The use of natural language processing on narrative medication schedules to compute average weekly dose

Medications with non-standard dosing and unstandardized units of measurement make the estimation of prescribed dose difficult from pharmacy dispensing data. A natural language processing tool named the SIG extractor was developed to identify and extract elements from narrative medication instructions to compute average weekly doses (AWDs) for disease-modifying antirheumatic drugs. The goal of this paper is to evaluate the performance of the SIG extractor.

This agreement study utilized Veterans Health Affairs pharmacy data from 2008 to 2012. The SIG extractor was designed to extract key elements from narrative medication schedules (SIGs) for 17 select medications to calculate AWD, and these medications were categorized by generic name and route of administration. The SIG extractor was evaluated against an annotator-derived reference standard for accuracy, which is the fraction of AWDs accurately computed.

The overall accuracy was 89% [95% confidence interval (CI) 88%, 90%]. The accuracy was ≥85% for all medications and route combinations, except for cyclophosphamide (oral) and cyclosporine (oral), which were 79% (95%CI 72%, 85%) and 66% (95%CI 58%, 73%), respectively.

The SIG extractor performed well on the majority of medications, indicating that AWD calculated by the SIG extractor can be used to improve estimation of AWD when dispensed quantity or days' supply is questionable or improbable. The working model for annotating SIGs and the SIG extractor are generalized and can easily be applied to other medications. Copyright © 2016 John Wiley & Sons, Ltd.