The opioid crisis is the deadliest drug epidemic in American history and new approaches are needed. One novel approach includes predicting likelihood of opioid use disorder (OUD) treatment retention by assessing someone?s risk of early departure from treatment. Current methods to improve treatment retention rely on providers using their intuition to identify when an individual is at risk of leaving treatment early in order to intervene, which often happens too late. Mobile health and machine learning predictive analytics offer a new opportunity to personalize OUD treatment, improve retention in OUD care, and mitigate the risk of relapse and overdose episodes. Project Motivate will combine physiological and behavioral data from disparate sources in order to predict when an individual is at risk of early departure from OUD treatment. This data will be displayed in a user-friendly manner so that providers can more effectively support patients to remain in treatment with timely intervention and responses.
Early departure from opioid use disorder treatment programs is common, with early termination rates over 50% for many opioid use disorder treatments, putting individuals at an increased risk of relapsing, overdose and death. Using physiological monitoring tools to predict the likelihood that someone is at risk of early departure from opioid use disorder treatment due to worsening symptoms and/or cravings will allow for proactive interventions that will improve treatment retention. Making an impact here will not only save lives, but it will also lower medical costs, municipal emergency response costs, recidivism, workplace accidents, lost workplace productivity and costs to families.
