Imprecise DEA framework for evaluating health-care performance of districts

Abstract

Purpose - This paper aims to address performance measurement in the health-care sector, which gains increasing importance for most countries because growing health expenditures and increased quality and competition in the health sector require hospitals to use their resources efficiently. Health policy-makers and health-care managers stress the need for developing a robust performance evaluation methodology for health-care organizations. Design/methodology/approach - This paper presents an imprecise data envelopment analysis (DEA) framework for evaluating the health-care performance of 26 districts in Istanbul, a metropolis with nearly 15 million inhabitants. The proposed methodology takes into account both quantitative and qualitative data represented as linguistic variables for performance evaluation. Moreover, this study reckons that weight flexibility in DEA assessments can lead to unrealistic weighting schemes for some inputs and outputs, which are likely to result in overstated efficiency scores for a number of decision-making units (in here, districts). To overcome this problem, a weight restricted imprecise DEA model that constrains weight flexibility in DEA is proposed. Findings - The proposed imprecise DEA approach sets forth a more realistic decision methodology for evaluating the relative health-care performance and also enables to determine the best district in terms of health-care performance in Istanbul. Originality/value - This paper includes the quality dimension, which has been overlooked in previous studies, into the health-care performance evaluation of districts. Moreover, it circumvents unrealistic weight flexibility which may distort the relative evaluation of health-care performance.