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Course Outline

Foundations of Agentic AI for Healthcare

  • Distinguishing agentic systems from tool-only LLM applications
  • Defining autonomy boundaries, policies, and human oversight
  • Understanding the healthcare data landscape and constraints (EHR, FHIR, PHI)

Designing Agent Workflows

  • Mechanisms for 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 techniques
  • Utilizing embeddings, vector stores, and relevance evaluation
  • Ensuring grounded responses and citation strategies

Healthcare Integrations and Interoperability

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

Safety, Risk, and Governance

  • Establishing guardrails, conducting red-teaming, and designing fail-safes
  • Managing PHI, de-identification processes, and access controls
  • Facilitating human-in-the-loop review and escalation paths

Evaluation and Monitoring

  • Conducting offline evaluations, utilizing golden sets, and defining KPIs
  • Detecting hallucinations and verifying factuality
  • Enhancing observability, logging, and managing cost/latency

Deployment Patterns and Hands-on Lab

  • Comparing API-based versus on-prem model choices
  • Building a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB
  • Practicing simulated incident response and rollback procedures

Summary and Next Steps

Requirements

  • A foundational understanding of Python programming
  • Prior 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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