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Data envelopment analysis and logistic regression model

Bastian Background In the United States, emergency departments EDs are constantly pressured to improve operational efficiency and quality in order to gain financial benefits and maintain a positive reputation. Objectives The first objective is to evaluate how efficiently EDs transform their input resources into quality outputs. The second objective is to investigate the relationship between the efficiency and quality performance of EDs and the factors affecting this relationship. Methods Using two data sources, we develop a data envelopment analysis DEA model to evaluate the relative efficiency of EDs. Based on the DEA result, we performed multinomial logistic regression to investigate the relationship between ED efficiency and quality performance.
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This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In the United States, emergency departments EDs are constantly pressured to improve operational efficiency and quality in order to gain financial benefits and maintain a positive reputation. The first objective is to evaluate how efficiently EDs transform their input resources into quality outputs. The second objective is to investigate the relationship between the efficiency and quality performance of EDs and the factors affecting this relationship. Using two data sources, we develop a data envelopment analysis DEA model to evaluate the relative efficiency of EDs.
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Diego Prior This paper has a dual aim. First, it analyses the total factor productivity of the Spanish banking sub-sectors throughout an eight-year time span. Hence, by means of an application where the base period is an established reference unit, the indicator offers two types of measures: Results show that benchmarks for private and savings banks have inferior scores than the mean sample level, while the ones for credit cooperatives are roughly equal to the sample mean.
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