Patient and clinician acceptability of automated extraction of social drivers of health from clinical notes in primary care.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 40085013

Abstract

OBJECTIVE: Artificial Intelligence (AI)-based approaches for extracting Social Drivers of Health (SDoH) from clinical notes offer healthcare systems an efficient way to identify patients' social needs, yet we know little about the acceptability of this approach to patients and clinicians. We investigated patient and clinician acceptability through interviews.

Authors

  • Serena Jinchen Xie
    Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Seattle, WA 98195, United States.
  • Carolin Spice
    Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Seattle, WA 98195, United States.
  • Patrick Wedgeworth
    Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Seattle, WA 98195, United States.
  • Raina Langevin
    Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Seattle, WA 98195, United States.
  • Kevin Lybarger
    University of Washington, Seattle, WA.
  • Angad Preet Singh
    Department of Medicine, University of Washington, Seattle, WA 98195, United States.
  • Brian R Wood
    Department of Medicine, University of Washington, Seattle, WA 98195, United States.
  • Jared W Klein
    Department of Medicine, University of Washington, School of Medicine, Seattle, WA 98195, United States.
  • Gary Hsieh
    University of Washington, WA.
  • Herbert C Duber
    Washington State Department of Health, Olympia, WA 98501, United States.
  • Andrea L Hartzler
    Group Health Research Institute, Seattle, WA.

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