Artificial Intelligence and Large Language Models as Decision-Support Tools in Hospital Compounding Pharmacy: A Proof-of-Concept Study.

Abstract

Objectives To evaluate the efficiency and reliability of a large language model (LLM) as a decision-support tool in hospital compounding pharmacy for pediatric extemporaneous preparations requiring assessment of drug crushability, regulatory compliance, and formulation feasibility. Methods A proof-of-concept study compared a structured LLM-assisted workflow with the traditional manual information retrieval process in a hospital pharmacy setting. The LLM (Claude Sonnet 4.6) was configured with a standardized prompt to extract and consolidate data from multiple authoritative sources: the Friuli Venezia Giulia "Do Not Crush" list, the Italian Medicines Agency (AIFA) database for Summary of Product Characteristics (SmPC), AIFA Law 648/96 off-label use lists, and the Stabilis database for oral liquid formulation stability. Two representative drugs, propranolol hydrochloride and imatinib mesylate, were analyzed. For each drug, the model generated a structured output including crushability, regulatory information, off-label status, and extemporaneous formulation data. The same queries were manually performed by an experienced hospital pharmacist. Primary outcome was information retrieval time; secondary outcomes included completeness and accuracy. Results The LLM-assisted workflow reduced retrieval time to less than 2 minutes per drug (mean 1 minute 45 seconds), compared with a mean of 20 minutes (range 15-25 minutes) for the manual process, plus an additional 5 to 10 minutes for transcription. Output completeness was 100%, with all predefined fields correctly populated. The model accurately classified drug crushability and correctly identified Law 648/96 regulatory status. For propranolol, the system identified crushability, pediatric off-label authorization, and SyrSpend-based formulations with stability data of up to 146 days at room temperature. For imatinib, the model highlighted cytotoxic handling precautions, identified the absence of SyrSpend formulations, and retrieved alternative formulation stability data (30 days refrigerated). Conclusions A properly configured LLM can function as an effective decision-support tool in hospital compounding pharmacy, improving efficiency while maintaining high standards of completeness, accuracy, and regulatory compliance. These preliminary results support further investigation into the integration of the LLM system into routine pediatric galenical preparation practice.