How Cleveland Clinic Uses Predictive Analytics to Reduce Hospital Readmissions and Improve Patient Outcomes

By: GoBeyond Team

July 3, 2025

3 min read

Cleveland Clinic predictive analytics dashboard showing readmission risk scores

Quick Overview

Cleveland Clinic developed a predictive “readmission risk score” model using electronic medical record (EMR) data and 18 clinical and social variables to identify patients at high risk of hospital readmission within 30 days. The model supports personalized care planning and targeted interventions to reduce unnecessary readmissions, improve quality of care, and lower healthcare costs.

Cleveland Clinic

Company Size

~67,000 employees

Revenue Range

$12B+ annual revenue

Primary Challenge

Reducing hospital readmissions and optimizing patient care transitions

Key Metrics

\- 15.9% average 30-day readmission rate over 3 years\- 10% reduction in readmissions over 2 years\- Risk score identifies top 5% high-risk patients\- Consistent model performance across diagnoses and demographics

The Problem

High readmission rates increased costs and reduced patient satisfaction; manual risk assessment was inefficient

The Solution

Developed and continuously validated a predictive risk score model using EMR data, integrated into clinical workflows to guide discharge planning and follow-up

Results

\- Reduced readmissions by 10%\- Improved personalized care and resource allocation\- Enhanced physician awareness of readmission risks\- Maintained model accuracy during COVID-19 pandemic