Prevalence of Crowding, Boarding and Staffing Levels in Swedish Emergency Departments - a National Cross Sectional Study

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Crowding has been associated with decreased quality of care and increased mortality, but the prevalence on a national level is unknown in most countries. Method: We performed a national, cross-sectional study on staffing levels, staff workload, occupancy rate and patients waiting for an in-hospital bed (boarding) at five time points during 24 hours in Swedish EDs. Results: Complete data were collected from 37 (51% of all) EDs in Sweden. High occupancy rate indicated crowding at twelve hospitals (37.5 %) at 31 out of 170 (18.2%) time points. Mean workload (measured on a scale from 1, no workload to 6, very high workload) was moderate at 2.65 (±1.25). Boarding was more prevalent in academic EDs than rural EDs (median 3 vs 0). There were an average of 2.6, 4.6 and 3.2 patients per registered nurse, enrolled nurse and physician, respectively. Conclusion: ED crowding based on occupancy rate was prevalent on a national level in Sweden and comparable with international data. Staff workload, boarding and patient to staff ratios were generally lower than previously described. Critical Care & Emergency Medicine Emergency Department Crowding Boarding Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background The emergency department (ED) is the nexus for patient inflow at a modern hospital. The combination of high acuity patients and frequent peaks in demand often results in crowding and a high workload for the staff [1, 2] . Crowding has been linked to increased inpatient mortality and decreased quality of care [3–6] . Many investigations have been conducted at a single ED or in local health care systems, with large variations in the extent of crowding, and nationwide data are lacking [6–8] . Patients waiting in the ED for an in-hospital bed, also known as boarding or access block, have been identified as an important factor for ED crowding [2] but prospective studies of the problems are scarce [9, 10] . Sweden, with a population of 10 million, has a universal publicly funded health care system granting emergency care with a small co-payment at 72 EDs spanning from small rural EDs to large urban academic EDs. Despite a long tradition of high-quality healthcare databases in Sweden, the emergency medicine register still lacks national coverage and includes limited data on crowding [11] . Crowding in Swedish EDs has previously been a limited problem [12] , but news reports have raised the issue in recent years, and several research projects on the topic have been initiated [13] . Sweden lacks unified national information about ED attendances but based on government reports from 2010 and 2015, it is clear that ED attendances and waiting times have increased [14] Similar trends are seen in Denmark which has a comparable health care system [15] . With no proportional increase in hospital beds during the last 30 years, Sweden now has the fewest inpatient beds per capita of all OECD countries [16] . Based on the conceptual input-throughput-output model [17] , there is a clear risk of crowding given the increasing number of ED attendances and decreasing number of hospital beds, limiting capacity to admit patients. Despite almost two decades of international research, there is no consensus on how to measure crowding, and several methods have proven reliable and valid [5, 18] . In this study we chose to measure occupancy rate and staff workload to encompass different aspects of crowding [17] . Occupancy rate is a simple numeric variable that accounts for the core resource, an ED treatment bed. Staff perception of crowding or workload has been used to derive the International Crowding Metric in Emergency Departments (ICMED), National Emergency Department Overcrowding Score (NEDOCS) and Swedish Emergency Department Assessment of Patient Load (SEAL), but is less studied outside these scores [19–21] . Workload is subjective in nature, but has face validity as a measure of human resource utilisation and may complement occupancy rate at times when available treatment beds does not reflect crowding. An example could be a surge in high acuity patients at a period with low staffing, which will result in a high workload at a low occupancy rate. We aimed to study the current levels of crowding at Swedish EDs by assessing patient attendance, occupancy rate, boarding as well as staff numbers and workload. Methods Study Design and Population We conducted a cross sectional, multi-centre study during 24 hours on April 25 th 2018. All Swedish EDs listed in the national healthcare institution registry were offered to participate by written invitation (e-mail) to the officially listed head of department. The written invitation was followed up by a telephone call. Participation was confirmed in writing by the department head. EDs were classified by their hospital status in Sweden (Academic, Community and Rural), where academic centres were the only centres with tertiary, highly specialised care (such as neurosurgery, cardiothoracic surgery, transplantations and advanced burn care). Data Collection During the 24 h period, each ED collected data at five pre-specified time points (00:00, 06:00, 12:00, 18:00, 23:59). A questionnaire was supplied by the research coordination centre and the method of data gathering was left to each ED. We did not collect information on the personnel gathering the data. Data included the number of registered ED patients, the number of patients waiting for an in-hospital bed (boarding), the number of enrolled nurses, registered nurses and physicians, occupancy rate and overall ED workload. Each ED also provided information on the annual and daily census in the previous year (2017) and the number of available treatment beds. Measurements and Definitions We defined occupancy rate like McCarthy et al. [18] as the number of patients divided by the number of beds where basic care could be provided, excluding corridor spaces. An occupancy rate above 1.0 was set as the cut-off to indicate crowding. Workload was assessed on a graded Likert scale with anchors from 1 (very low workload) to 6 (very high workload). It was used as a measure of staff perception of crowding in the ED and a score of 4.5 or higher was considered to indicate crowding [20] . A boarding patient was defined as a patient with a decision for admission who was still present in the ED, regardless of the duration. The EDs reported if the study period was representative in terms of workload and if there were any extraordinary events during the 24 h period. They also graded the supply of inpatient beds during the study period on a scale from 1 (good bed availability) to 10 (extreme bed shortage). Data was recorded prospectively on a paper-based report form by a senior staff member and subsequently submitted in a digital form to the study coordinator. Statistics Census was reported as median. Registered patients, staffing levels and workload were reported as means with standard deviations (SD). Boarding patients were reported as medians with interquartile range (IQR). Correlations were assessed using ordinary least-squares linear regression. To compare medians, the grand median for each group was calculated. A two by two table was created by