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Duration 35 hours
Course Outline
Foundations of LangGraph in Healthcare
- Review of core LangGraph architecture and guiding principles
- Key application areas: patient triage, medical documentation, and compliance automation
- Navigating constraints and leveraging opportunities in regulated settings
Medical Data Standards and Ontologies
- Overview of HL7, FHIR, SNOMED CT, and ICD frameworks
- Incorporating ontologies into LangGraph workflows
- Addressing data interoperability and integration complexities
Workflow Orchestration for Clinical Contexts
- Structuring patient-centric versus provider-centric workflows
- Implementing decision branching and adaptive planning in clinical scenarios
- Managing persistent state for longitudinal patient records
Compliance, Security, and Privacy Protocols
- Understanding HIPAA, GDPR, and regional healthcare regulations
- Techniques for de-identification, anonymization, and secure logging
- Establishing audit trails and traceability within graph execution
Ensuring Reliability and Explainability
- Designing for error handling, retries, and fault tolerance
- Incorporating human-in-the-loop decision support mechanisms
- Promoting explainability and transparency in medical workflows
Integration Strategies and Deployment
- Connecting LangGraph with EHR/EMR systems
- Containerization and deployment best practices for healthcare IT
- Managing monitoring, logging, and SLA requirements
Case Studies and Advanced Applications
- Automating medical coding and billing processes
- Supporting AI-assisted diagnosis and clinical triage
- Streamlining compliance reporting and documentation
Conclusion and Recommended Next Steps
Requirements
- Proficiency in Python and LLM application development at an intermediate level
- Working knowledge of healthcare data standards such as HL7 and FHIR
- Basic familiarity with LangChain or LangGraph concepts
Target Audience
- Domain technologists
- Solution architects
- Consultants specializing in LLM agents within regulated industries
