KOSMOS: Ontology-Based Knowledge Graph Scaffolding for Medical Documentation Generation

We investigate whether an ontology-typed knowledge graph (KG) can improve SOAP note generation from clinician–patient encounter transcripts by serving as a structured intermediate representation that organizes clinically salient content while preserving provenance. We introduce Knowledge graph Ontology Supported Medical Output System (KOSMOS), which extracts typed clinical entities with attributes and relationships, grounds entities to UMLS concepts and a schema, and retains links to supporting transcript turns. The resulting graph is provided as context for large language model (LLM)-based SOAP generation either alone (KG-only) or combined with the original transcript (Transcript + Nodes, Transcript + KG). We evaluate these conditions against DocLens and Ambient Clinical Intelligence Benchmark (ACI-BENCH) baselines on their benchmark, claim, and citation analyses. Across all three test sets, transcript-inclusive KOSMOS variants achieve the highest raw scores, numerically exceeding the transcript-only baselines. Claim-level evaluation shows modest, non-significant recall gains for Transcript + Nodes and low hallucination under transcript-conditioned GPT-5.2, while citation analysis shows about a 3% accuracy gain for KOSMOS (Transcript + KG) over DocLens GPT-5.2. Overall, ontology-guided KG structure appears most beneficial as a complementary scaffold paired with transcript access, while relationships provide limited additional benefit under current extraction quality.