Visualization of Medical Data Based on EHR Standards

Kopanitsa, Georgy; Hildebrand, Christian; Stausberg, Jürgen; Englmeier, Karl-Hans

doi:10.3414/me12-01-0016

Cited by 25 publications

(10 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a graphical representation of data and statistical concepts, data visualizations particularly support decision making [13]. Data analysts, scientists, and statistical experts have been among the primary users of data visualization to date [14], but digitization of health services together with demographic change [15] and the recently observable shift toward patient empowerment are leading to an increase in the number of older adults without special background knowledge using data visualizations [16][17][18][19][20][21]. Accordingly, research on the visualization of health data is increasingly taking into account the perspective of older adults for design and evaluation [22][23][24].…”

Section: Data Visualizationmentioning

confidence: 99%

A Task-Data Taxonomy for Health Data Visualizations: Web-Based Survey with Experts and Older Adults

Theis¹,

Rasche²,

Bröhl³

et al. 2017

Preprint

View full text Add to dashboard Cite

Background: Increasingly, eHealth involves health data visualizations to enable users to better understand their health situation. Selecting efficient and ergonomic visualizations requires knowledge about the task that the user wants to carry out and the type of data to be displayed. Taxonomies of abstract tasks and data types bundle this knowledge in a general manner. Task-data taxonomies exist for visualization tasks and data. They also exist for eHealth tasks. However, there is currently no joint task taxonomy available for health data visualizations incorporating the perspective of the prospective users. One of the most prominent prospective user groups of eHealth are older adults, but their perspective is rarely considered when constructing tasks lists.Objective: The aim of this study was to construct a task-data taxonomy for health data visualizations based on the opinion of older adults as prospective users of eHealth systems. eHealth experts served as a control group against the bias of lacking background knowledge. The resulting taxonomy would then be used as an orientation in system requirement analysis and empirical evaluation and to facilitate a common understanding and language in eHealth data visualization.Methods: Answers from 98 participants (51 older adults and 47 eHealth experts) given in an online survey were quantitatively analyzed, compared between groups, and synthesized into a task-data taxonomy for health data visualizations.Results: Consultation, diagnosis, mentoring, and monitoring were confirmed as relevant abstract tasks in eHealth. Experts and older adults disagreed on the importance of mentoring (χ 2 4 =14.1, P=.002) and monitoring (χ 2 4 =22.1, P<.001). The answers to the open questions validated the findings from the closed questions and added therapy, communication, cooperation, and quality management to the aforementioned tasks. Here, group differences in normalized code counts were identified for "monitoring" between the expert group (mean 0.18, SD 0.23) and the group of older adults (mean 0.08, SD 0.15; t 96 =2431, P=.02). Time-dependent data was most relevant across all eHealth tasks. Finally, visualization tasks and data types were assigned to eHealth tasks by both experimental groups. Conclusions:We empirically developed a task-data taxonomy for health data visualizations with prospective users. This provides a general framework for theoretical concession and for the prioritization of user-centered system design and evaluation. At the same time, the functionality dimension of the taxonomy for telemedicine-chosen as the basis for the construction of present taxonomy-was confirmed.

show abstract

Section: Data Visualizationmentioning

confidence: 99%

A Task-Data Taxonomy for Health Data Visualizations: Web-Based Survey with Experts and Older Adults

Theis¹,

Rasche²,

Bröhl³

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…In addition, presentation of uncertainty in a meaningful way, for example in the context of risk, is still an open research area. -Currently, according to Kopanitsa et al [60], there is a gap in transforming knowledge from domain model to interface model. Hence there is a need to turn hard-coded user interfaces into generic methods by a process of standardisation.…”

Section: Open Problemsmentioning

confidence: 99%

Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice

Bettencourt‐Silva

Mannu

Iglesia

2016

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Routinely collected data in hospital Electronic Medical Records (EMR) is rich and abundant but often not linked or analysed for purposes other than direct patient care. We have created a methodology to integrate patient-centric data from different EMR systems into clinical pathways that represent the history of all patient interactions with the hospital during the course of a disease and beyond. In this paper, the literature in the area of data visualisation in healthcare is reviewed and a method for visualising the journeys that patients take through care is discussed. Examples of the hidden knowledge that could be discovered using this approach are explored and the main application areas of visualisation tools are identified. This paper also highlights the challenges of collecting and analysing such data and making the visualisations extensively used in the medical domain. This paper starts by presenting the state-of-the-art in visualisation of clinical and other health related data. Then, it describes an example clinical problem and discusses the visualisation tools and techniques created for the utilisation of these data by clinicians and researchers. Finally, we look at the open problems in this area of research and discuss future challenges.

show abstract

“…Shapes that are preattentively processed are often simple shapes, and the different shapes that are chosen should be sufficiently distinct. Ware Other related recent health-data visualization approaches are reviewed in [13], [14] and [15], with a concrete example in [16].…”

Section: B Literature On Information Visualizationmentioning

confidence: 99%

Visual exploration and cohort identification of acute patient histories aggregated from heterogeneous sources

Sætre¹,

Nytrø²,

Nordbo³

et al. 2016

2016 IEEE 32nd International Conference on Data Engineering Workshops (ICDEW)

View full text Add to dashboard Cite

Abstract-How can we use information visualization to support retrospective, exploratory analysis of collections of histories for patients admitted to acute care? This paper describes a novel design for visual cohort identification and exploration. We have developed a tool that integrates multiple, heterogeneous clinical data sources and allows alignment, querying and abstraction in a common workbench. This paper presents results from two projects and a review of related work in the field of information visualization including both presentation and interactive navigation of the information. We have developed an interactive prototype and present the visualization aspect of this prototype and a brief demonstration of its use in a research project with a large cohort of patients.The prototype represents and reasons with patient events in different OWL-formalizations according to the perspective and use: One for integration and alignment of patient records and observations; Another for visual presentation of individual or cohort trajectories.Health researchers have successfully analyzed large cohorts (over 100,000 individuals) using the tool. We have also used the tool to produce interactive personal health time-lines (for more than 10,000 individuals) on the web. Utility, usability and effect have been tested extensively and the results so far are promising.We envision that clinicians who want to learn more about groups of patients and their treatment processes will find the tool valuable. In addition, we believe that the visualization can be useful to researchers looking at data to be statistically evaluated, in order to discover new hypotheses or get ideas for the best analysis strategies. Our main conclusion is that the tool is usable, but it can be challenging to use for large data sets.

show abstract

Visualization of Medical Data Based on EHR Standards

Cited by 25 publications

References 35 publications

A Task-Data Taxonomy for Health Data Visualizations: Web-Based Survey with Experts and Older Adults

A Task-Data Taxonomy for Health Data Visualizations: Web-Based Survey with Experts and Older Adults

Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice

Visual exploration and cohort identification of acute patient histories aggregated from heterogeneous sources

Contact Info

Product

Resources

About