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Course Outline

Foundations of Agentic AI for Healthcare

  • Distinctions between agentic systems and tool-only LLM applications
  • Defining autonomy boundaries, policies, and human oversight
  • Understanding the 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

  • Processing medical documents and chunking
  • Embeddings, vector stores, and relevance evaluation
  • Grounding responses and citation strategies

Healthcare Integrations and Interoperability

  • Fundamentals of FHIR/SMART for agent connectivity
  • Handling structured and unstructured clinical data
  • Eventing, APIs, and audit trails

Safety, Risk, and Governance

  • Implementing guardrails, red-teaming, and fail-safe design
  • Managing PHI, 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

  • Choosing between API-based and on-prem models
  • Building a retrieval-augmented agent with LangChain, FastAPI, and ChromaDB
  • Simulated incident response and rollback procedures

Summary and Next Steps

Requirements

  • A fundamental understanding of Python programming
  • Experience with data analysis or machine learning 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
 14 Hours

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