5 Big Data Production Examples in Healthcare

Healthcare costs are driving the demand for big data-driven healthcare applications. Technology decision-makers in healthcare systems cannot ignore the increased efficiencies, the attractive economics, and the rapid pace of innovation that can now be applied to delivering and paying for healthcare. Many are finding that new standards and incentives for the digitizing and sharing of healthcare data — along with improvements and decreasing costs in storage and parallel processing on commodity hardware — are causing a big data revolution in healthcare with the goal of better care at lower cost.  

The healthcare industry can benefit immensely from the use of advanced analytics and big data technologies, and the MapR Converged Data Platform offers the perfect solution. In this post, we will look at 5 big data production examples in healthcare. 

Valence Health is using the MapR Converged Data Platform to build a data lake that is the company’s main data repository. Valence consumes 3,000 inbound data feeds, with 45 different types of data, daily. This critical data includes lab test results, patient health records, prescriptions, immunizations, pharmacy benefits, claims and payments, and claims from doctors and hospitals, which are used to inform decisions about improving both healthcare outcomes and reimbursement. The company’s rapid client growth and the associated increasing volumes of data were straining its existing technology infrastructure.  

Prior to their MapR solution, if they received a feed with 20 million lab records, it would take 22 hours to process that data. MapR cut that cycle time down from 22 hours to 20 minutes, running on much less hardware. Valence Health is also now able to accommodate customer requests that were very difficult to address in the past. For example, a customer might call and say, “I sent you an incorrect file three months ago, and I need you to take that file out.” Their traditional database solution might take 3-4 weeks to get that data deleted. MapR snapshots provide point-in-time recovery that enables Valence to roll back and remove that file in minutes.  

UnitedHealthcare provides health benefits and services to nearly 51 million people. The company contracts with more than 850,000 physicians and care professionals and approximately 6,100 hospitals nationwide. Their Payment Integrity group has the tough job of ensuring that claims are paid correctly and on time. Their previous approach to managing more than one million claims every day (10 TB of data daily) was ad hoc, heavily rule-based and limited by data silos and a fragmented data environment. UnitedHealthcare came up with a unique dual model strategy, which meant focusing on operationalizing savings, while at the same time pursuing innovation to constantly leverage the latest technologies.  

Here’s how they are doing it: in terms of operationalizing savings, the group is building a predictive analytics “factory,” where they can identify inaccurate claims in a systematic, repeatable way. Hadoop is now the data framework for a single platform that’s equipped with tools to analyze a slew of information from claims, prescriptions, plan participants, contracted care providers, and associated claim review outcomes.  

They integrated all this data from multiple data silos across the business, including over 36 data assets. And they now have multiple predictive models (PCR, True Fraud, Ayasdi, etc.) at their fingertips that provide a rank-ordered list of potentially fraudulent providers they can pursue in a targeted, systematic way.  

Liaison Technologies provides cloud-based solutions to help organizations integrate, manage, and secure data across the enterprise.