FHIR Careplan - Universal FHIR Server and Tools

A comprehensive FHIR (Fast Healthcare Interoperability Resources) server and toolkit designed for healthcare data integration, patient care planning, and clinical decision support. This project provides a universal interface to multiple FHIR servers with advanced AI-powered clinical analysis capabilities.

🏥 Overview

The FHIR Careplan project consists of two main components:

1. Universal FHIR MCP Server (fhir_server.py) - A Model Context Protocol (MCP) server that provides standardized access to multiple FHIR servers

2. FHIR Tools Library (fhir_tools.py) - A comprehensive Python library for FHIR data manipulation, analysis, and AI-powered clinical insights

✨ Key Features

🔗 Multi-Server FHIR Integration

• Universal FHIR Interface: Connect to multiple FHIR servers (Epic, Cerner, HAPI, Firely, etc.)

• Vendor-Agnostic: Standardized API regardless of underlying FHIR server implementation

• Real-time Connectivity Testing: Automatic server health checks and diagnostics

• Intelligent Failover: Automatic switching between servers for optimal performance

🤖 AI-Powered Clinical Analysis

• OpenAI Integration: Extract clinical keywords and concepts from free text

• Semantic Mapping: Map clinical terms to standardized FHIR codes

• Similar Patient Matching: Find patients with similar clinical profiles

• Predictive Analytics: Generate care recommendations based on historical data

📊 Comprehensive Patient Data Access

• Complete Patient Records: Demographics, conditions, medications, procedures, encounters

• Vital Signs & Lab Results: Categorized observations with time-series data

• Care Plans & Teams: Treatment plans and healthcare provider information

• Allergies & Procedures: Complete medical history tracking

🚀 Performance Optimization

• Async Operations: Non-blocking I/O for high-performance data access

• Intelligent Caching: Condition codes and frequently accessed data caching

• Batch Processing: Efficient handling of multiple patient records

• Connection Pooling: Optimized HTTP connections for multiple servers

🛠 Installation

Prerequisites

• Python 3.11 or higher

• OpenAI API key (for AI features)

• Access to FHIR servers (local or remote)

Setting Up MCP Server

1. Clone the Repository

   git clone https://github.com/Kushagra-Dutta/Fhir-MCP.git cd FHIR-MCP

2. Install Dependencies

   pip install -r requirements.txt # or using uv uv sync

3. Configure Environment Variables

   # Create .env file echo "OPENAI_API_KEY=your_openai_api_key" > .env

4. Run the FHIR MCP Server

   python fhir_server.py

Setting Up Firely Test Database Locally

1. Obtain a Firely Server License Key

   • Visit Firely Server Trial Page

   • Fill out the form to receive a license key via email

   • You'll receive the license key and download files (license valid for 7 days)

   • Save the license file as firelyserver-license.json

2. Set Up Using Docker

   # Pull the Firely server image docker pull firely/server # Run the container # For Windows CMD: docker run -d -p 9090:4080 --name firely.server -v %CD%/firelyserver-license.json:/app/firelyserver-license.json firely/server # For PowerShell or macOS/Linux: docker run -d -p 9090:4080 --name firely.server -v ${PWD}/firelyserver-license.json:/app/firelyserver-license.json firely/server # Verify container is running docker ps

3. Load Test Data

   • Use Postman to load test data bundles

   • Create a new PUT request in Postman

   • Set request type to raw JSON

   • Copy content from test data bundles

   • Send requests to base URL (http://localhost:9090)

   • Repeat for each data bundle

After completing these steps, you'll have a working test database accessible at http://localhost:9090.

Setting Up MCP Chatbot Client

You can set up the MCP chatbot client either by forking the repository or using Docker. The chatbot will serve as the frontend interface for interacting with the FHIR server.

1. Get the Chatbot setup

   # Clone the repository git clone https://github.com/cgoinglove/mcp-client-chatbot.git cd mcp-client-chatbot

2. Install PNPM (if not installed)

   npm install -g pnpm

3. Choose Setup Method:

   Docker Compose Setup 🐳

   # Install dependencies pnpm i # Configure environment variables # Edit .env file with your API keys (created automatically after pnpm i) # Start all services including PostgreSQL pnpm docker-compose:up

   Local Setup 🚀

   # Install dependencies pnpm i # Setup environment variables pnpm initial:env # Start PostgreSQL (skip if already running) pnpm docker:pg # Run database migrations pnpm db:migrate # Start development server pnpm dev # Optional: Build & start for production-like testing pnpm build:local && pnpm start

