Toward automated workflow analysis and visualization in clinical environments
Toward automated workflow analysis and visualization in clinical environments
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DOI：
10.1016/j.jbi.2010.05.015
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发表时间：
2011-06-01
影响因子：
4.5
通讯作者：
Patel, Vimla L.
中科院分区：
文献类型：
--
作者：
Vankipuram, Mithra;Kahol, Kanav;Patel, Vimla L.
患者安全方面的失误与临床工作流程中的意外扰动有关。工作流分析的有效性对于了解这些扰动对患者结局的影响至关重要。用于工作流分析的典型方法，如人种学观察和访谈，在同时从不同角度捕捉活动的能力方面受到限制。这种限制，加上临床环境的复杂性和动态性质，使得理解临床工作流程的细微差别变得困难。本研究提出的方法旨在提供一种捕捉和分析工作流的量化手段。所采取的方法利用使用无线电识别标签和观察收集的临床团队的运动和位置的记录。此数据用于对重症监护环境中的活动进行建模。然后可以在3D虚拟现实环境中重放检测到的活动，以进行进一步的分析和培训。使用这种方法，建议的系统增强了现有的工作流分析方法，允许捕获复杂和动态环境中的工作流。该系统通过一组15个模拟的临床活动进行测试，当组合在一起时，这些活动代表了创伤病房的工作流程。自动识别活动的平均识别率达到87.5%。(C)2010 Elsevier Inc.保留所有权利。
Lapses in patient safety have been linked to unexpected perturbations in clinical workflow. The effectiveness of workflow analysis becomes critical to understanding the impact of these perturbations on patient outcome. The typical methods used for workflow analysis, such as ethnographic observations and interviewing, are limited in their ability to capture activities from different perspectives simultaneously. This limitation, coupled with the complexity and dynamic nature of clinical environments makes understanding the nuances of clinical workflow difficult. The methods proposed in this research aim to provide a quantitative means of capturing and analyzing workflow. The approach taken utilizes recordings of motion and location of clinical teams that are gathered using radio identification tags and observations. This data is used to model activities in critical care environments. The detected activities can then be replayed in 3D virtual reality environments for further analysis and training. Using this approach, the proposed system augments existing methods of workflow analysis, allowing for capture of workflow in complex and dynamic environments. The system was tested with a set of 15 simulated clinical activities that when combined represent workflow in trauma units. A mean recognition rate of 87.5% was obtained in automatically recognizing the activities. (C) 2010 Elsevier Inc. All rights reserved.