QI-dataset anonymisation is the removal of personal data from a quality extract under UK GDPR Art. 9 & DPA 2018. anonym.plus runs on your own device. The measures stay usable, but the rows no longer name anyone.
When this applies
A quality team pulls thousands of rows to track an outcome over time. Each row carries a patient, a clinician, and a date that must come out before analysis.
How anonym.plus handles it
- Point anonym.plus at the extract on your server.
- It scans ID columns and any free-text note fields.
- Steady labels keep links across joined rows intact.
- Review the summary and tune the column rules.
- Swap each identifier, shifting dates to keep the gaps.
- Save the clean extract. Source rows stay local.
What you need to provide
- The extract (CSV, JSON, or a bundle of files).
- A column map for known ID fields.
- Replace with a steady label map to keep joins.
Patient data entity types detected
| Category | anonym.plus entity type | Example |
|---|---|---|
| Patient | PERSON | patient_name → [PATIENT_n] |
| Staff | PERSON | attending → [CLINICIAN_n] |
| Record IDs | MEDICAL_RECORD_NUMBER | nhs_number column → [NHS_NUMBER_n] |
| Dates | DATE_TIME | event_date → shifted [DATE] |
| Location | LOCATION | unit address → [ADDRESS] |
| Free text | PERSON / LOCATION | inline names → labels |
Compliance achieved
- Strips personal data under UK GDPR Art. 9 & DPA 2018.
- Runs offline, so no supplier contract is needed.
- Keeps row links steady, so the measures stay analysable.
Anonymise QI datasets offline — see plans & start free →
Limitations & cautions
A quality extract mixes tidy columns with messy notes. Column rules handle the first well, but note fields need the same care as any chart. Test a sample first, and check that date-shifting keeps the gaps your trend needs.
Frequently asked questions
Can rows stay linkable after the swap?
Yes. A steady label map swaps each identifier the same way, so rows for one person still join while no real identity is left.
Why work locally rather than in the cloud?
Sending raw patient data to a cloud tool is itself a disclosure. Local work skips that exposure and any supplier-contract burden it brings.
Does it handle both CSV and document bundles?
Yes. Tidy columns and bundled documents are both supported in one run.