Artificial Intelligence Governance of Referral Pathway Routing Models Under Unmeasured Ambulance Dispatch Political Pressure: A Healthcare Robustness Evaluation

The problem is that AI governance evaluations of referral pathways typically assume that dispatch routing is insulated from political interference, yet in many health systems political actors influence ambulance allocation through informal channels that are not captured in administrative data. The paper characterises the operating characteristics of the BACI estimator under a declared variance structure with an intraclass correlation of 0.64, between-cluster standard deviation of 6.0, and residual standard deviation of 4.5. Power was estimated across 2000 design cells for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 54 clusters are required to detect a 5 percentage point effect at 80 per cent power. Unmeasured political pressure on dispatch routing operates as a cluster-level confounder that inflates the intraclass correlation and reduces effective power, and that governance evaluations must therefore treat cluster counts as a political as well as a statistical parameter. The contribution is a robustness framework that specifies the conditions under which BACI designs can credibly identify AI routing effects when political pressure is present but unmeasured.