AI-Powered Demand Forecasting for Outpatient Appointment Scheduling to Maximize Capacity Utilization and Patient Satisfaction

Abstract

Efficient outpatient scheduling is a persistent challenge in healthcare, where balancing provider capacity with unpredictable patient demand is critical to operational performance and patient satisfaction. Traditional scheduling systems often fail to adapt dynamically to complex, real-world variations in demand and resource availability. This paper presents a novel framework for outpatient appointment scheduling that integrates advanced machine learning techniques with stochastic optimization to maximize both capacity utilization and patient satisfaction. We develop a comprehensive approach that incorporates temporal patterns, patient demographics, diagnoses, and environmental factors to generate accurate demand forecasts. Our methodology integrates recurrent neural networks with attention mechanisms and Gaussian process regression to capture complex dependencies and uncertainty in healthcare demand. We further develop a multi-objective optimization model that transforms these forecasts into optimal scheduling policies, balancing institutional efficiency against patient experience metrics. Extensive simulations using both synthetic and real-world datasets demonstrate that our approach reduces patient wait times by 27.3\% while increasing provider utilization by 18.4\% compared to current best practices. Sensitivity analyses reveal that our method is robust to varying levels of appointment cancellations and no-shows, maintaining performance advantages across diverse clinical settings. By quantifying the tradeoffs between competing objectives and providing an interpretable framework for decision-making, this research constitutes a significant advancement in healthcare operations management and provides actionable insights for healthcare administrators seeking to optimize resource allocation while improving patient satisfaction.