classifying each value as above or below the grand median, and we then applied Fisher’s exact test. Staffing ratios were compared using parametric ANOVA and post-hoc testing with t-test. Boarding was compared using Kruskal-Wallis test and post-hoc Mann-Whitney-U test. The Holm method was used to adjust for multiple comparisons [22] . Data was imported into Pandas dataframes (version 0.23.4, https://pandas.pydata.org/ ) [23] and analysed with computer scripts in the Python programming language (version 3.7.2, https://www.python.org ) using the scipy scientific library (version 1.1, https://www.scipy.org/ ) [24, 25] and statsmodels (version 0.10, https://www.statsmodels.org ) [26] for statistical calculations. Ethics This study was carried out in accordance with The Declaration of Helsinki [27] . This study was approved for all sites by the regional ethics review board in Linköping, Sweden (permit reference: 2018/50-31) .Informed consent was waived since no identifiable personal data was collected. Results Participating sites Fifty-five out of 72 eligible EDs accepted participation and 37 (51%) delivered complete data for the number of patients and workload assessments (Figure 1) . Thirty-five (49%) EDs reported complete staffing data for all time points. Five out of Sweden's 7 (71%) university hospitals responded in the study. The geographic distribution of the responding EDs is shown in figure 2 . The median number of annual visits in the participating EDs were 35000 (range 3300 - 102000) with 15 (44%) reporting more than 40000 visits per year. The number of patients seen in the EDs during the 24 h period was not different compared to the daily census of the previous year (median 95 vs 93, p=1.00). Registered patients and boarding The number of registered patients showed a diurnal pattern in most EDs with a median of 20 (IQR 14-41) patients present at 18:00 and 4 (IQR 2-6) patients at 06:00. The number of patients boarding in the ED followed the same pattern (Figure 3) and correlated modestly to the number of patients in the ED (r 2 =0.31). Boarding was more prevalent in academic EDs than rural EDs with a median boarding of 3 (IQR 1-4) and 0 (IQR 0-1) patients respectively (p=0.008). There was no significant difference between urban EDs (median 1, IQ 0-2) and rural or academic EDs. Occupancy rate and Workload Occupancy rate was greater than 1.0 on at least one occasion at twelve EDs (37.5 %) and on a total of 31 out of 170 (18.2%) time points. Mean occupancy rate was higher in academic EDs compared to rural EDs (0.89 vs 0.45, difference 0.37, 95%CI 0.16-0.58, p<0.001) and for urban compared to rural EDs (0.54 vs 0.45, difference 0.24, 95%CI 0.016-0.48, p=0.037) but there was no significant difference between academic and urban centres (p=0.45). Mean workload was 2.65 (±1.25) and as higher than 4.5 at 14 out of 170 time points (8.2%). There was a moderate correlation between workload with occupancy rate (r 2 =0.36) and assessed workload showed a similar diurnal pattern as occupancy rate (Figure 4). Staffing levels During the 24 h period, there was an average of 2.6 (±1.6) patients in the ED per registered nurse, 4.6 (±3.1) per enrolled nurse and 3.2 (±2.2) per physician, with little difference between time points except 06:00 which had lower ratios for all providers (Figure 5). There were more patients per nurse in academic compared to rural EDs (4.4 vs 2.2, p=0.02) but not compared to urban EDs (4.4 vs 3.2, p=0.08) and no difference between rural and urban EDs (2.2 vs 3.2, p=0.13). There were more patients per physician at academic than rural EDs (4.4 vs 2.6, p=0.01), but there was no difference compared to urban EDs (4.4 vs 3.3, p=0.13) or between urban and rural EDs (3.3 vs 2.6, p=0.13). Non-clinical events There were no extraordinary incidents reported in any of the participating EDs’ catchment areas. Four sites (11%) reported hospital-specific disturbances. Of these, two were related to downtime in the electronic health records (EHRs) and two due to disturbances in other digital support systems (ancillary testing, registration and internal telephone system). None of these events were reported to affect the ED workflow. Discussion In this national cross-sectional study at Swedish EDs during 24 h, we provide a snapshot of current Swedish ED crowding, boarding and staffing, which has never been done before. We observed that boarding was common and occupancy rates were generally high, primarily in academic EDs but also in urban EDs. On average, patient to staff ratios for nurses were on par with internationally reported levels (see below), but lower for physicians. There were more patients per staff at academic centres compared to rural hospitals. Workload was mostly perceived as low to moderate, which indicated limited staff problems related to crowding during the study period. Occupancy rate correlated modestly with workload, which suggests that these may reflect different aspects of crowding. Workload was subjectively assessed by a single senior provider at each ED which limits the generalizability. However, subjective provider judgement was used as an outcome measure in the original NEDOCS trial and this has been validated in several different settings [19, 28, 29] . Physicians’ judgment has also proved to be equal or superior to structured decision support tools in many types of clinical decision-making ranging from imaging in trauma to the investigation in suspected pulmonary embolism [30] . Boarding was prevalent at many sites during this study. Generally however, boarding was reported as lower compared to the limited data from the United States (US) and Australia published so far. In a US cross sectional study of 89 EDs, 22% reported boarding patients and 73% of EDs had more than 2 patients boarding [9] . In a registry study of 139 509 ED visits in the US, median boarding time was 79 minutes [31] . In a study of 72 EDs in Australia, boarding ranged from 2 to 22 patients at two time points [10] . The difference in findings between the present and previous studies may be due to sampling errors or temporal effects, but it may also reflect possible differences in health care systems. Lack of inpatient beds is usually the basis for boarding patients in the ED. Since Sweden has fewer inpatient beds per capita than the US and Australia, our results support the claim that boarding may not be directly related to the number of hospital beds, but also to resource utilisation [32] , both in single hospitals and in the system as a whole. It is important to note that our definition of boarding did not include a minimum waiting time after the decision to admit, and that we did not gather any further information regarding the admissions. The staffing ratios were comparable at all study sites with most variation observed around midnight. This finding likely reflects that staffing is reduced at night-time and that staffing ratios therefore become more dependent on the inflow of patients. We did not collect information about work shifts at each ED and cannot exclude that this may explain some of the variation in staff ratios. The emergency medicine literature provides little data for comparison, but Schneider et al. reported higher mean ratios for nurses (4.2) and physicians (9.7) in the US in 2003 [9] . The difference, particularly for physicians, may partly be due to different