Optimizing Healthcare Scheduling Through Machine Learning: A Role-Based Doctor Appointment System

Improving access to healthcare services requires efficient digital solutions, particularly for managing doctor appointments. This study presents an advanced Healthcare Management System (HMS), a full-stack web application aimed at streamlining the appointment scheduling process in clinical environments. Built on the MERN stack—MongoDB, Express.js, React (with Vite), and Node.js—paired with Tailwind CSS for responsive design, the platform offers an intuitive interface for seamless user interaction. The system incorporates dedicated dashboards tailored to three user roles: Administrator, Doctor, and Patient, each providing secure, role-specific functionalities. Security is fortified through JWT-based authentication and Bcrypt.js for password encryption. The appointment module enables dynamic scheduling, real-time updates, and automated confirmations, with doctors managing their availability directly. Admins are equipped with tools for managing users, professionals, and appointments, alongside integrated analytics. Patients benefit from easy booking, flexible time selection, and secure transactions via Stripe and Razor pay gateways. The system’s development process emphasized maintainability and robustness through tools such as Visual Studio Code, GitHub, and Thunder Client. This HMS offers a scalable, secure, and intelligent approach to healthcare appointment management, with the potential for future integration of machine learning to enhance predictive scheduling and resource optimization. INDEX TERMS Healthcare Management System (HMS), Doctor Appointment Scheduling, MERN Stack, Machine Learning, Web Application, Role-Based Access Control, JWT Authentication, Stripe, Razor pay, MongoDB, React.js, Vite, Node.js, Express.js, Tailwind CSS, Secure Transactions, Real-Time Scheduling, Patient Portal, Admin Dashboard, Scalable Web Architecture.