[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100551139":3},{"organization":4,"outcomesModule":7,"designInfo":24,"detailedDescription":30,"studyPopulation":23,"armGroups":31,"interventions":42,"overallOfficials":23,"centralContacts":23,"locations":23,"responsibleParty":48,"collaborators":23,"id":52,"slug":23,"hasResults":53,"nctId":54,"briefTitle":55,"officialTitle":56,"acronym":57,"eligibilityCriteria":58,"healthyVolunteers":59,"sex":60,"minAge":61,"maxAge":62,"enrollmentInfo":63,"targetDuration":23,"studyType":66,"phases":67,"briefSummary":69,"conditions":70,"keywords":73,"overallStatus":78,"whyStopped":23,"lastUpdateSubmitDate":79,"lastUpdatePostDateStruct":80,"startDateStruct":83,"completionDateStruct":85,"leadSponsor":87,"locationsCount":23},{"fullName":5,"class":6},"Duke-NUS Graduate Medical School","OTHER",{"primaryOutcomes":8,"secondaryOutcomes":19,"otherOutcomes":23},[9,13,16],{"measure":10,"description":11,"timeFrame":12},"Report generation time","Time for radiologist to produce each individual CXR report","12 months",{"measure":14,"description":15,"timeFrame":12},"Turnaround Time","Time from patient arrival at radiography department to time for clinical team to receive report",{"measure":17,"description":18,"timeFrame":12},"Time to discharge","Time from patient arrival at radiography department to time to discharge from hospital",[20],{"measure":21,"description":22,"timeFrame":12},"30-day patient readmission rate","Rate of readmission of patient to hospital after discharge within 30 days",null,{"allocation":25,"interventionModel":26,"interventionModelDescription":23,"primaryPurpose":27,"observationalModel":23,"timePerspective":23,"maskingInfo":28},"RANDOMIZED","PARALLEL","DIAGNOSTIC",{"masking":29,"maskingDescription":23,"whoMasked":23},"NONE","Randomized, prospective selection of patients. Control group involves radiologists reporting chest radiographs as per reference standard clinical workflow Intervention group involves radiologists assisted with AI reporting an AI-triaged worklist of chest radiographs using an AI report generation tool Clinical outcomes on patients are studied at pre-determined study endpoints, including time to discharge from the hospital and re-admission rates.\n\nEconomic analysis on cost-avoidance from man-hours saved from report generation and triage.",[32,36],{"label":33,"type":34,"description":35,"interventionNames":23},"Control Arm","NO_INTERVENTION","Chest radiographs reported with AI assistance",{"label":37,"type":38,"description":39,"interventionNames":40},"AI assisted","ACTIVE_COMPARATOR","AI assisted detection, triage and reporting of CXR",[41],"Diagnostic Test: AI",[43],{"type":44,"name":45,"description":46,"armGroupLabels":47,"otherNames":23},"DIAGNOSTIC_TEST","AI","Artificial intelligence triage and reporting system",[37],{"type":49,"investigatorFullName":50,"investigatorTitle":51,"investigatorAffiliation":5,"oldNameTitle":23,"oldOrganization":23},"PRINCIPAL_INVESTIGATOR","Charlene Liew","Clinical Assistant Professor","100551139",false,"NCT06456203","Trial of Artificial Intelligence for Chest Radiography","Artificial Intelligence for Chest Radiography: Impact on Economics, Patient Outcomes and Radiology Service Delivery","ACER","Inclusion Criteria:\n\n* All patients attending radiography to have chest radiographs during the study period\n\nExclusion Criteria:\n\n* age below 14\n* deceased before discharge\n* chest radiograph performed in non-standard projections",true,"ALL","14 Years","130 Years",{"count":64,"type":65},10000,"ESTIMATED","INTERVENTIONAL",[68],"NA","Randomized Clinical Trial of the impact of Chest radiograph AI-assisted triage and report generation upon clinical outcomes and an economic analysis of impact of AI decision support on radiology service delivery.",[71,72],"Pneumonia","Lung Cancer",[74,75,76,77],"Radiology","artificial intelligence","triage","radiography","NOT_YET_RECRUITING","2024-06-11",{"date":81,"type":82},"2024-06-13","ACTUAL",{"date":84,"type":65},"2024-10",{"date":86,"type":65},"2025-12",{"name":5,"class":6}]