Epic Risk Score for Unplanned Hospital Readmissions

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

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests a tool designed to help doctors identify patients at high risk of returning to the hospital within 30 days after discharge. It aims to improve patient care by linking high-risk patients to existing support programs that help reduce readmissions. Participants will either have their risk score (Epic 30-day unplanned readmission risk score) visible to their doctors, who can then connect them to extra services, or will receive standard care without the risk score being shown. The trial seeks patients currently hospitalized at Atrium Health Wake Forest Baptist Hospital under general medicine or hospitalist care. As an unphased trial, this study offers patients the chance to contribute to innovative healthcare solutions that could enhance future patient care.

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 prior data suggests that this computerized clinical decision support tool is safe for identifying patients at high risk of unplanned readmission?

Research shows that the Epic 30-day unplanned readmission risk score helps predict which patients might need extra care to avoid early hospital readmission. Studies have found that it connects patients with care programs, reducing readmission rates by 14.3%. As a non-medicinal tool, it poses no side effects or safety concerns like those associated with drugs or surgeries. Instead, it provides information to help doctors and nurses make better decisions about a patient's post-hospital care. This makes it very safe, as it uses data to improve patient outcomes. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about this trial because it aims to refine how healthcare providers predict and manage the risk of patients being readmitted to the hospital within 30 days. Unlike standard care, where transitional and supportive services are available but not always utilized effectively, this trial introduces an Epic Systems risk score directly into the provider's workflow. This score highlights patients with a high risk of readmission, prompting timely interventions. The goal is to enhance patient outcomes by proactively connecting high-risk patients with necessary support services, potentially reducing the overall readmission rate.

What evidence suggests that this computerized clinical decision support tool is effective for reducing unplanned readmissions?

Research has shown that the Epic 30-day unplanned readmission risk score effectively predicts which patients might need to return to the hospital soon after discharge. In this trial, the intervention group will have this score placed in the providers' storyboard, enabling healthcare providers to identify high-risk patients and offer additional support and care. Other studies have confirmed that this tool accurately predicts readmissions, helping to reduce unexpected hospital returns. By using this score, doctors can focus on patients who need more help, potentially lowering the rate of unplanned readmissions. This tool aims to improve patient care and help keep patients out of the hospital after they go home. ^{2 4 6 7 8}

Who Is on the Research Team?

Donna Williams, MD

Principal Investigator

Wake Forest University Health Sciences

Are You a Good Fit for This Trial?

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 for a Trial Participant

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

What Are the Treatments Tested in This Trial?

Interventions

• Epic 30-day unplanned readmission risk score

How Is the Trial Designed?

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+

Citations

1. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC8589185/

External validation of EPIC's Risk of Unplanned Readmission ... The study endpoint was an unplanned 30-day readmission. Models were replicated using the original intercept and beta coefficients as reported. Otherwise, score ...

2. clinicaltrials.gov clinicaltrials.gov/study/NCT04306172

Validation of EPIC's Readmission Risk Model, the LACE+ ... Outcome 2: Patients with an unplanned readmission within 30 days of index hospitalization discharge date to the same hospital. An unplanned readmission was ...

3. jamanetwork.com jamanetwork.com/journals/jamanetworkopen/fullarticle/2836552

Electronic Health Record Interventions to Reduce Risk of ... Pooled results of additional 30-day readmission outcomes, including outcomes at 90 days and at 6, 12, and 24 months, can be found in Table 3 ...

4. bmcmedinformdecismak.biomedcentral.com bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-023-02200-9

Evaluating machine learning algorithms to Predict 30-day ... This study aims to develop a probability calculator to predict unplanned readmissions (PURE) within 30-days after discharge from the department of Urology.

5. himss.org himss.org/sites/hde/files/2022-02/30-day-readmissions-predictive-risk-model-jurong-health-campus.pdf

Predictive Risk Score for Reducing 30 Day Readmissions ... A risk score, developed using machine learning, predicts readmissions for hospitalized patients, enabling targeted interventions and reducing the 30-day ...

6. epicshare.org epicshare.org/share-and-learn/driving-down-readmissions-and-mortality

A Predictive Model Helps a Safety Net Hospital Reduce ... It connected these patients with standardized treatment and social care resources to help lower the rate of 30-day readmissions by 14.3%, retain ...

7. journals.plos.org journals.plos.org/plosone/article?id=10.1371/journal.pone.0258338

External validation of EPIC's Risk of Unplanned Readmission ... The main objective of this study is to externally validate the EPIC's Risk of Unplanned Readmission model and to compare it to the internationally, widely used ...

8. michigan.himss.org michigan.himss.org/sites/hde/files/media/file/2024/10/31/corewell-health-reducing-readmission-risk-_1.pdf

corewell-health-reducing-readmission-risk- ... The. LACE+ score predicts the patient's risk of mortality or urgent readmission within 30 days of discharge and is calculated using comorbidity ...