4. Configure Environment Variables Create/edit .env file with required API keys:

   # LLM Provider API Keys (add the ones you plan to use) OPENAI_API_KEY=**** GOOGLE_GENERATIVE_AI_API_KEY=**** ANTHROPIC_API_KEY=**** XAI_API_KEY=**** OPENROUTER_API_KEY=**** OLLAMA_BASE_URL=http://localhost:11434/api # Auth Configuration BETTER_AUTH_SECRET=**** # Generate with: npx @better-auth/cli@latest secret BETTER_AUTH_URL= # Optional: URL you access the app from # Database Configuration POSTGRES_URL=postgres://your_username:your_password@localhost:5432/your_database_name # MCP Configuration FILE_BASED_MCP_CONFIG=false # Optional OAuth Settings (for Google/GitHub login) GOOGLE_CLIENT_ID= GOOGLE_CLIENT_SECRET= GITHUB_CLIENT_ID= GITHUB_CLIENT_SECRET=

5. Connect MCP Server to Chatbot

   • Access the chatbot at http://localhost:3000

   • Create an account and login

   • Go to MCP configuration

   • Click "Add Server"

   • Copy-paste your .chatbot-config.json configuration

6. Configure System Prompt

   • Navigate to src/app/prompts.ts in the chatbot repository

   • Replace the default system prompt with your custom prompt from system_prompt.txt

   • Example system prompt structure:

     export const defaultSystemPrompt = `You are a powerful agentic AI coding assistant. You have access to a set of tools you can use to answer the user's question. <communication> 1. Be conversational but professional. 2. Refer to the USER in the second person and yourself in the first person. 3. Format your responses in markdown. 4. NEVER lie or make things up. </communication> <tool_calling> Follow these rules regarding tool calls: 1. ALWAYS follow the tool call schema exactly as specified. 2. NEVER call tools that are not explicitly provided. 3. Only calls tools when they are necessary. 4. Before calling each tool, first explain to the USER why you are calling it. </tool_calling> <making_code_changes> When making code changes: 1. Add all necessary import statements and dependencies 2. NEVER output code to the USER, unless requested 3. Use appropriate code edit tools for implementation </making_code_changes> `;

   • Customize the prompt sections based on your needs:

     • <communication>: Define how the AI should interact

     • <tool_calling>: Specify rules for using tools

     • <making_code_changes>: Set guidelines for code modifications

   • Save the changes and restart the development server

After completing these steps, your chatbot will be connected to the MCP server and ready to interact with the FHIR database.

🏗 Architecture

Core Components

Server Registry

The system maintains a comprehensive registry of FHIR servers:

• Firely Local (http://localhost:9090) - Local development server

• HAPI R4 (http://hapi.fhir.org/baseR4) - Public test server

• Epic, Cerner, Azure - Enterprise healthcare systems (configurable)

📖 Usage Guide

Basic FHIR Operations

1. Server Management

2. Patient Search

3. Patient Data Retrieval

AI-Powered Clinical Analysis

1. Clinical Text Extraction

2. FHIR Code Mapping

3. Similar Patient Matching

MCP Server Integration

The FHIR server can be used as an MCP server for integration with AI assistants:

🔧 Configuration

Server Configuration

The system supports multiple FHIR servers configured in fhir_tools.py:

Environment Variables

🔍 Available Tools

Core FHIR Operations

• switch_server(server_name) - Switch between FHIR servers

• test_server_connectivity(server_name) - Test server connectivity

• find_patient(search_criteria) - Search for patients

• get_comprehensive_patient_info(patient_id) - Get complete patient data

Clinical Data Access

• get_patient_observations(patient_id) - Get patient observations

• get_patient_conditions(patient_id) - Get patient conditions

• get_patient_medications(patient_id) - Get patient medications

• get_vital_signs(patient_id) - Get vital signs

• get_lab_results(patient_id) - Get laboratory results

• get_patient_encounters(patient_id) - Get patient encounters

• get_patient_allergies(patient_id) - Get patient allergies

• get_patient_procedures(patient_id) - Get patient procedures

AI-Powered Analysis

• extract_clinical_keywords(text) - Extract clinical information from text

• map_to_fhir_codes_fast(clinical_data) - Map terms to FHIR codes

• find_similar_patients_simple(criteria) - Find similar patients

• extract_condition_codes_from_fhir() - Extract all condition codes

System Management

• list_available_servers() - List all configured servers

• get_server_registry() - Get complete server registry

• diagnose_fhir_server(server_name) - Diagnose server capabilities

• clear_condition_cache() - Clear condition codes cache

• get_condition_cache_stats() - Get cache performance statistics

🧪 Testing Tools

MCP Inspector

The MCP (Model Context Protocol) Inspector is a powerful development and testing tool that helps debug and validate FHIR server interactions. It provides real-time inspection of server behavior and API responses.

