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 Duration 14 hours

Course Outline

Foundations of Agentic AI in Healthcare

  • Distinguishing agentic systems from simple tool-use LLM applications
  • Defining autonomy limits, policy adherence, and human oversight mechanisms
  • Navigating the healthcare data ecosystem and constraints (EHR, FHIR, PHI)

Designing Effective Agent Workflows

  • Integrating planning, memory, tool usage, and reflection loops
  • Advanced prompt engineering, function/tool integration, and action selection logic
  • Managing state and applying orchestration patterns

Building Retrieval-Augmented Agents

  • Ingesting and chunking medical documentation for optimal processing
  • Utilizing embeddings, vector stores, and assessing relevance
  • Ensuring grounded responses and implementing citation strategies

Healthcare Integration and Interoperability

  • Core FHIR/SMART principles for seamless agent connectivity
  • Processing both structured and unstructured clinical data
  • Implementing eventing, API management, and comprehensive audit trails

Safety, Risk Management, and Governance

  • Establishing guardrails, red-teaming practices, and fail-safe design
  • Managing PHI, de-identification techniques, and access controls
  • Implementing human-in-the-loop reviews and clear escalation paths

Evaluation and Continuous Monitoring

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

Deployment Strategies and Practical Lab

  • Choosing between API-based and on-premise model architectures
  • Constructing a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB
  • Simulating incident response protocols and rollback procedures

Conclusion and Future Steps

Requirements

  • Foundational proficiency in Python programming
  • Practical experience with data analysis or Machine Learning workflows
  • Knowledge of healthcare data standards and concepts (e.g., EHR, FHIR)

Target Audience

  • Data scientists and ML engineers specializing in healthcare
  • Teams focused on clinical informatics and digital health products
  • IT executives and innovation managers within the healthcare sector

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