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Implementation strategies of a contract-based MRI examination reservation process for stroke patients

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  • Geng, Na
  • Xie, Xiaolan
  • Jiang, Zhibin

Abstract

Timely imaging examinations are critical for stroke patients due to the potential life threat. We have proposed a contract-based Magnetic Resonance Imaging (MRI) reservation process [1] in order to reduce their waiting time for MRI examinations. Contracted time slots (CTS) are especially reserved for Neural Vascular Department (NVD) treating stroke patients. Patients either wait in a CTS queue for such time slots or are directed to Regular Time Slot (RTS) reservation. This strategy creates “unlucky” patients having to wait for lengthy RTS reservation. This paper proposes and analyzes other contract implementation strategies called RTS reservation strategies. These strategies reserve RTS for NVD but do not direct patients to regular reservations. Patients all wait in the same queue and are served by either CTS or RTS on a FIFO (First In First Out) basis. We prove that RTS reservation strategies are able to reduce the unused time slots and patient waiting time. Extensive numerical results are presented to show the benefits of RTS reservation and to compare various RTS reservation strategies.

Suggested Citation

  • Geng, Na & Xie, Xiaolan & Jiang, Zhibin, 2013. "Implementation strategies of a contract-based MRI examination reservation process for stroke patients," European Journal of Operational Research, Elsevier, vol. 231(2), pages 371-380.
    • Handle: RePEc:eee:ejores:v:231:y:2013:i:2:p:371-380
    DOI: 10.1016/j.ejor.2013.05.043
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    References listed on IDEAS

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    1. Lev, Benjamin & Revesz, George & Shea, Francis & Caltagirone, Robert, 1976. "Patient flow analysis and the delivery of radiology service+," Socio-Economic Planning Sciences, Elsevier, vol. 10(4), pages 159-166.
    2. Geng, Na & Xie, Xiaolan, 2012. "Optimizing contracted resource capacity with two advance cancelation modes," European Journal of Operational Research, Elsevier, vol. 221(3), pages 501-512.
    3. Schütz, Hans-Jörg & Kolisch, Rainer, 2012. "Approximate dynamic programming for capacity allocation in the service industry," European Journal of Operational Research, Elsevier, vol. 218(1), pages 239-250.
    4. Robert A. Shumsky & Fuqiang Zhang, 2009. "Dynamic Capacity Management with Substitution," Operations Research, INFORMS, vol. 57(3), pages 671-684, June.
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    6. Alexander Erdelyi & Huseyin Topaloglu, 2009. "Computing protection level policies for dynamic capacity allocation problems by using stochastic approximation methods," IISE Transactions, Taylor & Francis Journals, vol. 41(6), pages 498-510.
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    Cited by:

    1. Gang Du & Xinyue Li & Hui Hu & Xiaoling Ouyang, 2018. "Optimizing Daily Service Scheduling for Medical Diagnostic Equipment Considering Patient Satisfaction and Hospital Revenue," Sustainability, MDPI, vol. 10(9), pages 1-23, September.
    2. Paola Cappanera & Filippo Visintin & Carlo Banditori & Daniele Feo, 2019. "Evaluating the long-term effects of appointment scheduling policies in a magnetic resonance imaging setting," Flexible Services and Manufacturing Journal, Springer, vol. 31(1), pages 212-254, March.
    3. Bowen Pang & Xiaolei Xie & Feng Ju & James Pipe, 2022. "A dynamic sequential decision-making model on MRI real-time scheduling with simulation-based optimization," Health Care Management Science, Springer, vol. 25(3), pages 426-440, September.

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