Jan Scheer

Objectives: Medical practitioners analyze numerous types of data, often using archaic representations that do not meet their needs. Pneumologists who analyze lung function exams must often consult multiple exam records manually, making comparisons cumbersome. Such shortcomings can be addressed with interactive visualizations, but these must be designed carefully with practitioners’ needs in mind. Materials and Methods: A workshop with experts was conducted to gather user requirements and common tasks. Based on the workshop results, we iteratively designed a web-based prototype, continuously consulting experts along the way. The resulting application was evaluated in a formative study via expert interviews with 3 medical practitioners. Results: Participants in our study were able to solve all tasks in accordance with experts’ expectations and generally viewed our system positively, though there were some usability and utility issues in the initial prototype. An improved version of our system solves these issues and includes additional customization functionalities. Discussion: The study results showed that participants were able to use our system effectively to solve domain-relevant tasks, even though some shortcomings could be observed. Using a different framework with more fine-grained control over interactions and visual elements, we implemented design changes in an improved version of our prototype that needs to be evaluated in future work. Conclusion: Employing a user-centered design approach, we developed a visual analytics system for lung function data that allows medical practitioners to more easily analyze the progression of several key parameters over time.

Background: Visual analysis and data delivery in the form of visualizations are of great importance in health care, as such forms of presentation can reduce errors and improve care and can also help provide new insights into long-term disease progression. Information visualization and visual analytics also address the complexity of long-term, time-oriented patient data by reducing inherent complexity and facilitating a focus on underlying and hidden patterns. Objective: This review aims to provide an overview of visualization techniques for time-oriented data in health care, supporting the comparison of patients. We systematically collected literature and report on the visualization techniques supporting the comparison of time-based data sets of single patients with those of multiple patients or their cohorts and summarized the use of these techniques. Methods: This scoping review used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. After all collected articles were screened by 16 reviewers according to the criteria, 6 reviewers extracted the set of variables under investigation. The characteristics of these variables were based on existing taxonomies or identified through open coding. Results: Of the 249 screened articles, we identified 22 (8.8%) that fit all criteria and reviewed them in depth. We collected and synthesized findings from these articles for medical aspects such as medical context, medical objective, and medical data type, as well as for the core investigated aspects of visualization techniques, interaction techniques, and supported tasks. The extracted articles were published between 2003 and 2019 and were mostly situated in clinical research. These systems used a wide range of visualization techniques, most frequently showing changes over time. Timelines and temporal line charts occurred 8 times each, followed by histograms with 7 occurrences and scatterplots with 5 occurrences. We report on the findings quantitatively through visual summarization, as well as qualitatively. Conclusions: The articles under review in general mitigated complexity through visualization and supported diverse medical objectives. We identified 3 distinct patient entities: single patients, multiple patients, and cohorts. Cohorts were typically visualized in condensed form, either through prior data aggregation or through visual summarization, whereas visualization of individual patients often contained finer details. All the systems provided mechanisms for viewing and comparing patient data. However, explicitly comparing a single patient with multiple patients or a cohort was supported only by a few systems. These systems mainly use basic visualization techniques, with some using novel visualizations tailored to a specific task. Overall, we found the visual comparison of measurements between single and multiple patients or cohorts to be underdeveloped, and we argue for further research in a systematic review, as well as the usefulness of a design space.

BACKGROUND The visual analysis and delivery of data in the form of visualizations is of great importance in healthcare, as such forms of presentation can reduce errors and improve care, and can also help provide new insights into long-term disease progression. Information visualization and visual analytics also address the complexity of long-term, time-oriented patient data by reducing inherent complexity and facilitating focus on underlying and hidden patterns. OBJECTIVE Aim of this review is to give an overview of visualization techniques of time-oriented data in health care supporting the comparison of patients. We systematically collect literature and report on visualization techniques supporting the task of comparing time-based datasets of single patients with those of multiple patients or their cohorts, and summarize the usage of these techniques. Visualization techniques are grouped according to the medical characteristics and other relevant visualization aspects like data types, interactions and tasks. METHODS This scoping review used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. After all collected articles were screened by 16 reviewers according to the criteria, 6 reviewers extracted the set of variables under investigation. The characteristics of these variables were based on existing taxonomies or identified through open coding. RESULTS Out of 249 screened articles, we identified 22 fitting all criteria, and reviewed these in-depth. We collected and synthesized findings from these articles for medical aspects such as medical context, medical objective, and medical data type, as well as for the core investigated aspects of visualization technique, interaction technique, and supported tasks. The extracted articles were published between 2003 and 2019, and mostly situated in clinical research. The systems use a wide range of visualization techniques, most frequently showing some change over time. Timelines and temporal line charts occur eight times each, followed by histograms with seven occurrences and scatterplots with five. We report on the findings quantitatively through visual summarization, and qualitatively. CONCLUSIONS The articles under review in general mitigate complexity through visualization and support diverse medical objectives. We identified three distinct patient entities: single patients, multiple patients, and cohorts. Cohorts typically are visualized in condensed form either through prior data aggregation or through visual summarization, whereas visualization of individual patients often contains finer details. All systems provide mechanisms to view and compare patient data. Explicitly comparing a single patient to multiple patients or a cohort, however, is supported only by a few systems. These systems mainly use basic visualization techniques with some employing novel visualizations tailored for a specific task. Overall, we found the visual comparison of measurements between single and multiple patients or cohorts to be underdeveloped, and argue for further research in a systematic review, and the usefulness of a design space.