Performance of the ChatGPT Large Language Model for Decision Support in
Community Pharmacy
Abstract
Purpose: To assess the ChatGPT-4 (ChatGPT) large language model (LLM) on
tasks relevant to community pharmacy. Methods: ChatGPT was assessed with
community pharmacy-relevant test cases involving drug information
retrieval, identifying labeling errors, prescription interpretation,
decision making under uncertainty and multi-disciplinary consults. Drug
information on rituximab, warfarin, and St. John’s wort was queried. The
decision-support scenarios consisted of a subject with swollen eyelids,
and a maculopapular rash in a subject on lisinopril and ferrous sulfate.
The multi-disciplinary scenarios required integration of medication
management with nutrition and physical therapy. Results: The responses
from ChatGPT-4 for rituximab, warfarin, and St. John’s wort were
satisfactory and cited drug databases and drug-specific monographs.
ChatGPT identified labeling errors related to incorrect medication
strength, form, administration route, unit conversion, and directions.
For the patient with inflamed eyelids, the course of action developed by
GPT-4 was comparable to the pharmacist’s approach. For the patient with
the maculopapular rash, both the pharmacist and ChatGPT placed a drug
reaction to either lisinopril or ferrous sulfate at the top of the
differential. ChatGPT provided customized vaccination requirements for
travel to Brazil, guidance on management of drug allergies, and recovery
from a knee injury. ChatGPT provided satisfactory medication management
and wellness information for a diabetic on metformin and semaglutide.
Conclusions: LLMs have the potential to become a powerful tool in
community pharmacy. However, testing in validation studies across
diverse pharmacist queries, drug classes, and populations, and
engineering to secure patient privacy will be needed to enhance LLM
utility.