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Optimization and Simulation of Orthopedic Spine Surgery Cases at Mayo Clinic

Author

Listed:
  • Asli Ozen

    (University of Massachusetts Amherst, Amherst, Massachusetts 01003)

  • Yariv Marmor

    (ORT Braude College, 2161002 Karmiel, Israel; and Mayo Clinic, Rochester, Minnesota 55905)

  • Thomas Rohleder

    (3185 Rosemary Lane NE, Rochester, Minnesota 55906)

  • Hari Balasubramanian

    (University of Massachusetts Amherst, Amherst, Massachusetts 01003)

  • Jeanne Huddleston

    (Mayo Clinic, Rochester, Minnesota 55905)

  • Paul Huddleston

    (Mayo Clinic, Rochester, Minnesota 55905)

Abstract

Spine surgeries tend to be lengthy (mean time of 4 hours) and highly variable (with some surgeries lasting 18 hours or more). This variability along with patient preferences driving scheduling decisions resulted in both low operating room (OR) utilization and significant overtime for surgical teams at Mayo Clinic. In this paper we discuss the development of an improved scheduling approach for spine surgeries over a rolling planning horizon. First, data mining and statistical analysis was performed using a large data set to identify categories of surgeries that could be grouped together based on surgical time distributions and could be categorized at the time of case scheduling. These surgical categories are then used in a hierarchical optimization approach with the objective of maximizing a weighted combination of OR utilization and net profit. The optimization model is explored to consider trade-offs and relationships among utilization levels, financial performance, overtime allowance, and case mix. The new scheduling approach was implemented via a custom Web-based application that allowed the surgeons and schedulers to interactively identify best surgical days with patients. A pilot implementation resulted in a utilization increase of 19% and a reduction in overtime by 10%.

Suggested Citation

  • Asli Ozen & Yariv Marmor & Thomas Rohleder & Hari Balasubramanian & Jeanne Huddleston & Paul Huddleston, 2016. "Optimization and Simulation of Orthopedic Spine Surgery Cases at Mayo Clinic," Manufacturing & Service Operations Management, INFORMS, vol. 18(1), pages 157-175, February.
    • Handle: RePEc:inm:ormsom:v:18:y:2016:i:1:p:157-175
    DOI: 10.1287/msom.2015.0564
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    References listed on IDEAS

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    Cited by:

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    2. Grigory Korzhenevich & Anne Zander, 2024. "Leveraging the potential of the German operating room benchmarking initiative for planning: A ready-to-use surgical process data set," Health Care Management Science, Springer, vol. 27(3), pages 328-351, September.
    3. Wang, Lien & Demeulemeester, Erik & Vansteenkiste, Nancy & Rademakers, Frank E., 2024. "Capacity and surgery partitioning: An approach for improving surgery scheduling in the inpatient surgical department," European Journal of Operational Research, Elsevier, vol. 313(1), pages 112-128.
    4. Tinglong Dai & Sridhar Tayur, 2020. "OM Forum—Healthcare Operations Management: A Snapshot of Emerging Research," Manufacturing & Service Operations Management, INFORMS, vol. 22(5), pages 869-887, September.
    5. Gökalp, E. & Gülpınar, N. & Doan, X.V., 2023. "Dynamic surgery management under uncertainty," European Journal of Operational Research, Elsevier, vol. 309(2), pages 832-844.
    6. Jérémie Gallien & Alan Scheller-Wolf, 2016. "Introduction to the Special Issue on Practice-Focused Research," Manufacturing & Service Operations Management, INFORMS, vol. 18(1), pages 1-4, February.

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