Research conducted by Wolters Kluwer, an information and software company, suggests that artificial intelligence (AI) can potentially reduce drug diversion in hospitals. The study revealed that over half of managers who implemented AI-based diversion detection programs reported increased confidence in their effectiveness.
Drug diversion involves the illegal redirection of prescription medications from their intended clinical use, often involving medical staff either using these drugs for themselves or selling them for personal gain. The International Health Facility Diversion Association estimates that at least 37,000 diversion incidents occur annually in U.S. healthcare facilities.
Karen Kobelski, Vice President and General Manager of Clinical Surveillance Compliance & Data Solutions at Wolters Kluwer Health, highlighted that the issue has been exacerbated by the use of temporary and contract staff during the COVID-19 pandemic. She emphasized the importance of advanced technology in maintaining effective diversion detection programs, considering the risks to patient safety, clinical teams, and the hospital’s reputation and finances.
The survey, titled “The State of Drug Diversion 2023 Report,” revealed that 71 percent of respondents stated their drug diversion teams spend approximately eight hours on each investigation, impacting their ability to address the problem efficiently. The survey, conducted in the U.S. and produced by Eliciting Insights, involved 100 healthcare professionals. Its aim was to assess the confidence of healthcare facilities in their anti-drug diversion systems, both with and without the use of AI tools.
In 2019, only 29 percent of respondents used AI tracking to identify potential drug diversion cases. However, the 2023 results indicate a significant increase, with 56 percent of respondents now utilizing AI tools. Among those employing AI, more than half expressed confidence in the effectiveness of these tools in preventing the loss of prescription drugs.
Kobelski emphasized the value of AI-based diversion detection programs, as they can efficiently analyze large volumes of data to identify suspicious cases. This is especially beneficial for resource-strapped hospitals that may not have dedicated staff for continuous diversion detection programs, allowing them to effectively detect and address diversion incidents.