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Editorial
. 2022;43(1):956-963.
doi: 10.1080/08897077.2022.2060446.

Using data science to improve outcomes for persons with opioid use disorder

Corey J Hayes  1   2 , Michael A Cucciare  2   3   4 , Bradley C Martin  5 , Teresa J Hudson  2   3 , Keith Bush  6 , Weihsuan Lo-Ciganic  7   8 , Hong Yu  9   10 , Elizabeth Charron  11 , Adam J Gordon  11   12
Affiliations
Editorial

Using data science to improve outcomes for persons with opioid use disorder

Corey J Hayes et al. Subst Abus. 2022.

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.

Keywords: Opioid-related disorders; big data; machine learning.

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Conflict of interest statement

Conflicts of Interest: Dr. Lo-Ciganic is named as an inventor in a preliminary patent filing from the University of Florida for use of a machine learning algorithm for opioid risk prediction in Medicare. Dr. Lo-Ciganic has received grant funding from Merck Sharp & Dohme Corp and Bristol Myers Squibb, unrelated to this project. Dr. Martin receives royalties from TestleTree LLC for the commercialization of an opioid risk prediction tool, which is unrelated to this project.

References

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