Huang Albert Y, Joerger Guillaume, Salmon Remi, Dunkin Brian, Sherman Vadim, Bass Barbara L, Garbey Marc
Department of Surgery, Houston Methodist Hospital, 6550 Fannin St, Suite 1661, Houston, TX, 77030, USA.
Methodist Institute of Technology Innovation and Education, Houston, TX, USA.
Surg Endosc. 2016 Aug;30(8):3638-45. doi: 10.1007/s00464-015-4610-2. Epub 2015 Oct 30.
Optimization of OR management is a complex problem as each OR has different procedures throughout the day inevitably resulting in scheduling delays, variations in time durations and overall suboptimal performance. There exists a need for a system that automatically tracks procedural progress in real time in the OR. This would allow for efficient monitoring of operating room states and target sources of inefficiency and points of improvement.
We placed three wireless sensors (floor-mounted pressure sensor, ventilator-mounted bellows motion sensor and ambient light detector, and a general room motion detector) in two ORs at our institution and tracked cases 24 h a day for over 4 months.
We collected data on 238 total cases (107 laparoscopic cases). A total of 176 turnover times were also captured, and we found that the average turnover time between cases was 35 min while the institutional goal was 30 min. Deeper examination showed that 38 % of laparoscopic cases had some aspect of suboptimal activity with the time between extubation and patient exiting the OR being the biggest contributor (16 %).
Our automated system allows for robust, wireless real-time OR monitoring as well as data collection and retrospective data analyses. We plan to continue expanding our system and to project the data in real time for all OR personnel to see. At the same time, we plan on adding key pieces of technology such as RFID and other radio-frequency systems to track patients and physicians to further increase efficiency and patient safety.
手术室管理的优化是一个复杂的问题，因为每个手术室在一天中会进行不同的手术，这不可避免地导致手术安排延迟、手术时长变化以及整体表现欠佳。因此，需要一个能够在手术室中实时自动跟踪手术进程的系统。这将有助于高效监测手术室状态，找出低效率的根源和改进点。
我们在本机构的两个手术室中放置了三个无线传感器（地面安装的压力传感器、安装在呼吸机上的波纹管运动传感器和环境光探测器，以及一个普通房间运动探测器），并在四个多月的时间里每天24小时跟踪手术情况。
我们总共收集了238例手术的数据（107例腹腔镜手术）。还记录了176次周转时间，我们发现病例之间的平均周转时间为35分钟，而机构目标是30分钟。进一步检查发现，38%的腹腔镜手术存在某些方面的欠佳情况，拔管至患者离开手术室的时间是最大的影响因素（占16%）。
我们的自动化系统能够实现强大的无线实时手术室监测以及数据收集和回顾性数据分析。我们计划继续扩展我们的系统，并将数据实时投影给所有手术室工作人员查看。同时，我们计划添加诸如射频识别（RFID）和其他射频系统等关键技术，以跟踪患者和医生，进一步提高效率和患者安全。
