[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100646565":3},{"organization":4,"armGroups":7,"interventions":20,"overallOfficials":31,"centralContacts":35,"locations":41,"responsibleParty":59,"collaborators":26,"id":62,"slug":63,"hasResults":64,"nctId":65,"briefTitle":66,"officialTitle":66,"acronym":26,"eligibilityCriteria":67,"healthyVolunteers":64,"sex":68,"minAge":69,"maxAge":26,"enrollmentInfo":70,"targetDuration":26,"studyType":73,"phases":74,"briefSummary":76,"conditions":77,"keywords":79,"overallStatus":82,"whyStopped":26,"lastUpdateSubmitDate":83,"lastUpdatePostDateStruct":84,"startDateStruct":87,"completionDateStruct":89,"leadSponsor":91,"locationsCount":92},{"fullName":5,"class":6},"University of Michigan","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"Control Arm\u002FStandard of care","ACTIVE_COMPARATOR","The Control Arm, in which patients with a LACE Index score of 9 or greater on the day of hospital discharge will receive TOC interventions. This is the currently implemented allocation policy at Michigan Medicine. In this setting, approximately 60% of patients receive the TOC intervention. Thus, we will use a threshold of the 40th percentile for assigning the TOC telephone call bundle. Note that to maintain the 60% intervention rate throughout the study, the score threshold (LACE 9 or greater) will be monitored and dynamically adjusted. The LACE Index has four components: Length of Stay (L), Acuity of the Admission (A), Comorbidities (C), and Emergency Department Visits (E), and estimates the risk of a patient having an unplanned hospital readmission after being discharged from their current encounter.",[13],"Other: The LACE Index TOC assignment",{"label":15,"type":16,"description":17,"interventionNames":18},"Experimental Arm","EXPERIMENTAL","The Experimental Arm, in which Causal-Hospital reAdmission Risk Prediction Model (C-HARP) scores will be used to allocate TOC interventions. C-HARP is a machine learning model that leverages routinely collected and stored patient data in the electronic health record (EHR) to estimate how much a patient will benefit from receiving MM's TOC telephone call bundle. The assignment threshold will be defined as the 40th percentile of C-HARP scores within the most recent 100 days of the target cohort, so that approximately 60% of patients are assigned the telephone call bundle. Based on current retrospective data, this threshold corresponds to a C-HARP score of 18.",[19],"Device: C-HARP TOC assignment",[21,27],{"type":22,"name":23,"description":24,"armGroupLabels":25,"otherNames":26},"DEVICE","C-HARP TOC assignment","The Experimental Arm will allocate TOC interventions based on scores generated by the Causal-Hospital reAdmission Risk Prediction Model (C-HARP), C-HARP is a linear model that leverages routinely collected and stored patient data in the electronic health record (EHR) to estimate how much a patient will benefit from receiving Michigan Medicine's (MM's) TOC telephone call bundle.",[15],null,{"type":6,"name":28,"description":29,"armGroupLabels":30,"otherNames":26},"The LACE Index TOC assignment","The LACE Index has four components: Length of Stay (L), Acuity of the Admission (A), Comorbidities (C), and Emergency Department Visits (E), and estimates the risk of a patient having an unplanned hospital readmission after being discharged from their current encounter.",[9],[32],{"name":33,"affiliation":5,"role":34},"Jenna Wiens, PhD","STUDY_CHAIR",[36],{"name":37,"role":38,"phone":39,"phoneExt":26,"email":40},"Stephanie Shepard, PhD","CONTACT","734-647-1098","sdokeefe@umich.edu",[42],{"facility":43,"status":26,"city":44,"state":45,"zip":46,"country":47,"countryCode":48,"cosmosGeoPoint":49,"geoPoint":54,"contacts":55},"The University of Michigan","Ann Arbor","Michigan","48109","United States","US",{"type":50,"coordinates":51},"Point",[52,53],-83.74088,42.27756,{"lat":53,"lon":52},[56,57],{"name":37,"role":38,"phone":39,"phoneExt":26,"email":40},{"name":33,"role":58,"phone":26,"phoneExt":26,"email":26},"PRINCIPAL_INVESTIGATOR",{"type":58,"investigatorFullName":60,"investigatorTitle":61,"investigatorAffiliation":5,"oldNameTitle":26,"oldOrganization":26},"Jenna Wiens","Associate Professor of Electrical Engineering and Computer Science","100646565","evaluating-intervention-allocation-policies-for-reducing-hospital-readmissions-at-michigan-medicine-100646565",false,"NCT07690657","Evaluating Intervention Allocation Policies for Reducing Hospital Readmissions at Michigan Medicine","Inclusion Criteria:\n\n* Patient meets the primary care established rule at Michigan Medicine (MM), AND\n* Patient's primary care physician (PCP) Department is one of our General Medicine or Family Medicine or Medical-Pediatrics departments, AND\n* Inpatient class is Inpatient or Observation or Obs Greater than 48 hours or Outpatient in a Bed or Extended Recovery or Hospital Care at Home Inpatient or Hospital Care at Home Observation, AND\n* Discharged from General Medicine or Family Medicine or Medicine Observation services, AND\n* Disposition of home or home with home care\n\nExclusion Criteria:\n\n* Physician Organization of Michigan Accountable Care Organization (POM ACO)\n* Sepsis patients (had a diagnosis of sepsis during admission, active or resolved on the hospital problem list)","ALL","18 Years",{"count":71,"type":72},5000,"ESTIMATED","INTERVENTIONAL",[75],"NA","The researchers are investigating if using a risk-based prediction score or benefit-based prediction score to allocate transition of care (TOC) interventions is more effective in reducing the rate of unplanned hospital readmissions or death within 30 days of hospital discharge.",[78],"Transition of Care",[80,81],"Hospital readmission","death","NOT_YET_RECRUITING","2026-07-01",{"date":85,"type":86},"2026-07-08","ACTUAL",{"date":88,"type":72},"2026-07",{"date":90,"type":72},"2027-09",{"name":5,"class":6},1]