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Execution of Diagnostic Testing Has a Stronger Effect on Emergency Department Crowding than Other Common Factors: A Cross-Sectional Study

1 , * , 2 , 3

PLoS ONE

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      Abstract

      Study ObjectiveWe compared the effects of execution of diagnostic tests in the emergency department (ED) and other common factors on the length of ED stay to identify those with the greatest impacts on ED crowding.MethodsBetween February 2010 and January 2012, we conducted a cross-sectional, single-center study in the ED of a large, urban, teaching hospital in Japan. Patients who visited the ED during the study period were enrolled. We excluded (1) patients scheduled for admission or pharmaceutical prescription, and (2) neonates requiring intensive care transferred from other hospitals. Multivariate linear regression was performed on log-transformed length of ED stay in admitted and discharged patients to compare influence of diagnostic tests and other common predictors. To quantify the range of change in length of ED stay given a unit change of the predictor, a generalized linear model was used for each group.ResultsDuring the study period, 55,285 patients were enrolled. In discharged patients, laboratory blood tests had the highest standardized β coefficient (0.44) among common predictors, and increased length of ED stay by 72.5 minutes (95% CI, 72.8–76.1 minutes). In admitted patients, computed tomography (CT) had the highest standardized β coefficient (0.17), and increased length of ED stay by 32.7 minutes (95% CI, 40.0–49.9 minutes). Although other common input and output factors were significant contributors, they had smaller standardized β coefficients in both groups.ConclusionsExecution of laboratory blood tests and CT had a stronger influence on length of ED stay than other common input and output factors.

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      Most cited references 23

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      A conceptual model of emergency department crowding.

      Emergency department (ED) crowding has become a major barrier to receiving timely emergency care in the United States. Despite widespread recognition of the problem, the research and policy agendas needed to understand and address ED crowding are just beginning to unfold. We present a conceptual model of ED crowding to help researchers, administrators, and policymakers understand its causes and develop potential solutions. The conceptual model partitions ED crowding into 3 interdependent components: input, throughput, and output. These components exist within an acute care system that is characterized by the delivery of unscheduled care. The goal of the conceptual model is to provide a practical framework on which an organized research, policy, and operations management agenda can be based to alleviate ED crowding.
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        Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis

         Hae-Young Kim (2013)
        As discussed in the previous statistical notes, although many statistical methods have been proposed to test normality of data in various ways, there is no current gold standard method. The eyeball test may be useful for medium to large sized (e.g., n > 50) samples, however may not useful for small samples. The formal normality tests including Shapiro-Wilk test and Kolmogorov-Smirnov test may be used from small to medium sized samples (e.g., n 2.1 Kurtosis is a measure of the peakedness of a distribution. The original kurtosis value is sometimes called kurtosis (proper) and West et al. (1996) proposed a reference of substantial departure from normality as an absolute kurtosis (proper) value > 7.1 For some practical reasons, most statistical packages such as SPSS provide 'excess' kurtosis obtained by subtracting 3 from the kurtosis (proper). The excess kurtosis should be zero for a perfectly normal distribution. Distributions with positive excess kurtosis are called leptokurtic distribution meaning high peak, and distributions with negative excess kurtosis are called platykurtic distribution meaning flat-topped curve. 2) Normality test using skewness and kurtosis A z-test is applied for normality test using skewness and kurtosis. A z-score could be obtained by dividing the skew values or excess kurtosis by their standard errors. As the standard errors get smaller when the sample size increases, z-tests under null hypothesis of normal distribution tend to be easily rejected in large samples with distribution which may not substantially differ from normality, while in small samples null hypothesis of normality tends to be more easily accepted than necessary. Therefore, critical values for rejecting the null hypothesis need to be different according to the sample size as follows: For small samples (n < 50), if absolute z-scores for either skewness or kurtosis are larger than 1.96, which corresponds with a alpha level 0.05, then reject the null hypothesis and conclude the distribution of the sample is non-normal. For medium-sized samples (50 < n < 300), reject the null hypothesis at absolute z-value over 3.29, which corresponds with a alpha level 0.05, and conclude the distribution of the sample is non-normal. For sample sizes greater than 300, depend on the histograms and the absolute values of skewness and kurtosis without considering z-values. Either an absolute skew value larger than 2 or an absolute kurtosis (proper) larger than 7 may be used as reference values for determining substantial non-normality. Referring to Table 1 and Figure 1, we could conclude all the data seem to satisfy the assumption of normality despite that the histogram of the smallest-sized sample doesn't appear as a symmetrical bell shape and the formal normality tests for the largest-sized sample were rejected against the normality null hypothesis. 3) How strict is the assumption of normality? Though the humble t test (assuming equal variances) and analysis of variance (ANOVA) with balanced sample sizes are said to be 'robust' to moderate departure from normality, generally it is not preferable to rely on the feature and to omit data evaluation procedure. A combination of visual inspection, assessment using skewness and kurtosis, and formal normality tests can be used to assess whether assumption of normality is acceptable or not. When we consider the data show substantial departure from normality, we may either transform the data, e.g., transformation by taking logarithms, or select a nonparametric method such that normality assumption is not required.
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          The effect of hospital occupancy on emergency department length of stay and patient disposition.

