Healthcare & life-sciences data: anonymised, synthetic and real-world

Healthcare and life-sciences data is among the most valuable and the most sensitive data there is. It can only be sourced subject to applicable legal, privacy, ethical and contractual requirements. This guide explains the landscape and the responsible path to using it.

A cautious starting point

Health data is special-category data under the GDPR, and its use is tightly constrained. Everything below is subject to applicable legal, privacy, ethical and contractual requirements, and to appropriate approvals. The right default is anonymised, aggregated or synthetic data wherever possible.

The healthcare data landscape

• Anonymised and aggregated healthcare datasets and hospital-activity aggregates.
• Population and public health: population-health and epidemiological data and public-health statistics.
• Real-world and trials: real-world data and clinical-trial datasets, under strict conditions.
• Market and capacity: pharmaceutical-market data and healthcare-capacity indicators.
• Synthetic: synthetic healthcare data for testing and development.

Why synthetic and anonymised data lead

Because raw health data is so sensitive, synthetic datasets that mirror structure without real records, and robustly anonymised data, are often the only practical route for development, testing and many analytics. They let work begin while approvals for any real data proceed in parallel.

Common use cases

Population-health analytics, health-system capacity planning, pharmaceutical market analysis, and software development and testing using synthetic data.

Sourcing considerations

Legal basis, ethics approvals and contractual safeguards come first. Anonymisation must be robust against re-identification, which is especially hard for rich clinical data. Provenance and documentation are essential.

Delivery and governance

Delivery typically uses secure environments and controlled access. The GDPR and national health-data rules apply throughout, and practices aligned with NIS2 and ISO/IEC 27001 principles support the security expected for sensitive data.

The governance that comes first

In healthcare, governance precedes data. Any use of patient-level data depends on a clear legal basis, ethics approval where required, and contractual safeguards, and even then is usually confined to secure, controlled environments. The emerging European Health Data Space aims to standardise secondary use of health data across the EU under strong safeguards. The default for most commercial and development work is therefore anonymised, aggregated or synthetic data.

Why anonymisation is hard here

Clinical data is rich and high-dimensional, which makes robust anonymisation genuinely difficult: rare diagnoses, dates and locations can re-identify individuals even without direct identifiers. This is why synthetic healthcare data and carefully designed aggregates have become central, they let development, testing and many analyses proceed without exposing real patients, while real-data access follows its own governed track.

A healthcare data checklist

• Is there a lawful basis and, where needed, ethics approval before any access?
• Can the purpose be met with anonymised, aggregated or synthetic data?
• If real data is needed, is it handled in a secure, controlled environment?
• Has anonymisation been tested against re-identification, not just assumed?
• Are provenance and approvals fully documented?

Sources & further reading

• EUR-Lex: Regulation (EU) 2016/679 (GDPR), special categories of data.
• European Health Data Space (EHDS) proposals and guidance.
• EMA and ECDC: real-world and epidemiological data frameworks.
• European Data Protection Board: guidance on health data.