Health Care Technology
Company Overview of GNS Healthcare, Inc.
GNS Healthcare, Inc. provides analytics solutions for the healthcare industry. It offers Reverse Engineering and Forward Simulation Technology, a supercomputer-enabled framework that automates the extraction of causal network models directly from observational data and uses high-throughput simulations to generate new knowledge. The company also provides real-world outcomes solutions for data that comes from electronic health records, claims, clinical programs, and incentive and reward programs; product development solutions for biopharmaceutical, diagnostic, consumer product, and medical device companies; and publications, conference abstracts, and presentations. It serves pharmaceutical and...
One Charles Park
Cambridge, MA 02142-1254
Founded in 2000
Key Executives for GNS Healthcare, Inc.
Co-Founder, Chairman of the Board, Chief Executive Officer and President
Co-Founder and Executive Vice President
Chief Mathematics Officer
Compensation as of Fiscal Year 2015.
GNS Healthcare, Inc. Key Developments
GNS Healthcare, Inc. Announces Board Appointments
Sep 28 15
GNS Healthcare announced the expansion of its Strategic Advisory Board (SAB). Joining the SAB are Susan Weaver, MD, former Chief Medical Officer of Blue Cross and Blue Shield of North Carolina; Robert J. Perez, the former Chief Executive Officer of Cubist Pharmaceuticals, Inc.; Steve Eisenberg, MD, former Vice President, Healthcare Informatics for UnitedHealth Group subsidiary OptumHealth; and Timothy Thompson, Chairman and CEO of Intervention Insights. Dr. Weaver will assume a special role on the SAB, leading a Medical Care Management Task Force composed of clinical experts, including Dr. Eisenberg and Dr. Jan Berger, Vice Chair of the GNS Board of Directors and former Senior Vice President and Chief Clinical Officer for CVS Caremark.
GNS Healthcare, Inc. Presents at Boston Biotech Conferences 2015, Nov-12-2015 10:00 AM
Sep 22 15
GNS Healthcare, Inc. Presents at Boston Biotech Conferences 2015, Nov-12-2015 10:00 AM. Venue: Apella, 450 East 29th Street, NY 10016, New York, United States. Speakers: Iya Khalil, Co-Founder and Executive Vice President.
GNS Healthcare Introduces MAX for Metabolic Syndrome
Jun 3 15
GNS Healthcare announced the expansion of its big data-driven population health management suite of solutions with the introduction of MAX for Metabolic Syndrome. More than one-third of the U.S. population has metabolic syndrome, a disorder that can lead to chronic heart disease, stroke and diabetes. These conditions combine to account for almost 20% of overall healthcare costs in the United States. MAX for Metabolic Syndrome is the latest in a suite of population health management solutions from GNS. These GNS solutions are transforming population health management with a value-based approach that leverages precision medicine and transcends the traditional one-size-fits-all, rules-based model. GNS solutions analyze large, heterogeneous datasets to identify individuals who will benefit from programs to prevent disease progression, avoid complications, and improve individual health outcomes. The GNS solutions then match individuals to their personal optimal intervention. In contrast, rules-based approaches to population health rely on thresholds derived from averages across a population and, as a result, miss opportunities to deliver highly personalized care. MAX for Metabolic Syndrome predicts which of the five risk factors associated with metabolic syndrome will develop next in at-risk individuals. The GNS solution also matches each individual with the most efficient effective intervention to prevent the condition from developing. MAX for Metabolic Syndrome is based upon the unique GNS Meaningful Actions Accelerator (MAX) architecture. The MAX architecture leverages the company’s MeasureBase data warehouse and patented REFS machine learning and simulation platform to inform analytics models and discover new knowledge from large amounts of real-world data. GNS analytics models predict risk of negative outcomes, including adverse events, sub-optimal outcomes, and progression; quantify the effect of behavior changes; stratify populations based on individuals’ likelihood to engage; measure the power of interventions to change behavior; and match individuals to the most cost-efficient effective intervention.
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