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Duration 14 hours
Course Outline
Foundational Principles of Agentic AI in Healthcare
- Distinguishing agentic systems from simple tool-using LLM applications
- Defining autonomy limits, policy frameworks, and human oversight roles
- Navigating the healthcare data ecosystem and its constraints (EHR, FHIR, PHI)
Architecting Agent Workflows
- Integrating planning, memory, tool usage, and reflective feedback loops
- Advanced prompt engineering, function/tool selection, and decision-making processes
- Implementing state management and orchestration patterns
Developing Retrieval-Augmented Agents
- Ingesting and structuring medical documents for effective chunking
- Utilizing embeddings, vector stores, and assessing relevance
- Ensuring response grounding and developing robust citation strategies
Healthcare Integration and Interoperability
- Applying FHIR and SMART standards for seamless agent connectivity
- Processing both structured and unstructured clinical data efficiently
- Managing eventing, API interactions, and maintaining audit trails
Safety, Risk Management, and Governance
- Implementing guardrails, conducting red-team exercises, and designing fail-safes
- Handling PHI with proper de-identification and access control protocols
- Establishing human-in-the-loop review processes and escalation pathways
Performance Evaluation and Monitoring
- Conducting offline evaluations, defining golden sets, and establishing KPIs
- Detecting hallucinations and verifying factual accuracy
- Managing observability, logging, and optimizing cost and latency
Deployment Strategies and Practical Laboratory
- Comparing API-based versus on-premises model deployment options
- Constructing a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB
- Simulating incident response scenarios and practicing rollback procedures
Conclusions and Future Pathways
Requirements
- Proficiency in fundamental Python programming concepts
- Practical experience with data analysis or machine learning workflows
- Familiarity with key healthcare data standards (such as EHR and FHIR)
Intended Audience
- Healthcare data scientists and ML engineers
- Clinical informatics and digital health product teams
- IT leaders and innovation managers operating within the healthcare sector
