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Duration 35 hours
Course Outline
LangGraph Fundamentals for Healthcare
- Review of LangGraph architecture and core principles
- Primary healthcare applications: patient triage, medical documentation, and compliance automation
- Addressing constraints and identifying opportunities within regulated sectors
Healthcare Data Standards and Ontologies
- Overview of HL7, FHIR, SNOMED CT, and ICD
- Integrating ontologies into LangGraph workflows
- Navigating data interoperability and integration challenges
Workflow Orchestration in Healthcare
- Structuring 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
- Navigating HIPAA, GDPR, and regional healthcare regulations
- Techniques for de-identification, anonymization, and secure logging
- Maintaining audit trails and ensuring traceability during graph execution
Reliability and Explainability
- Designing for error handling, retries, and fault tolerance
- Incorporating human-in-the-loop decision support
- Ensuring explainability and transparency for medical workflows
Integration and Deployment
- Linking LangGraph with EHR/EMR systems
- Containerization and deployment strategies for healthcare IT environments
- Managing monitoring, logging, and SLAs
Case Studies and Advanced Scenarios
- Streamlining automated medical coding and billing processes
- Supporting AI-assisted diagnosis and clinical triage
- Automating compliance reporting and documentation
Summary and Future Directions
Requirements
- Intermediate proficiency in Python and LLM application development
- A working knowledge of healthcare data standards (such as HL7 and FHIR) is advantageous
- Basic familiarity with LangChain or LangGraph concepts
Target Audience
- Domain technologists
- Solution architects
- Consultants specializing in building LLM agents for regulated industries
