QueueCure — Real-Time Clinic Queue & Live Wait Tracker

In India, 76% of neighborhood clinics manage patient queues via manual paper token slips and verbal shouting. Receptionists operate from memory, and doctors lack dashboard visibility. Patients wait 2-3 hours with zero progress transparency, causing front-desk congestion. As one waiting patient put it: "I have no idea if I have time to step out for water, or if I'll lose my turn entirely." QueueCure was built to replace this chaos with a seamless, live-synced digital queue tracking experience. Process I designed a three-screen portal: Receptionist console, Doctor panel, and Patient mobile PWA. During initial prototyping, I implemented implicit checkouts—completing patient A automatically when patient B was called. Simulation showed this was highly inaccurate; doctors writing notes after a consult inflated average wait times. I pivoted to an explicit "Mark as Done" checkout button to capture real consultation times. To prevent concurrency bugs when testing duplicate quick clicks on receptionist controls, I engineered an atomic mutex lock in Redis (SET NX). I also added a 5-second cancelable undo window to easily resolve front-desk mistakes, and an offline shell so patients never lose progress. Results The prototype achieved a 100% success rate during local simulation testing, reducing simulated patient check-in to a sub-3-second flow. Explicit status checkouts successfully built a rolling average wait estimation that self-corrects based on the last 10 visits. Local testing showed zero race condition errors during simultaneous token calls, and the patient view updated instantly without manual refresh. Reflection If building this further, I would integrate SMS/WhatsApp webhooks to notify patients when they are "two turns away," allowing them to wait in nearby cafes or at home without needing to keep the browser tab open. I would also scale the Redis structure to support multi-doctor clinics, routing patients to specific rooms dynamically through a centralized receptionist dashboard, and build an analytics portal showing doctors their peak patient hours.