Abinav M

Queue Cure '26

Many clinics still manage patient queues manually using paper tokens and verbal calls. Patients often wait without knowing their position in the queue or expected waiting time. Receptionists must track appointments and queue order manually, which can lead to delays and mistakes. Queue Cure '26 solves this by providing a real-time digital queue system with appointment booking, doctor-wise queues, live updates, and estimated waiting times for patients. Process I started by analyzing the workflow of receptionists, doctors, and patients. The system was designed around a central queue database with real-time synchronization. First, I built appointment booking and token generation. Then I implemented separate queues for each doctor and status tracking for Waiting, Serving, Completed, and Missed patients. Socket.IO was added to instantly sync updates across screens. Finally, I integrated Supabase for persistent storage and added average consultation time calculation and daily queue reset functionality. Results Successfully developed a full-stack real-time clinic queue management platform. The system supports appointment booking, doctor-specific queues, live patient updates, queue status tracking, average consultation time calculation, and automated queue reset. Receptionists can manage queues efficiently while patients receive transparent waiting information. All updates are synchronized instantly without page refresh using Socket.IO and data is securely stored in Supabase. Reflection With more time, I would add SMS notifications, doctor mobile dashboards, . I would also improve waiting-time prediction using historical consultation data and conduct testing with real clinic staff and patients. These enhancements would improve usability, scalability, and make the platform suitable for production deployment in healthcare environments.