Queue Cure ' 26

Clinics and small hospitals still rely heavily on manual queue management, verbal announcements, paper tokens, or basic token displays that provide no visibility into actual waiting times. Patients often crowd reception desks to ask, “How much longer will it take?”, creating congestion and increasing staff workload. Receptionists struggle to manage high patient volumes, skipped patients, and multiple doctors simultaneously. Existing solutions rarely provide accurate wait-time estimates, leading to frustration, uncertainty, and poor patient experience. We analyzed how clinics currently manage patient queues and identified key challenges such as unclear wait times, repeated inquiries at reception, and limited visibility into queue progress. We mapped the entire patient journey from token generation to consultation completion and designed dedicated workflows for receptionists, administrators, and patients. Multiple dashboard concepts were explored before choosing a bold brutalist interface optimized for speed, readability, and visibility on large displays. Early wait-time models using fixed consultation durations proved unreliable, so we developed a dynamic algorithm based on historical consultation data and real-time patient progress. Firebase Firestore enabled instant synchronization across dashboards, TVs, and patient devices, while opt Queue Cure successfully delivers a real-time clinic queue management experience with instant synchronization across dashboards, TVs, and patient devices. Key outcomes include: Real-time queue visibility across multiple devices. Dynamic wait-time estimation based on actual consultation behavior. Single-click token management for receptionists, including skip and recall functionality. Support for multiple doctors and daily token resets. Reduced dependency on manual queue announcements and repetitive patient inquiries. Sub-second synchronization using Firebase Firestore listeners. If given more time, I would conduct structured usability testing with clinic receptionists, doctors, and patients to gather quantitative feedback and validate assumptions with real-world data. I would also introduce machine learning-based wait-time prediction models that consider doctor-specific consultation patterns, appointment types, and peak-hour behavior. Additionally, I would expand the platform with appointment scheduling, automated patient notifications, and multi-branch management features to transform Queue Cure from a queue management tool into a complete clinic operations platform