes Patients: Building a Dialogue on Equity of AI/ML Models in Clinical Care

糖尿病患者电子健康记录 (EHR) 中的盲/残疾和交叉偏差：建立关于临床护理中 AI/ML 模型公平性的对话

• 批准号: 10599633
• 负责人: Maya Sabatello
• 金额: $ 31.12万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-03-12 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10599633
• 关键词: Adult; Affect; African American population; Algorithms; All of Us Research Program; Black Populations; Black race; Blindness; Caring; Clinical; Clinical Research; Cohort Analysis; Communication; Communities; Competence; Computerized Medical Record; Data; Data Scientist; Data Set; Deposition; Descriptor; Diabetes Mellitus; Diabetic Retinopathy; Disabled Persons; Documentation; Educational workshop; Ethics; Ethnic group; Eye; Gender; General Population; Goals; Guidelines; Health; Health Disparities Research; Healthcare; Hearing; Inequality; Interdisciplinary Study; Justice; Knowledge; Label; Language; Libraries; Medical; Medical center; Medicine; Modeling; Outcome; Parents; Participant; Patients; Pattern; Perception; Pharmaceutical Preparations; Phenotype; Population; Prevalence; Quality of life; Race; Records; Reproducibility; Research; Research Personnel; Series; Sex Bias; Sexism; Subgroup; System; Training; Treatment outcome; Trust; Vision; Visually Impaired Persons; Woman; Work; analytical tool; base; blind; clinical care; clinical data warehouse; clinical implementation; cohort; cost; data modeling; demographics; design; disability; disorder risk; eHealth; ethical legal social implication; experience; health disparity; health disparity populations; health equity promotion; health record; improved; interdisciplinary collaboration; interest; intersectionality; marginalized population; member; men; multilevel analysis; open source; pilot test; precision medicine; racial and ethnic; racial bias; racial diversity; racial population; racism; recruit; social exclusion; social health determinants; trustworthiness

The use of AI/ML analytical tools to predict disease risk, onset and progression, and treatment outcomes is growing and holds promise for improving health outcomes for marginalized health disparities population. Yet, there is indication that people with disabilities—the largest health disparities group in the US—will not be able to reap the benefits of these scientific advancements. In the Parent R01, we explore the views of adults with vision, hearing, and mobility disabilities on trust in and trustworthiness of precision medicine research (PMR), a major training dataset for AI/ML applications. Community members in this R01 and the PI’s prior work identified disability bias in clinical and research settings as a key barrier to trust and participation in PMR. These findings are prominent for blind adults who both express the highest interest in participating in PMR and concern about disability bias in medical interactions. Studies also show that clinicians view blind patients as incompetent, regardless of abilities, and as difficult patients, despite structural issues that compromise the health outcomes of blind patients (e.g., inaccessible drug labels). Insofar as disability bias is presented in the medical documentation of blind patients, the use of such data in AI/ML models can affect care and reproduce, even worsen, existing health disparities. The worry is amplified for blind patients encountering intersectional marginalization, for whom health disparities are compounded. The prevalence of preventable blindness (e.g., diabetic retinopathy, a common and leading cause of blindness) is disproportionately high among women and marginalized racial/ethnic communities, especially Black/African American individuals, but also that gender and racial biases exist in electronic medical records (EHRs). Assessing whether disability bias—as an independent and intersectional factor—is presented in EHRs is thus crucial for AI/ML models to develop equitable analytical tools to improve health outcomes for all. Yet, no study has explored disability bias in EHRs, major training dataset for AI/ML models, or assessed how disability bias compounds racial and gender biases that are embedded in EHRs. The proposed study is led by a new interdisciplinary research team and uses an intersectionality framework and disability community-engaged model to begin closing the gaps. We will: 1) Develop, validate, and disseminate reproducible phenotype definitions for diabetes-related blindness and create cohorts for analyses using the EHRs of diabetes patients (2016-22) from a large urban medical center serving highly diverse racial/ethnic populations; 2) Identify and evaluate a list of blind/disability-related negative patient descriptors in clinical documentation; and 3) Assess the use of disability biased language in EHRs of diabetes patients (blind, nonblind) and if negative descriptors in EHRs varied intersectionally (men/women, Black/White). This project has the potential to inform equitable AI/ML models in clinical care, improve health outcomes of an often invisible but large and growing health disparity population, and build a dialogue on disability ethics and equity of AI/ML among clinicians, data scientists, blind adults, and ELSI researchers.

使用AI/ML分析工具来预测疾病风险、发病和进展以及治疗结果， 不断增长，并有望改善边缘化健康差距人口的健康成果。然而， 有迹象表明，残疾人-美国最大的健康差距群体-将无法 从这些科学进步中获益。在父母R 01中，我们探讨了成年人的观点， 视力、听力和行动能力障碍对精准医学研究（PMR）的信任和可信度的影响， AI/ML应用程序的主要训练数据集。本R 01中的社区成员和PI先前的工作已确定 临床和研究环境中的残疾偏见是PMR信任和参与的关键障碍。这些发现 对于那些对参与PMR表现出最高兴趣并关注 医疗互动中的残疾偏见研究还表明，临床医生认为盲人病人是不称职的， 无论能力如何，作为困难的病人，尽管存在损害健康结果的结构性问题， 失明患者（例如，无法访问的药物标签）。由于残疾偏见，在医疗 盲人患者的记录，在AI/ML模型中使用这些数据可能会影响护理和繁殖， 甚至加剧了现有的健康差距。对于遇到交叉学科的盲人患者来说， 边缘化，对他们来说，健康差距更加严重。可预防性失明的患病率（例如， 糖尿病性视网膜病变是一种常见的主要致盲原因， 边缘化的种族/族裔社区，特别是黑人/非裔美国人，但也有性别和 电子病历中存在种族偏见。评估是否残疾偏见-作为一个独立的 因此，EHR中的交叉因素对于AI/ML模型开发公平的分析模型至关重要。 改善所有人健康状况的工具。然而，没有研究探讨残疾偏见在电子健康档案，主要培训 AI/ML模型的数据集，或评估残疾偏见如何混合种族和性别偏见， 嵌入在电子病历中这项拟议中的研究由一个新的跨学科研究小组领导， 跨部门框架和残疾人社区参与模式，以开始缩小差距。我们将：1） 开发、验证和传播糖尿病相关失明的可重复表型定义， 使用来自大型城市医疗中心的糖尿病患者的EHR（2016-22）创建队列进行分析 服务于高度多样化的种族/民族人群; 2）确定和评估与盲人/残疾相关的负面清单 临床文件中的患者描述符;以及3）评估EHR中残疾偏见语言的使用， 糖尿病患者（盲人、非盲人）以及EHR中的阴性描述符是否存在交叉变化（男性/女性， 黑/白色）。该项目有可能为临床护理中的公平AI/ML模型提供信息， 一个往往看不见的，但巨大的和不断扩大的健康差距人口的结果，并建立对话， 临床医生、数据科学家、盲人和ELSI研究人员之间的AI/ML的残疾伦理和公平性。