denominators since we registered all physicians irrespective of training level in this study. In Sweden, a majority of the current ED physicians are pre-interns, interns or residents and only a minority are on site consultants [14] . This may result in higher numbers of physicians working in the ED compared to the US, where EDs are primarily staffed by residents and consultants. There are no national recommendations for staffing ratios in Sweden but our results are within the four patients to one nurse ratio legislated in the US state of California [33] . During the 24 h period, four study sites (11%) noted disturbances in the EHR or support systems, and this has previously been associated with increased ED crowding [34] . All EDs in Sweden use EHRs with a range of digital support systems for radiology, laboratory and other ancillary facilities. The reports may thus be an indicator of the fragility of complex digital systems to which ED providers must adapt. The lack of adverse events suggests mature systems with some resilience against unexpected downtime, leading to no serious disruption of clinical work. However, further studies will be needed to determine the frequency of EHR disturbances and their effects on emergency care. Limitations This was a cross sectional study during only 24 h, and the generalisability of the results is therefore limited. There may be both seasonal differences in the demand and availability of healthcare resources. However, given the range of EDs both in size and geographic location, we believe that the results are a representative snapshot of the ED situation on a national level in Sweden. The response rate was 51% among the eligible EDs regarding patient and crowding data, which is quite high compared to similar studies. Again, generalisability was most likely increased by the wide range of ED size and location. However, the fact that so many EDs chose to not participate emphasizes the need for mandatory and public reporting of this type of information for all EDs. Conclusion Based on this cross sectional study during 24 h in 37 EDs, crowding as measured by occupancy rate and ED boarding is prevalent in Sweden. Occupancy rates were comparable with international data, whereas boarding and patient to staff ratios were lower than reported in the limited existing literature. In contrast to occupancy rate and boarding, patient to staff ratios and perceived workload did not suggest high levels of crowding. These observations highlight the importance of measuring different aspects of the complex entity of ED crowding. Declarations Ethics approval and consent to participate This study was approved for all sites by the regional ethics review board in Linköping, Sweden (permit reference: 2018/50-31) .Informed consent was waived since no identifiable personal data was collected. Consent for publication Not applicable Availability of data and materials The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare no competing interests Funding This work was supported by two grants from Region Östergötland to author DBW (LIO-532001 and LIO-700271), and from Region Skåne to author UE. The funding bodies had no role or influence over any aspect of this study. Author contributions DBW conceived the study and obtained the ethical permit. DBW and JW designed the trial. DBW and JH coordinated data collection. JW managed the data, with the assistance of DBW, JH and UE. DBW and UE obtained the research funding. DBW supervised the conduct of the trial. DBW and JW drafted the manuscript. All authors contributed substantially to its revision. JW takes responsibility for the paper as a whole. All authors have read and approved the manuscript. Acknowledgements The authors like to acknowledge research nurse Erika Hörlin for her assistance with data collection. 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Cite Share Download PDF Status: Published Journal Publication published 18 Jun, 2020 Read the published version in BMC Emergency Medicine → Version 3 posted Editor assigned by journal 28 May, 2020 Editorial decision: Accept 28 May, 2020 Submission checks completed at journal 27 May, 2020 Editor invited by journal 27 May, 2020 You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Wilhelms","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIie3QIW/CQBjG8ae5pJij2GLGV3hJE2b2YXqZqCkEiVg61GHI9L7GzOlrLtlMCRYJHtG5kizLrqFmoqUScf+cueR+ee8OcLnuMV2vJeAPOHAEHgLWi5AlzJIYiPw+BDVBQ4S8JYKvXa5Lgnhjw/wo0iyRA05etWon42IR5++WSBY8k1BmLhknxot2Qjolwwn1yVkolL4Sr+N6tD+T+bmSx0qoLLH/QN7lt4Mc7BQ0UyAUi2uC4brjLYcz5VsKXy2J7MXMVDJ/afhnOwn2aVRWq6dostlOy2+VTUYj83GqXtpJU/h/q28Cl8vlcnX2BzSYSnteNFRdAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-6347-3970","institution":"","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"B","lastName":"Wilhelms","suffix":""}],"badges":[],"createdAt":"2020-01-21 13:22:27","currentVersionCode":3,"declarations":"","doi":"10.21203/rs.2.21604/v3","doiUrl":"https://doi.org/10.21203/rs.2.21604/v3","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12873-020-00342-x","type":"published","date":"2020-06-18T12:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":1250480,"identity":"bc092dad-2bc2-4970-8edf-4f8c8ea17bb0","added_by":"auto","created_at":"2020-06-04 14:23:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":135033,"visible":true,"origin":"","legend":"Flowchart of participating sites","description":"","filename":"figure1flowchart.png","url":"https://assets-eu.researchsquare.com/files/rs-12189/v3/figure1flowchart.png"},{"id":1250481,"identity":"15bb54b9-d6d4-4675-8265-e8544f2b93b3","added_by":"auto","created_at":"2020-06-04 14:23:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1834034,"visible":true,"origin":"","legend":"Map of Sweden and geographic distribution of enrolled and missing EDs","description":"","filename":"figure2map.png","url":"https://assets-eu.researchsquare.com/files/rs-12189/v3/figure2map.png"},{"id":1250482,"identity":"725469a6-347c-4960-87a7-0086a3a6ea6d","added_by":"auto","created_at":"2020-06-04 14:23:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":29252,"visible":true,"origin":"","legend":"Number of patients present and boarding for each hospital type at each time point","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-12189/v3/fig3.png"},{"id":1250483,"identity":"4f2f643e-0d17-4532-9105-52a814d95a43","added_by":"auto","created_at":"2020-06-04 14:23:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":30757,"visible":true,"origin":"","legend":"Occupancy rate in relation to workload at each time point","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-12189/v3/fig4.png"},{"id":1250484,"identity":"b657497b-9d81-4824-9487-ba407ee1762b","added_by":"auto","created_at":"2020-06-04 14:23:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":50514,"visible":true,"origin":"","legend":"Patient to staff ratio for each staff category at each time point","description":"","filename":"fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-12189/v3/fig5.png"},{"id":13535700,"identity":"27d18c06-e2ae-417d-957c-ec9f6d042a77","added_by":"auto","created_at":"2021-09-17 01:29:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":836161,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-12189/v3/48ca6392-60d7-411e-81ce-04c824b20b21.