MediQueue AI – Real-Time Patient Queue & Waiting Room Management System

Many clinics still rely on manual patient queue management, leading to long waiting times, repeated inquiries at reception, and a lack of transparency for patients. Receptionists must manually track patient order, while patients often do not know their queue position or expected waiting time. This creates operational inefficiencies, increased staff workload, and a poor patient experience. The challenge was to design a real-time system that streamlines patient registration, token assignment, queue management, and live waiting-room updates from a single platform. Process I started by understanding the workflow at an ordinary outpatient facility and finding out the pain areas for receptionists and patients. These included the problem of manual token management, constant querying about status, and lack of visibility of wait time. Two screens were created in tandem – Admin Dashboard for patient registration and queuing, and a Waiting Room Dashboard for patients. A real-time database was created to make sure that all updates are done in real time across both dashboards. Wait times are automatically computed depending on consultation time and number of people in the queue. Many revisions were made to streamline the process. The initial design had separate screens for registration and queue control, but after getting feedback from users, it was found that they Results 100% real-time synchronization between admin and waiting-room displays. Single-screen workflow for registration, token assignment, and patient calling. Live wait-time estimation based on queue length and consultation duration. Reduced patient uncertainty by providing transparent queue tracking. Improved operational efficiency by minimizing manual queue management tasks. Enhanced patient experience through live token status and waiting-room visibility. Future Improvements: SMS/WhatsApp notifications for token updates. Doctor-specific queues and appointment scheduling. Analytics dashboard for Reflection If I were to continue developing this project, I would focus on improving scalability, user experience, and real-time communication. Currently, the system uses Spring Boot, MySQL, HTML, and CSS to provide core queue management functionality. In the next iteration, I would implement WebSocket-based real-time updates to eliminate page refreshes completely and provide instant synchronization between the admin and patient dashboards. I would also enhance the frontend by adopting a modern framework such as React to create a more interactive and responsive user experience. Additionally, I would add