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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.
- Data interoperability and integration challenges.
Workflow Orchestration in Healthcare
- Designing patient-centric versus provider-centric workflows.
- Decision branching and adaptive planning in clinical contexts.
- Handling persistent state for longitudinal patient records.
Compliance, Security, and Privacy
- HIPAA, GDPR, and regional healthcare regulations.
- De-identification, anonymization, and secure logging practices.
- Audit trails and traceability within graph execution.
Reliability and Explainability
- Error handling, retries, and fault-tolerant design patterns.
- Implementing human-in-the-loop decision support.
- Explainability and transparency for medical workflows.
Integration and Deployment
- Connecting LangGraph with EHR/EMR systems.
- Containerization and deployment within healthcare IT environments.
- Monitoring, logging, and SLA management.
Case Studies and Advanced Scenarios
- Automated medical coding and billing workflows.
- AI-assisted diagnosis support and clinical triage.
- Compliance reporting and documentation automation.
Summary and Next Steps
Requirements
- Intermediate proficiency in Python and LLM application development.
- A working understanding of healthcare data standards (e.g., HL7, FHIR) is advantageous.
- Familiarity with the fundamental concepts of LangChain or LangGraph.
Target Audience
- Domain technologists.
- Solution architects.
- Consultants developing LLM agents for regulated industries.
