“Sentara is committed to delivering high quality healthcare and innovative services that meet the unique needs of the communities we serve,” said Michael Reagin, Sentara Healthcare senior vice president and chief information and innovation officer. “We collaborated with CitiusTech to develop an EDP that provides us more flexibility and scale to continue meeting the changing demands of our patients, care teams and partners across the care continuum.”
Sentara partnered with CitiusTech, a Microsoft partner, to architect, build and implement its EDP. It leveraged CitiusTech’s BigData solution H-Scale, to deploy an end-to-end data management platform to ingest, curate, transform and reconcile data from five different sources on a daily basis and create a 360-degree view of the patient record. Sentara built a robust enterprise-class system in less than two years gaining benefits of quick time to market along with significant implementation savings.
By implementing a cloud-first strategy, Sentara Healthcare has ensured that the EDP uses HIPAA compliant PaaS service offerings of Azure and can scale for large data volume. This is expected to save close to $1.5 million every year by moving away from an on-premises model.
“Next-gen interoperability and real-time data access have become imperative for healthcare organizations to enhance quality of care and align with value-based models.” Says Rizwan Koita, CEO of CitiusTech. “Sentara Healthcare with its cloud-first strategy has built an industry leading data platform using CitiusTech’s H-Scale on Microsoft Azure to support data-driven performance.”
Gareth Hall, director of business strategy for Worldwide Healthcare at Microsoft said, “Microsoft Azure enabled CitiusTech to deliver a cloud-based enterprise-wide healthcare data management solution. This enabled Sentara to get a holistic view of patient information across their enterprise. CitiusTech H-Scale, combined with Azure, helps customers achieve scale in healthcare data management.”
CitiusTech has further enabled Sentara to leverage aggregated information and generate actionable insights by deploying artificial intelligence and machine learning models at an enterprise scale. This is expected to save approximately $3 million a year through efficiency improvements across the organization.