ArticlePloS one2015
Skåne Emergency Department Assessment of Patient Load (SEAL)-A Model to Estimate Crowding Based on Workload in Swedish Emergency Departments.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning techniques for mortality prediction in emergency departments: a systematic review.BMJ open · 2021Pooled it
- Article
- Construction of an index system for assessing emergency department overcrowding in Chinese tertiary hospitals: a Delphi study.BMC emergency medicine · 2026Article
- Length of stay in the emergency department and its associated input-, throughput-, and output factors at two hospitals in Sweden.BMC emergency medicine · 2025Article
- Artificial intelligence-driven forecasting and shift optimization for pediatric emergency department crowding.JAMIA open · 2025Article
- Current Applications, Challenges, and Future Directions of Artificial Intelligence in Emergency Medicine: A Narrative Review.Archives of academic emergency medicine · 2025Article
- Emergency department crowding and mortality: an observational multicenter study in Sweden.Frontiers in public health · 2023Article
- Differentiating properties of occupancy rate and workload to estimate crowding: A Swedish national cross-sectional study.Journal of the American College of Emergency Physicians open · 2022Article
- Validation of the modified Skåne emergency department assessment of patient load (mSEAL) model for emergency department crowding and comparison with international models; an observational study.BMC emergency medicine · 2021Observational
- Prevalence of crowding, boarding and staffing levels in Swedish emergency departments - a National Cross Sectional Study.BMC emergency medicine · 2020Article
- Emergency Department Workload and Crowding During a Major Electronic Health Record Breakdown.Frontiers in public health · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesEmergency department (ED) crowding is an increasing problem in many countries. The purpose of this study was to develop a quantitative model that estimates the degree of crowding based on workload in Swedish EDs.
methodsAt five different EDs, the head nurse and physician assessed the workload on a scale from 1 to 6 at randomized time points during a three week period in 2013. Based on these assessments, a regression model was created using data from the computerized patient log system to estimate the level of crowding based on workload. The final model was prospectively validated at the two EDs with the largest census.
resultsWorkload assessments and data on 14 variables in the patient log system were collected at 233 time points. The variables Patient hours, Occupancy, Time waiting for the physician and Fraction of high priority (acuity) patients all correlated significantly with the workload assessments. A regression model based on these four variables correlated well with the assessed workload in the initial dataset (r2 = 0.509, p < 0.001) and with the assessments in both EDs during validation (r2 = 0.641; p < 0.001 and r2 = 0.624; p < 0.001).
conclusionsIt is possible to estimate the level of crowding based on workload in Swedish EDs using data from the patient log system. Our model may be applicable to EDs with different sizes and characteristics, and may be used for continuous monitoring of ED workload. Before widespread use, additional validation of the model is needed.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.