Advances in physician profiling: the use of DEA

Insurers, health plans, and individual physicians in the United States are facing increasing pressures to reduce costs while maintaining quality. In this study, motivated by our work with a large managed care organization, we use readily available data from its claims database with data envelopment analysis (DEA) to examine physician practices within this organization. Currently the organization evaluates primary care physicians using a profile of 16 disparate ratios involving cost, utilization, and quality. We employed these same factors along with indicators of severity to develop a single, comprehensive measure of physician efficiency through DEA. DEA enabled us to identify a reference set of “best practice” physicians tailored to each inefficient physician. This paper presents a discussion of the selection of model inputs and outputs, the development of the DEA model using a “stepwise” approach, and a sensitivity analysis using superefficiency scores. The stepwise and superefficiency analyses required little extra computation and yielded useful insights into the reasons as to why certain physicians were found to be efficient. This paper demonstrates that DEA has advantages for physician profiling and usefully augments the current ratio-based reports.     

A PCA–DEA approach to measure the quality of life in Estonian counties

This paper investigates the application of a PCA–DEA model to assess the quality of life (QOL) scores in Estonian counties and analyses the model's results. The dataset is a balanced panel of 15 Estonian counties covering the period from 2000 to 2011. We consider a PCA–DEA model as an alternative method to estimate and predict QOL scores and rankings of Estonian counties. The method consists of a two-stage analysis that begins with a principal component analysis. In the second stage, the standard DEA is used. The results from the conventional DEA model and the PCA–DEA model are compared and discussed. A comparison of the methodologies demonstrates that a PCA–DEA model provides a powerful tool for performance ranking. The rankings of Estonian counties using QOL scores for different model specifications are presented. Finally, the QOL ranking of Estonian counties is revised using PCA–DEA.     

A DEA-centric decision support system for evaluating Ciclovía-Recreativa programs in the Americas

Ciclovía-Recreativa (CR) is a community-based program with health and social benefits including physical activity promotion, social capital development, improvement in the population's quality of life, and reduction of air pollution and street noise. It is critical that these programs are evaluated through their operational performance and efficient use of resources. In this paper, we develop a DEA methodology that measures each CR efficiency relative to its peer programs, compares its performance to a benchmark system, identifies its sources of inefficiencies and offers recommendations for improvement. We examine the proposed methodology on programs in the region of the Americas as a case study and demonstrate the results and the recommendations. Finally, we present a spreadsheet-based DEA-centric Decision Support System (DSS) that facilitates the evaluation of the CR programs. Based on this study, an award called “Bicis de Calidad” (in English “Bikes of Quality”) was created to be granted to the best CR programs reaching full efficiency according to the DEA outcomes.     

Measuring and Evaluating Efficiency of Hospitals Through Total Quality Management

ABSTRACT

The purpose of this study is to measure and evaluate the efficiency of 12 hospitals in Turkey using a multi-criteria Data Envelopment Analysis (DEA) methodology. Number of beds, number of physicians and the critical factors of total quality management in the health care sector were used as inputs of the model. The outputs used in this analysis incorporated financial and non-financial performance of hospitals, number of outpatients and number of patient days. Performance of the hospitals was measured using subjective measures based on executive's perception of how their organization performed relative to the competition. Results provide management with information regarding the relatively best practice hospitals in the observation sets and locate the relatively inefficient hospitals by comparison with the best practice ones. At last some suggestions are made for the least efficient hospital.     

Data envelopment analysis: The evolution of the state of the art (1978–1995)

The purpose of this paper is to briefly trace the evolution of DEA from the initial publication by Charnes et al. (1978b) to the current state of the art (SOA). The state of development of DEA is characterized at four points in time to provide a perspective in both directions—past and future. An evolution map is provided which illustrates DEA growth during the 17-year period, the timing of the major events, and the interconnections and influences between topics. An extensive DEA bibliography is provided.     

