AI Clinical Workflow Infrastructure

Healthcare AI proof-of-concepts often look impressive at first, but many fail under technical review because the model answers without evidence. In a clinical setting, an unsupported answer is risky and difficult to trust. The core problem is how to let a clinician ask useful questions about a patient record while keeping the model grounded in actual FHIR data, refusing when information is missing, and leaving an audit trail that engineering and clinical teams can review.

We separated the clinical UI from the retrieval and answering layer. Synthetic Synthea FHIR bundles load into a local index; each question triggers intent classification and retrieval of only the most relevant Patient, Observation, Condition, MedicationRequest, AllergyIntolerance, Encounter, and Procedure resources. The LLM receives a constrained prompt built from that context alone, returns answers with resource-level citations, and logs every interaction. An evaluation harness with negative-control questions measures pass/fail behavior before production handoff.