Prediction of Radiology Appointment Cancellations Using Machine Learning Models: A Comparison of City and State Hospitals

Online appointment systems are widely used in healthcare to improve scheduling coordination and facilitate patient access to services.Compared with traditional appointment methods, these systems may reduce administrative workload, support more efficient resource utilization, and improve operational workflow through centralized scheduling and automated processes (4-7).Recent studies have demonstrated that machine learning models can effectively predict radiology appointment no-shows with moderate to high accuracy, typically achieving area under the curve (AUC) values ranging from approximately 0.75 to 0.93 depending on the dataset and clinical context (8-13).However, evidence also suggests that model performance may vary across healthcare settings and often declines when transitioning from retrospective analyses to real-world deployment, highlighting the need for context-specific evaluation and continuous recalibration (8).