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:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2026-03-25'}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'HEALTH_SERVICES_RESEARCH', 'interventionModel': 'PARALLEL', 'interventionModelDescription': "This study follows a mixed-methods research design using an explanatory sequential approach.\n\nThe 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.\n\nThe 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."}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 64}}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2025-12-01', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2026-03', 'completionDateStruct': {'date': '2026-02-10', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2026-03-11', 'studyFirstSubmitDate': '2026-03-03', 'studyFirstSubmitQcDate': '2026-03-03', 'lastUpdatePostDateStruct': {'date': '2026-03-13', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2026-03-06', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2026-02-10', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Electrocardiogram Interpretation Knowledge Score', 'timeFrame': 'Baseline (Pre-test) and 2 weeks post-intervention (Post-test)', 'description': '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.'}], 'secondaryOutcomes': [{'measure': 'Nursing Clinical Decision-Making Score using Case Vignettes', 'timeFrame': 'Baseline (Pre-test) and 2 weeks post-intervention (Post-test)', 'description': "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.\n\nThis 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."}, {'measure': 'Nursing Decision-Making Scale in Electrocardiogram Interpretation', 'timeFrame': 'Baseline (Pre-test) and 2 weeks post-intervention (Post-test)', 'description': '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).\n\nScoring and Interpretation:\n\nMinimum and Maximum Values: The total score ranges from a minimum of 8 to a maximum of 40.\n\nHigh Competence (30-40): Indicates high clinical judgment and professional confidence in interpreting heart rhythms.\n\nModerate Ability (19-29): Indicates moderate clinical judgment; the nurse may require supervision or further guidance.\n\nLow Competence (8-18): Indicates poor confidence and a high risk of errors in clinical judgment.\n\nHigher total scores reflect a better outcome, representing greater clinical competence and safer decision-making in cardiovascular care.'}, {'measure': 'General Self-Efficacy Scale for Electrocardiogram Interpretation and Clinical Tasks', 'timeFrame': 'Baseline (Pre-test) and two weeks after the completion of the training intervention (Follow-up test).', 'description': "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.\n\nScoring System:\n\nEach item is scored on a 4-point Likert scale:\n\n1. Not at all true\n2. Hardly true\n3. Moderately true\n4. Exactly true\n\nInterpretation of Total Score (Range 10-40):\n\nHigh Self-Efficacy (31-40): Indicates strong perceived competence and high self-belief in managing cardiovascular care challenges.\n\nModerate Self-Efficacy (21-30): Indicates moderate confidence in managing clinical demands.\n\nLow Self-Efficacy (10-20): Indicates low belief in the ability to cope with professional challenges in Electrocardiogram interpretation.\n\nHigher total scores indicate a better outcome, representing stronger perceived competence and higher self-efficacy among the nursing staff."}, {'measure': "Nurses' Perception and Satisfaction with Artificial Intelligence-Assisted Learning in Electrocardiogram Interpretation", 'timeFrame': 'Baseline (Pre-test) and 2 weeks post-intervention (Post-test)', 'description': "This qualitative outcome assesses the participants' acceptance, perceived usefulness, usability, and satisfaction with the Artificial Intelligence-assisted learning platform specifically designed for Electrocardiogram interpretation.\n\nData Collection and Analysis:\n\nMethod: Data will be collected through focus group discussions to explore the nursing staff's lived experiences and feedback.\n\nMethod of Analysis: Results will be analyzed using Thematic Analysis to identify recurring patterns, themes, and sub-themes related to the intervention."}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Electrocardiogram Interpretation', 'Artificial Intelligence in Nursing Education', 'Clinical Reasoning and Decision-Making', 'Nursing Staff Competency', 'Self-Efficacy in Clinical Practice', 'Innovative Simulation Training'], 'conditions': ['Electrocardiogram Interpretation', 'Nursing Education', 'Clinical Reasoning', 'Artificial Intelligence', 'Clinical Decision-Making', 'Cardiovascular Care']}, 'descriptionModule': {'briefSummary': "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).\n\nStudy 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.\n\nData Collection\n\nTo measure the impact of the training, the study will use four primary tools:\n\nAn Electrocardiogram Interpretation Knowledge Test to measure theoretical understanding.\n\nAn assessment of Nursing Decision-Making in Electrocardiogram Interpretation to evaluate practical clinical reasoning.\n\nA Self-Efficacy Scale for Artificial Intelligence-based Electrocardiogram Training to measure the participants' confidence in their skills.\n\nFocus 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."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria\n\n* Nursing staff currently employed in clinical practice.\n* Willingness to participate in the study and provide written informed consent.\n* Ability to use basic computer software or mobile applications to interact with the Artificial Intelligence platform.\n\nExclusion Criteria\n\n* Nurses who have attended advanced Electrocardiogram certification courses or specialized training within the past three months to avoid bias in the baseline knowledge assessment.\n* 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.'}, 'identificationModule': {'nctId': 'NCT07455357', 'briefTitle': 'Innovative Electrocardiogram Training Using Artificial Intelligence Clinical Scenarios for Nursing Staff', 'organization': {'class': 'OTHER', 'fullName': 'Alexandria University'}, 'officialTitle': 'Impact of Innovative ECG Training Using AI-Supported Clinical Scenarios on Knowledge, Clinical Reasoning, and Self-Efficacy Among Nursing Staff', 'orgStudyIdInfo': {'id': 'AU-20-6-392'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'Artificial Intelligence Driven Training Group', 'description': "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.", 'interventionNames': ['Device: Artificial Intelligence Driven Scenario-Based Learning Software']}, {'type': 'ACTIVE_COMPARATOR', 'label': 'Traditional Training Control Group', 'description': '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.', 'interventionNames': ['Other: Traditional Electrocardiogram Educational Program']}], 'interventions': [{'name': 'Artificial Intelligence Driven Scenario-Based Learning Software', 'type': 'DEVICE', 'description': "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.\n\nParticipants 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", 'armGroupLabels': ['Artificial Intelligence Driven Training Group']}, {'name': 'Traditional Electrocardiogram Educational Program', 'type': 'OTHER', 'description': '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.\n\nThe 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.', 'armGroupLabels': ['Traditional Training Control Group']}]}, 'contactsLocationsModule': {'locations': [{'zip': '21511', 'city': 'Alexandria', 'country': 'Egypt', 'facility': 'Faculty of Nursing, Alexandria University', 'geoPoint': {'lat': 31.20176, 'lon': 29.91582}}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Alexandria University', 'class': 'OTHER'}, 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Lecturer of Medical-Surgical Nursing', 'investigatorFullName': 'Mohamed Fakhry Ahmed Salem', 'investigatorAffiliation': 'Alexandria University'}}}}