Application of Data Envelopment Analysis (DEA) in choosing the proper Magnetic Resonance Imaging (MRI) machine

DEA in choosing proper MRI machine

  • Pooneh Dehghan Associate Professor of Radiology Imaging department, Taleghani Hospital Shahid Beheshti University of Medical Sciences
  • Alireza Rajaei Associate Professor of Rheumatology Head of Education Development Center Shahid Beheshti University of Medical Sciences
  • Reza Zandi Assistant Professor of Orthopaedics Department of Orthopedics, Taleghani Hospital Shahid Beheshti University of Medical Sciences
  • Shahin Mehdipour MRI product manager, Fanavari Azmayeshgahi Company; Advanced partner of Siemens Healthineers in Iran.
  • Salar Taki MRI product manager, Fanavari Azmayeshgahi Company; Advanced partner of Siemens Healthineers in Iran.
  • Homayoun Hadizadeh Kharazi Chief radiologist and CEO of Babak Imaging Center
  • Seyyed Hasan Langari Imaging department, Taleghani Hospital, Tehran, Iran
Keywords: Cost-benefit analysis, Data envelopment analysis, MRI machines, MRI

Abstract

This study is aimed to apply one of the decision-making tools, Data Envelopment Analysis (DEA) in the field of imaging in health care for choosing the most efficient model of Siemens MRI machines for clinical purposes. A list of Siemens MRI machines with their corresponding details such as price and technical characteristics were collected as mentioned in the machine booklets and through consultation with Siemens representative in the country. Variables were defined and categorized as input and output and the linear mathematical model for each machine was written and calculated using the super-efficiency model to find the most efficient Siemens MRI machine and rank the available models using DEA. The results showed that the most efficient model of Siemens MRI is Prisma (Super-efficiency score = 2.009302) followed by Skyra (Super-efficiency score = 1.697531) and Sola (Super-efficiency score = 1.683571).

Data Envelopment Analysis (DEA) is recommended as the decision-making tool for selecting advanced technologies in healthcare since it can handle substantial number of variables as input and output and unlike other decision-making tools such as Analytic Hierarchy Process (AHP) which is widely used in this industry, the weight of each variable is determined by the linear mathematical model which makes it reproduceable and reliable.

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Author Biography

Pooneh Dehghan, Associate Professor of Radiology Imaging department, Taleghani Hospital Shahid Beheshti University of Medical Sciences

Associate Professor of Radiology 

Imaging department, Taleghani Hospital

Shahid Beheshti University of Medical Sciences

References

Jones S, Cournane S, Sheehy N, Hederman L. A Business Analytics Software Tool for Monitoring and Predicting Radiology Throughput Performance. Journal of Digital Imaging. 2016;29(6):645-653.

Poulin P, Austin L, Scott C, et al. Introduction of new technologies and decision making processes: a framework to adapt a local health technology Decision Support Program for other local settings. Medical Devices: Evidence and Research. 2013;6:185-193.

eunethta. In. EUnetHTA network2016.

Ribeiro MM, O'Neil JG, Mauricio JC. Caraterização da Tecnologia Por Ressonância Magnética Em Portugal. Lisboa. In. ISBN:978-989-96573-1-1. Lisbon, Lisbone, Portugal2013.

Maia MJo. Decision-making process in radiology: the magnetic resonance example in the TA context. Enterprise and Work Innovation Studies. 2011;7(7):75-101.

Khouja M. The Use of Data Envelopmemt Analysis for Technology Selection. Computers and Industrial Engineering. 1995;28(1):123-131.

Lozano S, Villa G, Eguia I. Data Envelopment Analysis with multiple modes of functioning: Application to reconfigurable manufacturing systems. International Journal of Production Research. 2017;51(10).

Mardania A, Jusohb A, Nora KM, Khalifaha Z, Valipour A, Norhayati Z. Multiple criteria decision-making techniques and their applications – a review of the literature from 2000 to 2014. Economic Research. 2015;28(1):516-571.

Agarwal P, Sahai M, Mishra V, Bag M, Singh V. A review of multi-criteria decision making techniques for supplier evaluation and selection. International Journal of Industrial Engineering Computations. 2011;2:801-811.

Oliviera V, Sobral J, Riveiro MM. Development of a Tool for Selection and Acquisition of Medical Devices based on the Analytic Hierarchy Process. Paper presented at: 2019 IEEE 6th Portuguese Meeting2019; Lisbon.

Koksalmis GH, Calisir C, Durucu M, Calisir F. Selecting an MRI System: A Multi Criteria Decision Making Model for MRI Technicians. International Journal of business Analytics. 2018;5(3):22-30.

Kohl S, Schoenfelder J, Fugener A, Brunner JO. The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals. Health Care Management Science. 2018;22(2):245-286.

Li H, Dong S. Measuring and Benchmarking Technical Efficiency of Public Hospitals in Tianjin, China: A Bootstrap–Data Envelopment Analysis Approach. Inquiry. 2015;52.

Stefko R, Gavurova B, Kocisova K. Healthcare Efficiency Assessment using DEA analysis in the Slovak Republic. Health Economic Review. 2018;8(6).

Ali M, Debela M, Bamud T. Technical efficiency of selected hospitals in Eastern Ethiopia. Health Economics Review. 2017;7(24).

Kiani MM, Khanjankhani K, Mosavi Rigi SA, et al. Efficiency Evaluation of University Hospitals in Bushehr Province before and after the implementation of the Health System Development Plan. Evidence-Based Health Policy, Management and Economics. 2018;2(1):1-11.

Nouraei Motlagh S, Ghasempour S, Yusefzadeh H, Lotfi F, Astaraki P, Saki K. Evaluation of the Productivity of Hospitals Affiliated to Lorestan University of Medical Sciences Using the Malmquist and the Kendrick-Creamer Indices. Shiraz E-Medical Journal. 2019;20(7).

Ebrahim MD, Daneshvar S. Efficiency Analysis of Healthcare System in Lebanon Using Modified Data Envelopment Analysis. Journal of Healthcare Engineering. 2018.

Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision-making units. European Journal of Operational Research. 1978:429-444.

Cooper WW, Seiford LM, Tone K. Data Envelopment Analysis: A Comprehensive Text with Models. Application, References and DEA-Solver Software. New York: Springer; 2000.

Siemens Internal Data Sheet. In. Siemens MRI Internal Data Sheet. Tehran, Iran: Siemens company; 2019.

CITATION
DOI: 10.26838/MEDRECH.2020.8.2.479
Published: 2021-04-26
How to Cite
1.
Dehghan P, Rajaei A, Zandi R, Mehdipour S, Taki S, Hadizadeh Kharazi H, Langari SH. Application of Data Envelopment Analysis (DEA) in choosing the proper Magnetic Resonance Imaging (MRI) machine: DEA in choosing proper MRI machine. Med. res. chronicles [Internet]. 2021Apr.26 [cited 2021May16];8(2):79-8. Available from: https://medrech.com/index.php/medrech/article/view/479
Section
Original Research Article