Pseudonymising NHS Significant-Event Audit Reports – UK GDPR-compliant anonymisation per Common Law Duty of Confidentiality

NHS Significant-Event Audit (SEA) reports document clinical incidents by reference to individual patient cases, embedding patient identifiers alongside clinician accounts and root-cause analysis findings. The Common Law Duty of Confidentiality applies to clinical information in SEA reports. anonym.legal pseudonymises patient and staff identifiers while preserving the incident description, learning outcomes, and action plans for governance sharing and accreditation evidence.

When this applies

This task applies when SEA reports are shared with CCG/ICB clinical governance reviewers, GP appraisers, CQC inspectors, or external peer reviewers who require the learning content of the report but not the identities of the patient or clinical staff involved.

  1. Upload the SEA report (PDF or DOCX) to anonym.legal.
  2. The engine identifies patient name, date of birth, NHS number, and dates of clinical contact, together with the names of clinical staff involved.
  3. Each patient and named clinical staff member is pseudonymised consistently; the incident description, timeline, and contributing factors are preserved.
  4. Learning outcomes and action plan commitments are preserved in full.
  5. A reversible mapping table is produced with UK data residency.
  6. The pseudonymised report is released for governance sharing or accreditation submission.

What you provide

  • Significant-Event Audit report document
  • Any supporting clinical records appended to the report

Limitations & cautions

  • SEA reports submitted as GP appraisal evidence may require assessors to confirm the authenticity of the events described; a pseudonymised version may need a covering note from the appraiser confirming its relationship to a real event.
  • The tool does not assess whether the root-cause analysis methodology is complete or whether the learning outcomes are proportionate — obtain clinical peer review.
  • Named clinical staff in the report may have employment-related rights regarding disclosure of their involvement — confirm with HR and employment legal counsel.

FAQ

Can a pseudonymised SEA report be submitted as evidence to a CQC inspection?

CQC inspectors may accept pseudonymised SEA reports as evidence of learning culture during inspections. Confirm with the CQC inspector what format of evidence is acceptable and whether re-identification of the patient is required for specific concerns.

Does the tool remove incident dates from the report?

Incident dates are preserved by default, as they are typically essential to the clinical narrative and learning analysis. If specific dates are re-identifying in the context of very small practices, you can configure date generalisation to month and year.

Are SEA reports shared across GP federations eligible for pseudonymisation under this workflow?

Yes. SEA reports shared within a GP federation or primary care network for collective learning are a primary use case. The pseudonymised version allows federation members to discuss the learning without identifying the originating practice's patient.

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.