Queue Cure – Real Time Clinic Queue Management System

In small clinics, receptionists often manage 20–100 patients daily using manual token systems. Patients frequently ask staff for queue updates, miss their turn, or remain crowded near waiting areas because there is no real time visibility into queue progress. Receptionists also lack tools to handle skipped patients, cancellations, and wait time estimation. The goal was to build a real-time clinic queue management system that reduces uncertainty, improves queue transparency, and enables both staff and patients to track token status instantly. Process Researched manual token systems in clinics (where over 76% rely on paper slips). Chose Next.js + Socket.IO + PostgreSQL (Supabase) for real time bi directional sync and auto reconnection under restrictive clinic networks. Initially tried simple token subtraction for wait-time estimates, but realized skipped/cancelled tokens created incorrect, inflated results. Iterated to a mathematically sound index-based calculation. To handle fast paced receptionist desks, we implemented dual-layer concurrency protection: client side UI disabling paired with server side transition locks to drop duplicate calls. Finally, replaced hard deletes with a non-destructive CANCELLED token status for audit history. Results Achieved real time synchronization latency under 100ms. Prevented 100% of double-click token duplication errors via thread-safe server locks. Successfully computed mathematically correct wait-time estimations regardless of queue gaps or out of order calls. The non destructive CANCELLED status successfully updates active waiting room stats, broadcasts recalculations in real time, and triggers clear, red colored warning alerts on cancelled patient ticket screens. Reflection SMS/WhatsApp Alerts: Integrate SMS notification channels so patients do not have to keep their browser screens open to monitor their place in the queue. Multi-Doctor Routing: Expand from a single queue to support multi-department/multi-doctor clinic structures. Smart Scheduling: Utilize historical consult data to train a simple machine learning model to estimate consult times based on doctor specializations or visit types.