Rx Norm for Europe - Toward the representation of medicinal products in the OMOP CDM: Graph visualization and validation of two mapping approaches using the OHDSI USAGI tool and LLM

Abstract Medication product names in Swiss electronic health records are heterogeneous and often encode multiple attributes (e.g., ingredient, strength, dose form, packaging) in German free text. This limits interoperability and reduces the utility of ATC codes, which do not uniquely identify products. We compared two workflows for mapping Swiss medication products to RxNorm and RxNorm Extension: (i) an Observational Health Data Sciences and Informatics (OHDSI) USAGI workflow with lexical similarity and expert curation, and (ii) a large language model (LLM) workflow with constrained synonym generation and candidate selection. The LLM workflow applied explicit attribute priorities and allowed abstention when no suitable match was available. Mapping results were loaded into a Neo4j graph database. We assessed semantic proximity using the median shortest path length between mapped concepts. We evaluated 179 products; 151 products were not equally mappable at code level. For these discordant products, the LLM workflow mapped predominantly to branded-level classes (121/151, 80.1 percent), whereas manual/USAGI mapping more often selected clinical drug–level classes (87/151, 57.6 percent). Semantic proximity differed by target vocabulary. In RxNorm, the LLM workflow achieved a lower overall median path length than manual/USAGI (2.47 vs 2.81), in RxNorm Extension, manual/USAGI achieved a lower median path length than LLM (2.46 vs 2.66). Graph-based inspection supported identification of ambiguous cases and systematic differences in hierarchical level. The results show that LLM-assisted mapping can be efficient and competitive; performance depends on the target vocabulary and concept class. Improved European coverage in RxNorm extensions remains necessary for standardization.