Agentic AI in Healthcare Training Course
Agentic AI represents an approach where AI systems autonomously plan, reason, and utilize tools to achieve specific objectives within established boundaries.
This instructor-led live training, available both online or onsite, is designed for intermediate-level healthcare and data professionals seeking to design, evaluate, and govern agentic AI solutions for clinical and operational scenarios.
Upon completion of this training, participants will be capable of:
- Articulating the core concepts and constraints of agentic AI within healthcare environments.
- Designing secure agent workflows that incorporate planning, memory, and tool integration.
- Developing retrieval-augmented agents tailored to clinical documentation and knowledge repositories.
- Evaluating, monitoring, and governing agent behavior using guardrails and human-in-the-loop controls.
Course Format
- Interactive lectures paired with facilitated discussions.
- Guided laboratory exercises and code walkthroughs conducted in a sandbox environment.
- Scenario-based practical applications focusing on safety, evaluation, and governance.
Customization Options
- For tailored training arrangements for this course, please reach out to us to coordinate your specific needs.
Course Outline
Foundations of Agentic AI for Healthcare
- Distinguishing agentic systems from tool-only LLM applications
- Defining autonomy boundaries, policies, and human oversight requirements
- Navigating the healthcare data landscape and its constraints (EHR, FHIR, PHI)
Designing Agent Workflows
- Implementing planning, memory, tool use, and reflection loops
- Leveraging prompt engineering, functions/tools, and action selection strategies
- Mastering state management and orchestration patterns
Retrieval-Augmented Agents
- Processing medical documents through ingestion and chunking techniques
- Utilizing embeddings, vector stores, and relevance evaluation methods
- Ensuring grounded responses and effective citation strategies
Healthcare Integrations and Interoperability
- Understanding FHIR/SMART fundamentals for agent connectivity
- Handling structured and unstructured clinical data effectively
- Managing events, APIs, and maintaining comprehensive audit trails
Safety, Risk, and Governance
- Implementing guardrails, red-teaming exercises, and fail-safe design principles
- Managing PHI handling, de-identification processes, and access controls
- Establishing human-in-the-loop review mechanisms and escalation paths
Evaluation and Monitoring
- Conducting offline evaluations, utilizing golden sets, and defining KPIs
- Detecting hallucinations and verifying factuality
- Enhancing observability, logging practices, and managing cost/latency metrics
Deployment Patterns and Hands-on Lab
- Comparing API-based versus on-prem model deployment options
- Constructing 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
- Prior experience with data analysis or machine learning workflows
- Familiarity with key healthcare data concepts (e.g., EHR, FHIR)
Target Audience
- Healthcare data scientists and machine learning engineers
- Clinical informatics specialists and digital health product teams
- IT leaders and innovation managers within the healthcare sector
Open Training Courses require 5+ participants.
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