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Barriers and facilitators to developing and implementing artificial intelligence-based clinical decision support in
Hashim Kareemi1,2, Alex Colak3, Krishan Yadav4,5,6
1Department of Emergency Medicine, University of British Columbia, Vancouver, BC, Canada. hashim.kareemi@vch.ca.
CJEM
|June 22, 2026
Summary
Implementing artificial intelligence (AI) clinical decision support tools in the emergency department (ED) faces challenges in team capacity and data infrastructure. Engaging end-users and sharing resources are key facilitators for successful AI adoption.
Area of Science:
- Clinical Informatics
- Artificial Intelligence in Medicine
- Health Services Research
Background:
- Artificial intelligence (AI)-based clinical decision support (CDS) tools show promise for improving emergency department (ED) care.
- However, widespread clinical translation of these AI-CDS tools into the ED remains limited.
- Understanding the barriers and facilitators is crucial for successful implementation.
Purpose of the Study:
- To investigate the reasons behind the limited clinical translation of AI-CDS tools in the ED.
- To identify key barriers and facilitators influencing the development and implementation of AI-CDS tools in this setting.
Main Methods:
- A qualitative study design was employed, utilizing semi-structured interviews with researchers experienced in developing and implementing AI-CDS tools for the ED.
- Purposive and snowball sampling methods were used to recruit participants.
- Grounded theory framework guided the iterative analysis of anonymized transcripts by two coders to identify themes, barriers, and facilitators, adhering to SRQR and COREQ guidelines.
Main Results:
- Data saturation was achieved after ten interviews. Eight themes emerged regarding AI-CDS development and implementation in the ED, including team capacity, data infrastructure, problem definition, regulatory approval, legal/liability issues, model performance, time, and cost.
- The most impactful facilitators identified were engaging multiple healthcare end-users and sharing resources across departments.
Conclusions:
- Successful implementation of AI-CDS tools in the ED necessitates a clearly defined clinical problem, a strong data infrastructure, and a multidisciplinary research team.
- Anticipating and proactively addressing regulatory, legal, and financial challenges over an extended timeline is essential.
- Leveraging identified facilitators early in the development process can enhance the likelihood of successful AI tool integration.
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