De-identify hospital discharge summaries under HIPAA Safe Harbor – CCPA/HIPAA-compliant de-identification per 45 CFR §164.514(b)(2)

Hospital discharge summaries contain a concentrated set of HIPAA Safe Harbor identifiers — patient name, admission and discharge dates, diagnosis codes, and physician names — within a single document. anonym.legal applies the §164.514(b)(2) Safe Harbor method to discharge summaries, removing all 18 identifier categories while preserving diagnosis codes, procedure records, and discharge medication lists for clinical audit and benchmarking.

When this applies

Apply this workflow when discharge summaries are extracted for quality-improvement audits, hospital benchmarking consortia, or external clinical review, and the reviewing parties require episode-level clinical data without patient or clinician identity.

  1. Upload discharge summaries individually or in batch (PDF, DOCX, or HL7 CDA format).
  2. The engine identifies all 18 Safe Harbor identifier categories in document headers, structured fields, and free-text narrative sections.
  3. Patient name, MRN, admission date, discharge date, date of birth, address, phone, and insurer beneficiary number are removed or generalized; dates are reduced to year-only per §164.514(b)(2)(i)(C).
  4. Attending and consulting physician names are removed; provider role labels (e.g., 'attending physician', 'hospitalist') are retained.
  5. ICD-10-CM diagnosis codes, CPT procedure codes, and discharge medication names are preserved in full as non-identifying clinical content.
  6. A de-identification certificate is generated per the Safe Harbor compliance record requirement.
  7. The de-identified summary batch is exported for audit or benchmarking use.

What you provide

  • Discharge summary documents (PDF, DOCX, or HL7 CDA)
  • Patient encounter identifier list (to verify batch completeness)

Limitations & cautions

  • Discharge summaries describing unusual clinical presentations or rare procedure combinations may retain re-identification risk even after Safe Harbor de-identification; apply Expert Determination review for rare-condition cohorts.
  • Admission and discharge dates are generalized to year-only; time-series analyses requiring month or day granularity should use the limited data set pathway under §164.514(e) with a data-use agreement.
  • The tool does not validate clinical coding accuracy — a separate clinical coding review is required.

FAQ

Can de-identified discharge summaries be shared with a hospital benchmarking collaborative?

Yes. Once de-identified under the Safe Harbor standard, the summaries are no longer PHI and may be shared with benchmarking collaboratives without patient authorization or Privacy Rule restrictions, even if the collaborative is not a HIPAA covered entity.

Are physician DEA numbers treated as an identifier requiring removal?

DEA numbers are certificate or license numbers and fall under identifier category (11) in §164.514(b)(2)(i)(K). They are removed as part of Safe Harbor processing.

How are co-morbidities coded in the de-identified output?

ICD-10-CM comorbidity codes are retained in full — they are clinical classification codes, not identifiers. Only personal data fields (name, dates, MRN, etc.) are removed. The clinical coding record remains intact for audit purposes.

Healthcare Records

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.

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.