An approximate matching method for clinical drug names.

OBJECTIVE: To develop an approximate matching method for finding the closest drug names within existing RxNorm content for drug name variants found in local drug formularies. METHODS: We used a drug-centric algorithm to determine the closest strings between the RxNorm data set and local variants which failed the exact and normalized string matching searches. Aggressive measures such as token splitting, drug name expansion and spelling correction are used to try and resolve drug names. The algorithm is evaluated against three sets containing a total of 17,164 drug name variants. RESULTS: Mapping of the local variant drug names to the targeted concept descriptions ranged from 83.8% to 92.8% in three test sets. The algorithm identified the appropriate RxNorm concepts as the top candidate in 76.8%, 67.9% and 84.8% of the cases in the three test sets and among the top three candidates in 90-96% of the cases. CONCLUSION: Using a drug-centric token matching approach with aggressive measures to resolve unknown names provides effective mappings to clinical drug names and has the potential of facilitating the work of drug terminology experts in mapping local formularies to reference terminologies.

Methods for managing variation in clinical drug names.

OBJECTIVES: To develop normalization methods for managing the variation in clinical drug names. METHODS: Manual examination of drug names from RxNorm and local variants collected from formularies led to the identification of three types of drug-specific normalization rules: expansion of abbreviations (e.g., tab to tablet);reformatting of specific elements (e.g., space between number and unit); and removal of salt variants (e.g., succinate from metoprolol succinate). RESULTS: After drug-specific normalization, recall of 3397 previously non-matching names from formularies reaches 45% overall (70% of some subsets), compared to 10-20% after generic normalization. Ambiguity has not increased significantly in the RxNorm dataset. CONCLUSIONS: A limited number of drug-specific normalization operations provide significant improvement over general language normalization.