Michael Wang1,2, Matthew S Pantell3,4, Laura M Gottlieb4,5,6, Julia Adler-Milstein1,2. 1. Center for Clinical Informatics & Improvement Research, University of California, San Francisco, San Francisco, California, USA. 2. Department of Medicine, University of California, San Francisco, San Francisco, California, USA. 3. Department of Pediatrics, University of California, San Francisco, San Francisco, California, USA. 4. Center for Health and Community, University of California, San Francisco, San Francisco, California, USA. 5. Social Interventions Research and Evaluation Network, University of California, San Francisco, San Francisco, California, USA. 6. Department of Family and Community Medicine, University of California, San Francisco, San Francisco, California, USA.
Abstract
OBJECTIVE: Electronic Health Records (EHRs) increasingly include designated fields to capture social determinants of health (SDOH). We developed measures to characterize their use, and use of other SDOH data types, to optimize SDOH data integration. MATERIALS AND METHODS: We developed 3 measures that accommodate different EHR data types on an encounter or patient-year basis. We implemented these measures-documented during encounter (DDE) captures documentation occurring during the encounter; documented by discharge (DBD) includes DDE plus documentation occurring any time prior to admission; and reviewed during encounter (RDE) captures whether anyone reviewed documented data-for the newly available structured SDOH fields and 4 other comparator SDOH data types (problem list, inpatient nursing question, social history free text, and social work notes) on a hospital encounter basis (with patient-year metrics in the Supplementary Appendix). Our sample included all patients (n = 27 127) with at least one hospitalization at UCSF Health (a large, urban, tertiary medical center) over a 1-year period. RESULTS: We observed substantial variation in the use of different SDOH EHR data types. Notably, social history question fields (newly added at study period start) were rarely used (DDE: 0.03% of encounters, DBD: 0.26%, RDE: 0.03%). Free-text patient social history fields had higher use (DDE: 12.1%, DBD: 49.0%, RDE: 14.4%). DISCUSSION: Our measures of real-world SDOH data use can guide current efforts to capture and leverage these data. For our institution, measures revealed substantial variation across data types, suggesting the need to engage in efforts such as EHR-user education and targeted workflow integration. CONCLUSION: Measures revealed opportunities to optimize SDOH data documentation and review.
OBJECTIVE: Electronic Health Records (EHRs) increasingly include designated fields to capture social determinants of health (SDOH). We developed measures to characterize their use, and use of other SDOH data types, to optimize SDOH data integration. MATERIALS AND METHODS: We developed 3 measures that accommodate different EHR data types on an encounter or patient-year basis. We implemented these measures-documented during encounter (DDE) captures documentation occurring during the encounter; documented by discharge (DBD) includes DDE plus documentation occurring any time prior to admission; and reviewed during encounter (RDE) captures whether anyone reviewed documented data-for the newly available structured SDOH fields and 4 other comparator SDOH data types (problem list, inpatient nursing question, social history free text, and social work notes) on a hospital encounter basis (with patient-year metrics in the Supplementary Appendix). Our sample included all patients (n = 27 127) with at least one hospitalization at UCSF Health (a large, urban, tertiary medical center) over a 1-year period. RESULTS: We observed substantial variation in the use of different SDOH EHR data types. Notably, social history question fields (newly added at study period start) were rarely used (DDE: 0.03% of encounters, DBD: 0.26%, RDE: 0.03%). Free-text patient social history fields had higher use (DDE: 12.1%, DBD: 49.0%, RDE: 14.4%). DISCUSSION: Our measures of real-world SDOH data use can guide current efforts to capture and leverage these data. For our institution, measures revealed substantial variation across data types, suggesting the need to engage in efforts such as EHR-user education and targeted workflow integration. CONCLUSION: Measures revealed opportunities to optimize SDOH data documentation and review.
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