Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 14 hours
Course Outline
Core Principles of Agentic AI in Healthcare
- Distinguishing agentic systems from standard LLM tool applications
- Defining limits of autonomy, operational policies, and the role of human supervision
- Navigating the healthcare data environment and its constraints (including EHR, FHIR, and PHI)
Architecting Agent Workflows
- Integrating planning, memory, tool usage, and iterative reflection cycles
- Advanced prompt engineering, function integration, and strategic action selection
- Managing state and implementing orchestration patterns
Implementing Retrieval-Augmented Agents
- Processing and segmenting medical documentation
- Utilizing embeddings, vector databases, and assessing relevance
- Ensuring response accuracy and implementing citation methodologies
Healthcare Integration and System Interoperability
- Understanding FHIR and SMART standards for seamless agent connectivity
- Handling both structured and unstructured clinical data effectively
- Managing event triggers, API interactions, and maintaining comprehensive audit logs
Safety, Risk Management, and Governance
- Establishing guardrails, conducting red-teaming exercises, and designing fail-safes
- Protocols for PHI management, data anonymization, and access restrictions
- Incorporating human-in-the-loop validation and clear escalation procedures
Performance Evaluation and Continuous Monitoring
- Conducting offline assessments, defining golden sets, and establishing KPIs
- Identifying hallucinations and performing factuality verification
- Ensuring observability, detailed logging, and optimizing cost and latency
Deployment Strategies and Practical Laboratory Exercise
- Choosing between API-based and on-premises model deployments
- Constructing a retrieval-augmented agent using LangChain, FastAPI, and ChromaDB
- Simulating incident response protocols and executing rollback procedures
Conclusions and Path Forward
Requirements
- Proficiency in fundamental Python programming
- Practical experience with data analysis or machine learning workflows
- Knowledge of key healthcare data frameworks (such as EHR and FHIR)
Target Audience
- Data scientists and machine learning engineers in the healthcare industry
- Teams focused on clinical informatics and digital health products
- IT executives and innovation managers within the healthcare sector
