How to Move from an LLM Demo to a Production-Ready Healthcare AI Agent
Building an AI demo is easy.
Building a healthcare AI agent that works in production is a different problem.
A prototype only needs a chat UI, an API, and a prompt. But if your system touches patient info, billing, or EHR data, the architecture changes. The model is no longer the product. The system around the model becomes the product.
Focus on these layers before you build:
Data Flow and PHI Boundaries Do not just ask which model to use. Ask what sensitive data enters your system and where it goes. PHI can leak into logs, traces, embeddings, and monitoring tools. Define a strict boundary for where PHI enters, stays, and leaves.
Permissioned Retrieval (RAG) In healthcare, RAG is not just about quality. It is about permissions. A billing staff member should not retrieve the same documents as a physician. Use metadata filters and role-based access to ensure users only see what they are allowed to see.
Intentional Audit Logs Logs are not just for debugging. You must track who used the agent, what they asked, what data was retrieved, and if a human approved the output. Do not dump full prompts into logs if they contain PHI.
Human Review as Risk Control Human review is not just a feature. It is a safety layer. For high-risk tasks like clinical summaries or medical documentation, the AI must suggest a draft that a human approves before it reaches a patient or an EHR.
Reliable Integration Connecting to an EHR requires more than a simple API call. You must handle authentication, patient matching, FHIR mapping, and failure handling.
The real engineering work happens in the parts users do not see:
- Access control
- Auditability
- Data boundaries
- Retrieval permissions
- Human review workflows
The cost of healthcare AI is not the cost of the model. It is the cost of the system that makes the model safe for a regulated environment.
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