ABSTRACT
PURPOSE: The theft of drugs from healthcare facilities, also known as drug diversion, occurs frequently but is often undetected. This paper describes a research study to develop and test novel drug diversion detection methods. Improved diversion detection and reduction in diversion improves patient safety, limits harm to the person diverting, reduces the public health impact of substance use disorder, and mitigates significant liability risk to pharmacists and their organizations. METHODS: Ten acute care inpatient hospitals across 4 independent health systems extracted 2 datasets from various health information technology systems. Both datasets were consolidated, normalized, classified, and sampled to provide a harmonious dataset for analysis. Supervised machine learning methods were iteratively used on the initial sample dataset to train algorithms to classify medication movement transactions as involving a low or high risk of diversion. Thereafter, the resulting machine learning model classified the risk of diversion in a historical dataset capturing 8 to 24 months of history that included 27.9 million medication movement transactions by 19,037 nursing, 1,047 pharmacy, and 712 anesthesia clinicians and that included 22 known, blinded diversion cases to measure when the model would have detected the diversion compared to when the diversion was actually detected by existing methods. RESULTS: The machine learning model had 96.3% accuracy, 95.9% specificity, and 96.6% sensitivity in detecting transactions involving a high risk of diversion using the initial sample dataset. In subsequent testing using the much larger historical dataset, the analytics detected known diversion cases (n = 22) in blinded data faster than existing detection methods (a mean of 160 days and a median of 74 days faster; range, 7-579 days faster). CONCLUSION: The study showed that (1) consolidated datasets and (2) supervised machine learning can detect known diversion cases faster than existing detection methods. Users of the technology also noted improved investigation efficiency.
Subject(s)
Prescription Drug Diversion , Substance-Related Disorders , Algorithms , Humans , Machine Learning , PharmacistsABSTRACT
PURPOSE: Results of a quantitative assessment of emotional intelligence in a sample of pharmacists affiliated with the ASHP Research and Education Foundation's Pharmacy Leadership Academy (PLA) are presented. METHODS: A demographic questionnaire and a validated instrument for assessing emotional intelligence, the Emotional Quotient Inventory, version 2.0 (EQ-i 2.0), were administered to a group of practicing pharmacists who graduated from the PLA during the period 2008-12 (n = 82) and a control group of pharmacists who were accepted into the PLA in 2013 but had not begun leadership training (n = 40). The dependent variables were the mean total EQ-I 2.0 score and mean scores on five EQ-i 2.0 composite scales. The independent variables were PLA affiliation status (graduate versus matriculant) and demographic variables. Descriptive and inferential statistics were used to calculate between-group differences in EQ-i 2.0 scores. The relationship of demographic variables to EQ-i 2.0 scores was analyzed via multiple linear regression. RESULTS: Among the 122 pharmacists who completed both assessments, the overall mean total EQ-i 2.0 score was 101.11, which indicated an average level of emotional intelligence. There were significant differences between the PLA graduate group and the control group in total EQ-i 2.0 scores and in EQ-i 2.0 scores for self-expression, decision-making, interpersonal skills, and other aspects of emotional intelligence. The evaluated demographic factors were not found to be significant predictors of EQ-i 2.0 scores. CONCLUSION: The study results indicated an average level of emotional intelligence among all PLA affiliates but revealed significant differences in mean total EQ-i 2.0 scores and EQ-i 2.0 composite scale scores favoring PLA graduates.
