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Duration 35 hours
Course Outline
LangGraph Fundamentals in Healthcare
- Review of LangGraph architecture and core principles
- Key healthcare applications: patient triage, medical documentation, and compliance automation
- Constraints and opportunities within regulated environments
Healthcare Data Standards and Ontologies
- Overview of HL7, FHIR, SNOMED CT, and ICD
- Incorporating ontologies into LangGraph workflows
- Challenges in data interoperability and integration
Workflow Orchestration for Healthcare
- 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
- Navigating HIPAA, GDPR, and regional healthcare regulations
- Techniques for de-identification, anonymization, and secure logging
- Establishing audit trails and traceability in graph execution
Reliability and Explainability
- Error handling, retry mechanisms, and fault-tolerant design patterns
- Incorporating human-in-the-loop 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
- Workflows for automated medical coding and billing
- AI-assisted diagnostic support and clinical triage
- Automation of compliance reporting and documentation
Summary and Next Steps
Requirements
- Intermediate proficiency in Python and LLM application development
- 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
