HARSITHA S K

Queue Cure '26

76% of India's 1.5M clinics still run on paper token slips and shouting. Patients wait 2-3 hours with zero visibility into their turn. Receptionists track everything from memory, and doctors have no dashboard to monitor flow. Queue Cure '26 digitizes this with two live-synced screens — receptionist control and patient visibility — replacing guesswork with real-time data. Process Mapped the core sync problem first — chose Socket.io over polling/REST for true zero-refresh updates Designed a single server-side source of truth so both screens never disagree Built wait-time math from real consultation history, not static numbers Stress-tested edge cases (double-clicks, empty queue, race conditions) before adding polish Layered in scalability (multi-doctor) and UX features (voice, QR, dark mode) once the core was solid Results Zero-refresh live sync across Receptionist, Patient, and Doctor screens — confirmed in live testing Self-correcting wait times — shift from manual default to real data after 3+ patients Concurrency-safe: double-clicks, empty queues, multi-tab usage all handled cleanly Scales to multiple doctors/counters with isolated queues on one server Deployed free (Render) and version-controlled (GitHub) with documented sockets and thought process Reflection Treated no-shows as a first-class edge case — excluded from wait-time data instead of silently corrupting the average Designed multi-doctor support at the architecture level (Socket.io rooms), guaranteeing isolation at the transport layer rather than client-side filtering Closed the "no doctor dashboard" gap explicitly named in the brief by building a third screen Made wait-time math genuinely adaptive, so accuracy improves the longer the system runs