@article{Dehghan_Rajaei_Zandi_Mehdipour_Taki_Hadizadeh Kharazi_Langari_2021, title={Application of Data Envelopment Analysis (DEA) in choosing the proper Magnetic Resonance Imaging (MRI) machine: DEA in choosing proper MRI machine}, volume={8}, url={https://medrech.com/index.php/medrech/article/view/479}, DOI={10.26838/MEDRECH.2021.8.2.479}, abstractNote={<p>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).</p> <p>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.</p&gt;}, number={2}, journal={Medico Research Chronicles}, author={Dehghan, Pooneh and Rajaei, Alireza and Zandi, Reza and Mehdipour, Shahin and Taki, Salar and Hadizadeh Kharazi, Homayoun and Langari, Seyyed Hasan}, year={2021}, month={Apr.}, pages={79-88} }