Understanding the Opioid Epidemic: Human-Based Versus Algorithmic-Based Perceptions, Treatments, and Guidelines

Abstract

As a major public health crisis, the opioid epidemic caused over 556,000 deaths in the U.S. between 2000 and 2020. To control the epidemic, the Centers for Disease Control and Prevention (CDC) has developed some general guidelines, encouraging physicians to use opioid medications only when their benefits outweigh their risks. The CDC’s 2016 guidelines mainly left it to physicians to decide when the benefits outweigh the risks. A few years later (in 2022), the CDC made some modifications to make its recommendations a bit less reliant on each individual physician’s perception of benefits versus risks. In complex and high stake decision-making environments such as those pertaining the use of opioid medications, it is not clear whether and how human-based perceptions might differ from algorithmic-based ones. In this study, we first develop some longitudinal machine learning algorithms (e.g., historical random forest, recurrent neural networks, and long short-term memory networks) and train them on clinical evidence of more than 3 million patients. We then feed the best machine learning algorithm to a mathematical model that enables determining cost-effective treatments for each patient in a personalized manner. Through extensive numerical experiments, we compare the treatment options and recommendations from our algorithmic-based approach with human-based ones that are currently followed in the medical practice. Compared to the human-based approach, our results show that the average saving in quality-adjusted life years and costs obtained by following our algorithmic-based treatments are about 2.82 days and $461.46 per patient per year. Finally, we make use of our findings and generate insights for policymakers as well as individual physicians into better ways of managing opioid prescriptions (and hence, the opioid epidemic) by incorporating and interacting with our algorithmic-based approach.