The European Union's new Health Data Space regulation has fundamentally changed how organizations should negotiate data rights in life sciences collaborations. After years of political negotiations, the European Health Data Space (EHDS) Regulation has now entered into force, creating a framework that treats health data as a strategic asset in its own right, not merely a byproduct of research. For life sciences companies, universities, and technology partners, this shift means that collaboration agreements drafted before the EHDS framework was finalized may no longer reflect the true commercial value of the data they govern.

Why Are Companies Suddenly Rethinking Data Rights?

Traditionally, life sciences collaborations have centered on intellectual property, particularly patents. Negotiations focused on ownership of foreground IP, licensing rights, publication restrictions, and revenue-sharing arrangements. But the landscape has changed dramatically. The number of big technology companies entering the health data industry signals that access to high-quality datasets is becoming important in its own right. Organizations may now find that data access rights negotiated several years ago no longer reflect the strategic importance of the underlying datasets. A collaboration that was primarily about generating patentable inventions could generate significant further value from data-driven insights, AI training, or biomarker discovery.

The importance of this shift is further amplified by the rapid growth of artificial intelligence (AI) in healthcare and life sciences. Many organizations are exploring machine learning tools for drug discovery, diagnostics, patient stratification, and clinical decision support. The value of these tools is often heavily dependent on access to large, diverse, and well-structured datasets.

What Specific Issues Should Organizations Address in Their Agreements?

Legal experts and industry leaders are identifying critical gaps in existing collaboration agreements that were drafted before the EHDS framework was finalized. Organizations should scrutinize several key areas:

• Data-Use Flexibility: Do data-use provisions allow sufficient flexibility for future secondary uses beyond the original project scope?
• Access Rights Limitations: Are data access rights limited to specific projects or fields of use, potentially restricting future opportunities?
• Dataset Value Sharing: Who benefits if a dataset becomes significantly more valuable because it can be combined with other European health datasets?
• Derived Data Ownership: Have the parties adequately addressed rights in derived datasets, algorithms, models, and outputs?
• Governance Structures: Are governance structures capable of accommodating future regulatory requirements?

In many agreements, these issues receive considerably less attention than ownership of foreground intellectual property. Now may be a good time to reassess that balance.

How Should Organizations Handle AI-Generated Outputs?

As AI becomes more central to life sciences innovation, three specific contractual mechanisms deserve particular attention. Rather than prohibiting AI outright, collaboration agreements should address the specific contractual mechanisms that enable organizations to leverage data for AI purposes while managing risk.

• Permitted Use Definitions: Traditional data-use clauses may authorize "research purposes" or use within a defined "field of use," but these formulations were rarely drafted with AI in mind. Organizations should consider whether their agreements expressly permit the use of shared datasets for training, validating, and fine-tuning AI and machine learning models, and whether that permission extends to commercial deployment of the resulting tools or is limited to the collaboration itself.
• Ownership of AI-Generated Outputs: Collaboration agreements typically address ownership of foreground intellectual property, but trained models, model weights, and AI-generated predictions or insights often fall outside those traditional categories. Where one party contributes the data and the other contributes the AI capability, the question of who owns the resulting model and who can exploit it needs to be addressed expressly.
• Audit and Explainability Rights: As AI systems in healthcare increasingly fall within the scope of the EU AI Act's high-risk classification, contractual audit rights should go beyond traditional data protection compliance. Organizations may need the ability to inspect training data provenance, review model documentation, and require a degree of algorithmic transparency, not only as a matter of good governance but to meet emerging regulatory expectations.

Silence on these points is an invitation to dispute. Organizations that treat data merely as the raw ingredient for research may be missing an opportunity and could be creating risk.

What Should Organizations Do Now?

The EHDS does not mean every collaboration agreement requires immediate renegotiation. However, organizations will benefit from keeping an eye on the development of the EHDS and should think about how their template collaboration and licensing agreements treat data. Key areas to evaluate include whether existing data rights are aligned with new strategic objectives, whether agreements clearly address rights in derived data and AI-generated outputs, whether governance mechanisms can accommodate evolving regulatory requirements, whether value-sharing arrangements appropriately reflect the contribution of data assets, and whether future collaborations should place greater emphasis on data access alongside traditional IP provisions.

The EHDS represents part of a broader shift in how health data is viewed across Europe. While intellectual property will remain central to life sciences innovation, data is increasingly becoming a core commercial asset in its own right. For organizations entering new collaborations, the question is no longer simply who owns the IP. It is also who can access, use, and generate value from the data. Those who address that question proactively are likely to be better positioned to unlock opportunities arising from the next generation of data-driven life sciences innovation.