Agentic AI in Healthcare Training Course
Agentic AI refers to a methodology where artificial intelligence systems plan, reason, and utilize tools to achieve objectives within specified boundaries.
This instructor-led, live training (available online or onsite) is designed for intermediate-level healthcare and data professionals seeking to design, evaluate, and govern agentic AI solutions for clinical and operational applications.
Upon completion of this training, participants will be able to:
- Articulate the concepts and constraints of agentic AI within healthcare settings.
- Design secure agent workflows incorporating planning, memory, and tool integration.
- Construct retrieval-augmented agents that process clinical documents and knowledge bases.
- Evaluate, monitor, and govern agent behavior using guardrails and human-in-the-loop controls.
Course Format
- Interactive lectures and facilitated discussions.
- Guided labs and code walkthroughs in a sandbox environment.
- Scenario-based exercises focused on safety, evaluation, and governance.
Customization Options
- To request a customized version of this course, please contact us to arrange.
Course Outline
Foundations of Agentic AI for Healthcare
- Distinguishing agentic systems from 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
- A foundational understanding of Python programming
- Experience with data analysis or machine learning workflows
- Familiarity with healthcare data concepts (e.g., EHR, FHIR)
Target Audience
- Healthcare data scientists and ML engineers
- Clinical informatics and digital health product teams
- IT leaders and innovation managers in the healthcare sector
Open Training Courses require 5+ participants.
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