Reverse efficiency measures for environmental assessment in data envelopment analysis

Environmental issues are becoming more and more important in our everyday life. Data Envelopment Analysis (DEA) is a tool developed for measuring relative operational efficiency. DEA can also be employed to estimate environmental efficiency where undesirable outputs like greenhouse gases exist. The classical DEA method identifies best practices among a given empirical data set. In many situations, however, it is advantageous to determine the worst practices and perform efficiency evaluation by comparing DMUs with the full-inefficient frontier. This strategy requires that the conventional production possibility set is defined from a reverse perspective. In this paper, presence of both desirable and undesirable outputs is assumed and a methodological framework for performing an unbiased efficiency analysis is proposed. The reverse production possibility set is defined and new models are presented regarding the full-inefficient frontier. The operational, environmental and overall reverse efficiencies are studied. The important notion of weak disposability is discussed and the effects of this assumption on the proposed models are investigated. The capability of the proposed method is examined using data from a real-world application about paper production.     

Incorporating quality into the measurement of hospital efficiency: a double DEA approach

This paper uses “double DEA” to assess how accounting for quality influences DEA technical efficiency scores of a sample of 1,074 US hospitals. In the first use of DEA, quality indices are estimated using a variety of process and outcome measures of quality. In the second use of DEA, technical efficiency is assessed while controlling for quality. A variety of DEA quality indices and a DEA variety of efficiency models are compared to determine how the treatment of quality influences findings regarding technical efficiency. Controlling for efficiency does matter, with outcome measures having an apparently greater impact than process measures. Given the call for improved quality and better cost containment, controlling for quality is an important contribution to efficiency analysis.     

Lower Bound Restrictions on Intensities in Data Envelopment Analysis

We propose an extension to the basic DEA models that guarantees that if an intensity is positive then it must be at least as large as a pre-defined lower bound. This requirement adds an integer programming constraint known within Operations Research as a Fixed-Charge (FC) type of constraint. Accordingly, we term the new model DEA_FC. The proposed model lies between the DEA models that allow units to be scaled arbitrarily low, and the Free Disposal Hull model that allows no scaling. We analyze 18 datasets from the literature to demonstrate that sufficiently low intensities—those for which the scaled Decision-Making Unit (DMU) has inputs and outputs that lie below the minimum values observed—are pervasive, and that the new model ensures fairer comparisons without sacrificing the required discriminating power. We explain why the low-intensity phenomenon exists. In sharp contrast to standard DEA models we demonstrate via examples that an inefficient DMU may play a pivotal role in determining the technology. We also propose a goal programming model that determines how deviations from the lower bounds affect efficiency, which we term the trade-off between the deviation gap and the efficiency gap.     

Economic development and growth in Colombia: An empirical analysis with super-efficiency DEA and panel data models

In this paper, we analyse economic development and growth through traditional measures (gross domestic product and human development index) and Data Envelopment Analysis (DEA) in Colombian departments over the period 1993–2007. We use a DEA model to measure and rank economic development and growth from different approaches such as poverty, equality and security. The results show considerable variation in efficiency scores across departments. A second-stage panel data analysis with fixed effects reveals that higher levels of economic activity, quality life, employment and security are associated with a higher efficiency score based on the standards of living, poverty, equality and security. All findings of this analysis should demonstrate that economic development and growth could be achieved most effectively through a decrease in poverty, an increase in equality, a reduction in violence, and improved security. This indicates the need to generate effective policies that guarantee the achievement of these elements in the interest of all members of society.     

Online banking performance evaluation using data envelopment analysis and axiomatic fuzzy set clustering

This paper presents a hybrid model to conducting performance measurements for Internet banking by using data envelopment analysis (DEA) and axiomatic fuzzy set (AFS) clustering. For each bank, DEA is applied to select a best combination by computing an aggregated efficiency score based on outputs, such as web metrics and revenue; and inputs, such as equipment, operation cost and employees. Then AFS clustering method is used to classify the best combination into different groups and provide the definitely semantic interpretations for the evaluation results. Identification of operational fitness and business orientation of each firm, in this way, will yield insights into understanding the weaknesses and strengths of banks, which are considering moving into Internet banking.     

