Incentivizing Agentic Retrieval for Disease-Centric Clinical Case Search via Trajectory Memory

Clinical case search aims to retrieve disease-consistent prior cases from electronic health record databases for a natural-language patient query under a retrospective retrieval objective. The task is challenging because patient queries contain heterogeneous evidence, including symptoms, diagnoses, laboratory results, treatments, and timelines. These fields are often only partially specified, and fixed retrieval pipelines cannot adapt from prior search trajectories. Existing clinical retrieval methods typically flatten the query into a single representation, use static retrieval pipelines, or lack an explicit mechanism for cross-case adaptation. \textbf{PatientSearch-SE} addresses retrospective disease-centric clinical case search with three components: a \emph{sufficiency-aware planner} that separates dimensions suitable for direct matching from those requiring hypothesis completion, a \emph{hierarchical tool orchestration} module that maps these states to retrieval and re-ranking actions, and a \emph{trajectory-guided memory consolidation} mechanism that writes reusable disease cards and strategy templates without parameter updates. We also propose an evaluation framework that combines disease-centric retrieval outcomes with trajectory-level process probes for retrieval behavior analysis. Experiments on PMC-Patients and MIMIC-IV show higher disease-centric retrieval metrics for PatientSearch-SE than for the compared same-interface sparse, dense, and reasoning-based baselines, with larger gains on PMC-Patients and more modest gains on MIMIC-IV. Its memory-accumulation results are consistent with controlled external-memory adaptation, and its retrieved cases provide additional evidence for diagnosis-masked downstream few-shot RAG prediction under this retrospective protocol.