Optimization and simulation of orthopedic spine surgery cases at Mayo Clinic

Asli Ozen, Yariv Marmor, Thomas Rohleder, Hari Balasubramanian, Jeanne Huddleston, Paul Huddleston

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

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%.

Original languageEnglish (US)
Pages (from-to)157-175
Number of pages19
JournalManufacturing and Service Operations Management
Volume18
Issue number1
DOIs
StatePublished - Dec 1 2016

Keywords

  • Mixed-integer program
  • Operating room scheduling
  • Surgery scheduling

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research

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