Tanisha Thakur

EXPIRY ALERT ' 26

Queue Cure '26 — Real-Time Digital Queue Management for Clinics

Clinics in India manage patient queues on paper. Patients wait 45+ minutes with zero visibility into when they'll be called — no screen, no notification. Receptionists handle tokens manually: writing names in a register, checking the last token number, shouting names aloud. Emergency patients wait in the same line as routine cases, which is dangerous. Missed calls are common since patients can't always hear their name. No one — not the doctor, receptionist, or patient — has real-time visibility into the queue. I observed a real clinic receptionist managing 30+ patients with just a notebook, and mapped the old process: 10 manual steps from walk-in to doctor notification. I identified 3 core pain points — no real-time updates, no emergency priority, no voice announcements — and designed around fixing each one. Built Socket.io for real-time sync so every queue action broadcasts instantly to all screens. For priority, I chose a simple array-based approach: emergency patients insert at index 0, normal patients append at the end — fast and predictable rather than a complex scoring system. Added Web Speech API for free, offline voice announcements instead of paying for a TTS API. Used localStorage as a lightweight persistence layer instead of standing up a database, since session continuity mattered mo Reduced patient check-in from 10 manual steps to 3 clicks. Voice announcements via Web Speech API eliminated manual name-calling entirely — 0 seconds of receptionist shouting per patient. Emergency patients now bypass the normal queue automatically instead of waiting in line, removing a real safety risk. Socket.io sync pushes every queue update to all connected screens in under 1 second, so doctor, receptionist, and patient see the same live state. LocalStorage persistence means a page refresh causes zero data loss — tested by refreshing mid-queue with patients waiting. I'd build a full QR-based patient system: appointments booked entirely online, no waiting at reception. Each patient gets a QR code for check-in, payment, and token — all in one. They can check their slot and estimated wait time from anywhere — home, car, shop — and walk in only when their turn is near. Their next appointment gets booked automatically based on doctor availability, with a reminder sent ahead of time. Consultation fees are paid through the QR before entry — zero cash handling. Prescribed medicines are delivered through the same QR flow, removing the pharmacy visit entirely.

queue cure

Most neighbourhood clinics still rely on paper tokens and manual queue management, leading to long waiting times, confusion, and poor communication between receptionists, doctors, and patients. Patients have no visibility into their queue status or expected waiting time, while receptionists spend valuable time managing the queue manually. Queue Cure '26 addresses this by providing a real-time digital queue management system with live updates, smart token management, doctor selection, emergency prioritization, appointment booking, and accurate wait-time estimation to improve clinic efficiency a I began by studying how small clinics manage patient queues and identified key problems such as manual token handling, uncertain waiting times, and lack of real-time updates. Based on the hackathon requirements, I designed a two-screen solution for receptionists and patients. I built the frontend using React and the backend using Express.js with Socket.IO for instant synchronization. I implemented patient registration, automatic token generation, doctor selection, emergency priority, live queue updates, and dynamic wait-time calculation. I then added appointment booking, voice announcements, consultation history, search functionality, dashboard analytics, and queue export. Finally, I tested different queue scenarios to ensure accurate synchronization, smooth user experience, and reliable p The application successfully digitizes clinic queue management by enabling receptionists to register patients, assign doctors, prioritize emergency cases, book appointments, call the next patient, and complete consultations efficiently. Patients receive live queue updates, accurate waiting-time estimates, and voice announcements without refreshing the page. The dashboard displays patients served, waiting patients, emergency cases, consultation history, and real-time analytics, improving transparency, reducing manual effort, and enhancing the overall patient experience. If I continue developing this project, I would implement role-based authentication for receptionists, doctors, and administrators, add SMS/WhatsApp notifications to alert patients before their turn, support multiple doctors with independent queues, integrate online appointment scheduling, and deploy the application on the cloud with HTTPS. I would also enhance analytics with reports and predictive wait-time estimation using historical consultation data to make the system more scalable and suitable for real-world clinics.

Queue Cure '26 — Real-Time Digital Queue Management for Clinics

Clinics in India manage patient queues on paper. Patients wait 45+ minutes with zero visibility into when they'll be called — no screen, no notification. Receptionists handle tokens manually: writing names in a register, checking the last token number, shouting names aloud. Emergency patients wait in the same line as routine cases, which is dangerous. Missed calls are common since patients can't always hear their name. No one — not the doctor, receptionist, or patient — has real-time visibility into the queue. Process I observed a real clinic receptionist managing 30+ patients with just a notebook, and mapped the old process: 10 manual steps from walk-in to doctor notification. I identified 3 core pain points — no real-time updates, no emergency priority, no voice announcements — and designed around fixing each one. Built Socket.io for real-time sync so every queue action broadcasts instantly to all screens. For priority, I chose a simple array-based approach: emergency patients insert at index 0, normal patients append at the end — fast and predictable rather than a complex scoring system. Added Web Speech API for free, offline voice announcements instead of paying for a TTS API. Used localStorage as a lightweight persistence layer instead of standing up a database, since session continuity mattered mo Results Reduced patient check-in from 10 manual steps to 3 clicks. Voice announcements via Web Speech API eliminated manual name-calling entirely — 0 seconds of receptionist shouting per patient. Emergency patients now bypass the normal queue automatically instead of waiting in line, removing a real safety risk. Socket.io sync pushes every queue update to all connected screens in under 1 second, so doctor, receptionist, and patient see the same live state. LocalStorage persistence means a page refresh causes zero data loss — tested by refreshing mid-queue with patients waiting. Reflection I'd build a full QR-based patient system: appointments booked entirely online, no waiting at reception. Each patient gets a QR code for check-in, payment, and token — all in one. They can check their slot and estimated wait time from anywhere — home, car, shop — and walk in only when their turn is near. Their next appointment gets booked automatically based on doctor availability, with a reminder sent ahead of time. Consultation fees are paid through the QR before entry — zero cash handling. Prescribed medicines are delivered through the same QR flow, removing the pharmacy visit entirely.