Using RxNorm and NDF-RT to classify medication data extracted from electronic health records: experiences from the Rochester Epidemiology Project.
Electronic Health Records
Information Storage and Retrieval
Natural Language Processing
Terminology as Topic
RxNorm and NDF-RT published by the National Library of Medicine (NLM) and Veterans Affairs (VA), respectively, are two publicly available federal medication terminologies. In this study, we evaluate the applicability of RxNorm and National Drug File-Reference Terminology (NDF-RT) for extraction and classification of medication data retrieved using structured querying and natural language processing techniques from electronic health records at two different medical centers within the Rochester Epidemiology Project (REP). Specifically, we explore how mappings between RxNorm concept codes and NDF-RT drug classes can be leveraged for hierarchical organization and grouping of REP medication data, identify gaps and coverage issues, and analyze the recently released NLM's NDF-RT Web service API. Our study concludes that RxNorm and NDF-RT can be applied together for classification of medication extracted from multiple EHR systems, although several issues and challenges remain to be addressed. We further conclude that the Web service APIs developed by the NLM provide useful functionalities for such activities.