[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100549453":3},{"organization":4,"armGroups":7,"interventions":24,"overallOfficials":12,"centralContacts":34,"locations":12,"responsibleParty":44,"collaborators":12,"id":48,"slug":49,"hasResults":50,"nctId":51,"briefTitle":52,"officialTitle":52,"acronym":12,"eligibilityCriteria":53,"healthyVolunteers":54,"sex":55,"minAge":56,"maxAge":57,"enrollmentInfo":58,"targetDuration":12,"studyType":61,"phases":62,"briefSummary":64,"conditions":65,"keywords":12,"overallStatus":67,"whyStopped":12,"lastUpdateSubmitDate":68,"lastUpdatePostDateStruct":69,"startDateStruct":72,"completionDateStruct":74,"leadSponsor":76,"locationsCount":12},{"fullName":5,"class":6},"Boston Children's Hospital","OTHER",[8,13,19],{"label":9,"type":10,"description":11,"interventionNames":12},"Physician assessment before intervention","NO_INTERVENTION","No intervention. Physician is surveyed to provide their assessment of patient disposition.",null,{"label":14,"type":15,"description":16,"interventionNames":17},"Physician assessment after baseline model intervention","ACTIVE_COMPARATOR","Physician is shown a baseline model recommendation for patient disposition including description of factors driving the model prediction.",[18],"Diagnostic Test: Baseline model",{"label":20,"type":15,"description":21,"interventionNames":22},"Physician assessment after fairness-aware model intervention","Physician is shown a model recommendation form a model tuned for subgroup performance for patient disposition including description of factors driving the model prediction.",[23],"Diagnostic Test: Fairness-aware model",[25,30],{"type":26,"name":27,"description":28,"armGroupLabels":29,"otherNames":12},"DIAGNOSTIC_TEST","Baseline model","Model prediction of patient disposition including feature importance scores driving prediction.",[14],{"type":26,"name":31,"description":32,"armGroupLabels":33,"otherNames":12},"Fairness-aware model","Model prediction of patient disposition including feature importance scores driving prediction. This model has been tuned to minimize subgroup calibration errors.",[20],[35,40],{"name":36,"role":37,"phone":38,"phoneExt":12,"email":39},"William La Cava, PhD","CONTACT","4133200544","william.lacava@childrens.harvard.edu",{"name":41,"role":37,"phone":42,"phoneExt":12,"email":43},"Andrew Fine, MD","617-355-9696","andrew.fine@childrens.harvard.edu",{"type":45,"investigatorFullName":46,"investigatorTitle":47,"investigatorAffiliation":5,"oldNameTitle":12,"oldOrganization":12},"PRINCIPAL_INVESTIGATOR","William La Cava","Assistant Professor","100549453","effect-of-predictive-model-on-ed-physician-assessments-of-patient-disposition-100549453",false,"NCT06434220","Effect of Predictive Model on ED Physician Assessments of Patient Disposition","Inclusion Criteria:\n\n* Board certified emergency department attending physicians currently employed by Boston Children's Hospital\n\nExclusion Criteria:\n\n* Physicians are excluded from completely surveys for patients who they are currently caring for",true,"ALL","18 Years","65 Years",{"count":59,"type":60},10,"ESTIMATED","INTERVENTIONAL",[63],"NA","The goal of this study is to measure the impact of fairness-aware algorithms on physician predictions of ED patient admission. Using an experimentally validated machine learning model tuned for equitable outcomes, the investigators quantify the impact of model recommendations on ED physician assessments of admission risk in a silent, prospective study. The investigators survey ED physicians who are not currently caring for patients using live site data. To quantify the impact of the model on ED physician assessments of admission risk, the investigators collect physician assessments before and after consulting the (original or updated) model prediction.\n\nThe investigators measure ED physician adherence to model suggestions, along with the predictive accuracy and equity of downstream patient outcomes. The outcome of this study is an empirical measure of the extent to which fair ML models may influence admission decisions to mitigate health care disparities.",[66],"Patient Outcome Assessment","NOT_YET_RECRUITING","2026-04-07",{"date":70,"type":71},"2026-04-13","ACTUAL",{"date":73,"type":60},"2027-01-01",{"date":75,"type":60},"2027-09-01",{"name":5,"class":6}]