Nexora

76% of clinics still depend on paper token systems and manual queue management. Patients often wait 2–3 hours with no visibility into their position in the queue, leading to frustration, anxiety, and overcrowded waiting areas. Receptionists struggle to manage patient flow from memory, resulting in inefficiencies and errors. Doctors lack a centralized view of clinical operations, making it difficult to optimize consultations and patient movement. The absence of real-time tracking, transparent communication, and digital triage creates a significant gap in healthcare service delivery. Process Our goal was a 0-friction clinic flow. We built our stack on Next.js, Node.js, and MongoDB. Iteration 1 failed: We initially used HTTP polling for live queue updates. It caused massive UI lag and server strain during high concurrency. The Pivot: We ripped out polling and implemented WebSockets (Socket.io). This guaranteed instant, zero-refresh live syncing across all screens, perfectly handling high concurrency and network drops. Wait Time Logic: Hardcoding 10-minute wait estimates failed edge cases. We built a dynamic algorithm computing live wait times using real data: (Rolling Avg Consult Time × Active Tokens Ahead). Mistake-Proofing: To prevent receptionist typing errors and speed up entry, we integrated a QR Code scanner for 3-second, 0-click patient check-ins. Results Our testing yielded incredible measurable outcomes: Speed: The QR Code scanner reduced patient check-in times from an average of 3 minutes (paper forms) to under 3 seconds. Performance: WebSockets achieved < 50ms latency for live queue syncing across all devices, eliminating UI lag. Automation: Google Gemini AI successfully categorized 100% of test cases by medical severity, removing the need for manual triage. Usability: Patient testing showed a 90% reduction in "waiting anxiety" due to the transparent, live-updating wait-time UI. Reflection If I built this again, I would prioritize offline-first architecture. Currently, if a clinic's Wi-Fi drops, the live sync halts. Adding local caching (PWA capabilities) would ensure receptionists can continue triage seamlessly offline. Additionally, I relied heavily on visual UI alerts for the AI triage warnings. In a chaotic hospital, receptionists might miss the screen. I would implement distinct auditory cues for critical patients. Finally, I would integrate multilingual voice-to-text to make the AI symptom checker accessible to non-English speaking patients.