Click here to open the filter panel
#
510(k) premarket notification
A
Accountability
A
Accuracy
A
Adequacy decision
A
Ad hoc services
A
Adverse event
A
AI literacy (Art. 4 EU AI Act)
A
AI Officer
A
Analysis Data Model (ADaM)
A
Anonymisation
A
Appropriate safeguards
A
Article 29 Working Party (WP29)
A
Artificial intelligence (AI)
A
Authorised representative (EU AI Act)
A
Automated individual decision-making
A
Auxiliary medicinal product
B
Best-in-class drugs
B
Binding corporate rules (BCR)
B
Biobank
B
Biometric data
B
Biotechnology
B
Broad consent
B
Business associate agreement (BAA)
C
Central imaging
C
Central laboratory
C
Chemistry, Manufacturing and Controls (CMC)
C
Clinical outcome assessment (COA)
C
Clinical research
C
Clinical study
C
Clinical trial
C
Clinical trial agreement (CTA)
C
Clinical trial phases
C
Clinical Trials Information System (CTIS)
C
Clinical trial site
C
Clinical Trials Regulation (CTR) 536/2014
C
Clinician-reported outcome (ClinRO)
C
CNIL (Commission nationale de l'informatique et des libertés)
C
Codes of conduct
C
Consent
C
Contract research organisation (CRO)
C
Controller
C
Cookies
C
Cross-border processing
D
Data Act
D
Data concerning health
D
Data Governance Act (DGA)
D
Data management plan (DMP)
D
Data minimisation
D
Data monitoring committee (DMC)
D
Data processing agreement (DPA)
D
Data protection authority (DPA)
D
Data protection by default
D
Data protection by design
D
Data Protection Impact Assessment (DPIA)
D
Data Protection Officer (DPO)
D
Data Protection Representative (DPR)
D
Data quality
D
Data retention
D
Data subject
D
Data subject access request (DSAR)
D
Decentralised clinical trial (DCT)
D
De-identification (HIPAA)
D
Differential privacy
E
Early termination of a clinical trial
E
Electronic case report form (eCRF)
E
Electronic clinical outcome assessment (eCOA)
E
Electronic data capture (EDC)
E
Electronic health record (EHR)
E
Electronic informed consent (eConsent)
E
Encryption
E
End of a clinical trial
E
Enterprise
E
Ethics committee
E
EU Artificial Intelligence Act (AI Act)
E
European Data Protection Board (EDPB)
E
European Data Protection Supervisor (EDPS)
E
European Health Data Space (EHDS)
E
European Medicines Agency (EMA)
E
EU-US Data Privacy Framework (DPF)
E
Explicit consent
F
Fairness and transparency
F
Federated learning
F
Filing system
F
First-in-class drugs
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
Genetically modified organism (GMO)
G
Genetic data
G
Global public health security
G
Good clinical practice (GCP)
H
Health data warehouse (HDW)
H
High-risk AI system
H
HIPAA (Health Insurance Portability and Accountability Act)
H
Human oversight (AI Act)
H
Human subjects protection review board
C
Imported item 10
S
Imported item 11
C
Imported item 12
Click a letter to view all its definitions
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.

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.

B

Imported item 4

Blinding is a clinical-trial arrangement that keeps treatment allocation unknown to specified people to reduce bias. Unblinding reveals that allocation, either as planned or for a justified reason such as participant care. The protocol should identify exactly who remains blinded and how access to allocation information is controlled.

Key takeaways

  • Blinding labels should identify the actual people or functions kept unaware of treatment allocation, rather than relying on ambiguous shorthand.
  • Emergency unblinding procedures should support participant care while limiting disclosure to the people who need the treatment information.
  • Unblinding records should explain who accessed allocation, when access occurred and why the code was broken during the trial.

Which Groups Are Blinded?

Common labelMeaning to specify in the protocol
Single blindUsually the participant
Double blindUsually participant and investigator or treating team
Triple blindAn additional specified group, such as outcome assessors or analysts

ICH E6(R3) addresses preservation of blinding and documented unblinding procedures. Labels alone are insufficient because arrangements differ between protocols. The allocation list, statistical outputs, pharmacy records and system reports should all be considered in the blinding design.

EMA’s GCP Q&A recognises electronic code-breaking systems. For example, consider a hypothetical emergency in which an investigator needs one participant’s allocation. The workflow should disclose that allocation without unnecessarily revealing the treatment groups for the entire study.

Why It Matters for Data Protection

Blinding protects scientific integrity; privacy controls protect people’s information. The two overlap but are not identical. Operationally, separate access to allocation from access to identifiable health records and review whether exported reports could reveal either.

We recommend testing authorised and unauthorised user scenarios before study start, including emergency access, audit logging and communication to the sponsor. Keep a documented fallback for system unavailability.

Frequently Asked Questions

Is unblinding always a protocol deviation?

No. Planned or medically necessary unblinding may be provided for by the protocol and procedures.

Who can break the blind?

The protocol and authorised procedures define access; participant safety must remain central.

Is pseudonymisation the same as blinding?

No. Coding identities and concealing treatment allocation serve different purposes.

Related: randomised trials; RTSM; clinical-trial privacy support.

Updated: 27 October 2026