Improving the Delivery of Robotic Surgery

描述（由申请人提供）：机器人允许广泛的外科医生成功利用腹腔镜方法进行许多复杂手术。因此，自2007年以来，全世界使用机器人进行的手术几乎增加了两倍。然而，存在与手术机器人的使用相关的大量学习曲线，其可能取决于手术技术以外的元素。事实上，高质量，高效和安全的护理是通过手术室工作人员，任务，技术，环境和组织之间的复杂互动而产生的。尽管在其他手术中有越来越多的证据基础，但在这些系统模型的背景下，还没有发表研究检查手术机器人。我们的目标是将人为因素系统分析技术应用于机器人手术，以便更好地了解护理效率低下或次优的地方。我们将捕捉流程中断-小错误或偏离护理-这将为旨在改善机器人手术流程的干预奠定基础，从而提高与手术机器人相关的安全性，质量，效率和学习。在具体目标1中，我们试图识别和定义机器人手术过程中发生的流动中断。我们将通过对雪松-西奈医学中心100台机器人手术的连续观察来做到这一点。在具体目标2中，我们寻求识别可能影响外科医生学习曲线的导致血流中断的技术和非技术因素。我们试图确定流量如何根据手术类型和外科医生数量而变化。我们还将确定患者因素是否会影响流量中断率。我们将使用多变量统计分析，通过外科医生数量和专业、患者因素（如合并症和体重指数）、手术类型以及我们在整个观察过程中可能识别的其他因素来分析血流中断率。在具体目标3中，我们寻求进行试点干预，以帮助减少流量中断。我们将首先进行定性分析，将与类似原因或系统问题相关的中断分组。最后，通过与人为因素专家和外科医生的讨论，我们将制定一系列解决方案，作为试点干预措施加以实施和测试。然后，我们将通过对五个机器人案例的观察和分析来衡量这种干预的初步效果。

DESCRIPTION (provided by applicant): Robots allow a broad range of surgeons to utilize a laparoscopic approach successfully for many complex operations. As a consequence, the use of robotic procedures performed worldwide has nearly tripled since 2007. However, there is a substantial learning curve associated with the use of surgical robots that may depend on elements other than surgical technique. In fact, high quality, efficient and safe care arises through complex interactions between operating room staff, tasks, technology, environment and organization. Despite an increasing evidence base in other surgeries, there is an absence of published studies examining surgical robotics within the context of these systems models. Our goal is to apply human factors systems analysis techniques to robotic surgery in order to better understand where care is inefficient or suboptimal. We will capture flow disruptions - small errors or deviations from care - which will lay the foundation for an intervention that seeks to improve the flow of robotic surgery, and thus enhance the safety, quality, efficiency and learning associated with surgical robotics. In Specific Aim 1, we seek to identify and define flow disruptions that occur during robotic surgery. We will do this through consecutive observations of 100 robotic operations at Cedars- Sinai Medical Center. In Specific Aim 2, we seek to identify technical and non-technical factors that contribute to flow disruptions which may impact the surgeon learning curve. We seek to determine how flow varies by procedure type and by surgeon volume. We will also determine whether patient factors impact rates of flow disruptions. We will use multi-variable statistical analysis to analyze the rate of flow disruptions by surgeon volume and specialty, patient factors (such as comorbidities and body mass index), operation type, and other factors we may identify throughout the observation process. In Specific Aim 3, we seek to perform a pilot intervention that will assist in reducing flow disruptions. We will firs conduct a qualitative analysis in which we group disruptions together that relate to similar causes or system problems. Finally, through discussions with human factors experts and surgeons, we will develop a range of solutions that will be put in place and tested as a pilot intervention. We will then measure the preliminary effect of this intervention through the observation and analysis of five robotic cases.