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Duration 14 hours
Course Outline
Core Principles of Agentic AI in Healthcare
- Distinguishing agentic systems from standard tool-only LLM applications.
- Defining autonomy boundaries, establishing policies, and ensuring human oversight.
- Navigating the healthcare data landscape and its constraints (EHR, FHIR, PHI).
Architecting Agent Workflows
- Implementing planning, memory, tool usage, and reflection cycles.
- Advanced prompt engineering, function/tool integration, and action selection strategies.
- Managing state and adopting effective orchestration patterns.
Retrieval-Augmented Agents
- Ingesting and chunking medical documentation.
- Utilizing embeddings, vector stores, and assessing relevance.
- Grounding responses effectively and employing citation strategies.
Healthcare Integration and Interoperability
- Understanding FHIR/SMART fundamentals for seamless agent connectivity.
- Processing both structured and unstructured clinical data.
- Managing eventing, API interactions, and maintaining audit trails.
Safety, Risk Management, and Governance
- Applying guardrails, red-teaming techniques, and designing fail-safe mechanisms.
- Handling PHI, implementing de-identification, and enforcing access controls.
- Establishing human-in-the-loop review processes and escalation pathways.
Evaluation and Continuous Monitoring
- Conducting offline evaluations, curating golden sets, and defining KPIs.
- Detecting hallucinations and verifying factuality.
- Ensuring observability, logging, and optimizing cost/latency management.
Deployment Strategies and Practical Lab
- Comparing API-based versus on-premise model deployment choices.
- Developing a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB.
- Simulating incident response and executing rollback procedures.
Concluding 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 machine learning engineers.
- Teams specializing in clinical informatics and digital health products.
- IT executives and innovation managers within the healthcare sector.
