Artificial Intelligence Benchmarking of Drug-Drug Interaction Alert Suppression Under Unmeasured Renal Function Assay Rounding in District Formularies: A Healthcare Safety-Robustness Evaluation

Drug-drug interaction alert suppression in district formularies depends on renal function estimates that are themselves rounded before they reach the rule engine, and the safety consequences of that rounding are rarely quantified. the present analysis treats alert suppression as a decision rule operating on a discretised renal covariate and derives the operating characteristics of a before-after-control-impact design for evaluating suppression policies under unmeasured assay rounding. The design is characterised analytically and by a replication-based power analysis across 2000 design cells, with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64. Power curves, minimum detectable effect curves, a kernel density of estimated power, coefficient intervals, a power-versus-cluster scatter and a residual diagnostic are reported for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. Fifty-two clusters are required to detect a 5 percentage point change at 80 per cent power. The contribution is a formal statement of the rounding-induced bias in suppression thresholds and a design-level account of how that bias propagates into benchmark estimates, with implications for how district formularies should report renal covariates to artificial intelligence safety benchmarks.