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Duration 14 hours
Course Outline
Foundations of Agentic AI in Healthcare
- Distinguishing between agentic systems and standard tool-only LLM applications
- Defining autonomy boundaries, operational policies, and the role of human oversight
- Navigating the healthcare data ecosystem and its specific constraints (EHR, FHIR, PHI)
Architecting Agent Workflows
- Integrating planning, memory, tool usage, and reflection loops
- Applying prompt engineering, function/tool definitions, and intelligent action selection
- Managing state effectively and implementing robust orchestration patterns
Retrieval-Augmented Agents
- Ingesting and structuring medical documents for optimal processing
- Utilizing embeddings, vector stores, and evaluating relevance metrics
- Ensuring response grounding and implementing effective citation strategies
Healthcare Integrations and Interoperability
- Understanding FHIR/SMART fundamentals for seamless agent connectivity
- Processing both structured and unstructured clinical data streams
- Managing eventing, API interactions, and maintaining comprehensive audit trails
Safety, Risk Management, and Governance
- Implementing guardrails, conducting red-teaming, and designing fail-safe mechanisms
- Handling PHI responsibly, including de-identification and strict access controls
- Establishing human-in-the-loop review processes and clear escalation pathways
Evaluation and Monitoring
- Conducting offline evaluations, building golden sets, and defining key performance indicators
- Detecting hallucinations and performing rigorous factuality checks
- Enhancing observability, logging, and managing cost/latency metrics
Deployment Strategies and Practical Labs
- Comparing API-based versus on-premise model deployment options
- Developing a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB
- Simulating incident response scenarios and executing rollback procedures
Course Summary and Future Directions
Requirements
- Foundational knowledge of Python programming
- Practical experience with data analysis or machine learning workflows
- Familiarity with healthcare data standards and concepts (e.g., EHR, FHIR)
Target Audience
- Healthcare data scientists and ML engineers
- Teams in clinical informatics and digital health product development
- IT leaders and innovation managers operating within the healthcare sector
