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... 

1 IPU

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

Speakers: Tia Johnson MSHS, MT(AMT), CIC and Katharine Hoffman

Infection preventionists juggle urgent clinical demands, evolving guidelines, and a growing need for well-crafted policies and program infrastructure—often with limited time and support. Enter ChatGPT: your new AI-powered brainstorming partner, policy assistant, and documentation sidekick. This rapid-fire session explores how to leverage ChatGPT for developing infection prevention programs, drafting and refining policies, and generating supporting materials such as job aids, risk assessments, and surveillance protocols.

You’ll learn how to craft prompts that yield useful, accurate outputs aligned with evidence-based practice and regulatory standards. We’ll cover best practices for using ChatGPT to support decision-making, accelerate writing, and enhance team collaboration without sacrificing accuracy or professional integrity. Real-world examples will be shared, with live demonstrations of how to go from ""code red"" gaps to ""code written"" solutions in minutes—not weeks.

Whether you're leading a new initiative, revising outdated protocols, or trying to wrangle that policy binder into shape, this session will give you the tools to work smarter (not harder) with AI. No tech experience required—just curiosity and a willingness to think outside the binder.

IPUs: 

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

1 IPU

Tuesday, October 20, 2026 from 1:15-3:00 pm ET

Presentation 1: "Utilization of an Infection Prevention Chatbot for frontline Caregivers"

Speaker: Evan Sylvester, MPH, AL-CIP, LTC-CIP, CIC, WFR, MT(ASCP)CM

This session introduces participants to a centralized Infection Prevention chatbot built in Copilot Studio, designed to serve frontline nurses and physicians across 13 hospitals. Attendees will explore how the chatbot supports real-time clinical decision-making, reduces reliance on Infection Prevention staff, and ensures standardized guidance across multiple facilities. The session combines demonstrations of live interactions, analytics interpretation, and optimization strategies for improving adoption and accuracy.

Key themes:
1)Real-world use of AI chatbots for Infection Prevention.
2)Standardization of protocols across multi-hospital systems.
3)Interpreting data analytics for performance and knowledge coverage.
4)Strategies to continuously improve user adoption and chatbot resolution rates

Presentation 2: "Transforming Public Health Reporting with Scalable, Intelligent Automation"

Speaker: Jessica Chalk, RN

Background: Timely and accurate reporting of communicable diseases is an essential element of effective infection prevention programs. However, manual completion of Confidential Morbidity Report (CMR) forms is time consuming and presents operational challenges for frontline clinicians. To address these challenges, our health system implemented a scalable automation initiative to modernize reporting across a large, multi-site care delivery network.

 
Methods: In collaboration with an external partner, we developed an automated solution that extracts structured data from multiple electronic health record (EHR) platforms, generates CMR forms, and securely transmits them to 41 local health jurisdictions. This automated process standardizes reporting, supports secure encrypted data transmission, and strengthens public health partnerships.

 
Results: Between January and June 2025, 9,453 CMR forms were submitted electronically, saving an estimated 788 to 1,103 hours of provider time. Automation reduced administrative burden, improved reporting timeliness, and enhanced compliance to reporting requirements. Areas for improvement were identified in data completeness and case detection accuracy.

Conclusions: Automated public health reporting strengthens the interface between infection prevention and the local health jurisdiction, enabling faster, more reliable disease investigation and response. Identification of reportable conditions using lab results and diagnosis codes requires careful calibration to balance sensitivity and specificity. These remain active areas of focus as we refine our approach to improve accuracy, consistency, and system performance. Further enhancements include a dashboard which leverages historical disease thresholds to detect emerging outbreaks and flag anomalies in reporting trends, supporting early investigation and improve data integrity. This initiative offers a replicable model for healthcare systems seeking to modernize public health reporting through intelligent automation.

IPUs: 

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