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003ePrevalence of Crowding, Boarding and Staffing Levels in Swedish Emergency Departments - a National Cross Sectional Study\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eThe emergency department (ED) is the nexus for patient inflow at a modern hospital. The combination of high acuity patients and frequent peaks in demand often results in crowding and a high workload for the staff \u003ca href=\"https://paperpile.com/c/qkPHPn/8BOAg+dtKQS\"\u003e[1, 2]\u003c/a\u003e\u003cstrong\u003e. \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCrowding has been linked to increased inpatient mortality and decreased quality of care \u003ca href=\"https://paperpile.com/c/qkPHPn/wRTIP+rGyK6+CkqRe+wtbsZ\"\u003e[3\u0026ndash;6]\u003c/a\u003e. Many investigations have been conducted at a single ED or in local health care systems, with large variations in the extent of crowding, and nationwide data are lacking \u003ca href=\"https://paperpile.com/c/qkPHPn/wtbsZ+8VUh+kaLi\"\u003e[6\u0026ndash;8]\u003c/a\u003e. Patients waiting in the ED for an in-hospital bed, also known as boarding or access block, have been identified as an important factor for ED crowding \u003ca href=\"https://paperpile.com/c/qkPHPn/dtKQS\"\u003e[2]\u003c/a\u003e but prospective studies of the problems are scarce \u003ca href=\"https://paperpile.com/c/qkPHPn/ovT0L+3E6sQ\"\u003e[9, 10]\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eSweden, with a population of 10 million, has a universal publicly funded health care system granting emergency care with a small co-payment at 72 EDs spanning from small rural EDs to large urban academic EDs. Despite a long tradition of high-quality healthcare databases in Sweden, the emergency medicine register still lacks national coverage and includes limited data on crowding \u003ca href=\"https://paperpile.com/c/qkPHPn/Ti7jv\"\u003e[11]\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eCrowding in Swedish EDs has previously been a limited problem \u003ca href=\"https://paperpile.com/c/qkPHPn/Fp7nK\"\u003e[12]\u003c/a\u003e, but news reports have raised the issue in recent years, and several research projects on the topic have been initiated \u003ca href=\"https://paperpile.com/c/qkPHPn/UfzEf\"\u003e[13]\u003c/a\u003e. Sweden lacks unified national information about ED attendances but based on government reports from 2010 and 2015, it is clear that ED attendances and waiting times have increased \u003ca href=\"https://paperpile.com/c/qkPHPn/VyquN\"\u003e[14]\u003c/a\u003e Similar trends are seen in Denmark which has a comparable health care system \u003ca href=\"https://paperpile.com/c/qkPHPn/99tZ\"\u003e[15]\u003c/a\u003e. With no proportional increase in hospital beds during the last 30 years, Sweden now has the fewest inpatient beds per capita of all OECD countries \u003ca href=\"https://paperpile.com/c/qkPHPn/KQuCq\"\u003e[16]\u003c/a\u003e. Based on the conceptual input-throughput-output model \u003ca href=\"https://paperpile.com/c/qkPHPn/Tg9ol\"\u003e[17]\u003c/a\u003e, there is a clear risk of crowding given the increasing number of ED attendances and decreasing number of hospital beds, limiting capacity to admit patients.\u003c/p\u003e\n\u003cp\u003eDespite almost two decades of international research, there is no consensus on how to measure crowding, and several methods have proven reliable and valid \u003ca href=\"https://paperpile.com/c/qkPHPn/CkqRe+GR8ym\"\u003e[5, 18]\u003c/a\u003e. In this study we chose to measure occupancy rate and staff workload to encompass different aspects of crowding \u003ca href=\"https://paperpile.com/c/qkPHPn/Tg9ol\"\u003e[17]\u003c/a\u003e. Occupancy rate is a simple numeric variable that accounts for the core resource, an ED treatment bed. Staff perception of crowding or workload has been used to derive the International Crowding Metric in Emergency Departments (ICMED), National Emergency Department Overcrowding Score (NEDOCS) and Swedish Emergency Department Assessment of Patient Load (SEAL), but is less studied outside these scores \u003ca href=\"https://paperpile.com/c/qkPHPn/gn4aF+xGG1f+WWnFi\"\u003e[19\u0026ndash;21]\u003c/a\u003e. Workload is subjective in nature, but has face validity as a measure of human resource utilisation and may complement occupancy rate at times when available treatment beds does not reflect crowding. An example could be a surge in high acuity patients at a period with low staffing, which will result in a high workload at a low occupancy rate.\u003c/p\u003e\n\u003cp\u003eWe aimed to study the current levels of crowding at Swedish EDs by assessing patient attendance, occupancy rate, boarding as well as staff numbers and workload.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a cross sectional, multi-centre study during 24 hours on April 25\u003csup\u003eth\u003c/sup\u003e 2018. All Swedish EDs listed in the national healthcare institution registry were offered to participate by written invitation (e-mail) to the officially listed head of department. The written invitation was followed up by a telephone call. Participation was confirmed in writing by the department head. EDs were classified by their hospital status in Sweden (Academic, Community and Rural), where academic centres were the only centres with tertiary, highly specialised care (such as neurosurgery, cardiothoracic surgery, transplantations and advanced burn care).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the 24 h period, each ED collected data at five pre-specified time points (00:00, 06:00, 12:00, 18:00, 23:59). A questionnaire was supplied by the research coordination centre and the method of data gathering was left to each ED. We did not collect information on the personnel gathering the data. Data included the number of registered ED patients, the number of patients waiting for an in-hospital bed (boarding), the number of enrolled nurses, registered nurses and physicians, occupancy rate and overall ED workload. Each ED also provided information on the annual and daily census in the previous year (2017) and the number of available treatment beds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurements and Definitions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe defined occupancy rate like McCarthy et al.\u003ca href=\"https://paperpile.com/c/qkPHPn/GR8ym\"\u003e[18]\u003c/a\u003e as the number of patients divided by the number of beds where basic care could be provided, excluding corridor spaces. An occupancy rate above 1.0 was set as the cut-off to indicate crowding. Workload was assessed on a graded Likert scale with anchors from 1 (very low workload) to 6 (very high workload). It was used as a measure of staff perception of crowding in the ED and a score of 4.5 or higher was considered to indicate crowding \u003ca href=\"https://paperpile.com/c/qkPHPn/xGG1f\"\u003e[20]\u003c/a\u003e. A boarding patient was defined as a patient with a decision for admission who was still present in the ED, regardless of the duration.\u003c/p\u003e\n\u003cp\u003eThe EDs reported if the study period was representative in terms of workload and if there were any extraordinary events during the 24 h period. They also graded the supply of inpatient beds during the study period on a scale from 1 (good bed availability) to 10 (extreme bed shortage). Data was recorded prospectively on a paper-based report form by a senior staff member and subsequently submitted in a digital form to the study coordinator.