Anonymize FDA-regulated clinical trial records for electronic submission compliance – CCPA/HIPAA-compliant de-identification per 21 CFR Part 11

Clinical trial records managed under FDA electronic record and signature requirements at 21 CFR Part 11 must be maintained with audit trail integrity. When trial participant records are prepared for publication, sponsor review, or data-sharing submissions, anonym.legal de-identifies participant identifiers while preserving the electronic record structures — audit trails, electronic signatures, and version histories — required by 21 CFR Part 11.

When this applies

Apply this workflow when an FDA-regulated clinical trial's electronic records — eCRFs, informed consent forms, and adverse-event reports — must be shared with a data monitoring committee, sponsor, or submitted to a repository in de-identified form, and 21 CFR Part 11 audit trail integrity must be preserved.

  1. Upload clinical trial electronic records (PDF exports of eCRFs, electronic consent forms, and adverse-event narratives) to anonym.legal.
  2. The engine identifies participant PHI: name, date of birth, address, SSN, and any other HIPAA Safe Harbor identifiers appearing in trial record headers, consent form fields, and adverse-event narratives.
  3. All Safe Harbor identifiers are removed; trial-specific non-identifying data — randomization code, treatment arm assignment, dosing records, efficacy endpoint measurements — are preserved.
  4. Electronic signature metadata (signer role, signature date, signature status) is preserved in de-identified form; the signing individual's name is replaced with a role label.
  5. Audit trail entries — record creation timestamps, modification history, system access logs — are preserved with user role labels replacing named user identifiers.
  6. A de-identified record package meeting FDA data submission formatting requirements is produced for sponsor or repository use.

What you provide

  • Electronic clinical trial records (PDF eCRF exports, consent forms, adverse-event reports)
  • Trial data dictionary identifying PHI-bearing fields
  • Informed consent forms (if electronic consent under 21 CFR Part 11 scope)

Limitations & cautions

  • 21 CFR Part 11 requires that audit trails documenting record changes be maintained and not altered; the de-identification process generates a new de-identified record set — the original identified trial master file records must be retained separately with intact audit trails.
  • FDA may require access to identified trial records during inspections; de-identified copies are not a substitute for the identified trial master file.
  • Electronic signature authenticity verification relies on PKI infrastructure linked to the original signer's identity; de-identified records with role labels in place of signer names cannot be used for signature verification under 21 CFR §11.70.

FAQ

Does 21 CFR Part 11 apply to all electronic records in a clinical trial?

21 CFR Part 11 applies to electronic records that are created, modified, maintained, archived, retrieved, or transmitted under FDA requirements. This includes eCRFs, electronic informed consent forms, adverse-event reports submitted electronically, and records related to FDA-regulated drug or device trials. Records maintained for non-FDA purposes are not subject to Part 11.

Can the de-identified trial records be submitted to ClinicalTrials.gov data repositories?

Yes. De-identified participant-level data shared under ClinicalTrials.gov data-sharing plans must be de-identified per HIPAA standards when the trial data also constitutes PHI. This workflow produces data suitable for such submissions when participants are covered by HIPAA.

Are adverse-event narratives processed differently from structured eCRF fields?

Yes. Adverse-event free-text narratives require natural-language entity detection to identify participant names and dates embedded in clinical descriptions. The engine applies NLP-based detection to narrative fields separately from structured field processing, ensuring comprehensive identifier removal.

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.