As a member of the Forbes Technology Council, Kevin MacDonald recently penned an article on how AI technology can prevent opioid theft for the website’s Innovation section. In the Forbes article, MacDonald explains why preventing diversion is a difficult problem for hospitals and how artificial intelligence can help to solve it.
MacDonald defines diversion as “the polite way to say drug theft that occurs in medical institutions.” The difficulty of detecting diversion in hospitals is that the very people who are responsible for being the gatekeepers of controlled substances can also be clever about manipulating the system to hide drug theft. MacDonald calls this the “diversion gap” and explains the three common contributions to this gap: sampling, manual matching and the cover-up.
Each one takes advantage of the lack of tools hospitals have to provide a comprehensive view of the controlled substance use cycle, from the pharmacy to administration to its waste. MacDonald writes: “There is a tremendous amount of data related to controlled substances in hospitals. The combination of numerous storage locations, personnel with access to controlled substances, process variation, patient procedures and numerous medications generates a lot of data. For example, in just three dozen hospitals, our company is tracking activities of over 30,000 clinicians.”
However, MacDonald believes AI can help detect and prevent diversion by using machine learning algorithms to organize all this data, giving hospitals a more complete picture. Essentially, “separating the signal from the noise.” He also believes that in addition to machine learning, hospitals need to also implement 100% audits of controlled substance administration, additional physical security measures and education programs. To read his full article, click here or on the image below.