Pathology Report Anonymisation with anonym.plus

Hide the patient while the diagnosis and grade stay intact.

Pathology de-identification is the removal of patient identifiers from the file. UK GDPR Art. 9 and the DPA 2018 govern health data. anonym.plus runs this on your device. The gross and microscopic notes stay clear, but no one is named.

When this applies

A tumour board wants the case as a teaching example. The diagnosis must stay, yet the name, the dates, and the specimen ID have to be hidden first.

How anonym.plus handles it

  1. Open the file (PDF, DOCX, or scan) on your machine.
  2. Local OCR reads any scanned page so printed text is found.
  3. The app flags names, dates, IDs, and contact lines.
  4. Check each flag and protect terms like a tissue type.
  5. Swap each ID for a token, or remove it outright.
  6. Save the clean version. The source stays on disk.

What you need to provide

Patient data entity types detected

Categoryanonym.plus entity typeExample
NamesPERSONMargaret Osei → [PATIENT_1]
DatesDATE_TIMECollected 03/11/2025 → [DATE]
Record IDsMEDICAL_RECORD_NUMBERMRN 220784 → [MRN]
SpecimenIDSpecimen S26-1184 → [SPECIMEN]
LabORGANIZATIONSt Thomas' Histopathology → [LAB]
ContactEMAIL_ADDRESSm.osei@nhs.net → [EMAIL]

Compliance achieved

Anonymise pathology write-ups offline — see plans & start free →

Limitations & cautions

The tool removes the listed identifier types. It does not judge whether a very rare diagnosis could still single out a patient. You make that call. When in doubt, seek expert review of re-identification risk.

Frequently asked questions

Are specimen and accession numbers treated as identifiers?

Yes. Both are unique codes that can trace back to a single case. The tool flags them so you can swap or remove each one.

Will the microscopic description stay intact?

Yes. Clinical wording is left alone. Only the patient details change, so the cell findings and grade read exactly as the pathologist wrote them.

Can two cases share one stable token?

Yes, with a name map. The same patient gets the same label across files. That lets you group cases without ever exposing the real name.