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Duration 14 hours
Course Outline
Foundations of Agentic AI for Healthcare
- Agentic systems versus tool-only LLM applications
- Defining autonomy boundaries, policies, and human oversight
- The healthcare data landscape and its constraints (EHR, FHIR, PHI)
Designing Agent Workflows
- Planning, memory, tool use, and reflection loops
- Prompt engineering, functions/tools, and action selection
- State management and orchestration patterns
Retrieval-Augmented Agents
- Ingestion and chunking of medical documents
- Embeddings, vector stores, and relevance evaluation
- Grounding responses and citation strategies
Healthcare Integrations and Interoperability
- FHIR/SMART basics for agent connectivity
- Handling structured and unstructured clinical data
- Eventing, APIs, and audit trails
Safety, Risk, and Governance
- Guardrails, red-teaming, and fail-safe design
- PHI handling, de-identification, and access controls
- Human-in-the-loop review and escalation paths
Evaluation and Monitoring
- Offline evaluations, golden sets, and KPI definition
- Hallucination detection and factuality checks
- Observability, logging, and cost/latency management
Deployment Patterns and Hands-on Lab
- Comparing API-based versus on-prem model choices
- Building a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB
- Simulated incident response and rollback procedures
Summary and Next Steps
Requirements
- A foundational understanding of Python programming
- Practical experience with data analysis or ML workflows
- Familiarity with healthcare data standards and concepts (e.g., EHR, FHIR)
Target Audience
- Healthcare data scientists and ML engineers
- Clinical informatics and digital health product teams
- IT leaders and innovation managers within the healthcare sector
