Loading...
Loading...
Automated PII Detection and Redaction for Clinical Data
A medical research institution needed to share clinical data with external researchers while maintaining HIPAA compliance. Manual redaction of PII (names, dates, locations, medical record numbers) from hundreds of documents per month was time-consuming and carried high risk of human error exposing protected health information.
We developed an automated PII detection and redaction system using machine learning models trained on medical data. The system identifies 18 types of PHI, applies context-aware redaction, maintains document formatting, and generates detailed redaction reports. All processing happens in a HIPAA-compliant environment with encryption at rest and in transit.
Implementation timeline was 8 weeks: Week 1-2: PHI taxonomy definition and test dataset creation Week 3-4: ML model training and validation (95%+ accuracy target) Week 5-6: Redaction pipeline and document processing workflows Week 7: Security hardening and HIPAA compliance validation Week 8: User acceptance testing and knowledge transfer The system processes Word docs, PDFs, and spreadsheets with consistent accuracy.
The system has processed over 5,000 documents with 97% PII detection accuracy. Manual review time dropped from 20 minutes per document to 3 minutes for final verification. Zero PHI exposure incidents since deployment. The institution now shares data with external researchers 10x faster than before.
Key Outcomes: