From Bedside to Big Data: AI-Driven Chatbots for Infection Prevention and Public Health Reporting

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...