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCensus was reported as median. Registered patients, staffing levels and workload were reported as means with standard deviations (SD). Boarding patients were reported as medians with interquartile range (IQR).\u003c/p\u003e\n\u003cp\u003eCorrelations were assessed using ordinary least-squares linear regression. To compare medians, the grand median for each group was calculated. A two by two table was created by classifying each value as above or below the grand median, and we then applied Fisher\u0026rsquo;s exact test. Staffing ratios were compared using parametric ANOVA and post-hoc testing with t-test. Boarding was compared using Kruskal-Wallis test and post-hoc Mann-Whitney-U test. The Holm method was used to adjust for multiple comparisons \u003ca href=\"https://paperpile.com/c/qkPHPn/0xhwb\"\u003e[22]\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eData was imported into Pandas dataframes (version 0.23.4, \u003ca href=\"https://pandas.pydata.org/\"\u003ehttps://pandas.pydata.org/\u003c/a\u003e)\u003ca href=\"https://paperpile.com/c/qkPHPn/Af6p1\"\u003e[23]\u003c/a\u003e and analysed with computer scripts in the Python programming language (version 3.7.2, \u003ca href=\"https://www.python.org\"\u003ehttps://www.python.org\u003c/a\u003e) using the scipy scientific library (version 1.1, \u003ca href=\"https://www.scipy.org/\"\u003ehttps://www.scipy.org/\u003c/a\u003e)\u003ca href=\"https://paperpile.com/c/qkPHPn/MJXWx+kg2TC\"\u003e[24, 25]\u003c/a\u003e and statsmodels (version 0.10, \u003ca href=\"https://www.statsmodels.org\"\u003ehttps://www.statsmodels.org\u003c/a\u003e)\u003ca href=\"https://paperpile.com/c/qkPHPn/wnp5U\"\u003e[26]\u003c/a\u003e for statistical calculations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was carried out in accordance with The Declaration of Helsinki \u003ca href=\"https://paperpile.com/c/qkPHPn/egLst\"\u003e[27]\u003c/a\u003e. This study was approved for all sites by the regional ethics review board in Link\u0026ouml;ping, Sweden (permit reference: 2018/50-31) .Informed consent was waived since no identifiable personal data was collected.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParticipating sites \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFifty-five out of 72 eligible EDs accepted participation and 37 (51%) delivered complete data for the number of patients and workload assessments \u003cstrong\u003e(Figure 1)\u003c/strong\u003e. Thirty-five (49%) EDs reported complete staffing data for all time points. Five out of Sweden's 7 (71%) university hospitals responded in the study. The geographic distribution of the responding EDs is shown in \u003cstrong\u003efigure 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eThe median number of annual visits in the participating EDs were 35000 (range 3300 - 102000) with 15 (44%) reporting more than 40000 visits per year. The number of patients seen in the EDs during the 24 h period was not different compared to the daily census of the previous year (median 95 vs 93, p=1.00).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegistered patients and boarding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe number of registered patients showed a diurnal pattern in most EDs with a median of 20 (IQR 14-41) patients present at 18:00 and 4 (IQR 2-6) patients at 06:00. The number of patients boarding in the ED followed the same pattern (Figure 3) and correlated modestly to the number of patients in the ED (r\u003csup\u003e2\u003c/sup\u003e=0.31). Boarding was more prevalent in academic EDs than rural EDs with a median boarding of 3 (IQR 1-4) and 0 (IQR 0-1) patients respectively (p=0.008). There was no significant difference between urban EDs (median 1, IQ 0-2) and rural or academic EDs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOccupancy rate and Workload \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOccupancy rate was greater than 1.0 on at least one occasion at twelve EDs (37.5 %) and on a total of 31 out of 170 (18.2%) time points. Mean occupancy rate was higher in academic EDs compared to rural EDs (0.89 vs 0.45, difference 0.37, 95%CI 0.16-0.58, p\u0026lt;0.001) and for urban compared to rural EDs (0.54 vs 0.45, difference 0.24, 95%CI 0.016-0.48, p=0.037) but there was no significant difference between academic and urban centres (p=0.45). Mean workload was 2.65 (\u0026plusmn;1.25) and as higher than 4.5 at 14 out of 170 time points (8.2%). There was a moderate correlation between workload with occupancy rate (r\u003csup\u003e2\u003c/sup\u003e=0.36) and assessed workload showed a similar diurnal pattern as occupancy rate (Figure 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStaffing levels \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the 24 h period, there was an average of 2.6 (\u0026plusmn;1.6) patients in the ED per registered nurse, 4.6 (\u0026plusmn;3.1) per enrolled nurse and 3.2 (\u0026plusmn;2.2) per physician, with little difference between time points except 06:00 which had lower ratios for all providers (Figure 5). There were more patients per nurse in academic compared to rural EDs (4.4 vs 2.2, p=0.02) but not compared to urban EDs (4.4 vs 3.2, p=0.08) and no difference between rural and urban EDs (2.2 vs 3.2, p=0.13). There were more patients per physician at academic than rural EDs (4.4 vs 2.6, p=0.01), but there was no difference compared to urban EDs (4.4 vs 3.3, p=0.13) or between urban and rural EDs (3.3 vs 2.6, p=0.13).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNon-clinical events\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were no extraordinary incidents reported in any of the participating EDs\u0026rsquo; catchment areas. Four sites (11%) reported hospital-specific disturbances. Of these, two were related to downtime in the electronic health records (EHRs) and two due to disturbances in other digital support systems (ancillary testing, registration and internal telephone system). None of these events were reported to affect the ED workflow.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this national cross-sectional study at Swedish EDs during 24 h, we provide a snapshot of current Swedish ED crowding, boarding and staffing, which has never been done before. We observed that boarding was common and occupancy rates were generally high, primarily in academic EDs but also in urban EDs. On average, patient to staff ratios for nurses were on par with internationally reported levels (see below), but lower for physicians. There were more patients per staff at academic centres compared to rural hospitals. Workload was mostly perceived as low to moderate, which indicated limited staff problems related to crowding during the study period.