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Managing magnetic resonance imaging machines: support tools for scheduling and planning

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Abstract

We devise models and algorithms to estimate the impact of current and future patient demand for examinations on Magnetic Resonance Imaging (MRI) machines at a hospital radiology department. Our work helps improve scheduling decisions and supports MRI machine personnel and equipment planning decisions. Of particular novelty is our use of scheduling algorithms to compute the competing objectives of maximizing examination throughput and patient-magnet utilization. Using our algorithms retrospectively can help (1) assess prior scheduling decisions, (2) identify potential areas of efficiency improvement and (3) identify difficult examination types. Using a year of patient data and several years of MRI utilization data, we construct a simulation model to forecast MRI machine demand under a variety of scenarios. Under our predicted demand model, the throughput calculated by our algorithms acts as an estimate of the overtime MRI time required, and thus, can be used to help predict the impact of different trends in examination demand and to support MRI machine staffing and equipment planning.

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Notes

  1. Our data was collected and analyzed with the approval of the SCH Institutional Review Board.

  2. The makespan is the maximum completion time over all jobs.

  3. We suspect magnet availability is a mitigating factor.

  4. Using magnet on-times as an estimate of arrival rates does not take reneging into account. However, based on interviews with technologists and doctors at SCH, the occurrence of reneging was insignificant.

  5. We note that the increase could also be due to other factors such as improved efficiency in conducting examinations or an increased proportion of shorter MRI examinations required.

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Acknowledgements

We thank the referees and editor for their extremely helpful comments. Also, we thank Eyjo Ásgeirsson and Cliff Stein for many useful discussions and Tom Crockett for his help with the computational experiments. Finally, we thank the technologists and schedulers at SCH for their valuable information and insights.

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Authors and Affiliations

  1. Mathematics Department, The College of William & Mary, Williamsburg, VA, USA

    Adam P. Carpenter, Lawrence M. Leemis, Alan S. Papir & David J. Phillips

  2. Seattle Chilldren’s Hospital, Seattle, WA, USA

    Grace S. Phillips

Authors
  1. Adam P. Carpenter
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  2. Lawrence M. Leemis
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  3. Alan S. Papir
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  4. David J. Phillips
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  5. Grace S. Phillips
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Corresponding author

Correspondence to David J. Phillips.

Additional information

This paper was supported in part by NSF grants DMS-0703532, DUE-0849955, and a NASA/VSGC New Investigator grant.

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Carpenter, A.P., Leemis, L.M., Papir, A.S. et al. Managing magnetic resonance imaging machines: support tools for scheduling and planning. Health Care Manag Sci 14, 158–173 (2011). https://doi.org/10.1007/s10729-011-9153-z

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  • DOI: https://doi.org/10.1007/s10729-011-9153-z

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