Carroll Lauren N, Au Alan P, Detwiler Landon Todd, Fu Tsung-Chieh, Painter Ian S, Abernethy Neil F
Department of Biomedical Informatics and Medical Education, University of Washington, 850 Republican St., Box 358047, Seattle, WA 98109, United States.
Department of Biological Structure, University of Washington, 1959 NE Pacific St., Box 357420, United States.
J Biomed Inform. 2014 Oct;51:287-98. doi: 10.1016/j.jbi.2014.04.006. Epub 2014 Apr 16.
A myriad of new tools and algorithms have been developed to help public health professionals analyze and visualize the complex data used in infectious disease control. To better understand approaches to meet these users' information needs, we conducted a systematic literature review focused on the landscape of infectious disease visualization tools for public health professionals, with a special emphasis on geographic information systems (GIS), molecular epidemiology, and social network analysis. The objectives of this review are to: (1) identify public health user needs and preferences for infectious disease information visualization tools; (2) identify existing infectious disease information visualization tools and characterize their architecture and features; (3) identify commonalities among approaches applied to different data types; and (4) describe tool usability evaluation efforts and barriers to the adoption of such tools.
We identified articles published in English from January 1, 1980 to June 30, 2013 from five bibliographic databases. Articles with a primary focus on infectious disease visualization tools, needs of public health users, or usability of information visualizations were included in the review.
A total of 88 articles met our inclusion criteria. Users were found to have diverse needs, preferences and uses for infectious disease visualization tools, and the existing tools are correspondingly diverse. The architecture of the tools was inconsistently described, and few tools in the review discussed the incorporation of usability studies or plans for dissemination. Many studies identified concerns regarding data sharing, confidentiality and quality. Existing tools offer a range of features and functions that allow users to explore, analyze, and visualize their data, but the tools are often for siloed applications. Commonly cited barriers to widespread adoption included lack of organizational support, access issues, and misconceptions about tool use.
As the volume and complexity of infectious disease data increases, public health professionals must synthesize highly disparate data to facilitate communication with the public and inform decisions regarding measures to protect the public's health. Our review identified several themes: consideration of users' needs, preferences, and computer literacy; integration of tools into routine workflow; complications associated with understanding and use of visualizations; and the role of user trust and organizational support in the adoption of these tools. Interoperability also emerged as a prominent theme, highlighting challenges associated with the increasingly collaborative and interdisciplinary nature of infectious disease control and prevention. Future work should address methods for representing uncertainty and missing data to avoid misleading users as well as strategies to minimize cognitive overload.
已经开发出大量新工具和算法，以帮助公共卫生专业人员分析和可视化传染病控制中使用的复杂数据。为了更好地理解满足这些用户信息需求的方法，我们进行了一项系统的文献综述，重点关注面向公共卫生专业人员的传染病可视化工具领域，特别强调地理信息系统（GIS）、分子流行病学和社会网络分析。本次综述的目的是：（1）确定公共卫生用户对传染病信息可视化工具的需求和偏好；（2）识别现有的传染病信息可视化工具，并描述其架构和功能；（3）确定应用于不同数据类型的方法之间的共性；（4）描述工具可用性评估工作以及采用此类工具的障碍。
我们从五个书目数据库中识别出1980年1月1日至2013年6月30日期间以英文发表的文章。主要关注传染病可视化工具、公共卫生用户需求或信息可视化可用性的文章被纳入综述。
共有88篇文章符合我们的纳入标准。发现用户对传染病可视化工具有不同的需求、偏好和用途，现有的工具也相应地多种多样。工具的架构描述不一致，综述中很少有工具讨论可用性研究或传播计划的纳入情况。许多研究指出了对数据共享、保密性和质量的担忧。现有工具提供了一系列功能，允许用户探索、分析和可视化他们的数据，但这些工具通常用于孤立的应用程序。普遍提到的广泛采用的障碍包括缺乏组织支持、访问问题以及对工具使用的误解。
随着传染病数据的数量和复杂性增加，公共卫生专业人员必须综合高度不同的数据，以促进与公众的沟通，并为有关保护公众健康措施的决策提供信息。我们的综述确定了几个主题：考虑用户的需求、偏好和计算机素养；将工具集成到日常工作流程中；与理解和使用可视化相关的复杂性；以及用户信任和组织支持在采用这些工具中的作用。互操作性也成为一个突出的主题，突出了与传染病控制和预防日益协作和跨学科性质相关的挑战。未来的工作应解决表示不确定性和缺失数据的方法，以避免误导用户，以及尽量减少认知过载的策略。
