chukkareddy anusha

Real-Time Clinic Queue Management System

Healthcare clinics in India often rely on manual paper-based token systems, leading to long waiting times, confusion among patients, and no visibility of queue status. Patients are forced to wait without knowing when they will be called, while receptionists manage queues manually, which increases chances of errors and delays. There is a need for a simple real-time digital queue system that provides live updates to both receptionist and patients without requiring page refresh. Process I started by analyzing how small clinics manage patient queues and identified that the main issue was lack of real-time visibility. I designed a two-screen system: one for the receptionist and one for patients. The receptionist dashboard allows adding patients and calling the next token, while the patient screen updates automatically using real-time communication. I used a simple event-driven approach to ensure instant updates between both screens. Initially, I considered a static queue system, but it failed to solve the visibility problem, so I shifted to a real-time sync model using socket-based communication. I iterated on UI simplicity to ensure the system could be used quickly in a real clinic environment without training. Results The system enables instant queue updates without page refresh, improving visibility for patients and reducing manual coordination for receptionists. Patient flow becomes more transparent, and the receptionist can manage queues faster with fewer errors. The solution achieves real-time synchronization between both screens and reduces confusion in waiting areas. User flow testing showed that patients can understand their queue position instantly without asking staff repeatedly. Reflection Next time, I would improve the system by adding estimated wait time based on average consultation duration and dynamic queue load. I would also integrate notifications for patients when their turn is approaching. Additionally, I would focus on storing persistent data in a backend database to maintain queue history and improve scalability for multiple doctors or departments.