Published on 13-Mar-2012
"IBM Content and Predictive Analytics allows us to extract data and present it to clinicians where they need it most and assist them in helping make decisions that will improve the quality of care." - Dr. David Ramirez
Enterprise Content Management, Smarter Analytics
Seton Healthcare is a not-for-profit organization, the Seton Family is the leading provider of healthcare services in Central Texas, serving an 11-county population of 1.8 million.
To significantly reduce the occurrence of high cost CHF readmissions by proactively identifying patients likely to be readmitted on an emergent basis.
Seton Healthcare 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.
• Proactively targeted care management and reduced re-admission of CHF patients. • Identified patients likely for re-admission and introduced early interventions to reduce cost, mortality rates, and improved patient quality of life.
Seton Healthcare Video: Brief Description
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.