Computational Support for Substance Use Disorder Prevention, Detection, Treatment, and Recovery

TL;DR

This paper discusses how computational tools and sociotechnical approaches can improve prevention, detection, treatment, and recovery efforts for Substance Use Disorders, addressing a major public health challenge.

Contribution

It identifies three key opportunity areas for computational support in SUD management, emphasizing interdisciplinary and data-sharing strategies.

Findings

Identified three broad opportunity areas for computational support.

Highlighted the importance of social support networks.

Emphasized data sharing across care ecologies.

Abstract

Substance Use Disorders (SUDs) involve the misuse of any or several of a wide array of substances, such as alcohol, opioids, marijuana, and methamphetamine. SUDs are characterized by an inability to decrease use despite severe social, economic, and health-related consequences to the individual. A 2017 national survey identified that 1 in 12 US adults have or have had a substance use disorder. The National Institute on Drug Abuse estimates that SUDs relating to alcohol, prescription opioids, and illicit drug use cost the United States over $520 billion annually due to crime, lost work productivity, and health care expenses. Most recently, the US Department of Health and Human Services has declared the national opioid crisis a public health emergency to address the growing number of opioid overdose deaths in the United States. In this interdisciplinary workshop, we explored how computational support - digital systems, algorithms, and sociotechnical approaches (which consider how technology and people interact as complex systems) - may enhance and enable innovative interventions for prevention, detection, treatment, and long-term recovery from SUDs. The Computing Community Consortium (CCC) sponsored a two-day workshop titled "Computational Support for Substance Use Disorder Prevention, Detection, Treatment, and Recovery" on November 14-15, 2019 in Washington, DC. As outcomes from this visioning process, we identified three broad opportunity areas for computational support in the SUD context: 1. Detecting and mitigating risk of SUD relapse, 2. Establishing and empowering social support networks, and 3. Collecting and sharing data meaningfully across ecologies of formal and informal care.