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To bring order to test data chaos,

we hit HOME

In healthcare systems, bad test data doesn’t just slow testing. It skews results, creates compliance risks, and leaves teams scrambling to fix errors.

For one U.S. healthcare giant, manually generating data for medical, dental, and pharmacy testing had become unsustainable.

Manual processes introduced accuracy risks, slowed provisioning, and demanded heavy maintenance of custom-built tools.

Infosys Quality Engineering knew the fix wasn’t just faster tools. It was rethinking how test data is created, and controlled from the start.

The real question: Could AI turn this chaos into clean, on-demand, reliable data?

HOME for Reliable Data

Clean test data. On demand. No bottlenecks.

What got better

Prompt-based generation of member and dependent data using GPT LLMs + LangChain
Automated review, enrollment, and summary workflows
Human verification ensures accuracy before enrollment

What it led to

Testing moved faster: ~50% less effort to create and manage test data
Quicker provisioning, reducing delays for development and testing teams

More accurate, consistent data across medical, dental, and pharmacy domains
Lower maintenance overhead as manual processes and custom tools were minimized

Why it made
a difference

Teams regained control over test data quality and speed, enabling faster provisioning, more accurate and consistent data, and easier scaling of testing operations without additional setup resources.