Electrocardiogram InterpretationNCT07455357
Innovative Electrocardiogram Training Using Artificial Intelligence Clinical Scenarios for Nursing Staff
A study testing Artificial Intelligence Driven Scenario-Based Learning Software and Traditional Electrocardiogram Educational Program in people with Electrocardiogram Interpretation and Nursing Education, run by Alexandria University at 1 site in Egypt.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Electrocardiogram Interpretation, Nursing Education, Clinical Reasoning
- What is being tested?
- Artificial Intelligence Driven Scenario-Based Learning Software and Traditional Electrocardiogram Educational Program
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 64 participants
- Where
- 1 siteEgypt
- Study dates
- 1 Dec 2025to 10 Feb 2026
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Impact of Innovative ECG Training Using AI-Supported Clinical Scenarios on Knowledge, Clinical Reasoning, and Self-Efficacy Among 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.
Read more
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
- Being tested
- Artificial Intelligence Driven Scenario-Based Learning Software, Traditional Electrocardiogram Educational Program
- Keywords
- Electrocardiogram Interpretation, Artificial Intelligence in Nursing Education, Clinical Reasoning and Decision-Making, Nursing Staff Competency, Self-Efficacy in Clinical Practice, Innovative Simulation Training
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial Intelligence Driven Scenario-Based Learning SoftwareDevice
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
Traditional Electrocardiogram Educational ProgramOther
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: 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. Receives: Other: Traditional Electrocardiogram Educational Program |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Baseline (Pre-test) and 2 weeks post-intervention (Post-test)
Secondary outcomes (4)
Nursing Clinical Decision-Making Score using Case Vignettes
Measured using a specialized assessment tool consisting of 10 clinical case vignettes. Each vignette presents a specific patient scenario related to cardiovascular care and requires the participant to analyze Electrocardiogram findings and choose the most appropriate nursing intervention through Multiple-Choice Questions.
This tool evaluates the nursing staff's ability to apply theoretical knowledge to practical, real-life clinical situations, where scores range from a minimum of 0 to a maximum of 10, with higher scores indicating a better outcome, Higher total scores across the 10 vignettes indicate superior clinical decision-making skills and better professional judgment in Electrocardiogram interpretation.
Measured: Baseline (Pre-test) and 2 weeks post-intervention (Post-test)
Nursing Decision-Making Scale in Electrocardiogram Interpretation
This is an 8-item self-report tool designed to assess the clinical judgment and confidence of nursing staff regarding Electrocardiogram interpretation. Each item is rated on a 5-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly Agree).
Scoring and Interpretation:
Minimum and Maximum Values: The total score ranges from a minimum of 8 to a maximum of 40.
High Competence (30-40): Indicates high clinical judgment and professional confidence in interpreting heart rhythms.
Moderate Ability (19-29): Indicates moderate clinical judgment; the nurse may require supervision or further guidance.
Low Competence (8-18): Indicates poor confidence and a high risk of errors in clinical judgment.
Higher total scores reflect a better outcome, representing greater clinical competence and safer decision-making in cardiovascular care.
Measured: Baseline (Pre-test) and 2 weeks post-intervention (Post-test)
General Self-Efficacy Scale for Electrocardiogram Interpretation and Clinical Tasks
A 10-item psychometric scale adapted to assess the nursing staff's perceived confidence and self-belief in their ability to perform Electrocardiogram interpretation and associated clinical tasks effectively under various conditions.
Scoring System:
Each item is scored on a 4-point Likert scale:
- Not at all true
- Hardly true
- Moderately true
- Exactly true
Interpretation of Total Score (Range 10-40):
High Self-Efficacy (31-40): Indicates strong perceived competence and high self-belief in managing cardiovascular care challenges.
Moderate Self-Efficacy (21-30): Indicates moderate confidence in managing clinical demands.
Low Self-Efficacy (10-20): Indicates low belief in the ability to cope with professional challenges in Electrocardiogram interpretation.
Higher total scores indicate a better outcome, representing stronger perceived competence and higher self-efficacy among the nursing staff.
Measured: 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
This qualitative outcome assesses the participants' acceptance, perceived usefulness, usability, and satisfaction with the Artificial Intelligence-assisted learning platform specifically designed for Electrocardiogram interpretation.
Data Collection and Analysis:
Method: Data will be collected through focus group discussions to explore the nursing staff's lived experiences and feedback.
Method of Analysis: Results will be analyzed using Thematic Analysis to identify recurring patterns, themes, and sub-themes related to the intervention.
Measured: Baseline (Pre-test) and 2 weeks post-intervention (Post-test)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Mar 2026
- Study start1 Dec 2025
- Primary completion10 Feb 2026
- Study completion10 Feb 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Electrocardiogram InterpretationStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- 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.
- Masking
- None (open label)
- Enrolment
- 64 participants (actual)
Sponsor and investigators
- Lead sponsor
- Alexandria University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Mohamed Fakhry Ahmed Salem, Alexandria University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07455357
- Sponsor's protocol ID
- AU-20-6-392
- Status last verified
- Mar 2026
- First submitted
- 3 Mar 2026
- Last updated
- 13 Mar 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 13 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Impact of Innovative ECG Training Using AI-Supported Clinical Scenarios on Knowledge, Clinical Reasoning, and Self-Efficacy Among Nursing Staff
- ClinicalTrials.gov ID
- NCT07455357
- Acronym / sponsor ID
- AU-20-6-392
- 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
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 64 participants (actual)
- Lead sponsor
- Alexandria UniversityUniversity, hospital or other
- Study start
- 1 Dec 2025 (actual)
- Primary completion
- 10 Feb 2026 (actual)
- Study completion
- 10 Feb 2026 (actual)
- First posted
- 6 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- 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.
| Arm | Type | Receives |
|---|---|---|
| Artificial Intelligence Driven Training Group | Experimental | Device: Artificial Intelligence Driven Scenario-Based Learning SoftwareParticipants 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. |
| Traditional Training Control Group | Active comparator | Other: Traditional Electrocardiogram Educational ProgramParticipants 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (2 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial Intelligence Driven Scenario-Based Learning SoftwareThis 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 | Device | Artificial Intelligence Driven Training Group |
| Traditional Electrocardiogram Educational ProgramThis 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. | Other | Traditional Training Control Group |
Primary outcome
Electrocardiogram Interpretation Knowledge Score
Time frame: Baseline (Pre-test) and 2 weeks post-intervention (Post-test)
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.
4 secondary outcomes
Nursing Clinical Decision-Making Score using Case Vignettes
Time frame: Baseline (Pre-test) and 2 weeks post-intervention (Post-test)
Nursing Decision-Making Scale in Electrocardiogram Interpretation
Time frame: Baseline (Pre-test) and 2 weeks post-intervention (Post-test)
General Self-Efficacy Scale for Electrocardiogram Interpretation and Clinical Tasks
Time frame: 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
Time frame: Baseline (Pre-test) and 2 weeks post-intervention (Post-test)
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 13 Mar 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Artificial Intelligence Driven Scenario-Based Learning Software and Traditional Electrocardiogram Educational Program in people with Electrocardiogram Interpretation and Nursing Education, run by Alexandria University at 1 site in Egypt.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 13 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Egypt. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Electrocardiogram Interpretation Knowledge Score (time frame: Baseline (Pre-test) and 2 weeks post-intervention (Post-test)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 13 Mar 2026.