This brief is based on an analysis undertaken by EHTEL of a 2026 report produced by the WHO Regional Office for Europe, with Working Group 1 of its Strategic Partnership Initiative for Data and Digital Health (SPI-DDH). EHTEL has selected two areas from the report’s five areas of strategic value. They are: clinical documentation and workflow and predictive analytics and risk assessment. The latter relates to work on virtual digital twins.

This implementers’ brief is based on an analysis undertaken by EHTEL of a 2026 report produced by the WHO Regional Office for Europe. EHTEL has examined the 2026 report in depth, and translated the survey results into five key findings. Implications are laid out for implementers both today and in the immediate future. EHTEL adds further value to the 2026 report by identifying three top priorities for digital health leaders and identifying the road ahead for implementers.

This briefing paper deep dives into potential European-level expectations for data structures that would enable communication between electronic health record (EHR) systems and algorithm-based tools. It highlights that the data structures should be in line with FAIR data principles. The paper contains plenty of insights into data fluidity, FAIR data, artificial intelligence (AI)-based algorithms, use cases, typologies and samples of tools, and the large spectrum of actors/stakeholders to involve. It offers 10 key recommendations.

The EHDS regulation inevitably contained some blind spots in the face of the complexity of Europe’s healthcare delivery. These blind spots are now emerging and causing pain points for implementers. This working paper reflects on the EHR definition - often perceived as too broad - and offers perspectives on potential solutions from healthcare providers, industry, and Member States, and proposes policy recommendations to ensure a successful EHDS implementation. 

This working paper, prepared for Xt-EHR Joint Action, looks at the real requirements for meaningful interactions between electronic health records (EHRs) and AI solutions. Building on the EHDS, it highlights the importance of data fluidity and the role of FAIR data, data quality assurance, user-centred design, and governance. Achieving interoperable, trustworthy, and clinically meaningful AI supported clinical decision making requires comprehensive, multi-level strategies – from local hospital workflows up to European semantic frameworks – with human beings and AI working together to refine data ecosystems.