Abstract
Electrocardiograms (ECGs) and ECG monitoring are commonly used in healthcare for many reasons, such as assessing heart rate and rhythm, monitoring drug effects, or detecting arrhythmia or ischaemia. ECG monitoring encompasses both real-time surveillance systems, such as cardiac telemetry (typically 3- or 5-lead monitoring) and detailed 12-lead ECG moment-in-time analysis. ECG education and training remain crucial even as rapid advances in artificial intelligence (AI) and related technologies continue to evolve innovation. However, approaches to teaching, evaluating, and maintaining these skills vary. In this editorial, we use AI as an umbrella term encompassing machine learning (ML)-based approaches that underpin most contemporary ECG interpretation algorithms and refer to ECG monitoring and 12-lead ECG under the broader concept of ECG interpretation. This editorial posits that nurses, as key members of an interdisciplinary team, are well-positioned to recognise early signs of cardiac deterioration and apply their diagnostic and clinical reasoning skills, but require structured ECG education, assessment, and continuing professional development (CPD) to do so effectively.