ACCURACY OF A NATURAL LANGUAGE PROCESSING SOFTWARE DESIGNED TO COMPUTE AVERAGE WEEKLY DOSE FROM NARRATIVE MEDICATION SCHEDULE
Author(s)
Lu C1, Leng J1, Cannon G2, Zhou X1, Harrison DJ3, Shah N3, Sauer BC1
1Departments of Internal Medicine, University of Utah, Salt Lake City, UT, USA, 2VA Salt Lake City Health Care System, Salt Lake City, UT, USA, 3Amgen, Inc, Thousand Oaks, CA, USA
OBJECTIVES: Capturing dosing of biologic and non-biologic Disease Modifying Anti-Rheumatic Drugs (DMARDs) using electronic medical records is challenging. Precise estimates of weekly dose using structured pharmacy data alone are difficult since quantity dispensed is not standardized for injectable products. Natural Language Processing (NLP) software was developed to extract elements of the prescribing provider’s medication instructions (SIGs) to compute the average weekly dose. The objective was to evaluate the accuracy of the NLP software’s computation of the average weekly dose by medication and route. METHODS: The NLP software computed the average weekly dose for biologic and non-biologic DMARDs and was evaluated against the annotator-derived reference standard. Using an annotation guideline, trained annotators annotated relevant information from SIGs including unit strength, dose per administration event and the schedule of administration events. The NLP software was then trained on 11,937 records. A validated set of the annotator-derived reference standard that contained 140 SIGs per medication and route was used to evaluate the NLP accuracy and compute the 95% Confidence Interval (CI) of accuracy. RESULTS: The overall accuracy for injectable biologic and oral and injectable non-biologic DMARDs was 89.1% (95% CI: 87.9%-90.3%). Accuracy was 95.3% (95% CI: 91.9%-98.7%) for oral methotrexate, 84.7% (95% CI: 78.9%-90.5%) for injectable methotrexate, 87.9% (95% CI: 82.5%-93.3%) for sulfasalazine, and 92.9% (95% CI: 88.7%-97.2%) for hydroxychloroquine. For biologics, accuracy was 92.1% (95% CI: 87.6%-96.6%) for etanercept, 98.6% (95% CI: 96.7%-100%) for adalimumab and injectable abatacept 90.0% (95% CI: 85.2%-94.8%). The lower bound of the 95% CI ranged from 79.1%-100% for biologic DMARDs and from 78.9%-91.9% for non-biologic DMARDs. CONCLUSIONS: The lower bounds of the 95% CI for most medications were greater than 80%. These results indicate that the NLP software can be used to extract information to calculate the weekly dose of DMARDs from narrative medication schedules.
Conference/Value in Health Info
2014-05, ISPOR 2014, Palais des Congres de Montreal
Value in Health, Vol. 17, No. 3 (May 2014)
Code
PRM40
Topic
Real World Data & Information Systems
Topic Subcategory
Reproducibility & Replicability
Disease
Musculoskeletal Disorders