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Duration 14 hours
Course Outline
Foundations of Agentic AI for Healthcare
- Distinguishing agentic systems from standard tool-only LLM applications
- Defining autonomy limits, policy frameworks, and human oversight mechanisms
- Navigating the healthcare data landscape, including EHR, FHIR, and PHI constraints
Designing Agent Workflows
- Structuring planning processes, memory management, tool integration, and reflection cycles
- Advanced prompt engineering, function/tool definition, and strategic action selection
- Implementing effective state management and orchestration patterns
Retrieval-Augmented Agents
- Efficient ingestion and chunking of medical documentation
- Utilizing embeddings, vector databases, and assessing relevance accuracy
- Ensuring response grounding and developing robust citation strategies
Healthcare Integrations and Interoperability
- Foundational knowledge of FHIR/SMART for seamless agent connectivity
- Processing both structured and unstructured clinical data effectively
- Managing eventing, API integrations, and comprehensive audit trails
Safety, Risk, and Governance
- Implementing guardrails, conducting red-teaming exercises, and designing fail-safe systems
- Proper handling of PHI, de-identification techniques, and strict access controls
- Establishing human-in-the-loop review processes and clear escalation pathways
Evaluation and Monitoring
- Conducting offline evaluations, managing golden datasets, and defining key performance indicators
- Detecting hallucinations and performing rigorous factuality checks
- Enhancing observability, logging practices, and managing cost/latency metrics
Deployment Patterns and Hands-on Lab
- Evaluating API-based versus on-premise model deployment strategies
- Constructing a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB
- Simulating incident response scenarios and executing rollback procedures
Summary and Next Steps
Requirements
- Proficiency in fundamental Python programming concepts
- Practical experience with data analysis or Machine Learning (ML) workflows
- Working knowledge of healthcare data standards and concepts, including EHR and FHIR
Target Audience
- Healthcare data scientists and Machine Learning engineers
- Clinical informatics specialists and digital health product teams
- IT leaders and innovation managers operating within the healthcare sector
