On-Demand: Detecting Drug Diversion Faster using NIH-Funded Machine Learning Analytics
Program Date/Time: Wednesday, April 20, 2022, at 1 p.m. ET / 12 CT
The American Journal of Health-System Pharmacy (AJHP) published a 5-year National Institutes of Health (NIH) funded study on using machine learning and advanced analytics to improve drug diversion detection. The study compared using these technologies against traditional means of detection such as the use of monthly anomalous usage reports in 10 acute-care inpatient hospitals across four independent health systems.
The advanced analytics and machine learning technologies detected known diversion cases in blinded data an average of 160 days faster than existing, non-machine learning detection methods had. Additionally, the machine learning model demonstrated 96.3% accuracy, 95.9% specificity, and 96.6% sensitivity detecting transactions at high-risk of diversion in the dataset.
Join contributing authors Tom Knight and Pam Letzkus in a panel discussion on the results of the retrospective study detailing key benefits of using technology to improve drug diversion detection and investigation.
Learning Objectives:
- How hospitals and other healthcare facilities can consolidate data from existing IT systems to detect drug diversion
- How machine learning can detect drug diversion faster than traditional methods
Sponsored by:
