Integration of Data Envelopment Analysis and Clustering Methods

Hassan Najadat, Ahmad Alaiad, Sanaa Abu Alasal, Ghadeer Anwar Mrayyan and Izzat Alsmadi
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Hassan Najadat: Jordan University of Science & Technology, Jordan2Texas A& M, San Antonio, USA
Ahmad Alaiad: Jordan University of Science & Technology, Jordan2Texas A& M, San Antonio, USA
Sanaa Abu Alasal: Jordan University of Science & Technology, Jordan2Texas A& M, San Antonio, USA
Ghadeer Anwar Mrayyan: Jordan University of Science & Technology, Jordan2Texas A& M, San Antonio, USA
Izzat Alsmadi: Jordan University of Science & Technology, Jordan2Texas A& M, San Antonio, USA

Journal of Information & Knowledge Management (JIKM), 2020, vol. 19, issue 01, 1-19

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.

Keywords: Data envelopment analysis; constant return to scale model; variable return to scale model; hospitals efficiency; decision making units; data mining (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1142/S0219649220400067

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