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Duration 35 hours
Course Outline
Foundations of LangGraph in Healthcare
- Review of LangGraph architecture and core principles
- Key healthcare applications: patient triage, clinical documentation, and compliance automation
- Navigating constraints and leveraging opportunities in 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 complexities
Workflow Orchestration in Clinical Settings
- Designing patient-centric versus provider-centric workflows
- Implementing decision branching and adaptive planning for clinical contexts
- Managing persistent state for longitudinal patient records
Compliance, Security, and Privacy
- Compliance with HIPAA, GDPR, and regional healthcare regulations
- Strategies 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
- Enhancing explainability and transparency for medical workflows
Integration and Deployment Strategies
- Linking LangGraph with EHR/EMR systems
- Containerization and deployment within healthcare IT infrastructure
- Managing monitoring, logging, and SLAs
Case Studies and Advanced Scenarios
- Streamlining automated medical coding and billing processes
- Leveraging AI for diagnostic support and clinical triage
- Automating compliance reporting and documentation
Summary and Future Directions
Requirements
- Intermediate proficiency in Python and LLM application development
- A solid understanding of healthcare data standards, such as HL7 and FHIR, is advantageous
- Basic familiarity with LangChain or LangGraph
Intended Audience
- Domain technologists
- Solution architects
- Consultants developing LLM agents within regulated industries
