Measurement of hospital performance

The rationale for carrying out this research was to address the clear lack of knowledge surrounding the measurement of public hospital performance in Ireland. The objectives of this research were to develop a comprehensive model for measuring hospital performance and using this model to measure the performance of public acute hospitals in Ireland in 2007. Having assessed the advantages and disadvantages of various measurement models the Data Envelopment Analysis (DEA) model was chosen for this research. DEA was initiated by Charnes, Cooper and Rhodes in 1978 and further developed by Fare et al. (1983) and Banker et al. (1984). The method used to choose relevant inputs and outputs to be included in the model followed that adopted by Casu et al. (2005) which included the use of focus groups. The main conclusions of the research are threefold. Firstly, it is clear that each stakeholder group has differing opinions on what constitutes good performance. It is therefore imperative that any performance measurement model would be designed within parameters that are clearly understood by any intended audience. Secondly, there is a lack of publicly available qualitative information in Ireland that inhibits detailed analysis of hospital performance. Thirdly, based on available qualitative and quantitative data the results indicated a high level of efficiency among the public acute hospitals in Ireland in their staffing and non pay costs, averaging 98.5%. As DEA scores are sensitive to the number of input and output variables as well as the size of the sample it should be borne in mind that a high level of efficiency could be as a result of using DEA with too many variables compared to the number of hospitals. No hospital was deemed to be scale efficient in any of the models even though the average scale efficiency for all of the hospitals was relatively high at 90.3%. Arising from this research the main recommendations would be that information on medical outcomes, survival rates and patient satisfaction should be made publicly available in Ireland; that despite a high average efficiency level that many individual hospitals need to focus on improving their technical and scale efficiencies, and that performance measurement models should be developed that would include more qualitative data.

Assessment of national health systems performance in the WHO African region

There is growing peer and donor pressure on African countries to utilize available resources more efficiently in a bid to support the ongoing efforts to expand coverage of health interventions with a view to achieving the health-related Millennium Development Goals. The purpose of this study was to estimate the technical and scale efficiency of national health systems in African continent. Methods The study applied the Data Envelopment Analysis approach to estimate the technical efficiency and scale efficiency among the 53 countries of the African Continent. Results Out of the 38 low-income African countries, 12 countries national health systems manifested a constant returns to scale technical efficiency (CRSTE) score of 100%; 15 countries had a VRSTE score of 100%; and 12 countries had a SE score of one. The average variable returns to scale technical efficiency (VRSTE) score was 95% and the mean scale efficiency (SE) score was 59%; meaning that while on average the degree of inefficiency was only 5%, the magnitude of scale inefficiency was 41%. Of the 15 middle-income countries, 5 countries, 9 countries and 5 countries had CRSTE, VRSTE and SE scores of 100%. Ten countries, six countries and 10 countries had CRSTE, VRSTE and SE scores of less than 100%; and thus, they were deemed inefficient. The average VRSTE (i.e. pure efficiency) score was 97.6%. The average SE score was 49.9%. Conclusion There are large unmet need for health and health-related services among countries of the African Continent. Thus, it would not be advisable for health policy-makers address NHS inefficiencies through reduction in excess human resources for health. Instead, it would be more prudent for them to leverage health promotion approaches and universal access prepaid (tax-based, insurance-based or mixtures) health financing systems to create demand for under utilised health services/interventions with a view to increasing ultimate health outputs to efficient target levels.

Avaliação do desempenho das entidades gestoras dos serviços de resíduos urbanos em Portugal

Dissertação de Mestrado, Gestão Empresarial, Faculdade de Economia, Universidade do Algarve, 2016

The performance of the urban water and wastewater sectors in Australia

Substantial structural reform has occurred in the water and wastewater sectors of Australia's major urban centers over the past two decades. This reform has involved the corporatization of government assets and some vertical and horizontal separation. This paper analyses the performance of these sectors since the mid 1990s. In particular, it uses Malmquist Data Envelopment Analysis (DEA) to determine the different levels of productivity and efficiency improvement over this period. The results point to modest, but positive productivity gains in the larger urban centers, independent of industry structure. Further, it highlights the need to consider exogenous factors that can influence productivity outcomes in an industry generally associated with monopoly characteristics and dependent on water sources that are, to varying extents, unpredictable and uncontrollable.

Operating efficiency and return to scale of Malaysian REITS: The preliminary findings

This paper aims to present preliminary findings on measuring the technical efficiencies using Data Envelopment Analysis (DEA) in Malaysian Real Estate Investment Trusts (REITs) to determine the best practice for operations which include the asset allocation and scale size to improve the performance of Malaysian REITs. Variables identified as input and output will be assessed in this cross section analysis using the operational approach and Variable Return to Scale DEA (VRS-DEA) by focusing on Malaysian REITs for the year 2013. Islamic REITs have higher efficiency score as compared to the conventional REITs for both models. Diversified REITs are more efficient as compared to the specialised REIT using both models. For Model 1, the negative inefficient value is identified in the managerial inefficiency as compared to the scale inefficiency. This shows that inputs are not fully minimised to produce more outputs. However, when other expenses are considered as different input variables, the efficiency score becomes higher from 60.3% to 81.2%. In model 2, scale inefficiency produce greater inefficiency as compared to the managerial efficiency. The result suggests that Malaysian REITs have been operating at the wrong scale of operations as majority of the Malaysian REITs are operating at decreasing return to scale.

EFFICIENCY DYNAMICS ON SCALE-FREE NETWORKS WITH COMMUNITIES

The simple efficiency model is developed on scale-free networks with communities to study the effect of the communities in complex networks on efficiency dynamics. For some parameters, we found that the state of system will transit from a stagnant phase to a growing phase as the strength of community decreases.

