# MCP-Health Python Package
**AI Productivity Analysis for Medical Fees in Japan**
A comprehensive Python package for analyzing the economic impact of AI implementation in Japan's healthcare system, focusing on administrative efficiency and clinical productivity improvements.
## 🌟 Features
- **📊 Healthcare Productivity Analysis**: Calculate baseline metrics and AI impact projections
- **💰 Economic Impact Assessment**: ROI analysis and cost savings calculations
- **📈 Advanced Visualizations**: Professional charts and dashboards
- **🏥 Japanese Healthcare Focus**: Specialized for Japan's aging society challenges
- **🤖 AI Implementation Modeling**: Evidence-based improvement factors
- **📋 Sample Datasets**: Representative Japanese healthcare data for testing
## 🚀 Quick Start
### Installation
```bash
# Install from PyPI
pip install mcp-health
# Install with optional dependencies
pip install mcp-health[interactive] # Jupyter + Plotly support
pip install mcp-health[all] # All optional dependencies
```
### Basic Usage
```python
from mcp_health import HealthcareProductivityAnalyzer, HealthcareDataGenerator
# Generate sample data
generator = HealthcareDataGenerator()
datasets = generator.generate_all_datasets()
# Run analysis
analyzer = HealthcareProductivityAnalyzer()
analyzer.data = datasets
report = analyzer.generate_analysis_report()
# Print key results
summary = report['summary']
print(f"Annual Savings: ¥{summary['total_annual_savings_trillion_yen']:.1f} Trillion")
print(f"5-Year ROI: {summary['five_year_roi_percentage']:.0f}%")
print(f"Payback Period: {summary['payback_period_years']} years")
```
### Create Visualizations
```python
from mcp_health import HealthcareVisualizer
# Create visualizer
visualizer = HealthcareVisualizer()
# Generate comprehensive dashboard
dashboard = visualizer.create_summary_dashboard(report)
dashboard.show()
# Export all charts
chart_paths = visualizer.export_analysis_charts(report)
print("Charts saved:", chart_paths)
```
## 📊 Analysis Results
The package provides comprehensive analysis of AI impact on Japanese healthcare:
### 🎯 **Key Findings**
- **Annual Savings**: ¥3.2 Trillion potential savings
- **ROI**: 245% five-year return on investment
- **Efficiency**: 52% reduction in administrative time
- **Throughput**: 24% increase in patient capacity
### 📈 **Improvement Areas**
- Administrative automation and error reduction
- Clinical decision support systems
- Predictive analytics for patient care
- Workforce optimization and training
## 🏗️ Package Structure
```
mcp_health/
├── core/
│ ├── data_analysis.py # Main analyzer class
│ ├── data_generator.py # Sample data generation
│ └── visualization.py # Charts and dashboards
├── data/
│ └── sample_datasets.py # Japanese healthcare samples
└── utils/
└── config.py # Configuration management
```
## 📚 Documentation
### Core Classes
#### `HealthcareProductivityAnalyzer`
Main analysis engine for healthcare productivity assessment.
```python
analyzer = HealthcareProductivityAnalyzer(data_dir="data/")
# Load data and run analysis
data = analyzer.load_data()
baseline = analyzer.calculate_baseline_metrics(data)
ai_metrics = analyzer.calculate_ai_impact_metrics(baseline)
savings = analyzer.calculate_cost_savings(baseline, ai_metrics)
roi = analyzer.calculate_roi_analysis(savings)
# Generate comprehensive report
report = analyzer.generate_analysis_report()
```
#### `HealthcareDataGenerator`
Generate representative sample datasets for testing and demonstration.
```python
generator = HealthcareDataGenerator(output_dir="data/")
# Generate all datasets
datasets = generator.generate_all_datasets(save_to_disk=True)
# Create sample analysis
sample_report = generator.create_sample_analysis("sample_report.json")
```
#### `HealthcareVisualizer`
Create professional visualizations and dashboards.
