The time-oriented analysis of electronic patient records on (neonatal) intensive care units is a tedious and time-consuming task. Graphic data visualization should make it easier for physicians to assess the overall situation of a patient and to recognize essential changes over time.
Metaphor graphics are used to sketch the most relevant parameters for characterizing a patient's situation. By repetition of the graphic object in 24 frames the situation of the ICU patient is presented in one display, usually summarizing the last 24 h.
VIE-VISU is a data visualization system which uses multiples to present the change in the patient's status over time in graphic form. Each multiple is a highly structured metaphor graphic object. Each object visualizes important ICU parameters from circulation, ventilation, and fluid balance.
The design using multiples promotes a focus on stability and change. A stable patient is recognizable at first sight, continuous improvement or worsening condition are easy to analyze, drastic changes in the patient's situation get the viewers attention immediately.
"With electronic health record data, minimization of such disclosure risks is of high priority. In recent years, there has been a lot of work focusing on visualization of health record data, both at the individual record level  , and also at an aggregate level to look at cohort analysis and identifying temporal trends of treatments and patient plans  . In both these cases, data privacy is at risk because of the complex ecosystem of the health-care industry, involving both trusted and untrusted users . "
[Show abstract][Hide abstract] ABSTRACT: In this paper, we reflect on the use of visualization techniques for analyzing electronic health record data with privacy concerns. Privacy-preserving data visualization is a relatively new area of research compared to the more established research areas of privacy-preserving data publishing and data mining. We describe the opportunities and challenges for privacy-preserving visualization of electronic health record data by analyzing the different disclosure risk types, and vulnerabilities associated with commonly used visualization techniques.
IEEE VIS 2014 Workshop on Visualizing Electronic Health Record Data, Paris; 11/2014
"Some approaches are static visualizations, such as the one proposed by Powsner and Tufte, but most modern ones are interactive . Many of these support only a single EHR – Life- lines, Midgaard, Web-Based Interactive Visualization System VIE-VISU, to name a few. They generally focus on supporting physicians to quickly absorb a patient's potentially lengthy medical history in order to make better medical decisions. "
[Show abstract][Hide abstract] ABSTRACT: Current electronic health record (EHR) systems facilitate the storage, retrieval, persistence, and sharing of patient data. However, the way physicians interact with EHRs has not changed much. More specifically, support for temporal analysis of a large number of EHRs has been lacking. A number of information visualization techniques have been proposed to alleviate this problem. Unfortunately, due to their limited application to a single case study, the results are often difficult to generalize across medical scenarios. We present the usage data of Lifelines2, our information visualization system, and user comments, both collected over eight different medical case studies. We generalize our experience into an information-seeking process model for multiple EHRs. Based on our analysis, we make recommendations to future information visualization designers for EHRs on design requirements and future research directions.
ACM International Health Informatics Symposium, IHI 2010, Arlington, VA, USA, November 11 - 12, 2010, Proceedings; 06/2010
"In addition to patient-generated data, there are vast arrays of clinical data generated that document the treatment that is received by the patient. This data includes drug therapy, respiratory therapy, physical therapy and all other clinical interventions . "
[Show abstract][Hide abstract] ABSTRACT: One mission of medical informatics is to provide physicians, nurses, and other health care providers with the technology and tools for interpreting large and diverse data sets, so that appropriate critical care decisions can be facilitated. Ideally, medical data visualization provides the means to transform data into information and contextual knowledge suitable for interpretation and decision-making by Van Bemmel and Musen (1997) and Coiera (1997). The authors propose a model through which data is organized into multivariate multidimensional critical care patient data visualizations (CPDV). It does this as the primary means to represent and manage complex context-based patient data at various user-defined temporal resolutions. Furthermore, user-defined spatial organization of multiple (clinically related) datasets allows rapid visualization of significant trends that are related to several co-variables. Currently, anticipated findings from usability testing support the notion that the proposed model will facilitate medical decision making in a critical care environment
Information Visualization, 2006. IV 2006. Tenth International Conference on; 08/2006
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