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Seton Healthcare uses IBM Content and Predictive Analytics to improve care & lower CHF readmissions

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Published on Mar 2, 2012

What if you could augment the wisdom of care providers with the scope and speed of powerful analytics?

Seton Healthcare relies on IBM Content and Predictive Analytics to identify high-risk congestive heart failure (CHF) patients for interventive care to avoid preventable readmissions. Natural language processing enables analysis of both structured (i.e. lab results) and unstructured data (i.e. physician notes, discharge summaries), opening the door to rich clinical and operational insights that were hidden in inaccessible free text files. Seton can now identify trends and patterns in patient care and outcomes, uncovering sometimes obscure correlations or disparities buried in years of medical records; these can dramatically improve diagnosis and treatment. For instance, doctors can identify when a patient checks "non-smoker" and then conveys that they are "trying to quit smoking", which medications are being prescribed but not taken and other wild cards—such as lifestyle or living conditions—that may affect a patient's well being. This forecasting of risk enables the care team to choose the best treatment options and apply early interventions to prevent avoidable re-admissions.

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