Transcript de-identification is the removal of personal identifiers from a recorded telemedicine call. Health data in the transcript is special-category data under UK GDPR Art. 9 and DPA 2018. anonym.plus handles it on your own device. The spoken text stays readable, yet it no longer names the patient.
When this applies
Spoken consultations get typed into long transcripts full of names said aloud. Before you reuse one for review or model training, those spoken identifiers must go.
How anonym.plus handles it
- Open the typed transcript in anonym.plus on your device.
- Local OCR reads pasted screenshots of the dialogue too.
- The tool marks names, dates, places, and contact details.
- Skim each flag and fix any clinical term caught wrongly.
- Swap each identifier for a safe label, or black it out.
- Store the clean text. The source never leaves your machine.
What you need to provide
- The transcript (TXT, DOCX, PDF, or pasted dialogue).
- An operator: Replace, Redact, or Mask.
- Optional speaker map for [PATIENT] and [CLINICIAN] roles.
Patient data entity types detected
| Category | anonym.plus entity type | Example |
|---|---|---|
| Names | PERSON | “Hi, this is Chloe” → [PATIENT] |
| Dates | DATE_TIME | “since last Tuesday” → [DATE] |
| Contact | PHONE_NUMBER | callback +44 161 496 0182 → [PHONE] |
| Contact | EMAIL_ADDRESS | chloe@example.co.uk → [EMAIL] |
| Location | LOCATION | “I live in Bristol” → [PLACE] |
| Record IDs | MEDICAL_RECORD_NUMBER | NHS number 485 777 3366 → [NHS_NUMBER] |
Compliance achieved
- Strips special-category health data under UK GDPR Art. 9 & DPA 2018.
- Anonymisation assessed against the ICO Anonymisation Code motivated-intruder test.
- Runs fully offline — no cloud processor, no data-sharing agreement needed.
- Working files are protected with AES-256-GCM encryption.
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Limitations & cautions
Spoken words get typed loosely, so misheard names and unusual spellings can slip past occasionally. Always check the flags before export. Auto-typed audio is the riskiest source, since the speech-to-text step adds its own errors.
Frequently asked questions
Are spoken names harder to catch than typed ones?
A bit. Speech-to-text can mangle a name, which then reads oddly in the text. The tool flags clear names well; review the rest, above all on auto-typed audio.
Does this need a data-sharing agreement?
No. anonym.plus runs on your own device with no cloud step. No outside party touches the data, so the tool itself needs no processor agreement.
Can I keep speaker turns readable?
Yes. Replace puts a steady role label in each turn, so the back-and-forth still flows without naming a real person.