Patient and clinician acceptability of automated extraction of social drivers of health from clinical notes in primary care
Although AI approaches like SDoH autosuggest hold promise for efficiently identifying SDoH from clinical notes, they must also account for concerns of patients and clinicians to ensure these systems are acceptable and do not undermine trust.
Authors
- Carolin Spice
- Raina Langevin (ORCID: https://orcid.org/0009-0007-5616-3852)
- Angad Singh (ORCID: https://orcid.org/0009-0000-9956-038X)
- Herbert C. Duber (ORCID: https://orcid.org/0000-0002-5077-3170)
- Brian R. Wood (ORCID: https://orcid.org/0000-0001-5737-6197)
- Kevin Lybarger (ORCID: https://orcid.org/0000-0001-5798-2664)
- Gary Hsieh (ORCID: https://orcid.org/0000-0002-9460-2568)
- Patrick Wedgeworth (ORCID: https://orcid.org/0009-0002-7618-5921)
- Serena Jinchen Xie (ORCID: https://orcid.org/0000-0003-1826-7005)
- Jared W. Klein (ORCID: https://orcid.org/0000-0001-9110-2595)
- Andrea L. Hartzler (ORCID: https://orcid.org/0000-0003-4932-7314)
Institutions
- Washington State Department of Health (US)
- George Mason University (US)
- University of Washington (US)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2025-03-14
- DOI
- https://doi.org/10.1093/jamia/ocaf046
- Citations
- 1
- Primary Topic
- Healthcare Policy and Management
- Type
- article
- Field-Weighted Citation Impact
- 3.13