Journal of the American Medical Informatics Association

Arleo, Alessio; Chen, Annie T; Gotz, David; Kandaswamy, Swaminathan; Bernard, Jürgen

doi: 10.1093/jamia/ocae249pmid: 39429003

Marquard, Jenna; Austin, Robin; Rajamani, Sripriya

doi: 10.1093/jamia/ocae125pmid: 38833256

ObjectiveThis study experimentally evaluated how well lay individuals could interpret and use 4 types of electronic health record (EHR) patient-facing immunization visualizations.Materials and MethodsParticipants (n = 69) completed the study using a secure online survey platform. Participants viewed the same immunization information in 1 of 4 EHR-based immunization visualizations: 2 different patient portals (Epic MyChart and eClinicWorks), a downloadable EHR record, and a clinic-generated electronic letter (eLetter). Participants completed a common task, created a standard vaccine schedule form, and answered questions about their perceived workload, subjective numeracy and health literacy, demographic variables, and familiarity with the task.ResultsThe design of the immunization visualization significantly affected both task performance measures (time taken to complete the task and number of correct dates). In particular, those using Epic MyChart took significantly longer to complete the task than those using eLetter or eClinicWorks. Those using Epic MyChart entered fewer correct dates than those using the eLetter or eClinicWorks. There were no systematic statistically significant differences in task performance measures based on the numeracy, health literacy, demographic, and experience-related questions we asked.DiscussionThe 4 immunization visualizations had unique design elements that likely contributed to these performance differences.ConclusionBased on our findings, we provide practical guidance for the design of immunization visualizations, and future studies. Future research should focus on understanding the contexts of use and design elements that make tables an effective type of health data visualization.

Zhang, Siwei; Strayer, Nick; Vessels, Tess; Choi, Karmel; Wang, Geoffrey W; Li, Yajing; Bejan, Cosmin A; Hsi, Ryan S; Bick, Alexander G; Velez Edwards, Digna R; Savona, Michael R; Phillips, Elizabeth J; Pulley, Jill M; Self, Wesley H; Hopkins, Wilkins Consuelo; Roden, Dan M; Smoller, Jordan W; Ruderfer, Douglas M; Xu, Yaomin

Goodwin, Sarah; Saunders, Thom; Aitken, Joanne; Baade, Peter; Chandrasiri, Upeksha; Cook, Dianne; Cramb, Susanna; Duncan, Earl; Kobakian, Stephanie; Roberts, Jessie; Mengersen, Kerrie

doi: 10.1093/jamia/ocae212pmid: 39135444

ObjectiveThe Australian Cancer Atlas (ACA) aims to provide small-area estimates of cancer incidence and survival in Australia to help identify and address geographical health disparities. We report on the 21-month user-centered design study to visualize the data, in particular, the visualization of the estimate uncertainty for multiple audiences.Materials and MethodsThe preliminary phases included a scoping study, literature review, and target audience focus groups. Several methods were used to reach the wide target audience. The design and development stage included digital prototyping in parallel with Bayesian model development. Feedback was sought from multiple workshops, audience focus groups, and regular meetings throughout with an expert external advisory group.ResultsThe initial scoping identified 4 target audience groups: the general public, researchers, health practitioners, and policy makers. These target groups were consulted throughout the project to ensure the developed model and uncertainty visualizations were effective for communication. In this paper, we detail ACA features and design iterations, including the 3 complementary ways in which uncertainty is communicated: the wave plot, the v-plot, and color transparency.DiscussionWe reflect on the methods, design iterations, decision-making process, and document lessons learned for future atlases.ConclusionThe ACA has been hugely successful since launching in 2018. It has received over 62 000 individual users from over 100 countries and across all target audiences. It has been replicated in other countries and the second version of the ACA was launched in May 2024. This paper provides rich documentation for future projects.

Scholich, Till; Raj, Shriti; Lee, Joyce; Newman, Mark W

doi: 10.1093/jamia/ocae183pmid: 39003519

ObjectivesTo understand healthcare providers’ experiences of using GlucoGuide, a mockup tool that integrates visual data analysis with algorithmic insights to support clinicians’ use of patientgenerated data from Type 1 diabetes devices.Materials and MethodsThis qualitative study was conducted in three phases. In Phase 1, 11 clinicians reviewed data using commercial diabetes platforms in a think-aloud data walkthrough activity followed by semistructured interviews. In Phase 2, GlucoGuide was developed. In Phase 3, the same clinicians reviewed data using GlucoGuide in a think-aloud activity followed by semistructured interviews. Inductive thematic analysis was used to analyze transcripts of Phase 1 and Phase 3 think-aloud activity and interview.Results3 high level tasks, 8 sub-tasks, and 4 challenges were identified in Phase 1. In Phase 2, 3 requirements for GlucoGuide were identified. Phase 3 results suggested that clinicians found GlucoGuide easier to use and experienced a lower cognitive burden as compared to the commercial diabetes data reports that were used in Phase 1. Additionally, GlucoGuide addressed the challenges experienced in Phase 1.DiscussionThe study suggests that the knowledge of analytical tasks and task-specific visualization strategies in implementing features of data interfaces can result in tools that lower the perceived burden of engaging with data. Additionally, supporting clinicians in contextualizing algorithmic insights by visual analysis of relevant data can positively influence clinicians’ willingness to leverage algorithmic support.ConclusionTask-aligned tools that combine multiple data-driven approaches, such as visualization strategies and algorithmic insights, can improve clinicians’ experience in reviewing device data.

Li, Zuotian; Liu, Xiang; Tang, Ziyang; Jin, Nanxin; Zhang, Pengyue; Eadon, Michael T; Song, Qianqian; Chen, Yingjie V; Su, Jing

doi: 10.1093/jamia/ocae158pmid: 38916922

ObjectiveOur objective is to develop and validate TrajVis, an interactive tool that assists clinicians in using artificial intelligence (AI) models to leverage patients’ longitudinal electronic medical records (EMRs) for personalized precision management of chronic disease progression.Materials and MethodsWe first perform requirement analysis with clinicians and data scientists to determine the visual analytics tasks of the TrajVis system as well as its design and functionalities. A graph AI model for chronic kidney disease (CKD) trajectory inference named DisEase PrOgression Trajectory (DEPOT) is used for system development and demonstration. TrajVis is implemented as a full-stack web application with synthetic EMR data derived from the Atrium Health Wake Forest Baptist Translational Data Warehouse and the Indiana Network for Patient Care research database. A case study with a nephrologist and a user experience survey of clinicians and data scientists are conducted to evaluate the TrajVis system.ResultsThe TrajVis clinical information system is composed of 4 panels: the Patient View for demographic and clinical information, the Trajectory View to visualize the DEPOT-derived CKD trajectories in latent space, the Clinical Indicator View to elucidate longitudinal patterns of clinical features and interpret DEPOT predictions, and the Analysis View to demonstrate personal CKD progression trajectories. System evaluations suggest that TrajVis supports clinicians in summarizing clinical data, identifying individualized risk predictors, and visualizing patient disease progression trajectories, overcoming the barriers of AI implementation in healthcare.DiscussionThe TrajVis system provides a novel visualization solution which is complimentary to other risk estimators such as the Kidney Failure Risk Equations.ConclusionTrajVis bridges the gap between the fast-growing AI/ML modeling and the clinical use of such models for personalized and precision management of chronic diseases.

Warnking, René Pascal; Scheer, Jan; Becker, Franziska; Siegel, Fabian; Trinkmann, Frederik; Nagel, Till

doi: 10.1093/jamia/ocae113pmid: 38796836

ObjectivesMedical practitioners analyze numerous types of data, often using archaic representations that do not meet their needs. Pneumologists who analyze lung function exams must often consult multiple exam records manually, making comparisons cumbersome. Such shortcomings can be addressed with interactive visualizations, but these must be designed carefully with practitioners’ needs in mind.Materials and MethodsA workshop with experts was conducted to gather user requirements and common tasks. Based on the workshop results, we iteratively designed a web-based prototype, continuously consulting experts along the way. The resulting application was evaluated in a formative study via expert interviews with 3 medical practitioners.ResultsParticipants in our study were able to solve all tasks in accordance with experts’ expectations and generally viewed our system positively, though there were some usability and utility issues in the initial prototype. An improved version of our system solves these issues and includes additional customization functionalities.DiscussionThe study results showed that participants were able to use our system effectively to solve domain-relevant tasks, even though some shortcomings could be observed. Using a different framework with more fine-grained control over interactions and visual elements, we implemented design changes in an improved version of our prototype that needs to be evaluated in future work.ConclusionEmploying a user-centered design approach, we developed a visual analytics system for lung function data that allows medical practitioners to more easily analyze the progression of several key parameters over time.

