Vishnu Varadhan

QueueCure '26 – Real-Time Digital Clinic Queue Management System

Many neighborhood clinics in India still rely on paper token slips and manual announcements to manage patient queues. Patients often wait 2–3 hours without knowing when their turn will arrive, creating frustration and crowding in waiting areas. Receptionists manually manage queues, token numbers, and patient records, increasing the chances of mistakes. Doctors also lack visibility into queue status. QueueCure was built to digitize this process through a real-time queue management system that provides live updates to both patients and clinic staff. Process I started by analyzing the core problems faced by small clinics: slow patient registration, lack of queue visibility, and manual queue management. The solution was designed around two user groups: receptionists and patients. The first version focused on role-based authentication and queue management. I then implemented real-time synchronization so that queue changes made by receptionists instantly appear on patient screens without refreshing. To improve usability, patient information was stored and automatically retrieved using a Patient ID, reducing repeated data entry. Several iterations were made to improve privacy, validation, and usability. Patient medical reasons were hidden from other patients, emergency contact validation was added, and wait-time estimation was redesigned to use co Results QueueCure successfully replaces paper-based queue management with a digital system that updates in real time. Receptionists can register patients and assign tokens quickly, while patients can track queue progress, current token status, and estimated waiting time from their own device. Key outcomes include: Real-time queue updates without page refresh Centralized patient record management Reduced manual queue handling by receptionists Dynamic wait-time estimation based on operational data Improved patient transparency and waiting experience Privacy-focused patient dashboard design Reflection If given more time, I would integrate automated payment verification through a UPI payment gateway instead of manual payment confirmation. I would also enhance wait-time prediction using larger historical datasets and machine learning models to improve accuracy for different consultation types. Additional features such as appointment scheduling, doctor-specific queues, multilingual support, and clinic analytics dashboards would further improve the platform's usefulness in real-world deployments.