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

India has over 1.5 million clinics, with a significant number still relying on paper token slips and verbal announcements to manage patient queues. This manual process creates long waiting times, confusion about queue status, and increased workload for reception staff. Patients often wait 2–3 hours without visibility into when they will be called, leading to frustration and poor clinic experiences. Receptionists must manage appointments, token allocation, and patient inquiries simultaneously, increasing the likelihood of errors and inefficiencies. Process I began by understanding the pain points faced by both patients and clinic staff in traditional queue systems. I identified three primary user groups: receptionists, doctors, and patients. Based on the hackathon requirements, I mapped the end-to-end patient journey from registration to consultation completion. I designed separate role-based dashboards tailored to each user's responsibilities. The receptionist interface prioritized speed and error prevention, enabling quick patient registration, token generation, and queue control. The patient waiting view focused on transparency by displaying the current token being served, tokens ahead in the queue, and estimated waiting times. For real-time synchronization, I implemented Socket.IO to ensure that updates made by receptionists instantly . Results Queue Cure successfully demonstrated real-time queue synchronization across multiple user interfaces, ensuring that token updates appeared instantly without requiring page refreshes. The platform delivered dynamic wait-time estimates based on consultation patterns, providing patients with greater transparency and reducing uncertainty during clinic visits. The application supported multiple user roles through secure authentication and role-based access control. The receptionist workflow streamlined patient registration and token management, reducing manual overhead and minimizing operational . Reflection Given more time, I would enhance Queue Cure by integrating appointment scheduling and SMS/WhatsApp notifications to proactively inform patients about their queue status. I would also introduce predictive analytics using machine learning models to generate more accurate wait-time estimates based on doctor-specific consultation patterns and historical clinic data. From a technical perspective, I would implement automated testing pipelines, improve observability through centralized logging and monitoring, and adopt a distributed queue architecture to better support high-volume .