Beyond Patient-Level Triage: Pre-Arrival Care Navigation under Shared, Limited Capacity

Pre-arrival care navigation, recommending a care setting from a patient's pre-arrival description, offers an opportunity to direct patients to appropriate care before emergency-department (ED) resources are committed. EDs often care for patients whose needs could be met in lower-acuity settings, yet emergency triage largely assesses acuity and resource needs after ED arrival, when care-routing options have already narrowed. The decision serves two interests: each patient seeks capable and timely care, while the health system must keep care timely for patients with urgent needs; under constrained capacity, lower-acuity demand competes with later, more urgent arrivals. The patient's interest can be scored at every routing decision, but the system's interest is determined by a whole run of decisions and observed only after them, so it is hard to learn from single decisions. We introduce Stackelberg-Inspired Commitment Learning (SICL), a hierarchical framework for language-model routing policies that gives the system's interest a decision of its own. A system-level routing posture, held across a segment of arrivals, sets site-level incentives; each patient's destination is then chosen in the patient's own interest under those incentives; and during training the posture is credited with the segment's system-level outcome, according to how closely the routing followed it. We further construct TriageRoute, a dynamic emergency-care benchmark derived from MIMIC-IV-ED v2.2 that models heterogeneous care sites, travel delays, evolving queues, and workload-sensitive outcomes, where every policy sees the same patient information and care-network state and is given the same instructions. Despite identical information, policies differ widely in timely access for patients with urgent needs; in controlled simulation, SICL achieves the highest timely access among policies given the same information while also achieving the highest patient utility, at the cost of reduced capacity efficiency.