PHI in Generative AI
Using patient data to prompt LLMs risks exposing PHI to external APIs (OpenAI, Google). HIPAA compliance requires private, encrypted AI on AWS.
Generative AI on AWS
HIPAA-Compliant Generative AI for Healthcare
Healthcare organizations want AI on their patient data but must maintain HIPAA compliance. We deploy Bedrock models on encrypted PHI, ensuring patient privacy while unlocking AI productivity.
- A use case is scoped against real data.
- The model runs on Amazon Bedrock.
- A knowledge path retrieves from your sources.
- Guardrails apply policy and filters before output leaves.
- A named owner reviews anything that reaches customers or records.
- The feature ships inside the existing application.
Key Challenges We Solve
AI Model Explainability
Clinical AI decisions must be explainable for physician trust and regulatory compliance. Black-box models cannot be used in clinical decision-making.
Data Governance for AI
AI training on patient data requires data governance: de-identification, consent tracking, and audit logs of which patient data trained which models.
Our Approach
Bedrock for HIPAA-Compliant AI
Use Amazon Bedrock (HIPAA-eligible) with customer-managed KMS encryption to run generative AI on encrypted PHI. Your data never leaves AWS.
Clinical AI Use Cases
Clinical note summarization (converting doctor dictations to structured EHR notes), diagnostic assistance (symptom → differential diagnosis suggestions), and patient communication templates.
Audit & Governance
CloudTrail audit logs of all AI model prompts and outputs, de-identification pipelines to prevent re-identification, and consent tracking for data usage.
Frequently Asked Questions
Is Amazon Bedrock HIPAA-eligible?
Yes. Amazon Bedrock is HIPAA-eligible and AWS will sign a BAA. When using Bedrock with customer-managed KMS encryption, all model prompts and outputs are encrypted and auditable.
Can we fine-tune Bedrock models on patient data?
Yes, with proper data governance. Fine-tuning on patient data requires de-identification (removing PHI), consent tracking, and audit logging. We implement data governance pipelines that enable fine-tuning safely.
What AI use cases work in healthcare?
Clinical note summarization (transcribe → structured EHR), diagnostic assistance (symptom screening), prior authorization automation, and patient education (generate patient-friendly treatment explanations). We avoid use cases where AI decisions alone (without physician review) impact care.
Ready to Get Started?
Talk to our AWS experts about hipaa-compliant generative ai for healthcare.