Artificial Intelligence Can be Leveraged to Minimize Casualties of the Opioid Epidemic

The crisis of opioid use, abuse, addiction, and subsequent overdose deaths has reached epidemic proportions in America with no clear end in sight. As of March 2018, the National Institutes of Health reported that more than 115 Americans per day are dying as a direct result of opioid overdoses. In late 2017, it was reported that the US life expectancy had dropped for a second consecutive year, due in part to a surge in fatal opioid overdoses. 
 
For perspective, US life expectancy had not dropped for a single year since 1993, which at the time was a direct result of the AIDS epidemic, and had not dropped for consecutive years since the 1960s. Further, the CDC has estimated that as a result of only prescription opioid abuse, the total yearly economic burden to the United States totaled upwards of $78.5 billion—including the costs of health care, lost productivity, addiction treatment, and involvement of the criminal justice system—and clearly this shocking figure excludes the abuse of illicit opioids, such as heroin.
 
Complex problems require multidimensional answers. There are currently firms working to use artificial intelligence (AI), some through the use of machine learning (ML), in hopes that it will contribute to solving this problem or at least minimize its impact. An incredibly elementary explanation of the relationship between AI and ML is as follows:  ML is a subset of AI—and ML itself contains a further subset known as deep learning—and all ML can be considered AI, but not all AI is considered ML. To delve any deeper into the interconnected relationship between AI, ML, and deep learning would be beyond the scope of this article, but it is important to briefly note the distinction.
 
At times, there are conflicting views within the chemical dependency treatment field as to what the term recovery means, relating to the substances from which an opioid addict actually abstains. However, most would agree that recovery from opioid addiction, regardless of your view of what recovery means, is a challenging and sometimes seemingly impossible goal to not only achieve but also maintain on a long-term basis. 
 
Relapse is an unfortunate but common part of the story along the path of an opioid addict seeking recovery. And due to the fact that opioid tolerance can drastically decrease during a period of abstinence, relapse with opioids frequently leads to overdose and death. 
 
Here lies an area of opportunity in which AI may be useful in minimizing the deaths related to opioid overdose. Since 2014, a Chicago firm called Triggr Health has been using ML through its platform to both predict addiction recovery relapses and help target prevention strategies. 
 
Clients are referred by their health care provider to use the Triggr platform. As of mid-2018, Triggr was exclusively marketed to health care systems and providers, with future expansion planned that would also target individual consumers. After the client downloads the Triggr app to their smartphone, the ML-based platform monitors the client’s smartphone data and activity, such as texting patterns, phone location, and sleep history. 
 


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