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
Summary: The use of Artificial Intelligence and Machine Learning (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. Blind people experience significant health disparities and could benefit from AI/ML advances but disability bias in clinical settings—independently and compounded by racial and gender biases—may preclude the potential benefits from accruing to them. This study emerged from, and continues to employ, community-based participatory research. It builds on an intersectionality framework to explore disability and racial and gender biases in electronical medical records (EHRs) of blind and nonblind diabetes patients, and to develop reproduceable, publicly available phenotype definitions and a list of disability biasing language. Our multidisciplinary team comprises expertise in ELSI research, disability studies, medicine, computer science and biomedical informatics. 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.