DEVESH KUMAR T S CZ

ClinicQ: Real-time Smart Queue Management for Healthcare

Patients often experience high anxiety and wasted time due to unpredictable waits in clinic waiting rooms. Simultaneously, reception staff struggle to manage physical tokens, seamlessly handle emergency priority shifts, and communicate doctor availability efficiently. This lack of transparency leads to crowded lobbies, repetitive questions, and a frustrating experience for both patients and healthcare providers. Process I started by mapping the typical patient journey in a busy clinic to identify bottlenecks. The biggest issue was the manual, non-transparent token system. I decided to build a digital dashboard with separate, focused views: one for patients (to track their turn) and one for receptionists (to manage the flow). I initially considered using standard API polling for updates, but realized it wasn't responsive enough for a live waiting room. Instead, I pivoted to using Supabase for instant real-time data syncing, ensuring the queue updates immediately without page reloads. I also made sure to design a premium, high-contrast dark mode UI so the screen is easily readable from a distance. Receptionist ACCESS PASS: 1234 Results The outcome is a frictionless, fully digitized queue system. The receptionist dashboard successfully handles real-time token creation, emergency prioritization, and doctor status updates. The live patient view updates instantly, providing a single source of truth that reduces the need for patients to ask staff about wait times. The system successfully replaces physical tokens with a robust, real-time web interface. Reflection Next time, I would focus heavily on taking the experience off the screen and into the user's pocket. I'd integrate an automated SMS or WhatsApp notification service so patients can wait at home or a nearby cafe and get pinged when they are 2 tokens away. Additionally, I would build an analytics dashboard to track average consultation times over weeks to generate dynamic, AI-driven wait time predictions rather than relying on a manual baseline.