Quality Improvement Dataset Anonymization with anonym.plus

Clean a QI extract — columns and notes — without leaving your network.

QI-dataset anonymization is the removal of all 18 HIPAA Safe Harbor IDs (45 CFR §164.514(b)) from a quality extract. 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

  1. Point anonym.plus at the extract on your server.
  2. It scans ID columns and any free-text note fields.
  3. Steady labels keep links across joined rows intact.
  4. Review the summary and tune the column rules.
  5. Swap each ID, shifting dates to keep the gaps.
  6. Save the clean extract. Source rows stay local.

What you need to provide

PHI entity types detected

Categoryanonym.plus entity typeExample
PatientPERSONpatient_name → [PATIENT_n]
StaffPERSONattending → [CLINICIAN_n]
Record IDsMEDICAL_RECORD_NUMBERmrn column → [MRN_n]
DatesDATE_TIMEevent_date → shifted [DATE]
LocationLOCATIONunit address → [ADDRESS]
Free textPERSON / LOCATIONinline names → labels

Compliance achieved

Anonymize 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 ID 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 PHI to a cloud tool is itself a disclosure. Local work skips that exposure and the BAA burden it brings.

Does it handle both CSV and document bundles?

Yes. Tidy columns and bundled documents are both supported in one run.