From the Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital-Harvard Medical School, Boston, Massachusetts.
Department of Physiology.
Anesth Analg. 2024 Sep 1;139(3):639-646. doi: 10.1213/ANE.0000000000007003. Epub 2024 Jul 15.
Anesthesia monitors and devices are usually controlled with some combination of dials, keypads, a keyboard, or a touch screen. Thus, anesthesiologists can operate their monitors only when they are physically close to them, and not otherwise task-loaded with sterile procedures such as line or block placement. Voice recognition technology has become commonplace and may offer advantages in anesthesia practice such as reducing surface contamination rates and allowing anesthesiologists to effect changes in monitoring and therapy when they would otherwise presently be unable to do so. We hypothesized that this technology is practicable and that anesthesiologists would consider it useful.
A novel voice-driven prototype controller was designed for the GE Solar 8000M anesthesia patient monitor. The apparatus was implemented using a Raspberry Pi 4 single-board computer, an external conference audio device, a Google Cloud Speech-to-Text platform, and a modified Solar controller to effect commands. Fifty anesthesia providers tested the prototype. Evaluations and surveys were completed in a nonclinical environment to avoid any ethical or safety concerns regarding the use of the device in direct patient care. All anesthesiologists sampled were fluent English speakers; many with inflections from their first language or national origin, reflecting diversity in the population of practicing anesthesiologists.
The prototype was uniformly well-received by anesthesiologists. Ease-of-use, usefulness, and effectiveness were assessed on a Likert scale with means of 9.96, 7.22, and 8.48 of 10, respectively. No population cofactors were associated with these results. Advancing level of training (eg, nonattending versus attending) was not correlated with any preference. Accent of country or region was not correlated with any preference. Vocal pitch register did not correlate with any preference. Statistical analyses were performed with analysis of variance and the unpaired t -test.
The use of voice recognition to control operating room monitors was well-received anesthesia providers. Additional commands are easily implemented on the prototype controller. No adverse relationship was found between acceptability and level of anesthesia experience, pitch of voice, or presence of accent. Voice recognition is a promising method of controlling anesthesia monitors and devices that could potentially increase usability and situational awareness in circumstances where the anesthesiologist is otherwise out-of-position or task-loaded.
麻醉监测仪和设备通常通过组合使用表盘、键盘、键盘或触摸屏进行控制。因此，麻醉师只能在靠近监测仪时才能操作它们，而且在进行无菌操作（如置管或阻滞）时不能进行其他任务加载。语音识别技术已经很普遍，它可能在麻醉实践中具有优势，例如降低表面污染率，并允许麻醉师在无法进行操作时改变监测和治疗。我们假设该技术是可行的，并且麻醉师会认为它是有用的。
为 GE Solar 8000M 麻醉患者监护仪设计了一种新型的语音驱动原型控制器。该设备使用 Raspberry Pi 4 单板计算机、外部会议音频设备、Google Cloud Speech-to-Text 平台和修改后的 Solar 控制器来执行命令。五十名麻醉师对该原型进行了测试。评估和调查是在非临床环境中完成的，以避免因在直接患者护理中使用该设备而产生任何道德或安全问题。所有接受测试的麻醉师都是流利的英语使用者；其中许多人带有母语或原籍国的口音，反映了执业麻醉师群体的多样性。
该原型得到了麻醉师的一致好评。使用李克特量表评估易用性、有用性和有效性，平均值分别为 9.96、7.22 和 8.48。没有人口统计学因素与这些结果相关。进阶的培训水平（例如非主治医生与主治医生）与任何偏好都没有关联。口音或原籍国与任何偏好都没有关联。音高注册与任何偏好都没有关联。使用方差分析和独立样本 t 检验进行统计分析。
使用语音识别控制手术室监测仪受到麻醉师的好评。在原型控制器上很容易添加其他命令。可接受性与麻醉经验水平、音高或口音的存在之间没有发现不良关系。语音识别是一种很有前途的控制麻醉监测仪和设备的方法，它可能会增加在麻醉师无法操作或任务繁重的情况下的可用性和情境意识。
