Central Data Lake Enables Revenue-Cycle Analytics for LabCorp

LabCorp, one of the largest healthcare diagnostics companies in the world, employs more than 50,000 staff worldwide and serves 220,000 clients, including doctor’s offices, hospitals, managed care organizations, and biotechnology and pharmaceutical companies, with leading-edge medical laboratory tests and services. LabCorp wanted to improve billings, claims, and management with visibility into the health of its revenue cycle using a central data lake. 

Challenge: LabCorp had a large mainframe environment to handle enormous volumes of customer billing data. Data was stored in silos, making it difficult to do the data correlation needed to produce billing and claims reports. It was too onerous and time-consuming for the business to ask simple questions about billing and claims; therefore, reports were only generated on a monthly basis. LabCorp wanted a faster way to process billing data in order to have a more real-time picture of its business.  

Solution: Zaloni helped LabCorp offload data processing to a data lake and added an OLAP (online analytical processing) layer, which provided a user-friendly front-end foundation for analytics on various datasets. Zaloni’s DataOps platform, Arena, provided complete end-to-end governance of LabCorp’s central data lake. Arena’s data catalog provided easy self-service access to business users so they can easily find, prepare and provision data to their preferred business intelligence (BI) tools without going through IT. 

Results: LabCorp is seeing multiple benefits from having a unified, historical view of billing data with third party, patient and client-billed data formats in a single data lake platform. LabCorp business users are able to report billing and claims status daily versus monthly, giving the company increased visibility into revenue cycle KPIs. Business users also are able to customize reports without needing to involve developers from the IT team.  The central data lake enabled teams to quickly discover unified data sets, collaborate for better insights, and accelerate data monetization projects.