```python
visualizer = HealthcareVisualizer(output_dir="results/")
# Create specific charts
cost_chart = visualizer.plot_cost_comparison(baseline, ai_metrics)
roi_chart = visualizer.plot_roi_analysis(roi_data)
# Generate summary dashboard
dashboard = visualizer.create_summary_dashboard(analysis_report)
```
## 🔧 Configuration
Customize analysis parameters using the configuration system:
```python
from mcp_health.utils import load_config, DEFAULT_CONFIG
# Load default configuration
config = load_config()
# Modify AI improvement factors
config['ai_improvements']['admin_efficiency_gain'] = 0.60 # 60% improvement
# Update Japanese healthcare constants
config['japan_constants']['total_healthcare_cost'] = 48e12 # ¥48 trillion
# Use custom configuration
analyzer = HealthcareProductivityAnalyzer()
analyzer.ai_improvements = config['ai_improvements']
analyzer.japan_constants = config['japan_constants']
```
## 🌏 Japanese Healthcare Context
This package is specifically designed for Japan's unique healthcare challenges:
- **Aging Society**: 29.1% of population over 65 years
- **Healthcare Costs**: ¥45 trillion annual expenditure
- **Administrative Efficiency**: Currently 1.6% of total costs
- **Workforce Shortage**: 500K additional workers needed by 2025
## 📈 Use Cases
### 🏛️ **Policy Makers**
- Evidence-based AI investment decisions
- Healthcare budget planning and optimization
- Long-term strategic planning for aging society
### 🏥 **Hospital Administrators**
- ROI justification for AI implementation
- Operational efficiency assessment
- Staff productivity optimization
### 💼 **Technology Vendors**
- Market opportunity analysis
- Solution positioning and pricing
- Implementation planning and phasing
### 🔬 **Researchers**
- Healthcare AI impact studies
- Economic modeling and validation
- Comparative international analysis
## 🛠️ Development
### Setup Development Environment
```bash
# Clone the repository
git clone https://github.com/Tatsuru-Kikuchi/mcp-health-python.git
cd mcp-health-python
# Install in development mode
pip install -e .[dev]
# Run tests
pytest
# Format code
black mcp_health/
isort mcp_health/
# Type checking
mypy mcp_health/
```
### Running Tests
```bash
# Run all tests
pytest
# Run with coverage
pytest --cov=mcp_health --cov-report=html
# Run specific test file
pytest tests/test_data_analysis.py
```
## 📊 Sample Analysis Output
```json
{
"summary": {
"total_annual_savings_trillion_yen": 3.2,
"five_year_roi_percentage": 245,
"payback_period_years": 2,
"admin_time_reduction_percentage": 52,
"error_reduction_percentage": 76,
"throughput_increase_percentage": 24
},
"baseline_metrics": {
"admin_hours_per_patient": 2.0,
"processing_time_hours": 4.0,
"billing_error_rate": 0.025,
"patients_per_worker": 20,
"cost_per_patient": 250000
},
"ai_improved_metrics": {
"admin_hours_per_patient": 0.96,
"processing_time_hours": 1.0,
"billing_error_rate": 0.006,
"patients_per_worker": 24.8,
"cost_per_patient": 232250
}
}
```
## 🤝 Contributing
We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/enhancement`)
3. Make your changes and add tests
4. Ensure all tests pass (`pytest`)
5. Format your code (`black` and `isort`)
6. Submit a pull request
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🔗 Related Projects
- **[Original MCP-Health Dashboard](https://tatsuru-kikuchi.github.io/MCP-health/)**: Interactive web dashboard
- **[MCP-Health Repository](https://github.com/Tatsuru-Kikuchi/MCP-health)**: Original research project
## 📞 Support
- **Issues**: [GitHub Issues](https://github.com/Tatsuru-Kikuchi/mcp-health-python/issues)
- **Discussions**: [GitHub Discussions](https://github.com/Tatsuru-Kikuchi/mcp-health-python/discussions)
- **Email**: tatsuru.kikuchi@example.com
## 🙏 Acknowledgments
- Ministry of Health, Labour and Welfare (Japan) for healthcare statistics
- International healthcare AI research community
- Open source contributors and maintainers
---
<div align="center">
**⭐ Star this repository if you find it useful for healthcare AI research!**
[](https://github.com/Tatsuru-Kikuchi/mcp-health-python/stargazers)
[](https://github.com/Tatsuru-Kikuchi/mcp-health-python/network/members)
</div>
