Predictive Model Helps Prevent Congestive Heart Failure Hospitalizations


January 13, 2012

Clinical analytics company Humedica offers a predictive analytic model that identifies patients at high-risk for a congestive heart failure (CHF) hospitalization.  Designed to enable providers to identify high-risk CHF patients before they have been hospitalized, this tool can help reduce hospital admissions among the sickest, most costly patients in America. 

Developed with clinical data pulled directly from the electronic medical record (EMR), the Humedica system is more accurate and timely than other predictive analytic tools based upon claims-based risk predictors.

CHF is the most expensive and preventable cause of inpatient admissions in the United States, costing an estimated $35 billion per year.  Heart failure affects nearly 5 million patients nationwide with 500,000 new cases diagnosed annually.  Despite well-known and highly effective interventions, 40 percent of all Medicare patients with CHF are readmitted within 90 days.

For more information: