Barriers, facilitators and strategies for the implementation of artificial intelligence‐based electrocardiogram interpretation: A mixed‐methods study
Open accessAbstract
Introduction: The implementation of artificial intelligence‐based electrocardiogram interpretation (AI‐ECG) algorithms relies heavily on end‐user acceptance and a well‐designed implementation plan. This study aimed to identify the key barriers, facilitators and strategies for the successful adoption of AI‐ECG in clinical practice. Methods: A sequential explanatory mixed‐methods study was conducted among future AI‐ECG end‐users in the Netherlands, including doctors, nurses, and ambulance professionals, using a clinical scenario involving chest pain. Quantitative data were collected through a three‐round Delphi survey (n = 25) to identify key barriers and facilitators. Building on these findings, qualitative data were gathered through semi‐structured interviews (n = 7) and focus groups (n = 12) to further explain the barriers and facilitators, and discuss relevant implementation strategies. Results: Participants expressed a general openness to working with AI‐ECG. Four key barriers and twelve facilitators were identified in the quantitative phase. Participants mentioned the relative advantage of AI‐ECG in the context of recognizing subtle, or rare, ECG abnormalities and assisting in patient triage. However, successful implementation requires end‐users to have trust in the algorithm, clear protocols, actionable model output, integration with existing clinical systems and multidisciplinary implementation teams. Several strategies were proposed to address these challenges, including conducting local consensus discussions, identifying and preparing local champions and revising professional roles. Conclusions: This mixed‐methods study grounded in established theoretical frameworks identified several barriers and facilitators to AI‐ECG implementation and proposed strategies to address these challenges. These findings provide valuable insights for developing effective implementation plans for AI‐ECG in clinical practice.
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AI-ECG outputs do not improve care unless responsibility for action is clearly assigned.
“AI-ECG outputs do not improve care unless responsibility for action is clearly assigned.”
From AI-ECG for Echocardiography Triage in Structural Heart Disease: Evidence, Implementation, and Future Directions (2026) · cites this paper as [54]
The Results section (Structural characteristics) says role delineation and clear guidelines for acting on AI-generated outcomes are important before implementation. The Discussion also calls for clear protocols to realize AI-ECG’s potential. The paper does not establish the stronger claim that AI-ECG outputs cannot improve care unless responsibility is assigned; it reports implementation needs, not a measured condition for improved care. (LLM, conf=0.85)
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- Authors
- Arends, B. K. O., McCormick, J. M., van der Harst, P., Heus, P., & van Es, R.
- Affiliations
- University Medical Center Utrecht (NL)
- Utrecht University (NL)
- Department of Cardiology University Medical Center Utrecht Utrecht The Netherlands
- Corresponding author
- Arends, Bauke K. O.
- Venue
- European Journal of Clinical Investigation
- Publisher
- Wiley
- Published
- 2025-04
- Volume
- 55
- Issue
- S1
- ISSN
- 0014-2972 (ISSN-L)
- 1365-2362
- PMC
- PMC11973840
- OpenAlex
- W4409211507
- Cited by
- 5
- References
- 26
- Citations by year
- 2026: 3 · 2025: 2
- FWCI
- 1.20
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- European Journal of Clinical Investigation (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Funders
- Health~Holland (2022TKI2322)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- artificial intelligence, ECG interpretation, AI implementation barriers, end-user acceptance, chest pain, triage, clinical decision support, implementation strategies, professional roles, nurses, physicians
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