Artificial Intelligence replication of District Hospital Blood Bank Inventory Rotation Under Unmeasured Donor Deferral Misclassification: A Healthcare Robustness Evaluation

Donor deferral misclassification in district hospital blood banks is rarely observed directly, yet it propagates into inventory rotation decisions through the estimated effect of deferral status on unit issuance. the present analysis treats the problem as an errors-in-variables question and evaluates the robustness of ordinary least squares under a declared data-generating process. A replication-based evaluation was conducted: 600 data sets of N = 500 were drawn from a process with intercept 12.0, exposure 0.42, group -3.1, severity 1.35 and error SD 4.2, and the ['Intercept', 'exposure', 'group', 'severity'] estimator was characterised across replications. The exposure coefficient was estimated with bias -0.00102 and RMSE 0.016, while the intercept showed bias 0.05828 and RMSE 0.90541; empirical coverage of the nominal 95% intervals was 1.0 for both the intercept and the exposure coefficient. The sampling distribution of the exposure coefficient was approximately normal and the Q-Q plot was close to the identity line. The results indicate that under the stated process the estimator is essentially unbiased for the exposure coefficient, that interval coverage is conservative rather than nominal, and that the intercept is the fragile quantity. The contribution is a bounded robustness statement: misclassification of donor deferral status, when it enters as a mean-zero perturbation of the exposure, does not by itself destabilise the rotation-relevant coefficient, but the intercept and any rotation rule that depends on the level of issuance remain exposed.