Mapping MMI Pharmindex Items to RxNorm Semantic Clinical Drugs - An Evaluation Study.

INTRODUCTION: Terminologies are essential for the interoperability of healthcare data and enabling high-quality medical research. The Medical Informatics Initiative Germany currently relies on data from the MMI Pharmindex to identify medications; however, this database lacks international drug-level terminology. Therefore, this study aims to develop an algorithm to map RxNorm Semantic Clinical Drugs (SCDs) to medications in the MMI Pharmindex and to validate the accuracy of these mappings. METHODS: Using existing RxNorm identifiers at the substance level, we identified candidate RxNorm SCDs by using relationships within the RxNorm dataset. Candidates were retained if they matched MMI Pharmindex entries with respect to ingredients, strengths, and dose form. The resulting mappings were evaluated using a combination of automatic and manual validation procedures. RESULTS: At least one matching RxNorm SCD was identified for 18,326 medications. Among the evaluated mappings, different validation approaches indicated an approximate precision of 90%. The remaining discrepancies were predominantly attributable to ambiguous or suboptimal matches rather than to clearly incorrect mappings. The observed recall was 15% when considering the full medication set, including medications for which no SCD exists and which therefore cannot be mapped. CONCLUSION: We developed and validated an approach for mapping MMI Pharmindex medications to RxNorm SCDs, demonstrating high mapping precision where matches are available. While overall recall is limited by incomplete RxNorm concept coverage, the analysis highlights specific gaps and provides a structured basis for improving medication representation within the Medical Informatics Initiative Germany.