TY - GEN
T1 - Exploring Data Analytics to Identify Time-Dependent Factors of Emergency Department Crowding
AU - Huang, Wun Ci
AU - Teng, Wei-Guang
AU - Chi, Chih Hsien
AU - Hou, Ting Wei
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - During the period of the COVID-19 pandemic, there is a notable change in the congestion levels of emergency departments (ED). This phenomenon offers an opportunity to study the influence factors of ED crowding. In this work, we crawl real-time information from the ED of major hospitals in Taiwan and conduct data analytics to obtain a comprehensive view of the situation during the COVID-19 pandemic. Note that the data we used contain nonprivate information, avoiding the issue of confidentiality of data. Our goal is to provide valuable information on the appropriate timing of nonemergency patients' visits to the ED and to help nonemergency patients make informed decisions about when to visit the ED, ultimately improving their experience and the overall quality of medical care. The findings of this work have potential applications in developing intelligent systems or mobile applications that could offer valuable insights into optimizing nonemergency patient visits, thereby relieving the ED crowding problem.
AB - During the period of the COVID-19 pandemic, there is a notable change in the congestion levels of emergency departments (ED). This phenomenon offers an opportunity to study the influence factors of ED crowding. In this work, we crawl real-time information from the ED of major hospitals in Taiwan and conduct data analytics to obtain a comprehensive view of the situation during the COVID-19 pandemic. Note that the data we used contain nonprivate information, avoiding the issue of confidentiality of data. Our goal is to provide valuable information on the appropriate timing of nonemergency patients' visits to the ED and to help nonemergency patients make informed decisions about when to visit the ED, ultimately improving their experience and the overall quality of medical care. The findings of this work have potential applications in developing intelligent systems or mobile applications that could offer valuable insights into optimizing nonemergency patient visits, thereby relieving the ED crowding problem.
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U2 - 10.1109/ICCE-Taiwan58799.2023.10226752
DO - 10.1109/ICCE-Taiwan58799.2023.10226752
M3 - Conference contribution
AN - SCOPUS:85174971905
T3 - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
SP - 113
EP - 114
BT - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023
Y2 - 17 July 2023 through 19 July 2023
ER -