IBM uses predictive modeling of data to reduce heart failure risks

IBM, Epic and Carilion Clinic have conducted a pilot program and indentified 8,500 patients at risk for developing heart failure.

In a statement, IBM said it achieved the result 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.

Early detection and prevention of heart failure has proven difficult prior to the introduction of advanced analytics.

IBM’s natural language processing technology – also used in the IBM Watson cognitive system – can understand information posed in natural language and uncover insights from vast amounts of data.

Coupled with advanced predictive modeling, the pilot at Carilion Clinic using IBM Advanced Care Insights marks another example of IBM’s leadership in advancing predictive care and prevention.  IBM Advanced Care Insights combines predictive modeling with healthcare-specific content analysis.

Sean Hogan, vice president of global healthcare, IBM, said: “By tapping into the unstructured data, our clients have more complete and accurate information that allows them to make targeted interventions when appropriate that can help prevent more severe and costly medical complications.”

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