RxMap: an LLM-assisted tool for medication normalization

To develop a freely available, researcher-oriented system for accurate normalization of free-text medication strings to standardized RxNorm ingredient-level concepts.

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.

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.

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.