\u003c/p\u003e\n\u003cp\u003eOccupancy rate correlated modestly with workload, which suggests that these may reflect different aspects of crowding. Workload was subjectively assessed by a single senior provider at each ED which limits the generalizability. However, subjective provider judgement was used as an outcome measure in the original NEDOCS trial and this has been validated in several different settings \u003ca href=\"https://paperpile.com/c/qkPHPn/rI8yG+gn4aF+oEMYa\"\u003e[19, 28, 29]\u003c/a\u003e. Physicians\u0026rsquo; judgment has also proved to be equal or superior to structured decision support tools in many types of clinical decision-making ranging from imaging in trauma to the investigation in suspected pulmonary embolism \u003ca href=\"https://paperpile.com/c/qkPHPn/kS6q7\"\u003e[30]\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eBoarding was prevalent at many sites during this study. Generally however, boarding was reported as lower compared to the limited data from the United States (US) and Australia published so far. In a US cross sectional study of 89 EDs, 22% reported boarding patients and 73% of EDs had more than 2 patients boarding \u003ca href=\"https://paperpile.com/c/qkPHPn/ovT0L\"\u003e[9]\u003c/a\u003e. In a registry study of 139 509 ED visits in the US, median boarding time was 79 minutes \u003ca href=\"https://paperpile.com/c/qkPHPn/yQBCG\"\u003e[31]\u003c/a\u003e. In a study of 72 EDs in Australia, boarding ranged from 2 to 22 patients at two time points \u003ca href=\"https://paperpile.com/c/qkPHPn/3E6sQ\"\u003e[10]\u003c/a\u003e. The difference in findings between the present and previous studies may be due to sampling errors or temporal effects, but it may also reflect possible differences in health care systems. Lack of inpatient beds is usually the basis for boarding patients in the ED. Since Sweden has fewer inpatient beds per capita than the US and Australia, our results support the claim that boarding may not be directly related to the number of hospital beds, but also to resource utilisation \u003ca href=\"https://paperpile.com/c/qkPHPn/6qgDL\"\u003e[32]\u003c/a\u003e, both in single hospitals and in the system as a whole. It is important to note that our definition of boarding did not include a minimum waiting time after the decision to admit, and that we did not gather any further information regarding the admissions.\u003c/p\u003e\n\u003cp\u003eThe staffing ratios were comparable at all study sites with most variation observed around midnight. This finding likely reflects that staffing is reduced at night-time and that staffing ratios therefore become more dependent on the inflow of patients. We did not collect information about work shifts at each ED and cannot exclude that this may explain some of the variation in staff ratios. The emergency medicine literature provides little data for comparison, but Schneider et al. reported higher mean ratios for nurses (4.2) and physicians (9.7) in the US in 2003 \u003ca href=\"https://paperpile.com/c/qkPHPn/ovT0L\"\u003e[9]\u003c/a\u003e. The difference, particularly for physicians, may partly be due to different denominators since we registered all physicians irrespective of training level in this study. In Sweden, a majority of the current ED physicians are pre-interns, interns or residents and only a minority are on site consultants \u003ca href=\"https://paperpile.com/c/qkPHPn/VyquN\"\u003e[14]\u003c/a\u003e. This may result in higher numbers of physicians working in the ED compared to the US, where EDs are primarily staffed by residents and consultants. There are no national recommendations for staffing ratios in Sweden but our results are within the \u003cem\u003efour patients to one nurse\u003c/em\u003e ratio legislated in the US state of California \u003ca href=\"https://paperpile.com/c/qkPHPn/Vzlp5\"\u003e[33]\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eDuring the 24 h period, four study sites (11%) noted disturbances in the EHR or support systems, and this has previously been associated with increased ED crowding \u003ca href=\"https://paperpile.com/c/qkPHPn/nBYyb\"\u003e[34]\u003c/a\u003e. All EDs in Sweden use EHRs with a range of digital support systems for radiology, laboratory and other ancillary facilities. The reports may thus be an indicator of the fragility of complex digital systems to which ED providers must adapt. The lack of adverse events suggests mature systems with some resilience against unexpected downtime, leading to no serious disruption of clinical work. However, further studies will be needed to determine the frequency of EHR disturbances and their effects on emergency care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was a cross sectional study during only 24 h, and the generalisability of the results is therefore limited. There may be both seasonal differences in the demand and availability of healthcare resources. However, given the range of EDs both in size and geographic location, we believe that the results are a representative snapshot of the ED situation on a national level in Sweden.\u003c/p\u003e\n\u003cp\u003eThe response rate was 51% among the eligible EDs regarding patient and crowding data, which is quite high compared to similar studies. Again, generalisability was most likely increased by the wide range of ED size and location. However, the fact that so many EDs chose to not participate emphasizes the need for mandatory and public reporting of this type of information for all EDs.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBased on this cross sectional study during 24 h in 37 EDs, crowding as measured by occupancy rate and ED boarding is prevalent in Sweden. Occupancy rates were comparable with international data, whereas boarding and patient to staff ratios were lower than reported in the limited existing literature. In contrast to occupancy rate and boarding, patient to staff ratios and perceived workload did not suggest high levels of crowding. These observations highlight the importance of measuring different aspects of the complex entity of ED crowding.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eEthics approval and consent to participate\u003c/h3\u003e\n\u003cp\u003eThis study was approved for all sites by the regional ethics review board in Link\u0026ouml;ping, Sweden (permit reference: 2018/50-31) .Informed consent was waived since no identifiable personal data was collected.\u003c/p\u003e\n\u003ch3\u003eConsent for publication\u003c/h3\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch3\u003eAvailability of data and materials\u003c/h3\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis work was supported by two grants from Region \u0026Ouml;sterg\u0026ouml;tland to author DBW (LIO-532001 and LIO-700271), and from Region Sk\u0026aring;ne to author UE. The funding bodies had no role or influence over any aspect of this study.\u003c/p\u003e\n\u003ch3\u003eAuthor contributions\u003c/h3\u003e\n\u003cp\u003eDBW conceived the study and obtained the ethical permit. DBW and JW designed the trial. DBW and JH coordinated data collection. JW managed the data, with the assistance of DBW, JH and UE. DBW and UE obtained the research funding. DBW supervised the conduct of the trial. DBW and JW drafted the manuscript. All authors contributed substantially to its revision. JW takes responsibility for the paper as a whole. All authors have read and approved the manuscript.\u003c/p\u003e\n\u003ch3\u003eAcknowledgements\u003c/h3\u003e\n\u003cp\u003eThe authors like to acknowledge research nurse Erika H\u0026ouml;rlin for her assistance with data collection.