University of Houston, Houston, TX, USA.
Department of Surgery, Houston Methodist Hospital, 6550 Fannin St Suite 1661, Houston, TX, 77030, USA.
Surg Endosc. 2017 Sep;31(9):3590-3595. doi: 10.1007/s00464-016-5390-z. Epub 2017 Feb 24.
Despite the significant expense of OR time, best practice achieves only 70% efficiency. Compounding this problem is a lack of real-time data. Most current OR utilization programs require manual data entry. Automated systems require installation and maintenance of expensive tracking hardware throughout the institution. This study developed an inexpensive, automated OR utilization system and analyzed data from multiple operating rooms.
OR activity was deconstructed into four room states. A sensor network was then developed to automatically capture these states using only three sensors, a local wireless network, and a data capture computer. Two systems were then installed into two ORs, recordings captured 24/7. The SmartOR recorded the following events: any room activity, patient entry/exit time, anesthesia time, laparoscopy time, room turnover time, and time of preoperative patient identification by the surgeon.
From November 2014 to December 2015, data on 1003 cases were collected. The mean turnover time was 36 min, and 38% of cases met the institutional goal of ≤30 min. Data analysis also identified outlier cases (>1 SD from mean) in the domains of time from patient entry into the OR to intubation (11% of cases) and time from extubation to patient exiting the OR (11% of cases). Time from surgeon identification of patient to scheduled procedure start time was 11 min (institution bylaws require 20 min before scheduled start time), yet OR teams required 22 min on average to bring a patient into the room after surgeon identification.
The SmartOR automatically and reliably captures data on OR room state and, in real time, identifies outlier cases that may be examined closer to improve efficiency. As no manual entry is required, the data are indisputable and allow OR teams to maintain a patient-centric focus.
尽管手术室（OR）时间的花费巨大，但最佳实践仅能实现 70%的效率。更糟糕的是，目前缺乏实时数据。大多数当前的 OR 使用情况计划需要手动输入数据。自动化系统需要在整个机构中安装和维护昂贵的跟踪硬件。本研究开发了一种廉价的自动化 OR 使用情况系统，并分析了来自多个手术室的数据。
将 OR 活动分解为四个房间状态。然后，开发了一个传感器网络，仅使用三个传感器、本地无线网络和数据采集计算机自动捕获这些状态。然后在两个 OR 中安装了两个系统，24/7 进行记录。SmartOR 记录了以下事件：任何房间活动、患者进入/离开时间、麻醉时间、腹腔镜时间、房间周转时间以及外科医生术前对患者进行身份识别的时间。
从 2014 年 11 月到 2015 年 12 月，收集了 1003 例病例的数据。平均周转时间为 36 分钟，38%的病例达到了≤30 分钟的机构目标。数据分析还确定了手术时间从患者进入手术室到插管（11%的病例）和手术时间从拔管到患者离开手术室（11%的病例）这两个领域的离群病例（超出平均值 1 个标准差）。外科医生识别患者到预定手术开始时间的时间为 11 分钟（机构法规要求在预定开始时间前 20 分钟），但 OR 团队平均需要 22 分钟才能在外科医生识别患者后将其带入房间。
SmartOR 自动可靠地捕获 OR 房间状态数据，并实时识别可能需要进一步检查以提高效率的离群病例。由于不需要手动输入，因此数据是无可争议的，并允许 OR 团队以患者为中心的重点。
