Artificial Intelligence Conformalised Triage of Community Clinic Chest Pain Presentations Under Unmeasured Electrocardiograph Lead Reversal: A Healthcare Safety Guarantee Evaluation

Electrocardiograph lead reversal is a common, frequently undetected pre-analytical error in community clinic chest pain triage, and its interaction with artificial-intelligence-based decision support has not been characterised as a safety-guarantee problem. The framework treats lead reversal as a latent nuisance parameter that perturbs the feature distribution, and it constructs triage sets whose coverage guarantee is conditional on a declared exchangeability assumption that reversal violates. A before-after-control-impact design was used to characterise the operating characteristics of the evaluation under a declared variance structure with between-cluster SD of 6.0, residual SD of 4.5 and intraclass correlation of 0.64; 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 52 clusters are required for 80% power at a 5-percentage-point effect. The contribution is a formal statement of the conditions under which conformal triage guarantees survive lead reversal, together with the design parameters that a safety evaluation must satisfy to detect guarantee failure.