P K SOHAALIYA IT

Clinic Queue Intelligence Platform

Outpatient clinics often manage 80–150 patients daily using paper tokens and manual announcements. Patients wait 30–120+ minutes without knowing their queue position or expected consultation time, leading to anxiety and repeated enquiries at reception. Staff spend valuable time answering status questions and handling missed token calls. Clinics also lack analytics on consultation duration, peak hours, and bottlenecks. This system solves the issue by providing real-time queue tracking, smart ETA prediction, priority-based scheduling, waiting list display, and live operational insights Process I began by studying common issues in outpatient clinics through articles, healthcare reports, and user observations. The major pain points identified were long waiting times, repeated enquiries at reception, missed token calls, and lack of visibility into queue progress. I first explored simple token-display approaches, but they did not reduce patient anxiety because users still couldn't estimate their waiting time. I then iterated toward a real-time queue system with live tracking and ETA prediction. Multiple queue-sorting approaches were evaluated before selecting priority-based triage with fairness within each category. I also added Socket.IO for instant synchronization across receptionist, patient, and waiting-room screens. Design decisions focused on transparency and self-learning ETA Results Clinic Queue Intelligence Platform successfully transformed a manual clinic queue into a real-time digital workflow. Receptionists can register patients, manage priority queues, and monitor queue health from a single dashboard. Patients receive live queue visibility, estimated waiting time, and tracking through a unique QR-based link. Real-time synchronization across receptionist, patient, and waiting-room views reduced uncertainty, improved operational efficiency, and demonstrated how smart queue intelligence can enhance the outpatient clinic experience. Reflection Given more time, I would integrate actual WhatsApp/SMS notifications instead of simulated updates, add multi-doctor and multi-room support for larger clinics, and enhance the ETA engine using machine learning models trained on historical consultation patterns. I would also conduct usability testing with clinic staff and patients to refine workflows, improve accessibility, and validate the accuracy of wait-time predictions in real-world environments.