"Usability of data integration and visualization software for multidisciplinary pediatric intensive care: a human factors approach to assessing technology".
"Usability of data integration and visualization software for multidisciplinary pediatric intensive care: a human factors approach to assessing technology".
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DOI：
10.1186/s12911-017-0520-7
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发表时间：
2017-08-14
影响因子：
3.5
通讯作者：
Trbovich P
中科院分区：
文献类型：
--
作者：
Lin YL;Guerguerian AM;Tomasi J;Laussen P;Trbovich P
重症监护临床医生使用多个数据来源，以便为决策提供信息。我们开始评估一种名为T3™的新的交互式数据集成平台，该平台可用于儿科重症监护。支持三个主要功能：通过突出显示数据或估计患者不稳定的风险，跟踪生理信号、显示轨迹和触发决策。我们设计了一项人为因素研究，以确定界面可用性问题，衡量易用性，并描述可能支持或阻碍临床任务的界面功能。22名参与者，包括床边重症监护医生、护士和呼吸治疗师，在模拟实验室环境中测试了T3™接口。20个任务被执行，与真实设置，完全功能，原型，填充生理和治疗干预患者的数据。主要的数据可视化是时间序列，次要的可视化是：1)阴影超出目标值，2)带有夸大的最大值和最小值的小趋势(迷你图)，以及3)16参数指标的条形图。任务完成情况被记录下来，并使用使用错误评分表进行评估。可用性问题根据临床医生的任务和类型进行分类。严重程度评定量表被用来对可用性问题的潜在临床影响进行评级。时间序列支持跟踪单个参数，但部分支持使用多个参数确定患者轨迹。用多参数数据流观察到视觉模式过载。使用阴影和迷你图的自动数据处理经常被忽略，但16参数数据简化算法显示为持久的条形图，在视觉上是直观的。然而，通过选择或自动处理数据，触发辅助设备扭曲了临床医生经常使用的原始数据。因此，临床医生不能依赖新的数据表示法，因为他们不知道它们是如何建立或派生的。通过上下文使用观察到的可用性问题为数据集成软件的具体设计改进提供了方向，可能会减少使用错误并促进安全使用。数据驱动的决策可以受益于在模拟环境中涉及临床医生用户的迭代界面重新设计。这项研究是了解软件如何通过集成的连续监测数据支持临床医生决策的第一步。重要的是，所有可能成为临床医生用户的不同学科对类似平台的测试是了解决策辅助对临床结果的影响所必需的基本步骤。本文的在线版本(doi：10.1186/s12911-0170520-7)包含补充材料，授权用户可以使用。
Intensive care clinicians use several sources of data in order to inform decision-making. We set out to evaluate a new interactive data integration platform called T3™ made available for pediatric intensive care. Three primary functions are supported: tracking of physiologic signals, displaying trajectory, and triggering decisions, by highlighting data or estimating risk of patient instability. We designed a human factors study to identify interface usability issues, to measure ease of use, and to describe interface features that may enable or hinder clinical tasks. Twenty-two participants, consisting of bedside intensive care physicians, nurses, and respiratory therapists, tested the T3™ interface in a simulation laboratory setting. Twenty tasks were performed with a true-to-setting, fully functional, prototype, populated with physiological and therapeutic intervention patient data. Primary data visualization was time series and secondary visualizations were: 1) shading out-of-target values, 2) mini-trends with exaggerated maxima and minima (sparklines), and 3) bar graph of a 16-parameter indicator. Task completion was video recorded and assessed using a use error rating scale. Usability issues were classified in the context of task and type of clinician. A severity rating scale was used to rate potential clinical impact of usability issues. Time series supported tracking a single parameter but partially supported determining patient trajectory using multiple parameters. Visual pattern overload was observed with multiple parameter data streams. Automated data processing using shading and sparklines was often ignored but the 16-parameter data reduction algorithm, displayed as a persistent bar graph, was visually intuitive. However, by selecting or automatically processing data, triggering aids distorted the raw data that clinicians use regularly. Consequently, clinicians could not rely on new data representations because they did not know how they were established or derived. Usability issues, observed through contextual use, provided directions for tangible design improvements of data integration software that may lessen use errors and promote safe use. Data-driven decision making can benefit from iterative interface redesign involving clinician-users in simulated environments. This study is a first step in understanding how software can support clinicians’ decision making with integrated continuous monitoring data. Importantly, testing of similar platforms by all the different disciplines who may become clinician users is a fundamental step necessary to understand the impact on clinical outcomes of decision aids. The online version of this article (doi:10.1186/s12911-017-0520-7) contains supplementary material, which is available to authorized users.
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作者：
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