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eED - Emergency Department\u003c/p\u003e\n\u003cp\u003eEHR - Electronic Health Record\u003c/p\u003e\n\u003cp\u003eICMED - International Crowding Metric in Emergency Department\u003c/p\u003e\n\u003cp\u003eIQR - Interquartile Range\u003c/p\u003e\n\u003cp\u003eNEDOCS - National Emergency Department Overcrowding Score\u003c/p\u003e\n\u003cp\u003eSEAL - Swedish Emergency Department Assessment of Patient Load\u003c/p\u003e\n\u003cp\u003eSD - Standard Deviation\u003c/p\u003e\n\u003cp\u003eUS - United States\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003ca href=\"http://paperpile.com/b/qkPHPn/8BOAg\"\u003e Johnston A, Abraham L, Greenslade J, Thom O, Carlstrom E, Wallis M, et al. 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Front Public Health. 2019;7:267. doi:\u003c/a\u003e\u003ca href=\"http://dx.doi.org/10.3389/fpubh.2019.00267\"\u003e10.3389/fpubh.2019.00267\u003c/a\u003e\u003ca href=\"http://paperpile.com/b/qkPHPn/nBYyb\"\u003e.\u003c/a\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Emergency Department, Crowding, Boarding","lastPublishedDoi":"10.21203/rs.2.21604/v3","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.2.21604/v3","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eEmergency Department (ED) crowding occurs when demand for care exceeds the available resources. Crowding has been associated with decreased quality of care and increased mortality, but the prevalence on a national level is unknown in most countries. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethod: \u003c/strong\u003eWe performed a national, cross-sectional study on staffing levels, staff workload, occupancy rate and patients waiting for an in-hospital bed (boarding) at five time points during 24 hours in Swedish EDs. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eComplete data were collected from 37 (51% of all) EDs in Sweden. High occupancy rate indicated crowding at twelve hospitals (37.5 %) at 31 out of 170 (18.2%) time points. Mean workload (measured on a scale from 1, no workload to 6, very high workload) was moderate at 2.65 (±1.25). Boarding was more prevalent in academic EDs than rural EDs (median 3 vs 0). There were an average of 2.6, 4.6 and 3.2 patients per registered nurse, enrolled nurse and physician, respectively.\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eED crowding based on occupancy rate was prevalent on a national level in Sweden and comparable with international data. Staff workload, boarding and patient to staff ratios were generally lower than previously described.\u003c/p\u003e","manuscriptTitle":"Prevalence of Crowding, Boarding and Staffing Levels in Swedish Emergency Departments - a National Cross Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":3,"date":"2020-06-04 14:23:03","doi":"10.21203/rs.2.21604/v3","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2020-05-28T12:00:00+00:00","index":"","fulltext":""},{"type":"decision","content":"Accept","date":"2020-05-28T12:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2020-05-27T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-05-27T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}},{"code":2,"date":"2020-03-31 02:51:41","doi":"10.21203/rs.2.21604/v2","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revision","date":"2020-05-18T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-05-01T12:00:00+00:00","index":2,"fulltext":"Recommendation: Accept without revision\nForm responses:\n---\n\nComments to Author:\n---\nThank you again for a very interesting and relevant piece of work. All comments have been addressed and I have no further remarks.* Are the methods appropriate and well described?: **Yes**\n* Does the work include the necessary controls?: **Yes**\n* Are the conclusions drawn adequately supported by the data shown?: **Yes**\n* Are you able to assess any statistics in the manuscript or would you recommend an additional statistical review?: **I am able to assess the statistics**\n* Quality of written English: **Acceptable**\n* Declaration of competing interests: **I declare that I hve no competing interests**\n* Reviewer Publication Consent. I agree for my report to be made available under an Open Access Creative Commons CC-BY License (http://creativecommons.org/licenses/by/4.0) if this manuscript is accepted for publication. Any comments that I do not wish to be included in the published report have been included as confidential comments to the editor, which will not be published.: **I agree to the terms of the CC-BY 4.0 license; please do not publish my name with my report. (default)**\n"},{"type":"reviewerAgreed","content":"","date":"2020-04-14T12:00:00+00:00","index":2,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2020-04-09T12:00:00+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2020-04-09T12:00:00+00:00","index":1,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-04-09T12:00:00+00:00","index":1,"fulltext":"Recommendation: Accept without revision\nForm responses:\n---\n\nComments to Author:\n---\nDear Authors\nIn my opinion, the required revisions have been made properly.\nKind Regards* Are the methods appropriate and well described?: **Yes**\n* Does the work include the necessary controls?: **Yes**\n* Are the conclusions drawn adequately supported by the data shown?: **Yes**\n* Are you able to assess any statistics in the manuscript or would you recommend an additional statistical review?: **I recommend additional statistical review**\n* Quality of written English: **Acceptable**\n* Declaration of competing interests: **None declare**\n* I agree to the open peer review policy of the journal. I understand that my name will be included on my report to the authors and, if the manuscript is accepted for publication, my named report including any attachments I upload will be posted on the website along with the authors' responses. I agree for my report to be made available under an Open Access Creative Commons CC-BY license (http://creativecommons.org/licenses/by/4.0/). I understand that any comments which I do not wish to be included in my named report can be included as confidential comments to the editors, which will not be published.: **\nI agree to the open peer review policy of the journal**\n"},{"type":"editorAssigned","content":"","date":"2020-03-26T12:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2020-03-25T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-03-25T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2020-01-22 22:08:31","doi":"10.21203/rs.2.21604/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revision","date":"2020-03-01T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-02-27T12:00:00+00:00","index":1,"fulltext":"Recommendation: Accept after discretionary revisions\nForm responses:\n---\n\nComments to Author:\n---\nThe aim of this study is to conduct a temperature reading on workload and crowding in a 24 hour period in Sweden. The study is well designed, and the authors do a good job of reporting the findings. As with all multi-grouped analysis and findings, one could always wish for additional angles/segmentations of the results. E.g., while the study groups #patients by hospital type, it would be interesting to see differences between reported workload and site type.\n\nHaving read the paper a couple of times, I have a few questions.\n\nAre the shifts identical across participating sites? And were there any correlation between timepoints, shift takeover and assessment of workload?