QueueCure — Real-Time Digital Queue System for Neighbourhood Clinics

Many neighbourhood clinics still rely on paper tokens and verbal announcements, leaving patients with no visibility into their queue status. This often results in repeated visits to the reception desk, uncertainty about waiting times, and increased workload for staff. After discussing the issue with a relative who frequently visits hospitals and an MBBS student, a common concern emerged: “I don't mind waiting, but I wish I knew when my turn was coming.” This revealed a transparency gap that QueueCure aims to solve through real-time queue visibility. Process I began by studying how queues are managed in small clinics and identifying pain points for both patients and reception staff. My initial idea was a reception display showing live queue information, but feedback revealed that patients would still need to stay near the screen. After discussing the problem with an MBBS student, I shifted to a QR-based patient view accessible from any smartphone. The solution evolved into three modules: a Receptionist Dashboard for queue management, a Public Queue Display showing all doctors' queues within the clinic, and an Anonymous Patient View for tracking individual queue status. WhatsApp notifications were added for upcoming turns. Results QueueCure transformed traditional paper-token workflows into a real-time digital queue system. Patients can track their queue status through a QR code, while receptionists manage queues efficiently from a single dashboard. The solution provides 0% → 100% queue visibility and supports multiple doctors within the same clinic, making it more scalable than single-doctor queue systems. Real-time updates, public queue displays, and WhatsApp notifications reduce uncertainty and improve the waiting experience for patients. Reflection If I had more time, I would test QueueCure in a real clinic environment with a larger number of patients and reception staff to gather quantitative usability data. I would also improve the notification system with multilingual support and smarter wait-time predictions. While the current solution supports multiple doctors within a clinic, I would further enhance it with appointment booking, doctor-specific scheduling, queue analytics, and integration with hospital management systems to make it production-ready.