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Duration 35 hours
Course Outline
LangGraph Fundamentals for Healthcare
- Review of LangGraph architecture and core principles
- Key healthcare use cases: 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
- Mapping ontologies into LangGraph workflows
- Challenges in data interoperability and integration
Workflow Orchestration in Healthcare
- Designing patient-centric versus provider-centric workflows
- Decision branching and adaptive planning in clinical settings
- Managing persistent state for longitudinal patient records
Compliance, Security, and Privacy
- HIPAA, GDPR, and regional healthcare regulations
- Data 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
- Human-in-the-loop decision support strategies
- Ensuring explainability and transparency for medical workflows
Integration and Deployment
- Connecting LangGraph with EHR/EMR systems
- Containerization and deployment within healthcare IT landscapes
- Monitoring, logging, and SLA management
Case Studies and Advanced Scenarios
- Automated workflows for 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
- A foundational understanding of healthcare data standards (e.g., HL7, FHIR) is advantageous
- Familiarity with the basics of LangChain or LangGraph
Target Audience
- Domain technologists
- Solution architects
- Consultants developing LLM agents for regulated industries
