Effect of Metrics-Based Simulation Training to Proficiency on Procedure Quality and Errors Among Novice Cardiac Device Implanters: The IMPROF Randomized Trial

Utilization of and perceived need for simulators in clinical electrophysiology: results from an EHRA physician survey

AIMS

Simulator training has been recently introduced in electrophysiology (EP) programmes in order to improve catheter manipulation skills without complication risks. The aim of this study is to survey the current use of EP simulators and the perceived need for these tools in clinical training and practice.

METHODS AND RESULTS

A 20-item online questionnaire developed by the Scientific Initiatives Committee of the European Heart Rhythm Association (EHRA) in collaboration with EHRA Digital Committee was disseminated through the EHRA Scientific Research Network members, national EP groups, and social media platforms. Seventy-four respondents from 22 countries (73% males; 50% under 40 years old) completed the survey. Despite being perceived as useful among EP professionals (81%), EP simulators are rarely a part of the institutional cardiology training programme (20%) and only 18% of the respondents have an EP simulator at their institution. When available, simulators are mainly used in EP to train transseptal puncture, ablation, and mapping, followed by device implantation (cardiac resynchronization therapy [CRT], leadless, and conduction system pacing [CSP]). Almost all respondents (96%) believe that simulator programmes should be a part of the routine institutional EP training, hopefully developed by EHRA, in order to improve the efficacy and safety of EP procedures and in particular CSP 58%, CRT 42%, leadless pacing 38%, or complex arrhythmia ablations (VT 58%, PVI 45%, and PVC 42%).

CONCLUSION

This current EHRA survey identified a perceived need but a lack of institutional simulator programme access for electrophysiologists who could benefit from it in order to speed up the learning curve process and reduce complications of complex EP procedures.

Artificial intelligence in pediatric surgery

Artificial intelligence (AI) is rapidly changing the landscape of medicine and is already being utilized in conjunction with medical diagnostics and imaging analysis. We hereby explore AI applications in surgery and examine its relevance to pediatric surgery, covering its evolution, current state, and promising future. The various fields of AI are explored including machine learning and applications to predictive analytics and decision support in surgery, computer vision and image analysis in preoperative planning, image segmentation, surgical navigation, and finally, natural language processing assist in expediting clinical documentation, identification of clinical indications, quality improvement, outcome research, and other types of automated data extraction. The purpose of this review is to familiarize the pediatric surgical community with the rise of AI and highlight the ongoing advancements and challenges in its adoption, including data privacy, regulatory considerations, and the imperative for interdisciplinary collaboration. We hope this review serves as a comprehensive guide to AI's transformative influence on surgery, demonstrating its potential to enhance pediatric surgical patient outcomes, improve precision, and usher in a new era of surgical excellence.