Spain's collaborative strategy for artificial intelligence projects in healthcare provides a model for the rest of Europe as the continent rolls out the European Health Data Space (EHDS), according to a leading official from the country's National Health System. Álvaro Alonso Zorita, speaking at HIMSS26 Europe, said the Spanish approach of bringing together regional health systems, researchers, and technology partners has accelerated real-world clinical AI adoption.
The structure sidesteps fragmentation by aligning data standards and use cases across autonomous communities before scaling nationally. That coordination now offers a template for the EHDS, which aims to create a single market for health data across EU member states.
How Spain built a collaborative AI framework
Spain's health system operates through 17 regional services, each with independent IT infrastructures and clinical datasets. Rather than mandate top-down AI tools, national agencies created shared governance groups that set common technical requirements. Hospitals and research centres then co-developed AI for Healthcare applications that work across different electronic health record systems. The approach prioritised semantic interoperability-making sure data means the same thing from one region to another-before deploying algorithms.
"When you solve data harmonisation first, the AI implementation is much faster and safer," Zorita said. "We didn't start with the model. We started with the data infrastructure."
Lessons for the European Health Data Space
The EHDS legislation, now moving into implementation, sets rules for how health data can be accessed and reused for care delivery, research, and policy. Its success depends on consistent data quality and cross-border interoperability, challenges Spain already faced internally. Zorita pointed to the country's early investment in a minimum data set for clinical reports as a critical step that EHDS architects can replicate. Without that shared language, AI models trained in one hospital often fail in another.
Interoperability as the foundation, not the afterthought
Many health systems treat interoperability as a technical checklist item, but Spain's experience shows it must be the design principle. The work directly affects how clinical teams exchange information and how AI tools interpret patient records. For healthcare professionals managing electronic health data, the shift toward structured, standardised records changes daily workflows. Training that focuses on health information exchange and clinical data management-such as the AI Learning Path for Medical Records Clerks-can help staff adapt to the growing emphasis on data quality and machine-readable documentation.
Why this matters for healthcare professionals
Clinicians, IT staff, and data stewards will feel the EHDS's requirements long before patients notice any new service. Health records that lack consistent coding will limit the usefulness of AI decision-support tools. Professionals who understand the link between documentation standards and downstream AI performance will be better positioned to advocate for workflow changes and secure funding for data quality initiatives now, before the regulatory deadlines force rushed compliance.
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