Institute of Biomaterials and Biomedical Engineering, University of Toronto, Rosebrugh Building (RS), 164 College Street, Room 407, Toronto, ON, M5S 3G9, Canada.
Department of Critical Care Medicine, The Hospital for Sick Children, Canada, 555 University Ave., 2nd Floor, Atrium - Room 2830A, Toronto, ON, M5G 1X8, Canada.
BMC Med Inform Decis Mak. 2017 Aug 14;17(1):122. doi: 10.1186/s12911-017-0520-7.
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.
重症监护临床医生使用多种数据源来辅助决策。我们旨在评估一种新的交互式数据集成平台 T3™，该平台可用于儿科重症监护。它支持三种主要功能：通过突出显示数据或估计患者不稳定的风险来跟踪生理信号、显示轨迹和触发决策。我们设计了一项以人为中心的研究，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特征。
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™界面。使用真实设置、功能齐全的原型完成 20 项任务，原型中填充了生理和治疗干预患者数据。主要数据可视化是时间序列，次要可视化包括：1）阴影超出目标值，2）具有夸大最大值和最小值的迷你趋势（Sparklines），3）16 个参数指标的条形图。任务完成情况通过视频记录，并使用使用错误评分量表进行评估。将可用性问题分类为任务和临床医生类型。使用严重程度评分量表对可用性问题的潜在临床影响进行评分。
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。使用多个参数数据流观察到视觉模式过载。使用阴影和 Sparklines 进行自动数据处理通常会被忽略，但作为持久条形图显示的 16 个参数数据缩减算法在视觉上是直观的。然而，通过选择或自动处理数据，触发辅助工具会扭曲临床医生经常使用的原始数据。因此，临床医生无法依赖新的数据表示形式，因为他们不知道它们是如何建立或得出的。
通过上下文使用观察到的可用性问题，为数据集成软件的有形设计改进提供了方向，这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从涉及临床医生用户的模拟环境中的迭代界面重新设计中受益。这项研究是了解软件如何通过集成连续监测数据来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测试类似平台是了解决策辅助工具对临床结果影响的必要基础步骤。
