Visual Exploration of Time-Oriented Patient Data for Chronic Diseases: Design Study and Evaluation

Rind, Alexander; Aigner, Wolfgang; Miksch, Silvia; Wiltner, Sylvia; Pohl, Margit; Turic, Thomas; Drexler, Felix

doi:10.1007/978-3-642-25364-5_22

Cited by 34 publications

(22 citation statements)

References 23 publications

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“…Rind et al provide an extensive review of visualization tools for health data that they group into two categories [26]: those that focus on individual patients (e. g., LifeLines [22], graphical summary of patient status [23], MIVA [11], and VisuExplore [25]), and those that focus on a group of patients. Given our problem, we discuss the second category.…”

Section: Related Workmentioning

confidence: 99%

TimeSpan: Using Visualization to Explore Temporal Multi-dimensional Data of Stroke Patients

Loorak

Périn

Kamal

et al. 2016

IEEE Trans. Visual. Comput. Graphics

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-We present TimeSpan, an exploratory visualization tool designed to gain a better understanding of the temporal aspects of the stroke treatment process. Working with stroke experts, we seek to provide a tool to help improve outcomes for stroke victims. Time is of critical importance in the treatment of acute ischemic stroke patients. Every minute that the artery stays blocked, an estimated 1.9 million neurons and 12 km of myelinated axons are destroyed. Consequently, there is a critical need for efficiency of stroke treatment processes. Optimizing time to treatment requires a deep understanding of interval times. Stroke health care professionals must analyze the impact of procedures, events, and patient attributes on time-ultimately, to save lives and improve quality of life after stroke. First, we interviewed eight domain experts, and closely collaborated with two of them to inform the design of TimeSpan. We classify the analytical tasks which a visualization tool should support and extract design goals from the interviews and field observations. Based on these tasks and the understanding gained from the collaboration, we designed TimeSpan, a web-based tool for exploring multi-dimensional and temporal stroke data. We describe how TimeSpan incorporates factors from stacked bar graphs, line charts, histograms, and a matrix visualization to create an interactive hybrid view of temporal data. From feedback collected from domain experts in a focus group session, we reflect on the lessons we learned from abstracting the tasks and iteratively designing TimeSpan. Permanent repository link

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Section: Related Workmentioning

confidence: 99%

TimeSpan: Using Visualization to Explore Temporal Multi-dimensional Data of Stroke Patients

Loorak

Périn

Kamal

et al. 2016

IEEE Trans. Visual. Comput. Graphics

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“…For example, global health data can be represented in many different ways (Ola & Sedig, 2016. The same is true of patient data contained within an EHR, which can be displayed in many different ways (Monroe, Lan, Lee, Plaisant, & Shneiderman, 2013;Rind, 2013;Rind et al, 2011). Among the most common approaches to representing patient information are problem-oriented or source-oriented displays.…”

Section: The Representation Spacementioning

confidence: 99%

Aligning information technologies with evidence-based health-care activities: A design and evaluation framework

Sedig¹,

Naimi²,

Haggerty³

2017

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“…ChronoViz [11] is a visualization and analysis tool for time-based data from multiple sources. Visual exploration tools for patient data were proposed in [10], [13] and [24]. These tools are designed for handling a few long time-series, but not for showing an intuitive overview of multiple time-series.…”

Section: (Large) Time-series Data Visualizationmentioning

confidence: 99%

Stroscope: Multi-Scale Visualization of Irregularly Measured Time-Series Data

Cho

Kim

Bae

et al. 2014

IEEE Trans. Visual. Comput. Graphics

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For irregularly measured time-series data, the measurement frequency or interval is as crucial information as measurements are. A well-known time-series visualization such as the line graph is good at showing an overall temporal pattern of change; however, it is not so effective in revealing the measurement frequency/interval while likely giving illusory confidence in values between measurements. In contrast, the bar graph is more effective in showing the frequency/interval, but less effective in showing an overall pattern than the line graph. We integrate the line graph and bar graph in a unified visualization model, called a ripple graph, to take the benefits of both of them with enhanced graphical integrity. Based on the ripple graph, we implemented an interactive time-series data visualization tool, called Stroscope, which facilitates multi-scale visualizations by providing users with a graphical widget to interactively control the integrated visualization model. We evaluated the visualization model (i.e., the ripple graph) through a controlled user study and Stroscope through long-term case studies with neurologists exploring large blood pressure measurement data of stroke patients. Results from our evaluations demonstrate that the ripple graph outperforms existing time-series visualizations, and that Stroscope has the efficacy and potential as an effective visual analysis tool for (irregularly) measured time-series data.

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Visual Exploration of Time-Oriented Patient Data for Chronic Diseases: Design Study and Evaluation

Cited by 34 publications

References 23 publications

TimeSpan: Using Visualization to Explore Temporal Multi-dimensional Data of Stroke Patients

TimeSpan: Using Visualization to Explore Temporal Multi-dimensional Data of Stroke Patients

Aligning information technologies with evidence-based health-care activities: A design and evaluation framework

Stroscope: Multi-Scale Visualization of Irregularly Measured Time-Series Data

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