Pseudonymising Clinical Trial Data: Consent Forms & CRFs – UK GDPR-compliant anonymisation per Medicines for Human Use (Clinical Trials) Regulations 2004

Clinical trial datasets — including consent forms, case report forms (CRFs), and adverse-event narratives — identify trial participants by name, date of birth, and site identifiers alongside sensitive health and treatment data. Under the Medicines for Human Use (Clinical Trials) Regulations 2004, participant data must be managed confidentially. anonym.legal pseudonymises participant identifiers while preserving trial arm, dosing records, outcome measures, and adverse-event descriptions for regulatory review and publication.

When this applies

This task applies when clinical trial data is prepared for submission to a regulatory sponsor, shared with an independent data monitoring committee, or prepared for academic publication, and the receiving parties require the trial data but not the identities of individual trial participants.

  1. Upload consent forms, CRFs, and adverse-event narratives to anonym.legal.
  2. The engine identifies participant name, date of birth, site identifier (where linked to an individual), and any narrative descriptions of adverse events that name the participant.
  3. Each participant is assigned a consistent subject code pseudonym applied across all linked documents.
  4. Trial arm assignment, dosing records, efficacy outcome measures, and adverse-event grades and descriptions are preserved.
  5. Investigator names are pseudonymised; site codes (non-identifying) are preserved.
  6. A mapping table linking subject codes to participant identities is produced with UK data residency and role-based access control.

What you provide

  • Informed consent forms
  • Case report forms (CRFs) in PDF or electronic format
  • Adverse-event narrative reports
  • Site code mapping document (if site codes are identifying)

Limitations & cautions

  • Clinical trial participant data may be subject to additional obligations under the MHRA's clinical trials regulations; confirm that pseudonymisation is consistent with the approved trial protocol and ethics approval.
  • The Medicines for Human Use (Clinical Trials) Regulations 2004 framework requires trial data to be retained; pseudonymised copies used for publication do not replace the requirement to retain identified trial master file records.
  • Adverse-event narratives describing rare reactions in small sub-populations may retain re-identification risk; apply statistical disclosure control for very small participant groups.

FAQ

Does pseudonymisation of clinical trial data satisfy the ICH E6(R2) GCP guideline requirements for participant confidentiality?

ICH E6(R2) requires that participant confidentiality be maintained. Pseudonymisation supports this obligation when sharing data externally; however, the sponsor's data management plan should explicitly address how pseudonymisation interacts with trial master file obligations.

Can pseudonymised CRFs be included in a clinical trial publication?

Journals increasingly require data sharing with pseudonymised or anonymised participant data. anonym.legal produces pseudonymised CRFs suitable for supplementary materials; confirm with the target journal's data-sharing policy whether pseudonymised or fully anonymised data is required.

Are site identifiers treated as personal data if they can be linked to an individual?

If a site identifier combined with trial arm and adverse-event profile could re-identify a participant, it is processed as personal data. The engine's configuration allows site code pseudonymisation where identifying, with non-identifying numeric site codes preserved.

Healthcare Records

About this page

We update this page when our platform or the law changes.

Read our founder note for how we work.

Each change shows up in the timestamp at the top.

We follow these rules

  • GDPR (EU 2016/679).
  • ISO/IEC 27001:2022.
  • NIS2 (EU 2022/2555).
  • HIPAA safe harbor under 45 CFR § 164.514(b)(2).

Our promise

We do not sell your data.

We do not train models on your text.

We store your files in Germany.

You can delete your account at any time.

You own your work.

Where we run

Our servers live in Falkenstein, Germany.

We use Hetzner. They hold ISO 27001 certification.

All data stays in the EU.

Backups run every day.

Need help?

Email support@anonym.legal.

We reply within one business day.

How we test

We run a full check suite on every release.

Each surface gets its own sweep script and report.

Human reviewers spot-check the output each week.

We track recall and precision on a labelled set.

Bad runs block the deploy.

What we never do

  • We never sell your information to third parties.
  • We never train models on what you upload.
  • We never keep your work after you delete it.
  • We never share keys with any outside firm.
  • We never run ads inside the product.

Plans in plain words

We sell credits, not seats.

One credit covers one short job.

Long jobs use a few credits each.

You can top up at any time.

Unused credits roll over each month.

Read the plans page for current rates.

Who built this

A small team of engineers and lawyers built this.

We ship from Europe and work in the open.

Our founder note spells out why we started.

Where to start

How the parts fit

A browser add-on cleans text inside Chrome.

A Word plug-in handles drafts in Office.

A small desktop tool works on whole folders.

An agent protocol link feeds large models safely.

All four share one core engine and one rule set.

Words from our team

We started this work after a lunch about cookies.

One friend kept getting odd ads on her phone.

We asked why a court file leaked through a draft.

We sketched the first build on a napkin that week.

By month three we had a tiny demo for a friend.

She used it on her first case the next day.

Common questions we hear

Can the tool read scanned PDFs? Yes, with OCR.

Does it work on long files? Yes, in small chunks.

Can I roll my own rule set? Yes, save it as a preset.

Does it run offline? The desktop build runs offline.

Do you keep my files? No, the cloud build wipes after each run.

Will it learn from my work? No, we never train on inputs.

A short tour of the workflow

Upload a file or paste a snippet of prose.

Pick the entities you want gone from the draft.

Choose a method: replace, mask, hash, encrypt, or redact.

Press run and watch the side panel show each hit.

Skim the result and tweak any rule that misfired.

Save the cleaned file or send it to a teammate.