ArticleNursing open2023
Factors affecting the length of stay in the emergency department for critically Ill patients transferred to regional emergency medical center.
Article in Nursing open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Models to predict length of stay in the emergency department: a systematic literature review and appraisal.BMC emergency medicine · 2024Pooled it
- Article
- Exploring the tradeoff between data privacy and utility with a clinical data analysis use case.BMC medical informatics and decision making · 2024Article
- Factors affecting the length of stay in the emergency department for critically Ill patients transferred to regional emergency medical center.Nursing open · 2023Article
- Article
- The pattern of emergency department length of stay in Saudi Arabia: an epidemiological Nationwide analyses of secondary surveillance data.Frontiers in public health · 2023Article
- Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo identify the factors affecting Emergency Department Length of Stay for transferred critically ill patients.
backgroundThe 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.
methodsUsing 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.
resultsThe 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.
conclusionsThe 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.
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Registered trials
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