When legal drugs turn illegal and how AI can help

Mar 15, 2019

The opioid crisis is at the forefront of conversations across the nation, and data scientists are looking for ways to help solve the problem using big data and artificial intelligence. Opioids are responsible for the majority of overdose deaths in the United States. Clinical diversion, data sharing restrictions, and a lack of behavioral analytics to track addiction risks all factor into the growing problem, but data scientists have been brainstorming solutions that have the potential to shift the pendulum toward a lasting solution.

A lesser known issue contributing to the opioid crisis is clinical diversion. Clinical diversion occurs when the employees of a healthcare organization steal, misuse, or tamper with controlled substances. Data on reported incidents estimate that clinical drug diversion has led to the loss of over 18.7 million pills and $164 million in the first half of 2018 alone.Not only is clinical diversion part of the problem, but the lack of data sharing and drug monitoring across and within states also contributes to the growing crisis. While some states utilize Prescription Drug Monitoring Programs (PDMP) to address drug diversion, abuse, and “doctor shopping,” only 35 states have operational PDMPs. Because not all states have a PDMP, data sharing and interoperability between states has not been implemented.

A lack of behavioral analytics to track risk factors that lead to addiction means patients are at risk of being prescribed more medication with the potential for addiction. For example, the more operations and medical conditions someone has that include an opioid prescription during recovery, the more likely they are to become addicted. According to David Hom, an expert in health analytics, prescribed patient medications are often hidden from the doctor, and patients with different conditions have different risk factors when it comes to using opioids or recovering from addiction. Combining medical records with patient behavior and history to determine risk factors using analytic tools is one of the best ways to fight the epidemic.

Other tools that can be used to fight the epidemic involve AI and big data.Using structured and unstructured data, AI can be used to understand how these controlled substances move through an organization and can detect patterns and anomalies. Combining data from electronic health records, pharmacy systems, HR systems, and scheduling systems, health care organizations can effectively ingest mountains of data, pick out inconsistencies, and understand who is at risk for addiction and diversion. This can help organizations educate their staff on how to identify the signs of addiction and diversion and get them the help they need. The crisis continues to worsen as opioid overdose deaths become more common than traffic accident deaths, and a staggering 80% of heroin users develop their drug addiction through the misuse of prescription opioids.While many actions are being taken to provide access to treatment for people already facing addiction, the use of analytics as a tool to evaluate the data to make better policies and decisions around the opioid crisis is a crucial step in helping the healthcare industry, law enforcement, and government agencies.


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