Starren J, Chan S, Tahil F, White T
Departments of Medical Informatics and Radiology, Columbia University, New York, NY, USA.
Proc AMIA Symp. 2000:833-7.
Time-motion (TM) studies are often considered the gold-standard for measurements of the impact of computer systems on task flow and duration. However, in many clinical environments tasks occur too rapidly and have too short of a duration to be captured with conventional paper-based TM methods. Observers may also with to categorize caregiver activities along multiple axes simultaneously. This multi-axial characteristic of clinical activity has been modeled as multiple, parallel finite-state sets and implemented in three computerized data collection tools. Radiology reporting is a domain in which tasks can be characterized by multiple attributes. A radiologist may also switch among multiple tasks in a single minute. The use of these tools to measure the impact of an Automated Speech Recognition (ASR) system on Radiology reporting is presented.
时间动作(TM)研究通常被视为衡量计算机系统对任务流程和持续时间影响的黄金标准。然而,在许多临床环境中,任务发生得太快且持续时间太短,无法用传统的基于纸张的TM方法进行记录。观察者可能还希望同时沿多个轴对护理人员的活动进行分类。临床活动的这种多轴特征已被建模为多个并行的有限状态集,并在三种计算机化数据收集工具中实现。放射学报告是一个任务可以由多个属性表征的领域。放射科医生也可能在一分钟内切换多个任务。本文介绍了使用这些工具来衡量自动语音识别(ASR)系统对放射学报告的影响。