Adjusting for cultural differences,a new DEA model applied to a merged bank

The usefulness and application of Data Envelopment Analysis (DEA) efficiency measurements is usually limited by the requirement of consistent operating circumstances. However, in many real world situations this is not the case, so to overcome this problem, this paper reports on a new strategy by inventing a Culturally Adjusted DEA model to benchmark business units that operate under different cultural (business) environments. This is especially useful when these environmental factors are partial causes of inefficiency and can not be simply incorporated into a DEA model as inputs or outputs. A simulation analysis is conducted to examine the effectiveness of the CA-DEA model for controlling these environmental effects. This model is applied to a real life efficiency study of two major financial firms in Canada in 2000, when the two entities started to consolidate and merge their branch networks. Two cultural indices are identified to represent a firm’s unique operating environment, one to capture the nature of a firm’s corporate strategies (Corporate Index), and the other to estimate the effectiveness of a firm’s operational systems (Service Index). The results show that a firm’s corporate culture has a significant influence on its branches’ efficiency and this, we found, is often neglected in such studies. This paper also makes a contribution to the bank merger literature by providing an internal view of the potential benefits that may result from sharing cultural advantages while identifying the true managerial inefficiencies.     

Some extensions of techniques to handle non-discretionary factors in data envelopment analysis

Data Envelopment Analysis (DEA) assumes, in most cases, that all inputs and outputs are controlled by the Decision Making Unit (DMU). Inputs and/or outputs that do not conform to this assumption are denoted in DEA asnon-discretionary (ND) factors. Banker and Morey [1986] formulated several variants of DEA models which incorporated ND with ordinary factors. This article extends the Banker and Morey approach for treating nondiscretionary factors in two ways. First, the model is extended to allow for thesimultaneous presence of ND factors in both the input and the output sets. Second, a generalization is offered which, for the first time, enables a quantitative evaluation ofpartially controlled factors. A numerical example is given to illustrate the different models.The editor for this paper was Wade D. Cook.     

Major weapons procurement: An efficiency-based approach for the selection of fighter jets

This study applies the epsilon-based measure method in data envelopment analysis (DEA) to evaluate processes in procuring fighter jets (FJs), which are indicative of the technology and capability of major weapons. Moreover, multidimensional scaling analysis and sensitivity analysis were included to assess the selection of FJs. The DEA analysis indicates that the overall efficiency of the sample of 26 FJs was 0.824 on average. Besides, procurers rely on price and capability to decide which FJ models to purchase. Overall, the evaluation results can be provided to procurement decision makers even in selecting other types of major weapon in a cost-effective manner.     

A cross-national comparison and taxonomy of DEA-based hospital efficiency studies

This paper provides the first taxonomy of hospital efficiency studies that uses data envelopment analysis (DEA) and related techniques. We provide a systematic review of 79 such studies published from 1984–2004 that represent 12 countries. Only studies written in English are considered. A cross-national comparison reveals significant differences with respect to important study characteristics such as type of DEA model selected and choice of input and output categories. Compared with US studies, European efforts are more likely to measure allocative rather than technical efficiency, use longitudinal data, and use fewer observations. We take a longitudinal perspective that illustrates the life cycle of this research, as well as its diffusion across disciplines. Our taxonomy can be used by policy makers and researchers to review past, and assemble new, DEA models.     

