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3. Dual-read labs Our audit found HbA1c 8.2% in SQL but missing from FHIR Observations. FHIR-only would've missed the main diabetes teaching fact. So: FHIR primary → REST fallback → same ledger shape. Trends computed in code, not inferred by the model.
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Key Decisions 1. Structural verification We don't fact-check the LLM after the fact. The model only phrases rows that exist. Every agenda item & explain sentence carries ledgerRowIds[] — orphan claims don't render. Hallucinations become unrenderable, not just detectable.
I've built and shipped my first production level agent. 🤖🔥 As part of the AI engineering fellowship at @gauntletai we were tasked with building and integrating an AI agent into @OpenEMR , the most used open-source codebase to manage medical records. 🧵 loom.com/share/5fbded11…
2: Gateway before the LLM PHP gateway inside OpenEMR handles session, ACL, and FHIR/REST reads. Python sidecar runs LangGraph + LLM — but never touches FHIR directly. Scoped capability token: userId + patientUuid + correlationId. Trust stays in the EHR layer.
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