Scaling medical content review at Flo Health with Amazon Bedrock – Part 2

This article describes how Flo Health built a production-grade AI-powered medical content review and generation system using Amazon Bedrock, scaling their medical team's capabilities without expanding headcount.

• Implemented specialized AI Judges for different review dimensions (medical accuracy, legal compliance, brand style) using MACROS architecture
• Reduced medical content review time by 60% and tripled content throughput without expanding the medical team
• Built AI content generation system with Retrieval Augmented Generation (RAG) grounded in trusted medical sources and internal guidelines
• Used chain-of-thought prompting with Claude Haiku for lightweight tasks and Claude Sonnet for high-fidelity content generation
• Adopted three-layer validation: internal guidelines, trusted external sources, and human expert review with AI-annotated content
• Captured expert corrections as reusable rules and examples, reducing repeated errors by over 70%
• Prioritized AI as intelligent assistant augmenting human expertise rather than replacing medical professionals

The system demonstrates how domain-specific AI judges, structured feedback loops, and tiered model selection maintain rigorous medical standards while scaling content production.