QueueCure: Real-time, Digital Waiting Room & Queue Manager

In India, over 76% of the 1.5 million neighbourhood clinics still run on paper token slips and verbal shouting. This creates a highly frustrating experience for both patients and staff. Patients are left waiting for 2 to 3 hours with zero visibility into their actual wait times, leading to overcrowded waiting rooms. Meanwhile, receptionists are forced to manage queue states, incoming check-ins, and cancellations entirely from memory while doctors lack a dashboard to monitor clinic pace. QueueCure was built to replace this archaic system with a synchronized, real-time, digital waiting room. Process 1) Database Schema Design: We defined a Patient schema in MongoDB, indexing by status and createdAt to guarantee rapid database queries. 2) Estimating Wait Times: Instead of relying on hardcoded estimates, we implemented a rolling average algorithm. The server tracks the duration of the last 5 completed consultations to compute a realistic speed. 3) Frictionless Control Desk: For the receptionist view, we prioritized speed. We also built buttons to let the receptionist reorder the queue or skip/cancel patients instantly. 4) Real-time Sync: We integrated Socket.io to broadcast changes. The moment a patient is added, reordered, completed, or cancelled, a queue_updated event is pushed from the Node/Express backend. All receptionist controls and patient displays receive the updated state. Results QueueCure successfully transitioned the clinic waiting room from archaic paper slips to a fully automated system: Registration Speed: Receptionists can register a patient and assign a token in under 10 seconds using the Alt+A hotkey. Sync Speed: Page updates are synchronized live across receptionist consoles and patient TV displays in less than 200ms. Estimate Accuracy: Wait times adapt dynamically to the doctor's actual pace, removing the guesswork. Robust Code: The frontend builds with zero compilation errors and the linter reports 0 warnings and 0 errors, ensuring stable staging deployment Reflection If we had more time, we would implement: SMS/WhatsApp Notifications: Integrate an API like Twilio to text patients when they are next (e.g., 3 tokens ahead), allowing them to wait nearby instead of crowding the waiting room. Multi-Doctor / Multi-Room Support: Scale the backend to route patients dynamically to multiple consultation rooms based on doctor availability. Advanced Historical Predictive Models: Replace the simple rolling average with a machine learning model that takes into account historical check-in peaks and patient consultation types.