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Methods: Systematic scoping review of peer-reviewed literature that used patient-level EHR data to assess 1 \u00b1 6 social risk domains: housing, transportation, food, utilities, safety, social support\/isolation. Results: 111\/9022 identified articles met inclusion criteria. By domain, social support\/isolation was most often included ( N = 68\/111), predominantly defined by marital\/partner status ( N = 48\/68) and extracted from structured sociodemographic data ( N = 45\/48). Housing risk was defined primarily by homelessness ( N = 39\/49). Structured housing data was extracted most from billing codes and screening tools ( N = 15\/30, 13\/30, respectively). Across domains, data were predominantly sourced from structured fields ( N = 89\/111) versus unstructured free text ( N = 32\/111). Conclusion: We identified wide variability in how social domains are defined and extracted from EHRs for research. More consistency, particularly in how domains are operationalized, would enable greater insights across studies.<\/jats:p>","DOI":"10.1177\/14604582231200300","type":"journal-article","created":{"date-parts":[[2023,9,7]],"date-time":"2023-09-07T14:22:41Z","timestamp":1694096561000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Evaluating the comparability of patient-level social risk data extracted from electronic health records: A systematic scoping review"],"prefix":"10.1177","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3578-4696","authenticated-orcid":false,"given":"Gaia H","family":"Linfield","sequence":"first","affiliation":[{"name":"School of Medicine, University of California, San Francisco, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1181-0669","authenticated-orcid":false,"given":"Shyam","family":"Patel","sequence":"additional","affiliation":[{"name":"School of Medicine, University of California, San Francisco, CA, USA"}]},{"given":"Hee Joo","family":"Ko","sequence":"additional","affiliation":[{"name":"School of Medicine, University of California, San Francisco, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0874-6707","authenticated-orcid":false,"given":"Benjamin","family":"Lacar","sequence":"additional","affiliation":[{"name":"Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, CA, USA; Berkeley Institute for Data Science, University of California, Berkeley"}]},{"given":"Laura M","family":"Gottlieb","sequence":"additional","affiliation":[{"name":"Department of Family & Community Medicine, University of California, San Francisco, CA, USA"}]},{"given":"Julia","family":"Adler-Milstein","sequence":"additional","affiliation":[{"name":"School of Medicine, University of California, San Francisco, CA, USA; Center for Clinical Informatics and Improvement Research, University of California, San Francisco, CA, USA"}]},{"given":"Nina V","family":"Singh","sequence":"additional","affiliation":[{"name":"California School of Professional Psychology, Alliant International University, Emeryvilla, CA, USA"}]},{"given":"Matthew S","family":"Pantell","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, University of California, San Francisco, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1782-1609","authenticated-orcid":false,"given":"Emilia H","family":"De Marchis","sequence":"additional","affiliation":[{"name":"Department of Family & Community Medicine, University of California, San Francisco, CA, USA"}]}],"member":"179","published-online":{"date-parts":[[2023,9,7]]},"reference":[{"key":"bibr1-14604582231200300","doi-asserted-by":"publisher","DOI":"10.1377\/forefront.20191025.776011"},{"key":"bibr2-14604582231200300","unstructured":"Center for Medicare & Medicaid Services. 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