Drug Utilisation Review Anonymisation with anonym.plus

Strip the IDs from the utilisation data while the patterns stay.

DUR anonymisation is the removal of patient IDs from a utilisation dataset. It meets UK GDPR Art. 9 and DPA 2018. anonym.plus does this locally. The use patterns and prescriber-level data stay; no row names the person.

When this applies

A utilisation dataset ties fill patterns to named individuals. For a use review or a quality study, you strip the IDs but keep the patterns and the timing.

How anonym.plus handles it

  1. Point anonym.plus at the dataset on your device.
  2. It scans ID columns and any free-text fields.
  3. Patient names, NHS numbers, and dates get flagged.
  4. Confirm the flags; the medicines stay as non-IDs.
  5. Swap IDs with a steady map to keep cohort links.
  6. Save the clean data; the source stays on your machine.

What you need to provide

Patient data entity types detected

Categoryanonym.plus entity typeExample
NamesPERSONpatient_name → [PATIENT_n]
Record IDsMEDICAL_RECORD_NUMBERmrn field → [MRN_n]
DatesDATE_TIMEfill dates → shifted [DATE]
LocationLOCATIONpostcode → [POSTCODE]
AgeAGEage 92 → [AGE_BAND]
IdentifiersNHS_NUMBERNHS no. → [NHS_NUMBER]

Compliance achieved

Anonymise utilisation datasets offline — see plans & start free →

Limitations & cautions

Ages over 89 should be banded under UK GDPR Recital 26, and a full postcode should be truncated. The medicines and patterns stay. Check that date-shift keeps the gaps your study needs before a full run.

Frequently asked questions

Can cohorts stay linkable after the swap?

Yes. A steady label map swaps each ID the same way, so an individual's rows still group while no real identity is left.

How are very old ages handled?

The ICO Anonymisation Code treats age over 89 as a heightened re-identification risk, so band it. The tool flags it for you to review.

Are the medicines kept?

Yes. Drug names are not personal identifiers, so they stay and the use patterns remain readable.