ADITYA SONAWANE

Real-Time Digital Queue Management System for Neighborhood Clinics

Neighborhood clinics manage patient queues using paper token slips and verbal announcements. Patients have no way to track their position without standing near the reception desk, leading to crowded waiting areas, constant interruptions to staff, and high uncertainty about wait times. Receptionists waste time answering "how long?" repeatedly instead of focusing on registration. There was no affordable, real-time digital solution built specifically for small clinics — so we built one from scratch. Process We started by identifying the two core users — the receptionist and the waiting patient — and mapped their pain points separately. The receptionist needed speed: registering a patient had to take under 10 seconds. The patient needed clarity: position in queue, patients ahead, and a realistic wait estimate. We chose React + Vite for the frontend, Express.js for the backend, Socket.IO for real-time sync, and MySQL for persistent storage. Early on we considered polling instead of WebSockets, but rejected it because even 5-second polling would feel laggy during a live demo and under real clinic load. The hardest design decision was the ETA calculation. We avoided hardcoded values and instead built a rolling average from actual completed consultation durations, which makes predictions improve Results The system registers a patient and updates all connected screens in under 1 second. The receptionist dashboard handles the full workflow — register, call next, complete, and skip — each in a single click. Patients on their phones see their exact queue position, number of people ahead, and a dynamic estimated wait time that recalculates after every consultation. The rolling average ETA becomes more accurate as the day progresses. Both screens work across all devices with no app install, no login, and no page refresh required. Reflection I'd add a QR code on the receptionist screen that patients can scan to instantly open their personal token view — removing the need to type the URL manually. I'd also build a daily analytics view showing peak hours and average consultation times per day so clinic staff can plan staffing better. On the technical side, I'd extract the ETA logic into a dedicated service and write unit tests for the concurrency-sensitive queue operations. I underestimated how much time the database transaction design would take and would allocate more time to it upfront.