Trends in labor efficiency among American hospital markets

The health care sector is one of the most labor intensive sectors of the economy. As a major player in health service delivery, hospitals must closely examine their largest cost — labor expenses. This study evaluates trends in the efficiency of health care labor among urban hospital markets. More specifically, it assesses the hypothesis that technological changes and market and regulatory pressures have increased aggregate labor efficiency. Using data envelopment analysis (DEA), this study evaluates labor efficiency in 1989 and 1993 in 298 metropolitan statistical areas (MSAs) with two or more hospitals. Results of this study suggest that between 1989 and 1993, hospital markets generally demonstrated higher labor inefficiencies. The U.S. health care system could save approximately $16.6 billion in 1993 by eliminating hospitals' excessive use of health care provider labor.     

The relationship between the efficiency of orthopedic wards and the socio-economic status of their patients

The purpose of this paper is to study the effect of the socio-economic status of patients on the efficiency of orthopedic wards in acute hospitals in Israel (20 hospitals), from the viewpoint of the regulator—Israel Ministry of Health. At the first stage, data envelopment analysis is used with two inputs, and three outputs, where one output is undesirable—“number of deaths”—which also reflects the quality of the health services. At the second stage, various nonparametric tests are utilized to test the relationship between the socio-economic status of patients and the efficiency. As by-product DEA provides benchmark analysis, which indicates the peers of each inefficient ward, and the I/O improvements are needed for achieving efficiency. Two versions of DEA were used: the output oriented version (variable returns to scale), and the non-oriented version (Additive). Further analysis provides comparison of the results with other simple efficiency measures. We also compare between the efficiency from the regulator viewpoint and the hospitals’ viewpoint.     

Do mergers enhance the performance of hospital efficiency?

Due to the wave of mergers that have taken place in the USA, the early 1990s could be labelled as a restructuring era for health care systems. The question of whether mergers have an impact on organizational performance is still an area of interest for health services researchers. In this study, we examined the impacts of horizontal mergers of US hospital's technical efficiency before and after merger using longitudinal Data Envelopment Analysis (DEA). The findings of our study illustrate that mergers do increase a hospital's level of efficiency. Constant returns-to-scale model indicated an overall reduction in input utilisation after merger, compared to variable returns-to-scale model. This indicates the role of scale efficiency as a dominant source of improvement in inefficiency of hospitals involved in horizontal mergers, but not for technical efficiency. Suggestions for future study are provided.     

A new proposed model of restricted data envelopment analysis by correlation coefficients

The concept of efficiency in data envelopment analysis (DEA) is defined as weighted sum of outputs/weighted sum of inputs. In order to calculate the maximum efficiency score, each decision making unit (DMU)’s inputs and outputs are assigned to different weights. Hence, the classical DEA allows the weight flexibility. Therefore, even if they are important, the inputs or outputs of some DMUs can be assigned zero (0) weights. Thus, these inputs or outputs are neglected in the evaluation. Also, some DMUs may be defined as efficient even if they are inefficient. This situation leads to unrealistic results. Also to eliminate the problem of weight flexibility, weight restrictions are made in DEA. In our study, we proposed a new model which has not been published in the literature. We describe it as the restricted data envelopment analysis ((ARIII(COR))) model with correlation coefficients. The aim for developing this new model, is to take into account the relations between variables using correlation coefficients. Also, these relations were added as constraints to the CCR and BCC models. For this purpose, the correlation coefficients were used in the restrictions of input–output each one alone and their combination together. Inputs and outputs are related to the degree of correlation between each other in the production. Previous studies did not take into account the relationship between inputs/outputs variables. So, only with expert opinions or an objective method, weight restrictions have been made. In our study, the weights for input and output variables were determined, according to the correlations between input and output variables. The proposed new method is different from other methods in the literature, because the efficiency scores were calculated at the level of correlations between the input and/or output variables.     

Efficiency and productivity change in Taiwan’s hospitals: a non-radial quality-adjusted measurement

This study measures the quality-adjusted hospital efficiency and productivity index of a production unit. We propose a non-radial output-oriented directional distance function approach to analyze Taiwan’s hospital productivity, which embeds the quality of care and environment variables simultaneously. There are two major advantages of this model. First, it considers all the radial and non-radial slacks that the model can identify, and hence is able to provide a more accurate performance measure and improve the discriminating power of the analysis. Second, it allows us to identify the source of the inefficiency. Our results show that the productivity indices of most of Taiwan’s hospitals got worse during the 2002–2004 period, during which both technology and efficiency performance deteriorated, but divergence appeared among different types of hospitals. We confirmed the need to incorporate quality factors while measuring a hospital’s efficiency and productivity. Nevertheless, there is no evidence to support the idea that healthcare quality is undermined by the cost-saving efforts by the care providers after the implantation of a global budget system.     

Efficiency analysis of a multisectoral economic system

The present paper is concerned with efficiency analysis applied to a single economy represented by the Leontief input–output-model extended by the constraints for primary factors. First, the efficiency frontier is generated using a multi-objective optimization model instead of having to use data from different decision making units. The solutions of the multi-objective optimization problems define efficient virtual decision making units and the efficiency of the given economy is defined as the difference between the potential of an economy and its actual performance and can be obtained as a solution of a DEA model. It can be shown that the solution of the above defined DEA model yields the same efficiency score and the same shadow prices as the models by ten Raa (Linear analysis of competitive economics, LSE handbooks in economics. Harvester Wheatsheaf, New York, 1995; The economics of input–output analysis. Cambridge University Press, Cambridge, 2005) despite the different variables used in both models. Using duality theory of linear programming the equivalence of the approaches permits a clear economic interpretation. In the second part of the paper this approach is extended to the Leontief augmented model including emissions of pollutants and abatement activities. In this way the eco-efficiency of an economy can be analyzed.

Recessions are easily recognizable from a decrease in GDP. What really should interest us, however, is the difference between the potential of an economy and its actual performance (J. Stiglitz, 2002).

     

Measurement and sources of overall and input inefficiencies: Evidences and implications in hospital services

Traditional data envelopment analysis (DEA) focuses exclusively on measuring the overall efficiency of a decision making unit (DMU). Yet, variables that have explanatory power for the overall operational inefficiency of a DMU may not necessarily be the same as those that affect its individual input inefficiencies. On many occasions, variables that explain the overall inefficiency of a DMU can be inconsistent or incongruent with those that cause its individual input inefficiencies. Therefore, we conjecture that an overall inefficiency score alone may have limited value for decision making since such a process requires fine-tuning and adjustments of specific input factors of the DMU in order to maximize its overall efficiency. In this paper, the utilization and financial data of a set of hospitals in California is used to empirically test the above conjecture.Our study has several important contributions and practical implications. First, we fine-tune previous efficiency measures on hospitals by refining input and output measures. Second, with variables on organization, management, demographics, and market competition, we identify specific factors associated with a hospital's overall operational inefficiency. More importantly, by decomposing the overall DEA operational inefficiency score into different individual input inefficiencies (including slacks), we further identify specific variables that cause individual input inefficiency. Third, significant differences are observed among factors of the overall inefficiency and individual input inefficiencies. These findings have important implications for identifying congruent factors for performance standard setting and evaluation; it also provides invaluable information for guiding effective resource allocation and better decision making for improving hospital operational efficiency.     

Factors affecting primary health care centers’ economic and production efficiency

Little is known about the efficiency of health centers, despite their central role in primary health care strategy for several countries. This study evaluates the health centers in Greece, and identifies factors impeding the achievement of efficiency, with the aim of determining how their efficiency could be improved. Two alternative conceptual models are used to ensure the consistency of the efficiency results: one model is focusing on production efficiency and the other on economic efficiency. Subsequently a second stage analysis is performed to account for the impact of explanatory variables on efficiency. The use of DEA models alongside with bootstrap techniques allows calculating more accurately the efficiency scores that can reflect the performance of health centers more properly. The main drivers of health centers’ technical efficiency for both conceptual models were the location characteristics, the population growth, the mortality rate and the competition. The scale efficiency of health centers in production model is reflected by the size of their respective covered populations, the location characteristics and the mortality rate while the economic model is affected by their size, the location characteristics and the percentage of population working in agriculture. Determining how these variables influence on efficiency is essential for determining performance improvement strategies.     

