Statistical learning methodologies and admission prediction in an emergency department

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: Admission predictive model; Emergency departments; Exploratory data analysis; Key performance indicators, machine learning.