Medical Research Institution

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.