Kavin Balagurunathan

Queue Cure – Real-Time Healthcare Queue Management System

Many small clinics and healthcare centers still rely on paper-based token systems and manual queue management. Patients often wait for long periods without knowing their position in the queue or estimated waiting time, leading to frustration and overcrowding. Receptionists must manually manage patient flow, while doctors have limited visibility into queue status. Queue Cure addresses this challenge by providing a real-time digital queue management system that enables patient registration, token generation, priority-based queue handling, wait time estimation, and live synchronization across rec Process The development process started by understanding the day-to-day challenges faced in clinic queue management, such as long waiting times, lack of visibility into the queue, and the continued use of paper-based token systems. We focused on understanding the needs of both patients and receptionists to identify where delays and communication gaps commonly occur. Based on this research, we designed Queue Cure as a simple and efficient real-time queue management solution. The MERN stack (MongoDB, Express.js, React, and Node.js) was chosen because it provides a scalable and reliable foundation for full-stack web development. Socket.IO was integrated to ensure that any queue updates made by the receptionist are instantly reflected on patient-facing screens without requiring manual refreshes. The Results Queue Cure successfully transformed the traditional paper-based token system into a real-time digital queue management platform. The solution provides patients with clear visibility into their queue position and estimated waiting time, reducing uncertainty and improving the overall waiting experience. Receptionists can efficiently manage patient flow through token generation and priority-based handling, while real-time synchronization ensures that updates are instantly reflected across all connected screens. The final system demonstrates improved operational efficiency, reduced manual effort, Reflection Given more time and resources, I would expand Queue Cure beyond receptionist and patient views by introducing a dedicated doctor dashboard that provides visibility into upcoming patients, consultation history, and daily schedules. I would also incorporate AI-based wait-time prediction using historical consultation data, patient priority levels, and queue patterns to generate more accurate waiting time estimates. Additional AI features such as intelligent patient prioritization, appointment demand forecasting, and automated insights for clinic staff could further improve operational efficiency.