Metaphor graphics to visualize ICU data over time (original) (raw)
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Visualization of multivariate time-series data in a neonatal ICU
IBM Journal of Research and Development, 2012
Visualization of electronic medical data in the Neonatal Intensive Care Unit (NICU) is mainly tabular or in the form of stacked univariate plots of variables over time. In the NICU, norm values differ significantly from adult values, which determine scales and alarm limits in current clinical displays. Thus, the value of information displayed in traditional interfaces is limited by standard visualizations. Providers have difficulties identifying pertinent changes in the patient's condition resulting in delayed diagnosis and harm. We developed a novel interface that allows clinicians to visualize variables critical in the detection of a patent ductus arteriosus (PDA) in a neonate. The interface was designed to allow users to quickly determine changes in variables and the direction of the change. By providing a personalized view that normalizes data points to the patient's state over the total time period reviewed, minor changes in the patient's condition are more easily detected and may allow for earlier diagnosis and treatment of a PDA. By allowing providers to experience the changes in multiple variables simultaneously, we hope to identify patterns that can be recognized by providers as changes in patient status (no PDA vs. PDA). We present the design of a multivariate time series visualization that is interactive and animated, and personalized to an individual patient, such that medical personnel can quickly and efficiently recognize significant changes in the patient's condition.
A novel approach to ICU data visualization and communication integration
The intensive care unit (ICU) is a highly complex environment that houses critically ill patients requiring constant monitoring and care, as well as vast amounts of time-oriented data disseminated through a range of health information technologies (HIT), e.g., bedside and clinical decision support systems. Studies show the occurrence of medical mishaps due to diagnostic errors, impacting patient safety in spite of advances in HIT. Available visual representations of data, although time-oriented and multivariate, lack contextual information for communication among the ICU intensivists. We present a medical data visualization system (MIVA) that delivers multivariate data via a visualization display. The system organizes data into controllable time resolutions, providing contextual knowledge and communication tools at point-of-care. When comparing MIVA to paper charts, findings from two studies suggest that MIVA enabled significantly greater speed and accuracy during an in-lab experime...
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The time-oriented analysis of electronic patient records at a (neonatal) intensive care unit is a tedious and time-consuming task. The vast amount of data available makes it hard for the physician to recognize the essential changes over time. VIE-VISU is a data visualization system which uses multiples to present the change in the patient's status over time in graphic form. Metaphor graphics is used to sketch the parameters most relevant in characterizing the situation of a patient.
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Hospital intensive care unit (ICU) bedside devices and electronic medical record (EMR) technology do not yet adequately address the visualization of patient data in the context of cognitive overload and its impact on patient safety. We respond to these challenges through the design of a novel visualization dashboard for use in the ICU: MIVA 2.0 (Medical Information Visualization Assistant, v.2). MIVA 2.0 is designed to support rapid analysis and interpretation of real-time clinical data-trends and communication for clinical work and information flow. This paper describes the system design, functionality, and prior studies of MIVA 2.0.
An animated multivariate visualization for physiological and clinical data in the ICU
IHI'10 - Proceedings of the 1st ACM International Health Informatics Symposium, 2010
Current visualizations of electronic medical data in the Intensive Care Unit (ICU) consist of stacked univariate plots of variables over time and a tabular display of the current numeric values for the corresponding variables and occasionally an alarm limit. The value of information is dependent upon knowledge of historic values to determine a change in state. With the ability to acquire more historic information, providers need more sophisticated visualization tools to assist them in analyzing the data in a multivariate fashion over time. We present a multivariate time series visualization that is interactive and animated, and has proven to be as effective as current methods in the ICU for predicting an episode of acute hypotension in terms of accuracy, confidence, and efficiency with only 30-60 minutes of training.
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Artificial Intelligence in Medicine, 2000
We propose a methodology for the extraction of local trends from a stream of data. It has been designed to suit the needs of interpretation-oriented visualization and symbolization from ICU monitoring data. After giving implementation details for efficient computation of local trends, we propose the use of a characteristic analysis span for each variable. This characteristic span is obtained from a set of criteria that we compare and evaluate in regard of analysis of ICU monitoring data gathered within the Aiddaig project. The processing results in a rich visual representation and a framework for the local symbolization of the data stream based on its dynamics.
Proceedings of the International Conference on Computer Graphics Theory and Applications, 2012
In this paper we present a case study of interactive visual analysis and exploration of a large ICU data set. The data consists of patients' records containing scalar data representing various patients' parameters (e.g. gender, age, weight), and time series data describing logged parameters over time (e.g. heart rate, blood pressure). Due to the size and complexity of the data, coupled with limited time and resources, such ICU data is often not utilized to its full potential, although its analysis could contribute to a better understanding of physiological, pathological and therapeutic processes, and consequently lead to an improvement of medical care. During the exploration of this data we identified several analysis tasks and adapted and improved a coordinated multiple views system accordingly. Besides a curve view which also supports time series with gaps, we introduced a summary view which allows an easy comparison of subsets of the data and a box plot view in a coordinated multiple views setup. Furthermore, we introduced an inverse brush, a secondary brush which automatically selects non-brushed items, and updates itself accordingly when the original brush is modified. The case study describes how we used the system to analyze data from 1447 patients from the ICU at Guy's & St. Thomas' NHS Foundation Trust in London. We were interested in the relationship between serum sodium concentration, its rate of change and their effect on ICU mortality rates. The interactive visual analysis led us to findings which were fascinating for medical experts, and which would be very difficult to discover using conventional analysis methods usually applied in the medical field. The overall feedback from domain experts (coauthors of the paper) is very positive.
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Patient data visualization can help healthcare providers gain an overview of their patient’s condition and assist in decision-making about the next steps on management and communication. We explore the acceptance and opinion of five different visualizations that can be used to summarize patient data, including a Text Summary, text and frequency-based Word Cloud, a Bar Graph, a time-based Line Graph and a newly developed Text Graph that combines text and time-based distribution. Results from a user study with 15 professional healthcare providers, 16 firstor secondyear medical students, and 17 third or greater year medical students show that most visualizations are useful in extracting patient information and are received positively by the users. In addition, Text Summary and Text Graph are rated to be the most useful visualizations in extracting patient health information.
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