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Duration 35 hours
Course Outline
Core LangGraph Principles in Healthcare
- Review of LangGraph architecture and underlying principles
- Key healthcare applications: patient triage, medical documentation, and compliance automation
- Navigating constraints and leveraging opportunities in regulated environments
Healthcare Data Standards and Ontologies
- Overview of HL7, FHIR, SNOMED CT, and ICD
- Incorporating ontologies into LangGraph workflows
- Addressing data interoperability and integration hurdles
Workflow Orchestration in Clinical Settings
- Designing patient-centric versus provider-centric workflows
- Implementing decision branching and adaptive planning in clinical contexts
- Managing persistent state for longitudinal patient records
Compliance, Security, and Privacy
- Compliance with HIPAA, GDPR, and regional healthcare regulations
- Techniques for de-identification, anonymization, and secure logging
- Maintaining audit trails and traceability during graph execution
Reliability and Explainability
- Error handling, retry mechanisms, and fault-tolerant design
- Human-in-the-loop approaches to decision support
- Ensuring explainability and transparency in medical workflows
Integration and Deployment
- Connecting LangGraph with EHR/EMR systems
- Containerization and deployment strategies for healthcare IT environments
- Monitoring, logging, and SLA management
Case Studies and Advanced Scenarios
- Automated workflows for medical coding and billing
- AI-assisted diagnostic support and clinical triage
- Automating compliance reporting and documentation
Recap and Future Directions
Requirements
- Intermediate proficiency in Python and LLM application development
- Working knowledge of healthcare data standards (e.g., HL7, FHIR) is advantageous
- Basic familiarity with LangChain or LangGraph concepts
Target Audience
- Domain technologists
- Solution architects
- Consultants developing LLM agents for regulated industries
