Agentic AI in Healthcare Training Course
Agentic AI represents a paradigm where AI systems autonomously plan, reason, and execute tool-based actions to achieve objectives within established regulatory and operational constraints.
This instructor-led training, available online or onsite, is designed for intermediate-level healthcare and data professionals aiming to design, assess, and govern agentic AI solutions for clinical and operational applications for government.
Upon completion of this course, participants will be equipped to:
- Articulate the concepts and limitations of agentic AI within healthcare sectors.
- Engineer secure agent workflows incorporating planning, memory, and tool integration.
- Construct retrieval-augmented agents utilizing clinical documents and institutional knowledge bases.
- Assess, monitor, and regulate agent performance through guardrails and human-in-the-loop oversight.
Instructional Methodology
- Interactive lectures combined with facilitated professional discussion.
- Structured laboratories and code reviews within a secure sandbox environment.
- Scenario-driven exercises focused on safety, evaluation, and governance standards.
Curriculum Tailoring Options
- To solicit a customized training program aligned with this curriculum, please contact our office for coordination.
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
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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