PulseQueue

Clinics with multiple doctors often manage 30–100+ patients daily using manual queues or basic token systems. Patients frequently crowd near consultation rooms because they lack reliable wait-time estimates and fear missing their turn. Receptionists must manually handle delays, skipped patients, and room coordination, creating confusion and inefficiency. PulseQueue was built to provide real-time queue visibility, smart ETA prediction, voice-assisted patient calling, and synchronized management across multiple consultation cabins. Process I started with a simple queue system but quickly realized real clinics need support for multiple doctors. I redesigned the backend into a multi-cabin architecture with independent queues, real-time synchronization, and dynamic ETA prediction. One challenge was voice announcements overlapping when patients were called from different cabins simultaneously, which required redesigning the event flow. I also spent significant time improving TV screen readability by testing layouts, font sizes, and status indicators. Inspired by a real clinic visit where I had to guess when to return, I added QR-based queue tracking so patients can monitor their position remotely. I planned to add multilingual announcements using Sarvam AI, but cant complete that because of submission deadline. Results The system supports multiple doctor cabins with independent queues and real-time sync across receptionist and patient displays. Patients can check live queue status via QR code without waiting nearby, while receptionists manage priorities, skips, delays, and recalls from one dashboard. It evolved from a single-cabin prototype into a multi-cabin platform with ETA prediction, voice alerts, queue persistence, and delay handling. Tested across devices, it is fully deployed and accessible online. Reflection If I had more time, I would integrate Sarvam AI for multilingual voice announcements, replace file-based storage with a database for scalability, implement proper authentication and route protection for secure access control, and conduct testing with actual clinic staff and patients. These improvements would make the system more secure, scalable, and suitable for real-world deployment.