Morgenshtern, Gabriela; Rutishauser, Yves; Haag, Christina; von Wyl, Viktor; Bernard, Jürgen

doi: 10.1093/jamia/ocae230pmid: 39348270

ObjectivesThis article describes the design and evaluation of MS Pattern Explorer, a novel visual tool that uses interactive machine learning to analyze fitness wearables’ data. Applied to a clinical study of multiple sclerosis (MS) patients, the tool addresses key challenges: managing activity signals, accelerating insight generation, and rapidly contextualizing identified patterns. By analyzing sensor measurements, it aims to enhance understanding of MS symptomatology and improve the broader problem of clinical exploratory sensor data analysis.Materials and MethodsFollowing a user-centered design approach, we learned that clinicians have 3 priorities for generating insights for the Barka-MS study data: exploration and search for, and contextualization of, sequences and patterns in patient sleep and activity. We compute meaningful sequences for patients using clustering and proximity search, displaying these with an interactive visual interface composed of coordinated views. Our evaluation posed both closed and open-ended tasks to participants, utilizing a scoring system to gauge the tool’s usability, and effectiveness in supporting insight generation across 15 clinicians, data scientists, and non-experts.Results and DiscussionWe present MS Pattern Explorer, a visual analytics system that helps clinicians better address complex data-centric challenges by facilitating the understanding of activity patterns. It enables innovative analysis that leads to rapid insight generation and contextualization of temporal activity data, both within and between patients of a cohort. Our evaluation results indicate consistent performance across participant groups and effective support for insight generation in MS patient fitness tracker data. Our implementation offers broad applicability in clinical research, allowing for potential expansion into cohort-wide comparisons or studies of other chronic conditions.ConclusionMS Pattern Explorer successfully reduces the signal overload clinicians currently experience with activity data, introducing novel opportunities for data exploration, sense-making, and hypothesis generation.

Ondov, Brian; Patel, Harsh B; Kuo, Ai-Te; Kastner, John; Han, Yunheng; Wei, Hong; Elmqvist, Niklas; Samet, Hanan

doi: 10.1093/jamia/ocae234pmid: 39167120

ObjectiveThe COVID-19 pandemic emphasized the value of geospatial visual analytics for both epidemiologists and the general public. However, systems struggled to encode temporal and geospatial trends of multiple, potentially interacting variables, such as active cases, deaths, and vaccinations. We sought to ask (1) how epidemiologists interact with visual analytics tools, (2) how multiple, time-varying, geospatial variables can be conveyed in a unified view, and (3) how complex spatiotemporal encodings affect utility for both experts and non-experts.Materials and MethodsWe propose encoding variables with animated, concentric, hollow circles, allowing multiple variables via color encoding and avoiding occlusion problems, and we implement this method in a browser-based tool called CoronaViz. We conduct task-based evaluations with non-experts, as well as in-depth interviews and observational sessions with epidemiologists, covering a range of tools and encodings.ResultsSessions with epidemiologists confirmed the importance of multivariate, spatiotemporal queries and the utility of CoronaViz for answering them, while providing direction for future development. Non-experts tasked with performing spatiotemporal queries unanimously preferred animation to multi-view dashboards.DiscussionWe find that conveying complex, multivariate data necessarily involves trade-offs. Yet, our studies suggest the importance of complementary visualization strategies, with our animated multivariate spatiotemporal encoding filling important needs for exploration and presentation.ConclusionCoronaViz’s unique ability to convey multiple, time-varying, geospatial variables makes it both a valuable addition to interactive COVID-19 dashboards and a platform for empowering experts and the public during future disease outbreaks. CoronaViz is open-source and a live instance is freely hosted at http://coronaviz.umiacs.io.

Jeffs, Lily V; Dunbar, Julia C; Syed, Sanaa; Ng, Chelsea; Pollack, Ari H

doi: 10.1093/jamia/ocae206pmid: 39078283

ObjectivesPatients with chronic illnesses, including kidney disease, consider their sense of normalcy when evaluating their health. Although this concept is a key indicator of their self-determined well-being, they struggle to understand if their experience is typical. To address this challenge, we set out to explore how to design personal health visualizations that aid participants in better understanding their experiences post-transplant, identifying barriers to normalcy, and achieving their desired medical outcomes.Materials and MethodsPediatric kidney transplant patients and their caregivers participated in three asynchronous design sessions involving sharing experiences, presenting symbolic objects, and providing feedback on visualizations to understand their perceptions of normalcy post-transplant. Data analysis of design session 1 and 2 comprised deductive and inductive analysis. We used affinity diagramming to identify thematic areas about participants’ transplant experiences. Comprehension of design session three normalcy visualizations was also evaluated.ResultsParticipants effectively engaged in the design sessions, revealing diverse perspectives on their experiences. We found there is a significant need for visualizations that depict normalcy to better inform patients and caregivers about their health.DiscussionNormalcy Visualizations should incorporate three key design principles: personal values, facilitating peer and self-comparison, and seamlessly communicating abstract concepts to help youth kidney transplant recipients comprehend and contextualize if their transplant experience is normal and what normalcy means to them.ConclusionBy incorporating holistic aspects of patients’ and caregivers’ lives into personal health visualizations, they can be cognizant of their progress to normalcy and empowered to make decisions that help them feel normal.