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Duration 14 hours
Course Outline
Agentic AI Fundamentals for Healthcare
- Distinguishing agentic systems from tool-only LLM applications.
- Defining autonomy limits, policies, and human oversight roles.
- Navigating the healthcare data environment and constraints (EHR, FHIR, PHI).
Architecting Agent Workflows
- Integrating planning, memory, tool usage, and reflective loops.
- Applying prompt engineering, function/tool integration, and action selection.
- Implementing state management and orchestration strategies.
Retrieval-Augmented Agents
- Ingesting and segmenting medical documentation.
- Utilizing embeddings, vector databases, and relevance assessment.
- Grounding responses and employing citation methodologies.
Healthcare Integration and Interoperability
- Foundational FHIR/SMART principles for agent connectivity.
- Processing structured and unstructured clinical data.
- Managing events, APIs, and audit trails.
Safety, Risk Management, and Governance
- Implementing guardrails, red-teaming, and fail-safe designs.
- Managing PHI, de-identification, and access control.
- Establishing human-in-the-loop review and escalation procedures.
Evaluation and Monitoring
- Conducting offline assessments, defining golden sets, and establishing KPIs.
- Detecting hallucinations and verifying factual accuracy.
- Managing observability, logging, and cost/latency optimization.
Deployment Strategies and Practical Lab
- Comparing API-based versus on-premise model options.
- Building a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB.
- Simulating incident response and executing rollback procedures.
Overview and Future Directions
Requirements
- Fundamental proficiency in Python programming
- Background in data analysis or ML pipelines
- Knowledge of healthcare data frameworks (e.g., EHR, FHIR)
Target Audience
- Healthcare data scientists and ML engineers
- Clinical informatics and digital health product teams
- IT executives and innovation managers within healthcare
