Abstract

As Artificial Intelligence (AI) conversational agents become widespread, people are increasingly using them for health information seeking. The use of off-the-shelf conversational agents for health information seeking could place high metacognitive demands (the need for extensive monitoring and control of one's own thought process) on individuals, which could compromise their experience of seeking health information. However, currently, the specific demands that arise while using conversational agents for health information seeking, and the strategies people use to cope with those demands, remain unknown. To address these gaps, we conducted a think-aloud study with 15 participants as they sought health information using our off-the-shelf AI conversational agent. We identified the metacognitive demands such systems impose, the strategies people adopt in response, and propose considerations for designing beyond off-the-shelf interfaces to reduce these demands and support better user experiences and affordances in health information seeking.

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Abstract

We were approached by a group of healthcare providers who are involved in the care of chronic patients looking for potential technologies to facilitate the process of reviewing patient-generated data during clinical visits. Aiming at understanding the healthcare providers’ attitudes towards reviewing patient-generated data, we (1) conducted a focus group with a mixed group of healthcare providers. Next, to gain the patients’ perspectives, we (2) interviewed eight chronic patients, collected a sample of their data and designed a series of visualizations representing patient data we collected. Last, we (3) sought feedback on the visualization designs from healthcare providers who requested this exploration. We found four factors shaping patient-generated data: data & context, patient’s motivation, patient’s time commitment, and patient’s support circle. Informed by the results of our studies, we discussed the importance of designing patient-generated visualizations for individuals by considering both patient and healthcare provider rather than designing with the purpose of generalization and provided guidelines for designing future patient-generated data visualizations.

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Abstract

Lens visualization has been a prominent research area in the visualization community, fueled by the continuous need to mitigate visual clutter and occlusion resulting from the increasingly large datasets. Interactive lenses for 3D data, particularly, challenge visualization designers to conceive design strategies that facilitates the analysis of dense, multifaceted data with spatial referents. Given their relevance, the overarching research goal of this dissertation is to investigate how visualization lenses may support 3D data exploration and analysis—across both conventional and immersive environments. To this end, we begin with (1) modeling lenses by conducting a systematic review of existing lenses operating within spatial contexts. From this survey, we derive a design space that captures core design dimensions and choices involved in constructing spatially-embedded visualization lenses. Building upon this theoretical foundation, we proceed with (2) designing lenses, through the design, development, and evaluation of four immersive lenses tailored to support distinct forms of 3D data analysis: i) a deformable quadric lens for heterogeneous feature analysis, ii) a dual-imagery lens for multivariate, multi-geometry analysis, iii) a view-dependent lens for reservoir uncertainty analysis, and iv) a shape-conformal lens for spatiotemporal urban data analysis. Afterwards, we probe (3) evaluating lenses by complementing our prior theoretical and practical endeavors with empirical evidence from a controlled study comparing the efficacy of four lens-based designs in supporting time-varying urban data analysis. Throughout the course of our research agenda, we document domain-specific lessons learned and translate them into generalizable design guidelines intended to inform the broader visualization community. Finally, this dissertation concludes by suggesting future research directions for advancing visualization lenses in 3D data exploration and analysis—across both traditional and immersive environments. In the long term, we hope that this dissertation serve valuable reference points for visualization researchers and practitioners operating at either a theoretical, design, or empirical level.

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Abstract

Establishing strong patient-provider communication during clinical visits can have positive impacts on patient health outcomes. On the other hand, miscommunication between patients and healthcare providers can cause harm or in extreme cases cause death to patients. Factors such as healthcare providers' limited time, inefficient clinical environments, and patients' difficulties in communicating their concerns can be the causes of this miscommunication. In this thesis, I explore the design of visualizations to facilitate communication between healthcare providers and patients during clinical visits. In the first part (i) of this thesis, I present the results of a literature review I have conducted to expand our understanding of patients' and providers' communication challenges during in-clinic visits. In the second phase (ii) I discuss the results of interviews with healthcare providers and I contrast and compare patients' and providers' perspectives in the context of each other to unveil the roots of their communication challenges. Among the communication challenges we identified, I focus on exploring the challenges and the realities patients and healthcare providers face tracking and sharing patient-generated health data. In the third part of this dissertation (iii), I discuss the results of a series of interviews and focus groups with patients and healthcare providers I have conducted to gain a better understanding of patient-generated data communication challenges. I leverage this understanding to propose potential visualization designs representing patient-generated data collections to improve the process of reviewing and communicating these data between patients and healthcare providers. In the fourth part of this dissertation (iv), I discuss the results of the interviews with healthcare providers seeking their reflection on the proposed visualization designs. Finally, in collaboration with our healthcare provider team in Alberta Healthcare Services, I implement the prototypes of a number of carefully selected visualization designs. In the last part of this dissertation (v), I outline insights, lessons learned, and future research directions that arise from these studies and the design process. I hope this research provides more support for considering patients' and healthcare providers' individualities when designing technologies and visualizations in healthcare settings.

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