What Infection Preventionists Need to Know About Artificial Intelligence: A Practical Framework for Informed Engagement

1 IPU

Tuesday, October 20, 2026 from 10:00 am ET - 11:45 am ET

10-10:15 am ET: Opening of Program (Devin Jopp, CEO APIC)

10:15-10:45 am ET: Keynote Speaker

10:45-11:45 am ET: Session 1: What Infection Preventionists Need to Know About Artificial Intelligence: A Practical Framework for Informed Engagement

Speaker: John Delano

Artificial intelligence (AI) is rapidly entering healthcare operations, but for most infection preventionists (IP), the gap between what AI promises and what it actually delivers in infection prevention and control (IPC) practice remains wide. This session translates a collaboratively developed ""Ten Key Points"" framework into a practical, evidence-grounded overview designed to help IPs move from uncertainty to informed engagement. Rather than showcasing a single AI product or pilot, this session takes a field-level view: What types of AI tools are IPs most likely to encounter? Where does published evidence suggest these tools have the most traction in IPC, and where do significant gaps remain? What organizational, data quality, workforce, and governance factors determine whether AI tools succeed or fail in practice? And critically, what readiness questions should IP teams be asking before adoption?

 
The session is structured around four content blocks drawn from the Ten Key Points framework, each pairing core concepts with illustrative examples from recent IPC literature:
AI foundations for IPs -- distinguishing AI types (machine learning, Natural language processing (NLPs)/Large language models (LLMs), generative AI), understanding what each is designed to do, and recognizing that AI supports but does not replace IP judgment.
Where AI meets IPC practice -- current and emerging applications in HAI surveillance and case finding, predictive analytics for targeted prevention, and compliance monitoring and communication workflows, with specific examples from published studies.
What makes AI work (or fail) in practice -- data quality, standardized definitions, bias and fairness considerations, human factors including alert fatigue and overreliance, and the critical role of workflow fit.
Governance, ethics, and readiness -- privacy, accountability, transparency, and a structured set of readiness questions IP teams can apply before adopting any AI tool.

 
Learning Objectives:
Upon completion of this session, participants will be able to:
Differentiate between machine learning, natural language processing, large language models, and generative AI and describe how each may apply to infection prevention work.
Identify at least three IPC application areas where AI tools are being explored, including their current evidence base and practical limitations.
Identify key readiness questions IP teams should consider before adopting an AI tool in infection prevention and control.

IPUs: 

• APIC designates this activity for 1 infection prevention unit(s). 
• For more information, please see https://www.cbic.org/CBIC/Rece...