Evidence map›Paper›PMID 42844923›Full record

ArticleJournal of medical Internet research2026

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

Marianthi Markatou, Raktim Mukhopadhyay, Jeff Good, Yihao Tan, Arpan Dharia, Lawrence S Brown, Andrew H Talal

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Marianthi MarkatouDepartment of Biostatistics, SPHHP, University at Buffalo, State University of New York, Kimball Tower, Buffalo, NY, 14214, United States, 1 7168292894.ORCID http://orcid.org/0000-0002-1453-8229
Raktim MukhopadhyayDepartment of Biostatistics, SPHHP, University at Buffalo, State University of New York, Kimball Tower, Buffalo, NY, 14214, United States, 1 7168292894.ORCID http://orcid.org/0009-0007-8972-6682
Jeff GoodDepartment of Linguistics, CAS, University at Buffalo, State University of New York, Buffalo, NY, United States.ORCID http://orcid.org/0000-0001-8679-4654
Yihao TanDepartment of Biostatistics, SPHHP, University at Buffalo, State University of New York, Kimball Tower, Buffalo, NY, 14214, United States, 1 7168292894.ORCID http://orcid.org/0009-0002-9196-8166
Arpan DhariaDivision of Gastroenterology, Hepatology and Nutrition, JSMBS, University at Buffalo, State University of New York, Buffalo, NY, United States.ORCID http://orcid.org/0000-0001-9015-5337
Lawrence S BrownDepartment of Public Health, Weill Cornell Medicine, New York, NY, United States.ORCID http://orcid.org/0000-0001-5694-9774
Andrew H TalalDivision of Gastroenterology, Hepatology and Nutrition, JSMBS, University at Buffalo, State University of New York, Buffalo, NY, United States.ORCID http://orcid.org/0000-0002-5565-7515

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Analgesics, OpioidLarge Language ModelsOpioid-Related DisordersHumansPatient-Centered CareAnalgesics, OpioidCREATE frameworkengagementevaluationevidence generationLarge language modelsmedically underserved populationsopioid use disorderrisk

Identifiers

PMID42844923
PMCPMC13646446

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.