{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T21:02:48Z","timestamp":1775595768110,"version":"3.50.1"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2020,6,25]],"date-time":"2020-06-25T00:00:00Z","timestamp":1593043200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,12,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Accumulating evidence demonstrates the impact of bias that reflects social inequality on the performance of machine learning (ML) models in health care. Given their intended placement within healthcare decision making more broadly, ML tools require attention to adequately quantify the impact of bias and reduce its potential to exacerbate inequalities. We suggest that taking a patient safety and quality improvement approach to bias can support the quantification of bias-related effects on ML. Drawing from the ethical principles underpinning these approaches, we argue that patient safety and quality improvement lenses support the quantification of relevant performance metrics, in order to minimize harm while promoting accountability, justice, and transparency. We identify specific methods for operationalizing these principles with the goal of attending to bias to support better decision making in light of controllable and uncontrollable factors.<\/jats:p>","DOI":"10.1093\/jamia\/ocaa085","type":"journal-article","created":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T19:09:27Z","timestamp":1588360167000},"page":"2024-2027","source":"Crossref","is-referenced-by-count":88,"title":["Patient safety and quality improvement: Ethical principles for a regulatory approach to bias in healthcare machine learning"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6476-2165","authenticated-orcid":false,"given":"Melissa D","family":"McCradden","sequence":"first","affiliation":[{"name":"Bioethics Department, The Hospital for Sick Children, Toronto, Ontario, Canada"}]},{"given":"Shalmali","family":"Joshi","sequence":"additional","affiliation":[{"name":"Vector Institute, Toronto, Ontario, Canada"}]},{"given":"James A","family":"Anderson","sequence":"additional","affiliation":[{"name":"Bioethics Department, The Hospital for Sick Children, Toronto, Ontario, Canada"},{"name":"Institute\u00a0for Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada"},{"name":"Joint Centre for Bioethics, University of Toronto, Toronto, Ontario, Canada"}]},{"given":"Mjaye","family":"Mazwi","sequence":"additional","affiliation":[{"name":"Department of Critical Care Medicine, The Hospital for Sick Children, Toronto,\u00a0Ontario, Canada"}]},{"given":"Anna","family":"Goldenberg","sequence":"additional","affiliation":[{"name":"Vector Institute, Toronto, Ontario, Canada"},{"name":"Genetics and Genome Biology, The Hospital for Sick Children, Peter Gilgan Centre for Research and Learning, Toronto, Ontario, Canada"},{"name":"Department of Computer Science, University of Toronto, Toronto, Ontario, Canada"},{"name":"CIFAR, Toronto, Ontario, Canada"}]},{"given":"Randi","family":"Zlotnik Shaul","sequence":"additional","affiliation":[{"name":"Bioethics Department, The Hospital for Sick Children, Toronto, Ontario, Canada"},{"name":"Department of Paediatrics, University of Toronto, Toronto, ON, Canada"},{"name":"Child Health Evaluative Sciences, The Hospital for Sick Children, Peter Gilgan Centre for Research, Toronto, Ontario, Canada"}]}],"member":"286","published-online":{"date-parts":[[2020,6,25]]},"reference":[{"issue":"2","key":"2020121009234544600_ocaa085-B1","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1001\/amajethics.2019.167","article-title":"Can AI help reduce disparities in general medical and mental health care?","volume":"21","author":"Chen","year":"2019","journal-title":"AMA J Ethics"},{"key":"2020121009234544600_ocaa085-B2","first-page":"110","author":"Zhang","year":"2020"},{"key":"2020121009234544600_ocaa085-B3","first-page":"271","author":"Pfohl","year":"2019"},{"key":"2020121009234544600_ocaa085-B4","volume-title":"Unequal Treatment: 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