Selecting the Good vs. Rejecting the Bad: Regulatory Focus Effects on Staffing Decision Making

Although staffing decisions are typically conceptualized as motivated by the desire to select the best employees, many managers and human resources practitioners may be more concerned with avoiding bad hires. Regulatory focus theory provides a rich and well‐developed framework for understanding how prevention and promotion orientations shape judgments and decisions. This work is the first to examine this theory's implications for understanding staffing, and it illustrates how these fundamental underlying motivations alter the salience of costs and benefits associated with staffing practices, as well as subjective judgments about individual applicants. Regulatory focus is a function of situational factors as well as individual differences, and predominant motivational orientations also vary with business cycles. This model therefore offers broad cross‐level explanatory power for understanding dynamic factors influencing staffing, as well as a novel perspective on ways to improve the quality of staffing decision making. © 2014 Wiley Periodicals, Inc.     

An index of education and child health in the Horn of Africa

Synthetic indicators are intended to provide an overview of the situation of individuals in a community in relation to a concept to be studied such as quality of life. This study aims to propose a territorial measure and classification of education and child health for countries of the Horn of Africa based on the criteria set out in the United Nations Declaration of the Millennium Development Goals. We construct a synthetic indicator from a multidimensional approach taking into account a large number of variables defined in the Millennium Declaration. For this purpose, we use the Pena distance method for the year 2010, which is the latest year for which data is available. We believe that the situation of the countries in the Horn of Africa differ with respect to the Millennium Development Goal indicators for education and child health. The most striking differences relate to basic quality of life variables.     

Management of nursing homes using data envelopment analysis.

Data envelopment analysis (DEA) is used to evaluate the relative technical efficiency and assist in the management of a chain of nursing homes. As with any DEA model, variables chosen are particularly important. The study looks at two possibly critical issues. The first is the appropriateness of models that include only financial and economic measures to evaluate administrators when quality care is an expected output. The second issue is the appropriateness of using noncontrollable variables, in this case operating income, to evaluate administrators. We show how efficiency scores differ when quality variables and/or operating income are included. We also demonstrate the usefulness of DEA information to both the home administrator and chain managers for improving operating efficiency.     

An insight into the specification of the input-output set for DEA-based bank efficiency measurement

Data envelopment analysis (DEA) has become one of the most widely used instruments for measuring bank efficiency. However, its application encounters many problems, which is evidenced by continuous evolvements in the DEA method so far. Our paper addresses the pitfalls of DEA in the context of measuring bank efficiency, with focus on the specification of performance factors. We aim at examining whether the input-output specification for banks in DEA applications is in consistence with the criteria upon which banks make decisions. Four bank behaviour models which are most popularly employed to determine input and output factors in DEA studies—the intermediation approach, production approach, user cost approach and value added approach—are comprehensively discussed and reviewed. The comparative reflection on the bank behaviour models and the standard DEA models shows that the input-output related pitfalls of a DEA application are associated with its implicitly fixed preference structure, flexible weight determination and limited explanatory power. Due to the pitfalls, the conventional DEA models may fail to capture bank behaviours. In such cases, DEA results can hardly reflect the performance in its true sense, i.e. how banks perform against the goals that they decide to pursue. The findings suggest focusing on (DEA-based) performance measurement from a goal-oriented perspective, i.e. from the point of view of multi criteria decision making.     

Efficiency,growth and market power in the banking industry: New approach to efficient structure hypothesis

We extend the work of Homma, Tsutsui, and Uchida (2014) to provide empirical evidence on nexus of relationships in efficient structure (ES) hypothesis. In this framework, we test causality from cost efficiency to bank growth and then from bank growth to market concentration. We apply this approach to banking industry in Association of South East Asian (ASEAN) over the period of 1999–2014. The efficiency scores have been estimated by employing Slack Based Measurements Data Envelopment Analysis (SMB DEA). We apply Two-step system Generalized Method of Moments (GMM) and Panel Vector Auto Regression (PVAR) to account for endogeneity in estimation models. The results show that cost efficiency enables the banks to grow and obtain higher market share. The resultant growth then leads to higher market concentration/bank market power. There is also some evidence to support for quiet life (QL) hypothesis. Therefore, both ES and QL hypotheses may coexist in ASEAN banking industry.