Building HIPAA-Compliant AI Pipelines

Healthcare AI presents a unique challenge: the data you need to train models on is also the most sensitive data your organization holds.

The Challenge

Our client, a US-based telehealth provider, wanted to use natural language processing to triage patient messages. But every message potentially contained PHI — protected health information under HIPAA.

Our Architecture

We designed a three-layer pipeline:

• •Ingestion Layer: All patient data enters through an encrypted gateway with TLS 1.3. Data at rest uses AES-256 encryption with customer-managed keys in AWS KMS.

• •De-identification Layer: Before any data touches our ML models, it passes through a custom NER (Named Entity Recognition) model trained specifically on medical text.

• •Processing Layer: The de-identified text is processed by our fine-tuned LLM for triage classification. The model runs in an isolated VPC with no internet egress.

Results

• •98.7% PHI detection accuracy
• •Zero HIPAA violations in 18 months of production
• •40% reduction in nurse triage time
• •Passed OCR audit with zero findings