Skåne Emergency Department Assessment of Patient Load (SEAL)-A Model to Estimate Crowding Based on Workload in Swedish Emergency Departments
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. At five different EDs, the head nurse and physician assessed the workload on a scale fr...
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| Veröffentlicht in: | PloS one 2015-06, Vol.10 (6), p.e0130020-e0130020 |
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| description | 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.
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.
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).
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. |
| doi_str_mv | 10.1371/journal.pone.0130020 |
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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.
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).
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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0130020</identifier><identifier>PMID: 26083596</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Acuity ; Akutsjukvård ; Annan klinisk medicin ; Assessments ; Clinical Medicine ; Crowding ; Data collection ; Departments ; Emergencies ; Emergency medical care ; Emergency medical services ; Emergency Medicine ; Emergency Service, Hospital - statistics & numerical data ; Emergency services ; Health Care Service and Management, Health Policy and Services and Health Economy ; Health Sciences ; Hospitals ; Hälso- och sjukvårdsorganisation, hälsopolitik och hälsoekonomi ; Hälsovetenskap ; Klinisk medicin ; Medical and Health Sciences ; Medicin och hälsovetenskap ; Medicine ; Models, Statistical ; Mortality ; Other Clinical Medicine ; Patient assessment ; Patient Load ; Patients ; Physicians ; Regression Analysis ; Regression models ; Reproducibility of Results ; Surveys and Questionnaires ; Sweden ; Working conditions ; Workload ; Workload - statistics & numerical data ; Workloads ; Överbelastning</subject><ispartof>PloS one, 2015-06, Vol.10 (6), p.e0130020-e0130020</ispartof><rights>2015 Wretborn et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2015 Wretborn et al 2015 Wretborn et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c595t-d62e598a307ccf2f19e12f381b3234b253f900521177b4ec7ee89a8a3e51d29c3</citedby><cites>FETCH-LOGICAL-c595t-d62e598a307ccf2f19e12f381b3234b253f900521177b4ec7ee89a8a3e51d29c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4470939/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4470939/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,550,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26083596$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://lup.lub.lu.se/record/7485154$$DView record from Swedish Publication Index$$Hfree_for_read</backlink></links><search><creatorcontrib>Wretborn, Jens</creatorcontrib><creatorcontrib>Khoshnood, Ardavan</creatorcontrib><creatorcontrib>Wieloch, Mattias</creatorcontrib><creatorcontrib>Ekelund, Ulf</creatorcontrib><title>Skåne Emergency Department Assessment of Patient Load (SEAL)-A Model to Estimate Crowding Based on Workload in Swedish Emergency Departments</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>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.
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.
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).
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.</description><subject>Acuity</subject><subject>Akutsjukvård</subject><subject>Annan klinisk medicin</subject><subject>Assessments</subject><subject>Clinical Medicine</subject><subject>Crowding</subject><subject>Data collection</subject><subject>Departments</subject><subject>Emergencies</subject><subject>Emergency medical care</subject><subject>Emergency medical services</subject><subject>Emergency Medicine</subject><subject>Emergency Service, Hospital - statistics & numerical data</subject><subject>Emergency services</subject><subject>Health Care Service and Management, Health Policy and Services and Health Economy</subject><subject>Health Sciences</subject><subject>Hospitals</subject><subject>Hälso- och sjukvårdsorganisation, hälsopolitik och hälsoekonomi</subject><subject>Hälsovetenskap</subject><subject>Klinisk medicin</subject><subject>Medical and Health Sciences</subject><subject>Medicin och hälsovetenskap</subject><subject>Medicine</subject><subject>Models, Statistical</subject><subject>Mortality</subject><subject>Other Clinical Medicine</subject><subject>Patient assessment</subject><subject>Patient Load</subject><subject>Patients</subject><subject>Physicians</subject><subject>Regression Analysis</subject><subject>Regression models</subject><subject>Reproducibility of Results</subject><subject>Surveys and Questionnaires</subject><subject>Sweden</subject><subject>Working conditions</subject><subject>Workload</subject><subject>Workload - 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The purpose of this study was to develop a quantitative model that estimates the degree of crowding based on workload in Swedish EDs.
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.
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).
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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>26083596</pmid><doi>10.1371/journal.pone.0130020</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Acuity Akutsjukvård Annan klinisk medicin Assessments Clinical Medicine Crowding Data collection Departments Emergencies Emergency medical care Emergency medical services Emergency Medicine Emergency Service, Hospital - statistics & numerical data Emergency services Health Care Service and Management, Health Policy and Services and Health Economy Health Sciences Hospitals Hälso- och sjukvårdsorganisation, hälsopolitik och hälsoekonomi Hälsovetenskap Klinisk medicin Medical and Health Sciences Medicin och hälsovetenskap Medicine Models, Statistical Mortality Other Clinical Medicine Patient assessment Patient Load Patients Physicians Regression Analysis Regression models Reproducibility of Results Surveys and Questionnaires Sweden Working conditions Workload Workload - statistics & numerical data Workloads Överbelastning |
| title | Skåne Emergency Department Assessment of Patient Load (SEAL)-A Model to Estimate Crowding Based on Workload in Swedish Emergency Departments |
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