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麻醉报警中的用户图像不匹配:认知系统分析。

User image mismatch in anaesthesia alarms: a cognitive systems analysis.

机构信息

a Department of Anesthesia , Faculty of Health Sciences, McMaster University , Hamilton, Ontario , Canada.

出版信息

Ergonomics. 2013;56(10):1525-34. doi: 10.1080/00140139.2013.830151. Epub 2013 Sep 11.

Abstract

UNLABELLED

In this study, principles of Cognitive Systems Engineering are used to better understand the human-machine interaction manifesting in the use of anaesthesia alarms. The hypothesis is that the design of the machine incorporates built-in assumptions of the user that are discrepant with the anaesthesiologist's self-assessment, creating 'user image mismatch'. Mismatch was interpreted by focusing on the 'user image' as described from the perspectives of both machine and user. The machine-embedded image was interpreted through document analysis. The user-described image was interpreted through user (anaesthesiologist) interviews. Finally, an analysis was conducted in which the machine-embedded and user-described images were contrasted to identify user image mismatch. It is concluded that analysing user image mismatch expands the focus of attention towards macro-elements in the interaction between man and machine. User image mismatch is interpreted to arise from complexity of algorithm design and incongruity between alarm design and tenets of anaesthesia practice.

PRACTITIONER SUMMARY

Cognitive system engineering principles are applied to enhance the understanding of the interaction between anaesthesiologist and alarm. The 'user image' is interpreted and contrasted from the perspectives of machine as well as the user. Apparent machine-user mismatch is explored pertaining to specific design features.

摘要

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在这项研究中,使用认知系统工程学原理来更好地理解麻醉警报中体现的人机交互。假设是机器的设计包含了与麻醉师自我评估不一致的内置用户假设,从而产生“用户图像不匹配”。通过关注从机器和用户角度描述的“用户图像”来解释不匹配。通过文档分析解释机器嵌入的图像。通过用户(麻醉师)访谈解释用户描述的图像。最后,进行了一项分析,将机器嵌入和用户描述的图像进行对比,以确定用户图像不匹配。结论是,分析用户图像不匹配会将注意力扩展到人机交互中的宏观元素。解释用户图像不匹配是由于算法设计的复杂性以及报警设计与麻醉实践原则之间的不和谐而产生的。

从业者总结

认知系统工程学原理被应用于增强对麻醉师和警报之间交互的理解。从机器和用户的角度解释和对比“用户图像”。探索与特定设计特征相关的明显机器-用户不匹配。

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