FHIR-Hopper: A Neuro-Symbolic Agent for Reasoning over EHRs

Electronic health records (EHRs), increasingly exchanged via the Fast Healthcare Interoperability Resources (FHIR) standard, encode patient histories as deeply nested, multi-relational bundles that are difficult for large language models (LLMs) to reason over directly. We introduce \emph{FHIR-Hopper}, a neuro-symbolic agentic framework that projects a FHIR bundle into an episodic chronological graph, materializes a budgeted, saliency-ranked linearization as the LLM's initial context, and exposes deterministic graph-traversal tools so the agent can recover details on demand. Across three realistic FHIR clinical benchmarks, FHIR-Hopper attains the highest accuracy across multiple base LLMs \emph{while keeping average input-token usage stable at (\sim20)k tokens, a typical (\sim10\times) reduction over the strongest retrieval baseline on long records}. These results suggest that decoupling structure-aware retrieval from neural reasoning is an effective design for clinical question answering over structured EHRs.