Better Big Data Analytics Leads to Improved Financial Growth and Better Patient Outcomes
Philips Wellcentive is a leader in delivering superior cloud-based population health management solutions to healthcare institutions, health systems, employers, and payers. The Philips Wellcentive platform processes data daily to create and update profiles of over 35 million patients. It analyzes data and derives insights from approximately 3 billion medical records for 60,000 managed care populations.
The platform enables health systems, providers, and payers to collaborate information, and build and implement comprehensive and effective care plans for patients. It also determines their level of health and qualification for particular services, programs, and payment models. This functionality results in better revenue growth, faster business transformation, and better clinical outcomes for patients.
The Challenge: Unreliable Data Capture Leading to Cluster Overload
Philips Wellcentive used MapReduce and deployed a Hadoop team to monitor and assess thousands of patient metrics while capturing incremental data from the previous day. The task, involving billions of patient medical records, ran for eight hours every night.
Leveraging a “slash and burn” methodology for incremental data capture, the Hadoop team had to perform absolute re-pull and analysis to document daily changes. The approach proved to be ineffective given the constant influx of new data and the large volume of temporary data generated. As the amount of temporary data increased, jobs began to fail.
The Solution: Pepperdata Platform Spotlight & Pepperdata Capacity Optimizer
Pepperdata Platform Spotlight allowed Philips Wellcentive’s Hadoop team to gather hundreds of metrics based on tasks, containers, users, jobs, and groups. Pepperdata Capacity Optimizer helped increase cluster throughput and accelerate operation processes. This enabled Philips Wellcentive to meet their daily revenue targets.
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