\n\nAlso, what was the reason for relying solely on physician-based workload estimation? I would assume that there is a potential profession-based bias here?\n\nFigure 2 is illustrative in depicting the distribution of enrolled ED types and placement. However, it would be interesting to include non-respondents. The authors highlight the response rate of academic EDs, but not at the level of urban and rural EDs. To get a better overview of the responses/non-participants, I would appreciate an overview of the data + the additional dimensions mentioned above, listed in a table. Also, please consider using patterns for demarcation of hospital type in Figure 2. It is hard to distinguish between urban and academic sites when the paper is printed in B/W.\n\nA recently published study in Denmark describes the changes and effects of restructured uptake organization and referral structure. (Fløjstrup M, Bogh SB, Henriksen DP, et al. Increasing emergency hospital activity in Denmark, 2005-2016: a nationwide descriptive study. BMJ Open 2020. doi:10.1136/ bmjopen-2019-031409). I think this work would complement the points raised in the Discussion. And strengthen the argument for the importance of registering the ED metrics utilized in your work.\n\nI am intrigued by the high occurrence of IT-related incidents. Is this a typical pattern in a typical 24h period? If so, this issue definitely needs further investigation.\n\nMinor comments: There seems to be some issues with the reference manager used in the paper. Please check and correct insertion of references and use of punctuation.\n\nAll in all, an interesting study that paves the way for a better systemic understanding of the challenges faced by ED management. In the future I would very much like to see an extension to this study which samples across all seasons and discusses the challenges from a longitudinal perspective.\n* Are the methods appropriate and well described?: **Yes**\n* Does the work include the necessary controls?: **Yes**\n* Are the conclusions drawn adequately supported by the data shown?: **Yes**\n* Are you able to assess any statistics in the manuscript or would you recommend an additional statistical review?: **I am able to assess the statistics**\n* Quality of written English: **Acceptable**\n* Declaration of competing interests: **I declare that I have no competing interests**\n* I agree to the open peer review policy of the journal. I understand that my name will be included on my report to the authors and, if the manuscript is accepted for publication, my named report including any attachments I upload will be posted on the website along with the authors' responses. I agree for my report to be made available under an Open Access Creative Commons CC-BY license (http://creativecommons.org/licenses/by/4.0/). I understand that any comments which I do not wish to be included in my named report can be included as confidential comments to the editors, which will not be published.: ** I agree to the open peer review policy of the journal**\n"},{"type":"reviewerAgreed","content":"","date":"2020-02-25T12:00:00+00:00","index":2,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2020-02-25T12:00:00+00:00","index":2,"fulltext":"Recommendation: Accept after minor essential revisions\nForm responses:\n---\n\nComments to Author:\n---\nDear Authors\nI received your paper entitle \"Prevalence of Crowding, Boarding and Staffing Levels in Swedish Emergency Departments - a National Cross Sectional Study\" as a reviewer. It seems that you conducted this study to emphasize on the importance of growing ED crowding in Swedish EDs. All in all, the paper is acceptable from my viewpoint but needs minor revisions. You can find my comments below:\n- You have mentioned that \"Following marked 78 reductions in hospital beds during the last 30 years\"! it is better to say \"no proportional increase\" than \"reduction\".\n- In \"Measurements and Definitions\" just define the scales you used, so delete the first paragraph under this subheading.\n- Who was responsible for data gathering in each hospital? please mention his/her characteristics (degree, position, etc.) and how did he/she gather the require data.\n- What exact variables were recorded? Type of boarded patients? Indication of admission? They require to be admitted in which hospital unit? ICU? CCU? Surgery or what? It is important to report such detailed data and also discuss in this regard; Or if the related data is not accessible, at least mention this important point as limitations.\n_ I also want you to know that the crowding problem in developed country like Sweden is so far from developing countries (like Iran) and under-developed ones. So I suggest you to consider this point in your introduction and discussion parts. You can check the published paper below in this regard:\nhttp://dx.doi.org/10.5812/ircmj.15609\nhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6548098/\n\nKind Regards\n* Are the methods appropriate and well described?: **Yes**\n* Does the work include the necessary controls?: **Yes**\n* Are the conclusions drawn adequately supported by the data shown?: **Yes**\n* Are you able to assess any statistics in the manuscript or would you recommend an additional statistical review?: **I recommend additional statistical review**\n* Quality of written English: **Acceptable**\n* Declaration of competing interests: **None declare**\n* I agree to the open peer review policy of the journal. I understand that my name will be included on my report to the authors and, if the manuscript is accepted for publication, my named report including any attachments I upload will be posted on the website along with the authors' responses. I agree for my report to be made available under an Open Access Creative Commons CC-BY license (http://creativecommons.org/licenses/by/4.0/). I understand that any comments which I do not wish to be included in my named report can be included as confidential comments to the editors, which will not be published.: ** I agree to the open peer review policy of the journal**\n"},{"type":"reviewerAgreed","content":"","date":"2020-02-10T12:00:00+00:00","index":1,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2020-02-06T12:00:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2020-01-20T12:00:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2020-01-19T12:00:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2020-01-19T12:00:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"","date":"2020-01-17T12:00:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"996e816b-bc64-4798-b4e9-c8e47dcd9a57","owner":[],"postedDate":"June 4th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":76799,"name":"Critical Care \u0026 Emergency Medicine"}],"tags":[],"updatedAt":"2020-06-21T15:01:17+00:00","versionOfRecord":{"articleIdentity":"rs-12189","link":"https://doi.org/10.1186/s12873-020-00342-x","journal":{"identity":"bmc-emergency-medicine","isVorOnly":false,"title":"BMC Emergency Medicine"},"publishedOn":"2020-06-18 12:00:00","publishedOnDateReadable":"June 18th, 2020"},"versionCreatedAt":"2020-06-04 14:23:03","video":"","vorDoi":"10.1186/s12873-020-00342-x","vorDoiUrl":"https://doi.org/10.1186/s12873-020-00342-x","workflowStages":[]},"version":"v3","identity":"rs-12189","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"identity":"rs-12189","version":["v3"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}