Optimizing Order Sets With a Large Language Model–Powered Multiagent System
In this cohort study of an LLM-powered multiagent system for optimizing order sets, leveraging LLMs and multiagent systems provided a scalable approach. Alignment with a small set of expert ratings significantly enhanced the LLM evaluation. Future research could refine reasoning capabilities and integrate useful suggestions into electronic health records, while engaging end-users as artificial intelligence-supported reviewers.
Authors
- Sara Horst (ORCID: https://orcid.org/0000-0003-3585-9643)
- Adam T. Wright (ORCID: https://orcid.org/0000-0001-6844-145X)
- Aileen P. Wright (ORCID: https://orcid.org/0000-0002-7550-4284)
- Sean S Huang
- Allison B. McCoy (ORCID: https://orcid.org/0000-0003-2292-9147)
- Siru Liu (ORCID: https://orcid.org/0000-0002-5003-5354)
Institutions
- Vanderbilt University (US)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- JAMA Network Open
- Published
- 2025-09-23
- DOI
- https://doi.org/10.1001/jamanetworkopen.2025.33277
- Citations
- 3
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 2.42