Jens Wretborn1,2, Håkan Starkenberg3,4, Thoralph Ruge5,6, Daniel B Wilhelms7,8, Ulf Ekelund9. 1. Department of Emergency Medicine, Local Health Care Services in Central Östergötland, Linköping, Östergötland, Sweden. jens.wretborn@med.lu.se. 2. Department of Clinical Sciences Lund, Emergency Medicine, Faculty of Medicine, Lund University, Lund, Sweden. jens.wretborn@med.lu.se. 3. Enköping Hospital, Region Uppsala, Sweden. 4. Department of Emergency Medicine Solna, Karolinska Institutet, Solna, Sweden. 5. Department of Emergency and Internal Medicine, Skåne University Hospital, Malmö, Sweden. 6. Department of Clinical Sciences Malmö, Faculty of Medicine, Lund University, Lund, Sweden. 7. Department of Emergency Medicine, Local Health Care Services in Central Östergötland, Linköping, Östergötland, Sweden. 8. Department of Biomedical and Clinical Sciences, Faculty of Health Sciences, Linköping University, Linköping, Sweden. 9. Department of Clinical Sciences Lund, Emergency Medicine, Faculty of Medicine, Lund University, Lund, Sweden.
Abstract
BACKGROUND: Emergency Department crowding is associated with increased morbidity and mortality but no measure of crowding has been validated in Sweden. We have previously derived and internally validated the Skåne Emergency Department Assessment of Patient Load (SEAL) score as a measure of crowding in Emergency Departments (ED) in a large regional healthcare system in Sweden. Due to differences in electronic health records (EHRs) between health care systems in Sweden, all variables in the original SEAL-score could not be measured reliably nationally. We aimed to derive and validate a modified SEAL (mSEAL) model and to compare it with established international measures of crowding. METHODS: This was an observational cross sectional study at four EDs in Sweden. All clinical staff assessed their workload (1-6 where 6 is the highest workload) at 5 timepoints each day. We used linear regression with stepwise backward elimination on the original SEAL dataset to derive and internally validate the mSEAL score against staff workload assessments. We externally validated the mSEAL at four hospitals and compared it with the National Emergency Department Overcrowding Score (NEDOCS), the simplified International Crowding Measure in Emergency Department (sICMED), and Occupancy Rate. Area under the receiver operating curve (AuROC) and coefficient of determination was used to compare crowding models. Crowding was defined as an average workload of 4.5 or higher. RESULTS: The mSEAL score contains the variables Patient Hours and Time to physician and showed strong correlation with crowding in the derivation (r2 = 0.47), internal validation (r2 = 0.64 and 0.69) and in the external validation (r2 = 0.48 to 0.60). AuROC scores for crowding in the external validation were 0.91, 0.90, 0.97 and 0.80 for mSEAL, Occupancy Rate, NEDOCS and sICMED respectively. CONCLUSIONS: The mSEAL model can measure crowding based on workload in Swedish EDs with good discriminatory capacity and has the potential to systematically evaluate crowding and help policymakers and researchers target its causes and effects. In Swedish EDs, Occupancy Rate and NEDOCS are good alternatives to measure crowding based on workload.
BACKGROUND: Emergency Department crowding is associated with increased morbidity and mortality but no measure of crowding has been validated in Sweden. We have previously derived and internally validated the Skåne Emergency Department Assessment of Patient Load (SEAL) score as a measure of crowding in Emergency Departments (ED) in a large regional healthcare system in Sweden. Due to differences in electronic health records (EHRs) between health care systems in Sweden, all variables in the original SEAL-score could not be measured reliably nationally. We aimed to derive and validate a modified SEAL (mSEAL) model and to compare it with established international measures of crowding. METHODS: This was an observational cross sectional study at four EDs in Sweden. All clinical staff assessed their workload (1-6 where 6 is the highest workload) at 5 timepoints each day. We used linear regression with stepwise backward elimination on the original SEAL dataset to derive and internally validate the mSEAL score against staff workload assessments. We externally validated the mSEAL at four hospitals and compared it with the National Emergency Department Overcrowding Score (NEDOCS), the simplified International Crowding Measure in Emergency Department (sICMED), and Occupancy Rate. Area under the receiver operating curve (AuROC) and coefficient of determination was used to compare crowding models. Crowding was defined as an average workload of 4.5 or higher. RESULTS: The mSEAL score contains the variables Patient Hours and Time to physician and showed strong correlation with crowding in the derivation (r2 = 0.47), internal validation (r2 = 0.64 and 0.69) and in the external validation (r2 = 0.48 to 0.60). AuROC scores for crowding in the external validation were 0.91, 0.90, 0.97 and 0.80 for mSEAL, Occupancy Rate, NEDOCS and sICMED respectively. CONCLUSIONS: The mSEAL model can measure crowding based on workload in Swedish EDs with good discriminatory capacity and has the potential to systematically evaluate crowding and help policymakers and researchers target its causes and effects. In Swedish EDs, Occupancy Rate and NEDOCS are good alternatives to measure crowding based on workload.
Entities:
Keywords:
Boarding; Crowding; Emergency department
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