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Evaluating Intervention Allocation Policies for Reducing Hospital Readmissions at Michigan Medicine

NCT07690657 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 5000

Last updated 2026-07-08

No results posted yet for this study

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.

Conditions

  • Transition of Care

Interventions

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.

OTHER

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.

Sponsors & Collaborators

Principal Investigators

  • Jenna Wiens, PhD · University of Michigan

Study Design

Allocation
RANDOMIZED
Purpose
PREVENTION
Masking
TRIPLE
Model
PARALLEL

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-07-31
Primary Completion
2027-08-31
Completion
2027-09-30
FDA Device
Yes

Countries

  • United States

Study Locations

More Related Trials

Entities

Companies

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07690657 on ClinicalTrials.gov