{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T22:28:02Z","timestamp":1786660082829,"version":"3.56.0"},"reference-count":153,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2018,5,16]],"date-time":"2018-05-16T00:00:00Z","timestamp":1526428800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,8,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Health informatics interventions are designed to help people avoid, recover from, or cope with disease and disability, or to improve the quality and safety of healthcare. Unfortunately, they pose a risk of producing intervention-generated inequalities (IGI) by disproportionately benefiting more advantaged people. In this perspective paper, we discuss characteristics of health-related interventions known to produce IGI, explain why health informatics interventions are particularly vulnerable to this phenomenon, and describe safeguards that can be implemented to improve health equity. We provide examples in which health informatics interventions produced inequality because they were more accessible to, heavily used by, adhered to, or effective for those from socioeconomically advantaged groups. We provide a brief outline of precautions that intervention developers and implementers can take to guard against creating or worsening inequality through health informatics. We conclude by discussing evaluation approaches that will ensure that IGIs are recognized and studied.<\/jats:p>","DOI":"10.1093\/jamia\/ocy052","type":"journal-article","created":{"date-parts":[[2018,5,3]],"date-time":"2018-05-03T23:36:39Z","timestamp":1525390599000},"page":"1080-1088","source":"Crossref","is-referenced-by-count":624,"title":["Good intentions are not enough: how informatics interventions can worsen inequality"],"prefix":"10.1093","volume":"25","author":[{"given":"Tiffany C","family":"Veinot","sequence":"first","affiliation":[{"name":"School of Information and Department of Health Behavior and Health Education, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hannah","family":"Mitchell","sequence":"additional","affiliation":[{"name":"Department of Healthcare Policy & Research, Division of Health Informatics, Weill Cornell Medical College, New York, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jessica S","family":"Ancker","sequence":"additional","affiliation":[{"name":"Department of Healthcare Policy & Research, Division of Health Informatics, Weill Cornell Medical College, New York, New York, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2018,5,16]]},"reference":[{"key":"2020110612234823900_ocy052-B1","volume-title":"National Healthcare Disparities Report 2009","author":"Agency for Healthcare Research and Quality","year":"2010"},{"issue":"2","key":"2020110612234823900_ocy052-B2","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1377\/hlthaff.24.2.343","article-title":"Health disparities by race and class: why both matter","volume":"24","author":"Kawachi","year":"2005","journal-title":"Health Aff (Millwood)"},{"key":"2020110612234823900_ocy052-B3","volume-title":"Disparities in Health and Health Care: Five Key Questions and 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