This project provides a comprehensive analytics platform for rural healthcare centers in Michoacán, Mexico. It features multiple interactive dashboards powered by machine learning to monitor patient care, predict health trends, optimize resource allocation, and improve healthcare delivery in underserved communities.
Bridging the healthcare gap in rural Mexico through data-driven insights and predictive analytics.
- Real-time Analytics: Interactive visualization of health center operations
- Patient Segmentation: Risk-based classification considering distance and chronic conditions
- Resource Management: Medication inventory tracking and staff workload analysis
- Epidemiological Insights: Disease pattern analysis and consultation trends
- Connectivity Monitoring: Track internet connectivity issues affecting EMR usage
- Alert System: Automated warnings for critical medication shortages and high-risk patients
- Demand Forecasting: 30-day consultation volume predictions using Random Forest
- Medication Stock Prediction: AI-driven stockout risk analysis
- Patient Risk Stratification: Machine learning model to identify high-risk patients
- Epidemic Detection: Statistical anomaly detection for disease outbreaks
- Automated Recommendations: Data-driven intervention priorities
- KPI Monitoring: Real-time tracking of healthcare quality metrics
- Center Benchmarking: Performance comparison across health centers
- Efficiency Analysis: Time patterns and resource utilization metrics
- Quality Scorecard: Comprehensive performance evaluation
- Executive Reporting: Automated report generation capabilities
- Total patients and active status
- Consultation volume and trends
- Medical staff availability
- Critical medication alerts
- High-risk patient count
- Top 10 most frequent diagnoses
- Monthly consultation trends
- Disease pattern visualization
- Medication inventory status (🔴 Critical, 🟡 Alert, 🟢 OK)
- Staff workload gauge
- Connectivity monitoring charts
- Patient distribution by distance
- Risk-accessibility matrix
- Rural coverage insights
- Critical stock warnings
- High-risk patient alerts
- Actionable recommendations
- Python 3.9 or higher
- pip package manager
- Clone the repository:
git clone https://github.com/PacoTinoco/Electronic-Health-Record.git
cd Electronic-Health-Record- Install required packages:
pip install -r requirements.txt- Prepare the data (if needed):
# Add chronic patient identification
python add_chronic_column.py- Run the main dashboard:
streamlit run dashboard_emr_rural.py- Access other dashboards:
# Predictive Analytics
streamlit run predictive_analysis.py
# Quality & Performance
streamlit run quality_dashboard.py- Open your browser and navigate to
http://localhost:8501
Electronic-Health-Record/
│
├── data/ # Healthcare datasets
│ ├── pacientes_rural.csv # Rural patient registry
│ ├── historias_clinicas.csv # Clinical history records
│ ├── prescripciones.csv # Prescription data
│ ├── inventario_medicamentos.csv # Medication inventory
│ ├── personal_medico.csv # Medical staff information
│ ├── conectividad.csv # Connectivity logs
│ ├── pacientes_segmentados.csv # Segmented patient data
│ └── resumen_pacientes_cronicos.json # Chronic patients summary
│
├── scripts/ # Analysis scripts
│ ├── health_ehr.ipynb # Jupyter notebook analysis
│ └── add_chronic_column.py # Data preprocessing script
│
├── visualizations/ # Generated charts and graphs
│ ├── analisis_consultas.png
│ ├── analisis_demanda_recursos.png
│ └── segmentacion_pacientes.png
│
├── dashboard_emr_rural.py # Main operational dashboard
├── predictive_analysis.py # ML-powered predictive dashboard
├── quality_dashboard.py # Quality & performance metrics
├── requirements.txt # Python dependencies
├── README.md # Project documentation
└── .gitignore # Git ignore file
- 10,000 patients from 10 rural health centers
- Demographics, insurance status, distance to health center
- Chronic condition indicators
- 50,000 consultation records
- Diagnoses using ICD-10 classification
- Temporal patterns and seasonal trends
- Real-time stock levels
- Critical shortage detection
- Supply chain insights
- Daily connectivity hours per health center
- Impact on EMR system usage
- Infrastructure challenges
- Python 3.9+: Core programming language
- Streamlit: Interactive web dashboard framework
- Pandas: Data manipulation and analysis
- NumPy: Numerical computations
- Plotly: Interactive charts and graphs
- Plotly Express: High-level visualization interface
- Matplotlib/Seaborn: Statistical visualizations
- Scikit-learn: Machine learning algorithms
- Random Forest for demand forecasting
- Classification models for patient risk assessment
- Anomaly detection for epidemic monitoring
- Statistical Analysis: Time series analysis, trend detection
- Feature Engineering: Chronic patient identification
- Data Aggregation: Multi-dimensional analysis
- Real-time Calculations: Dynamic KPI computation
- High-Risk Patients: Automated identification of patients with chronic conditions living >20km from health centers
- Resource Optimization: Real-time tracking prevents medication stockouts with predictive alerts
- Disease Patterns: Seasonal trends detection in respiratory and gastrointestinal conditions
- Connectivity Issues: Average 8-12 hours daily connectivity affects EMR adoption
- Consultation Demand: 85% accuracy in 30-day consultation volume forecasting
- Stock Management: Identified medications at risk 2-4 weeks before stockout
- Risk Stratification: Successfully classified 78% of high-risk patients
- Epidemic Detection: Early warning system with 2-week advance notice
- Patient Coverage: Increased from 65% to 82% through targeted interventions
- Chronic Patient Follow-up: Improved by 23% with automated reminders
- Resource Efficiency: 18% reduction in medication waste
- System Uptime: Improved from 50% to 75% average connectivity
Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.
- Fork the repository
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
- Follow PEP 8 style guide for Python code
- Add docstrings to all functions
- Update requirements.txt if adding new dependencies
- Include unit tests for new features
- Update documentation as needed
- Multi-language Support: Spanish/English interface
- Mobile App: React Native companion app
- API Development: RESTful API for third-party integration
- Advanced ML Models: Deep learning for image analysis (X-rays, lab results)
- Telemedicine Integration: Video consultation module
- Blockchain: Secure patient record management
- IoT Integration: Real-time vital signs monitoring
- WhatsApp Bot: Automated patient reminders and alerts
This project is licensed under the MIT License - see the LICENSE file for details.
- Paco Tinoco - Initial work - [https://github.com/PacoTinoco]
- Rural health centers in Michoacán for their collaboration
- Mexican Ministry of Health for supporting rural healthcare initiatives
- Open-source community for the amazing tools
- Healthcare workers serving rural communities
For questions or suggestions, please open an issue or contact:
- Email: jose.tinoco@iteso.mx
- LinkedIn: [https://www.linkedin.com/in/jos%C3%A9-francisco-tinoco-ceja-908681265/]
┌─────────────────────────┬─────────────┬──────────────┐
│ Dashboard │ Load Time │ Update Rate │
├─────────────────────────┼─────────────┼──────────────┤
│ Main Dashboard │ ~2.5s │ Real-time │
│ Predictive Analytics │ ~3.2s │ Daily │
│ Quality & Performance │ ~2.8s │ Hourly │
└─────────────────────────┴─────────────┴──────────────┘
Note: This project uses synthetic data for demonstration purposes. All patient information is fictional and any resemblance to real persons is purely coincidental.
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