The preventive power of analytics
Through predictive modeling of data in its electronic medical records (EMRs), the Carilion Clinic health system in Virginia has identified 8,500 patients at risk for developing heart failure.
The clinic’s pilot project involved using IBM’s natural language processing and predictive analytics technology to analyze data in EMRs, which included unstructured data such as clinicians’ notes and discharge documents that are not often analyzed. With the goal of identifying at risk patients for earlier intervention and better care, the pilot applied content analytics and predictive modeling to identify at-risk patients with an 85 percent accuracy rate. The model found an additional 3,500 patients that would have been missed with traditional methods, according to IBM.
Steve Morgan, M.D., chief medical information officer at Carilion Clinic, says, “We’ve learned that predictive analytics insights from both structured and unstructured data is imperative to meet our goal of improving patient care at lower costs. We were amazed at the accuracy and usability of IBM’s predictive modeling, which the IBM team developed and deployed in six weeks. These results and innovations are helping us move the needle on quality and the costs of care.”
Patients identified in the pilot as being at-risk for heart failure were expected to develop the disease within one year and are candidates for care management and early interventions. 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; and
- lifestyle and environmental factors, such as occupation and marital status.
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