- https://doi.org/10.1109/codit66093.2025.11321846
Data-driven Models for Predicting No-show Rates and Service Times in Outpatient Appointment Scheduling
- Jul 15, 2025
- M Fall +3 more
Outpatient clinics are integral to healthcare, offering vital services without the need for hospital admission. However, appointment scheduling in these settings remains challenging due to uncertainties such as patient no-shows and variable service times. This study proposes a data-driven approach to minimize physician idle time and patient waiting time by analyzing an eight-year (2016-2023) dataset from primary and specialized care for American veterans. Adopting the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology, four predictive models: Random Forest, Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), and Artificial Neural Networks (ANN), were developed for both classification (noshow) and regression (service time) tasks. The ANN model demonstrated superior predictive performance in both domains. The key predictors of no-shows included waiting time, type of care and care provider, while type of care, care provider, and veteran ZIP code were the most influential in forecasting service time. These findings highlight the potential of machine learning to improve appointment scheduling in outpatient clinics.
- # Type Of Care Provider
- # Standard Process For Data Mining
- # Cross Industry Standard Process For Data Mining
- # Need For Hospital Admission
- # Service Time
- # Extreme Gradient Boosting
- # Patient No-shows
- # Outpatient Clinics
- # Adaptive Boosting
- # Appointment Scheduling
