Course Outline

Foundations of Agentic AI for Healthcare

  • Differentiating between agentic and tool-only LLM applications in healthcare settings
  • Establishing autonomy boundaries, policies, and human oversight mechanisms
  • Understanding the healthcare data landscape and constraints (EHR, FHIR, PHI)

Designing Agent Workflows for Government

  • Planning, memory management, tool utilization, and reflective loops in agent design
  • Techniques for prompt engineering, function/tool integration, and action selection
  • Strategies for state management and orchestration patterns

Retrieval-Augmented Agents for Government

  • Ingestion and chunking of medical documents for effective retrieval
  • Utilizing embeddings, vector stores, and relevance evaluation methods
  • Ensuring grounded responses and appropriate citation strategies

Healthcare Integrations and Interoperability for Government

  • Basics of FHIR/SMART standards for agent connectivity in healthcare systems
  • Methods for working with structured and unstructured clinical data
  • Implementing eventing, APIs, and audit trails for compliance and transparency

Safety, Risk, and Governance for Government

  • Establishing guardrails, conducting red-teaming exercises, and designing fail-safe mechanisms
  • Handling Protected Health Information (PHI), de-identification processes, and access control measures
  • Implementing human-in-the-loop review processes and defining escalation paths

Evaluation and Monitoring for Government

  • Conducting offline evaluations, defining golden sets, and establishing key performance indicators (KPIs)
  • Techniques for detecting hallucinations and ensuring factual accuracy
  • Ensuring observability, logging practices, and managing cost and latency

Deployment Patterns and Hands-on Lab for Government

  • Evaluating API-based versus on-premises model deployment options
  • Building a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB
  • Simulating incident response scenarios and rollback procedures

Summary and Next Steps for Government

Requirements

  • A foundational knowledge of Python programming for government applications
  • Practical experience with data analysis or machine learning workflows for government projects
  • Familiarity with healthcare data concepts, including Electronic Health Records (EHR) and Fast Healthcare Interoperability Resources (FHIR)

Audience

  • Healthcare data scientists and machine learning engineers for government
  • Clinical informatics professionals and digital health product teams in the public sector
  • IT leaders and innovation managers within healthcare organizations for government
 14 Hours

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