Agentic AI in Healthcare Training Course
Agentic AI represents a paradigm where artificial intelligence systems autonomously plan, reason, and utilize tools to achieve specific objectives within established boundaries.
This instructor-led training, available online or onsite, is designed for intermediate-level healthcare and data teams looking to design, evaluate, and govern agentic AI solutions for both clinical and operational scenarios.
Upon completion of this course, participants will be capable of:
- Articulating the core concepts and constraints of agentic AI within healthcare environments.
- Constructing secure agent workflows that incorporate planning, memory mechanisms, and tool integration.
- Developing retrieval-augmented agents tailored to clinical documents and knowledge bases.
- Implementing evaluation, monitoring, and governance frameworks for agent behavior using guardrails and human-in-the-loop controls.
Course Format
- Interactive lectures combined with facilitated group discussions.
- Guided laboratory exercises and code walkthroughs within a sandbox environment.
- Scenario-based practical exercises focusing on safety, evaluation, and governance.
Customization Options
- To request tailored training for this course, please reach out to us to arrange.
Course Outline
Foundations of Agentic AI for Healthcare
- Distinction between agentic systems and tool-only LLM applications
- Defining autonomy boundaries, policies, and human oversight requirements
- Overview of the healthcare data landscape and its constraints (EHR, FHIR, PHI)
Designing Agent Workflows
- Mechanisms for planning, memory, tool usage, and reflection loops
- Prompt engineering, function/tool invocation, and action selection strategies
- Patterns for state management and orchestration
Retrieval-Augmented Agents
- Ingestion and chunking of medical documents
- Utilizing embeddings, vector stores, and evaluating relevance
- Grounding responses and managing citation strategies
Healthcare Integrations and Interoperability
- Fundamentals of FHIR and SMART for agent connectivity
- Processing structured and unstructured clinical data
- Managing eventing, APIs, and audit trails
Safety, Risk, and Governance
- Implementing guardrails, conducting red-teaming, and designing fail-safe mechanisms
- Managing PHI, de-identification techniques, and access controls
- Human-in-the-loop review processes and escalation paths
Evaluation and Monitoring
- Conducting offline evaluations, utilizing golden sets, and defining KPIs
- Detecting hallucinations and verifying factuality
- Ensuring observability, logging, and managing cost/latency
Deployment Patterns and Hands-on Lab
- Comparing API-based versus on-prem model options
- Building a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB
- Practicing simulated incident response and rollback procedures
Summary and Next Steps
Requirements
- Foundational knowledge of Python programming
- Practical experience with data analysis or machine learning workflows
- Familiarity with healthcare data standards (e.g., EHR, FHIR)
Target Audience
- Healthcare data scientists and machine learning engineers
- Clinical informatics and digital health product teams
- IT leaders and innovation managers within the healthcare sector
Open Training Courses require 5+ participants.
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