Detecting drug diversion in health-system data using machine learning and advanced analytics.
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 s...
| Published in: | American Journal of Health-System Pharmacy Vol. 79; no. 16; pp. 1345 - 1355 |
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| Main Authors: | , , , , , |
| Format: | research tables/charts Journal Article |
| Published: |
Oxford University Press / USA
8/15/2022
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158394078&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158394078 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10792082 1X3 jtl: American Journal of Health-System Pharmacy issn: 10792082 maglogo: N pubinfo: dt: 8/15/2022 vid: 79 iid: 16 pid: 622 pub: Oxford University Press / USA artinfo: ui: 158394078 158394078 158394078 10.1093/ajhp/zxac035 158394078 ppf: 1345 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Detecting drug diversion in health-system data using machine learning and advanced analytics. aug: au: Knight, Tom May, Bernie Tyson, Don McAuley, Scott Letzkus, Pam Enright, Sharon Murphy affil: Invistics Corporation , Peachtree Corners, GA , USA sug: subj: Machine Learning Methods Data Analytics Theft Drugs Human Patient Safety Substance Use Disorders Prevention and Control Cross Infection Controlled Substances ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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