Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
School of Nursing, Columbia University, New York, New York, USA.
J Am Med Inform Assoc. 2024 Jan 18;31(2):525-530. doi: 10.1093/jamia/ocad137.
Data visualizations can be effective and inclusive means for helping people understand health-related data. Yet numerous high-quality studies comparing data visualizations have yielded relatively little practical design guidance because of a lack of clarity about what communicators want their audience to accomplish. When conducting rigorous evaluations of communication (eg, applying the ISO 9186 method), describing the process simply as evaluating "comprehension" or "interpretation" of visualizations fails to do justice to the true range of outcomes being studied. We present newly developed taxonomies of outcome measures and tasks that are guiding a large-scale systematic review of the health numbers communication literature. Using these taxonomies allows a designer to determine whether a specific data presentation format or feature supports or inhibits the desired audience cognitions, feelings, or behaviors. We argue that taking a granular, outcomes-based approach to designing and evaluating information visualization research is essential to deriving practical, actionable knowledge from it.
数据可视化是帮助人们理解健康相关数据的有效且包容性的手段。然而，由于沟通者不清楚他们希望受众完成什么目标，许多对数据可视化进行了严格评估的高质量研究并没有产生多少实际的设计指导。在对沟通进行严格评估（例如，应用 ISO 9186 方法）时，简单地将过程描述为评估对可视化的“理解”或“解释”，并不能公正对待正在研究的真实结果范围。我们提出了新开发的结果衡量标准和任务分类法，这些分类法正在指导对健康数字沟通文献的大规模系统综述。使用这些分类法可以让设计者确定特定的数据呈现格式或功能是否支持或抑制了预期的受众认知、情感或行为。我们认为，采取基于结果的细粒度方法来设计和评估信息可视化研究对于从中得出实用的、可操作的知识至关重要。
