CliniGuard 🏥

AI-Powered Healthcare Management Platform

A modern solution for streamlined clinic operations and enhanced patient care

📋 Table of Contents

• Overview
• Features
• Technology Stack
• System Architecture
• Installation
• Environment Setup
• API Documentation
• Screenshots
• Contributing
• License
• Author

🔍 Overview

CliniGuard is a comprehensive web-based healthcare platform designed to revolutionize clinic operations and enhance patient care through AI-powered medical services. The platform seamlessly integrates symptom analysis, disease prediction, appointment management, and telemedicine capabilities to provide a complete healthcare solution for both patients and healthcare providers.

By leveraging advanced AI technologies, CliniGuard offers intelligent symptom assessment and disease prediction, helping patients understand their health concerns before consulting with healthcare professionals. The platform also facilitates efficient appointment scheduling and management, ensuring optimal resource utilization for healthcare facilities.

✨ Features

🔬 AI-Powered Health Assessment

• Intelligent Symptom Analysis: Interactive symptom selection interface with AI-powered analysis
• Advanced Disease Prediction: Machine learning-based disease prediction using comprehensive patient data
• Multi-Step Health Forms: User-friendly health assessment with intuitive progress tracking

📅 Appointment Management System

• Dual Booking Pathways: Choose between AI-guided or direct booking options based on user preference
• Interactive Calendar: Dynamic date selection with real-time availability management
• Specialized Doctor Matching: Connect patients with healthcare providers based on specialization
• Priority Booking: Expedited access for urgent care needs

🏥 Healthcare Provider Management

• Comprehensive Doctor Profiles: Detailed healthcare provider information management
• Multi-Role Administration: Role-based access control for system administrators
• Secure Patient Records: HIPAA-compliant patient data management system

🎥 Telemedicine Support

• Secure Video Consultations: End-to-end encrypted remote healthcare consultations
• Real-Time Communication: Low-latency communication system for optimal telehealth experience

💻 Technology Stack

Category	Technologies
Frontend	React.js, Tailwind CSS, Framer Motion
Backend	Node.js, Express.js
Database	MongoDB
Authentication	JWT
Real-time Communication	WebSockets
AI Services	Priaid API, Google Gemini
Deployment	Docker, Nginx

🏗️ System Architecture

graph TD
    Start["User Access"] --> Auth["Authentication"]
    Auth --> Dashboard["User Dashboard"]
    
    Dashboard --> HealthAssess["Health Assessment"]
    Dashboard --> Appointment["Appointment Booking"]
    Dashboard --> Reports["Medical Reports"]
    
    HealthAssess --> SymptomForm["Symptom Selection"]
    SymptomForm --> AIAnalysis["AI Disease Prediction"]
    AIAnalysis --> Recommendations["Treatment Recommendations"]
    
    Appointment --> CompFlow["Comprehensive Flow"]
    Appointment --> SimpleFlow["Simple Booking"]
    
    CompFlow --> SymptomInput["Symptom Input"]
    SymptomInput --> DiseasePredict["Disease Prediction"]
    DiseasePredict --> BookingFlow["Appointment Booking"]
    
    SimpleFlow --> DateSelect["Date Selection"]
    DateSelect --> DoctorSelect["Doctor Selection"]
    DoctorSelect --> TimeSelect["Time Selection"]
    TimeSelect --> Confirmation["Booking Confirmation"]

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Data Models

User Management

erDiagram
    Admin {
        ObjectId _id
        String userType
        Number adminID
        String adminName
        String email
        String password
        Array doctors
        Array patients
    }
    
    Doctor {
        ObjectId _id
        String name
        String specialization
        String email
        Number docID
        String password
    }
    
    Patient {
        ObjectId _id
        String name
        Number age
        String gender
        String email
        String phone
    }
    
    Admin ||--o{ Doctor : manages
    Admin ||--o{ Patient : manages

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Health Assessment Data Flow

graph LR
    subgraph "Health Form Data"
        BasicInfo["Basic Information<br/>age, gender, height, weight"]
        VitalSigns["Vital Signs<br/>BP, heart rate, temperature"]
        Lifestyle["Lifestyle<br/>smoking, exercise, diet"]
        Medical["Medical History<br/>conditions, medications"]
    end
    
    subgraph "AI Processing"
        SymptomAnalysis["Symptom Analysis"]
        DiseasePredict["Disease Prediction"]
        InfoGeneration["Medical Information"]
    end
    
    BasicInfo --> SymptomAnalysis
    VitalSigns --> SymptomAnalysis
    Lifestyle --> DiseasePredict
    Medical --> DiseasePredict
    
    SymptomAnalysis --> InfoGeneration
    DiseasePredict --> InfoGeneration

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Setup Instructions

Prerequisites

• Node.js (v14 or higher)
• MongoDB
• npm or yarn package manager

Installation

1. Clone the repository

2. Install dependencies

   npm install
   ``` [13](#0-12) 
   

3. Environment Configuration Create .env file with required API keys and database configuration

4. Run the application

   npm run dev
   ``` [14](#0-13) 
   

API Endpoints

Doctor Management

• GET /doctors - Fetch all doctors 15
• POST /doctors/register - Register new doctor 5
• POST /doctors/login - Doctor authentication 16
• PATCH /doctors/:doctorId - Update doctor information 17

Reports Management

• GET /reports - Fetch medical reports 18
• POST /reports/create - Create new report 19
• PATCH /reports/:reportId - Update report 20

Component Architecture

Frontend Components

• Health Assessment: Multi-step form with progress tracking
• Appointment Booking: Dual-flow booking system with calendar integration 21
• Symptom Analysis: Interactive symptom selection interface
• Dashboard: Role-based user interfaces

Styling System

The application uses a hybrid styling approach:

• CSS Modules: Component-specific styling 22
• Tailwind CSS: Utility-first responsive design
• Custom Design System: Healthcare-focused color schemes and layouts

Notes

Made with ❤️ by Nupur