Automated identification of unstandardized medication data: A scalable and flexible data standardization pipeline using RxNorm on GEMINI multicenter hospital data

ObjectivePatient data repositories often assemble medication data from multiple sources, necessitating standardization prior to analysis. We implemented and evaluated a medication standardization procedure for use with a wide range of pharmacy data inputs across all drug categories, which supports research queries at multiple levels of granularity. MethodsThe GEMINI-RxNorm system automates the use of multiple RxNorm tools in tandem with other datasets to identify drug concepts from pharmacy orders. GEMINI-RxNorm was used to process 2,090,155 pharmacy orders from 245,258 hospitalizations between 2010 and 2017 at 7 hospitals in Ontario, Canada. The GEMINI-RxNorm system matches drug-identifying information from pharmacy data (including free-text fields) to RxNorm concept identifiers. A user interface allows researchers to search for drug terms and returns the relevant original pharmacy data through the matched RxNorm concepts. Users can then manually validate the predicted matches and discard false positives. We designed the system to maximize recall (sensitivity) and enable excellent precision (positive predictive value) with minimal manual validation. We compared the performance of this system to manual coding (by a physician and pharmacist) of 13 medication classes. ResultsManual coding was performed for 1,948,817 pharmacy orders and GEMINI-RxNorm successfully returned 1,941,389 (99.6%) orders. Recall was greater than 98.5% in all 13 drug classes, and the F-Measure and precision remained above 90.0% in all drug classes, facilitating efficient manual review to achieve 100.0% precision. GEMINI-RxNorm saved time substantially compared to manual standardization, reducing the time taken to review a pharmacy order row from an estimated 30 seconds to 5 seconds and reducing the number of rows needed to be reviewed by up to 99.99%. Discussion and ConclusionGEMINI-RxNorm presents a novel combination of RxNorm tools and other datasets to enable accurate, efficient, flexible, and scalable standardization of pharmacy data. By facilitating efficient minimal manual validation, the GEMINI-RxNorm system can allow researchers to achieve near-perfect accuracy in medication data standardization.