Combining stochastic DEA with Bayesian analysis to obtain statistical properties of the efficiency scores: An application to Greek public hospitals

Mitropoulos, Panagiotis; Talias, Μichael A.; Mitropoulos, Ioannis

doi:10.1016/j.ejor.2014.11.012

Cited by 85 publications

(55 citation statements)

References 24 publications

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“…The efficiency obtained in the Brazilian public hospitals analyzed was on average 0.79, a number similar to that found by Pérez-Romero et al (2017) Mitropoulos et al (2015), combining stochastic DEA with Bayesian analysis to extract statistical properties of efficiency scores of 117 Greek hospitals, concluded that while similar production technologies were used by tertiary and secondary hospitals, a large technology gap existed between primary care hospitals and those at the frontier of efficiency.…”

Section: Discussionsupporting

confidence: 65%

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: 65%

A model for multidimensional efficiency analysis of public hospital management

2017

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“…Mitropoulos et al [43] proposed a combined application of a CC-DEA model that is integrated with a stochastic mechanism from Bayesian techniques with an application in the Greek health system. Jimenez et al [33] used MC-DEA to evaluate the relative technical efficiency of small health care areas in probabilistic terms with respect to both mental health care, as well as the efficiency of the entire system.…”

Section: Literature Reviewmentioning

confidence: 99%

Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems

Bastian

Ekin²,

Kang

et al. 2016

Health Care Manag Sci

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The management of hospitals within fixed-input health systems such as the U.S. Military Health System (MHS) can be challenging due to the large number of hospitals, as well as the uncertainty in input resources and achievable outputs. This paper introduces a stochastic multi-objective auto-optimization model (SMAOM) for resource allocation decision-making in fixed-input health systems. The model can automatically identify where to re-allocate system input resources at the hospital level in order to optimize overall system performance, while considering uncertainty in the model parameters. The model is applied to 128 hospitals in the three services (Air Force, Army, and Navy) in the MHS using hospital-level data from 2009 - 2013. The results are compared to the traditional input-oriented variable returns-to-scale Data Envelopment Analysis (DEA) model. The application of SMAOM to the MHS increases the expected system-wide technical efficiency by 18 % over the DEA model while also accounting for uncertainty of health system inputs and outputs. The developed method is useful for decision-makers in the Defense Health Agency (DHA), who have a strategic level objective of integrating clinical and business processes through better sharing of resources across the MHS and through system-wide standardization across the services. It is also less sensitive to data outliers or sampling errors than traditional DEA methods.

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“…For example, fuzzy theory was combined with DEA in order to deal with a vague data [12]. Stochastic DEA is proposed to deal with a dynamic environment [13]. However, most of previous researches focus on the way to judge suppliers on the current condition, only few studies provide improvement plan for the future.…”

Section: Evaluating Suppliers' Perfor-mancementioning

confidence: 99%

Finding Weak Links in Supply Chain Case Study: Thai Frozen Shrimp Industry

Kawakami¹,

Hattori²

2017

IJDIWC

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Usually, a manufacture receives materials from one or more suppliers in a supply chain. Performance of each supplier in a chain affects on the chain's performance. Thus, evaluating suppliers in a supply chain is important for achieving efficient supply chain. Many methods have already been developed for evaluating performance of suppliers. Most of the existing methods show us suppliers' performance, but they do not indicate directions for improving supply chains. With the proposed method, we can find that which factors of which suppliers should be improved to achieve efficient condition. Thus, manufacturers can make decision for improving or changing suppliers more easily. A case study of Thai frozen shrimp industry is represented to show the applicability of the proposed scheme.

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Combining stochastic DEA with Bayesian analysis to obtain statistical properties of the efficiency scores: An application to Greek public hospitals

Cited by 85 publications

References 24 publications

A model for multidimensional efficiency analysis of public hospital management

A model for multidimensional efficiency analysis of public hospital management

Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems

Finding Weak Links in Supply Chain Case Study: Thai Frozen Shrimp Industry

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