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Course Outline

Fundamental Concepts of Autonomous AI Systems in Healthcare

  • Distinctions between autonomous agents and tool-assisted large language model applications
  • Establishing autonomy limits, regulatory policies, and required human oversight
  • Overview of the healthcare data ecosystem and compliance constraints (EHR systems, FHIR standards, PHI protection)

Architecting Agent Operational Workflows

  • Implementation of planning mechanisms, memory modules, tool interaction, and reflective feedback loops
  • Strategies for prompt engineering, function invocation, and dynamic action selection
  • Patterns for state management and process orchestration

Retrieval-Augmented Generation in Agent Design

  • Ingestion methodologies and segmentation of medical documentation
  • Utilization of embeddings, vector databases, and relevance assessment criteria
  • Ensuring factual grounding and establishing citation protocols for government stakeholders

Healthcare System Integration and Interoperability Standards

  • Fundamentals of FHIR and SMART frameworks for agent connectivity
  • Processing structured and unstructured clinical information
  • Implementation of event-driven architecture, API interactions, and audit trail maintenance

Risk Management, Safety Protocols, and Governance Frameworks

  • Deployment of safety guardrails, adversarial testing (red-teaming), and fail-safe mechanisms
  • Protocols for PHI handling, data de-identification, and role-based access controls
  • Human-in-the-loop validation procedures and escalation pathways

Performance Evaluation and Operational Monitoring

  • Conducting offline assessments, utilizing benchmark datasets, and defining key performance indicators
  • Mechanisms for hallucination detection and verification of factual accuracy
  • System observability, logging requirements, and management of computational costs and latency

Deployment Strategies and Practical Application Lab

  • Comparison of API-hosted versus on-premises model deployment options
  • Construction of a retrieval-augmented agent utilizing LangChain, FastAPI, and ChromaDB
  • Simulation of incident response protocols and system rollback procedures for government operations

Executive Summary and Strategic Next Steps

Requirements

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

Audience

  • Data scientists specializing in healthcare and machine learning engineers
  • Clinical informatics specialists and digital health product development teams
  • Information technology executives and innovation managers within the healthcare sector, leveraging solutions designed for government
 14 Hours

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