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是 它正在增长，并有望改善被边缘化的健康差距人群的健康状况。然而， 有迹象表明，残疾人--美国最大的健康差距群体--将无法 以获取这些科学进步的好处。在家长R01中，我们探索成年人的观点 视力、听力和行动能力障碍对精确医学研究(PMR)的信任和可信度，a AI/ML应用程序的主要训练数据集。本R01中的社区成员和PI之前的工作确定 临床和研究环境中的残疾偏见是信任和参与PMR的关键障碍。这些发现 对于盲人成年人来说是突出的，他们既对参与PMR表现出最高的兴趣，也对 医疗互动中的残疾偏见。研究还表明，临床医生认为盲人患者无能， 无论能力如何，都是困难的患者，尽管结构性问题会影响健康结果 对于失明患者(例如，无法获取的药品标签)。在医学上出现残疾偏见的情况下 对于盲人患者的记录，在AI/ML模型中使用此类数据可能会影响护理和生殖， 更糟糕的是，现有的健康差距。对于盲人患者来说，这种担忧被放大了 边缘化，对他们来说，健康差距加剧了。可预防失明的流行(例如， 糖尿病视网膜病变是导致失明的常见和主要原因)在女性和 被边缘化的种族/族裔社区，特别是黑人/非裔美国人，而且还有性别和 电子病历(EHR)中存在种族偏见。评估残疾偏见--作为一名独立人士 和交叉因子--在EHR中呈现，因此对于AI/ML模型开发公平的分析是至关重要的 改善所有人健康结果的工具。然而，还没有研究探索EHR中的残疾偏见，这是主要的培训 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 都嵌入了电子病历中。这项拟议的研究由一个新的跨学科研究团队领导，并使用了 交叉框架和残疾人社区参与模式，以开始弥合差距。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和 使用大型城市医疗中心的糖尿病患者(2016-22年)的EHR创建分析队列 服务于高度多样化的种族/民族人口；2)确定和评估与失明/残疾相关的负面清单 临床文件中的患者描述；以及3)评估在电子病历中残疾偏见语言的使用 糖尿病患者(盲人和非盲人)，如果EHR中的负面描述因素有交叉变化(男性/女性， 黑/白)。该项目有可能为临床护理中公平的AI/ML模型提供信息，改善健康 经常看不见但健康差距很大且不断扩大的人口的结果，并就以下问题开展对话 临床医生、数据科学家、失明成年人和ELSI研究人员之间的残疾伦理和AI/ML的公平。