Ignite Creation Date:
2025-12-25 @ 12:56 AM
Ignite Modification Date:
2026-03-01 @ 8:56 PM
Study NCT ID:
NCT05272267
Status:
COMPLETED
Last Update Posted:
2023-08-01
First Post:
2022-02-28
Is Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Transforming ED Throughput With AI-Driven Clinical Decision Support System
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'RANDOMIZED', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'HEALTH_SERVICES_RESEARCH', 'interventionModel': 'CROSSOVER'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 4016}}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2022-08-30', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2022-07', 'completionDateStruct': {'date': '2023-04-27', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2023-07-28', 'studyFirstSubmitDate': '2022-02-28', 'studyFirstSubmitQcDate': '2022-02-28', 'lastUpdatePostDateStruct': {'date': '2023-08-01', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2022-03-09', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2022-12-31', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'ED length of stay', 'timeFrame': 'From ED arrival to 3 days after ED discharge. For hospitalized patients with cardiac arrest, the outcome ascertainment continues until hospital discharge.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Critical Care', 'Emergency Treatment', 'Triage', 'Readmission'], 'conditions': ['Critical Care', 'Emergency Treatment', 'Triage', 'Readmission']}, 'descriptionModule': {'briefSummary': 'The aims of this study is to integrate real-time data flow infrastructure between hospital information system and AI models and to conduct a cluster randomized crossover trial to evaluate the efficacy of the AI models in improving patient flow and relieving ED crowding.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '20 Years', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* ED patients aged 20 years or older\n* Patients were treated by the recruited 16 ED attendings.\n\nExclusion Criteria:\n\n* Patients aged less than 20 years.\n* Patients were not treated by the recruited 16 ED attendings.'}, 'identificationModule': {'nctId': 'NCT05272267', 'acronym': 'TEDAI', 'briefTitle': 'Transforming ED Throughput With AI-Driven Clinical Decision Support System', 'organization': {'class': 'OTHER', 'fullName': 'National Taiwan University Hospital'}, 'officialTitle': 'Transforming ED Throughput With AI-Driven Clinical Decision Support System (TEDAI): The Impact on the Delivery of Care and Patient Experience', 'orgStudyIdInfo': {'id': '202108090RINC'}}, 'armsInterventionsModule': {'armGroups': [{'type': 'ACTIVE_COMPARATOR', 'label': 'AI-assisted', 'description': 'AI-assisted models providing diagnosis and prognostic information', 'interventionNames': ['Other: AI-assisted models providing diagnosis and prognostic information']}, {'type': 'PLACEBO_COMPARATOR', 'label': 'Usual care', 'description': 'usual care without AI-assisted models providing diagnosis and prognostic information', 'interventionNames': ['Procedure: Critical treatment']}], 'interventions': [{'name': 'AI-assisted models providing diagnosis and prognostic information', 'type': 'OTHER', 'description': 'AI-assisted models providing diagnosis and prognostic information in the ED, including triage, ICD coding, chest x ray alerts, critical event alerts, readmission prediction, and post-cardiac arrest prognostication.', 'armGroupLabels': ['AI-assisted']}, {'name': 'Critical treatment', 'type': 'PROCEDURE', 'description': 'Critical treatment of the emergency room', 'armGroupLabels': ['Usual care']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Taipei', 'country': 'Taiwan', 'facility': 'National Taiwan University Hospital', 'geoPoint': {'lat': 25.05306, 'lon': 121.52639}}], 'overallOfficials': [{'name': 'Dr. Huang', 'role': 'STUDY_CHAIR', 'affiliation': 'National Taiwan University Hospital'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'National Taiwan University Hospital', 'class': 'OTHER'}, 'responsibleParty': {'type': 'SPONSOR'}}}}