The early-stage risk capital market in Scotland since 2000: issues of scale, characteristics and market efficiency

In this paper we provide a detailed profile and analysis of the regional risk capital market in Scotland, using an innovative methodology and specially developed databases which cover risk capital investment in young companies in the periods 2000–04 and 2005–07. This identifies the investment activity of all actors in the market and provides estimates of the total flow of risk capital investment into early-stage Scottish companies over the period. The paper concludes by drawing out the implications for policy makers (providing a more robust evidence base for the development, implementation and monitoring of policy) and for academic researchers (on the methodologies for estimating market scale and efficiency).

Optimizing the efficiency of the sub-map technique for large-scale simultaneous localization and mapping

A technique for optimizing the efficiency of the sub-map method for large-scale simultaneous localization and mapping (SLAM) is proposed. It optimizes the benefits of the sub-map technique to improve the accuracy and consistency of an extended Kalman filter (EKF)-based SLAM. Error models were developed and engaged to investigate some of the outstanding issues in employing the sub-map technique in SLAM. Such issues include the size (distance) of an optimal sub-map, the acceptable error effect caused by the process noise covariance on the predictions and estimations made within a sub-map, when to terminate an existing sub-map and start a new one and the magnitude of the process noise covariance that could produce such an effect. Numerical results obtained from the study and an error-correcting process were engaged to optimize the accuracy and convergence of the Invariant Information Local Sub-map Filter previously proposed. Applying this technique to the EKF-based SLAM algorithm (a) reduces the computational burden of maintaining the global map estimates and (b) simplifies transformation complexities and data association ambiguities usually experienced in fusing sub-maps together. A Monte Carlo analysis of the system is presented as a means of demonstrating the consistency and efficacy of the proposed technique.

Comparing Input- and Output-Oriented Measures of Technical Efficiency to Determine Local Returns to Scale in DEA Models

This paper shows how one can infer the nature of local returns to scale at the input- or output-oriented efficient projection of a technically inefficient input-output bundle, when the input- and output-oriented measures of efficiency differ.

Methods for increasing the computational efficiency of input-output and related large scale matrix operations /

Includes bibliographical references.

A combined neural network and DEA for measuring efficiency of large scale datasets

Data Envelopment Analysis (DEA) is one of the most widely used methods in the measurement of the efficiency and productivity of Decision Making Units (DMUs). DEA for a large dataset with many inputs/outputs would require huge computer resources in terms of memory and CPU time. This paper proposes a neural network back-propagation Data Envelopment Analysis to address this problem for the very large scale datasets now emerging in practice. Neural network requirements for computer memory and CPU time are far less than that needed by conventional DEA methods and can therefore be a useful tool in measuring the efficiency of large datasets. Finally, the back-propagation DEA algorithm is applied to five large datasets and compared with the results obtained by conventional DEA.

Determining the contribution of technical change, efficiency change and scale change to productivity growth in the privatized English and Welsh water and sewerage industry:1985-2000

The water and sewerage industry of England and Wales was privatized in 1989 and subjected to a new regime of environmental, water quality and RPI+K price cap regulation. This paper estimates a quality-adjusted input distance function, with stochastic frontier techniques in order to estimate productivity growth rates for the period 1985-2000. Productivity is decomposed so as to account for the impact of technical change, efficiency change, and scale change. Compared with earlier studies by Saal and Parker [(2000) Managerial Decision Econ 21(6):253-268, (2001) J Regul Econ 20(1): 61-90], these estimates allow a more careful consideration of how and whether privatization and the new regulatory regime affected productivity growth in the industry. Strikingly, they suggest that while technical change improved after privatization, productivity growth did not improve, and this was attributable to efficiency losses as firms appear to have struggled to keep up with technical advances after privatization. Moreover, the results also suggest that the excessive scale of the WaSCs contributed negatively to productivity growth. © 2007 Springer Science+Business Media, LLC.

Evaluation efficiency of large-scale data set with negative data:an artificial neural network approach

Data envelopment analysis (DEA) is the most widely used methods for measuring the efficiency and productivity of decision-making units (DMUs). The need for huge computer resources in terms of memory and CPU time in DEA is inevitable for a large-scale data set, especially with negative measures. In recent years, wide ranges of studies have been conducted in the area of artificial neural network and DEA combined methods. In this study, a supervised feed-forward neural network is proposed to evaluate the efficiency and productivity of large-scale data sets with negative values in contrast to the corresponding DEA method. Results indicate that the proposed network has some computational advantages over the corresponding DEA models; therefore, it can be considered as a useful tool for measuring the efficiency of DMUs with (large-scale) negative data.

Energy efficiency optimization in hardware-constrained large-scale MIMO systems

Large-scale multiple-input multiple-output (MIMO) communication systems can bring substantial improvement in spectral efficiency and/or energy efficiency, due to the excessive degrees-of-freedom and huge array gain. However, large-scale MIMO is expected to deploy lower-cost radio frequency (RF) components, which are particularly prone to hardware impairments. Unfortunately, compensation schemes are not able to remove the impact of hardware impairments completely, such that a certain amount of residual impairments always exists. In this paper, we investigate the impact of residual transmit RF impairments (RTRI) on the spectral and energy efficiency of training-based point-to-point large-scale MIMO systems, and seek to determine the optimal training length and number of antennas which maximize the energy efficiency. We derive deterministic equivalents of the signal-to-noise-and-interference ratio (SINR) with zero-forcing (ZF) receivers, as well as the corresponding spectral and energy efficiency, which are shown to be accurate even for small number of antennas. Through an iterative sequential optimization, we find that the optimal training length of systems with RTRI can be smaller compared to ideal hardware systems in the moderate SNR regime, while larger in the high SNR regime. Moreover, it is observed that RTRI can significantly decrease the optimal number of transmit and receive antennas.