Creating health analytics as a service

Health data is cumbersome. Privacy requirements are rigid, much information is still collected on paper and claims are difficult to process. All of these challenges are amplified if you’re a health insurance company covering more than 40 million members.

Taking into account the evolving landscape of healthcare data, a health insurance company wanted to find a way to provide health analytics as a service to its members. However, the methods of ingesting, curating and storing data in their existing platform was not robust or scalable enough to provide this new service. The lack of such items as standard design patterns, a consistent data architecture and coordinated governance was preventing them from building a new analytics business offering.


Starting small to achieve big outcomes

Starting small to achieve big outcomes

Working with IBM, the company implemented the IBM Digital Insights Platform (DIP) to plan and build a data lake in the cloud. This new resource would serve as the foundation for a subscription-based healthcare analytics solution for this insurer as well as future healthcare payers.

The platform enabled the health insurance company to start small in developing and migrating their existing analytic capabilities to the cloud, creating common design patterns for data ingestion and curation.

The final analytics platform was built to scale the company’s data foundation and architecture, and optimized to help onboard new products, analytic capabilities, and customers quickly and efficiently.

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Curating raw data into a common language

Curating raw data into a common language

The solution included a series of strategic steps. First, IBM determined an overall platform strategy and initial scope to help the company start small and then grow. The team developed a solution architecture based on DIP’s reference architecture for both the initial roll out and a more comprehensive future state.

Next, IBM developed a data lake for ingesting and curating raw data to create a common data model. Then they regression tested and migrated analytical models to the cloud platform. To validate the data sets and analytical models, IBM executed system integration and user acceptance testing.

IBM also created a deployment and onboarding strategy for integrating new products and customers into the cloud platform. This plan is designed to help the health insurer more quickly scale out the platform for future use cases.

The benefits

  • Ingested and profiled over 100 source files within three weeks
  • Built 23 curated data sets within three months
  • Delivered 14 new analytic capabilities in five months
  • Created a single cloud-based data lake and platform to speed long-term scalability

Case studies

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