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Duration 14 hours
Course Outline
Foundations of Agentic AI in Healthcare
- Distinguishing agentic systems from basic tool-only LLM applications
- Defining autonomy boundaries, policy frameworks, and the role of human oversight
- Navigating the healthcare data ecosystem and its constraints (including EHR, FHIR, and PHI)
Designing Effective Agent Workflows
- Implementing planning strategies, memory management, tool usage, and reflection loops
- Advanced prompt engineering, function/tool integration, and decision-making for action selection
- Managing state and applying orchestration patterns for complex tasks
Building Retrieval-Augmented Agents
- Ingesting and chunking medical documents for efficient processing
- Utilizing embeddings, vector stores, and evaluating relevance
- Ensuring response accuracy through grounding and strategic citation
Healthcare Integrations and Interoperability
- Core FHIR/SMART concepts for enabling agent connectivity
- Handling both structured and unstructured clinical data sources
- Managing event-driven systems, API interactions, and maintaining audit trails
Safety, Risk Management, and Governance
- Establishing guardrails, conducting red-teaming exercises, and designing fail-safes
- Managing PHI, ensuring de-identification, and implementing robust access controls
- Integrating human-in-the-loop reviews and defining clear escalation paths
Evaluation and Continuous Monitoring
- Conducting offline evaluations, creating golden sets, and defining key performance indicators (KPIs)
- Detecting hallucinations and performing rigorous factuality checks
- Ensuring observability, comprehensive logging, and optimizing cost and latency
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 scenarios and executing rollback procedures
Conclusion and Future Directions
Requirements
- Proficiency in fundamental Python programming
- Practical experience with data analysis or machine learning workflows
- Familiarity with core healthcare data standards and concepts (such as EHR and FHIR)
Target Audience
- Healthcare data scientists and machine learning engineers
- Teams focused on clinical informatics and digital health product development
- IT executives and innovation managers within the healthcare industry
