Artificial Intelligence replication of District Hospital Pathology Laboratory Sample Routing Under Unmeasured Barcode Misread Rates: A Healthcare Robustness Evaluation

Barcode misread rates in district hospital pathology laboratories are rarely measured, yet routing decisions depend on them. The study characterises the behaviour of the ordinary least squares estimator under a declared data-generating process with 600 replications of 500 observations each, using a seed of 666930726. The estimator's bias for the intercept was -0.00116 and for the exposure coefficient 0.00042; root mean squared errors were 0.85157 and 0.01567 respectively; empirical coverage of nominal 95% confidence intervals was 1.0 for both parameters. These results indicate that under the stated process the estimator is approximately unbiased and that interval coverage is conservative. Robustness evaluation of routing replications must distinguish between parameter recovery and decision recovery, and it proposes a decision-theoretic criterion for that distinction. The contribution is a formal framework for evaluating routing replications when the misread rate is a latent quantity, together with a demonstration that parameter-level robustness does not guarantee decision-level robustness.