Integration of Data Envelopment Analysis and Clustering Methods

Abstract

Data Envelopment Analysis (DEA) has been applied creatively in various study domains to compare and evaluate different Decision Making Units (DMUs) based on multiple input–output attributes. In this paper, the performance of Jordanian public hospitals is assessed via a methodology combining DEA with data mining methods, specifically, clustering. Initially, inputs of inefficient hospitals were altered to check for waste in the allocated resources. Then, the number of inputs–outputs was manipulated to test if the number is strongly influencing the productivity of the DMUs. The number of DMUs used was 27 public hospitals and the applicable efficiency measurements used were constant return to scale (CRS) and variable return to scale (VRS) through the DEAP software. Experiments showed that the efficiency of a hospital might be more meaningfully assessed if it is compared with a group of hospitals that are similar in some factors. More specifically, results of applying the CRS model proved that 77% of the hospitals were efficient. Additionally, we found that the inefficiencies of some hospitals are linked to weak resource utilization. It is concluded that number of inputs–outputs inserted in the efficiency evaluation process impacts the resulted values.

Measuring operational efficiency of isolation hospitals during COVID-19 pandemic using data envelopment analysis: a case of Egypt

“…These results are compatible with other related studies (e.g. Habib and Shahwan, 2020;William et al, 2020;Najadat et al, 2020;Gandhi and Sharma, 2018;Mogha et al, 2015), they suggested that the combination of important elements of health resources (inputs) is one of the factors influencing the operational efficiency of hospitals and managing the demand for the health service, which can help alleviate disturbances in healthcare operations related to the lack and inadequacy of infrastructure and human health resources.…”

“…DEA has been extensively used by researchers and practitioners to evaluate hospital performance and in measuring the efficiency of various types of healthcare organizations including public hospitals (Alsabah et al, 2020;Ngobeni et al, 2020;Najadat et al, 2020;Kocisova et al, 2018;Almiman, 2018;Mitropoulos et al, 2015;Mogha et al, 2015;Najadat and Alsmadi, 2011;Akazili et al, 2008), university and research hospitals (Akkan et al, 2020;Ilgun and Knoca, 2019;Bahrami et al, 2018;Chowdhury and Zelenyuk, 2016) and private hospitals (Habib and Shahwan, 2020;Gandhi and Sharma, 2018;Mogha et al, 2012). Ngobeni et al (2020) measured the technical efficiency of public hospitals in nine provinces in South Africa based on CRS and VRS-DEA during the period 2017-2018.…”

“…During the study period, the level of efficiency increased steadily despite the slow decline in technological development. To evaluate the performance of 30 Jordanian public hospitals, Najadat et al (2020) combined DEA with data mining techniques. The results showed that the BIJ 28,7 efficiency of hospitals became more significant when compared to many analogous hospitals in some factors, and 77% of hospitals have achieved high levels of efficiency.…”

Research on commodity intelligent recommendation system based on data mining

“…Data mining is a kind of information processing technology 4 . It is the process of revealing hidden information from data in a database and analyzing it.…”

The application of data mining technology in hospital performance management under the background of big data

“…Data preparation is generally carried out when building a data warehouse, and this stage can be subdivided into: data integration, data selection, and data preprocessing. The data preparation stage is to obtain a large amount of data from the various heterogeneous systems of the hospital, and perform data association, cleaning, and standardization, so as to organize and simplify the data into standardized data that is easy to identify and has clear semantics [11] . If the data mining is carried out on the basis of the data warehouse, this part of the data will be stored in the data warehouse.…”