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E
Explicit consent
F
Fairness and transparency
F
FDPIC (Swiss Federal Data Protection and Information Commissioner)
F
Federated learning
F
Filing system
F
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F
First-in-human (FIH) trial
F
Food and Drug Administration (FDA)
F
Fundamental rights impact assessment (FRIA)
G
General Data Protection Regulation (GDPR)
G
General-purpose AI model (GPAI)
G
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G
Genetic data
G
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G
Good clinical practice (GCP)
H
HDS certification (Hébergeur de Données de Santé)
H
Health data access body (HDAB)
H
Health data warehouse (HDW)
H
HHS Office for Civil Rights (OCR)
H
High-risk AI system
H
HIPAA (Health Insurance Portability and Accountability Act)
H
Homomorphic encryption
H
Human oversight (AI Act)
H
Human subjects protection review board
I
ICH E6(R3)
I
Incapacitated subject
I
Information Commissioner's Office (ICO)
I
Informed consent
I
Inspection
I
Institutional Review Board (IRB)
I
Interactive response technology (IRT)
I
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I
Interactive web response system (IWRS)
I
International data transfer
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I
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I
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I
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Joint controllers
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k-anonymity
K
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K
Key (cryptographic key and pseudonymisation key)
K
Kinetics (pharmacokinetics and pharmacodynamics)
L
Large language model (LLM)
L
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L
Lay summary
L
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L
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L
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L
Legitimate interest assessment (LIA)
L
LGPD (Brazil's General Data Protection Law)
L
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L
Local site investigator
M
Main establishment
M
MedDRA
M
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M
Membership inference attack
M
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M
MHRA (Medicines and Healthcare products Regulatory Agency)
N
Natural history study
N
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N
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N
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O
Observational study
O
Observer-reported outcome (ObsRO)
O
One-stop-shop mechanism and lead supervisory authority
O
Orphan drug designation (ODD)
P
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P
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P
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P
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P
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P
Processing
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Processor
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P
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P
Provider and deployer (AI Act roles)
P
Pseudonymisation
P
Purpose limitation
Q
Qualified Person for Pharmacovigilance (QPPV)
Q
Quality management system (QMS)
Q
Quasi-identifier
Q
Query (clinical data query)
R
Randomisation and trial supply management (RTSM)
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Term of the Day

Natural history study

A natural history study is a preplanned observational study intended to track the course of a disease over time, identifying demographic, genetic, environmental and other variables that correlate with its development and outcomes in the absence of intervention, or under standard of care. Designs may be retrospective (chart review of existing records) or prospective (longitudinal follow-up of a cohort or registry).

Natural history data is particularly important in rare and paediatric diseases, where randomised placebo-controlled trials may be infeasible or unethical. The FDA (guidance on rare disease natural history studies, 2019) and the EMA accept well-designed natural history studies to define endpoints and biomarkers, identify patient subgroups, estimate sample sizes and, in some cases, serve as external or historical control arms for single-arm trials supporting orphan products.

Because they are non-interventional, natural history studies fall outside the CTR and are governed by national law (for example France's MR-003 or MR-004 reference methodologies) and by the GDPR. They typically involve secondary use of medical records, long-term follow-up, genetic data and small populations in which anonymisation is rarely achievable, so pseudonymisation, a DPIA and a robust research legal basis under Art. 9(2)(j) are essential. Registries maintained by patient organisations or academic consortia raise additional questions of joint controllership and data access governance.

T

Trusted research environment (TRE)

A trusted research environment (TRE) is a controlled setting where authorised researchers analyse sensitive data without receiving unrestricted copies. Access, permitted projects, technical controls and output checks work together. In health research, a TRE is a governance model as well as a secure computing platform.

Key Takeaways

  • TRE governance considers people, projects, settings, data and outputs rather than security infrastructure alone.
  • Researchers should not assume that data become anonymous simply because analysis takes place inside a secure environment.
  • The EHDS introduces secure processing environments for secondary use through a phased implementation framework.

What Are the Five Safes?

ElementMeaning
People and projectsApproved users and permitted research
Settings and dataControlled systems and protected information
OutputsDisclosure checks before release

The UK secure data environment policy uses the Five Safes framework to organise these controls. This separates approval to enter the environment from approval to take results out. A researcher may be allowed to run an analysis but still need an output review before exporting a table. The European Health Data Space (EHDS) uses the related concept of a secure processing environment; its secondary-use rules are not all already applicable in 2026.

Primary guidance: UK secure data environment policy; European Commission EHDS overview.

Why It Matters for Data Protection

For a hypothetical rare-disease project, we recommend checking small subgroup results before release, not just removing names from the input file. Set out who approves researchers, who authorises a new purpose and who checks exports. The contract and operating procedure should also address logging, incident escalation, retention and project closure. Treat these as connected decisions: strong access controls do not repair an unsuitable research purpose, and a sound purpose does not remove the need to protect the data.

Frequently Asked Questions

Is a TRE just cloud hosting?

No. It also includes researcher approval, project governance and release controls.

Can researchers download raw data?

That depends on the environment and permissions; unrestricted export is not the model.

Does a TRE replace data protection assessment?

No. Assess the purpose, people, information and safeguards for the particular project.

Related reading: secondary use of health data; european health data space ehds.

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Updated: 27 October 2026