Rush University Medical Center provides care for more than 300,000 patients per year, and boasts annual growth of its EMR data at 10TB. Until moving to the cloud, Rush’s on-premises infrastructure struggled to keep up with its data and application growth.
That was a huge problem because to innovate solutions based on machine learning (ML) and artificial intelligence (AI) Rush and other healthcare organizations must first manage this exponential growth in data.
In this case study, attendees will learn that in addition to data, AI enablement requires agility and elasticity of infrastructure. Additionally, Rush’s data and analytic leaders will explain why, for their advanced analytics platform, they chose to adopt a mix of cloud IaaS and PaaS offerings, an Hadoop distribution and cloud data lake.
• Security is critical for cloud deployment and must be built from the ground up to satisfy compliance requirements.
• How to use the cloud to predict cost and understand care gaps in geographies of interest for intervention.
• Cloud infrastructure is not necessarily cheaper than traditional data center. Rush will share learned lessons on how it used metered features of cloud to be cost effective.