Evaluation of Purchase of MRI-Machine

This presentation provides an overview of the factors to consider when evaluating the purchase of an MRI-Machine. It covers trends and fitness of the machine, time value of money and cost, advancements, and questions and options to consider. The presentation also includes references for further reading.

Evaluation the Purchase of
MRI-Machine
Student’s Name
Institutional Affiliations

 Determination of the current MRI trends
 Performance Capability
 Risks of breakdown or failure
 Expected increase in health outcomes
 Operational efficiency
 Clinical outcome
 Patient volume
 Staff management and retention
 Financial performance
Trends and Fitness of the
Machine

 Expected lifetime of the MRI-Machine
 Availability of upgrading
 Asset retention
 Organizational suitability
 Installation cost
 Training cost
 Energy consumption
Time Value of Money and
Cost

 Neuro imaging
 Cardiac imaging
 Vascular imaging
 Body imaging
 Breast imaging
 Pediatric imaging
 MSK imaging
Advancements

 It is important to ask questions and consider
different options in the market to determine
whether the machine is best suited for the
organization. Some of these questions which
relate to outcome include:
 Clinical excellence
 Patient satisfaction
 Operational efficiency
 Strategic growth
 Capital planning
 MRI Manufacturers
Questions and Options

 Ahmad, R., Potter, L. C., Rich, A., Simonetti, O. P., & Ting, S. T. (2018). U.S. Patent
Application No. 15/877,741.
 Chakravarty, A., & Debnath, J. (2015). Life cycle costing as a decision making tool for
technology acquisition in radio-diagnosis. Medical Journal Armed Forces India, 71(1),
38-42.
 Coelho, D. H., Tang, Y., Suddarth, B., & Mamdani, M. (2018). MRI surveillance of
vestibular schwannomas without contrast enhancement: clinical and economic
evaluation. The Laryngoscope, 128(1), 202-209.
 Hell, E., Rasche, V., & Ulrici, J. (2018). U.S. Patent Application No. 15/868,179.
 Hutchinson, R., & Lotan, Y. (2017). Cost consideration in utilization of multiparametric
magnetic resonance imaging in prostate cancer. Translational andrology and urology,
6(3), 345.
 Moradi, E., Pepe, A., Gaser, C., Huttunen, H., Tohka, J., & Alzheimer's Disease
Neuroimaging Initiative. (2015). Machine learning framework for early MRI-based
Alzheimer's conversion prediction in MCI subjects. Neuroimage, 104, 398-412.
 Schnack, H. G., Nieuwenhuis, M., van Haren, N. E., Abramovic, L., Scheewe, T. W.,
Brouwer, R. M., ... & Kahn, R. S. (2014). Can structural MRI aid in clinical
classification? A machine learning study in two independent samples of patients with
schizophrenia, bipolar disorder and healthy subjects. Neuroimage, 84, 299-306.
 Wang, Y., Chattaraman, V., Kim, H., & Deshpande, G. (2015). Predicting Purchase
Decisions Based on Spatio-Temporal Functional MRI Features Using Machine Learning.
IEEE Transactions on Autonomous Mental Development, 7(3), 248-255.
References