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Duration 35 hours
Course Outline
LangGraph Fundamentals for Healthcare
- Review of LangGraph architecture and core 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
- Integrating ontologies into LangGraph workflows
- Addressing data interoperability and integration challenges
Workflow Orchestration in Healthcare
- Developing patient-centric versus provider-centric workflows
- Implementing decision branching and adaptive planning in clinical settings
- Managing persistent state for longitudinal patient records
Compliance, Security, and Privacy
- Understanding HIPAA, GDPR, and regional healthcare regulations
- Techniques for de-identification, anonymization, and secure logging
- Establishing audit trails and ensuring traceability in graph execution
Reliability and Explainability
- Designing for error handling, retries, and fault tolerance
- Incorporating human-in-the-loop decision support
- Enhancing explainability and transparency in medical workflows
Integration and Deployment
- Connecting LangGraph with EHR/EMR systems
- Containerization and deployment strategies for healthcare IT environments
- Managing monitoring, logging, and SLAs
Case Studies and Advanced Scenarios
- Automating medical coding and billing workflows
- AI-assisted diagnosis support and clinical triage
- Streamlining compliance reporting and documentation automation
Summary and Next Steps
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
Target Audience
- Domain technologists
- Solution architects
- Consultants developing LLM agents for regulated industries