Innovative Electrocardiogram Training Using Artificial Intelligence Clinical Scenarios for Nursing Staff

Background and Purpose Accurate interpretation of an Electrocardiogram is a vital skill for nursing staff to ensure patient safety and timely intervention in cardiovascular care. Traditional training methods often lack the interactive and complex nature of real-life clinical situations. This study aims to evaluate the effectiveness of an innovative training program that uses Artificial Intelligence to create realistic clinical scenarios. The goal is to determine if this technology-enhanced approach improves nurses' knowledge, their ability to make clinical decisions (clinical reasoning), and their confidence in performing these tasks (self-efficacy). Study Design and Methodology The researchers will conduct a study involving nursing staff to compare their performance before and after the training intervention. Participants will engage with Artificial Intelligence supported clinical scenarios specifically designed for Electrocardiogram interpretation. Data Collection To measure the impact of the training, the study will use four primary tools: An Electrocardiogram Interpretation Knowledge Test to measure theoretical understanding. An assessment of Nursing Decision-Making in Electrocardiogram Interpretation to evaluate practical clinical reasoning. A Self-Efficacy Scale for Artificial Intelligence-based Electrocardiogram Training to measure the participants' confidence in their skills. Focus group discussions will be held at the end of the study to gain deeper qualitative insights into the nursing staff's experiences and perceptions of using technology in their professional development.

NCT Number	NCT07455357
Lead Sponsor	Alexandria University
Conditions	Electrocardiogram Interpretation, Nursing Education, Clinical Reasoning, Artificial Intelligence, Clinical Decision-Making, Cardiovascular Care
Enrollment	64 participants
Start Date	2025-12-01
Primary Completion	2026-02-10 (estimated)
Study Completion	2026-02-10 (estimated)
Updated on ClinicalTrials.gov	2026-03-13