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.

Keywords:

• Opioid-related disorders
• machine learning
• big data

Acknowledgements

This work was supported by the Veterans Affairs Health Services Research and Development Service under Award Number IK2HX003358, the Translational Research Institute (TRI), grant UL1 TR003107 through the National Center for Advancing Translational Sciences of the National Institutes of Health (NIH), and R01DA050676 through NIH/National Institute on Drug Abuse (NIDA). Infrastructure support for author AJG was provided, in part, by the VA HSR&D Informatics, Decision-Enhancement, and Analytic Sciences (IDEAS) Center of Innovation (CIN 13-414) and the NIDA under the Greater Intermountain Node (GIN; NIH/NIDA 1UG1DA049444-01). The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

The authors are solely responsible for the content of this article, which does not represent the official views of the US Federal Government, including the Department of Veterans Affairs, Veterans Health Administration, the National Institute of Health, or any of the academic affiliations of the authors.

Author contributions

CJH and AJG developed the Editorial concept. CJH drafted the Editorial. All authors edited and reviewed the final manuscript. All authors approved the final version for publication.

Disclosure statement

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.

Additional information

Funding

This work was supported by the Veterans Affairs Health Services Research and Development Service under Award Number IK2HX003358, the Translational Research Institute (TRI), grant UL1 TR003107 through the National Center for Advancing Translational Sciences of the National Institutes of Health (NIH), and R01DA050676 through NIH/National Institute on Drug Abuse (NIDA). Infrastructure support for author AJG was provided, in part, by the VA HSR&D Informatics, Decision-Enhancement, and Analytic Sciences (IDEAS) Center of Innovation (CIN 13-414) and the NIDA under the Greater Intermountain Node (GIN; NIH/NIDA 1UG1DA049444-01). The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.