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

Disciplines

Analysis | Health Information Technology | Health Services Research | Technology and Innovation

Abstract (300 words maximum)

Overcrowding in emergency departments continues to be a hot button issue in global healthcare especially in hospitals that cater to large populations. The recent and ongoing pandemic has only exacerbated this situation with wait times reaching as high as nine hours between 2020 and 2021 (Rodriguez,2022). Also, as a result of the pandemic, several hospitals have experienced staffing shortages which is one direct correlation to overcrowding in the ER. As a consequence, the rate at which patients leave the ER without being seen has doubled since 2021(Mann, 2022), which then leads to severe outcomes for patients such as deaths. Our study is an attempt to remedy the situation. Our preliminary research in spring 2022 used a small sample data gathered with a non-invasive wearable smart device known as the CareTaker 4 to record vitals and sort them in a priority listing. This showed that patients can indeed be triaged into a system that orders vitals according to urgency. This is a continuation of those preliminary findings with an increased data set gathered from 20 participants. The data will be analyzed using a mathematical model that will create a better 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. It would automatically move patients up in the priority list for immediate attention as their vitals deteriorate. This will be especially helpful in short staffing situations where the few staff on duty can rely on a model to determine who gets attended to next instead of using the current first-come-first-serve system that’s in place at many facilities which so far has proven to be ineffective.

Academic department under which the project should be listed

Information Technology

Primary Investigator (PI) Name

Maria Valero

Additional Faculty

Liang Zhao, Computing and Software Engineering, lzhao10@kennesaw.edu

Luisa Valentina, Industrial and Systems Engineering, lvallad1@kennesaw.edu

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

Overcrowding in emergency departments continues to be a hot button issue in global healthcare especially in hospitals that cater to large populations. The recent and ongoing pandemic has only exacerbated this situation with wait times reaching as high as nine hours between 2020 and 2021 (Rodriguez,2022). Also, as a result of the pandemic, several hospitals have experienced staffing shortages which is one direct correlation to overcrowding in the ER. As a consequence, the rate at which patients leave the ER without being seen has doubled since 2021(Mann, 2022), which then leads to severe outcomes for patients such as deaths. Our study is an attempt to remedy the situation. Our preliminary research in spring 2022 used a small sample data gathered with a non-invasive wearable smart device known as the CareTaker 4 to record vitals and sort them in a priority listing. This showed that patients can indeed be triaged into a system that orders vitals according to urgency. This is a continuation of those preliminary findings with an increased data set gathered from 20 participants. The data will be analyzed using a mathematical model that will create a better 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. It would automatically move patients up in the priority list for immediate attention as their vitals deteriorate. This will be especially helpful in short staffing situations where the few staff on duty can rely on a model to determine who gets attended to next instead of using the current first-come-first-serve system that’s in place at many facilities which so far has proven to be ineffective.