Jeevan kumar M

Queue Cure '26 – Smart Queue. Better Care.

76% of India's 1.5 million clinics still run on paper token slips and shouting. Patients wait 2–3 hours with zero visibility into when they'll be called. Doctors have no dashboard. Receptionists manage everything from memory. Process started by analyzing how small and mid-sized clinics currently manage patient queues. The biggest issues were paper token systems, lack of wait-time visibility, and heavy reliance on receptionists for status updates. Patients repeatedly asked how long they would wait, while doctors had no centralized view of queue progress. To solve this, I designed a real-time dual-display system consisting of a Receptionist Dashboard and a Patient Lobby Display. I implemented Socket.IO to instantly synchronize queue updates across screens and built department-wise token management for General Medicine, Dental Care, ENT, and Pediatrics. Initially, I explored manual refresh mechanisms, but they created delays and poor user experience. This led to adopting WebSockets for live updates. I also introduced dy Results Queue Cure '26 successfully modernizes traditional clinic queue management by replacing paper-based token systems with a synchronized digital workflow. The platform provides real-time visibility into patient queues, consultation progress, and estimated waiting times through dedicated receptionist and lobby displays. Key achievements include: • Real-time synchronization between administrative and patient-facing screens using Socket.IO. • Dynamic wait-time estimation based on configurable consultation benchmarks. • Department-specific queue management for General Medicine, Dental Care, ENT. Reflection I would expand Queue Cure into a multi-clinic platform capable of managing queues across multiple branches from a centralized dashboard. I would also integrate AI-driven prediction models that learn from historical consultation patterns to provide increasingly accurate wait-time forecasts. While the current implementation effectively solves clinic queue visibility and management challenges, these improvements would further strengthen scalability, predictive accuracy, and enterprise adoption potential.