AI-driven innovation and a robust focus on data unification are reshaping the global healthcare landscape, as demonstrated by TELUS Health and Quantiphi during their session at Google Cloud Next 2026 in Las Vegas.

Breaking Down Healthcare Data Silos

Healthcare has long struggled with fragmented data systems. Each stakeholder—patients, providers, payers, and employers—sits on disparate datasets, creating inefficiencies and bottlenecks. As Fawad Shaikh, general manager of TELUS Health’s Health Data Office, noted, these so-called "data silos" were historically accepted as the cost of doing business.

However, the tide is turning in 2026. Shaikh emphasized that outdated practices are no longer acceptable, particularly in the wake of COVID-19. The pandemic laid bare the critical need for streamlined data sharing within healthcare ecosystems, forcing governments and organizations to reevaluate long-standing barriers. Regulatory advancements, such as the implementation of Canada’s digital health standards in Nova Scotia and the U.S. Centers for Medicare & Medicaid Services' (CMS) FHIR framework, have set the stage for transformation. These frameworks promote secure, efficient data sharing while prioritizing privacy and compliance.

For TELUS Health, Shaikh clarified, this shift is not just about technology. "It’s about meeting the demands of the healthcare ecosystem—improving outcomes for patients while reducing administrative burdens for providers," he stated.

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Quantiphi’s Role: Building a New “Nervous System” for Healthcare

Adding to the discussion, Quantiphi’s Saurabh Mishra, global head of Google Cloud business, elaborated on the technical complexities involved in transforming healthcare data systems. Far from being a simple data consolidation exercise, Quantiphi worked with TELUS Health to create what Mishra described as "a new nervous system for a global healthcare platform."

This ambitious project required tackling three critical components:

1. Data Foundation Expertise: Ensuring that the underlying datasets are organized, reliable, and interoperable.
2. Advanced Platform Engineering: Re-architecting systems to enable vertical integration across silos while maintaining horizontal scalability.
3. Healthcare-Specific Knowledge: Understanding the intricate workings of personal health information (PHI), claims processes, and provider network ecosystems.

Using its proprietary AI platform, Codeaira, Quantiphi dramatically accelerated processes like data migration and integration of legacy systems. Unlike traditional engineering workflows, Codeaira automated much of the pipeline creation and eased the consolidation of multiple datasets, allowing TELUS Health to scale operations swiftly and securely.

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From Data Consolidation to True Unification

The team’s efforts extended beyond mere consolidation. Borrowing from accepted terminology in computer science, Shaikh and Mishra described "data unification" as the more complex process of harmonizing datasets to create meaningful, actionable insights. For TELUS Health, this challenge was exacerbated by their global scope and the acquisition of multiple companies over the years, each bringing its own legacy systems.

Shaikh explained their approach: "We started modestly, focusing on simple use cases like reporting. We consolidated our data, implemented master data management strategies, and standardized ingestion pipelines." This incremental strategy allowed them not only to centralize their systems but also to ensure contextual semantic understanding, critical when healthcare providers and patients operate across borders.

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Use Cases: AI Beyond Search

True to its theme, the session delved into AI’s role as more than just a sophisticated search tool. Instead of merely retrieving data, AI in TELUS Health’s ecosystem enables reasoning and decision-making. Examples included:

1. E-Fax Management: Machine learning algorithms were employed to structure incoming e-faxes, match them to patient records, and build missing profiles. This automation halved the processing workload, freeing medical staff for more urgent tasks.
2. Automated Payment Processing: Leveraging microservices, TELUS Health streamlined direct deposit procedures for provider payments, effectively eliminating third-party services altogether. This improved efficiency and reduced costs.

Both use cases relied on reusable data models—segmented datasets optimized for specific uses—that allowed the AI to scale horizontally and vertically across the organization.

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Addressing the AI Adoption Gap

Though impressive, these advancements required careful planning. Mishra stressed that organizations often jump straight to AI-focused initiatives without first addressing underlying data challenges. "A rock-solid data strategy is the foundation for any successful AI strategy," Mishra said. By curating high-quality datasets, TELUS Health built scalable data models that function as reusable building blocks for future AI-driven solutions.

Both speakers underscored the need for rigorous governance to prevent AI hallucinations (a phenomenon where AI generates false or misleading outputs). To mitigate risks, teams established clear guardrails and constraints—ensuring context-driven, high-quality outputs.

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Balancing Innovation With Core Modernization

As the session closed, both Shaikh and Mishra offered insights for other organizations looking to replicate TELUS Health’s success. Shaikh advocated for a unified data mission, where grassroots innovation complements top-down leadership. Success hinges on making data and AI an accessible conversation across every level of an organization.

"Leadership must guide where technology is deployed for impactful outcomes, and grassroots teams must embrace the tools to modernize their workflows," Shaikh explained. Mishra, on the other hand, highlighted the importance of taking a launchpad-mindset approach, solving data challenges first before scaling to AI-focused use cases.

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A Clear Path Forward

The collaboration between TELUS Health and Quantiphi offers powerful lessons in how data readiness, AI, and intelligent platform engineering can propel healthcare into the modern era. By addressing long-standing silos and focusing on unification, they’ve not only set a precedent for scalability but have also prioritized better health outcomes and efficient processes.

Google Cloud Next 2026 served as the perfect venue for showcasing these achievements, demonstrating that with the right strategy, the healthcare sector can confidently advance toward a future supported by transformative, ethically governed AI.