Yu Xiaofen, Wang Xinyu, Hou Wenwen, Wang Zheng
Cancer Center, Department of the Operating Room, Zhejiang Provincial People's Hospital, (Affiliated People's Hospital of Hangzhou Medical College), Hangzhou, 310014, China.
Xiasha Campus of Hangzhou First People's Hospital (Hangzhou Rehabilitation Hospital), Hangzhou, 310018, China.
J Robot Surg. 2025 Aug 13;19(1):482. doi: 10.1007/s11701-025-02651-4.
To develop a fault identification, troubleshooting, and rapid recovery system for da Vinci robot-assisted laparoscopic surgery (referred to as the "robot") to enhance clinical decision-making and ensure patient safety during robotic procedures. We retrospectively analyzed 4,784 surgeries performed using the "Si" robot at our hospital from September 2014 to May 2023, identifying 105 fault instances, and 776 surgeries using the "Xi" robot from April 2022 to May 2023, with 18 fault instances. Fault repair videos, images, and data from both robots were collected, supplemented by technical support from Chindex Medical Limited and related literature. Based on these data, we established a fault identification, troubleshooting, and rapid recovery system. The system's effectiveness was evaluated by comparing fault rates, fault categories, nurses' accuracy in fault identification and troubleshooting, and repair times before and after system implementation. Significant differences were observed in fault rates, fault categories, nurses' accuracy in fault identification and troubleshooting, and repair times before and after system implementation for both the "Si" and "Xi" robots (all P < 0.05). The "Si" robot showed a significant reduction in non-recoverable fault rates (P < 0.05), while the "Xi" robot demonstrated a significant improvement in recoverable fault rates (P < 0.05). The developed system improved nurses' adherence to operational standards, enhanced fault identification accuracy, reduced repair times, increased robotic surgery efficiency, and improved nursing collaboration quality.
开发一种用于达芬奇机器人辅助腹腔镜手术（以下简称“机器人”）的故障识别、故障排除及快速恢复系统，以加强临床决策并确保机器人手术过程中的患者安全。我们回顾性分析了2014年9月至2023年5月在我院使用“Si”机器人进行的4784例手术，识别出105例故障实例，以及2022年4月至2023年5月使用“Xi”机器人进行的776例手术，其中有18例故障实例。收集了两种机器人的故障修复视频、图像和数据，并得到了美中互利医疗有限公司的技术支持及相关文献的补充。基于这些数据，我们建立了一个故障识别、故障排除及快速恢复系统。通过比较系统实施前后的故障率、故障类别、护士在故障识别和故障排除方面的准确性以及修复时间，对该系统的有效性进行了评估。“Si”和“Xi”机器人在系统实施前后的故障率、故障类别、护士在故障识别和故障排除方面的准确性以及修复时间上均存在显著差异（均P<0.05）。“Si”机器人的不可恢复故障率显著降低（P<0.05），而“Xi”机器人的可恢复故障率显著提高（P<0.05）。所开发的系统提高了护士对操作标准的遵守程度，提高了故障识别准确性，缩短了修复时间，提高了机器人手术效率，并改善了护理协作质量。
