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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.
- Navigating constraints and identifying opportunities in regulated environments.
Healthcare Data Standards and Ontologies
- Overview of HL7, FHIR, SNOMED CT, and ICD standards.
- Mapping ontologies within LangGraph workflows.
- Addressing data interoperability and integration challenges.
Workflow Orchestration in Healthcare
- Designing workflows centered on either patient-centric or provider-centric approaches.
- Implementing decision branching and adaptive planning in clinical contexts.
- Managing persistent state for longitudinal patient records.
Compliance, Security, and Privacy
- Adhering to HIPAA, GDPR, and regional healthcare regulations.
- Techniques for de-identification, anonymization, and secure logging.
- Establishing audit trails and ensuring traceability during graph execution.
Reliability and Explainability
- Strategies for error handling, retries, and fault-tolerant design.
- Incorporating human-in-the-loop decision support mechanisms.
- Ensuring explainability and transparency in medical workflows.
Integration and Deployment
- Connecting LangGraph with EHR/EMR systems.
- Containerization and deployment strategies for healthcare IT environments.
- Implementing monitoring, logging, and SLA management.
Case Studies and Advanced Scenarios
- Automating medical coding and billing workflows.
- AI-assisted diagnosis support and clinical triage.
- Automating compliance reporting and documentation.
Summary and Next Steps
Requirements
- Intermediate proficiency in Python and LLM application development.
- Understanding of healthcare data standards (e.g., HL7, FHIR) is advantageous.
- Basic familiarity with LangChain or LangGraph concepts.
Target Audience
- Domain technologists.
- Solution architects.
- Consultants developing LLM agents within regulated industries.