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"description": "# MCP-Health Python Package\n\n\n\n\n\n\n**AI Productivity Analysis for Medical Fees in Japan**\n\nA comprehensive Python package for analyzing the economic impact of AI implementation in Japan's healthcare system, focusing on administrative efficiency and clinical productivity improvements.\n\n## \ud83c\udf1f Features\n\n- **\ud83d\udcca Healthcare Productivity Analysis**: Calculate baseline metrics and AI impact projections\n- **\ud83d\udcb0 Economic Impact Assessment**: ROI analysis and cost savings calculations\n- **\ud83d\udcc8 Advanced Visualizations**: Professional charts and dashboards\n- **\ud83c\udfe5 Japanese Healthcare Focus**: Specialized for Japan's aging society challenges\n- **\ud83e\udd16 AI Implementation Modeling**: Evidence-based improvement factors\n- **\ud83d\udccb Sample Datasets**: Representative Japanese healthcare data for testing\n\n## \ud83d\ude80 Quick Start\n\n### Installation\n\n```bash\n# Install from PyPI\npip install mcp-health\n\n# Install with optional dependencies\npip install mcp-health[interactive] # Jupyter + Plotly support\npip install mcp-health[all] # All optional dependencies\n```\n\n### Basic Usage\n\n```python\nfrom mcp_health import HealthcareProductivityAnalyzer, HealthcareDataGenerator\n\n# Generate sample data\ngenerator = HealthcareDataGenerator()\ndatasets = generator.generate_all_datasets()\n\n# Run analysis\nanalyzer = HealthcareProductivityAnalyzer()\nanalyzer.data = datasets\nreport = analyzer.generate_analysis_report()\n\n# Print key results\nsummary = report['summary']\nprint(f\"Annual Savings: \u00a5{summary['total_annual_savings_trillion_yen']:.1f} Trillion\")\nprint(f\"5-Year ROI: {summary['five_year_roi_percentage']:.0f}%\")\nprint(f\"Payback Period: {summary['payback_period_years']} years\")\n```\n\n### Create Visualizations\n\n```python\nfrom mcp_health import HealthcareVisualizer\n\n# Create visualizer\nvisualizer = HealthcareVisualizer()\n\n# Generate comprehensive dashboard\ndashboard = visualizer.create_summary_dashboard(report)\ndashboard.show()\n\n# Export all charts\nchart_paths = visualizer.export_analysis_charts(report)\nprint(\"Charts saved:\", chart_paths)\n```\n\n## \ud83d\udcca Analysis Results\n\nThe package provides comprehensive analysis of AI impact on Japanese healthcare:\n\n### \ud83c\udfaf **Key Findings**\n- **Annual Savings**: \u00a53.2 Trillion potential savings\n- **ROI**: 245% five-year return on investment \n- **Efficiency**: 52% reduction in administrative time\n- **Throughput**: 24% increase in patient capacity\n\n### \ud83d\udcc8 **Improvement Areas**\n- Administrative automation and error reduction\n- Clinical decision support systems\n- Predictive analytics for patient care\n- Workforce optimization and training\n\n## \ud83c\udfd7\ufe0f Package Structure\n\n```\nmcp_health/\n\u251c\u2500\u2500 core/\n\u2502 \u251c\u2500\u2500 data_analysis.py # Main analyzer class\n\u2502 \u251c\u2500\u2500 data_generator.py # Sample data generation\n\u2502 \u2514\u2500\u2500 visualization.py # Charts and dashboards\n\u251c\u2500\u2500 data/\n\u2502 \u2514\u2500\u2500 sample_datasets.py # Japanese healthcare samples\n\u2514\u2500\u2500 utils/\n \u2514\u2500\u2500 config.py # Configuration management\n```\n\n## \ud83d\udcda Documentation\n\n### Core Classes\n\n#### `HealthcareProductivityAnalyzer`\nMain analysis engine for healthcare productivity assessment.\n\n```python\nanalyzer = HealthcareProductivityAnalyzer(data_dir=\"data/\")\n\n# Load data and run analysis\ndata = analyzer.load_data()\nbaseline = analyzer.calculate_baseline_metrics(data)\nai_metrics = analyzer.calculate_ai_impact_metrics(baseline)\nsavings = analyzer.calculate_cost_savings(baseline, ai_metrics)\nroi = analyzer.calculate_roi_analysis(savings)\n\n# Generate comprehensive report\nreport = analyzer.generate_analysis_report()\n```\n\n#### `HealthcareDataGenerator`\nGenerate representative sample datasets for testing and demonstration.\n\n```python\ngenerator = HealthcareDataGenerator(output_dir=\"data/\")\n\n# Generate all datasets\ndatasets = generator.generate_all_datasets(save_to_disk=True)\n\n# Create sample analysis\nsample_report = generator.create_sample_analysis(\"sample_report.json\")\n```\n\n#### `HealthcareVisualizer`\nCreate professional visualizations and dashboards.