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

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.