HIMANSHU KHANDELWAL

Queue Cure — Real-Time OPD Queue Management System

Many small clinics still rely on paper tokens, verbal announcements, and manual queue management. This creates two major problems: patients have no visibility into when their turn will arrive, and receptionists must continuously manage the queue manually. As a result, patients often wait for long periods without knowing their position in the queue, while reception staff spend significant time handling token flow instead of patient assistance. The challenge was to create a simple digital queue management system that provides real-time queue visibility, live updates across multiple screens, an Process I began by identifying the minimum workflow required inside a clinic: patient registration, queue management, token calling, and patient visibility. Instead of building a large hospital management system, I focused only on the queueing problem defined in the challenge. The solution was divided into two synchronized interfaces: 1. Reception Dashboard - Add patients - Call next token - Manage queue state 2. Patient Waiting Room - Display active token - Show patients ahead - Show estimated waiting time To ensure both screens remained synchronized without refreshes, Socket.IO was implemented for real-time communication. Consultation durations are recorded as patients are processed, and waiting times are recalculated dynamically using actual consultation data rather than h Results The final solution successfully delivers real-time queue updates across both screens without requiring page refreshes. Queue actions performed by the receptionist are reflected instantly in the patient waiting room display. Patients can view the currently active token, understand how many patients are ahead of them, and receive estimated waiting times based on real consultation data. The system also reduces receptionist workload by providing a structured dashboard for patient registration and token management. Reflection Given additional time, I would improve wait-time prediction by considering visit type, historical consultation patterns, and doctor-specific consultation speeds rather than relying only on average consultation duration. I would also introduce persistent database storage, multi-doctor support, and analytics for daily patient flow. These additions would make the system better suited to larger clinics while preserving the simplicity of the current experience.