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Evaluating Intervention Allocation Policies for Reducing Hospital Readmissions at Michigan Medicine
Sponsor: University of Michigan
Summary
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.
Key Details
Gender
All
Age Range
18 Years - Any
Study Type
INTERVENTIONAL
Enrollment
5000
Start Date
2026-07
Completion Date
2027-09
Last Updated
2026-07-08
Healthy Volunteers
No
Conditions
Interventions
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.
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.
Locations (1)
The University of Michigan
Ann Arbor, Michigan, United States