ArticleJAMIA open2026
RxMap: an LLM-assisted tool for medication normalization.
Article in JAMIA open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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4 authors.
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Abstract
Objective: To develop a freely available, researcher-oriented system for accurate normalization of free-text medication strings to standardized RxNorm ingredient-level concepts. Methods: RxMap implements a fully automated normalization pipeline that combines deterministic RxNorm candidate generation with large language model (LLM)-assisted lexical parsing and hierarchical ingredient-level reconciliation. Raw medication strings are normalized to RxNorm ingredient-level (IN/MIN) concepts. ATC codes are assigned post-normalization, and an optional review interface supports transparent human review. Results: Evaluation on 22 624 unique medication strings from the IPUMS MEPS dataset demonstrated substantial improvements over deterministic RxNorm matching alone. The best-performing RxMap configuration improved RxCUI-level precision, recall, and F1-score from 0.865, 0.853, and 0.859 to 0.969, 0.964, and 0.966, respectively, with similar gains at the ingredient level. Conclusion: RxMap provides accurate, scalable normalization of free-text medication data to RxNorm concepts. The system operates fully automatically, offers optional review for quality control, and enables reproducible batch processing for research workflows.
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