8,500 patients at risk for developing heart failure identified using Predictive Analytics

8,500 patients at risk for developing heart failure identified using Predictive Analytics: Heart failure currently afflicts more than five million U.S. adults, half of whom will not survive five years after diagnosis according to the Centers for Disease Control and Prevention. Heart failure is one of the most common causes of hospitalization for people age 65 and older, and costs the nation $32 billion each year.

8,500 patients at risk for developing heart failure identified using Predictive Analytics

IBM announced that Virginia health system Carilion Clinic has indentified 8,500 patients at risk for developing heart failure in a pilot project. The pilot was completed in collaboration with IBM, Epic and Carilion Clinic.

The results were achieved through predictive modeling of data in Carilion Clinic’s electronic medical record (EMRs), including “unstructured” data such as clinicians’ notes and discharge documents that are not often analyzed. Using IBM’s natural language processing technology to analyze and understand these notes in the context of the EMR, the inclusion of unstructured data provides a more complete and accurate understanding of each patient. The pilot applied content analytics and predictive modeling to identify at-risk patients with an 85 percent accuracy rate. The model identified an additional 3500 patients that would have been missed with traditional methods. Each of these patients might benefit from targeted preventive care.

Predictors included:
• Physiological data such as maximum systolic blood pressure
• Prescription drug use of alpha blockers, beta blockers, beta agonists, and others
• Previous diagnoses such as Chronic Obstructive Pulmonary Disease
• Obesity
• Lifestyle and environmental factors, such as occupation and marital status