A probabilistic model for reducing medication errors: A sensitivity analysis using Electronic Health Records data. (March 2019) - Huang, Chu-Ya; Nguyen, Phung-Anh; Yang, Hsuan-Chia; Islam, Md Mohaimenul; Liang, Chia-Wei; Lee, Fei-Peng; (Jack) Li, Yu-Chuan
Highlights: Succeeded performing the analysis of a probabilistic model for reducing medication errors with various thresholds. Picking the optimal threshold is both an art and a science—it should be done with careful reference to both specialties and the purpose of the application. The AESOP model was observed over 80% accurate (accuracy) for overall departments. Inappropriate prescriptions were determined with a lower rate (i.e. 1%–3%) and the positive predictive value (PPV) ranged from 40%−60%. Abstract: Objectives: Medication-related clinical decision support systems have already been considered as a sophisticated method to improve healthcare quality, however, its importance has not been fully recognized. This paper's aim was to validate an existing probabilistic model that can automatically identify medication errors by performing a sensitivity analysis from electronic medical record data. Methods: We first built a knowledge base that consisted of 2.22 million disease-medication (DM) and 0.78 million medication-medication (MM) associations using Taiwan Health and Welfare data science claims data between January 1st, 2009 and December 31st, 2011. Further, we collected 0.6 million outpatient visit prescriptions from six departments across five different medical centers/hospitals. Afterward, we employed the data to our AESOP model and validated it using a sensitivity analysis of 11 various thresholds (α = [0.5; 1.5]) that were used to identify positive DM and MM associations. We randomly selected 2400 randomly prescriptions and compared them to the gold standard of 18 physicians' manual review for appropriateness. Results: One hundred twenty-one results of 2400 prescriptions with various thresholds were tested by the AESOP model. Validation against the gold standard showed a high accuracy (over 80%), sensitivity (80–96%), and positive predictive value (over 85%). The negative predictive values ranged from 45 to 75% across three departments, cardiology, neurology, and ophthalmology. Conclusion: We performed a sensitivity analysis and validated the AESOP model in different hospitals. Thus, picking the optimal threshold of the model depended on balancing false negatives with false positives and depending on the specialty and the purpose of the system.