Queue Cure '26 — Real-Time Clinic Queue Management System

Many small clinics still manage queues manually through paper token slips, verbal calling, and receptionist memory. This gives patients no visibility into how many people are ahead or how long they may have to wait, often leading to 2–3 hour uncertain waits, repeated desk enquiries, and a crowded reception area. It also puts the entire queue workflow on the receptionist, increasing the risk of missed turns and confusion. QueueCure was built to replace this with a live digital queue system for both reception staff and patients. Process I started by splitting the clinic queue problem into two user roles: the receptionist and the waiting patient. From there, I mapped the flow as add patient → issue token → call next → update the queue live. I built QueueCure as two synced interfaces: a Receptionist Dashboard and a Waiting Room TV. My main focus was keeping the currently serving token, next-up queue, waiting count, and estimated wait time in sync. Once the logic worked, I refined the UI to make both screens easy to read and use. Results QueueCure turned a manual queue workflow into a live digital system with two synchronized views: one for reception staff and one for patients. The final build allowed the receptionist to add patients, issue tokens, and call the next token from one interface, while patients could see the current token, upcoming queue, and estimated wait time in real time. It reduced the “blind waiting” experience for patients and created a clearer, more organized front-desk workflow. Reflection If I continued QueueCure, I would validate it with real clinic staff to test edge cases like cancellations, missed turns, emergency patients, and multiple doctors. I would also improve the ETA logic by making it adaptive instead of relying only on a fixed consultation time. On the product side, I would add multi-doctor support, patient mobile tracking, and clinic analytics such as average wait time and daily patient count