MediTrack is a real-time, centralized patient monitoring system signed to assist nurses in hospital wards. The platform integrates nurse scheduling, patient monitoring, data analytics, and AI-driven insights** to improve response time and patient care.
- Frontend: Next.js - Modern React framework for UI development.
- Backend: MongoDB - NoSQL database for efficient data storage.
- Hardware Integration: Raspberry Pis equipped with cameras to read real-time patient monitoring devices that lack direct communication capabilities.
- AI & Analytics: Machine learning insights for early warning detection and patient deterioration prediction.
- Deployment: Hosted on cloud services for scalability and reliability.
- Live dashboard displaying patient vitals.
- Automated alerts for abnormal health readings.
- Integration with monitoring devices (via Raspberry Pi cameras for non-communicative devices).
- Shift scheduling system with optimized workload distribution.
- Automated nurse-patient assignments based on real-time conditions.
- Predictive alerts for patient deterioration.
- Data-driven recommendations for improved patient care and resource allocation.
- Historical trends and real-time patient data visualization.
- Aggregated hospital performance reports.
- Improved nurse response times with automated prioritization.
- Greater agency for healthcare workers through real-time insights.
MediTrack incorporates machine learning models to:
- Detect early signs of patient deterioration.
- Predict potential complications based on vitals history.
- Optimize hospital resource allocation based on demand.
git clone https://github.com/Hoodini231/MediTrack.git
cd MediTrack