Sutter Health, IBM, Geisinger Health System awarded $2 million
Sutter Health , IBM Research and Geisinger Health System were awarded a $2 million joint research grant by the NIH to develop new and sophisticated big data analytics and application methods that could help doctors detect heart failure years sooner than is now possible.
Heart disease is the leading cause of death, disability and costly hospitalizations in the U.S. One in five adults will develop heart failure, a type of heart disease that remains nearly impossible to detect early. About half of people who have heart failure die within five years of diagnosis.
Sutter Health, IBM and Geisinger Health System will use the NIH funding to develop practical and cost-effective early-detection methods for application in primary care practices with an electronic health record (EHR) system. The research aims to create a deeper understanding of how to use the data contained within EHRs and advanced analytics to help detect heart failure earlier and identify best practices that help health systems nationwide integrate big data analytics into primary care. This “Smarter Care” approach will help doctors and caregivers use evidence-based insights to better partner with patients and identify more tailored treatment options and holistic approaches to disease management personalized for each individual.
EHR data provides an expansive view of a patient’s health history that may include a variety of big data, including demographics, medical history, medication and allergies and laboratory test results. Sophisticated analysis of this data could help doctors identify a patient’s risk of heart failure and reveal signals and patterns indicative of such outcome. Once patients are identified as high-risk for heart failure, physicians can better monitor their status, help motivate a patient to make potentially life-saving lifestyle changes and test clinical interventions to potentially slow or possibly reverse heart failure progression.
“Heart failure will remain among our nation’s most deadly and costly diseases unless we discover new methods to detect the illness much earlier,” said Walter “Buzz” Stewart, Ph.D., MPH, chief research and development officer for Sutter Health and principal investigator for the project. “Sophisticated analysis of EHR data could reveal the unique presentation of these symptoms at earlier stages and allow doctors and patients to work together sooner to do something about it. Through this research we could transform how heart failure is managed in the future.”
“IBM is applying advanced tools for analyzing medical data, including text, and reviewing a patient’s health records for new insight,” said Shahram Ebadollahi, Ph.D., program director, health informatics research for IBM T.J. Watson Research Center and co-principal investigator for the project. “By pairing IBM’s expertise in Big Data analytics with the domain knowledge and data of our healthcare partners this project will result in the development of new analytic algorithms for more accurate detection of the early onset of heart failure. Ultimately, we hope to advance a smarter approach to care for patients with heart failure.”
Steve Steinhubl, M.D., a cardiologist member of the research team from Geisinger, said, “Our earlier research showed that signs and symptoms of heart failure in patients are often documented years before a diagnosis and that the pattern of documentation can offer clinically useful signals for early detection of this deadly disease. Now we have the technology to enable earlier diagnosis and intervention of serious conditions like heart failure, leading to better outcomes for patients.”
The NIH funding allows the team to look deeper into the progression of predictors of heart failure so clinicians can implement timely care-management plans to improve health outcomes. They will begin testing predictive methods for heart failure in clinical practice over the next several years. Their findings also may provide insights for providers to use EHR data to improve health outcomes for other chronic conditions.
The three parties began their initial research in 2009.