          Emergency department (ED) overcrowding is a common problem. Despite a widespread belief that low hospital bed availability contributes to ED overcrowding, there are few data demonstrating this effect. To identify the effect of hospital occupancy on ED length of stay for admitted patients and patient disposition. This was an observational study design using administrative data at a 500-bed acute care teaching hospital. All patients presenting to the ED between April 1993 and June 1999 were included in the study. The predictor variable was daily hospital occupancy. Outcome measures included daily ED length of stay for admitted patients, daily consultation rate, and daily admission rate. The models controlled for the average daily age of ED patients and the average daily "arrival density" index, which adjusts for patient volume and clustering of patient arrivals. The average hospital occupancy was 89.7%. On average 155 patients visited the ED daily; 21% were referred to hospital physicians and 19% were admitted. The median ED length of stay for admitted patients was 5 hours 54 minutes (interquartile range 5 hr 12 min to 6 hr 42 min). Daily ED length of stay for admitted patients increased 18 minutes (95% CI = 12 to 24) when there was an absolute increase in occupancy of 10%. The ED length of stay appeared to increase extensively when hospital occupancy exceeded a threshold of 90%. Consultation and admission rates were not influenced by hospital occupancy. Increased hospital occupancy is strongly associated with ED length of stay for admitted patients. Increasing hospital bed availability might reduce ED overcrowding.
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            Author and article information

            Affiliations
            [1 ]Department of Emergency Medicine, University of Fukui Hospital, Fukui, Japan
            [2 ]Department of Primary Care and Emergency Medicine, Kyoto University Graduate School of Medicine, Kyoto city, Japan
            [3 ]Department of General Medicine, University of Fukui Hospital, Fukui, Japan
            University of Utah School of Medicine, United States of America
            Author notes

            Competing Interests: The authors have declared that no competing interests exist.

            Conceived and designed the experiments: TK KN. Analyzed the data: TK KN HH. Contributed reagents/materials/analysis tools: TK KN HH. Contributed to the writing of the manuscript: TK.

            Contributors
            Role: Editor
            Journal
            PLoS One
            PLoS ONE
            plos
            plosone
            PLoS ONE
            Public Library of Science (San Francisco, USA )
            1932-6203
            2014
            13 October 2014
            : 9
            : 10
            25310089
            4195592
            PONE-D-14-25818
            10.1371/journal.pone.0108447
            (Editor)

            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 credited.

            Counts
            Pages: 9
            Funding
            Financial incentives were provided to the authors by the Emergency Promotion Foundation ( http://www.fasd.or.jp/). This foundation has no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
            Categories
            Research Article
            Medicine and Health Sciences
            Clinical Medicine
            Critical Care and Emergency Medicine
            Diagnostic Medicine
            Clinical Laboratory Sciences
            Custom metadata
            The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper and its Supporting Information files.
            ScienceOpen disciplines:

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