Using MCP Inspector

Features

• Real-time Monitoring: Watch FHIR server interactions as they happen

• Request/Response Logging: Detailed logs of all API calls and responses

• Error Detection: Immediate feedback on API errors or misconfigurations

• Performance Metrics: Track response times and server performance

• Debug Mode: Enhanced logging for development troubleshooting

Log File Location

The inspector writes detailed logs to:

Best Practices

• Use MCP Inspector during development to validate server behavior

• Monitor the log file for unexpected errors or performance issues

• Run the inspector when implementing new FHIR endpoints

• Use it to debug connection issues with external FHIR servers

🏥 Business Applications

Hospital Engagement Platform

This toolkit can power automated hospital engagement platforms:

1. Patient Care Coordination

   • Automated care plan generation

   • Treatment timeline management

   • Multi-disciplinary team coordination

2. Clinical Decision Support

   • Evidence-based treatment recommendations

   • Risk assessment and early warnings

   • Outcome prediction based on similar cases

3. Resource Optimization

   • Predictive analytics for resource allocation

   • Automated scheduling and capacity management

   • Cost optimization through data-driven insights

4. Quality Improvement

   • Automated quality metrics tracking

   • Compliance monitoring and reporting

   • Performance analytics and benchmarking

🔐 Security & Compliance

• HIPAA Compliance: Designed with healthcare data privacy in mind

• Secure Communication: HTTPS/TLS encryption for all server communications

• Authentication Support: Multiple authentication methods (OAuth, API keys, etc.)

• Audit Logging: Comprehensive logging for compliance and debugging

📊 Performance Features

• Async Operations: Non-blocking I/O for high throughput

• Connection Pooling: Efficient HTTP connection management

• Intelligent Caching: Condition codes and metadata caching

• Batch Processing: Efficient handling of multiple records

• Error Handling: Robust error handling and retry mechanisms

🧪 Testing

Running Tests

Server Diagnostics

📝 Logging

The system provides comprehensive logging:

🤝 Contributing

1. Fork the repository

2. Create a feature branch

3. Make your changes

4. Add tests for new functionality

5. Submit a pull request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

For support and questions:

• Create an issue in the GitHub repository

• Check the logs directory for detailed error information

• Use the diagnostic tools for server troubleshooting

🔄 Version History

• v0.1.0 - Initial release with core FHIR functionality

• v0.2.0 - Added AI-powered clinical analysis

• v0.3.0 - Enhanced multi-server support and caching

🎯 Roadmap

• Advanced analytics dashboard

• Real-time data streaming

• Machine learning model integration

• Enhanced security features

• Mobile application support

• Cloud deployment templates

🕒 FHIR Timeline Agent

The FHIR Timeline Agent (fhir_timeline_agent.py) is a specialized agent designed to generate detailed clinical treatment timelines from patient queries using real FHIR data. It's specifically configured to work with the Firely Local FHIR server.

Features

• Natural Language Processing: Converts free-text patient queries into structured clinical data

• Real Patient Data Analysis: Uses actual FHIR patient records for timeline generation

• AI-Powered Timeline Generation: Leverages OpenAI GPT-4 for accurate clinical timelines

• Interactive CLI Interface: User-friendly command-line interface with rich formatting

• Comprehensive Patient Matching: Finds similar patients based on age, gender, and conditions

Usage

Example Queries

Timeline Generation Process

1. Query Processing

   • Extracts clinical keywords from natural language

   • Identifies age, gender, conditions, stage, and biomarkers

2. FHIR Code Mapping

   • Maps clinical terms to standardized FHIR codes

   • Uses Firely Local server's code systems

3. Similar Patient Search

   • Finds matching patients in the FHIR database

   • Scores matches based on age, gender, and conditions

4. Data Aggregation

   • Collects comprehensive medical history

   • Includes procedures, medications, encounters

5. Timeline Generation

   • Creates detailed treatment timeline using AI

   • Organizes events chronologically with clinical context

Output Format

The agent generates rich, formatted output including:

• Patient Profile: Demographics, diagnosis, stage, biomarkers

• Treatment Timeline: Step-by-step clinical events with dates

• Clinical Outcomes: Treatment response, survival status, toxicity

• Data Sources: Server information and analysis metrics

Configuration

The agent is hardcoded to use:

• Firely Local FHIR server (http://localhost:9090)

• OpenAI GPT-4 for timeline generation

• Rich console output for formatted display

Requirements

• OpenAI API key (set in .env file)

• Running Firely Local FHIR server

• Python packages:

  openai>=1.86.0 rich>=13.7.0 python-dotenv>=1.0.0 aiohttp>=3.8.0

Best Practices

• Provide complete patient information in queries

• Include age, gender, and primary diagnosis

• Add stage and biomarker information when available

• Use specific clinical terms for better matching

Error Handling

The agent includes robust error handling for:

• Missing patient information

• FHIR server connectivity issues

• AI generation failures

• Data parsing errors

Each error is displayed with helpful suggestions for resolution.

Built with ❤️ for healthcare interoperability and patient care improvement