Abstract
Background
The quality of an emergency department (ED) is highly dependent on its ability to
supply efficient, as well as high-quality treatment for all patients. Key performance
indicators are important when measuring the performance of an emergency department.
This study aimed to perform an exploratory data analysis and to develop an admission
prediction model based on a dataset that was constructed from key performance indicators
selected by a panel of expert physicians, nurses and hospital administrators.
Methods
A dataset of 172,695 records was retrospectively collected from an Emergency Department.
The relationships within the dataset were analyzed and three machine learning algorithms
were compared for an admission predictive model based on the initial patient information.
Results
The dataset showed that mean length of stay was similar in the different weekdays,
there was a positive linear relationship between the length of stay and patient age
and the admission predictive model yielded an AUC of 0.79.
Conclusions
The selected indicators can be used to study whether emergency department allocates
its resources properly to cope with overcrowding and the predictive model may be employed
by Hospital and ED administrates to fill information gaps and support decision making
for the improvement of the key performance indicators.
Keywords
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Article info
Publication history
Published online: January 15, 2021
Accepted:
November 25,
2020
Received in revised form:
October 7,
2020
Received:
August 29,
2020
Identification
Copyright
© 2020 Published by Elsevier Ltd on behalf of College of Emergency Nursing Australasia.
