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Multiscale Modeling of Surgical Flow in a Large Operating Room Suite: Understanding the Mechanism of Accumulation of Delays in Clinical Practice

机译:大型手术室套件中手术流量的多尺度建模:了解临床实践中延迟累积的机制

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Improving operating room (OR) management in large hospitals has been a challenging problem that remains largely unresolved [7]. Fifty percent of hospital income depends on OR activities and among the main concerns in most institutions is to improve efficiency of a large OR suite that. We advocate that optimizing surgical flow in large OR suites is a complex multifactorial problem with an underlying multiscale structure. Numerous components of the system can combine nonlinearly result in the large accumulated delays observed in daily clinical practice. We propose a multiscale agent-based model (ABM) of surgical flow. We developed a smartOR system that utilizes a dedicated network of non-invasive, wireless sensors to automatically track the state of the OR and accurately computes major indicators of performances such as turnover time between procedures. We show that our model can fit these time measurements and that a multiscale description of the system is possible. We will discuss how this model can be used to quantify and target the main limiting factors in optimizing OR suite efficiency.
机译:在大型医院中,改善手术室(OR)管理一直是一个具有挑战性的问题,至今仍未解决[7]。医院收入的50%取决于手术室活动,在大多数机构中,主要关注的问题是提高大型手术室套件的效率。我们主张在大型手术室中优化手术流程是一个复杂的多因素问题,具有潜在的多尺度结构。系统的许多组件可以非线性组合在一起,从而导致在日常临床实践中观察到较大的累积延迟。我们提出了一种基于多尺度Agent的手术流程模型(ABM)。我们开发了一个smartOR系统,该系统利用非侵入性无线传感器的专用网络自动跟踪OR的状态,并准确计算性能的主要指标,例如程序之间的周转时间。我们证明了我们的模型可以适合这些时间测量,并且可以对系统进行多尺度描述。我们将讨论如何使用此模型来量化和确定优化OR套件效率的主要限制因素。

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