Agentic AI in Healthcare Training Course
Agentic AI is an approach where artificial intelligence systems are designed to plan, reason, and take tool-using actions to achieve specific goals within defined parameters.
This instructor-led, live training (online or onsite) is aimed at intermediate-level healthcare and data teams who wish to design, evaluate, and govern agentic AI solutions for clinical and operational use cases in the public sector.
By the end of this training, participants will be able to:
- Explain agentic AI concepts and constraints within healthcare contexts.
- Design safe agent workflows that incorporate planning, memory, and tool usage.
- Develop retrieval-augmented agents using clinical documents and knowledge bases.
- Evaluate, monitor, and govern agent behavior with guardrails and human-in-the-loop controls to ensure compliance and safety for government applications.
Format of the Course
- Interactive lecture and facilitated discussion.
- Guided labs and code walkthroughs in a sandbox environment.
- Scenario-based exercises focusing on safety, evaluation, and governance for government use cases.
Course Customization Options
- To request a customized training for this course tailored to specific needs, please contact us to arrange.
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
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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