From Innovation to Impact: The CREATE Framework as a Blueprint for Large Language Model Adoption in Opioid Treatment...

Large language model (LLM)-based systems have tremendous potential to improve patient-centered health care, 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. This viewpoint reports the lessons learned from our experience and research with the CREATE (Culture, Respect, Education, Advancement, Trust, and Expertise) framework for engaging multiple stakeholders to guide the integration of LLM-based systems for improved health care delivery. We propose an extension of the traditional hierarchy of evidence model and identify key junction points in clinical workflows at which LLM-based systems can potentially be used to improve patient care. The key messages can be summarized as follows: (1) users of AI technologies, such as LLM-based tools, must be aware of the strengths, limitations, and impact of these technologies on health care delivery workflows and on the quality of generated evidence on which health care recommendations are based; (2) users must also be aware of the impact of data quality on AI outputs and on the evidence generated from the use of these systems; (3) the evidence pyramid provides a framework that facilitates the evaluation of the output generated by AI systems; (4) the CREATE framework facilitates the engagement of a variety of stakeholders. We propose concrete approaches for its validation and implementation; (5) 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 health care system, and we discuss various aspects of the evaluation process necessary to build trust; (6) to discuss research directions that need to be addressed before LLM-based systems can be integrated usefully in patient health care.