Factors affecting the length of stay in the emergency department for critically Ill patients transferred to regional emergency medical center. Issue 5 (27th December 2022)
- Record Type:
- Journal Article
- Title:
- Factors affecting the length of stay in the emergency department for critically Ill patients transferred to regional emergency medical center. Issue 5 (27th December 2022)
- Main Title:
- Factors affecting the length of stay in the emergency department for critically Ill patients transferred to regional emergency medical center
- Authors:
- Lee, Hyungbok
Lee, Sangrim
Kim, Hyeoneui - Abstract:
- Abstract: 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 resourceAbstract: 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. … (more)
- Is Part Of:
- Nursing open. Volume 10:Issue 5(2023)
- Journal:
- Nursing open
- Issue:
- Volume 10:Issue 5(2023)
- Issue Display:
- Volume 10, Issue 5 (2023)
- Year:
- 2023
- Volume:
- 10
- Issue:
- 5
- Issue Sort Value:
- 2023-0010-0005-0000
- Page Start:
- 3220
- Page End:
- 3231
- Publication Date:
- 2022-12-27
- Subjects:
- critically ill patient -- emergency department length of stay -- inter‐hospital transfer -- machine learning -- prediction model
610.73 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2054-1058 ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1002/nop2.1573 ↗
- Languages:
- English
- ISSNs:
- 2054-1058
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 26885.xml