Predicting Patient No-Shows Using Machine Learning to Optimize Clinic Scheduling (The Science Post, 2025)
Patient no-shows for scheduled appointments represent a critical operational and financial challenge for healthcareproviders, leading to wasted resources, reduced access to care, and increased wait times. This paper investigates theapplication of machine learning (ML) to predict the risk of a patient missing their appointment. By analyzinghistorical electronic health record (EHR) and scheduling data, predictive models can identify patients with a highprobability of no-show, enabling clinics to implement targeted interventions such as reminder calls, overbookingstrategies, or offering slots to waitlisted patients. This research details the development of several classificationalgorithms, including Logistic Regression, Random Forest, and Gradient Boosting, to forecast no-show events. Theresults demonstrate that ensemble methods can accurately identify high-risk patients, providing a data-drivenfoundation for improving clinic efficiency, maximizing resource utilization, and enhancing patient access to care