Towards actionable knowledge: A systematic analysis of mobile patient portal use

As the aging population grows, chronic illness increases, and our healthcare costs sharply increase, patient portals are positioned as a central component of patient engagement through the potential to change the physician-patient relationship and enable chronic disease self-management. A patient’s engagement in their healthcare contributes to improving health outcomes, and information technologies can support health engagement. In this chapter, we extend the existing literature by discovering design gaps for patient portals from a systematic analysis of negative users’ feedback from the actual use of patient portals. Specifically, we adopt a topic modeling approach, latent Dirichlet allocation (LDA) algorithm, to discover design gaps from online low rating user reviews of a common mobile patient portal, EPIC’s mychart. To validate the extracted gaps, we compared the results of LDA analysis with that of human analysis. Overall, the results revealed opportunities to improve collaboration and to enhance the design of portals intended for patient-centered care. Incorporating these changes may enable the technologies to have stronger position to influence health improvement and wellness

Guidelines For Pursuing and Revealing Data Abstractions

Many data abstraction types, such as networks or set relationships, remain unfamiliar to data workers beyond the visualization research community. We conduct a survey and series of interviews about how people describe their data, either directly or indirectly. We refer to the latter as latent data abstractions. We conduct a Grounded Theory analysis that (1) interprets the extent to which latent data abstractions exist, (2) reveals the far-reaching effects that the interventionist pursuit of such abstractions can have on data workers, (3) describes why and when data workers may resist such explorations, and (4) suggests how to take advantage of opportunities and mitigate risks through transparency about visualization research perspectives and agendas. We then use the themes and codes discovered in the Grounded Theory analysis to develop guidelines for data abstraction in visualization projects. To continue the discussion, we make our dataset open along with a visual interface for further exploration

Supporting Parental Decisions About Genomic Sequencing for Newborn Screening: The NC NEXUS Decision Aid

Advances in genomic sequencing technology have raised fundamental challenges to the traditional ways genomic information is communicated. These challenges will become increasingly complex and will affect a much larger population in the future if genomics is incorporated into standard newborn screening practice. Clinicians, public health officials, and other stakeholders will need to agree on the types of information that they should seek and communicate to parents. Currently, few evidence-based and validated tools are available to support parental informed decision-making. These tools will be necessary as genomics is integrated into clinical practice and public health systems. In this article we describe how the North Carolina Newborn Exome Sequencing for Universal Screening study is addressing the need to support parents in making informed decisions about the use of genomic testing in newborn screening. We outline the context for newborn screening and justify the need for parental decision support. We also describe the process of decision aid development and the data sources, processes, and best practices being used in development. By the end of the study, we will have an evidenced-based process and validated tools to support parental informed decision-making about the use of genomic sequencing in newborn screening. Data from the study will help answer important questions about which genomic information ought to be sought and communicated when testing newborns

Theory-driven Visual Design to Support Reflective Dietary Practice via mHealth: A Design Science Approach

Design for reflection in human-computer interaction (HCI) has evolved from focusing on an abstract and outcome-driven design subject towards exposing procedural or structural reflection characteristics. Although HCI research has recognized that an individual\u27s reflection is a long-lasting, multi-layered process that can be supported by meaningful design, researchers have made few efforts to derive insights from a theoretical perspective about appropriate translation into end-user visual means. Therefore, we synthesize theoretical knowledge from reflective practice and learning and argue for a differentiation between time contexts of reflection that design needs to address differently. In an interdisciplinary design-science-research project in the mHealth nutrition promotion context, we developed theory-driven guidelines for “reflection-in-action” and “reflection-on-action”. Our final design guidelines emerged from prior demonstrations and a final utility evaluation with mockup artifacts in a laboratory experiment with 64 users. Our iterative design and the resulting design guidelines offer assistance for addressing reflection design by answering reflective practice’s respective contextual requirements. Based on our user study, we show that reflection in terms of “reflection- in-action” benefits from offering actionable choice criteria in an instant timeframe, while “reflection-on-action” profits from the structured classification of behavior-related criteria from a longer, still memorable timeframe

How Do UX Practitioners Communicate AI as a Design Material? Artifacts, Conceptions, and Propositions

UX practitioners (UXPs) face novel challenges when working with and communicating artificial intelligence (AI) as a design material. We explore how UXPs communicate AI concepts when given hands-on experience training and experimenting with AI models. To do so, we conducted a task-based design study with 27 UXPs in which they prototyped and created a design presentation for a AI-enabled interface while having access to a simple AI model training tool. Through analyzing UXPs' design presentations and post-activity interviews, we found that although UXPs struggled to clearly communicate some AI concepts, tinkering with AI broadened common ground when communicating with technical stakeholders. UXPs also identified key risks and benefits of AI in their designs, and proposed concrete next steps for both UX and AI work. We conclude with a sensitizing concept and recommendations for design and AI tools to enhance multi-stakeholder communication and collaboration when crafting human-centered AI experiences