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Coordination of Intraoperative Neurophysiologic Monitoring Technologist and Surgery Schedules

  • Implementation Science & Operations Management
  • Published:
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Abstract

Resource coordination in surgical scheduling remains challenging in health care delivery systems. This is especially the case in highly-specialized settings such as coordinating Intraoperative Neurophysiologic Monitoring (IONM) resources. Inefficient coordination yields higher costs, limited access to care, and creates constraints to surgical quality and outcomes. To maximize utilization of IONM resources, optimization-based algorithms are proposed to effectively schedule IONM surgical cases and technologists and evaluate staffing needs. Data with 10 days of case volumes, their surgery durations, and technologist staffing was used to demonstrate method effectiveness. An iterative optimization-based model that determines both optimal surgery and technologist start time (operational scenario 4) was built in an Excel spreadsheet along with Excel’s Solver settings. It was compared with current practice (operational scenario 1) and optimization solution on only surgery start time (operational scenario 2) or technologist start time (operational scenario 3). Comparisons are made with respect to technologist overtime and under-utilization time. The results conclude that scenario 4 significantly reduces overtime by 74% and under-utilization time by 86% as well as technologist needs by 10%. For practices that do not have flexibility to alter surgeon preference on surgery start time or IONM technologist staffing levels, both scenarios 2 and 3 also result in substantial reductions in technologist overtime and under-utilization. Moreover, IONM technologist staffing options are discussed to accommodate technologist preferences and set constraints for surgical case scheduling. All optimization-based approaches presented in this paper are able to improve utilization of IONM resources and ultimately improve the coordination and efficiency of highly-specialized resources.

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Authors and Affiliations

  1. Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, 55905, Rochester, MN, USA

    Yu-Li Huang

  2. Systems Science and Industrial Engineering, State University of New York, 13902, Binghamton, NY, USA

    Ankit Bansal

  3. Division of Health Policy and Management, School of Public Health, University of Minnesota, 55455, Minneapolis, MN, USA

    Bjorn P. Berg

  4. Department of Neurology, Mayo Clinic, 55905, Rochester, MN, USA

    Christopher P. Tommaso & Ruple S. Laughlin

  5. Division of Health Care Delivery Research, Mayo Clinic, 55905, Rochester, MN, USA

    Yu-Li Huang

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  1. Yu-Li Huang
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  5. Ruple S. Laughlin
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Correspondence to Yu-Li Huang.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The authors have no conflicts of interest regarding this study.

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Huang, YL., Bansal, A., Berg, B.P. et al. Coordination of Intraoperative Neurophysiologic Monitoring Technologist and Surgery Schedules. J Med Syst 46, 67 (2022). https://doi.org/10.1007/s10916-022-01855-7

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  • DOI: https://doi.org/10.1007/s10916-022-01855-7

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