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的风险，发作和进展以及治疗结果是 增长和持股有望改善边缘化健康分布人群的健康成果。然而， 有迹象表明残疾人（美国最大的健康差异群体）将无法 获得这些科学进步的好处。在父母R01中，我们探讨了成年人的观点 关于精密医学研究（PMR）的信任和可信度的远见，听力和流动性障碍， AI/ML应用程序的主要培训数据集。 R01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信任和参与PMR的关键障碍。这些发现 对于盲人成年人而言，他们对参与PMR的最高兴趣以及对 医疗互动中的残疾偏见。研究还表明，临床医生认为盲人无能为力， 不管能力和困难的患者如何，都会损害健康结果的绝望结构性问题 盲人患者（例如，无法访问的药物标签）。就医疗而出现了残疾偏见 盲人患者的文献，在AI/ML模型中使用此类数据会影响护理和繁殖， 更糟糕的是，现有的健康差异。对于遇到室内及的盲人患者的担忧是放大的 边缘化，健康差异更加复杂。可预防失明的患病率（例如 糖尿病性视网膜病，是失明的常见和主要原因）在女性中， 边缘化的种族/族裔社区，尤其是黑人/非裔美国人的个人，也是性别和性别 电子病历（EHRS）中存在种族偏见。评估残疾偏见是否是独立的 与EHR中的交叉因子 - 因此，对于AI/ML模型，具有公平分析性至关重要 改善所有人健康成果的工具。然而，没有研究探索EHR的残疾偏见，重大培训 AI/ML模型的数据集，或评估残疾偏见如何使种族和性别偏见如何 嵌入在EHR中。拟议的研究由新的跨学科研究团队领导，并使用 交叉性框架和残疾社区参与模型开始缩小差距。我们将：1） 开发，验证和传播与糖尿病相关的失明和 使用糖尿病患者的EHR（2016-22）从一个大型城市医学中心创建分析的同类 为高度潜水员的种族/民族人口服务； 2）识别和评估与盲人/残疾相关的负面的列表 临床文档中的患者描述符； 3）评估在EHR中使用残疾偏见语言的使用 糖尿病患者（盲人，非盲）以及EHR中的负面描述在交叉上变化（男性/女性， 黑色/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，改善健康状况 经常看不见但较大且不断增长的健康差异人口的结果，并建立对话 临床医生，数据科学家，盲人成年人和ELSI研究人员中AI/ML的残疾伦理和平等。