Project Title

Collaborative Emergency Department Crowd Management Framework using Wearable Devices and Data Analytics

Academic department under which the project should be listed

CCSE - Information Technology

Faculty Sponsor Name

Dr Maria Valero De Clemente

Additional Faculty

Dr, Liang Zhao, Information Technology, lzhao10@kennesaw.edu

Abstract (300 words maximum)

According to the Catalyst Journal, Emergency department (ED) visits have risen to more than 60% since 1997, with more than 90% of U.S EDs being over-stretched due to overcrowding which has only been compounded by the recent pandemic. Consequences for ED overcrowding ranges from less severe effects such as patient inconvenience to more severe outcomes such as patient fatality. Research shows poor crowd management at the ED doesn’t only affect patients but takes a toll on ED staff as well. To attempt to address this issue, our study researches how patient vitals collected and transmitted in real time to ED staff can help manage patients in the ED using a triage system that orders vitals in an urgent priority listing. We gathered data from participants using non-invasive wearable devices (CareTaker4 & Oximeter) to collect vital signs information such as heart rate, respiratory rate, blood pressure and oxygen levels. We aim use the data to feed a mathematical model that will create a priority algorithm which can sort patients in an ED according to the urgency of their vital signs and transmit the data in real time to health personnel. This way, the patients can be moved automatically in the list as they deteriorate while waiting. We were able to plot the data to show which patients’ health are deteriorating quickly and that would require immediate attention. This will be very instrumental by helping ED staff attend to pressing cases faster and help control crowds according to medical urgency instead of a first come first serve basis which is not always effective.

Referenced Journal: https://catalyst.nejm.org/doi/full/10.1056/CAT.21.0217

Advisors:

Dr. Maria Valero (mvalero2@kennesaw.edu)

Dr. Liang Zhao (lzhao10@kennesaw.edu)

Disciplines

Data Science | Health Information Technology | Technology and Innovation

Project Type

Poster

How will this be presented?

Yes, in person

This document is currently not available here.

Share

COinS
 

Collaborative Emergency Department Crowd Management Framework using Wearable Devices and Data Analytics

According to the Catalyst Journal, Emergency department (ED) visits have risen to more than 60% since 1997, with more than 90% of U.S EDs being over-stretched due to overcrowding which has only been compounded by the recent pandemic. Consequences for ED overcrowding ranges from less severe effects such as patient inconvenience to more severe outcomes such as patient fatality. Research shows poor crowd management at the ED doesn’t only affect patients but takes a toll on ED staff as well. To attempt to address this issue, our study researches how patient vitals collected and transmitted in real time to ED staff can help manage patients in the ED using a triage system that orders vitals in an urgent priority listing. We gathered data from participants using non-invasive wearable devices (CareTaker4 & Oximeter) to collect vital signs information such as heart rate, respiratory rate, blood pressure and oxygen levels. We aim use the data to feed a mathematical model that will create a priority algorithm which can sort patients in an ED according to the urgency of their vital signs and transmit the data in real time to health personnel. This way, the patients can be moved automatically in the list as they deteriorate while waiting. We were able to plot the data to show which patients’ health are deteriorating quickly and that would require immediate attention. This will be very instrumental by helping ED staff attend to pressing cases faster and help control crowds according to medical urgency instead of a first come first serve basis which is not always effective.

Referenced Journal: https://catalyst.nejm.org/doi/full/10.1056/CAT.21.0217

Advisors:

Dr. Maria Valero (mvalero2@kennesaw.edu)

Dr. Liang Zhao (lzhao10@kennesaw.edu)

blog comments powered by Disqus