Hospital crises rarely emerge as isolated events; they evolve as progressive trajectories of fragilization in which pressure accumulates, instability increases, and decision windows gradually close. Yet most operational AI remains event-centric, optimizing forecasts or alarms rather than supporting the sequential, constrained decisions that govern real crisis response. This paper proposes a decision-oriented neuro-symbolic approach to crisis anticipation, where the goal is not merely to predict failure but to preserve controllable time through governance-ready outputs: an interpretable operational state, a near-horizon escalation outlook, and an auditable Evidence-to-State-to-Trajectory-to-Action justification chain.

We instantiate the framework on 169 weeks of NHS weekly A&E sitreps published by NHS England, leveraging routinely reported indicators (demand, 4-hour performance, and severe delay markers) to extract weak-signal trajectory primitives capturing sustained strain and loss of stability. A symbolic layer then maps these primitives into a compact four-state fragilization timeline (LOW/STRAIN/UNSTABLE/CRITICAL) with explicit semantics and constrained escalation logic, ensuring stable and defensible transitions rather than noisy alerting. In time-respecting evaluation, the neuro component achieves approximately 94% discrimination (AUC ≈ 0.94) for next-week performance breach, while the neuro-symbolic fusion converts this predictive power into actionable, explainable-by-design escalation states suitable for operational governance.

The contribution is a deployable anticipation layer that reframes crisis AI around trajectories and decision windows: not “will a crisis happen,” but how resilience is eroding and what can still be changed before escalation becomes inevitable.