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

• URL: http://arxiv.org/abs/2006.13259v1
• Date: Tue, 23 Jun 2020 18:30:20 GMT
• Title: Computational Support for Substance Use Disorder Prevention, Detection, Treatment, and Recovery
• Authors: Lana Yarosh, Suzanne Bakken, Alan Borning, Munmun De Choudhury, Cliff Lampe, Elizabeth Mynatt, Stephen Schueller, and Tiffany Veinot
• Abstract summary: Substance Use Disorders involve the misuse of alcohol, opioids, marijuana, and methamphetamine. 1 in 12 U.S. adults have or have had a substance use disorder. National Institute on Drug Abuse estimates that SUDs cost the U.S. $520 billion annually.
• Score: 62.9980747784214
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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