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Stakeholder Perspectives on Clinical Decision Support Tools to Inform Clinical Artificial Intelligence Implementation: Protocol for a Framework Synthesis for Qualitative Evidence

Lookup NU author(s): Dr Mo Al-Zubaidy, Dr Jeffry Hogg, Dr Gregory Maniatopoulos, Professor Dawn Teare, Fiona Beyer



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2022 JMIR Research Protocols.. All rights reserved. Background: Quantitative systematic reviews have identified clinical artificial intelligence (AI)-enabled tools with adequate performance for real-world implementation. To our knowledge, no published report or protocol synthesizes the full breadth of stakeholder perspectives. The absence of such a rigorous foundation perpetuates the “AI chasm,” which continues to delay patient benefit. Objective: The aim of this research is to synthesize stakeholder perspectives of computerized clinical decision support tools in any health care setting. Synthesized findings will inform future research and the implementation of AI into health care services. Methods: The search strategy will use MEDLINE (Ovid), Scopus, CINAHL (EBSCO), ACM Digital Library, and Science Citation Index (Web of Science). Following deduplication, title, abstract, and full text screening will be performed by 2 independent reviewers with a third topic expert arbitrating. The quality of included studies will be appraised to support interpretation. Best-fit framework synthesis will be performed, with line-by-line coding completed by 2 independent reviewers. Where appropriate, these findings will be assigned to 1 of 22 a priori themes defined by the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability framework. New domains will be inductively generated for outlying findings. The placement of findings within themes will be reviewed iteratively by a study advisory group including patient and lay representatives. Results: Study registration was obtained from PROSPERO (CRD42021256005) in May 2021. Final searches were executed in April, and screening is ongoing at the time of writing. Full text data analysis is due to be completed in October 2021. We anticipate that the study will be submitted for open-access publication in late 2021. Conclusions: This paper describes the protocol for a qualitative evidence synthesis aiming to define barriers and facilitators to the implementation of computerized clinical decision support tools from all relevant stakeholders. The results of this study are intended to expedite the delivery of patient benefit from AI-enabled clinical tools.

Publication metadata

Author(s): Al-Zubaidy M, Hogg HJ, Maniatopoulos G, Talks J, Teare MD, Keane PA, Beyer FR

Publication type: Article

Publication status: Published

Journal: JMIR Research Protocols

Year: 2022

Volume: 11

Issue: 4

Print publication date: 01/04/2022

Online publication date: 01/04/2022

Acceptance date: 09/02/2022

Date deposited: 27/04/2022

ISSN (electronic): 1929-0748

Publisher: JMIR Publications Inc.


DOI: 10.2196/33145


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