ArticlePloS one2017
Predicting Length of Stay among Patients Discharged from the Emergency Department-Using an Accelerated Failure Time Model.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 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
- What emergency department length of stay reveals about aging-related vulnerability and declining resilience.GeroScience · 2026Article
- Article
- Hospital length of stay and its predictors among surgical patients at public hospitals in Addis Ababa, Ethiopia.Frontiers in surgery · 2025Article
- Current Applications, Challenges, and Future Directions of Artificial Intelligence in Emergency Medicine: A Narrative Review.Archives of academic emergency medicine · 2025Article
- Predictors of length of hospital stay after pediatric Ebstein anomaly corrective surgery: a retrospective cohort study.BMC pediatrics · 2024Article
- Artificial Intelligence in Emergency Medicine: Viewpoint of Current Applications and Foreseeable Opportunities and Challenges.Journal of medical Internet research · 2023Review
- Prolonged length of stay and its associated factors at adult emergency department in amhara region comprehensive specialized hospitals, northwest Ethiopia.BMC emergency medicine · 2023Article
- Length of stay in the emergency department and its associated factors among pediatric patients attending Wolaita Sodo University Teaching and Referral Hospital, Southern, Ethiopia.BMC emergency medicine · 2022Article
- Evaluation of factors that influenced the length of hospital stay using data mining techniques.BMC medical informatics and decision making · 2022Article
- Predicting Patient Length of Stay in Australian Emergency Departments Using Data Mining.Sensors (Basel, Switzerland) · 2022Article
- Machine learning-based triage to identify low-severity patients with a short discharge length of stay in emergency department.BMC emergency medicine · 2022Article
- Impact of New Bed Assignment Information System on Emergency Department Length of Stay: An Effect Evaluation for Lean Intervention by Using Interrupted Time Series and Propensity Score Matching Analysis.International journal of environmental research and public health · 2022Article
- Towards accurate prediction of patient length of stay at emergency department: a GAN-driven deep learning framework.Journal of ambient intelligence and humanized computing · 2022Article
- Quantifying the impact of addressing data challenges in prediction of length of stay.BMC medical informatics and decision making · 2021Article
- Analysis of Factors Associated With Length of Stay of Opioid-Related Emergency Department Visits.Cureus · 2021Article
- Areas of delay related to prolonged length of stay in an emergency department of an academic hospital in South Africa.African journal of emergency medicine : Revue africaine de la medecine d'urgence · 2021Article
- Use of Neuroimaging for Children With Seizure in General and Pediatric Emergency Departments.The Journal of emergency medicine · 2021Article
- Increasing consultant-level staffing as a proportion of overall physician coverage improves emergency department length of stay targets.BMC emergency medicine · 2021Article
- Exploring Potential Association Between Emergency Department Crowding Status and Patients' Length of Stay at a University Hospital in Saudi Arabia.Open access emergency medicine : OAEM · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEmergency department (ED) crowding continues to be an important health care issue in modern countries. Among the many crucial quality indicators for monitoring the throughput process, a patient's length of stay (LOS) is considered the most important one since it is both the cause and the result of ED crowding. The aim of this study is to identify and quantify the influence of different patient-related or diagnostic activities-related factors on the ED LOS of discharged patients.
methodsThis is a retrospective electronic data analysis. All patients who were discharged from the ED of a tertiary teaching hospital in 2013 were included. A multivariate accelerated failure time model was used to analyze the influence of the collected covariates on patient LOS.
resultsA total of 106,206 patients were included for analysis with an overall medium ED LOS of 1.46 (interquartile range = 2.03) hours. Among them, 96% were discharged by a physician, 3.5% discharged against medical advice, 0.5% left without notice, and only 0.02% left without being seen by a physician. In the multivariate analysis, increased age (>80 vs <20, time ratio (TR) = 1.408, p<0.0001), higher acuity level (triage level I vs. level V, TR = 1.343, p<0.0001), transferred patients (TR = 1.350, p<0.0001), X-rays obtained (TR = 1.181, p<0.0001), CT scans obtained (TR = 1.515, p<0.0001), laboratory tests (TR = 2.654, p<0.0001), consultation provided (TR = 1.631, p<0.0001), observation provided (TR = 8.435, p<0.0001), critical condition declared (TR = 1.205, p<0.0001), day-shift arrival (TR = 1.223, p<0.0001), and an increased ED daily census (TR = 1.057, p<0.0001) lengthened the ED LOS with various effect sizes. On the other hand, male sex (TR = 0.982, p = 0.002), weekend arrival (TR = 0.928, p<0.0001), and adult non-trauma patients (compared with pediatric non-trauma, TR = 0.687, p<0.0001) were associated with shortened ED LOS. A prediction diagram was made accordingly and compared with the actual LOS.
conclusionsThe influential factors on the ED LOS in discharged patients were identified and quantified in the current study. The model's predicted ED LOS may provide useful information for physicians or patients to better anticipate an individual's LOS and to help the administrative level plan its staffing policy.
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