Ignite Creation Date:
2026-03-26 @ 3:16 PM
Ignite Modification Date:
2026-03-30 @ 2:19 AM
Study NCT ID:
NCT07455357
Status:
COMPLETED
Last Update Posted:
2026-03-13
First Post:
2026-03-03
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Innovative Electrocardiogram Training Using Artificial Intelligence Clinical Scenarios for Nursing Staff
Sponsor:
Alexandria University
Organization:
Study Overview
Official Title:
Impact of Innovative ECG Training Using AI-Supported Clinical Scenarios on Knowledge, Clinical Reasoning, and Self-Efficacy Among Nursing Staff
Status:
COMPLETED
Status Verified Date:
2026-03
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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.
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.
Detailed Description:
None
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: