Agentic AI in Healthcare Training Course
Agentic AI represents a paradigm where AI systems independently plan, reason, and utilize tools to achieve objectives within specific boundaries.
This instructor-led live training, available both online and onsite, is tailored for intermediate-level healthcare and data teams looking to design, evaluate, and govern agentic AI solutions for clinical and operational scenarios.
Upon completion of this training, participants will be equipped to:
- Articulate the core concepts and constraints of agentic AI within healthcare environments.
- Design secure agent workflows that incorporate planning, memory, and tool integration.
- Develop retrieval-augmented agents utilizing clinical documents and knowledge bases.
- Evaluate, monitor, and govern agent behavior through the use of guardrails and human-in-the-loop controls.
Course Format
- Interactive lectures and facilitated discussions.
- Guided labs and code walkthroughs conducted in a sandbox environment.
- Scenario-based exercises focusing on safety, evaluation, and governance.
Customization Options
- For those interested in a customized version of this course, please reach out to us to make arrangements.
Course Outline
Foundations of Agentic AI for Healthcare
- Differentiating agentic systems from tool-only LLM applications.
- Defining autonomy boundaries, policies, and the role of human oversight.
- Understanding the healthcare data landscape and its constraints (EHR, FHIR, PHI).
Designing Agent Workflows
- Implementing planning, memory, tool use, and reflection loops.
- Techniques for prompt engineering, function/tool integration, and action selection.
- Strategies for state management and orchestration patterns.
Retrieval-Augmented Agents
- Processes for ingesting and chunking medical documents.
- Utilizing embeddings, vector stores, and methods for relevance evaluation.
- Ensuring response grounding and developing citation strategies.
Healthcare Integrations and Interoperability
- Basics of FHIR/SMART for establishing agent connectivity.
- Managing structured and unstructured clinical data.
- Handling eventing, APIs, and maintaining audit trails.
Safety, Risk, and Governance
- Implementing guardrails, conducting red-teaming, and designing fail-safes.
- Managing PHI, applying de-identification techniques, and enforcing access controls.
- Establishing human-in-the-loop review processes and escalation paths.
Evaluation and Monitoring
- Conducting offline evaluations, utilizing golden sets, and defining KPIs.
- Detecting hallucinations and performing factuality checks.
- Ensuring observability, logging, and managing costs/latency.
Deployment Patterns and Hands-on Lab
- Comparing API-based versus on-prem model 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.
- Practical experience with data analysis or machine learning workflows.
- Familiarity with healthcare data concepts, such as EHR and FHIR.
Target Audience
- Healthcare data scientists and ML 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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