Proactive Polypharmacy Management Using Large Language Models: Opportunities to Enhance Geriatric Care.

Artificial intelligence Large language models Polypharmacy Primary care

Journal

Journal of medical systems
ISSN: 1573-689X
Titre abrégé: J Med Syst
Pays: United States
ID NLM: 7806056

Informations de publication

Date de publication:
18 Apr 2024
Historique:
received: 09 01 2024
accepted: 25 03 2024
medline: 18 4 2024
pubmed: 18 4 2024
entrez: 17 4 2024
Statut: epublish

Résumé

Polypharmacy remains an important challenge for patients with extensive medical complexity. Given the primary care shortage and the increasing aging population, effective polypharmacy management is crucial to manage the increasing burden of care. The capacity of large language model (LLM)-based artificial intelligence to aid in polypharmacy management has yet to be evaluated. Here, we evaluate ChatGPT's performance in polypharmacy management via its deprescribing decisions in standardized clinical vignettes. We inputted several clinical vignettes originally from a study of general practicioners' deprescribing decisions into ChatGPT 3.5, a publicly available LLM, and evaluated its capacity for yes/no binary deprescribing decisions as well as list-based prompts in which the model was prompted to choose which of several medications to deprescribe. We recorded ChatGPT responses to yes/no binary deprescribing prompts and the number and types of medications deprescribed. In yes/no binary deprescribing decisions, ChatGPT universally recommended deprescribing medications regardless of ADL status in patients with no overlying CVD history; in patients with CVD history, ChatGPT's answers varied by technical replicate. Total number of medications deprescribed ranged from 2.67 to 3.67 (out of 7) and did not vary with CVD status, but increased linearly with severity of ADL impairment. Among medication types, ChatGPT preferentially deprescribed pain medications. ChatGPT's deprescribing decisions vary along the axes of ADL status, CVD history, and medication type, indicating some concordance of internal logic between general practitioners and the model. These results indicate that specifically trained LLMs may provide useful clinical support in polypharmacy management for primary care physicians.

Identifiants

DOI: 10.1007/s10916-024-02058-y PMID: 38632172
pubmed: 38632172
doi: 10.1007/s10916-024-02058-y
pii: 10.1007/s10916-024-02058-y
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

41

Subventions

Organisme : NIGMS NIH HHS
ID : T32GM144273
Pays : United States

Informations de copyright

© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Références

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Auteurs

Arya Rao (A)

Harvard Medical School, Boston, MA, USA.
Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations Research Center (MESH IO), Massachusetts General Hospital, Boston, MA, USA.
Massachusetts General Hospital, Department of Radiology, 55 Fruit Street, Boston, MA, 02114, USA.

John Kim (J)

Harvard Medical School, Boston, MA, USA.
Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations Research Center (MESH IO), Massachusetts General Hospital, Boston, MA, USA.
Massachusetts General Hospital, Department of Radiology, 55 Fruit Street, Boston, MA, 02114, USA.

Winston Lie (W)

Harvard Medical School, Boston, MA, USA.
Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations Research Center (MESH IO), Massachusetts General Hospital, Boston, MA, USA.
Massachusetts General Hospital, Department of Radiology, 55 Fruit Street, Boston, MA, 02114, USA.

Michael Pang (M)

Harvard Medical School, Boston, MA, USA.
Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations Research Center (MESH IO), Massachusetts General Hospital, Boston, MA, USA.
Massachusetts General Hospital, Department of Radiology, 55 Fruit Street, Boston, MA, 02114, USA.

Lanting Fuh (L)

Massachusetts General Hospital, Department of Radiology, 55 Fruit Street, Boston, MA, 02114, USA.

Keith J Dreyer (KJ)

Harvard Medical School, Boston, MA, USA.
Data Science Office, Mass General Brigham, Boston, MA, USA.

Marc D Succi (MD)

Harvard Medical School, Boston, MA, USA. msucci@mgh.harvard.edu.
Medically Engineered Solutions in Healthcare Incubator, Innovation in Operations Research Center (MESH IO), Massachusetts General Hospital, Boston, MA, USA. msucci@mgh.harvard.edu.
Massachusetts General Hospital, Department of Radiology, 55 Fruit Street, Boston, MA, 02114, USA. msucci@mgh.harvard.edu.
ORCID: 0000-0002-1518-3984

Classifications MeSH