🚑 AI-Powered Emergency Cardiac Care System

🌟 Project Overview

This project is an intelligent, accessible, and automated system designed to improve emergency cardiac care by using AI-driven symptom triaging, lab report analysis, appointment scheduling, and patient-friendly interaction. The system is designed with scalability and ease of use in mind, especially for differently-abled patients.

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🎯 Problem Statement

Patients in emergency cardiac situations face delays in triaging, lab report analysis, and appointment scheduling. Differently-abled patients find it particularly difficult to navigate medical systems under stress. Manual processes slow down care delivery, reducing the chances of timely intervention.

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💡 Solution

Our solution automates key parts of emergency cardiac care:

1. Patients enter their symptoms in simple language.
2. AI (OpenAI GPT API) analyzes symptoms to provide a triage result (High, Medium, Low Risk).
3. Patients can upload lab reports (PDF/Image), and the system extracts key medical data automatically using OpenAI’s Document Analysis API.
4. Smart appointment recommendations are generated based on triage and lab report data.
5. A patient-friendly chatbot helps explain medical terms and guides patients in simple language.
6. Clean and accessible UI design supports differently-abled users (large buttons, simple forms, voice interaction support).

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🚀 Tech Stack

• Frontend: React, Tailwind CSS
• Backend: Node.js, Express
• APIs: OpenAI API for AI functionality (Symptom Analysis, Document Extraction, Chatbot)

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⚡ Prototype Mode

For the prototype demonstration, we use mock data to simulate:

• Symptom input and triage result
• Lab report uploads and analysis
• Appointment scheduling This allows demonstration without requiring OpenAI API keys at the initial stage.

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📈 Scalability Plan

The system is designed to scale easily:

• Add more hospitals, doctors, and appointment slots via database configuration.
• Integrate OpenAI API keys in production for real-time intelligent analysis.
• Support multiple languages for wider accessibility.
• Handle multiple patient requests simultaneously using cloud infrastructure.

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✅ How to Run Locally

1. Clone the repo:

   git clone https://github.com/ANDEV-afk/cardiac-care.git
   

2. Install dependencies:

   npm install

3. Set up environment variables in a .env file:

   OPENAI_API_KEY=your_openai_api_key (optional for prototype)

4. Start the frontend (if applicable):

   pnpm run dev

License

MIT License

Future Improvements->

Fully integrate OpenAI API keys for real-time AI-powered triaging and document analysis.

Multi-language support for a global user base.

Voice interaction directly powered by OpenAI APIs.

Offline mode support using cached data.