ArticleJournal of the American Medical Informatics Association : JAMIA2025
Negative descriptors in electronic health records of patients with diabetes.
Article in Journal of the American Medical Informatics Association : JAMIA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- From language matters to stigma-free communication in diabetes: Evolution, challenges, and next steps.Diabetic medicine : a journal of the British Diabetic Association · 2026Review
- Harnessing data to advance health and health equity.Journal of the American Medical Informatics Association : JAMIA · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundNegative descriptors in electronic health records (EHR) contribute to worse health outcomes; studies show they are also more prevalent in EHRs of women and racial minorities and affect downstream research biases. Similar and unique patterns of negative descriptors may also exist in the records of blind patients, including those with diabetic retinopathy. Diabetic retinopathy is a preventable but leading cause of blindness in the US that is disproportionally high among women and racial and ethnic minorities.
methodsUsing EHR from a large medical center, we created "matched" cohorts of patients with a type 2 diabetes-only diagnosis and patients with a diagnosis of diabetic retinopathy. We identified previously used and new, disability and patient-related negative descriptors and assessed patterns of biased language in the EHR, comparing patients by retinopathy diagnosis (yes/no), and changes in patterns of language usage pre- and post- the retinopathy diagnosis. We also assessed differences between patients with type 2 diabetes at the intersection of blindness (ie, retinopathy diagnosis) and self-reported gender and race and ethnicity marginalization.
resultsThe EHRs of patients with diabetic retinopathy were significantly more likely than those of patients with diabetes-only diagnoses to contain biased language, across queried negative descriptors. The biasing language was consistently more prevalent in EHRs of patients with diabetic retinopathy identifying as women, Black/African Americans and Hispanic compared to White men and more likely to occur following patients' retinopathy diagnosis.
conclusionsOur study indicates the presence of both disability- and intersectional biases in EHRs. We discuss findings' implications and suggest steps to address them.
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