Dynamic Network Data Envelopment Analysis Model Usage in Measuring and Ranking the Financial Performance of Social Security Hospitals Based on their Size

Sayed Aliakbar  Mousavinezhad-Naini; Mohammad  Tamimi; Allahkaram  Salehi

doi:10.18502/mshsj.v7i2.10683

Sayed Aliakbar Mousavinezhad-Naini Ph.D. student of Financial Management, Department of Management, Masjed-Soleiman Branch, Islamic Azad University, Masjed-Soleiman, Iran
Mohammad Tamimi Assistant professor, Department of Accouting, Dezful Branch, Islamic Azad University, Dezful, Iran
Allahkaram Salehi Assistant Professor, Department of Accouting, Masjed-Soleiman Branch, Islamic Azad University, Masjed-Soleiman, Iran

DOI: https://doi.org/10.18502/mshsj.v7i2.10683

Keywords: Data envelopment analysis mathematical model, Performance, Hospital size, Efficiency

Abstract

Background: Measuring the hospitals financial performance in the health care system is of great importance. This is because hospitals with good financial performance can maintain reliable systems and provide necessary resources to improve quality. The aim of this study was to measure, compare and rank the financial performance of social security hospitals based on their size using a dynamic network data envelopment analysis model.

Methods: This descriptive-analytical study was conducted using information sources in social security hospitals. Data were collected from financial statements from 2016 to 2019. The network efficiency analysis of the units was performed by GAMS 28 software.

Results: According to the findings from 50 hospitals, among the hospitals with less than 100 active beds, the highest financial performance score belonged to Aras Ardabil hospital (0.79) and the lowest to Shabikhani Kashan hospital (0.24). Among the hospitals with 100 to 200 active beds, the highest financial performance score was obtained by Gharzi Malayer hospital (0.78) and the lowest by Imam Reza Islamshahr hospital (0.27). Imam Reza Urmia hospital with a score of 0.87 and Beheshti Shiraz hospital with a score of 0.39, achieved the highest and lowest financial performance in the dynamic network usage, among the hospitals with more than 200 active beds.

Conclusion: Using the dynamic network data envelopment analysis model, the researchers measured the input and output of each decision-making unit over time. They also provided information about the system and internal structure in order to achieve overall efficiency. It is suggested that policymakers and hospital managers abandon the idea that a higher input rate determines a higher level of efficiency. They need to consider the compatibility of hospital size and internal structure in order to improve efficiency.