Multi-Year Replication of Multi-Flow Collapse Prediction: Intersection Coverage Validation of the Ω Collapse-Risk Functional in NHS Healthcare System Dynamics

Boundary note (added 2026-07-23): the Ω-centered empirical layer was self-retracted on 2026-05-31. Ω-centered validation is not currently supported as the active empirical basis; this record remains a historical, fixed, Ω-centered record and must not be cited as current validation. See the VOT Empirical Layer Boundary Note — https://doi.org/10.5281/zenodo.20474232. A later four-dataset head-to-head found that order gated by critical slowing does not beat free-energy signals (10.5281/zenodo.21422215, negative result). This study presents a multi-year replication analysis of the Ω collapse-risk integration functional using NHS England regional healthcare operational data. Building upon the prior single-year empirical evaluation (DOI: 10.5281/zenodo.18611100), this research validates the temporal stability and cross-cycle predictive consistency of multi-flow collapse prediction using deterministic intersection coverage methodology. The study integrates heterogeneous operational data streams including ambulance response performance, discharge delay metrics, workforce absence indicators, and urgent care system stress signals. Collapse events are operationalized using fixed percentile thresholding with persistence duration criteria, preserving methodological continuity with the prior study. A deterministic intersection coverage panel was constructed to ensure multi-source temporal overlap without imputation, prioritizing cross-flow structural validity over raw observation volume. Predictive performance of the Ω functional was evaluated against utilization-only baseline indicators across multiple collapse domains. Results demonstrate consistent predictive improvement across multiple healthcare system collapse domains, including workforce capacity failure and ambulance response performance collapse, supporting the robustness of multi-flow integration as a collapse-risk detection framework. This study focuses on methodological replication and directional predictive stability rather than statistical inference estimation. Bootstrap uncertainty quantification is intentionally excluded and identified as a future research extension. Scientific Scope Boundary: The Ω functional is defined strictly as a collapse-risk integration predictor and does not represent a conserved physical quantity, causal law, or normative optimization metric. Cross-DOI Research Continuity: This publication extends a structured research program investigating multi-flow collapse detection and infrastructure failure prediction through deterministic replication design. Keywords emphasize healthcare system resilience monitoring, rare-event collapse prediction, multi-flow integration, and reproducible infrastructure risk analytics.