In healthcare, data is the backbone of patient management- from medical history and family records to reaching new members, a robust database is what lets a business move fast and serve people well. For a managed-care services provider carrying voluminous amounts of that data, the problem was that it lived in disparate systems, making it hard to access when it mattered most. The business had bigger ambitions: simplify and consolidate its application landscape, expand its member base, and get new, innovative healthcare products to market faster. But every one of those goals put pressure on a single function- quality, which needed to move at a pace the existing testing model simply couldn’t match. The client needed a trusted partner to help establish best practices and build an entirely new quality operating model, and turned to Ascendion to make it happen.
Ascendion designed a new Quality Engineering (QE) methodology and operating model using the AAVA Intelligent Test Automation (ITA) studio – taking a digital-first approach to completely shift the client’s testing model from Quality Assurance to Quality Engineering, so quality was built into the engineering process itself rather than tested out at the end.
The testing lifecycle was automated to shorten time-to-market for products that directly enhanced member care, while machine learning capabilities were introduced to bring next-generation data reconciliation across multiple data sources. AAVA ITA studio capabilities modernized test data generation for faster releases, and a data pattern analyzer solution was integrated to improve real-time data generation for testing – rounding out a full-cycle approach spanning test case development, test execution, test automation, and test data management.
Tech Stack:
60% elimination of non-value-added QA tasks and 20% increase in speed-to-market of products through an ML-based data pattern analyzer.
20% increase in test productivity.
$1.5 million in savings in 2021 through an automated reconciliation data solution.
$15 million in anticipated savings in the quality function over the next 3 years.
Helped the client meet growing business demands through faster time-to-market for testing cycles, while improving overall efficiency in delivering timely, effective care to members.
Better positioned the client for future acquisitions and expansion by reducing the time and effort spent testing and reconciling data.
