Generalization of structured graph constraints to real-world clinical documentation

Determine whether the structured graph constraints used in MedCEG—specifically the Evidence Graphs and Critical Evidence Graphs that supervise reinforcement learning—generalize effectively to unstructured, noisy real-time clinical documentation such as Electronic Health Records without additional adaptation.

Background

MedCEG introduces Evidence Graphs (EGs) and Critical Evidence Graphs (CEGs) to explicitly supervise medical reasoning, improving both accuracy and logical soundness on textual benchmarks. The training pipeline includes a cold-start phase using linearized EGs and a reinforcement learning phase guided by CEG-based rewards, with evaluations conducted on structured medical QA datasets.

The authors note that their experiments focus on textual benchmarks and that real-world clinical environments involve unstructured, noisy data (e.g., EHRs). They explicitly state uncertainty about whether the graph-based constraints that work on curated text generalize to raw clinical documentation without further adaptation, identifying this as a limitation and an open issue for future validation.