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.

使用人工智能/机器学习分析工具来预测疾病风险、发病和进展以及治疗结果 增长并有望改善健康差距人口的健康结果。 有迹象表明，残疾人——美国最大的健康差距群体——将无法 为了从这些科学进步中获益，我们在《Parent R01》中探讨了成年人的观点。 视力、听力和行动障碍对精准医学研究 (PMR) 的信任和可信度的影响 本 R01 中的 AI/ML 应用程序的主要培训数据集和 PI 之前的工作已确定。 临床和研究环境中的残疾偏见是信任和参与 PMR 的主要障碍。 对于盲人成年人来说尤为突出，他们既表达了参与 PMR 的最高兴趣，又担心 研究还表明，偏爱认为盲人患者无能力， 无论能力如何，作为困难的患者，尽管结构性问题会损害健康结果 盲人患者的情况（例如，难以接近的药物标签）。 盲人患者的记录，在 AI/ML 模型中使用此类数据可能会影响护理和繁殖， 即使是长期存在的健康差异，对于遇到交叉问题的盲人患者来说，这种担忧也会加剧。 边缘化，使他们的健康差距更加复杂 可预防的失明的流行（例如， 糖尿病性视网膜病变（一种常见的导致失明的主要原因）在女性和女性中发病率不成比例地高 有偏见的种族/族裔社区，特别是黑人/非裔美国人，而且性别和 评估电子病历 (EHR) 中是否存在种族偏见——作为独立因素。 因此，EHR 中呈现的交叉因素对于 AI/ML 模型开发公平的分析至关重要 然而，还没有研究探讨电子病历、主要培训中的残疾偏见。 AI/ML 模型的数据集，或评估残疾偏见如何加剧种族和性别偏见 嵌入电子病历中 拟议的研究由一个新的跨学科研究小组领导，并使用 交叉框架和残疾人社区参与模式开始缩小差距，我们将：1) 开发、验证和传播糖尿病相关失明和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 (2016-22) 的 EHR 创建队列进行分析 为高度多样化的种族/族裔人群提供服务；2) 识别并评估与盲人/残疾相关的负面清单 临床文件中的患者描述；以及 3) 评估 EHR 中使用残疾偏见语言的情况； 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差异（男性/女性、 Black/White）。该项目有潜力为临床护理中的公平 AI/ML 模型提供信息，改善健康状况。 人口健康差距往往是看不见的，但规模巨大且不断扩大，并就这一问题建立对话 残障伦理以及人工智能/机器学习在追随者、数据科学家、盲人成年人和 ELSI 研究人员中的公平性。