In Nursing open
AIM : To identify the factors affecting Emergency Department Length of Stay for transferred critically ill patients.
BACKGROUND : The Length of Stay of the transferred patients is an important indicator of Emergency Department service quality; thus, understanding the factors affecting the Emergency Department Length of Stay of transferred critically ill patients is essential.
METHODS : Using the electronic medical records of 968 transferred critically ill Emergency Department patients of a tertiary hospital in Korea, prediction models for Emergency Department Length of Stay were built using various machine learning algorithms.
RESULTS : The logistic regression (AUROC 0.85) models showed the best performance, followed by random forest (AUROC 0.83) and Naive Bayes (AUROC 0.83). The logistic regression model indicated that fewer consultations, the highest acuity level, need for an emergency operation or angiography, need for ICU admission, severe emergency disease and fewer diagnoses were the statistically significant predictors for Emergency Department Length of Stay of 6 h or less.
CONCLUSIONS : The transferred critically ill patients analysed in this study who required immediate or specialized care tended to receive needed care on time at the study site.
IMPLICATIONS FOR NURSING MANAGEMENT : Understanding the factors affecting the Emergency Department Length of Stay of transferred critically ill patients is crucial for developing strategies to manage the nursing resource of Emergency Department successfully.
Lee Hyungbok, Lee Sangrim, Kim Hyeoneui
2022-Dec-27
critically ill patient, emergency department length of stay, inter-hospital transfer, machine learning, prediction model
