Using Data Science to Improve Outcomes for Persons with Opioid use Disorder

Abstract

Medication treatment for opioid use disorder (MOUD) is an effective evidence-based therapy for decreasing opioid-related adverse outcomes. Effective strategies for retaining persons on MOUD, an essential step to improving outcomes, are needed as roughly half of all persons initiating MOUD discontinue within a year. Data science may be valuable and promising for improving MOUD retention by using “big data” (e.g., electronic health record data, claims data mobile/sensor data, social media data) and specific machine learning techniques (e.g., predictive modeling, natural language processing, reinforcement learning) to individualize patient care. Maximizing the utility of data science to improve MOUD retention requires a three-pronged approach: (1) increasing funding for data science research for OUD, (2) integrating data from multiple sources including treatment for OUD and general medical care as well as data not specific to medical care (e.g., mobile, sensor, and social media data), and (3) applying multiple data science approaches with integrated big data to provide insights and optimize advances in the OUD and overall addiction fields.

Prospective acceptability of digital phenotyping among pregnant and parenting people with opioid use disorder: A multisite qualitative study

“…This approach has been widely used in behavioral medicine and psychiatry to identify clinical markers of disease risk, monitor symptoms and outcomes, and tailor treatments in mental health and addictive disorders, including substance use overdose and relapse prediction, detection, and intervention ( 20 , 21 ). Because data collection occurs in everyday contexts, it is an important tool for understanding behaviors of hard-to-reach populations who experience barriers to care and have intermittent healthcare utilization ( 22 ).…”

The evolution of Big Data in neuroscience and neurology

“…The US economic cost of OUD alone and fatal opioid overdoses was $471 billion and $550 billion, respectively, in 2017 [ 156 ]. Treatments focus on replacement (e.g., nicotine and opioid replacement) and abstinence and are often combined with self-help groups or psychotherapy [ 157 , 158 ].…”

“…Similarly, studies implementing data sets from the UK BioBank and 23andMe (representing > 140,000 subjects) have been used for developing the Alcohol Use Disorder Identification Test (AUDIT) to identify the genetic basis of alcohol consumption and alcohol use disorder [ 160 ]. Big Data is also being used to devise strategies for retaining patients on medication for OUD, as roughly 50% of persons discontinue OUD therapy within a year [ 158 ]. The Veterans Health Administration is spearheading such an initiative based on data (including clinical, insurance claim, imaging, and genetic data) from > 9 M veterans [ 158 ].…”

Data Science Implementation Trends in Nursing Practice: A Review of the 2021 Literature

“…All rights reserved. science can help improve some of the biggest health problems, such as health disparities 4 , opioid use disorder 5 , and poor birth outcomes 6 to name just a few.…”

“…In addition to more traditionally used data, such as electronic health record (EHR) and health registry data, nontraditional data and sources are now being incorporated into data science datasets, 1 such as social determinants of health, 2 wearable technology, and the Internet of Things, 3 creating even more potential for innovation. Every sector of health care now has the potential to use data science for scientific discovery and clinical practice improvement with hopes that data science can help improve some of the biggest health problems, such as health disparities, 4 opioid use disorder, 5 and poor birth outcomes, 6 to name just a few.…”