Cloud-Based Data Platform for Efficient Claims Management

Next-Gen Operational Efficiency for Healthcare

Transforming health

Nextgen claims

Healthcare and medical insurance is a 1.2 trillion-dollar industry with a 4.9% average growth per year.

As the burden of dealing with an ever-increasing number of claims prevents their employees from providing timely and efficient service, healthcare companies of all sizes are struggling with negative sentiment from customers and providers.

Consistent data management is key to transforming the claims process into a faster, more efficient, more accurate engine that drives customer satisfaction, cost savings, and employee productivity.

The challenge

Modern, modular, cloud-based, intelligent nextgen claims platform

Launch worked with a Fortune 50 healthcare company that has significant technical debt due to the array of legacy systems that manage data, including systems inherited via acquisitions. Besides being complex, difficult to support, and inflexible, these systems result in insurance claims processes that require heavy manual effort and are error prone. Well over a thousand employees were required to manually perform benefits plan configuration processing of more than 30 million claims per year.

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This company recognized their systems were outdated, costly to support, and wanted to become more flexible and efficient by developing a modern, cloud-based Claims Platform.

Because of the US healthcare system's inefficiency, administrative costs in the United States account for around 8% of their overall healthcare costs. In other developed countries, the average is between 1% and 3%.

Mastering data and improving quality using artificial intelligence

Provider data matching and validation

Transforming a health insurance industry leader with technology that automatically links and validates data within a data quality framework driven by natural language processing (NLP) and machine learning.

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The results

  • Dramatically reduced the need for manual intervention by surfacing low confidence records
  • Established trust in the system data
  • Organized data frameworks for maintaining quality over time
  • Enabled expedited system migrations
  • Improved the accuracy of the system data by fixing errors
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