mcp-health 0.1.0

AI Productivity Analysis for Medical Fees in Japan

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

# 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

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

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.

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.

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.

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:

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

# 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

# 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

{
  "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 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 file for details.

🔗 Related Projects

• Original MCP-Health Dashboard: Interactive web dashboard
• MCP-Health Repository: Original research project

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Email: [e-mail]

🙏 Acknowledgments

• Ministry of Health, Labour and Welfare (Japan) for healthcare statistics
• International healthcare AI research community
• Open source contributors and maintainers

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