EHDS standardizes access to health data across Europe but does not solve execution challenges on its own
The real bottleneck is slow, fragmented workflows, not lack of data
EHDS introduces secure processing environments, where data can be analyzed but not moved
The shift is from one-off data use to repeatable, scalable research execution
AI is critical to automate access, preparation, and validation workflows
Without operational readiness, organizations will not capture the value of EHDS
Turning European Health Data Space access into scalable, secure health data workflows
At the June 2026 AWS AI Health Leaders Forum in Zürich, Timo Kanninen, Chief Science Officer at BC Platforms, made one thing clear: the European Health Data Space (EHDS) will only deliver if organizations can turn new access rights into usable, governed research workflows.
EHDS creates the framework for secure, cross-border health data access. But access alone is not the breakthrough. The real test is whether data can be discovered, prepared, analyzed, and validated across systems, countries, and regulatory environments. As Timo put it:
The real challenge is not accessing data — it’s making it usable across systems, countries, and regulatory environments.
The European Health Data Space (EHDS) is often presented as a breakthrough for data access. That framing misses the point. The challenge is execution — not access.
What is EHDS?
The EHDS is an EU framework designed to enable secure, cross-border access and reuse of health data for research, innovation, and healthcare delivery. It establishes:
A common European model for health data use
Secure environments for data analysis
Cross-border access without data movement
For pharma organizations, EHDS creates both opportunity and pressure: access to broader datasets — combined with stricter requirements for how that data is used.
Data is made available in secure processing environments
Data cannot leave governed infrastructure
This creates the right foundation — but it does not solve execution on its own. In practice, with current systems:
With current systems, it can take months or even years before analysis is completed.
If nothing else changes, EHDS risks becoming another compliance layer, another approval step, another bottleneck. Access without usability does not accelerate research. It slows it down. This is why timing matters. Organizations must move now to align infrastructure, workflows, and governance with EHDS requirements before the 2028 deadline.
EHDS and the shift from access to data usability at scale
What EHDS enables — if implemented correctly — is a structural shift from fragmented, one-off data use, to repeatable, scalable research execution. That requires:
Federated data infrastructure, not silos
Standardized workflows, not project-by-project setups
Secure-by-design environments, not retrofitted compliance
AI embedded across the lifecycle, not layered on top
You can analyze the data—but you don’t move it. That changes how the entire system must operate.
As outlined in European Health Data Space: shaping the future of global health data, EHDS represents a shift toward a unified, secure, and innovation-driven ecosystem, with all EU member states expected to be ready by 2028 — and the potential to set a global benchmark for trusted, AI-ready health data use.
AI will only deliver if the system works
EHDS is closely tied to AI — but AI impact depends entirely on data usability. Today:
Data discovery is manual
Access requests are slow
Extraction and anonymization are fragmented
Output validation is resource-intensive
AI can support the entire process — from identifying relevant datasets to verifying that outputs meet regulatory requirements.
If done right, this means:
Study setup in days, not months
Scalable cross-border research
AI trained on real, governed datasets
This shift is already shaping how AI will be deployed in practice, as Timo outlines in his Bio-IT World commentary on how the European Health Data Space will drive the development of AI-based tools — from data harmonization to privacy-preserving federated model training.
Early proof: the model is already emerging
This model is not theoretical — it is already emerging in practice. Countries like Finland are already operating EHDS-aligned models through the Act on the Secondary Use of Health and Social Data (2019) and centralized data permit authorities such as Findata, which acts as a one-stop shop for multi-source health data access. In practice, this type of model combines three core components:
Clear governance frameworks for secondary data use
Centralized data access approval bodies
Certified secure processing environments
These foundations closely align with EHDS requirements and demonstrate that national-scale health data ecosystems can operate under strict regulatory controls while supporting research.
Early implementations also show that standardized workflows and centralized approvals can reduce data access and study setup timelines by an order of magnitude when executed properly. Across Europe, pilot initiatives are already demonstrating:
Cross-institutional collaboration at scale
Integrated, end-to-end research workflows
AI-supported data preparation, access, and validation
As Timo puts it:
The goal is to move from months or years to days or weeks.
This is the benchmark EHDS points toward — not incremental improvement, but step-change acceleration in research execution when the right infrastructure, governance, and workflows are in place.
The bottom line
EHDS will not create value by making data available. It will create value only if organizations can use that data consistently, securely, and at scale across studies, partners, and borders.
This is where most EHDS strategies break down: they focus on access and compliance, not on making data usable across real-world research workflows. Anything less — and organizations will struggle to turn EHDS access into real research outcomes.
Will your organization be ready to use EHDS data at scale?
The organizations that move first will do more than comply — they will redesign how health data is used across the research lifecycle.the research lifecycle.
See how BC Platforms helps make EHDS-ready research work at scale!
The European Health Data Space (EHDS) is an EU framework for secure, cross-border access to health data for research, innovation, and healthcare through controlled applications and certified environments.
How will EHDS affect pharmaceutical research?
EHDS can expand access to multi-country health data, helping pharma run larger and faster studies—if access, preparation, analysis, and validation workflows are operationally ready.
Why does data usability matter for EHDS?
Data usability determines whether approved data can be consistently discovered, prepared, analyzed, and validated across studies, partners, countries, and secure environments.
Will EHDS make health data easier to use?
EHDS improves the access framework, but data becomes easier to use only when organizations have the right infrastructure, governance, workflows, and AI-enabled tools in place.
What are secure processing environments in EHDS?
Secure processing environments are certified spaces where approved users can analyze health data without moving it outside governed infrastructure.
How can organizations prepare for EHDS readiness?
Organizations can prepare by building federated infrastructure, standardizing workflows, strengthening governance, operating secure environments, and embedding AI across the research lifecycle.
