# Komodo Health's MapAI: Democratizing Healthcare Analytics Through LLM Integration ## Company Overview and Problem Statement Komodo Health, a healthcare technology company, identified a critical challenge in the healthcare industry where non-technical team members often faced lengthy delays (weeks or months) in obtaining data insights. This bottleneck affected various roles including brand leads, Medical Affairs managers, and clinical researchers, ultimately impacting decision-making efficiency and product lifecycle management. ## Technical Solution Architecture ### LLM Integration and Model Selection - Implemented a multi-model approach evaluating several foundation models: - Created specialized GenAI agents for different healthcare queries - Developed an intelligence agent system to understand query intent and delegate to appropriate specialized agents ### Technical Infrastructure - Adopted an API-first architecture ### Deployment and Orchestration - Implemented LangChain framework for: - Utilized LangGraph for: - Automated the complete LLM lifecycle to: ## Platform Integration and Features ### MapLab Platform Integration - MapAI is integrated as a core feature within the MapLab enterprise platform - Supports multiple skill levels through different interfaces: ### Query Capabilities and Use Cases - Natural Language Processing Features: - Supported Query Types: ### Data Management and Reusability - Implements cohort and codeset saving functionality - Enables reuse of analyzed data in deeper analytical processes - Maintains consistency across different analysis levels - Integrates with comprehensive Healthcare Map™ data source ## Production Implementation ### Scalability and Performance - Built to handle enterprise-wide deployment - Supports concurrent users across different skill levels - Maintains performance with complex healthcare data queries ### Security and Compliance - Integrated with existing healthcare data privacy standards - Maintains HIPAA compliance in data processing - Implements secure API access protocols ### User Experience Considerations - Chat-style interface for intuitive interaction - Self-service functionality for immediate insight access - Support for varying technical expertise levels - Simplified workflow for common healthcare queries ## Future-Proofing and Maintenance ### System Evolution Strategy - Automated LLM lifecycle management enables: - Continuous integration of new healthcare data sources - Regular updates to query capabilities and features ### Enterprise Integration Benefits - Reduces dependency on technical teams - Accelerates decision-making processes - Standardizes insight generation across organization - Minimizes vendor management overhead - Ensures consistency in data analysis and reporting ## Impact and Results - Transformed weeks-long data analysis processes into instant insights - Enabled non-technical users to perform complex healthcare data analysis - Streamlined enterprise-wide access to healthcare insights - Reduced operational bottlenecks in data analysis workflows - Improved decision-making speed across different organizational roles The implementation represents a significant advancement in healthcare analytics accessibility, demonstrating successful integration of modern LLM technologies in a highly regulated industry. The system's architecture and deployment strategy showcase effective LLMOps practices, from model selection and integration to production deployment and maintenance.