Big data sets sights on heart disease
The National Institutes of Health has given a $2 million research grant to IBM, Sutter Health and Geisinger Health System as they partner to develop data analytics tools to help physicians detect heart failure sooner.
The funding will go towards development practical and cost-effective early-detection methods for primary care practices with an electronic health record system, officials say. The health systems hope to arrive at a deeper understanding of how to use the data contained within EHRs and advanced analytics to help detect heart failure earlier.
Another goal is to look for ways to help other hospitals and health systems integrate big data strategies into primary care, helping doctors and caregivers use evidence-based insights to better partner with patients and identify more tailored treatment options and holistic approaches to disease management that are personalized for each individual.
With EHR data offering an expansive view of a patient's health history – including demographics, medical history, medication and allergies, laboratory test results, and more – it's hoped that more sophisticated analysis of this data could help doctors identify patient's risk of heart failure and reveal signals and patterns that are indicative of such outcome, officials say.
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, chief research and development officer for Sutter Health and principal investigator for the project, in a press statement.
"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," he said. "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, program director of health informatics research for IBM's T.J. Watson Research Center and co-principal investigator for the project. His hope is to arrive at "new analytic algorithms for more accurate detection of the early onset of heart failure," he said.
IBM, Geisinger and Sutter first began their research in 2009. Their areas of focus was prediction modeling using EHR data, tackling challenges, strategies, and a comparison of machine learning approaches; automatic identification of heart failure diagnostic criteria using text analysis of clinical notes from electronic health records; and combining knowledge and data driven insights for identifying risk factors using electronic health records.
The NIH funding allows the team to look deeper into the progression of factors that are 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 may also provide insights for providers to use EHR data to improve health outcomes for other chronic conditions.
"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," said Steve Steinhubl, MD, a cardiologist member of the research team from Geisinger. "Now we have the technology to enable earlier diagnosis and intervention of serious conditions like heart failure, leading to better outcomes for patients."
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