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

by   Lana Yarosh, et al.

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.


page 3

page 7

page 15

page 17

page 19

page 20

page 21

page 22


Research Opportunities in Sociotechnical Interventions for Health Disparity Reduction

The implicit and explicit biases built into our computing systems are be...

Analyzing Partitioned FAIR Health Data Responsibly

It is widely anticipated that the use of health-related big data will en...

Social-Cultural Factors in the Design of Technology for Hispanic People with Stroke

Stroke is a leading cause of serious, long-term disability in the United...

Assisted Living in the United States: an Open Dataset

An assisted living facility (ALF) is a place where someone can live, hav...

NSF Broadband Research 2020 Report

The internet has become a critical communications infrastructure, and ac...

Discovering heterogeneous subpopulations for fine-grained analysis of opioid use and opioid use disorders

The opioid epidemic in the United States claims over 40,000 lives per ye...

A new measure for the analysis of epidemiological associations: Cannabis use disorder examples

Analyses of population-based surveys are instrumental to research on pre...

Please sign up or login with your details

Forgot password? Click here to reset