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Barriers, facilitators and strategies for the implementation of artificial intelligence‐based electrocardiogram interpretation: A mixed‐methods study

Year: 2025

Type: article

Abstract: 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.

Source: European Journal of Clinical Investigation

DOI: 10.1111/eci.14387

Authors: Bauke K. O. Arends, Jenna M. McCormick, Pim van der Harst, Pauline Heus, René van Es

Institutions: University Medical Center Utrecht, Utrecht University

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $4,100