Bauke K. O. Arends, Jenna M. McCormick, Pim van der Harst, Pauline Heus, René van Es
{"title":"实施基于人工智能的心电图判读的障碍、促进因素和策略：一项混合方法研究","authors":"Bauke K. O. Arends, Jenna M. McCormick, Pim van der Harst, Pauline Heus, René van Es","doi":"10.1111/eci.14387","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Introduction</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>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 (<i>n</i> = 25) to identify key barriers and facilitators. Building on these findings, qualitative data were gathered through semi-structured interviews (<i>n</i> = 7) and focus groups (<i>n</i> = 12) to further explain the barriers and facilitators, and discuss relevant implementation strategies.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>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.</p>\n </section>\n </div>","PeriodicalId":12013,"journal":{"name":"European Journal of Clinical Investigation","volume":"55 S1","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/eci.14387","citationCount":"0","resultStr":"{\"title\":\"Barriers, facilitators and strategies for the implementation of artificial intelligence-based electrocardiogram interpretation: A mixed-methods study\",\"authors\":\"Bauke K. O. Arends, Jenna M. McCormick, Pim van der Harst, Pauline Heus, René van Es\",\"doi\":\"10.1111/eci.14387\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Introduction</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>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 (<i>n</i> = 25) to identify key barriers and facilitators. Building on these findings, qualitative data were gathered through semi-structured interviews (<i>n</i> = 7) and focus groups (<i>n</i> = 12) to further explain the barriers and facilitators, and discuss relevant implementation strategies.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>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.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>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. 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Barriers, facilitators and strategies for the implementation of artificial intelligence-based electrocardiogram interpretation: A mixed-methods study
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.
期刊介绍：
EJCI considers any original contribution from the most sophisticated basic molecular sciences to applied clinical and translational research and evidence-based medicine across a broad range of subspecialties. The EJCI publishes reports of high-quality research that pertain to the genetic, molecular, cellular, or physiological basis of human biology and disease, as well as research that addresses prevalence, diagnosis, course, treatment, and prevention of disease. We are primarily interested in studies directly pertinent to humans, but submission of robust in vitro and animal work is also encouraged. Interdisciplinary work and research using innovative methods and combinations of laboratory, clinical, and epidemiological methodologies and techniques is of great interest to the journal. Several categories of manuscripts (for detailed description see below) are considered: editorials, original articles (also including randomized clinical trials, systematic reviews and meta-analyses), reviews (narrative reviews), opinion articles (including debates, perspectives and commentaries); and letters to the Editor.
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