A Knowledge-Based Probabilistic Framework for Enhancing Reliability in Healthcare Appointment Scheduling

Missed appointments in outpatient healthcare settings continue to be a widespread operational and clinical challenge, resulting in inefficiencies, disrupting care continuity, and contributing to financial losses. Addressing this problem requires analytical frameworks that extend beyond descriptive reporting toward interpretable, decision-oriented models. This study develops a knowledge-based probabilistic framework that integrates advanced machine learning (ML), probabilistic graphical models, and causal inference to predict and mitigate nonattendance. Leveraging 110,526 outpatient records from Brazilian healthcare facilities, the framework applies a structured pipeline that combines systematic feature selection, hybrid class balancing, and model training within a stratified nested cross-validation design to ensure unbiased performance estimation and reduce overfitting. The best-performing model achieved an accuracy of 0.93, an AUC of 0.83, and a macro-F1 of 0.83, surpassing benchmark approaches and providing a foundation for subsequent analyses. Building on these predictive results, Bayesian belief networks (BBNs) were employed to capture probabilistic dependencies among patient, temporal, and appointment-related variables, thereby enhancing interpretability, supporting scenario-based reasoning, and providing the foundation for causal analyses. In the final stage, average treatment effect (ATE) estimation and uplift modeling were applied to evaluate the impact of SMS reminders. Results demonstrate an average 15% reduction in nonattendance probability, with the strongest uplift observed among patients with repeated missed visits. This reduction corresponds to an estimated annual saving of approximately $670,000 in unused clinical capacity. Collectively, the integration of predictive modeling, probabilistic knowledge representation, and causal inference provides a coherent framework that is both analytically sound and operationally relevant, offering actionable tools to optimize scheduling, allocate resources efficiently, and enhance patient access.