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Duration 14 hours
Course Outline
Core Principles of Agentic AI in Healthcare
- Differentiating agentic systems from standard tool-using LLM applications
- Defining autonomy limits, policy frameworks, and the role of human supervision
- Understanding healthcare data ecosystems and restrictions (EHR, FHIR, PHI)
Architecting Agent Workflows
- Integrating planning, memory, tool usage, and reflective processes
- Refining prompt engineering, function/tools, and decision-making selection
- Managing state and implementing effective orchestration patterns
Developing Retrieval-Augmented Agents
- Processing medical documentation through ingestion and chunking strategies
- Utilizing embeddings, vector databases, and assessing relevance
- Ensuring response accuracy and implementing citation methodologies
Healthcare System Integration and Interoperability
- Understanding FHIR/SMART fundamentals for seamless agent connectivity
- Handling both structured and unstructured clinical data sources
- Managing event streaming, APIs, and comprehensive audit trails
Ensuring Safety, Risk Management, and Governance
- Implementing guardrails, conducting red-team exercises, and designing fail-safes
- Managing PHI, de-identification processes, and strict access controls
- Establishing human-in-the-loop reviews and clear escalation protocols
Performance Evaluation and Continuous Monitoring
- Conducting offline assessments, utilizing golden datasets, and defining KPIs
- Detecting hallucinations and verifying factual accuracy
- Enhancing observability, logging practices, and managing cost/latency
Deployment Strategies and Practical Lab
- Comparing API-based versus on-premise model deployment options
- Developing a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB
- Simulating incident response and executing rollback procedures
Course Wrap-up and Future Directions
Requirements
- Proficiency in fundamental Python programming
- Prior experience in data analytics or machine learning workflows
- Working knowledge of healthcare data standards (e.g., EHR, FHIR)
Intended Audience
- Healthcare data scientists and ML engineers
- Teams in clinical informatics and digital health product development
- IT executives and innovation managers within the healthcare sector
