Innovative Electrocardiogram Training Using Artificial Intelligence Clinical Scenarios for Nursing Staff

NCT07455357 | Completed

• 1 other identifier: AU-20-6-392
• Study Type: interventional
• Target: 64
• Locations: 1 country
• Sites: 1

Brief 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

Keywords

Electrocardiogram Interpretation; Artificial Intelligence in Nursing Education; Clinical Reasoning and Decision-Making; Nursing Staff Competency; Self-Efficacy in Clinical Practice; Innovative Simulation Training

Outcome Measures

Primary Outcomes (1)

• Electrocardiogram Interpretation Knowledge Score

  A comprehensive assessment tool designed to evaluate the theoretical and practical knowledge of nursing staff: Consists of 15 multiple-choice questions specifically designed to assess the cognitive knowledge level of nurses regarding the fundamental principles of Electrocardiogram interpretation.such as analysis of basic waveform components, calculating heart rate, identification of common arrhythmias, atrioventricular conduction abnormalities, and evaluation of life-threatening cardiac rhythms. Each correct answer is awarded one point, with a total possible score of 15. where scores range from a minimum of 0 to a maximum of 15, Higher scores indicate a higher level of knowledge in Electrocardiogram interpretation.

  Baseline (Pre-test) and 2 weeks post-intervention (Post-test)

Secondary Outcomes (4)

• Nursing Clinical Decision-Making Score using Case Vignettes

  Baseline (Pre-test) and 2 weeks post-intervention (Post-test)

• Nursing Decision-Making Scale in Electrocardiogram Interpretation

  Baseline (Pre-test) and 2 weeks post-intervention (Post-test)

• General Self-Efficacy Scale for Electrocardiogram Interpretation and Clinical Tasks

  Baseline (Pre-test) and two weeks after the completion of the training intervention (Follow-up test).

• Nurses' Perception and Satisfaction with Artificial Intelligence-Assisted Learning in Electrocardiogram Interpretation

  Baseline (Pre-test) and 2 weeks post-intervention (Post-test)

Study Arms (2)

Artificial Intelligence Driven Training Group

EXPERIMENTAL

Participants in this group will utilize an original, specifically designed learning software developed by the researcher that integrates Artificial Intelligence to provide dynamic clinical scenarios. The intervention focuses on interactive training for Electrocardiogram interpretation. Each scenario is tailored to the learner's performance, providing immediate feedback and simulating real-world cardiovascular care challenges. This group will complete pre-test and post-test assessments, followed by focus group discussions to explore their qualitative experiences with the software.

Device: Artificial Intelligence Driven Scenario-Based Learning Software

Traditional Training Control Group

ACTIVE COMPARATOR

Participants in this group will receive the standard educational intervention for Electrocardiogram interpretation used in traditional nursing education. This typically includes conventional classroom lectures, printed educational materials, and standard presentation slides without the interactive or adaptive features of Artificial Intelligence. This group will complete the same pre-test and post-test assessments as the intervention group to provide a baseline for comparing the effectiveness of the new technology-enhanced method.

Other: Traditional Electrocardiogram Educational Program

Interventions

Artificial Intelligence Driven Scenario-Based Learning Software DEVICE

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

Artificial Intelligence Driven Training Group

Traditional Electrocardiogram Educational Program OTHER

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.

Traditional Training Control Group

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Nursing staff currently employed in clinical practice.
• Willingness to participate in the study and provide written informed consent.
• Ability to use basic computer software or mobile applications to interact with the Artificial Intelligence platform.

You may not qualify if:

• Nurses who have attended advanced Electrocardiogram certification courses or specialized training within the past three months to avoid bias in the baseline knowledge assessment.
• Nurses who have previously participated in formal training or research studies involving Artificial Intelligence-driven educational platforms or clinical decision-support systems to ensure responses and perceived self-efficacy are not influenced by prior familiarity.

Sponsors & Collaborators

• Alexandria University: lead

Study Sites (1)

Faculty of Nursing, Alexandria University

Alexandria, 21511, Egypt

Location

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Lecturer of Medical-Surgical Nursing

Model Details: This study follows a mixed-methods research design using an explanatory sequential approach. The quantitative phase utilizes a quasi-experimental design to evaluate an original, specifically designed Artificial Intelligence-based learning software developed by the researcher. This software is tailored to improve Electrocardiogram Interpretation Knowledge, Nursing Decision-Making, and Self-Efficacy among nursing staff. Participants are assigned to either the intervention group, which utilizes this innovative software, or the control group, which receives traditional training. The qualitative phase consists of focus group discussions to explore the participants' experiences and feedback regarding the usability and effectiveness of the developed Artificial Intelligence software.

Study Record Dates

• First Submitted: March 3, 2026
• First Posted: March 6, 2026
• Study Start: December 1, 2025
• Primary Completion: February 10, 2026
• Study Completion: February 10, 2026
• Last Updated: March 13, 2026

Record last verified: 2026-03

Locations

EG (1)