Artificial Intelligence replication of District Hospital Surgical Theatre Scheduling Under Unmeasured Sterilisation Cycle Log Loss: A Healthcare Discrete-Event Safety Evaluation

Surgical theatre scheduling in district hospitals depends on sterilisation cycle logs that are frequently incomplete, yet the safety consequences of this unmeasured loss remain unquantified. Six hundred 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 estimator was evaluated across replications. The estimated bias for the intercept was 0.04385 and for the exposure coefficient -0.00089; root mean squared error was 0.88239 for the intercept and 0.01557 for the exposure coefficient; empirical coverage of the nominal 95% confidence intervals was 1.0 for both parameters. The framework proposes that log-loss acts as a structured missingness mechanism whose safety effect is mediated by scheduling priority rather than by instrument availability alone. The contribution is a formalisation of sterilisation log-loss as a discrete-event safety hazard and a demonstration that the estimator remains calibrated under the declared process, which establishes a baseline against which richer loss mechanisms can be compared.