GenAI Quality Engineering for Healthcare Diagnostics

Modernizing healthcare diagnostics via GenAI quality engineering

Replacing legacy testing constraints with a scalable, AI-led quality engineering model

10 min read

Overview

The organization is a Fortune 500 healthcare services leader specializing in diagnostic information that supports critical clinical decision‑making. Operating at a global scale, it enables essential testing across a wide range of complex disease areas while serving hospitals and care providers worldwide. To sustain these mission‑critical services, the company manages a large, sophisticated technology ecosystem designed for reliability, security and performance. Its operations play a vital role in advancing patient outcomes and supporting modern healthcare systems.

The Challenge

To maintain high standards of diagnostic accuracy while managing a massive volume of data, the customer identified several opportunities to modernize its application ecosystem:

Legacy platform dependencies: A significant portion of the application landscape relied on legacy platforms, requiring frequent and intensive manual reviews to ensure test cases remained relevant during updates.
UI-centric testing constraints: The testing strategy was predominantly focused on the User Interface (UI), which limited scalability and increased the effort required for maintenance.
Operational bottlenecks: The heavy reliance on UI-based validation led to longer application change cycles and reduced overall operational efficiency.
Limited API integration: A lack of broad API-level testing coverage created an imbalance that constrained the speed of functional enhancements.

The Solution

Guided by HCLTech’s Value Stream Mapping (VSM) approach that is powered by proven AI adoption service catalogue to infuse AI in the software testing lifecycle value chain, high-impact use cases were identified and prioritized to drive a comprehensive Testing and Quality Engineering transformation, shifting away from maintenance-intensive methods toward a scalable, AI-led model.

Key solution pillars:

API-first strategy: Transformed the testing framework by prioritizing API-level validation, significantly improving reliability and reducing reliance on UI-heavy testing.
GenAI and Agentic AI integration: Leveraged advanced AI platforms like GHCP to automate critical engineering tasks, including:
- Intelligent test design: Accelerated test design cycles and expanded coverage through AI-driven test case creation.
- Automated scripting: Generated standardized test scripts to ensure consistency across applications.
- Proactive analysis: Utilized AI for advanced code analysis to identify potential quality issues early in the development lifecycle.
AI-driven incident triage: Implemented intelligent triage to enable faster analysis and prioritization of defects and production incidents.
Phased rollout: Initiated the solution with a pilot program to validate measurable benefits before progressively scaling it across the broader application landscape.

The Impact

The shift to a GenAI-led Quality Engineering solution has delivered strong, measurable results in the pilot phase for three applications. The program is now scaling across 91 applications and benefit metrics will be updated at the end of the scale phase.

High trust and adoption of GenAI at scale, the program achieved a 77% GenAI adoption rate, with a 90% acceptance rate for AI-generated recommendations, enabling customers to confidently embed AI into quality engineering workflows and accelerate decision making with minimal resistance from engineering teams.
41% productivity improvement measured, delivering clear business value
- Test case maintainability reached 98%, while test automation coverage increased to 15%, significantly reducing regression risk and ensuring consistent quality across frequent application changes in the customer’s laboratory and testing systems.
- Faster validation: High throughput and improved efficiency enabled significantly faster validation cycles for critical application changes.
- AI-driven test case creation delivered teams to redirect effort toward higher value validation, innovation and continuous improvement initiatives.

Beyond the Numbers

HCLTech’s impact on customers goes beyond technical metrics, fostering a culture of high accountability and seamless collaboration. By integrating HCLTech experts directly with client teams, the engagement model ensured that priorities remained perfectly aligned and communication stayed transparent. This collaborative environment allowed for rapid feedback and proactive problem-solving, turning complex legacy challenges into opportunities for innovation. The result is an engineering ecosystem that is not only faster but also more sustainable, empowering customers to meet tight timelines for critical certifications and major performance-enhancing migrations with total confidence.

Celebrating Success

The partnership has consistently met major milestones, including successful high-stakes migrations of complex logic to modern architectures that improved both performance and cost-efficiency. Customer has highly valued the responsiveness and ownership demonstrated by HCLTech teams, particularly in supporting critical reporting and certification requirements under tight deadlines.

Looking ahead, HCLTech plans to expand this success by:

Increasing automation: Further scaling test and process automation coverage across new business initiatives.
Predictive analytics: Introducing AI/ML-driven analytics to anticipate and resolve potential issues before they impact the business.
Accelerating go-to-market: Continuously improving the end-customer experience by enabling faster releases of new features and digital channels.