Several key messages emerged from a series of expert meetings held in late April 2026 by the Cyprus Presidency of the European Union. Three have considerable relevance for the European Health Data Space (EHDS) and its future developments, and are especially close to implementers’ hearts:

• From policy to implementation to create value with the EHDS
• Integrating primary and secondary use of data e.g. for public health purposes
• Artificial intelligence (AI) as an enabler of this integration

Source: Cyprus Presidency

From policy to implementation to create value with the EHDS

EHDS success will be judged by measurable improvements. This too is the message of the new ESHIA alliance. EHDS improvements need to be seen as improving patient care, health systems performance, as well as the expansion/growth of European digital health and care industries.

• As a concrete example, the new ESHIA alliance is building a trusted home for interoperability standards – a platform where policy, innovation, and implementation come together to make health data flow effectively and securely across borders.
• In addition, in terms of the shift towards concrete implementation of the EHDS, learning cycles can work well. The “EHDS learning cycle” involves the integration of policy, technology, governance, and stakeholders.

Integrate primary and secondary use of data for e.g., public health purposes

Integration of health and care data is a key goal. There needs to be change away from the historic fact that primary and secondary uses of health data were kept separate – the different sources of data need to be brought together and made useful.

• A March 2026 work by the xShare project similarly highlights this message. More on these xShare outcomes is coming soon!

AI is an enabler of integration of data

AI can help “structurise” data. AI is essential for scaling up the secondary use of health and care data – especially where messy, “real-world” data is concerned.

• EHTEL’s working papers 2 and 3, and a further upcoming paper, can be of real help when looking at future uses of AI.

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Overall, to sum up, this identification of what is needed in terms of the integration of primary and secondary uses of data, AI, and important remaining challenges shows that there are many concrete areas in which implementers voices need to be heard, and on which implementers can work.