Validation and Implementation Design of an Epic Systems 30-day Unplanned Readmission Risk Model

Overseen By Brad Rowland, MD

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Wake Forest University Health Sciences

Disqualifiers: Psychiatric diagnoses, AMA, planned readmission, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

68 Participants Needed

Trial Summary

What is the purpose of this trial?

This trial is testing a software tool that helps doctors identify patients who are likely to need to come back to the hospital after being discharged. The tool calculates a risk score and suggests additional care services for high-risk patients. The goal is to reduce unplanned readmissions by providing extra support to those who need it most.

Do I need to stop my current medications for this trial?

The trial information does not specify whether you need to stop taking your current medications. It seems to focus on assessing a tool for identifying patients at risk of readmission, so it might not require changes to your medication.

What data supports the effectiveness of the treatment Epic 30-day unplanned readmission risk score?

The Epic 30-day unplanned readmission risk score has been shown to effectively predict which patients are at higher risk for readmission, with a predictive capability reflected in an AUC/C-statistic of 0.716-0.760 for all patients. This suggests it can help healthcare providers identify and potentially reduce unplanned readmissions by alerting them in real-time before discharge. ^{1 2 3 4 5}

Is the Epic 30-day unplanned readmission risk score safe for humans?

The Epic 30-day unplanned readmission risk score is a tool used to predict the likelihood of a patient being readmitted to the hospital. It does not involve any direct treatment or medication, so there are no safety concerns for humans associated with its use. ^{1 2 4 6 7}

Research Team

Donna Williams, MD

Principal Investigator

Wake Forest University Health Sciences

Eligibility Criteria

Inclusion Criteria

meeting inpatient criteria

Providers on the hospitalist or general medicine teaching services

Patients of Atrium Health Wake Forest Baptist Hospital (AHWFBH)

Exclusion Criteria

died during index admission

Providers who opt out of the study

planned readmission

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Implementation

Providers are randomly allocated to intervention or control groups, with the intervention group receiving the Epic 30-day unplanned readmission risk score in their workflow

4 weeks

Ongoing monitoring during hospital stay

Follow-up

Participants are monitored for the number of transitional and supportive care services (TSC) referrals and provider satisfaction with the clinical decision support tool

4 weeks

Treatment Details

Interventions

• Epic 30-day unplanned readmission risk score

Participant Groups

Group I: intervention group (IG) Experimental Treatment 1 Intervention

The IG will consist of placing the Epic 30-day unplanned readmission risk score in the story board of providers - If a patient is identified as having a high risk of readmission defined as readmission risk \>20%, a link to transitional and supportive care services (TSC) will be appear in the story board

Group II: control group (CG) Active Control 1 Intervention

The CG will have the same transitional and supportive care services (TSC) orders available to them; however, they will not have the risk of readmission score present in their workflow

Who Is Running the Clinical Trial?

Wake Forest University Health Sciences

Lead Sponsor

Trials

1,432

Recruited

2,506,000+

References

1. United States pubmed.ncbi.nlm.nih.gov

External validation of EPIC's Risk of Unplanned Readmission model, the LACE+ index and SQLape as predictors of unplanned hospital readmissions: A monocentric, retrospective, diagnostic cohort study in Switzerland. [2021]

2. Switzerland pubmed.ncbi.nlm.nih.gov

Implementation and Continuous Monitoring of an Electronic Health Record Embedded Readmissions Clinical Decision Support Tool. [2020]

3. England pubmed.ncbi.nlm.nih.gov

Measuring readmissions: focus on the time factor. [2019]

4. United States pubmed.ncbi.nlm.nih.gov

Risk Factors Associated with Unplanned Hospital Readmissions in Adults with Cancer. [2015]

5. England pubmed.ncbi.nlm.nih.gov

Are readmissions avoidable? [2019]

6. United States pubmed.ncbi.nlm.nih.gov

What Are They Worth? Six 30-Day Readmission Risk Scores for Medical Inpatients Externally Validated in a Swiss Cohort. [2021]

7. Brazil pubmed.ncbi.nlm.nih.gov

Development and validation of predictive model for long-term hospitalization, readmission, and in-hospital death of patients over 60 years old. [2022]