Retrieval-augmented generation for medication safety: A case study using drug package inserts
Abstract
Background: Retrieval-augmented generation (RAG) has shown promise in mitigating hallucinations in large language models (LLMs), although its utility in medication safety education remains underexplored. To evaluate the performance of RAG-based LLMs in generating patient medication safety education materials, and to assess the agreement between LLM-as-a-judge evaluations and clinical expert judgments.
Methods: Two senior clinical pharmacists selected medications and defined key safety education entries and developed an ontology mapping these entries to sections of drug package inserts. Using this ontology, GPT-4o, OAGLLM, and the pharmacists each independently generated educational materials. Seven licensed clinical pharmacists conducted blinded evaluations using a 5-point Likert scale across six dimensions (Turing Test, Coverage, Relevance, Accuracy, Harmfulness, and Severity of Harm), with each entry independently assessed by one domain-matched pharmacist. DeepSeek-V3, DeepSeek-R1, Qwen-Plus, Claude Sonnet 4.5 also assessed the materials using the same evaluation criteria as the judge models. Inter-rater agreement was assessed using weighted kappa and Spearman correlation coefficients.
Results: Across 70 medications, human experts achieved the highest overall score (0.89 ± 0.16), followed by GPT-4o (0.86 ± 0.20) and OAGLLM (0.84 ± 0.17). Human-generated materials performed best in Coverage, Relevance, and Accuracy, while GPT-4o achieved the lowest Harmfulness score.…
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