Analysis of the anti-scalping mechanism of hospital appointment registration based on Bayesian theory.

Objective: To evaluate the clinical effectiveness of a Bayesian-based anti-scalping mechanism integrated into a hospital appointment system in improving fairness, efficiency, and patient accessibility while reducing speculative bookings. Methods: A retrospective analysis was conducted from January 2019 to December 2022 (defined as the core observation period, with the intervention occurring in January 2021). Results up to mid-2023 are provided solely to demonstrate long-term trend stability. The Bayesian model identified "abnormal behaviors" based on a validated ground truth set, defined as accounts meeting at least two of the following criteria: (1) more than three cancelations within a 48 h window, (2) a single device ID associated with more than five medical card IDs, or (3) registration completion speeds faster than the 99th percentile of manual operation times (e.g., <2 s). These labels were manually verified by a cross-departmental audit team to minimize misclassification in the training set. Statistical comparisons were made using χ Results: After the anti-scalping mechanism was implemented, the appointment completion rate increased from 64.6% (2019) to 78.0% (2022) (χ Conclusion: Integrating Bayesian inference into outpatient appointment systems can effectively enhance fairness, efficiency, and patient trust by reducing speculative bookings and improving accessibility. The findings demonstrate that data-driven management frameworks can bridge the gap between algorithmic modeling and real-world hospital operations, providing a replicable strategy for intelligent, equitable healthcare resource allocation.