Are hospitals producing quality care efficiently? An analysis using Dynamic Network Data Envelopment Analysis (DEA)

Khushalani, Jaya; Özcan, Yaşar A.

doi:10.1016/j.seps.2017.01.009

Cited by 109 publications

(80 citation statements)

References 26 publications

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“…Their DEA model showed that, although cost efficiency did not change in the study period, allocative efficiency (refers to how different resource inputs are combined to produce a mix of different outputs -Medeiros and Schwierz (2015)) decreased significantly. Similar results and significantly different efficiency levels were also find by Khushalani and Ozcan (2017), who studied the efficiency of general hospitals in the United States between 2009 and 2013, also using the DEA.…”

Section: Discussionsupporting

confidence: 80%

“…In addition, a multidimensional analysis of the efficiency of such organizations may also provide historical understandings for current results. Khushalani and Ozcan (2017) used DEA to evaluate hospitals in the United States, computing efficiency scores for hospital sub-divisions and quality. They found that the efficiency of quality production improved significantly between 2009 and 2013 with no trade-off between efficiency of producing quality outputs and efficiency of producing medical care.…”

Section: Introductionmentioning

confidence: 99%

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A model for multidimensional efficiency analysis of public hospital management

2017

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Introduction: Multidimensional efficiency analysis can provide important insights into the performance of hospitals.In this paper, we propose a multidimensional model based on Data Envelopment Analysis (DEA) to investigate and compare the efficiency of public hospitals in Brazil. Methods: Data from 21 public hospitals were collected from public databases (OECD -Organization for Economic Co-operation and Development; SIH-SUS -SUS Hospital Information System, Datasus, Brazil). Four inputs (Number of medical and non-medical staff, Annual revenue, Number of beds, Average length of patient hospitalization), four Variables of Influence (Type of hospital, Accredited hospital, Number of medical specialties, Resources from government) and four Outputs (Number of outpatient care services, Number of hospitalizations, Number of surgeries, Number of exams) were used to feed the DEA model. Results: Seven hospital units reach 100% efficiency and, according to DEA, can be considered efficient units. Two units were considered "almost efficient" and the remaining twelve units perform poorly, considering the data supplied to the DEA model. As a whole, the average efficiency of the hospitals investigated was 79% (0.79). Conclusion: A very heterogeneous performance has been found among the Brazilian public hospitals investigated. Besides, the reasonably low average efficiency seems to indicate that the system has a large potential for improvement in almost all areas associated with the input and output variables investigated in this paper.Keywords Hospital efficiency, Data Envelopment Analyses, Public hospital.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 work is properly cited.How to cite this article: Soares AB, Pereira AA, Milagre ST. A model for multidimensional efficiency analysis of public hospital management.

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Section: Discussionsupporting

confidence: 80%

Section: Introductionmentioning

confidence: 99%

A model for multidimensional efficiency analysis of public hospital management

2017

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“…Alternative longitudinal analysis techniques based on DEA are for example the DEA window analysis [82,83] as well as the dynamic network DEA (see e.g., Guo et al [84] or Khushalani and Ozcan [85]): For the DEA window analysis e.g., Vlontzos and Pardalos [86] outline a longitudinal efficiency analysis regarding the GHG emissions from EU agricultural industries. Similarly, Halkos and Tzeremes [87] have analysed the hypothesis of an environmental Kuznets curve with this technique.…”

Section: Efficiency Measurementmentioning

confidence: 99%

How to Achieve Supply Chain Sustainability Efficiently? Taming the Triple Bottom Line Split Business Cycle

Klumpp

2018

Sustainability

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For sustainable supply chains, specific concepts regarding how to efficiently improve sustainability are needed in a global comprehensive triple bottom line (TBL) approach, especially for forwarders as central actors in supply chain design. Such specific advice is provided by reporting empirical DEA Malmquist index findings from seven large European forwarders regarding a TBL sustainability analysis from 2006 to 2016. A major obstacle in improving sustainability consists in the newly discovered fact that with the business cycle, the three TBL areas of economic, ecologic and social objectives for logistics are undergoing different up-and down-ward trends, making it very hard to improve all three simultaneously. Additional factors are identified in the characteristics of size and government influence regarding the sustainability efficiency of forwarders. This has important impacts on supply chain design like e.g., with selection criteria.

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“…Most scholars include government fiscal health expenditure or health expenditure into health care financial input indicators [31,32]. With references on the efficiency of health systems, a scattered picture study based on OECD data, government financial expenditure on health was selected as a financial input indicator [33,34].…”

Section: Variable Selectionmentioning

confidence: 99%

Is the allocation of medical and health resources effective? Characteristic facts from regional heterogeneity in China

Peng

Zhang

2020

Preprint

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Background: Over the last decade, the expenditure on public medical and health has increased greatly in China, however, problems as low efficiency and unfairness still exist. How to accurately describe the effectiveness of existing medical and health resources in combination with regional heterogeneity is of great significance to China's medical and health reform.Methods: Based on provincial panel data for the period of 2005 to 2017, this paper constructs a super-efficiency three-stage SBM-DEA model, combining expected output and unexpected output, to measure and analyze the spatial-temporal heterogeneity characteristics and influencing factors of public medical and health efficiency (PMHE). Results: After the impacts of random error and external environmental factors are removed, the mean value of overall PMHE is 0.9274, failing to reach DEA efficiency, and PMHE shows a fluctuated downward trend. In addition, apart from eastern regions, the mean values of PMHE in central, western and north-eastern are all underestimated. (2) The adjusted PMHE level shows a prominent spatial imbalance at the stage 3. The average efficiency level is ranked by the east > the western > the central > the northeast region. (3) The increases of GDP per capita and population density are beneficial to the improvement of PMHE, while income level and education level are disadvantageous to PMHE, and the urbanization level, an uncertain effect.Conclusion: This study innovatively incorporates undesired outputs of health care into the efficiency evaluation framework by constructing the main efficiency evaluation indicators. The results of the robust evaluation conclude that China's existing investment in medical and health resources is generally not effective. Therefore, although China's health care reform has achieved certain results, it is still necessary to expand the investment in health care resources.

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Are hospitals producing quality care efficiently? An analysis using Dynamic Network Data Envelopment Analysis (DEA)

Cited by 109 publications

References 26 publications

A model for multidimensional efficiency analysis of public hospital management

A model for multidimensional efficiency analysis of public hospital management

How to Achieve Supply Chain Sustainability Efficiently? Taming the Triple Bottom Line Split Business Cycle

Is the allocation of medical and health resources effective? Characteristic facts from regional heterogeneity in China

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