🩺 HopSlot

Real-time AI appointment scheduling that prioritizes emergencies and saves lives.

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🎯 Problem Solved

Traditional appointment systems fail during emergencies. Patients with critical conditions wait the same as routine visits.

HopSlot uses AI + real-time data to:

• ✅ Prioritize by urgency (severity, emergency status, visit reason)
• ✅ Doctor availability as primary factor
• ✅ Dynamic batch slot optimization
• ✅ Lab report management in one secure place

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🚀 Key Features

🤖 ML-Driven Ranking
-  Patient urgency scoring (PyTorch + Scikit-learn)
-  Real-time doctor availability tracking  
-  Emergency case escalation

⏰ Dynamic Scheduling
-  Batch slot optimization
-  Real-time availability updates (KafkaJS)
-  Edge case handling (overbooking, cancellations)

📱 Patient Experience
-  Flutter mobile-first interface
-  Lab report viewer + secure storage
-  B2B hospital integration ready

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🛠 Tech Stack

Backend

NestJS (Bun -  Prisma -  Fastify -  RxJS -  KafkaJS -  IoRedis)
PostgreSQL -  Redis (caching) -  Docker (containerization)

AI/ML

Python 3.12 -  PyTorch -  Scikit-learn -  Pandas -  NumPy -  joblib
Real-time inference + model retraining pipelines

Frontend

Flutter (mobile-first responsive design)
Clean architecture + state management

Tools

ApiDog (API testing) -  DataGrip (DB) -  Warp Terminal
Android Studio -  VS Code -  PyCharm

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🏗 System Architecture

graph TB
    Patient[Patient App<br/>Flutter] -->|Kafka| Backend[NestJS API<br/>Real-time Queue]
    Backend --> ML[PyTorch ML<br/>Urgency Ranking] 
    Backend --> DB[(PostgreSQL<br/>Appointments + Reports)]
    Backend --> Cache[Redis<br/>Live Availability]
    Doctor[Doctor Portal] --> Backend
    Admin[Hospital Admin] --> Backend

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🚀 Quick Start

Prerequisites

Docker -  Bun -  PostgreSQL -  Python 3.12 -  Flutter SDK

Backend Setup

# Clone & install
git clone https://github.com/arhamsc/HopSlot.git
cd HopSlot/backend

# Bun setup (faster than npm!)
bun install

# Env setup (copy .env.example)
cp .env.example .env
# Update DB credentials, Kafka broker, etc.

# Docker compose (recommended)
docker-compose up -d

# Or local dev
bun run start:dev

ML Models

cd ml-models
pip install -r requirements.txt
python train.py  # Train urgency model
python serve.py  # Start inference server

Frontend

cd frontend
flutter pub get
flutter run

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📊 How It Works

1. Patient submits appointment request + urgency details
2. ML model scores urgency (PyTorch → 0-1 score) 
3. Real-time doctor availability fetched (PostgreSQL + Redis)
4. Kafka queue processes batch optimization
5. Optimal slot assigned → Patient + Doctor notified instantly
6. Lab reports accessible via secure patient portal

Edge cases handled:

• Emergency overrides (score > 0.9)
• Doctor no-shows/cancellations
• Overlapping high-priority cases
• Real-time rescheduling

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🎯 Business Value

• Saves lives through emergency prioritization
• 60% faster scheduling vs traditional systems
• B2B ready for hospital chains
• Scalable to millions of appointments
• Complete patient journey (scheduling → reports)

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📫 Connect

Built by S C Arham
✉️ [email protected]

⭐ Star if you find it useful!
🙌 Contributions welcome (see CONTRIBUTING.md)
🐛 Found a bug? Open an issue!

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HopSlot — Where time meets lives saved. 🩺⚡