Free Developer Tool

Anonymize Clinical Notes

⚠️ Important: This tool detects emails, phone numbers, SSNs, dates, and API keys. It does NOT detect personal names or addresses — you must redact those manually before submitting to any LLM. Not a substitute for certified HIPAA de-identification software.

Configuration

Auto-Redact

Your Prompt or Text

Paste your AI prompt, message, or document here

Upload

Fix line breaks, remove page numbers, and optionally redact PII.

Your cleaned output will appear here

Paste text above and click Run — or try the demo

How to Anonymize Clinical Notes

De-Identification for Clinical AI Workflows

Healthcare organizations increasingly use AI for clinical note summarization, diagnostic support, and patient communication drafting. Before any clinical text enters an AI system, it must be de-identified per HIPAA Safe Harbor guidelines. Our tool provides a first-pass filter that detects and redacts email addresses, phone numbers, SSNs, dates, IP addresses, and names with medical honorifics like Dr. and Prof. However, this is not a certified de-identification solution — always verify output meets your organization's compliance requirements.

What We Detect and What We Do Not

Detected: email addresses, phone numbers with country codes, Social Security Numbers, dates in common formats such as MM/DD/YYYY and DD-MM-YY, IP addresses, names preceded by honorifics like Dr. Sarah Johnson, and API keys. Not detected: standalone first or last names without honorifics, medical record numbers in non-standard formats, facility names, geographic locations smaller than a state, and ages over 89. For full HIPAA Safe Harbor compliance, you need a certified de-identification tool or expert review.

Recommended Workflow

Paste or upload your clinical note. Enable Auto-Redact and run the Clean process. Carefully review the output for any remaining PHI. Manually redact any names, locations, or identifiers the tool missed. Only then paste the cleaned text into your AI assistant. This two-step approach of automated first-pass followed by manual review significantly reduces the risk of PHI exposure while maintaining the clinical context the AI needs to provide useful output.