Alan Kolett

MediQueue – Real-Time Patient Flow Management for Clinics and Hospitals

Healthcare clinics often rely on inefficient paper tokens and manual announcements, causing long, opaque waits and staff burnout. The "Queue Cure 2026" challenge highlighted this critical friction. We built MediQueue, a real-time patient flow platform designed to provide instant queue visibility and accurate wait-time estimation. By synchronizing updates across patients, staff, and displays, MediQueue eliminates operational bottlenecks and improves the patient experience without requiring expensive hospital infrastructure. Process I identified four key stakeholders: receptionists, patients, doctors, and admins. My core architectural decision was implementing Socket.IO to ensure instant, real-time synchronization across all dashboards, eliminating the need for page refreshes. I mapped the patient journey—from registration to completion—and designed role-specific dashboards for each. A major hurdle was dynamic wait-time estimation; I replaced hardcoded logic with a calculation based on queue position and average consultation time. I iterated heavily on the UI, moving from a "command center" aesthetic to a clean, accessible healthcare interface. I also focused on concurrency, ensuring that token generation and status updates remained consistent across all clients, even during disconnections. Results MediQueue successfully delivers a synchronized, real-time patient flow system. Key results include instant queue updates via Socket.IO, role-based dashboards, and a robust, concurrency-aware design. The solution directly addresses the Queue Cure 2026 criteria by providing transparency for patients and reducing administrative burden. It proves that high-impact queue management can be achieved without complex or expensive infrastructure, significantly streamlining clinic operations and improving the patient experience. Reflection To move beyond a prototype, I would implement persistent PostgreSQL storage for data reliability and add SMS/WhatsApp notifications for patients. Future iterations would include multi-department support, advanced analytics for forecasting patient flow, and secure patient authentication. Most importantly, I would conduct formal usability testing with actual clinical staff and patients to refine the workflows. While the current prototype validates the core real-time architecture, these additions would be essential for a production-grade healthcare solution.