Building longitudinal medication dose data using medication information extracted from clinical notes in electronic health records

Objective: To develop an algorithm for building longitudinal medication dose datasets using information extracted from clinical notes in electronic health records (EHRs). Materials and Methods: We developed an algorithm that converts medication information extracted using natural language processing (NLP) into a usable format and builds longitudinal medication dose datasets. We evaluated the algorithm on 2 medications extracted from clinical notes of Vanderbilt's EHR and externally validated the algorithm using clinical notes from the MIMIC-III clinical care database. Results: For the evaluation using Vanderbilt's EHR data, the performance of our algorithm was excellent; F1-measures were >= 0.98 for both dose intake and daily dose. For the external validation using MIMIC-III, the algorithm achieved F1-measures >= 0.85 for dose intake and >= 0.82 for daily dose. Discussion: Our algorithm addresses the challenge of building longitudinal medication dose data using information extracted from clinical notes. Overall performance was excellent, but the algorithm can perform poorly when incorrect information is extracted by NLP systems. Although it performed reasonably well when applied to the external data source, its performance was worse due to differences in the way the drug information was written. The algorithm is implemented in the R package, "EHR," and the extracted data from Vanderbilt's EHRs along with the gold standards are provided so that users can reproduce the results and help improve the algorithm. Conclusion: Our algorithm for building longitudinal dose data provides a straightforward way to use EHR data for medication-based studies. The external validation results suggest its potential for applicability to other systems.