\n\n```python\nvisualizer = HealthcareVisualizer(output_dir=\"results/\")\n\n# Create specific charts\ncost_chart = visualizer.plot_cost_comparison(baseline, ai_metrics)\nroi_chart = visualizer.plot_roi_analysis(roi_data)\n\n# Generate summary dashboard\ndashboard = visualizer.create_summary_dashboard(analysis_report)\n```\n\n## \ud83d\udd27 Configuration\n\nCustomize analysis parameters using the configuration system:\n\n```python\nfrom mcp_health.utils import load_config, DEFAULT_CONFIG\n\n# Load default configuration\nconfig = load_config()\n\n# Modify AI improvement factors\nconfig['ai_improvements']['admin_efficiency_gain'] = 0.60 # 60% improvement\n\n# Update Japanese healthcare constants\nconfig['japan_constants']['total_healthcare_cost'] = 48e12 # \u00a548 trillion\n\n# Use custom configuration\nanalyzer = HealthcareProductivityAnalyzer()\nanalyzer.ai_improvements = config['ai_improvements']\nanalyzer.japan_constants = config['japan_constants']\n```\n\n## \ud83c\udf0f Japanese Healthcare Context\n\nThis package is specifically designed for Japan's unique healthcare challenges:\n\n- **Aging Society**: 29.1% of population over 65 years\n- **Healthcare Costs**: \u00a545 trillion annual expenditure\n- **Administrative Efficiency**: Currently 1.6% of total costs\n- **Workforce Shortage**: 500K additional workers needed by 2025\n\n## \ud83d\udcc8 Use Cases\n\n### \ud83c\udfdb\ufe0f **Policy Makers**\n- Evidence-based AI investment decisions\n- Healthcare budget planning and optimization\n- Long-term strategic planning for aging society\n\n### \ud83c\udfe5 **Hospital Administrators** \n- ROI justification for AI implementation\n- Operational efficiency assessment\n- Staff productivity optimization\n\n### \ud83d\udcbc **Technology Vendors**\n- Market opportunity analysis\n- Solution positioning and pricing\n- Implementation planning and phasing\n\n### \ud83d\udd2c **Researchers**\n- Healthcare AI impact studies\n- Economic modeling and validation\n- Comparative international analysis\n\n## \ud83d\udee0\ufe0f Development\n\n### Setup Development Environment\n\n```bash\n# Clone the repository\ngit clone https://github.com/Tatsuru-Kikuchi/mcp-health-python.git\ncd mcp-health-python\n\n# Install in development mode\npip install -e .[dev]\n\n# Run tests\npytest\n\n# Format code\nblack mcp_health/\nisort mcp_health/\n\n# Type checking\nmypy mcp_health/\n```\n\n### Running Tests\n\n```bash\n# Run all tests\npytest\n\n# Run with coverage\npytest --cov=mcp_health --cov-report=html\n\n# Run specific test file\npytest tests/test_data_analysis.py\n```\n\n## \ud83d\udcca Sample Analysis Output\n\n```json\n{\n \"summary\": {\n \"total_annual_savings_trillion_yen\": 3.2,\n \"five_year_roi_percentage\": 245,\n \"payback_period_years\": 2,\n \"admin_time_reduction_percentage\": 52,\n \"error_reduction_percentage\": 76,\n \"throughput_increase_percentage\": 24\n },\n \"baseline_metrics\": {\n \"admin_hours_per_patient\": 2.0,\n \"processing_time_hours\": 4.0,\n \"billing_error_rate\": 0.025,\n \"patients_per_worker\": 20,\n \"cost_per_patient\": 250000\n },\n \"ai_improved_metrics\": {\n \"admin_hours_per_patient\": 0.96,\n \"processing_time_hours\": 1.0,\n \"billing_error_rate\": 0.006,\n \"patients_per_worker\": 24.8,\n \"cost_per_patient\": 232250\n }\n}\n```\n\n## \ud83e\udd1d Contributing\n\nWe welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.\n\n1. Fork the repository\n2. Create a feature branch (`git checkout -b feature/enhancement`)\n3. Make your changes and add tests\n4. Ensure all tests pass (`pytest`)\n5. Format your code (`black` and `isort`)\n6. Submit a pull request\n\n## \ud83d\udcc4 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## \ud83d\udd17 Related Projects\n\n- **[Original MCP-Health Dashboard](https://tatsuru-kikuchi.github.io/MCP-health/)**: Interactive web dashboard\n- **[MCP-Health Repository](https://github.com/Tatsuru-Kikuchi/MCP-health)**: Original research project\n\n## \ud83d\udcde Support\n\n- **Issues**: [GitHub Issues](https://github.com/Tatsuru-Kikuchi/mcp-health-python/issues)\n- **Discussions**: [GitHub Discussions](https://github.com/Tatsuru-Kikuchi/mcp-health-python/discussions)\n- **Email**: tatsuru.kikuchi@example.com\n\n## \ud83d\ude4f Acknowledgments\n\n- Ministry of Health, Labour and Welfare (Japan) for healthcare statistics\n- International healthcare AI research community\n- Open source contributors and maintainers\n\n---\n\n<div align=\"center\">\n\n**\u2b50 Star this repository if you find it useful for healthcare AI research!**\n\n[](https://github.com/Tatsuru-Kikuchi/mcp-health-python/stargazers)\n[](https://github.com/Tatsuru-Kikuchi/mcp-health-python/network/members)\n\n</div>\n",
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