NIMALAN MANI M

ClinicFlow – Real-Time Clinic Queue Management System

Many clinics still rely on paper tokens and manual announcements. Patients often do not know their queue status and repeatedly ask for updates, while receptionists manually track patient flow. ClinicFlow was created to provide a real-time digital queue system that improves transparency, reduces administrative effort, and gives patients live visibility into waiting times and token progression. Process The project began by analyzing common clinic queue workflows and identifying the need for real-time updates. React was chosen for the frontend due to its component-based architecture, while Node.js and Express were used for backend operations. Socket.IO was selected because instant synchronization between screens was the core requirement. Initially, queue data was stored in server memory, but this approach failed when the server restarted and data was lost. To solve this, Supabase PostgreSQL was integrated for persistent storage. Additional iterations introduced duplicate patient prevention, wait-time estimation, QR-code access, and a patient-facing display. Results ClinicFlow successfully delivers real-time queue synchronization between receptionist and patient screens without requiring page refreshes. Queue updates, token progression, and patient registrations are reflected instantly across connected devices through Socket.IO. The platform provides live queue visibility, estimated waiting times, duplicate patient prevention, QR-code access, and persistent storage using Supabase. By replacing manual queue tracking with a digital workflow, ClinicFlow improves transparency for patients while reducing administrative effort for receptionists. Reflection With additional development time, I would expand ClinicFlow into a complete clinic operations platform. Planned enhancements include appointment scheduling, doctor dashboards, SMS and WhatsApp notifications, multilingual support, and voice-based token announcements. I would also implement role-based authentication, multi-clinic support, and predictive wait-time models using machine learning. Conducting structured usability testing with clinic staff and patients would provide valuable insights for improving accessibility, workflow efficiency, and overall user experience.