
Build a PHI de-identification pipeline for medical documents
Delivery in
7 days
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What you get with this Offer
Need to use medical data for AI, research or sharing without exposing patient identity? I will build a pipeline that detects and redacts protected health information (PHI) in documents and text, following the HIPAA Safe Harbor identifiers.
What you get:
- Detection of names, dates, addresses, phone numbers, emails, MRNs, IDs and more
- Redaction or replacement with consistent placeholders
- Works on PDFs, scanned documents (OCR) and plain text
- Audit report of what was redacted and confidence scores
- Runs privately in your own environment (for example AWS) so data never leaves your control
Why work with me: 7+ years of Python, a 5.0 rating on PeoplePerHour with 40+ reviews, and production AI systems built for healthcare and business automation. Full source code and 7 days of free support are included.
Please message me before ordering with a short description of your project so I can confirm the scope.
What you get:
- Detection of names, dates, addresses, phone numbers, emails, MRNs, IDs and more
- Redaction or replacement with consistent placeholders
- Works on PDFs, scanned documents (OCR) and plain text
- Audit report of what was redacted and confidence scores
- Runs privately in your own environment (for example AWS) so data never leaves your control
Why work with me: 7+ years of Python, a 5.0 rating on PeoplePerHour with 40+ reviews, and production AI systems built for healthcare and business automation. Full source code and 7 days of free support are included.
Please message me before ordering with a short description of your project so I can confirm the scope.
Get more with Offer Add-ons
-
I can deploy the pipeline to your AWS account
Additional 2 working days
+$150 -
I can deliver all work in 3 working days
+$120
What the Freelancer needs to start the work
1. Sample de-identified or synthetic documents in your real formats.
2. Which identifiers must be removed or kept.
3. Where the pipeline will run (your cloud or local server).
4. Expected volume of documents.
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