Technical efficiency of private sector hospitals in India using data envelopment analysis

Abstract

Purpose The purpose of this paper is to ascertain the performance of Indian hospitals in recent past and derive meaningful insights for policy makers and practicing managers in this area. Design/methodology/approach This paper analyses the technical efficiency of select Indian private hospitals using three related methodologies: data envelopment analysis (DEA), Malmquist Productivity Index (MPI) and Tobit regression. Two output variables (i.e. total income and profit after tax) and four input variables (i.e. cost of labour, net fixed assets, current assets and other operating expenses) were selected for the purpose of the study. Findings DEA analysis has shown that 14 out of 37 hospitals are found to be efficient under the Cooper and Rhodes model of DEA and 20 out of 37 hospitals are efficient under the Banker, Charles and Cooper model of DEA. The empirical results pertaining to MPI indicate an overall productivity progress in the private Indian hospital industry during the study period, which is largely due to technological advancement in the industry. Tobit regression demonstrates that chain affiliated, specialized and multi-city located hospitals exhibit a higher technical efficiency. Research limitations/implications This study has a limitation with reference to the unavailability of data on the input and output parameters of the model. The data related to the number of beds, number of doctors, number of nurses, etc., were not available for the period under consideration. Originality/value This study seems to be one of the few studies applying productivity and performance analysis using DEA, MPI and Tobit regression for the Indian private hospital industry.

Systematic review and meta-analysis of public hospital efficiency studies in Gulf region and selected countries in similar settings

“…Predominant inputs were the capital (number of beds) and labour (number of health workers with different professional categories) variables. Three studies [37–39] used capital expenses in the inputs, and one study [41] included prices of capital and labour. Numerous output dimensions were used in the efficiency models: the mean was 3.7 (range: 1–7) and the median was 3.5 variables.…”

“…Moreover, overestimates of efficiency scores on DEA can occur if the number of hospitals is small relative to the number of input and output variables [49]. Several empirical analyses have had a small sample size in comparison with the number of the variables used and reported high-efficiency scores [27, 31, 35, 39, 40]. To remedy such problems, Hollingsworth suggested that the number of units used in efficiency assessment should be at least three times the combined counts of inputs and outputs altogether [49].…”

Innovative methods of evaluating business efficiency: a comparative study

“…Thus, the cost of returns, wage efficiency, and material efficiency was quantified by applying the modified matrix system. The adequacy of selecting the above inputs and outputs was confirmed after comparing scientific studies focused on applying DEA models in the context of evaluating business effectiveness (Fenyves et al, 2015;Buyukkeklik et al, 2016;Gandhi and Sharma, 2018). The correlation analysis for each combination of variables confirmed that selected input and output variables for efficiency evaluations are appropriate since neither high nor low correlation rates were found.…”

Managers’ Competences in Private Hospitals for Investment Decisions during the COVID-19 Pandemic

“…The training of health managers reflects the concern with the leadership capacity [28] that supports the organizational strategy [29]. Clarity of objectives [30], motivational factors [31], communication [25], decentralized decision-making [14], the establishment of bonds relational [32], transformational leadership [33] and innovation [34] are fundamental for the training of health managers [35].…”