TimeSpan: Using Visualization to Explore Temporal Multi-Dimensional Data of Stroke PatientsIEEE Trans. Visual. Comput. Graphics

About

Authors
Mona Loorak, Charles Perin, Noreen Kamal, Michael Hill, Sheelagh Carpendale
Year
2015
DOI
10.1109/TVCG.2015.2467325
Subject
Signal Processing / Software / Computer Vision and Pattern Recognition / Computer Graphics and Computer-Aided Design

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1077-2626 (c) 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TVCG.2015.2467325, IEEE Transactions on Visualization and Computer Graphics

TimeSpan: Using Visualization to Explore Temporal

Multi-dimensional Data of Stroke Patients

Mona Hosseinkhani Loorak, Charles Perin, Noreen Kamal, Michael Hill, and Sheelagh Carpendale

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.

Index Terms—Multi-dimensional data, Temporal event sequences, Electronic health records 1 INTRODUCTION

Working closely with domain experts, we have designed, implemented and studied TimeSpan, to develop a better understanding of temporal data of acute stroke patients. Stroke is the second leading cause of death globally and the major cause of acquired neurological disability in adults [17]. Fast and efficient treatment of stroke patients can reduce stroke related mortality and disability. Ischemic stroke is caused by a sudden blockage of a brain artery. It can be treated with tissue plasminogen activator (tPA), but this is a time critical treatment.

Rapid administration of tPA to open a blocked artery in the brain will be beneficial on average within 4.5 hours of stroke onset. However, treatment must commence as soon as possible and all delays from onset-to-hospital and hospital arrival-to-treatment must be minimized.

The time from when a patient arrives in the hospital to when tPA is administered is called door-to-needle (DTN) time. There are various delays in DTN time due to patient and hospital related factors [8].

Examples of such delays include delay in obtaining CT scan, delay in patient registration, delay due to patient high blood pressure, and delay in getting blood lab results. Multiple small delays that a patient encounters may add up to a large delay in onset-to-treatment.

We are working with a group of stroke professionals who are studying clinically acquired temporal stroke treatment data to better understand the varying time spans in DTN. Understanding the factors that contribute to these delays needs careful examination and analysis of the temporal multivariate data. Improving the support for exploration of these data may contribute to finding unforeseen reasons for delay, and lead to novel approaches to providing faster treatment. Our mandate is to design a tool that can help this professional group with their analysis.

Currently, the standard technique for representing and analyzing these data are statistical process control (SPC) charts [4] borrowed from industry. SPC charts aggregate data to give an overview of the • Mona Hosseinkhani Loorak, Charles Perin, and Sheelagh Carpendale are with the Department of Computer Science, University of Calgary. E-mail: hossem,charles.perin, sheelagh@ucalgary.ca. • Noreen Kamal and Michael Hill are with the Department of Clinical

Neurosciences, University of Calgary. E-mail: nrkamal, michael.hill@ucalgary.ca.

Manuscript received 31 Mar. 2015; accepted 1 Aug. 2015; date of publication xx Aug. 2015; date of current version 25 Oct. 2015.

For information on obtaining reprints of this article, please send e-mail to: tvcg@computer.org. data and make it possible to perform statistical tests on this data. Using this method, many hospitals have improved the quality of care [30].

However, SPC charts do not demonstrate the detailed timing of events, cannot represent every patient in detail, and do not include the multidimensional aspects of the data. Crucial information can be missed or not observable because of aggregation. Indeed, stroke patient data is multi-typed, consisting of temporal, ordinal, quantitative, and nominal data types, making it a challenging problem in data visualization.

To design TimeSpan, we conducted a series of observations in the emergency department of a large tertiary-care hospital and eight oneon-one interviews with stroke professionals with various expertise (e. g., stroke neurologist, quality assurance analyst, and stroke nurse).

These studies provided an understanding of the current practices and challenges of our target domain. From the interviews, we extracted, analyzed, and classified the analytical tasks a visualization should support.