Agentic AI in Healthcare Training Course
Agentic AI represents a methodology wherein AI systems engage in planning, reasoning, and utilizing tools to achieve objectives within established boundaries.
This instructor-led live training, available either 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:
- Explaining the core concepts and constraints of agentic AI within healthcare environments.
- Designing secure agent workflows incorporating planning, memory, and tool integration.
- Developing retrieval-augmented agents that operate over clinical documents and knowledge repositories.
- Evaluating, monitoring, and governing agent behavior through the implementation of guardrails and human-in-the-loop controls.
Course Format
- Interactive lectures and facilitated discussions.
- Guided labs and code walkthroughs conducted within a sandbox environment.
- Scenario-based exercises focusing on safety, evaluation, and governance.
Customization Options
- For information regarding customized training for this course, please reach out to us to arrange.
Course Outline
Foundations of Agentic AI for Healthcare
- Distinguishing agentic AI from tool-only LLM applications.
- Establishing autonomy boundaries, policies, and human oversight.
- Understanding the healthcare data landscape and constraints, including EHR, FHIR, and PHI.
Designing Agent Workflows
- Implementing planning, memory, tool usage, and reflection loops.
- Mastering prompt engineering, functions/tools, and action selection.
- Managing state and applying orchestration patterns.
Retrieval-Augmented Agents
- Ingesting and chunking medical documents.
- Utilizing embeddings, vector stores, and evaluating relevance.
- Grounding responses and employing citation strategies.
Healthcare Integrations and Interoperability
- Understanding FHIR/SMART basics 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.
- Handling PHI, de-identification, and access controls.
- Facilitating human-in-the-loop review and escalation paths.
Evaluation and Monitoring
- Conducting offline evaluations, using 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 choices.
- Building a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB.
- Simulating incident response and executing rollback procedures.
Summary and Next Steps
Requirements
- A foundational understanding 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 machine learning engineers.
- Clinical informatics and digital health product teams.
- IT leaders and innovation managers within the healthcare sector.
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