Medical Report De-identification with Generative AI

Project Snapshot

• Industry: Healthcare / HealthTech

• Client Type: Healthcare Providers & Enterprises

• Duration: Multi-phase implementation

• Deployment Model: Cloud-based microservices (AWS)

• Technologies: Python, FastAPI, PyTorch, Docker, Open Source LLM (LLaMA 3.1), DocTr, OpenCV, Prompt Engineering, EC2, S3, CI/CD, HIPAA Compliance, Generative AI

The Challenge

Healthcare organizations face strict requirements for protecting patient privacy under HIPAA and other compliance frameworks. Challenges included:

• Automating the de-identification of sensitive medical data from reports while ensuring regulatory compliance

• Accurately identifying PHI (personally identifiable health information) in both structured and unstructured text

• Delivering a scalable, cloud-native solution that integrates seamlessly with existing healthcare data pipelines

• Maintaining secure storage and deployment practices while optimizing for performance and accessibility

Our Solution

We engineered a HIPAA-compliant Generative AI microservice designed specifically for medical report de-identification:

• Text Detection with DocTr → Reliable extraction of medical report text from scanned or digital files

• Sensitive Data Identification with LLaMA 3.1 → Open-source LLMs with multi-shot prompting to improve extraction accuracy for patient identifiers

• Data Masking → OpenCV-based pixel-level masking and redaction of sensitive fields

• Cloud Deployment → FastAPI microservice deployed on AWS EC2, with secure storage on S3 and Dockerized CI/CD pipelines for efficient updates

• HIPAA Compliance → Designed with privacy, security, and compliance at the core

The Impact

This solution delivered measurable improvements for healthcare data management:

• 100% HIPAA-Compliant → Secure and compliant handling of medical data

• Improved Accuracy → Multi-shot prompting significantly boosted de-identification reliability

• Automated Redaction → Reduced manual review workload and minimized human error

• Cloud-Native Scalability → Seamless integration and scaling across healthcare systems

• Secure Storage → AWS-based deployment ensured compliance and accessibility

Our Role

We partnered with the client to:

• Architect and develop a HIPAA-compliant AI microservice

• Integrate LLM-based sensitive data detection with advanced prompting techniques

• Deploy and scale the solution in a cloud-native environment

• Implement CI/CD pipelines for continuous delivery and updates

• Ensure compliance-driven system design

Client Testimonial

“The medical report de-identification system has streamlined compliance and reduced manual review significantly. With AI-driven accuracy and HIPAA compliance, we are confident in our ability to handle sensitive data securely.”
— Chief Technology Officer, Healthcare Enterprise