Automated Safety Plan Scoring in Outpatient Mental Health Settings Using Large Language Models: Exploratory Study
These findings highlight LLMs' potential to provide clinicians with timely and accurate feedback on safety plan quality, which could greatly improve its implementation in community practice.
Authors
- Sy Hwang (ORCID: https://orcid.org/0000-0003-3851-9521)
- Christa D. Labouliere (ORCID: https://orcid.org/0000-0003-1772-5481)
- Hayoung Kim Donnelly (ORCID: https://orcid.org/0000-0002-5633-1488)
- Michael B. Steinberg (ORCID: https://orcid.org/0000-0003-2661-2120)
- Emily Schriver (ORCID: https://orcid.org/0000-0003-4522-1029)
- Haitisha Mehta
- David S. Mandell (ORCID: https://orcid.org/0000-0001-8240-820X)
- María A. Oquendo (ORCID: https://orcid.org/0000-0002-7901-5082)
- Kelly L. Green (ORCID: https://orcid.org/0000-0003-4207-0173)
- Danielle L. Mowery (ORCID: https://orcid.org/0000-0003-3802-4457)
- Megan Reilly (ORCID: https://orcid.org/0000-0002-8620-4869)
- Uğurcan Vurgun (ORCID: https://orcid.org/0000-0001-6021-4316)
- Gregory K. Brown (ORCID: https://orcid.org/0000-0002-4294-6879)
Institutions
- University of Pennsylvania Health System (US)
- Columbia University (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- JMIR Mental Health
- Published
- 2025-09-03
- DOI
- https://doi.org/10.2196/79010
- Primary Topic
- Healthcare Decision-Making and Restraints
- Type
- article
- Field-Weighted Citation Impact
- 0.00