我国资本市场产出效率与生产函数的实证分析

本文运用数据包络分析对我国资本市场的产出效率进行了考察，运用两阶段的DEA-回归方法建立符合经济学定义的我国资本市场生产函数。在此基础上，对我国资本市场的产出效率作了实证分析。     

Evaluating the performance of Brazilian university hospitals

In order to demonstrate how DEA modeling can be helpful to aid decision making relative to the Brazilian Teaching Hospital Policy by means of hospital performance assessment, we develop a case study with 30 general hospitals linked to Brazilian Federal Universities. We consider data on medical care (Medical Model—MM), teaching and research (Teaching-Research Model—TRM) and use the software IDEAL (Interactive Data Envelopment Analysis Laboratory) as a tool for the units’ efficiency evaluation. IDEAL, developed in Brazil, is unique in providing a 3-D frontiers visualization, assisting in exploratory analysis and allowing a better understanding of the DEA modeling (envelopment and multiplier). Both models are input-oriented and each hospital is categorized according to its relative efficiency in the MM and TRM. In this phase, it is very important to discuss with the decision-makers the results and patterns of the DEA models. Finally, the modelling indicates the necessary changes for the inefficient units and generates recommendations for teaching ratios and public financing.     

A new non-oriented model for classifying flexible measures in DEA

In conventional data envelopment analysis (DEA), measures are classified as either input or output. However, in some real cases there are variables which act as both input and output and are known as flexible measures. Most of the previous suggested models for determining the status of flexible measures are oriented. One important issue of these models is that unlike standard DEA, even under constant returns to scale the input- and output-oriented model may produce different efficiency scores. Also, can be expected a flexible measure is selected as an input variable in one model but an output variable in the other model. In addition, in all of the previous studies did not point to variable returns to scale (VRS), but the VRS assumption is prevailed on many real applications. To deal with these issues, this study proposes a new non-oriented model that not only selects the status of each flexible measure as an input or output but also determines returns to scale status. Then, the aggregate model and an extension with the negative data related to the proposed approach are presented.     

A microsimulation model of the health care system: the role of the hospital sector

A microeconometric model of the health care system of the United States was estimated by applying econometric techniques to extensive data. The model treats the microanalytic behavior of individuals and institutions comprising the health care system, and is structured in terms of five submodels pertaining to consumers, hospital and physician services, physician and nonphysician manpower. In each submodel there is an economic market on which demands and supplies from the individuals and institutions interact and on which the allocation of resources is determined. A detailed treatment of participant attributes facilitates study of the distributional impacts of alternative policies. This paper presents an overview of the complete system, and summarizes the relationships used for the hospital sector.     

A new approach for technical efficiency estimation in multiple output production

The nonparametric technique of Data Envelopment Analysis (DEA) has been used to measure technical efficiency. This approach has proven useful because, unlike regression analyses, it allows multiple outputs and does not require a priori functional form specification. DEA does, however, require correct model specification; inclusion of inappropriate variables or omission of relevant variables leads to distortions. The purpose of this paper is to develop an alternative methodology based on canonical correlation to measure technical efficiency for multiple output production correspondences. Using simulated data, the new methodology is compared with DEA. The results indicate that the canonical regression approach outperforms DEA in most cases.     

基于复合DEA模型高校办学效益评价方法研究

传统DEA只能在固定投入(或产出)的情况下,将产出(或投入)尽量扩大(或缩小),而造成决策单元非DEA有效的原因,既有投入方面的因素,也有产出方面的因素,是由投入和产出两方面的原因共同造成的,而不仅仅是一方面的原因.本文考虑投入和产出两方面的因素,构造了复合DEA模型,并研究了基于复合DEA模型高校办学效益评价方法.     

