Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Mar 22, 2024
Date Accepted: Nov 17, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Are treatment services ready for the use of big data analytics and artificial intelligence in managing opioid use disorder?: Viewpoint Paper
ABSTRACT
In this viewpoint paper we explore the use of big data analytics and artificial intelligence (AI) in improving outcomes for people with opioid use disorder (OUD) and bring to the table relevant challenges that must be addressed if new technologies are to be utilised ethically, effectively and equitably. First, we explore the conceptualisation of big data analytics and AI and its relevance to OUD treatment services. We then explore potential challenges as well as benefits of leveraging big data analytics and AI to enhance patient care in keeping with international standards for the treatment of OUD. Finally, we lay out strategic and operational principles which OUD treatment services need to address to maximize the potential of big data and AI. These include greater algorithmic transparency, a framework for clinician-technology interfacing, protections for vulnerable situations and people, adequate capture of salient data specific to OUD treatment environments, adequate resources to respond to big data analytical outputs, rebuilding and respecting public trust in institutions and technology, and tackling digital exclusion. Ultimately, effective big data analytics and AI-driven change requires full and open engagement with an OUD treatment system’s complexity, avoiding reductive approaches which may discount existing organisational cultures or exaggerate unhelpful attitudes and practices. We hope, through this paper, to equip the clinician and policy maker to engage with and respond to potential implementation challenges of AI and big data technologies into OUD services.
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.