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Duration 14 hours
Course Outline
Core Principles of Agentic AI in Healthcare
- Distinguishing agentic frameworks from standard tool-only LLM applications
- Defining autonomy limits, policy frameworks, and the role of human oversight
- Navigating the healthcare data environment and its constraints (including EHR, FHIR, and PHI)
Architecting Agent Workflows
- Integrating planning, memory, tool interaction, and reflective loops
- Advanced prompt engineering, function/tool selection, and action determination
- Managing state and applying orchestration patterns effectively
Developing Retrieval-Augmented Agents
- Ingesting and chunking medical documentation for processing
- Utilizing embeddings, vector databases, and assessing relevance
- Ensuring response grounding and implementing citation strategies
Healthcare Integration and Interoperability
- Understanding FHIR/SMART fundamentals for agent connectivity
- Processing structured and unstructured clinical data
- Managing eventing, API interactions, and maintaining audit trails
Safety, Risk Management, and Governance
- Implementing guardrails, conducting red-teaming exercises, and designing fail-safe mechanisms
- Handling PHI, de-identification techniques, and access control protocols
- Establishing human-in-the-loop review processes and escalation paths
Assessment and Continuous Monitoring
- Conducting offline evaluations, creating golden datasets, and defining KPIs
- Detecting hallucinations and performing factuality verification
- Ensuring observability, robust logging, and managing cost and latency
Deployment Strategies and Practical Lab
- Selecting between API-based and on-premise model deployment options
- Developing a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB
- Simulating incident response procedures and rollback protocols
Conclusions and Future Directions
Requirements
- Fundamental proficiency in Python programming
- Prior experience with data analysis or Machine Learning pipelines
- Knowledge of healthcare data structures and standards (such as EHR and FHIR)
Target Audience
- Healthcare data scientists and ML engineers
- Teams in clinical informatics and digital health product development
- IT executives and innovation leaders within the healthcare industry
