Rule-based task assignment and scheduling of medical examinations for heterogeneous MRI machines

Xiaodan Wu, Juan Li and Mohammad T. Khasawneh

Journal of Simulation, 2020, vol. 14, issue 3, 189-203

Abstract: The management of heterogeneous magnetic resonance imaging (MRI) machines involves a set of complex processes, which encompass multiple examination types, different machine functionalities, and patient priorities. We design three types of rules for task assignment, patient scheduling, and appointment duration in heterogeneous MRI facilities with the consideration of patient priorities to increase system throughput, decrease patient waiting time, and improve machine utilisation. We use a real-world case study to investigate the performance of the proposed rules using discrete event simulation. The simulation results show that allocating appointments with similar durations to the same MRI machines reduces patient waiting time effectively. Moreover, queue pooling is an effective method when appointment durations are within a specific range. Finally, changing the appointment duration based on both service time and number of patients can be more effective in increasing the overall machine utilisation.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/17477778.2019.1623988 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjsmxx:v:14:y:2020:i:3:p:189-203

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjsm20

DOI: 10.1080/17477778.2019.1623988

Access Statistics for this article

Journal of Simulation is currently edited by Christine Currie

More articles in Journal of Simulation from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().