ArticleSensors (Basel, Switzerland)2022
Predicting Patient Length of Stay in Australian Emergency Departments Using Data Mining.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Artificial Intelligence and Machine Learning for Emergency Department Overcrowding: A Systematic Review with Large Language Model-Assisted Screening.Healthcare (Basel, Switzerland) · 2026Review
- High- and Low-Complexity Features of Non-Critical Adult Patients in the Emergency Department.Journal of clinical medicine · 2026Article
- Predicting Procedure Time in Pediatric Dental Rehabilitation Under General Anesthesia: The Role of Preoperative Factors and Age-Based Models.Paediatric anaesthesia · 2026Article
- Developing interpretable machine learning models to predict length of stay and disposition decision for adult patients in emergency departments.BMJ health & care informatics · 2025Article
- Factors associated with extended length of stay for paediatric mental health presentations to EDs in South Western Sydney, Australia.Emergency medicine Australasia : EMA · 2025Article
- Benefits of emergency department routine blood test performance on patients whose allocated triage category is not time critical: a retrospective study.BMC health services research · 2024Observational
- Length of stay and associated factors among pediatric patients in the pediatric emergency unit of the Tikur Anbessa specialized hospital, Addis Ababa, Ethiopia.BMC emergency medicine · 2024Article
- Factors Relating to Decision Delay in the Emergency Department: Effects of Diagnostic Tests and Consultations.Open access emergency medicine : OAEM · 2023Article
- Advances in E-Health and Mobile Health Monitoring.Sensors (Basel, Switzerland) · 2022Article
- Analysis of factors influencing length of stay in the emergency department in public hospital, Padang, Indonesia.Medical journal, Armed Forces IndiaArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Length of Stay (LOS) is an important performance metric in Australian Emergency Departments (EDs). Recent evidence suggests that an LOS in excess of 4 h may be associated with increased mortality, but despite this, the average LOS continues to remain greater than 4 h in many EDs. Previous studies have found that Data Mining (DM) can be used to help hospitals to manage this metric and there is continued research into identifying factors that cause delays in ED LOS. Despite this, there is still a lack of specific research into how DM could use these factors to manage ED LOS. This study adds to the emerging literature and offers evidence that it is possible to predict delays in ED LOS to offer Clinical Decision Support (CDS) by using DM. Sixteen potentially relevant factors that impact ED LOS were identified through a literature survey and subsequently used as predictors to create six Data Mining Models (DMMs). An extract based on the Victorian Emergency Minimum Dataset (VEMD) was used to obtain relevant patient details and the DMMs were implemented using the Weka Software. The DMMs implemented in this study were successful in identifying the factors that were most likely to cause ED LOS > 4 h and also identify their correlation. These DMMs can be used by hospitals, not only to identify risk factors in their EDs that could lead to ED LOS > 4 h, but also to monitor these factors over time.
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