Sainath

Queue Cure – Smart Digital Queue Management System

Many clinics still rely on paper token slips and manual queue management. Patients often wait for long periods without knowing when their turn will arrive, creating frustration and uncertainty. Receptionists manually track tokens and patient order, which can lead to errors and inefficiencies. Doctors also lack visibility into the current queue status. The absence of a digital system results in poor patient experience and increased workload for clinic staff. Our goal was to create a simple and accessible queue management solution that provides real-time queue visibility, estimated waiting times Process We first analyzed the workflow of a typical clinic reception system and identified the main pain points: lack of queue visibility, manual token tracking, and patient uncertainty. We designed a simple web-based solution with two views: a receptionist dashboard and a patient waiting room. Using HTML, CSS, and JavaScript, we implemented automatic token generation, queue tracking, estimated waiting time calculation, and live interface updates. Local Storage was used to preserve queue data. Multiple UI iterations were made to ensure simplicity, responsiveness, and ease of use for both patients and receptionists. Results The project successfully digitizes clinic queue management and provides patients with clear visibility into their waiting status. Receptionists can efficiently manage patient flow with a simple interface, while patients can instantly see current token status and estimated wait times. The system reduces confusion, improves operational efficiency, and enhances patient satisfaction. The solution is lightweight, responsive, and can be deployed easily without complex infrastructure. Reflection In future versions, I would add real-time synchronization across multiple devices using a backend server and WebSockets. Additional features such as SMS notifications, doctor dashboards, appointment scheduling, and analytics would further improve usability and scalability. Integration with cloud databases would also allow deployment across multiple clinics.