Are treatment services ready for the use of big data analytics and artificial intelligence in managing opioid use disorder?

Matthew Amer; Rosalind Gittins; Antonio Martinez Millana; Florian Scheibein; Maricia Ferri; Babak Tofighi; Francis Sullivan; Margaret Handley; Monty Ghosh; Alexander Baldacchino; Joseph  Tay Wee Teck

doi:10.2196/58723

Are treatment services ready for the use of big data analytics and artificial intelligence in managing opioid use disorder?

Matthew Amer, Rosalind Gittins, Antonio Martinez Millana, Florian Scheibein, Maricia Ferri, Babak Tofighi, Francis Sullivan, Margaret Handley, Monty Ghosh, Alexander Baldacchino, Joseph Tay Wee Teck

Research output: Contribution to journal › Article › peer-review

Abstract

In this viewpoint, we explore the use of big data analytics and artificial intelligence (AI) and discuss important challenges to their ethical, effective, and equitable use within opioid use disorder (OUD) treatment settings. Applying our collective experiences as OUD policy and treatment experts, we discuss 8 key challenges that OUD treatment services must contend with to make the most of these rapidly evolving technologies: data and algorithmic transparency, clinical validation, new practitioner-technology interfaces, capturing data relevant to improving patient care, understanding and responding to algorithmic outputs, obtaining informed patient consent, navigating mistrust, and addressing digital exclusion and bias. Through this paper, we hope to critically engage clinicians and policy makers on important ethical considerations, clinical implications, and implementation challenges involved in big data analytics and AI deployment in OUD treatment settings.

Original language	English
Article number	e58723
Pages (from-to)	1-15
Number of pages	15
Journal	Journal of Medical Internet Research
Volume	27
DOIs	https://doi.org/10.2196/58723
Publication status	Published - 28 Apr 2025

Keywords

Machine learning
ML
Artificial intelligence
AI
Algorithm
Predictive model

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.2196/58723Licence: CC BY

Amer-2025-Are-treatment-services-ready-JMIR-2025-27-e58723-CCBY
Copyright © Matthew Amer, Rosalind Gittins, Antonio Martinez Millana, Florian Scheibein, Marica Ferri, Babak Tofighi, Frank Sullivan, Margaret Handley, Monty Ghosh, Alexander Baldacchino, Joseph Tay Wee Teck. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 28.04.2025. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
Final published version, 466 KBLicence: CC BY

https://preprints.jmir.org/preprint/58723/acceptedLicence: CC BY

Cite this

Amer, M., Gittins, R., Millana, A. M., Scheibein, F., Ferri, M., Tofighi, B., Sullivan, F., Handley, M., Ghosh, M., Baldacchino, A., & Tay Wee Teck, J. (2025). Are treatment services ready for the use of big data analytics and artificial intelligence in managing opioid use disorder? Journal of Medical Internet Research, 27, 1-15. Article e58723. https://doi.org/10.2196/58723

@article{a86c2cba2dfb41b9b3d42fa2e8e50a4e,

title = "Are treatment services ready for the use of big data analytics and artificial intelligence in managing opioid use disorder?",

abstract = "In this viewpoint, we explore the use of big data analytics and artificial intelligence (AI) and discuss important challenges to their ethical, effective, and equitable use within opioid use disorder (OUD) treatment settings. Applying our collective experiences as OUD policy and treatment experts, we discuss 8 key challenges that OUD treatment services must contend with to make the most of these rapidly evolving technologies: data and algorithmic transparency, clinical validation, new practitioner-technology interfaces, capturing data relevant to improving patient care, understanding and responding to algorithmic outputs, obtaining informed patient consent, navigating mistrust, and addressing digital exclusion and bias. Through this paper, we hope to critically engage clinicians and policy makers on important ethical considerations, clinical implications, and implementation challenges involved in big data analytics and AI deployment in OUD treatment settings.",

keywords = "Machine learning, ML, Artificial intelligence, AI, Algorithm, Predictive model",

author = "Matthew Amer and Rosalind Gittins and Millana, {Antonio Martinez} and Florian Scheibein and Maricia Ferri and Babak Tofighi and Francis Sullivan and Margaret Handley and Monty Ghosh and Alexander Baldacchino and {Tay Wee Teck}, Joseph",

note = "Funding: Support for this research was provided by the Commonwealth Fund.",

year = "2025",

month = apr,

day = "28",

doi = "10.2196/58723",

language = "English",

volume = "27",

pages = "1--15",

journal = "Journal of Medical Internet Research",