VIVA: Virtual Healthcare Interactions Using Visual Analytics, With Controllability Through Configuration

2025-08-12

AI Summary

During the early phase of the COVID-19 pandemic, HealthLink BC (HLBC) urgently required efficient analysis of heterogeneous virtual healthcare service usage data to optimize triage protocols, improve patient outcomes and satisfaction, and alleviate systemic strain. To address this, we designed and implemented VIVA, a visual analytics tool grounded in the “Scan–Act–Adapt” interactive workflow abstraction model, enhancing understanding of clinical interaction patterns and supporting evidence-based decision-making. Our key methodological contribution is the “configuration-for-controllability” paradigm—integrating developer-defined configurations with log-driven automatic configuration—to bridge the scalability gap between hard-coded systems and fully interactive interfaces. Through three domain-expert-led case studies, VIVA demonstrated robust support for real-world analytical tasks and informed iterative system architecture evolution. This work advances both the methodology and practical application of visual analytics in public health emergency response.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Computer Vision: Scene Analysis & Understanding
• Data Mining & Knowledge Management: Data Visualization & Summarization

Application Category

• Web Mining and Content Analysis: Web data visualization
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience
• Responsible Web: Ethical and legal aspects of web-scale data analysis, uses and collection practices

Abstract

At the beginning of the COVID-19 pandemic, HealthLink BC (HLBC) rapidly integrated physicians into the triage process of their virtual healthcare service to improve patient outcomes and satisfaction with this service and preserve health care system capacity. We present the design and implementation of a visual analytics tool, VIVA (Virtual healthcare Interactions using Visual Analytics), to support HLBC in analysing various forms of usage data from the service. We abstract HLBC's data and data analysis tasks, which we use to inform our design of VIVA. We also present the interactive workflow abstraction of Scan, Act, Adapt. We validate VIVA's design through three case studies with stakeholder domain experts. We also propose the Controllability Through Configuration model to conduct and analyze design studies, and discuss architectural evolution of VIVA through that lens. It articulates configuration, both that specified by a developer or technical power user and that constructed automatically through log data from previous interactive sessions, as a bridge between the rigidity of hardwired programming and the time-consuming implementation of full end-user interactivity. Availability: Supplemental materials at https://osf.io/wv38n

Problem

• Analyzing virtual healthcare service usage data
• Designing visual analytics tool for healthcare triage
• Balancing system configurability and user interactivity

Innovation

• Visual analytics tool for healthcare data
• Controllability Through Configuration model
• Interactive workflow Scan, Act, Adapt