Cohen Tara N, Anger Jennifer T, Shamash Kevin, Catchpole Kenneth R, Avenido Raymund, Ley Eric J, Gewertz Bruce L, Shouhed Daniel
Department of Surgery, Cedars-Sinai Medical Center, 8687 Melrose Ave., Suite G-555, West Hollywood, CA, 90069, USA.
Department of Urology, University of California San Diego, 9400 Campus Point Drive #7897, La Jolla, CA, 92037, USA.
World J Surg. 2022 Jun;46(6):1300-1307. doi: 10.1007/s00268-022-06487-z. Epub 2022 Feb 26.
Challenges associated with turnover time are magnified in robotic surgery. The introduction of advanced technology increases the complexity of an already intricate perioperative environment. We applied a human factors approach to develop systematic, data-driven interventions to reduce robotic surgery turnover time.
Researchers observed 40 robotic surgery turnovers at a tertiary hospital [20 pre-intervention (Jan 2018 to Apr 2018), 20 post-intervention (Jan 2019 to Jun 2019)]. Components of turnover time, including cleaning, instrument and room set-up, robot preparation, flow disruptions, and major delays, were documented and analyzed. Surveys and focus groups were used to investigate staff perceptions of robotic surgery turnover time. A multidisciplinary team of human factors experts and physicians developed targeted interventions. Pre- and post-intervention turnovers were compared.
Median turnover time was 67 min (mean: 72, SD: 24) and 22 major delays were noted (1.1/case). The largest contributors were instrument setup (25.5 min) and cleaning (25 min). Interventions included an electronic dashboard for turnover time reporting, clear designation of roles and simultaneous completion of tasks, process standardization of operating room cleaning, and data transparency through monthly reporting. Post-intervention turnovers were significantly shorter (U = 57.5, p = .000) and ten major delays were noted.
Human factors analysis generated interventions to improve turnover time. Significant improvements were seen post-intervention with a reduction in turnover time by a 26 min and decrease in major delays by over 50%. Future opportunities to intervene and further improve turnover time include targeting pre- and post-operative care phases.
机器人手术中周转时间相关的挑战更为突出。先进技术的引入增加了本就复杂的围手术期环境的复杂性。我们采用人因学方法来制定系统的、基于数据的干预措施,以缩短机器人手术的周转时间。
研究人员在一家三级医院观察了40次机器人手术周转情况[干预前20次(2018年1月至2018年4月),干预后20次(2019年1月至2019年6月)]。记录并分析了周转时间的各个组成部分,包括清洁、器械和房间设置、机器人准备、流程中断和重大延误。通过调查和焦点小组来探究工作人员对机器人手术周转时间的看法。由人因学专家和医生组成的多学科团队制定了针对性的干预措施。比较了干预前后的周转情况。
周转时间中位数为67分钟(均值:72分钟,标准差:24),记录到22次重大延误(每次手术1.1次)。最大的影响因素是器械设置(25.5分钟)和清洁(25分钟)。干预措施包括用于周转时间报告的电子仪表盘、明确角色分工和任务同步完成、手术室清洁流程标准化以及通过月度报告实现数据透明。干预后的周转时间显著缩短(U = 57.5,p = .000),记录到10次重大延误。
人因学分析产生了改善周转时间的干预措施。干预后有显著改善,周转时间减少了26分钟,重大延误减少了50%以上。未来干预及进一步改善周转时间的机会包括针对术前和术后护理阶段。