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

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Τίτλος: From Innovation to Impact: The CREATE Framework as a Blueprint for Large Language Model Adoption in Opioid Treatment Programs.
Συγγραφείς: Markatou M; Department of Biostatistics, SPHHP, University at Buffalo, State University of New York, Kimball Tower, Buffalo, NY, 14214, United States, 1 7168292894., Mukhopadhyay R; Department of Biostatistics, SPHHP, University at Buffalo, State University of New York, Kimball Tower, Buffalo, NY, 14214, United States, 1 7168292894., Good J; Department of Linguistics, CAS, University at Buffalo, State University of New York, Buffalo, NY, United States.; Department of AI and Society, CAS, University at Buffalo, State University of New York, Buffalo, NY, United States., Tan Y; Department of Biostatistics, SPHHP, University at Buffalo, State University of New York, Kimball Tower, Buffalo, NY, 14214, United States, 1 7168292894., Dharia A; Division of Gastroenterology, Hepatology and Nutrition, JSMBS, University at Buffalo, State University of New York, Buffalo, NY, United States., Brown LS; Department of Public Health, Weill Cornell Medicine, New York, NY, United States., Talal AH; Division of Gastroenterology, Hepatology and Nutrition, JSMBS, University at Buffalo, State University of New York, Buffalo, NY, United States.
Πηγή: Journal of medical Internet research [J Med Internet Res] 2026 Oct 07; Vol. 28, pp. e97131. Date of Electronic Publication: 2026 Oct 07.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: JMIR Publications Country of Publication: Canada NLM ID: 100959882 Publication Model: Electronic Cited Medium: Internet ISSN: 1438-8871 (Electronic) Linking ISSN: 14388871 NLM ISO Abbreviation: J Med Internet Res Subsets: MEDLINE
Imprint Name(s): Publication: <2011- > : Toronto : JMIR Publications
Original Publication: [Pittsburgh, PA? : s.n., 1999-
Ιατρικοί όροι (MeSH): Opioid-Related Disorders*/therapy , Analgesics, Opioid*/therapeutic use , Large Language Models*, Humans ; Patient-Centered Care
Περίληψη: Unlabelled: 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.
(© Marianthi Markatou, Raktim Mukhopadhyay, Jeff Good, Yihao Tan, Arpan Dharia, Lawrence S Brown, Andrew H Talal. Originally published in the Journal of Medical Internet Research (https://www.jmir.org).)
Contributed Indexing: Keywords: CREATE framework; Large language models; engagement; evaluation; evidence generation; medically underserved populations; opioid use disorder; risk
Substance Nomenclature: 0 (Analgesics, Opioid)
Entry Date(s): Date Created: 20261008 Date Completed: 20261008 Latest Revision: 20261008
Update Code: 20261008
DOI: 10.2196/97131
PMID: 42844923
Βάση Δεδομένων: MEDLINE
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  Data: From Innovation to Impact: The CREATE Framework as a Blueprint for Large Language Model Adoption in Opioid Treatment Programs.
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  Data: &lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Markatou+M%22&quot;&gt;Markatou M&lt;/searchLink&gt;; Department of Biostatistics, SPHHP, University at Buffalo, State University of New York, Kimball Tower, Buffalo, NY, 14214, United States, 1 7168292894.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Mukhopadhyay+R%22&quot;&gt;Mukhopadhyay R&lt;/searchLink&gt;; Department of Biostatistics, SPHHP, University at Buffalo, State University of New York, Kimball Tower, Buffalo, NY, 14214, United States, 1 7168292894.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Good+J%22&quot;&gt;Good J&lt;/searchLink&gt;; Department of Linguistics, CAS, University at Buffalo, State University of New York, Buffalo, NY, United States.; Department of AI and Society, CAS, University at Buffalo, State University of New York, Buffalo, NY, United States.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Tan+Y%22&quot;&gt;Tan Y&lt;/searchLink&gt;; Department of Biostatistics, SPHHP, University at Buffalo, State University of New York, Kimball Tower, Buffalo, NY, 14214, United States, 1 7168292894.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Dharia+A%22&quot;&gt;Dharia A&lt;/searchLink&gt;; Division of Gastroenterology, Hepatology and Nutrition, JSMBS, University at Buffalo, State University of New York, Buffalo, NY, United States.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Brown+LS%22&quot;&gt;Brown LS&lt;/searchLink&gt;; Department of Public Health, Weill Cornell Medicine, New York, NY, United States.&lt;br /&gt;&lt;searchLink fieldCode=&quot;AU&quot; term=&quot;%22Talal+AH%22&quot;&gt;Talal AH&lt;/searchLink&gt;; Division of Gastroenterology, Hepatology and Nutrition, JSMBS, University at Buffalo, State University of New York, Buffalo, NY, United States.
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  Data: Unlabelled: 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.&lt;br /&gt; (&#169; Marianthi Markatou, Raktim Mukhopadhyay, Jeff Good, Yihao Tan, Arpan Dharia, Lawrence S Brown, Andrew H Talal. Originally published in the Journal of Medical Internet Research (https://www.jmir.org).)
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      – SubjectFull: Opioid-Related Disorders therapy
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      – SubjectFull: Analgesics, Opioid therapeutic use
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              Text: 2026 Oct 07
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