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 Duration 14 hours

Course Outline

Foundations of Agentic AI for Healthcare

  • Differentiating agentic systems from tool-only LLM applications
  • Defining autonomy limits, policies, and human oversight frameworks
  • Navigating the healthcare data landscape and associated constraints (EHR, FHIR, PHI)

Designing Agent Workflows

  • Planning, memory management, tool utilization, and reflection cycles
  • Prompt engineering, function/tool integration, and action selection logic
  • State management techniques and orchestration patterns

Retrieval-Augmented Agents

  • Ingestion and chunking strategies for medical documents
  • Utilizing embeddings, vector stores, and assessing relevance
  • Strategies for grounding responses and effective citation

Healthcare Integrations and Interoperability

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

Safety, Risk, and Governance

  • Establishing guardrails, conducting red-teaming, and fail-safe design
  • PHI management, de-identification techniques, and access control protocols
  • Human-in-the-loop review processes and escalation pathways

Evaluation and Monitoring

  • Conducting offline evaluations, defining golden sets, and establishing KPIs
  • Detecting hallucinations and performing factuality verification
  • Ensuring observability, logging, and managing cost/latency metrics

Deployment Patterns and Hands-on Lab

  • Comparing API-based versus on-premise model deployment choices
  • Constructing a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB
  • Simulating incident response protocols and rollback procedures

Summary and Next Steps

Requirements

  • Fundamental proficiency in Python programming
  • Prior experience with data analysis or machine learning workflows
  • Familiarity with healthcare data standards such as EHR and FHIR

Target Audience

  • Healthcare data scientists and ML engineers
  • Teams involved in clinical informatics and digital health product development
  • IT leaders and innovation managers within the healthcare sector

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