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 Duration 14 hours

Course Outline

Core Principles of Agentic AI for Government Healthcare Operations

  • Distinguishing agentic systems from standard LLM tool integrations
  • Defining autonomy limits, regulatory policies, and human oversight frameworks
  • Navigating healthcare data ecosystems and constraints (EHR, FHIR, PHI) for government compliance

Architecting Agent Workflows

  • Implementing planning, memory, tool utilization, and iterative reflection processes
  • Prompt engineering, function definitions, and decision selection mechanisms
  • State management and orchestration strategies for public sector efficiency

Retrieval-Augmented Agent Implementation

  • Ingestion and segmentation of medical documentation
  • Embeddings, vector storage, and relevance assessment protocols
  • Ensuring response accuracy and citation methodologies for accountability

Healthcare Interoperability and Integration

  • FHIR and SMART fundamentals for secure agent connectivity
  • Processing structured and unstructured clinical data for government health systems
  • Event-driven architecture, API management, and comprehensive audit trails

Safety, Risk Management, and Governance

  • Establishing guardrails, conducting red-team exercises, and designing fail-safes
  • Handling PHI, de-identification standards, and strict access controls for government use
  • Human-in-the-loop verification and defined escalation protocols

Evaluation and Continuous Monitoring

  • Offline testing, golden dataset creation, and KPI establishment
  • Detecting hallucinations and validating factual accuracy
  • Observability, logging, and management of cost and latency metrics

Deployment Strategies and Practical Application

  • Evaluating API-based versus on-premise model deployments
  • Developing retrieval-augmented agents using LangChain, FastAPI, and ChromaDB for government environments
  • Simulating incident response and executing rollback procedures

Summary and Strategic Recommendations

Requirements

  • Foundational proficiency in Python programming
  • Practical experience with data analytics or machine learning workflows
  • Knowledge of healthcare data standards and frameworks (e.g., EHR, FHIR)

Intended Audience

  • Healthcare data scientists and machine learning engineers
  • Clinical informatics specialists and digital health product teams
  • IT executives and innovation managers within public health organizations

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