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Duration 35 hours
Course Outline
Foundations of LangGraph in Healthcare
- Review of LangGraph architecture and core principles
- Key use cases: patient triage, medical documentation, and compliance automation
- Exploring constraints and opportunities within 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 challenges
Workflow Orchestration in Clinical Settings
- Designing workflows centered on patient versus provider perspectives
- Implementing decision branching and adaptive planning for 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
- Maintaining audit trails and traceability during graph execution
Reliability and Explainability
- Designing fault-tolerant systems with effective error handling and retries
- Implementing human-in-the-loop decision support
- Ensuring explainability and transparency in medical workflows
Integration and Deployment
- Connecting LangGraph with EHR/EMR systems
- Containerization strategies for healthcare IT environments
- Managing monitoring, logging, and SLAs
Case Studies and Advanced Scenarios
- Automating medical coding and billing workflows
- Supporting AI-assisted diagnosis and clinical triage
- Automating compliance reporting and documentation
Summary and Next Steps
Requirements
- Intermediate proficiency in Python and LLM application development
- Working knowledge of healthcare data standards (e.g., HL7, FHIR)
- Familiarity with the fundamentals of LangChain or LangGraph
Target Audience
- Domain technologists
- Solution architects
- Consultants developing LLM agents for regulated industries
