Currently submitted to: JMIR AI
Date Submitted: Sep 16, 2026
Open Peer Review Period: Sep 21, 2026 - Nov 16, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited.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.
Between Craving and Connection: A Mixed Methods Provider Perspectives on Generative AI Chatbots for Alcohol Treatment
ABSTRACT
Background:
Generative artificial intelligence (AI) chatbots are increasingly proposed as scalable tools to support behavioral health care, including alcohol use reduction interventions. However, little is known about how providers perceive their clinical value, risks, and implementation needs, particularly for patients with co-occurring mental health concerns.
Objective:
This study aimed to examine behavioral health providers’ perspectives on the potential roles, value, and implementation considerations of AI chatbots designed to support alcohol use reduction, particularly for patients with co-occurring anxiety or depression.
Methods:
We conducted a mixed-methods study with 22 behavioral health providers who had experience treating alcohol and mental health problems across primary care, psychiatry, and addiction medicine within a large integrated U.S. health system. Providers completed semi-structured interviews exploring perceived roles, engagement factors, safety considerations, and equity implications of AI chatbots for alcohol care, followed by a survey assessing attitudes toward AI using an adapted technology acceptance scale. Interviews were thematically analyzed, and quantitative data were summarized descriptively.
Results:
Quantitative findings indicated moderately positive overall attitudes toward AI chatbots in the context of alcohol and mental health care, with strong endorsement of their potential for psychoeducation, increased access to care, and motivation enhancement, alongside concerns regarding trustworthiness and the need for strict clinical oversight. Qualitative analysis identified five themes: cautious optimism; targeting cravings, high-risk moments, and relapse prevention ; prerequisites for engagement (personalization and emotional responsiveness); safety and escalation needs; and extending the alcohol care continuum to patients not ready for treatment.
Conclusions:
Providers viewed AI chatbots as a promising adjunctive alcohol treatment, particularly for patients with co-occurring mental health needs and those experiencing stigma, ambivalence, or limited care access, with notable caveats. Successful integration requires human-centered design, robust safety guardrails, and alignment with clinical workflows. Clinical Trial: N/A
Citation
Request queued. Please wait while the file is being generated. It may take some time.
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.