Transforming clinical scheduling with technology using MERN stack application in healthcare

Efficient clinical scheduling is critical to improving patient access, reducing no-shows, and optimizing healthcare resource utilization. Traditional appointment systems, often manual and fragmented, lead to inefficiencies, overbookings, and patient dissatisfaction. This paper presents CureConnect, a web-based doctor appointment booking and scheduling platform developed using the MERN stack (MongoDB, Express.js, React.js, Node.js) with integrated machine learning capabilities. The proposed system combines real-time booking, secure payments, role-based dashboards, and predictive intelligence to streamline the end-to-end scheduling workflow. A Gradient Boosting model (XGBoost) predicts appointment no-shows with an accuracy of 87.4% (AUC-ROC: 0.91), while an optimization engine dynamically reallocates slots to improve fairness and resource utilization. Experimental results demonstrate a 24.3% reduction in no-shows, a 15.6% increase in provider utilization, and a 19.8% decrease in patient waiting time, all with low inference latency (~120 ms). Furthermore, explainability is ensured through SHAP-based interpretation, and fairness audits confirm unbiased performance across demographic groups. By leveraging modern full-stack development with integrated predictive analytics, CureConnect offers a scalable, secure, and transparent solution for transforming clinical scheduling and advancing digital healthcare delivery.