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

Many clinics in India still rely on paper token slips and manual announcements to manage patient queues. This creates confusion, long waiting times, and poor visibility for patients regarding when they will be called. Receptionists often struggle to manage queues efficiently, especially during peak hours. We built Queue Cure '26 to digitize this process by providing a real-time queue management system with live updates, estimated waiting times, and voice announcements to improve both patient experience and clinic operations. Process We first analyzed the workflow of a typical clinic and identified key pain points faced by patients and receptionists. Based on these findings, we designed two interfaces: a receptionist dashboard and a patient waiting screen. Flask was used as the backend framework, while Socket.IO enabled real-time synchronization between screens. We implemented features such as token generation, calling the next patient, voice announcements, wait-time estimation, and queue reset functionality. The application was tested by simulating multiple patients to ensure smooth updates and a better user experience. Results Queue Cure '26 successfully provided real-time synchronization between receptionist and patient screens without requiring page refreshes. The system reduced confusion in patient queues by displaying the currently serving token, upcoming patients, and estimated waiting times. Voice announcements improved accessibility and ensured patients did not miss their turn. Through testing, the solution demonstrated a faster and more organized clinic workflow compared to traditional paper-based token systems. Reflection Given more time, I would integrate a database to persist queue data across server restarts and support multiple clinics simultaneously. I would also add SMS/WhatsApp notifications for patients, analytics for clinic administrators, and improve the wait-time prediction model using historical consultation data. Further user testing with actual clinics would help refine the interface and uncover additional edge cases.