Safety, efficiency and learning curves in robotic surgery: a human factors analysis
Safety, efficiency and learning curves in robotic surgery: a human factors analysis
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DOI：
10.1007/s00464-015-4671-2
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发表时间：
2016-09-01
影响因子：
3.1
通讯作者：
Anger, Jennifer T.
中科院分区：
文献类型：
--
作者：
Catchpole, Ken;Perkins, Colby;Anger, Jennifer T.
使用效率、学习曲线、工作流集成和严重事件的日益普遍都可能成为采用的障碍。我们探索了一种观察方法和初始诊断，以提高机器人手术中的整体系统性能。在多个手术室中使用两种不同的手术机器人（S和Si）观察了89例机器人手术病例，涉及多个专业（泌尿科、妇科和心脏外科）。主要的测量指标是手术持续时间和血流中断率--被描述为“偏离手术的自然进程，从而可能危及安全或效率”。收集的背景参数包括外科医生的经验水平和培训、手术类型、机器人模型和患者因素。在四个手术阶段（手术室切口前;机器人对接;主要手术干预;控制台后）进行观察。平均每小时9.62次血流中断（95% CI 8.78-10.46）主要由协调、沟通、设备和培训问题引起。手术持续时间和血流中断率因外科医生经验（分别为p = 0.039; p < 0.001）、培训病例（p = 0.012; p = 0.007）和手术类型（均为p < 0.001）而异。某些阶段的流量中断率对机器人模型和患者特征也很敏感。流量中断率对系统上下文很敏感，并产生改进诊断。复杂的手术机器人设备增加了技术故障的可能性，增加了整个团队的沟通要求，并可能降低在手术区域保持视力的能力。这些数据表明了减少培训成本和学习曲线的具体机会。
Expense, efficiency of use, learning curves, workflow integration and an increased prevalence of serious incidents can all be barriers to adoption. We explored an observational approach and initial diagnostics to enhance total system performance in robotic surgery.Eighty-nine robotic surgical cases were observed in multiple operating rooms using two different surgical robots (the S and Si), across several specialties (Urology, Gynecology, and Cardiac Surgery). The main measures were operative duration and rate of flow disruptions-described as 'deviations from the natural progression of an operation thereby potentially compromising safety or efficiency.' Contextual parameters collected were surgeon experience level and training, type of surgery, the model of robot and patient factors. Observations were conducted across four operative phases (operating room pre-incision; robot docking; main surgical intervention; post-console).A mean of 9.62 flow disruptions per hour (95 % CI 8.78-10.46) were predominantly caused by coordination, communication, equipment and training problems. Operative duration and flow disruption rate varied with surgeon experience (p = 0.039; p < 0.001, respectively), training cases (p = 0.012; p = 0.007) and surgical type (both p < 0.001). Flow disruption rates in some phases were also sensitive to the robot model and patient characteristics.Flow disruption rate is sensitive to system context and generates improvement diagnostics. Complex surgical robotic equipment increases opportunities for technological failures, increases communication requirements for the whole team, and can reduce the ability to maintain vision in the operative field. These data suggest specific opportunities to reduce the training costs and the learning curve.