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An area of increasing importance has been the use of quality measures in the study of health care. One specific application involves the performance of nursing homes. Previous studies using data envelopment analysis (DEA) methodology to study this problem have revealed several problems, including the selection of quality output measures and the assignment of weights to these measures that result in minimizing their impact. In this chapter, we will use weight restrictions as an effective means of including important quality measures in the DEA model and allowing the DEA results to discriminate among high- and low-quality performing nursing homes.

As state funding for public higher education has declined, there is a rising demand for accountability. Past studies have relied on indicator ratios to look at the relationship between funding and performance measures. This approach has some inherent problems that make it difficult to identify inefficiencies. This chapter will study efficiency in state systems of higher education by applying data envelopment analysis (DEA). DEA methodology converts multiple variables into a single comprehensive measure of performance efficiency and has the ability to perform benchmarking for the purpose of establishing performance goals. The advantages of DEA modeling will be shown by comparing results with those from a recent study of higher education finance based on publicly available data. DEA is shown to be feasible and implementable for studying state systems of higher education, and provides useful information in identifying “best practice” state systems and guidance for improvement. The value of DEA modeling to state policy makers and education researchers is discussed.

The purpose of this paper is to analyze the productive efficiency and the best operational practices in an armaments manufacturer.

A longitudinal case study is performed using data envelopment analysis (DEA). Using DEA, an assessment of six years in the company manufacturing process is conducted. The research aims at developing an internal benchmark on three production lines of the company.

The results show that only one of the three analyzed production lines increased efficiency over time. With this result, the most efficient production line may be used as a reference in relation to the best operational practices of the company. Moreover, it was found that the current indicators to evaluate efficiency are insufficient and may lead to wrong management decisions.

This research could allow a larger understanding of the factors that really contribute to increased operational efficiency. This is due to internal benchmark assist in the identification of the best practices. The identification of best practices can contribute to enhance the efficiency of inefficient operations without the need for external comparisons.

DEA contributes due to its robustness, for the evaluation of productive efficiency. One of the contributions of this study is to identify opportunities for improvement in key components of the operation through targets, internal benchmarking and robust assessment of productive efficiency.

The aim of this research study is to develop a queue assessment model to evaluate the inflow of walk-in outpatients in a busy public hospital of an emerging economy, in the absence of appointment systems, and construct a dynamic framework dedicated towards the practical implementation of the proposed model, for continuous monitoring of the queue system.

The current study utilizes data envelopment analysis (DEA) to develop a combined queuing–DEA model as applied to evaluate the wait times of patients, within different stages of the outpatients' department at the Combined Military Hospital (CMH) in Lahore, Pakistan, over a period of seven weeks (23rd April to 28th May 2014). The number of doctors/personnel and consultation time were considered as outputs, where consultation time was the non-discretionary output. The two inputs were wait time and length of queue. Additionally, VBA programming in Excel has been utilized to develop the dynamic framework for continuous queue monitoring.

The inadequate availability of personnel was observed as the critical issue for long wait times, along with overcrowding and variable arrival pattern of walk-in patients. The DEA model displayed the “required” number of personnel, corresponding to different wait times, indicating queue build-up.

The current study develops a queue evaluation model for a busy outpatients' department in a public hospital, where “all” patients are walk-in and no appointment systems. This model provides vital information in the form of “required” number of personnel which allows the administrators to control the queue pre-emptively minimizing wait times, with optimal yet dynamic staff allocation. Additionally, the dynamic framework specifically targets practical implementation in resource-poor public hospitals of emerging economies for continuous queue monitoring.