Thanusha Thanusha

Queue Cure '26 – Real-Time Smart Clinic Queue Management System

Most small and medium-sized clinics in India still rely on manual token systems and verbal announcements to manage patient queues. Patients often wait for long periods without knowing when they will be called, leading to frustration and uncertainty. Receptionists manually track queue status, while doctors have limited visibility into patient flow. The goal was to build a digital queue management system that provides real-time updates, estimated waiting times, and transparent queue tracking for all stakeholders. Process I started by analyzing the workflow of a typical clinic and identifying the needs of three primary users: receptionists, patients, and doctors. The system was designed around real-time communication, ensuring that any action taken by the receptionist would instantly reflect across all connected screens. A WebSocket-based architecture was implemented to synchronize queue updates without requiring page refreshes. The receptionist dashboard allows patient registration, token generation, and queue management. The patient waiting room displays the currently served token, tokens ahead, and estimated waiting time. Additional features such as QR-based queue tracking, voice announcements, smart wait prediction, and consultation analytics were added to improve usability and transparency. Results The final solution successfully digitizes clinic queue management through a centralized real-time platform. Receptionists can efficiently manage patient flow, patients gain visibility into their waiting status, and doctors can monitor queue activity through dedicated dashboards. Key outcomes include: Real-time queue synchronization across multiple interfaces. Dynamic wait-time estimation based on consultation duration. QR-based patient tracking without requiring manual inquiries. Voice announcements for token calling. Live analytics and queue monitoring capabilities. Reflection Given additional time, I would integrate persistent cloud database storage instead of local storage to support multiple clinic branches and long-term data retention. I would also implement role-based authentication, appointment scheduling, SMS/WhatsApp notifications, and AI-powered wait-time prediction using historical consultation data. Additional improvements would include multilingual support, mobile-first optimization, and advanced analytics dashboards to help clinics identify bottlenecks and improve patient flow management.