Assessing the Relative Performance of Nurses Using Data Envelopment Analysis Matrix (DEAM)

Assessing employee performance is one of the most important issue in healthcare management services. Because of their direct relationship with patients, nurses are also the most influential hospital staff who play a vital role in providing healthcare services. In this paper, a novel Data Envelopment Analysis Matrix (DEAM) approach is proposed for assessing the performance of nurses based on relative efficiency. The proposed model consists of five input variables (including type of employment, work experience, training hours, working hours and overtime hours) and eight output variables (the outputs are amount of hours each nurse spend on each of the eight activities including documentation, medical instructions, wound care and patient drainage, laboratory sampling, assessment and control care, follow-up and counseling and para-clinical measures, attendance during visiting and discharge suction) have been tested on 30 nurses from the heart department of a hospital in Iran. After determining the relative efficiency of each nurse based on the DEA model, the nurses’ performance were evaluated in a DEAM format. As results the nurses were divided into four groups; superstars, potential stars, those who are needed to be trained effectively and question marks. Finally, based on the proposed approach, we have drawn some recommendations to policy makers in order to improve and maintain the performance of each of these groups. The proposed approach provides a practical framework for hospital managers so that they can assess the relative efficiency of nurses, plan and take steps to improve the quality of healthcare delivery

Quantifying the effects of modelling choices on hospital efficiency measures: A meta-regression analysis

It has often been argued that the results of efficiency analyses in health care are influenced by the modelling choices made by the researchers involved. In this paper we use meta-regression analysis in an attempt to quantify the degree to which modelling factors influence efficiency estimates. The data set is derived from 253 estimated models reported in 95 empirical analyses of hospital efficiency in the 22-year period from 1987 to 2008. A meta-regression model is used to investigate the degree to which differences in mean efficiency estimates can be explained by factors such as: sample size; dimension (number of variables); parametric versus non-parametric method; returns to scale (RTS) assumptions; functional form; error distributional form; input versus output orientation; cost versus technical efficiency measure; and cross-sectional versus panel data. Sample size, dimension and RTS are found to have statistically significant effects at the 1% level. Sample size has a negative (and diminishing) effect on efficiency; dimension has a positive (and diminishing) effect; while the imposition of constant returns to scale has a negative effect. These results can be used in improving the policy relevance of the empirical results produced by hospital efficiency studies.

Spatial Data Envelopment Analysis Method for the Evaluation of Regional Infrastructure Disparities

Purpose—to achieve a more detailed assessment of regional differences, exploring regional infrastructure and human capital usage efficiency and to display analysis capabilities of spatial data efficient frontier method.Design/methodology/approach—the data envelopment analysis (DEA) is applied to find the efficient frontier, which extends the application of production function of the regions. This method of mathematical programming optimization allows assessing the effectiveness of the regional spatial aspects presented. In recent studies this method is applied for evaluating the European Union regional policy issues.Findings—the application of DEA reveals its feasibility for regional input and output studies to evaluate more detailed and more reasonable fund allocation between Lithuanian regions. This analysis shows that in the comparatively efficient Lithuanian regions, such as Vilnius and Klaipėda, “the bottleneck” of usage of transport infrastructure and regional specific human capital is reached. It is stated that decision-making units could enhance region attractiveness for private investors by improving indirect factors in these regions. For practical significance of the study the results are compared with German regional analysis, conducted by Schaffer and other researchers (2011).Practical implications—the practical value of this work is based on giving more accurate planning tools for fund allocation decisions in Lithuanian regions while planning infrastructure and human capital development. The regional indicators were analyzed for 2010.Research type—case study

Assessing the queuing process using data envelopment analysis:an application in health centres

Queuing is one of the very important criteria for assessing the performance and efficiency of any service industry, including healthcare. Data Envelopment Analysis (DEA) is one of the most widely-used techniques for performance measurement in healthcare. However, no queue management application has been reported in the health-related DEA literature. Most of the studies regarding patient flow systems had the objective of improving an already existing Appointment System. The current study presents a novel application of DEA for assessing the queuing process at an Outpatients’ department of a large public hospital in a developing country where appointment systems do not exist. The main aim of the current study is to demonstrate the usefulness of DEA modelling in the evaluation of a queue system. The patient flow pathway considered for this study consists of two stages; consultation with a doctor and pharmacy. The DEA results indicated that waiting times and other related queuing variables included need considerable minimisation at both stages

The Size and Service Offering Efficiencies of U.S. Hospitals.

