Agentic AI in Healthcare Training Course
Agentic AI is an approach where artificial intelligence systems are designed to plan, reason, and take tool-using actions to achieve specific goals within predefined constraints.
This instructor-led, live training (available both online and on-site) is tailored for intermediate-level healthcare and data teams who aim to design, evaluate, and govern agentic AI solutions for clinical and operational applications.
By the end of this training, participants will be able to:
- Understand and explain the principles and limitations of agentic AI in healthcare settings.
- Develop safe workflows for agents that include planning, memory, and tool usage capabilities.
- Create retrieval-augmented agents to access clinical documents and knowledge bases effectively.
- Assess, monitor, and manage agent behavior using guardrails and human-in-the-loop controls.
Format of the Course
- Interactive lectures and facilitated discussions.
- Guided labs and code walkthroughs in a secure sandbox environment.
- Scenario-based exercises focused on safety, evaluation, and governance.
Course Customization Options
- For customized training sessions tailored to your specific needs, please contact us to arrange.
Course Outline
Foundations of Agentic AI for Healthcare
- Agentic vs. tool-only LLM applications
- Autonomy boundaries, policies, and human oversight
- Healthcare data landscape and constraints (EHR, FHIR, PHI)
Designing Agent Workflows
- Planning, memory, tool use, and reflection loops
- Prompt engineering, functions/tools, and action selection
- State management and orchestration patterns
Retrieval-Augmented Agents
- Medical document ingestion and chunking
- Embeddings, vector stores, and relevance evaluation
- Grounding responses and citation strategies
Healthcare Integrations and Interoperability
- FHIR/SMART basics for agent connectivity
- Working with structured and unstructured clinical data
- Eventing, APIs, and audit trails
Safety, Risk, and Governance
- Guardrails, red-teaming, and fail-safe design
- PHI handling, de-identification, and access controls
- Human-in-the-loop review and escalation paths
Evaluation and Monitoring
- Offline evaluations, golden sets, and KPI definition
- Hallucination detection and factuality checks
- Observability, logging, and cost/latency management
Deployment Patterns and Hands-on Lab
- API-based vs. on-prem model choices
- Building a retrieval-augmented agent with LangChain, FastAPI, and ChromaDB
- Simulated incident response and rollback procedures
Summary and Next Steps
Requirements
- An understanding of basic Python programming
- Experience with data analysis or ML workflows
- Familiarity with healthcare data concepts (e.g., EHR, FHIR)
Audience
- Healthcare data scientists and ML engineers
- Clinical informatics and digital health product teams
- IT leaders and innovation managers in healthcare
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