Pseudonymising Multidisciplinary Team Meeting Minutes – UK GDPR-compliant anonymisation per UK GDPR Art. 9

Multidisciplinary Team (MDT) meeting minutes record clinical discussions about named patients, embedding diagnoses, treatment recommendations, and dissenting clinical opinions alongside patient identifiers in a format shared across multiple clinical specialties. anonym.legal pseudonymises patient and clinician identifiers in MDT minutes while preserving the clinical recommendation, evidence base, and action ownership for governance review, clinical audit, or training.

When this applies

This task applies when MDT minutes are shared with clinical governance teams, external peer reviewers, or training developers who require the clinical decision-making content but not the identities of individual patients or the clinicians who contributed to the meeting.

  1. Upload the MDT minutes (PDF or DOCX) to anonym.legal.
  2. The engine identifies patient names, dates of birth, NHS numbers, and named clinicians — including specialties represented — across all agenda items.
  3. Each patient and named clinician is pseudonymised consistently throughout the minutes.
  4. Clinical findings, staging or grading data, treatment recommendations, and action owners (by role) are preserved in clear text.
  5. Specialty labels and MDT structure are preserved; only natural-person identifiers are pseudonymised.
  6. A mapping table is produced with UK data residency.

What you provide

  • MDT meeting minutes document
  • Any case-summary sheets prepared for the MDT listing patient details

Limitations & cautions

  • The tool does not assess the clinical appropriateness of MDT decisions — obtain peer clinical review separately.
  • MDT minutes used as training case studies should be drawn from a sufficiently large case pool to avoid patients being identifiable from rare diagnosis combinations.
  • Action owners are preserved by role rather than by name in the pseudonymised version; ensure role descriptions are sufficient for action tracking purposes.

FAQ

Can pseudonymised MDT minutes be used in a Royal College audit of clinical decision-making quality?

Yes. Pseudonymised minutes are suitable for audit submissions where the focus is on decision-making process and outcome, rather than individual patient pathways. Confirm with the Royal College that pseudonymised records meet their audit data specification.

Are dissenting clinical opinions recorded in the minutes preserved or pseudonymised?

The substance of dissenting opinions is preserved in clear text. The named clinician who expressed the dissent is pseudonymised by consistent pseudonym, so the clinical reasoning is available without identifying the individual.

Does the tool handle minutes where patients are discussed across multiple MDT meetings?

Yes. Upload minutes from multiple meetings in a single batch. A patient discussed at multiple meetings receives the same pseudonym throughout, preserving longitudinal case-review continuity.

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.