September 25, 2017 |
Big Data Analytics Can Predict Individualized Risk of Metabolic Syndrome
July 1, 2014  | 

Cambridge, MAResearch published online in the American Journal of Managed Care demonstrates that analysis of patient records using state-of-the-art data analytics can predict future risk of metabolic syndrome - which affects more than a third of the U.S. population. The conditions metabolic syndrome can lead to - chronic heart disease, stroke and diabetes - combine to account for almost 20 percent of overall health care costs in the U.S. The study was conducted by Aetna and GNS Healthcare Inc. (GNS).

“This study demonstrates how integration of multiple sources of patient data can help predict patient-specific medical problems,” said lead author Dr. Gregory Steinberg, head of clinical innovation at Aetna Innovation Labs. “We believe the personalized clinical outreach and engagement strategies, informed by data from this study, can help improve the health of people with metabolic syndrome and reduce the associated costs.”

“The breakthrough in this study is that we are able to bring to light hyper-individualized patient predictions, including quantitatively identifying which individual patients are most at risk, which syndrome factors are most likely to push that patient past a threshold, and which interventions will have the greatest impact on that individual,” said Colin Hill, co-founder and CEO of GNS. “The GNS automated data analytics platform paired with Aetna’s deep clinical expertise produced these results on extremely large datasets in just three months, a testament to the ability of both groups.” Continue>

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