Redacta

Pseudonymise medical and clinical documents before they're processed by AI or shared. Redacta replaces patient identifiers with labelled tokens — [PATIENT_NAME_1], [NHS_NUMBER_1], [DATE_OF_BIRTH_1], … — while leaving the clinical meaning intact, and returns a redaction report alongside the cleaned text.

It started as an Agent Skill and is now one engine shipped across eight surfaces — an iOS app, agent skill, MCP server, a self-hosted HTTP service with a Kubernetes deployment, two libraries, a CLI, and a FigJam whiteboard plugin.

Running this in production? Redacta offers a small number of fixed-price design-partner integrations for teams shipping AI agents on clinical or patient data — deployment in your environment, one real workflow integrated, and a data-flow document written for your DPO. Details →

One engine, many surfaces

The detection logic lives in one place — the TypeScript engine (@pharmatools/redacta, in npm-package/), which the MCP server and the FigJam plugin consume, and which the iOS app runs on-device via JavaScriptCore. The Python package mirrors it for pip users; the agent skill adds LLM reasoning for free-text names on top of the deterministic patterns.

How it works

Two layers:

• Patterns (deterministic). A bundled script (scripts/redact_structured.py, Python standard library only, no network) matches fixed-format identifiers: NHS numbers (Modulus-11 validated), UK National Insurance numbers, dates of birth, UK postcodes, phone numbers, emails, and hospital/MRN numbers. US SSN and ZIP codes are also handled.
• Reasoning (judgement). The skill then has the agent handle what patterns can't: patient names (told apart from the clinicians treating them), relatives and carers, postal addresses, and identifying ages.
• Self-check. A final pass re-reads the output for any identifier that slipped through before the report is written.

It also works in reverse. Re-identification (scripts/reinstate.py) takes the token map from an earlier redaction and restores the original values — so you can redact a document, run it through another AI tool, and put the real details back locally. Redact → process → re-identify is a complete round trip, and identifiers only ever exist on your machine.

Safe Harbor mode. Ask for HIPAA Safe Harbor (or "US de-identification") and Redacta applies a stricter pass: all dates (not just the date of birth), all specific ages, and the remaining HIPAA identifiers — fax, certificate/licence, device serial, VIN, and health-plan beneficiary numbers.

Self-hosting on Kubernetes

Organisations that can't let identifiable text leave their environment can run Redacta inside their own infrastructure: a small HTTP service (gateway-service/) deployable into an existing Kubernetes cluster with plain YAML — two stateless replicas behind a Service for redact/reinstate, an optional single-replica session boundary for the protect → release loop, health probes, resource limits, restrictive security defaults, and no-PHI logging. Text is pseudonymised before it reaches any external AI service, and the processing boundary stays under your control. Walkthrough (local kind cluster included): gateway-service/k8s/README.md · concepts: docs/KUBERNETES.md. Deploying somewhere a DPO will ask questions? There's a one-page security & data-protection summary at pharmatools.ai/redacta-security.

Install

Claude Code

git clone https://github.com/nickjlamb/redacta ~/.claude/skills/redacta

Then invoke it with /redacta, or let it trigger automatically when you ask to redact or de-identify clinical text.

Claude apps / API

Zip the repository folder and upload it as a skill.

Contents

Run the tests:

python3 scripts/test_redact_structured.py
python3 scripts/test_reinstate.py

A note on limits

Redacta is a strong first line of defence, not a guarantee. It won't catch every possible identifier and isn't a substitute for formal data-protection processes. Always review the redaction report before sharing text.

License

MIT-0 (MIT No Attribution). Built by PharmaTools.AI.