{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T21:07:21Z","timestamp":1763500041192,"version":"3.44.0"},"reference-count":54,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"National Human Genome Research Institute of the National Institutes of Health","award":["3R01HG0 10868-04S1"],"award-info":[{"award-number":["3R01HG0 10868-04S1"]}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["3R01HG010868-04S1"],"award-info":[{"award-number":["3R01HG010868-04S1"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Negative 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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>Using EHR from a large medical center, we created \u201cmatched\u201d 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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The 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\u2019 retinopathy diagnosis.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>Our study indicates the presence of both disability- and intersectional biases in EHRs. We discuss findings\u2019 implications and suggest steps to address them.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocaf132","type":"journal-article","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T11:53:02Z","timestamp":1753271582000},"page":"1589-1597","source":"Crossref","is-referenced-by-count":2,"title":["Negative descriptors in electronic health records of patients with diabetes"],"prefix":"10.1093","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9946-0709","authenticated-orcid":false,"given":"Tony Y","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, Columbia University Irving Medical Center , New York, NY, 10032,","place":["United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2504-4494","authenticated-orcid":false,"given":"Mika","family":"Baugh","sequence":"additional","affiliation":[{"name":"Indiana University School of Public Health\u2014Bloomington , IN,","place":["United States"]}]},{"given":"Emily R","family":"Gordon","sequence":"additional","affiliation":[{"name":"Columbia University Vagelos College of Physicians and Surgeons , New York, NY, 10032,","place":["United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6891-7865","authenticated-orcid":false,"given":"Cameron","family":"Ekanayake","sequence":"additional","affiliation":[{"name":"Columbia University Vagelos College of Physicians and Surgeons , New York, NY, 10032,","place":["United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5660-5573","authenticated-orcid":false,"given":"Nathalie","family":"Moise","sequence":"additional","affiliation":[{"name":"Center for Behavioral Cardiovascular Health, Columbia University Irving Medical Center , New York, NY,","place":["United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9721-5240","authenticated-orcid":false,"given":"Noemie","family":"Elhadad","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center , New York, NY, 10032,","place":["United States"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4444-5389","authenticated-orcid":false,"given":"Maya","family":"Sabatello","sequence":"additional","affiliation":[{"name":"Center for Precision Medicine and Genomics, Department of Medicine, Columbia University Irving Medical Center , New York, NY, 10032,","place":["United States"]},{"name":"Division of Ethics, Department of Medical Humanities and Ethics, Columbia University Irving Medical Center , New York, NY, 10032,","place":["United States"]}]}],"member":"286","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"key":"2025092208421867900_ocaf132-B1","doi-asserted-by":"crossref","first-page":"e26391","DOI":"10.2196\/26391","article-title":"A typology of existing machine learning-based predictive analytic tools focused on reducing costs and improving quality in health care: systematic search and 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