A Hackathon Solution to Optimize Emergency Response and Patient Redirection
SAMU regulators face the challenge of rapidly assessing emergency situations and directing patients to the most appropriate hospitals. This process requires quick decision-making and accurate information.
EmerSwift is an AI-powered tool designed to streamline emergency dispatch for SAMU regulators by:
- Rapid Triage: Quickly assessing patient conditions based on critical information.
- Intelligent Hospital Redirection: Suggesting optimal hospitals based on patient needs and real-time data.
- Enhanced Decision-Making: Providing actionable insights to support informed decisions.
Caution
MVP Demo Scope: This demonstration focuses on the core triage and hospital redirection workflow for a limited set of emergency scenarios. Real-world SAMU operations involve significantly more complex factors, which will be addressed in future development phases.
- Intuitive Emergency Triage Form: A user-friendly interface for inputting critical patient information.
- AI-Powered Triage and Recommendation: Real-time analysis of patient data to suggest appropriate hospitals.
- Mocked OpenStreetMap Integration: A visual representation of hospital locations and potential redirection routes.
Important
Time Savings: EmerSwift's streamlined triage and hospital recommendation process is designed to significantly reduce the time SAMU regulators spend on initial assessment and hospital selection, allowing for faster patient care.
- Emergency Triage: SAMU regulators input critical patient information into the form.
- AI Analysis: SwiftDispatch processes the input data to assess urgency and identify key needs.
- Hospital Recommendations: The tool suggests optimal hospitals based on the analysis.
- Visualized Redirection: A mocked map displays the recommended hospitals and their locations.
Built for speed, scalability, and rapid prototyping:
- Frontend: React for a dynamic and responsive user interface.
- Backend: Node.js and Express for a robust API.
- AI/ML: Scikit-learn and Statsmodels for basic data analysis and modeling.
- Data Storage: PostgreSQL for lightweight data management.
- Deployment: Docker for containerization and easy deployment.
Future iterations of EmerSwift for SAMU will include:
- Advanced AI Models: More sophisticated AI algorithms for improved triage and recommendations.
- Real-time Hospital Capacity Data: Integration with real-time hospital data to optimize redirection.
- Integration with SAMU Systems: Seamless integration with existing SAMU systems for efficient data exchange.
- Enhanced User Interface: A more intuitive and user-friendly interface for SAMU regulators.
| # | Name | Background |
|---|---|---|
| 1 | Romain Malengrez | Freelance Product Designer |
| 2 | Thibaut Tebi | Biomedical Engineer |
| 3 | Dimitri Abitbol | Data Scientist |
| 4 | Samy Chouam | Software Engineer |
| 5 | Colin Peugnet | Software Engineer |
| 6 | Arnaud Durand | Software Engineer |
- Clone the repository:
git clone https://github.com/xxx/Hackathon-xxx - Navigate to the project directory:
cd Hackathon-AISummit - Run
npm installto install all of the required packages - Run
npm run devto start the front-end - Run
npm run serverto start the server - Access the SAMU Dispatch Interface in your browser:
http://localhost:5173 - Make sure the server is
healthywithcurl -X GET http://localhost:3000/health
Tip
The server port is either 5174 or 3000.
Quick Start: For the fastest demo setup, ensure both your front-end and server terminals are open before accessing http://localhost:5173 in your browser. This ensures a smooth initial loading experience.
Let's revolutionize emergency response with SwiftDispatch! 🚀
Note
Looking Ahead: EmerSwift's current capabilities are a foundation. We envision future versions integrating real-time data feeds, advanced AI algorithms, and seamless integration with existing SAMU infrastructure to create a truly transformative emergency dispatch solution.