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Batch Processing 50K Clinical Notes Locally

A February 2026 SDNY ruling found AI-processed documents lose attorney-client privilege if not anonymized before processing.

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

Running 50K Clinical Notes Locally: HIPAA Guide

Research teams that need to de-identify large note archives face a common gap. Cloud tools often can't handle the volume. Many rules require on-site work. Manual review takes too long. Local batch runs are the answer.

This guide covers the key rules, the setup, and the records you need.

See our compliance overview and security practices for how we support HIPAA.

Why Cloud Does Not Work Here

HIPAA's Expert Determination method sets a clear bar. De-identified data must carry "very small risk" of re-identification. A qualified person must verify that. An IRB that approves research with de-identified patient data also needs records. You must document the method used, the entity types removed, and the quality checks applied.

That records requirement is key. De-identification can't be a black box. You must show what was found, what was removed, and how you checked the result.

Uploading 500,000 files to a cloud API is slow and costly. Rate limits and long transfer times make it hard. Cloud runs are rarely practical for large research datasets.

HIPAA adds a second concern. Sending protected health information (PHI) to a Business Associate — even a de-identification vendor — requires a Business Associate Agreement (BAA). For IRB research, BAA rules may intersect with IRB data use terms. Legal review is often needed. Local runs remove the data-transfer concern entirely.

Why the Privilege Case Matters

A February 2026 SDNY ruling found that AI-processed documents lose attorney-client privilege if not anonymized first. The court held that sending privileged documents to an external AI service was a disclosure. That disclosure waived privilege for the content analyzed.

The healthcare parallel is clear. Physician notes sent to cloud NLP tools carry similar risk. Therapist records sent to outside AI services do too. Local runs — where documents never leave your site — avoid that risk.

See our guide on HIPAA cloud and zero-knowledge PHI for more on keeping data on-site.

How to Set Up for 50K Notes

Batch size: The Desktop App handles 1–5,000 files per batch based on your plan. Ten batches of 5,000 covers all 50,000 notes in one overnight job. No manual steps are needed in between.

Speed: Running 1–5 files at once boosts output. A single overnight job finishes the full set with no extra work.

Entity types: Healthcare-specific types include MRN formats, NPI numbers, DEA numbers, health plan IDs, and HIPAA date formats. Set them once in a named preset. That preset applies to every batch. De-identification stays uniform across all files.

Audit logs: Each batch job exports a CSV or JSON file. It records the file name, entity types found, confidence scores, and a time stamp. This log meets the IRB Expert Determination requirement. You can show what was found and removed in each file.

IRB Records Checklist

Before you file your IRB protocol, confirm you can show:

  • Name and version of the de-identification tool
  • Full list of entity types in the preset
  • Test results on a held-out sample
  • Batch logs for each run (file name, entity counts, time stamp)
  • Proof that no PHI left your on-site environment

Local batch runs make each item easy to produce. Logs are auto-generated. The preset is saved and versioned. The site boundary is clear.

Sources

Ready to protect your data?

Start anonymizing PII with 285+ entity types across 48 languages.

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.