Could data analytics mean a little less surgery in people's lives?[/caption] A new partnership between Aetna and GNS Healthcare aims to reduce incidences of heart disease, stroke and diabetes. GNS Healthcare specializes in data analytics for pharmaceutical companies, healthcare companies, hospitals, and other care-focused organizations. Under the terms of the partnership, it will feel claims and other health data from Aetna through its Reverse Engineering and Forward Simulation (REFS) platform, generating a model for determining individual risk of developing metabolic syndrome. REFS attempts to reveal the underlying causal relationships between various pieces of data, which GNS Healthcare claims makes it a more effective tool than those platforms that merely make associations between data. According to PubMed Health, metabolic syndrome is a name “for a group of risk factors that occur together and increase the risk for coronary artery disease, stroke and type 2 diabetes.” For the purpose of the model, Aetna and GNS Healthcare are defining an individual as having the syndrome if they meet three of five criteria, including large waist size, high blood pressure, high triglycerides, low HDL (‘good’) cholesterol, and high blood sugar. Using the individual’s health information, the model can make an educated guess about any conditions they might develop in the future. In GNS Healthcare’s own example, someone with sufficiently high triglycerides and low HDL has a high probability of experiencing high blood pressure within 12 months—a prediction that a physician can then use to tailor interventions that could head that condition off before it develops. “GNS has a long history of using REFS to understand the pathways for different conditions and treatments,” Carol McCall, Chief Strategy Officer for GNS Healthcare, wrote in a Sept. 26 statement. “We expect our models, built from the millions of distinct data elements Aetna provides, to reveal causal relationships among hundreds of variables and how they evolve over time that lead to actionable insights into precursors of metabolic disease.” Big Data and analytics are the subject of continuing debate among those in the health care community. While many see analytics as potentially making patient care more efficient, with consulting firm Frost & Sullivan recently predicting that hospitals and healthcare centers will adopt advanced analytics software in significant numbers over the next five years, significant concerns exist about cost and privacy. Indeed, regulations governing privacy—in addition to politics and policy—could determine the ultimate availability of data for analytics platforms. For its part, GNS Healthcare claims that the data used in its models “will be secure and comply with applicable privacy laws.” Image: Dmitriy Shironosov/Shutterstock.com GNS Healthcare, Aetna Team on Analytics to Combat Metabolic Syndrome
[caption id="attachment_4781" align="aligncenter" width="500"] Could data analytics mean a little less surgery in people's lives?[/caption] A new partnership between Aetna and GNS Healthcare aims to reduce incidences of heart disease, stroke and diabetes. GNS Healthcare specializes in data analytics for pharmaceutical companies, healthcare companies, hospitals, and other care-focused organizations. Under the terms of the partnership, it will feel claims and other health data from Aetna through its Reverse Engineering and Forward Simulation (REFS) platform, generating a model for determining individual risk of developing metabolic syndrome. REFS attempts to reveal the underlying causal relationships between various pieces of data, which GNS Healthcare claims makes it a more effective tool than those platforms that merely make associations between data. According to PubMed Health, metabolic syndrome is a name “for a group of risk factors that occur together and increase the risk for coronary artery disease, stroke and type 2 diabetes.” For the purpose of the model, Aetna and GNS Healthcare are defining an individual as having the syndrome if they meet three of five criteria, including large waist size, high blood pressure, high triglycerides, low HDL (‘good’) cholesterol, and high blood sugar. Using the individual’s health information, the model can make an educated guess about any conditions they might develop in the future. In GNS Healthcare’s own example, someone with sufficiently high triglycerides and low HDL has a high probability of experiencing high blood pressure within 12 months—a prediction that a physician can then use to tailor interventions that could head that condition off before it develops. “GNS has a long history of using REFS to understand the pathways for different conditions and treatments,” Carol McCall, Chief Strategy Officer for GNS Healthcare, wrote in a Sept. 26 statement. “We expect our models, built from the millions of distinct data elements Aetna provides, to reveal causal relationships among hundreds of variables and how they evolve over time that lead to actionable insights into precursors of metabolic disease.” Big Data and analytics are the subject of continuing debate among those in the health care community. While many see analytics as potentially making patient care more efficient, with consulting firm Frost & Sullivan recently predicting that hospitals and healthcare centers will adopt advanced analytics software in significant numbers over the next five years, significant concerns exist about cost and privacy. Indeed, regulations governing privacy—in addition to politics and policy—could determine the ultimate availability of data for analytics platforms. For its part, GNS Healthcare claims that the data used in its models “will be secure and comply with applicable privacy laws.” Image: Dmitriy Shironosov/Shutterstock.com
Could data analytics mean a little less surgery in people's lives?[/caption] A new partnership between Aetna and GNS Healthcare aims to reduce incidences of heart disease, stroke and diabetes. GNS Healthcare specializes in data analytics for pharmaceutical companies, healthcare companies, hospitals, and other care-focused organizations. Under the terms of the partnership, it will feel claims and other health data from Aetna through its Reverse Engineering and Forward Simulation (REFS) platform, generating a model for determining individual risk of developing metabolic syndrome. REFS attempts to reveal the underlying causal relationships between various pieces of data, which GNS Healthcare claims makes it a more effective tool than those platforms that merely make associations between data. According to PubMed Health, metabolic syndrome is a name “for a group of risk factors that occur together and increase the risk for coronary artery disease, stroke and type 2 diabetes.” For the purpose of the model, Aetna and GNS Healthcare are defining an individual as having the syndrome if they meet three of five criteria, including large waist size, high blood pressure, high triglycerides, low HDL (‘good’) cholesterol, and high blood sugar. Using the individual’s health information, the model can make an educated guess about any conditions they might develop in the future. In GNS Healthcare’s own example, someone with sufficiently high triglycerides and low HDL has a high probability of experiencing high blood pressure within 12 months—a prediction that a physician can then use to tailor interventions that could head that condition off before it develops. “GNS has a long history of using REFS to understand the pathways for different conditions and treatments,” Carol McCall, Chief Strategy Officer for GNS Healthcare, wrote in a Sept. 26 statement. “We expect our models, built from the millions of distinct data elements Aetna provides, to reveal causal relationships among hundreds of variables and how they evolve over time that lead to actionable insights into precursors of metabolic disease.” Big Data and analytics are the subject of continuing debate among those in the health care community. While many see analytics as potentially making patient care more efficient, with consulting firm Frost & Sullivan recently predicting that hospitals and healthcare centers will adopt advanced analytics software in significant numbers over the next five years, significant concerns exist about cost and privacy. Indeed, regulations governing privacy—in addition to politics and policy—could determine the ultimate availability of data for analytics platforms. For its part, GNS Healthcare claims that the data used in its models “will be secure and comply with applicable privacy laws.” Image: Dmitriy Shironosov/Shutterstock.com 