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Duration 14 hours
Course Outline
Foundations of Agentic AI in Healthcare
- Distinguishing agentic systems from 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 utilization, and reflection loops
- Prompt engineering, functions/tools, and action selection strategies
- State management and orchestration patterns
Retrieval-Augmented Agents
- Ingesting and chunking medical documents
- Utilizing embeddings, vector stores, and assessing relevance
- Grounding responses and implementing citation strategies
Healthcare Integrations and Interoperability
- FHIR/SMART fundamentals for agent connectivity
- Managing structured and unstructured clinical data
- Eventing, APIs, and maintaining audit trails
Safety, Risk, and Governance
- Implementing guardrails, red-teaming, and fail-safe design
- Handling PHI, de-identification, and enforcing access controls
- Establishing human-in-the-loop review and escalation paths
Evaluation and Monitoring
- Offline evaluations, defining golden sets, and setting KPIs
- Detecting hallucinations and verifying factuality
- Observability, logging, and managing cost/latency
Deployment Patterns and Hands-on Lab
- Choosing between API-based and on-prem model deployment
- Developing a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB
- Simulated incident response and rollback procedures
Summary and Next Steps
Requirements
- A foundational understanding of basic Python programming
- Practical experience with data analysis or machine learning workflows
- Familiarity with key 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
