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From Innovation to Impact: The CREATE Framework as a Blueprint for Large Language Model Adoption in Opioid Treatment Programs
ABSTRACT
Large language model-based systems (LLMs) have tremendous potential to improve patient-centered healthcare, especially for medically underserved populations. However, realizing this potential requires careful consideration of the socio-technical contexts in which LLMs are used. The design of such systems should consider the vulnerabilities of any medically underserved group that the system intends to support and provide trustworthy evidence for its use. In this work, we study the adoption of LLM-based systems as socio-technical systems from the diffusion of innovations (DOI) theory perspective. We extend the CREATE (Culture, Respect, Education, Advancement, Trust and Expertise) framework to guide integration of LLM-based systems for improved healthcare delivery. We also propose an extension of the traditional hierarchy of evidence model appropriate for our context and discuss risks associated with the use of such systems. Furthermore, we identify key junction points in clinical workflows at which LLM-based systems can potentially be used to improve patient care. In addition to general considerations associated with the development of any patient-oriented LLM-based system, any system designed to support people with opioid use disorder (OUD) needs to consider the overall lack of trust and stigmatizing experiences of this population with the healthcare system. We therefore discuss various aspects of the evaluation process necessary to build trust.
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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.