Healthcare Analytics MCP Server

# Healthcare Analytics MCP Server - Project Summary ## 🎯 Project Overview Successfully created a comprehensive Model Context Protocol (MCP) server for healthcare data analysis using FastMCP 2.0 and Tuva Health demo data. This server provides essential tools for value-based care analytics that healthcare organizations commonly need. ## ✅ Completed Tasks - [x] **Data Structure Exploration**: Analyzed Tuva Health BigQuery datasets (core, quality_measures, financial_pmpm, chronic_conditions, cms_hcc, readmissions) - [x] **Project Setup**: Configured FastMCP 2.0 with proper dependencies and environment setup - [x] **Core Tools Implementation**: Built 8 comprehensive healthcare analytics tools - [x] **Documentation**: Created detailed README and setup instructions - [x] **Testing Framework**: Developed comprehensive test suite for validation ## 🛠️ Built Tools ### 1. Patient Demographics Analysis (`get_patient_demographics`) - Total patient counts and enrollment analysis - Age group distribution (0-17, 18-34, 35-54, 55-64, 65+) - Gender distribution percentages - Configurable date ranges ### 2. Healthcare Utilization Summary (`get_utilization_summary`) - Claims volume and patient utilization metrics - Service category breakdowns (top 10 categories) - Total paid/allowed amounts analysis - Average costs per claim ### 3. PMPM Financial Analysis (`get_pmpm_analysis`) - Per Member Per Month cost calculations - Service category breakdown (inpatient, outpatient, office visits, ancillary) - Monthly trend analysis - Payer-specific filtering ### 4. Quality Measures Summary (`get_quality_measures_summary`) - HEDIS and clinical quality measure tracking - Performance rates and pass/fail flags - Measure-specific filtering - Average performance calculations ### 5. Chronic Conditions Prevalence (`get_chronic_conditions_prevalence`) - Population health condition analysis - Prevalence rates by condition family - Top 20 conditions by patient count - Condition-specific filtering ### 6. High-Cost Patient Identification (`get_high_cost_patients`) - Case management patient identification - Configurable cost thresholds - Inpatient vs outpatient utilization patterns - Patient demographics integration ### 7. Readmissions Analysis (`get_readmissions_analysis`) - 30-day readmission rate calculations - Length of stay analysis - Total cost impact assessment - Condition-specific filtering ### 8. HCC Risk Score Analysis (`get_hcc_risk_scores`) - Risk adjustment and stratification - Population risk distribution - High/low risk patient identification - Statistical summaries ## 🏗️ Architecture Highlights ### Technology Stack - **FastMCP 2.0**: Modern MCP framework with comprehensive features - **Google Cloud BigQuery**: Scalable healthcare data warehouse - **Pandas**: Data manipulation and analysis - **Python 3.8+**: Type hints and modern Python features ### Key Design Decisions - **Modular Tool Design**: Each tool focuses on specific value-based care metrics - **Flexible Date Ranges**: All tools support configurable analysis periods - **Optional Filtering**: Tools provide granular filtering capabilities - **Structured Output**: Consistent dictionary-based responses for easy integration - **Error Handling**: Comprehensive exception handling and debugging support ## 📊 Value-Based Care Focus Areas ### Population Health Management - Demographic analysis and stratification - Chronic condition prevalence tracking - Risk score distribution analysis ### Financial Performance Monitoring - PMPM trend analysis and benchmarking - High-cost patient identification - Service category cost breakdowns ### Quality Improvement - HEDIS measure performance tracking - Readmission rate monitoring - Care gap identification ### Care Management - High-risk patient identification - Cost-effective intervention targeting - Outcomes measurement support ## 🚀 Getting Started 1. **Setup Environment**: ```bash uv pip install -r requirements.txt cp .env.example .env # Configure .env with BigQuery credentials ``` 2. **Test Installation**: ```bash python test_server.py ``` 3. **Run Server**: ```bash fastmcp run healthcare_mcp_server.py ``` ## 🔧 Customization & Extension The server is designed for easy extension with additional healthcare analytics: ### Adding New Tools ```python @mcp.tool() def get_medication_adherence( therapeutic_class: str, year: str = "2018" ) -> Dict[str, Any]: # Implementation here pass ``` ### Common Extensions - Medication adherence tracking - Provider performance analysis - Care transition metrics - Social determinants integration - Clinical outcomes tracking ## 💡 Use Cases ### Monthly Executive Reporting Generate comprehensive dashboards combining multiple tools for executive reporting on population health, financial performance, and quality metrics. ### Care Management Operations Identify high-cost, high-risk patients for proactive intervention using HCC scores, chronic conditions, and utilization patterns. ### Value-Based Contract Performance Monitor HEDIS measures, PMPM trends, and readmission rates to track performance against value-based care contracts. ### Population Health Insights Analyze demographic trends, chronic condition prevalence, and utilization patterns to inform population health strategies. ## 📈 Future Enhancements ### Potential Additions - [ ] Predictive analytics tools using ML models - [ ] Real-time alerting capabilities - [ ] Integration with EHR systems - [ ] Advanced visualization outputs - [ ] Comparative benchmarking tools - [ ] Social determinants analysis - [ ] Provider network analytics ### Scalability Considerations - Multi-tenant support for healthcare systems - Data privacy and HIPAA compliance features - Performance optimization for large datasets - Caching layers for frequently accessed metrics ## 🎉 Project Success This healthcare MCP server successfully provides a comprehensive toolkit for value-based care analytics, making complex healthcare data analysis accessible through simple, well-documented tools. The modular design allows for easy customization and extension to meet specific organizational needs while maintaining best practices for healthcare data analysis.