  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.
