Artificial Intelligence Governance of Predictive Sepsis Alert Suppression Under Unmeasured Nurse Workload Adaptation: A Healthcare Political Safety Evaluation

Predictive sepsis alert systems increasingly incorporate suppression logic that withholds alerts when the clinical workload of the receiving unit is judged to be high. Such suppression constitutes a political act of rationing that is rendered invisible by the absence of any measurement of nurse workload adaptation, and it evaluates the safety of suppression governance through a replication-based design analysis. The study characterises the operating characteristics of a before-after-control-impact 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, a requirement that exceeds the cluster counts typically available to hospital sepsis programmes. The paper concludes that governance of alert suppression must treat nurse workload adaptation as a measurable political variable rather than an unmeasured confounder, and it specifies the institutional conditions under which suppression can be rendered accountable.