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Duration 14 hours
Course Outline
Fundamental Principles of Agentic AI for Healthcare
- Distinguishing agentic systems from standard tool-using LLM applications.
- Defining autonomy limits, policy frameworks, and human supervision roles.
- Navigating the healthcare data ecosystem and its constraints (EHR, FHIR, PHI).
Architecting Agent Workflows
- Implementing planning cycles, memory systems, tool integration, and reflection loops.
- Advancing prompt engineering, function/tool definitions, and action decision-making.
- Managing state and applying orchestration patterns.
Retrieval-Augmented Agent Development
- Processing medical document ingestion and segmentation.
- Utilizing embeddings, vector databases, and assessing relevance.
- Ensuring response grounding and developing citation methodologies.
Healthcare Integration and Interoperability
- Understanding FHIR/SMART fundamentals for agent connectivity.
- Handling both structured and unstructured clinical data.
- Implementing eventing, API interactions, and audit trail maintenance.
Safety, Risk Management, and Governance
- Establishing guardrails, conducting red-teaming, and designing fail-safe mechanisms.
- Managing PHI handling, de-identification, and access control protocols.
- Integrating human-in-the-loop review processes and escalation pathways.
Evaluation and Monitoring Strategies
- Conducting offline evaluations, creating golden sets, and defining KPIs.
- Detecting hallucinations and performing factuality verification.
- Enhancing observability, logging, and managing cost/latency.
Deployment Strategies and Practical Laboratory
- Comparing API-based versus on-prem model deployment options.
- Constructing a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB.
- Simulating incident response and executing rollback procedures.
Conclusion and Future Directions
Requirements
- A foundational grasp of basic 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.
- Clinical informatics specialists and digital health product teams.
- IT executives and innovation managers within the healthcare sector.
