Adriana Arcia, PhD, RN (University of San Diego, San Diego, CA) Natalie Benda, PhD (Weill Cornell Medical College, New York, NY) Amanda Makulec, MPH (Data Visualization Society, Washington, DC) Danny Wu, PhD, MS (University of Cincinnati, Cincinnati, OH)
Since the Health Information Technology for Economic and Clinical Health (HITECH) Act incentivized the utilization of clinical informatics systems, the volume of health data that learning health systems are collecting and aggregating on patients has grown exponentially. Patients are also generating their own data through digital health tools that they and the health system want to leverage to improve health. In parallel with vast quantities of data, the 21st Century Cures Act requires that electronic health information be freely accessible and authorizes penalties for those who block data from patients. There are also non-clinical streams of data (e.g., environmental exposure, disease transmission) that are increasingly accessible to the public. Though barriers to accessing data are being lifted, the data are often available in a raw format that is rarely comprehensible without a significant amount of pre-processing. Once processed, data may still require contextualization to the person or the community of interest to make it actionable. Therefore, the development and evaluation of visualizations of health data for lay audiences is an important area of inquiry. We define lay audiences as those interacting with informatics tools in a non-professional capacity (e.g., patients, caregivers, community members, research participants). To create useful, usable visualizations, the design process must be grounded in scientific principles, such as human-centered design. Human-centered design “is an approach to interactive systems development that aims to make systems usable and useful by focusing on the users, their needs and requirements, and by applying human factors/ergonomics, and usability knowledge and techniques. This approach enhances effectiveness and efficiency [of the interventions designed], improves human well-being, user satisfaction, accessibility and sustainability; and counteracts possible adverse effects of use on human health, safety and performance.” The next steps to supporting design of human-centered visualizations of health data are to disseminate and implement mechanisms for developing visualization tools for supporting productive interactions, generating insights, and extracting value from the data.
Despite well-intentioned investment, there are substantive gaps in using rigorous methodology and appropriate human-centered design to support the development of meaningful health visualizations for patients, family caregivers, and the public. A systematic review of patient-facing visualizations demonstrated that almost half of the included studies did not involve relevant lay audience end users at all in the design or evaluation process, and there was a lack of consistency and rigor in the methods described for those that did involve lay audiences. Moreover, others that involved lay audience end users had fundamental misconceptions regarding human-centered design, such as assuming that soliciting user requests was sufficient. Studies that do involve lay audiences also often fail to go beyond an initial assessment of user preferences, leading to the development of interventions that are not actionable, understandable, or do not lead to the desired outcomes.3 When studies do involve lay audience participants in visualization development, participants are often not representative of the diversity of the intended audience’s characteristics such as level of education, race/ethnicity, age, health status, literacy/numeracy, experience with technology, and physical abilities.
The onset of the COVID-19 pandemic spurred innovation in the development and accelerated adoption of remote tools for clinical care (i.e., telehealth) and research. For example, many participatory design sessions moved online leading to the development of innovative mechanisms for obtaining user feedback that mitigate geographic barriers. There is an opportunity to blend remote, hybrid, and in-person approaches to gain inclusive perspectives for human-centered design of health data visualizations. Designing for lay audiences is inherently challenging because their needs are often not well-articulated and their requests may even violate basic design principles. Methods that work well for domain experts (e.g., clinicians) may not be appropriate for lay audiences.
Given these challenges, methods are needed to understand when to adhere to individuals’ preferences but also take into account when their preferences may not match their cognitive support needs. Therefore, we aspire to bring together best practices across informatics, human factors, human computer interaction, cognitive science, data science, and computer science to develop visualizations that are inclusive of and informed by the target audience's needs. Now is the time for consensus-building and dissemination of best practices for participatory human-centered design applications, methods, and evaluation tactics. Our goals with this focus issue on visualizations for lay audiences are to highlight: 1) rigorous human-centered design methods, and 2) noteworthy applications of those methods. We welcome a variety of supplementary materials to help illustrate applications such as those that manualize processes, provide datasets and code, and include replications of the visualizations or infographics tested to encourage reproducibility.
This focus issue welcomes original research and applications papers, perspectives, brief communications, case reports, and review articles addressing a range of topics associated with this area including, but not limited to, the following areas:
Please email any questions regarding the focus issue or submissions to Ashley Anderson (jamia.editorialoffice@jjeditorial.com) at the editorial office with the subject line “Focus Issue on Visualization for Lay Audiences”.
