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.
摘要：
在医疗保健提供系统中，手术计划中的资源协调仍然具有挑战性。在高度专业化的环境中尤其如此，例如协调术中神经生理监测（IONM）资源。效率低下的协调会产生更高的成本，获得护理的机会有限，并对手术质量和结果产生限制。为了最大限度地利用IONM资源，提出了基于优化的算法，以有效地安排IONM手术病例和技术人员，并评估人员需求。10天病例卷的数据，他们的手术持续时间，技术人员的人员配备被用来证明方法的有效性。在Excel电子表格中建立了一个基于迭代优化的模型，该模型确定了最佳手术和技术人员的开始时间（操作方案4）以及Excel的求解器设置。将其与当前实践（操作场景1）和仅在手术开始时间（操作场景2）或技术专家开始时间（操作场景3）上的优化解决方案进行比较。对技术人员的加班时间和未充分利用时间进行了比较。结果得出的结论是，情景4显着减少了74％的加班时间和86％的未充分利用时间，以及技术专家的需求减少了10％。对于不能灵活改变外科医生对手术开始时间或IONM技术人员人员配备水平的偏好的做法，方案2和方案3也导致技术专家加班和利用率不足的大幅减少。此外，讨论了IONM技术人员的人员配备选项，以适应技术人员的偏好并为手术病例安排设置约束。本文提出的所有基于优化的方法都能够提高IONM资源的利用率，并最终提高高度专业化资源的协调和效率。