Hospital productivity has been a research topic for over two decades. We expand on this research to include measures of dis/economies of scope. By using the Free Coordination Hull (FCH) we are able to determine if hospitals in our sample can become more efficient if they provide more services (diseconomies of scope) or if two smaller hospitals with a reallocation of resources could become more efficient (economies of scope). Using data from the American Hospital Association for the years 2004-2007, we found variations among hospital markets (measured by the Core Based Statistical Area). We can determine whether dis/economies of scope exist by comparing the results from two linear programming problems. Focusing on four markets: Los Angeles, Philadelphia, Madison, WI, and New Orleans we found variations in how best these hospitals operating in these markets could change in order to increase both scale and scope efficiencies. This approach could be used by policy makers and managers in order to reduce costs by sharing, reducing, or expanding services in hospitals. Findings from a study such as this should aid reform programs by providing more information on the sources of hospital inefficiency.Hospital, Efficiency, Economies of Scope, Hospital Markets

Managing healthcare performance in analytical framework

Purpose – The purpose of the paper is to develop an integrated framework for performance management of healthcare services. Design/methodology/approach – This study develops a performance management framework for healthcare services using a combined analytic hierarchy process (AHP) and logical framework (LOGFRAME). The framework is then applied to the intensive care units of three different hospitals in developing nations. Numerous focus group discussions were undertaken, involving experts from the specific area under investigation. Findings – The study reveals that a combination of outcome, structure and process-based critical success factors and a combined AHP and LOGFRAME-based performance management framework helps manage performance of healthcare services. Practical implications – The proposed framework could be practiced in hospital-based healthcare services. Originality/value – The conventional approaches to healthcare performance management are either outcome-based or process-based, which cannot reveal improvement measures appropriately in order to assure superior performance. Additionally, they lack planning, implementing and evaluating improvement projects that are identified from performance measurement. This study presents an integrated approach to performance measurement and implementing framework of improvement projects

Measuring the Efficiency of Tertiary Care Hospitals and Medical Colleges in Punjab, an Application of Non- Parametric Approach DEA

Efficient utilization of resources required that the healthcare units should operate at their full capacity and increase their efficiency to yields best quality healthcare services and more savings. To measure the capacity utilization of medical collages/attached teaching hospitals in Punjab, the study incorporated the Data Envelopment Analysis (DEA). This is a cross sectional study for the academic year 2015, by using the primary data collected by “Punjab Economic Research Institute (PERI)“. The main objective to address the efficiency issue in this study is to provide empirical evidence for public policy to provide for tertiary health care facilities. The results showed that 78% medical colleges in a sample data are unity, indicating that they are perfectly utilizing their resources while the remaining 22% have more close to unity, showing that they have only small capacity to increase their output within available resources. In case of teaching hospitals, there are 56% hospitals in Punjab, which are not operating at their maximum level of output indicating that they have the capacity to provide more services. While 44% are using their resources efficiently. The higher efficiency score of medical colleges/teaching hospital in provincial capital reflected the fact that Decision Making Units (DMUs) in Lahore are relatively more efficient in the management of resources. In the second stage, by using a Tobit regression, the inefficacies are regressed against various indicators of inputs and outputs. Finally, the finding of this study suggests some policy recommendation to make the medical colleges/teaching hospitals more resource effective in order to accelerate the highest efficiency scores

Health care performance management : insights from applications of data envelopment analysis

The comprehensive measurement of efficiency and performance in the Health Service in the UK has become one of the most important managerial developments of recent years. The reasons for this development were examined, particularly in relation to the difficulties involved with performance assessment in such a context. The most widely utilised techniques were evaluated from the perspective of the Health Care Manager and a number of serious limitations were identified. In response to these limitations, the technique of Data Envelopment Analysis was evaluated as an alternative. It has been proposed as an appropriate and useful tool for the assessment of efficiency, although the literature on DEA showed limited practical application to public sector services in the UK. The many facets of the technique were investigated and literature on its application to hospital data was reviewed. A two-stage application procedure for the DEA technique was developed in response to this evaluation, to be used in the measurement hospital efficiency. The procedure was based on a deep theoretical understanding of the DEA methodology. The most important elements of the process were related to selection of the initial sample, the identification of the variables to be included in the DEA model and the definition of the weight restrictions to be incorporated. Input from Health Care Managers was used to guide the application and data from a sample of acute hospitals in Scotland was utilised in the analysis. The application procedure showed how the practicalities of the DEA technique could be enhanced, in particular through the inclusion of weight restrictions. This led to the development of efficiency strategies for the inefficient hospitals, which could be related to the policy objectives or managerial structure of the hospitals in the sample. It was concluded that there were many potential benefits of the DEA approach to efficiency assessment and the two-stage application procedure defined here, which could be seen to fulfil many of the requirements of the Health Care Manager. It was determined that combining theoretical and practical issues can enhance the applicability of the DEA methodology