Westenskow D R, Orr J A, Simon F H, Bender H J, Frankenberger H
Department of Anesthesiology, University of Utah, Salt Lake City.
Anesthesiology. 1992 Dec;77(6):1074-9. doi: 10.1097/00000542-199212000-00005.
The proliferation of monitors and alarms in the operating room may lead to increased confusion and misdiagnosis unless the information provided is better organized. Intelligent alarm systems are being developed to organize these alarms, on the assumption that they will shorten the time anesthesiologists need to detect and correct faults. This study compared the human response time (the time between the sounding of an alarm and the resolution of a fault) when anesthesiologists used a conventional alarm system and when they used an intelligent alarm system. In a simulated operating room environment, we asked 20 anesthesiologists to resolve seven breathing circuit faults as quickly as possible. Human response time was 62% faster, decreasing from 45 to 17 s, when the intelligent alarm system was used. The standard deviations in response time were only half as large for the intelligent alarm system. It appears that the computer-based neural network in the intelligent alarm system diagnosed faults more rapidly and consistently than did the anesthesiologists. This study indicates that breathing circuit faults may be more rapidly corrected when the anesthesiologist is guided by intelligent alarms.
手术室中监测器和警报器的激增可能会导致混乱加剧和误诊，除非所提供的信息能得到更好的整理。目前正在开发智能警报系统来整理这些警报，其假设是这些系统将缩短麻醉医生检测和纠正故障所需的时间。本研究比较了麻醉医生使用传统警报系统和智能警报系统时的人体反应时间（从警报响起至故障解决的时间）。在模拟手术室环境中，我们要求20名麻醉医生尽快解决七个呼吸回路故障。使用智能警报系统时，人体反应时间快了62%，从45秒降至17秒。智能警报系统的反应时间标准差仅为传统警报系统的一半。智能警报系统中基于计算机的神经网络似乎比麻醉医生能更快速、更一致地诊断故障。这项研究表明，在智能警报的引导下，麻醉医生可以更快速地纠正呼吸回路故障。
