Innovative Electrocardiogram Training Using Artificial Intelligence Clinical Scenarios for Nursing Staff

Summary

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.

Conditions

• Electrocardiogram Interpretation
• Nursing Education
• Clinical Reasoning
• Artificial Intelligence
• Clinical Decision-Making
• Cardiovascular Care

Interventions

DEVICE

Artificial Intelligence Driven Scenario-Based Learning Software

This intervention consists of an original educational software designed and developed by the researcher. The software utilizes Artificial Intelligence to generate interactive and adaptive clinical scenarios focused on Electrocardiogram interpretation. Participants interact with high-fidelity simulations where the Artificial Intelligence engine adjusts the complexity of the case based on the user's responses. The software provides immediate feedback, rationales for correct nursing decisions, and tracks the progress of the nursing staff in real-time. Training sessions are structured to enhance clinical reasoning and self-efficacy through immersive, technology-enhanced learning

OTHER

Traditional Electrocardiogram Educational Program

This intervention represents the standard educational approach for nursing staff. It includes traditional classroom-based lectures and the use of static educational materials such as printed manuals and PowerPoint presentations. The content covers the same theoretical and practical principles of Electrocardiogram interpretation as the intervention group but without the use of Artificial Intelligence or interactive clinical scenarios. The sessions are led by an instructor in a conventional learning environment, focusing on passive knowledge acquisition and standardized clinical examples.

Sponsors & Collaborators

• Alexandria University

  lead OTHER

Study Design

Allocation

RANDOMIZED

Purpose

HEALTH_SERVICES_RESEARCH

Masking

NONE

Model

PARALLEL

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

Yes

Timeline & Regulatory

Start

2025-12-01

Primary Completion

2026-02-10

Completion

2026-02-10

Countries

• Egypt

Study Locations