Local AI Copilot Sharply Improves Pharmacist Prescription Screening
This crossover study shows that a privacy-preserving, locally hosted large language model used as a prescreening tool substantially raises the accuracy and sensitivity of outpatient prescription review while cutting review time roughly in half.
large language modelQwen3-14Bartificial intelligenceprescription reviewmedication safetyclinical decision supporthallucinationcrossover studyambulatory carepharmacy informaticsAmbulatory Care / MTM
Background Information
LLMs boost accuracy and privacy in outpatient prescription review.
Locally deployed large language models (LLMs) can enhance outpatient prescription review accuracy while ensuring data privacy in hospitals. Human-AI collaboration improved review accuracy to 97.2% compared to 82.6% unaided, and reduced review time by approximately 51.9%. This study demonstrates the utility of LLMs in improving prescription accuracy and efficiency without external data transfer, which is crucial for privacy. Locally deployed LLMs significantly enhance outpatient prescription review accuracy while maintaining data privacy.
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Authors:Liu Z, Ding Y, Chen J, Yan Z, Cheng X, Zhou W, Fu P, Wang Z
Citation:Liu Z, Ding Y, Chen J, et al. Locally Deployed Large Language Models for AI-Assisted Outpatient Prescription Review: Crossover Study. JMIR medical informatics. 2026;14:e97520. doi:10.2196/97520
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Executive Summary
Study Design
Patient Population
Primary Outcomes
Secondary Outcomes & Safety Profile
Pharmacist Implications
Clinical Pearls
Limitations
Controversies & Evidence Gaps
Cost & Logistics
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