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Skåne Emergency Department Assessment of Patient Load (SEAL)—A Model to Estimate Crowding Based on Workload in Swedish Emergency Departments
OBJECTIVES: Emergency 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. METHODS: At five different EDs, the head nurse and physician assessed the w...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4470939/ https://www.ncbi.nlm.nih.gov/pubmed/26083596 http://dx.doi.org/10.1371/journal.pone.0130020 |
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author | Wretborn, Jens Khoshnood, Ardavan Wieloch, Mattias Ekelund, Ulf |
author_facet | Wretborn, Jens Khoshnood, Ardavan Wieloch, Mattias Ekelund, Ulf |
author_sort | Wretborn, Jens |
collection | PubMed |
description | OBJECTIVES: Emergency 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. METHODS: At 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. RESULTS: Workload 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). CONCLUSIONS: It 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. |
format | Online Article Text |
id | pubmed-4470939 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-44709392015-06-29 Skåne Emergency Department Assessment of Patient Load (SEAL)—A Model to Estimate Crowding Based on Workload in Swedish Emergency Departments Wretborn, Jens Khoshnood, Ardavan Wieloch, Mattias Ekelund, Ulf PLoS One Research Article OBJECTIVES: Emergency 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. METHODS: At 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. RESULTS: Workload 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). CONCLUSIONS: It 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. Public Library of Science 2015-06-17 /pmc/articles/PMC4470939/ /pubmed/26083596 http://dx.doi.org/10.1371/journal.pone.0130020 Text en © 2015 Wretborn et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Wretborn, Jens Khoshnood, Ardavan Wieloch, Mattias Ekelund, Ulf Skåne Emergency Department Assessment of Patient Load (SEAL)—A Model to Estimate Crowding Based on Workload in Swedish Emergency Departments |
title | Skåne Emergency Department Assessment of Patient Load (SEAL)—A Model to Estimate Crowding Based on Workload in Swedish Emergency Departments |
title_full | Skåne Emergency Department Assessment of Patient Load (SEAL)—A Model to Estimate Crowding Based on Workload in Swedish Emergency Departments |
title_fullStr | Skåne Emergency Department Assessment of Patient Load (SEAL)—A Model to Estimate Crowding Based on Workload in Swedish Emergency Departments |
title_full_unstemmed | Skåne Emergency Department Assessment of Patient Load (SEAL)—A Model to Estimate Crowding Based on Workload in Swedish Emergency Departments |
title_short | Skåne Emergency Department Assessment of Patient Load (SEAL)—A Model to Estimate Crowding Based on Workload in Swedish Emergency Departments |
title_sort | skåne emergency department assessment of patient load (seal)—a model to estimate crowding based on workload in swedish emergency departments |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4470939/ https://www.ncbi.nlm.nih.gov/pubmed/26083596 http://dx.doi.org/10.1371/journal.pone.0130020 |
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