Streaming Media

 
Media is loading

Publication Date

2026

Presentation Length

Poster/Gallery presentation

College

Gordon E. Inman College of Nursing

Department

Nursing, School of

Student Level

Undergraduate

Faculty Mentor

Dr. Linda Wofford

Presentation Type

Poster

Summary

This project proposes a nurse-led pilot of AI-supported predictive mobility monitoring on a cardiac medical-surgical unit, to improve early identification of high-risk mobility behaviors and enhance patient safety. Current fall-prevention strategies, such as bed alarms and virtual sitters, often alert staff after unsafe movement has begun, creating a reactive cycle that contributes to alarm fatigue and delayed intervention (Abbe & O’Keeffe, 2020). This initiative uses monitors with contactless infrared sensors to identify movement before patients exit the bed, an alert system that goes straight to the nurse and PCT phones, and a central console for floor management and care coordination. Falls are tracked through patient safety indicators such as fall-associated fracture rates and at the unit level using NDNQI metrics, including total falls and falls with injury per 1,000 patient days, reinforcing the need for earlier mobility risk recognition (VirtuSense Technologies, 2020). The pilot will be implemented in six high fall-risk rooms over three months and led by the unit educator and nurse leadership team. Implementation will include staff training, workflow integration, and evaluation at 30, 60, and 90 days. Expected outcomes include reductions in total falls and falls with injury, improved response time to mobility risk behaviors, and decreased reliance on continuous sitter observation. Outcomes will be evaluated using NDNQI fall metrics, incident reports, and staff feedback. This pilot offers a sustainable way to strengthen proactive fall prevention while supporting nursing workflow and patient safety.

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.