# 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

> **NIH NIH R01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2022 · $311,234

## Abstract

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...

## Key facts

- **NIH application ID:** 10599633
- **Project number:** 3R01HG010868-04S1
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Maya Sabatello
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $311,234
- **Award type:** 3
- **Project period:** 2021-03-12 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10599633

## Citation

> US National Institutes of Health, RePORTER application 10599633, 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 (3R01HG010868-04S1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10599633. Licensed CC0.

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