Health Data Analytics
Health Data Analytics Institute (HDAI), an innovator in healthcare predictive analytics solutions, today announced that their Population Health Production Environment and Longevity Production Environment hosted at Amazon Web Services (AWS) have earned Certified status for information security by HITRUST.
“Our partners count on us to meet complex compliance and privacy requirements that include technical and process elements,” said Nassib Chamoun, President and CEO at HDAI. “We are proud to be one of the first predictive analytics companies to achieve HITRUST Risk-based, 2-year Certification. This is a tangible demonstration of our commitment to the highest standards for data protection and information security.”
HDAI’s certified Population Health and Longevity environments support their versatile analytic platform. The company’s mission is to create a shared understanding of quantified health risks to inform actions with the greatest potential to benefit patients. The company uses advanced statistical models for predicting patient and population risks of adverse events, utilization, and developing chronic conditions to prevent avoidable encounters and improve the quality of patients’ lives.HDAI has pioneered a method for performance comparison based on clinically matched digital twins to understand individual and population risk more precisely, while protecting and securing health data.
“The HITRUST Assurance Program is the most rigorous available, consisting of a multitude of quality assurance checks, both automated and manual,” said Bimal Sheth, Executive Vice President, Standards Development & Assurance Operations, HITRUST. “The fact that Health Data Analytics Institute has achieved HITRUST Risk-based, 2-year Certification attests to the high quality of their information risk management and compliance program.”
Health Data Analytics Institute (HDAI) is an analytics company with a versatile analytic platform that creates a shared understanding of quantified health risks and personalized care profiles to inform actions with the greatest potential to benefit patients. The predictors are built on extensive underlying data assets and risk modeling methodology refined over 20 years and validated in peer-reviewed articles from JAMA to PLOS One. For more information