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

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.

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

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