Welcome to the
Health Data Semantics Resource Hub
Across countries, regulators, partners, and internal systems, the same drug, condition, or clinical concept is often represented using different names, codes, and definitions. When data doesn’t share a consistent meaning, it becomes difficult to exchange information, connect datasets, or apply analytics and AI reliably.
These inconsistencies fragment data across organizations, create silos, and force teams to spend significant time reconciling data inconsistencies instead of generating insight.
This resource hub brings together expert perspectives and practical guidance to help you understand the health data semantics problem at the root of these issues — and how you can solve it.
What you'll explore:
- How data without semantics is fragmented
- The cost of manual reconciliation
- How AI at scale requires semantic alignment
- Health data semantics solutions
Browse expert insights and resources — no registration required.
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Health Data Semantics Information for All Levels of Experience
Select the option that best matches your experience with health data semantics, from basic concepts to advanced implementation strategies.
The Problem: Semantic Inconsistency Across Health Data
Healthcare organizations often use different terminology for the same concepts. Even something as common as Tylenol can appear under many different names. In this short video, see how semantic inconsistency creates major challenges for sharing and using health data.
The Hidden Cause of Data’s Biggest Problems
In this short video, see why health data semantics matters now. Inconsistent terminology across systems drains resources, slows time to market, complicates longitudinal analytics, and limits the potential of AI-driven innovation.
The Solution: How Harmonization Makes Data and AI Work
Organizations may use different terminologies, but their data can still be interoperable. Watch this short video and learn how to address semantic inconsistency in healthcare data.
How consistent meaning in data is a core enabler of trustworthy AI
This executive brief explains how inconsistent meaning in healthcare data slows analytics and AI — and outlines a practical path to fixing it. Ideal for senior leaders who need to quickly ramp up on this topic, and for data and analytics teams looking for a clear resource to educate stakeholders and drive alignment.
How to Harness Semantics in Health Data for AI Innovation at Scale
Learn how semantic interoperability breaks down data silos and enables the high-quality, structured data needed to power AI-driven innovation across healthcare.
IQVIA’s Center for Health Data Semantics
Learn how consistent, transparent, and logically linked terms and concepts can eliminate data confusion and create a stronger foundation for data innovation at scale.
AI Raises the Stakes: Why Semantics Can’t Be Ignored
AI is the future of healthcare innovation, but it can’t move forward without semantic alignment. See how health data semantics will allow you to scale AI with confidence.
SNOMED in the EHDS: From Regulatory Requirement to Strategic Advantage
The European Health Data Space (EHDS) turns interoperability from a “nice-to-have” into a strategic advantage. Learn why SNOMED CT is fundamental for interoperability in European healthcare, how organizations can move it from mandate to measurable value, and what IQVIA can do to provide the platform and expertise needed to reduce risk and accelerate SNOMED CT implementation.
IQVIA Health Data Semantics Suite
Discover the future of healthcare terminology management
This fact sheet introduces the IQVIA Health Data Semantics Suite, a set of tools that helps healthcare and life sciences organizations standardize and manage complex healthcare terminologies. It shows how aligning data with global standards can improve interoperability, support analytics and AI, and unlock greater value from healthcare data.
