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Batchverwerking van Klinische Notities: HIPAA Lokaal

Academische medische centra verwerken 500.000+ klinische notities per jaar. Cloud-PHI-verwerking is geblokkeerd door CISOs. Hier leest u hoe lokale batch-de-identificatie HIPAA-compliant werkt.

April 11, 20268 min lezen
batch PHI de-identificationclinical notes processingHIPAA local processingresearch dataset complianceIRB requirements

Het Klinische Notitie Volume Probleem

Academische medische centra genereren enorme hoeveelheden klinische notities. Onderzoekers hebben de-geïdentificeerde datasets nodig voor studies naar ziektepatronen, behandelingsresultaten en kwaliteitsverbetering. Maar HIPAA-regels gelden voor elk stuk PHI dat het zorgsysteem verlaat.

Cloud-verwerking is voor de meeste grote zorgsystemen geen optie. CISOs blokkeren PHI-overdracht naar externe platforms na de reeks van zorgdatalekken in 2024.

Wat Lokale Batchverwerking Vereist

Een HIPAA-conforme lokale batchoplossing voor klinische notities moet:

  • Offline draaien zonder vereiste internetverbinding voor model-inferentie
  • Alle 18 PHI-categorieën detecteren zoals vereist door HIPAA Safe Harbor
  • Ziekenhuisspecifieke MRN-formaten herkennen via aangepaste entiteitsdefinities
  • Auditspoor produceren voor elke verwerkte notitie met entiteitstype en verwijderingsmethode
  • Schaalbaar zijn voor 100.000+ records per dag op standaard ziekenhuishardware

De Drie-Laags Aanpak

Laag 1 — Regelgebaseerde detectie: Structurele PHI: SSN's, telefoonnummers, e-mailadressen, datums. Regex met checksum-validatie voor MRN-formaten.

Laag 2 — NER-modellen: Namen, adressen en contextuele PHI in vrije tekst. Klinisch getrainde NER presteert significant beter dan algemene modellen op medische tekst.

Laag 3 — Aangepaste entiteiten: Ziekenhuisspecifieke formaten: MRN-patronen per faciliteitscode, staf-ID-formaten, interne locatiecodes.

Alleen het drie-laags ontwerp bereikt de sub-5% misratio die nodig is voor HIPAA Safe Harbor Expert Determination.

Bekijk de HIPAA-nalevingsdocumentatie en de desktopapp voor lokale implementatieopties.

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About this page

We update this page when our platform or the law changes.

Read our founder note for how we work.

Each change shows up in the timestamp at the top.

Related reading

We follow these rules

  • GDPR (EU 2016/679).
  • ISO/IEC 27001:2022.
  • NIS2 (EU 2022/2555).
  • HIPAA safe harbor under 45 CFR § 164.514(b)(2).

Our promise

We do not sell your data.

We do not train models on your text.

We store your files in Germany.

You can delete your account at any time.

You own your work.

Where we run

Our servers live in Falkenstein, Germany.

We use Hetzner. They hold ISO 27001 certification.

All data stays in the EU.

Backups run every day.

Need help?

Email support@anonym.legal.

We reply within one business day.

How we test

We run a full check suite on every release.

Each surface gets its own sweep script and report.

Human reviewers spot-check the output each week.

We track recall and precision on a labelled set.

Bad runs block the deploy.

What we never do

  • We never sell your information to third parties.
  • We never train models on what you upload.
  • We never keep your work after you delete it.
  • We never share keys with any outside firm.
  • We never run ads inside the product.

Plans in plain words

We sell credits, not seats.

One credit covers one short job.

Long jobs use a few credits each.

You can top up at any time.

Unused credits roll over each month.

Read the plans page for current rates.

Who built this

A small team of engineers and lawyers built this.

We ship from Europe and work in the open.

Our founder note spells out why we started.

Where to start

How the parts fit

A browser add-on cleans text inside Chrome.

A Word plug-in handles drafts in Office.

A small desktop tool works on whole folders.

An agent protocol link feeds large models safely.

All four share one core engine and one rule set.

Words from our team

We started this work after a lunch about cookies.

One friend kept getting odd ads on her phone.

We asked why a court file leaked through a draft.

We sketched the first build on a napkin that week.

By month three we had a tiny demo for a friend.

She used it on her first case the next day.

Common questions we hear

Can the tool read scanned PDFs? Yes, with OCR.

Does it work on long files? Yes, in small chunks.

Can I roll my own rule set? Yes, save it as a preset.

Does it run offline? The desktop build runs offline.

Do you keep my files? No, the cloud build wipes after each run.

Will it learn from my work? No, we never train on inputs.

A short tour of the workflow

Upload a file or paste a snippet of prose.

Pick the entities you want gone from the draft.

Choose a method: replace, mask, hash, encrypt, or redact.

Press run and watch the side panel show each hit.

Skim the result and tweak any rule that misfired.

Save the cleaned file or send it to a teammate.