具有偏好锥的链式网络DEA模型

对链式网络DEA模型进行推广,将"偏好锥"引入网络DEA模型.针对中间产出重要性以及决策者评价时的偏好,建立带有产出锥和投入锥相应的两阶段生产可能集,对具有"偏好锥"的链式网络DEA模型,证明了决策单元为网络DEA有效的充要条件,给出了网络DEA有效性与各阶段弱DEA有效性的关系.另外,文章结合具体算例说明了偏好锥的变化对效率评价的影响.关于两阶段的模型以及相关结论可以推广到多阶段网络结构.     

DEA with non-monotonic variables. Application to EU governments’ macroeconomic efficiency

Conventional Data Envelopment Analysis (DEA) considers monotonic variables, ie the lower the inputs and the larger the outputs, the better. There are, however, occasions when the monotonicity of a variable with respect to efficiency depends on the value of the variable, that is in a certain range of values an increase in the variable is desirable, while in another range it is a decrease of the variable that is desirable. In this paper, a DEA model that solves problems considering non-monotonic variables is proposed. An application to assess the macroeconomic efficiency of European Union Member States, as regards taxation, gross debt, GDP growth and employment is presented.     

Do mergers improve hospital productivity?

This paper complements the existing literature on hospital mergers by using data envelopment analysis (DEA) to generate both efficiency and productivity measures to ascertain whether hospital mergers, at least in the short run, result in performance gains. Using data over the period 1996–1998, we apply DEA, both pre-merger and post-merger, to set of hospitals that merged in 1997 as well as to a matching control group of non-merging hospitals over the same timeframe. A comparison of DEA efficiency scores and the Malmquist index values across the case and control hospitals allow us to assess whether any increase in productivity is the result of a merger rather than simply and randomly adding two hospitals' inputs and outputs together.     

DEA的交形式生产可能集及其应用

DEA理论、模型及方法可用以评价给定决策单元之间的相对有效性,其在经济学中的应用体现在经验生产可能集的构造上.DEA的生产可能集有两种等价形式—和形式及交形式.相比较而言,交形式更具几何直观性及计算便利性.     

A cognitive analytics management framework to select input and output variables for data envelopment analysis modeling of performance efficiency of banks using random forest and entropy of information

The efficiency of banks has a critical role in development of sound financial systems of countries. Data Envelopment Analysis (DEA) has witnessed an increase in popularity for modeling the performance efficiency of banks. Such efficiency depends on the appropriate selection of input and output variables. In literature, no agreement exists on the selection of relevant variables. The disagreement has been an on-going debate among academic experts, and no diagnostic tools exist to identify variable misspecifications. A cognitive analytics management framework is proposed using three processes to address misspecifications. The cognitive process conducts an extensive review to identify the most common set of variables. The analytics process integrates a random forest method; a simulation method with a DEA measurement feedback; and Shannon Entropy to select the best DEA model and its relevant variables. Finally, a management process discusses the managerial insights to manage performance and impacts. A sample of data is collected on 303 top-world banks for the periods 2013 to 2015 from 49 countries. The experimental simulation results identified the best DEA model along with its associated variables, and addressed the misclassification of the total deposits. The paper concludes with the limitations and future research directions.

     

An extended slacks-based measure model for data envelopment analysis with negative data

Data envelopment analysis (DEA) is a non-parametric approach based on linear programming that has been widely applied for evaluating the relative efficiency of a set of homogeneous decision-making units (DMUs) with multiple inputs and outputs. The original DEA models use positive input and output variables that are measured on a ratio scale, but these models do not apply to the variables in which negative data can appear. However, with the widespread use of interval scale data and undesirable data, the emphasis has been directed towards the simultaneous consideration of the positive and negative data in DEA models. In this paper, using the slacks-based measure, we propose an extended model to evaluate the efficiency of DMUs, even if some variables are measured on an interval scale and some on a ratio scale. Moreover, the extended model allows for the presence of all interval-scale variables, which are capable of taking both negative and positive values.