An Adaptive Control Approach to Treatment Selection for Substance Use Disorders

Despite the massive costs and widespread harms of substance use, most individuals with substance use disorders (SUDs) receive no treatment at all. Digital therapeutics platforms are an emerging low-cost and low-barrier means of extending treatment to those who need it. While there is a growing body of research focused on how treatment providers can identify which patients need SUD support (or when they need it), there is very little work that addresses how providers should select treatments that are most appropriate for a given patient. Because SUD treatment involves months or years of voluntary compliance from the patient, treatment adherence is a critical consideration for the treatment provider. In this paper we focus on algorithms that a treatment provider can use to match the burden-level of proposed treatments to the time-varying engagement state of the patient to promote adherence. We propose structured models for a patient's engagement over time and their treatment adherence decisions. Using these models we pose a stochastic control formulation of the treatment-provider's burden selection problem. We propose an adaptive control approach that estimates unknown patient parameters as new data are observed. We show that these estimates are consistent and propose algorithms that use these estimates to make appropriate treatment recommendations.