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

ObjectiveTo develop an algorithm for building longitudinal medication dose datasets using information extracted from clinical notes in electronic health records (EHRs).Materials and MethodsWe 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.ResultsFor 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.DiscussionOur 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.ConclusionOur 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.     

Computerized support for neuropsychological test interpretation in clinical situations

Abstract

This study describes and evaluates a computer program developed for improving the speed and quality of neuropsychological test interpretation. The study shows that the within-session (i.e., during testing session) interpretation of test results can support the hypothesis-testing approach. Within-session interpretation facilitates flexible selection of tests according to patient-specific criteria. The core of the program is a syndrome profile that reflects the likelihood of each predefined cognitive function as an explanatory hypothesis for poor test results. A clinical comparison shows clear consistency between the syndromes detected by the program and the diagnostic reports created by experienced neuropsychologists. The overall results encourage conducting further studies on modeling the within-session decision making process for patient-specific neuropsychological testing.