gschwandtner2011carecruiser

CareCruiser: Exploring and visualizing plans, events, and effects interactively

Concept

The authors introduce CareCruiser to support the exploration of effects on a patient's condition after clinical actions [1].

CareCruiser aims to provide an enhanced visual analysis system to explore the result of each applied clinical action and identities sub-optimal treatment choices. This helps clinicians investigate and optimize their treatment plans.

Implementation

Figure 1 shows an overview of CareCruiser visualizing a patient's historical oxygen saturation(tcpSO2) and carbon dioxide(PCO2) pressure values during treatment plan executions. Colors are coded to represent the deviation of the value from its initial point, only brushed sections will be highlighted in color. Details of the brushed treatment plans are shown on the top left. The lower left shows a tree graph visualizing brushed treatment plans and sub-plans in a hierarchical structure. User options to filter and search clinical actions within the brushed area are shown above the main view.

Below the main view is the plan bar, glyphs are used to represent clinical actions along the x-axis which represents time. Actions applied that are not part of the plan are laid below the plan bar.

Related Work

The filtering of clinical events are inspired by LifeLine 2 [2] and PatternFinder [3].

KNAVE II [4] introduces filtering on different granularities of time and event alignment with absolute and relative time scales.

LiveRAC [5] arranges charts in a matrix and provides reordering of rows and columns and zooming in multiple levels.

The authors only find a few tools that support visualization of applied treatment plans in combination with patient data. GOT [6] shows basic characteristics of the treatment plan with a focus on patient data. CareVis [7] supports temporal constraints of treatment plans. Midgaard [8] visualizes treatment plans in a hierarchical structure.

Data Characteristics

  • Data source: unknown
  • Size: 1 patient
  • Spatial dimensionality: 2D
  • Temporal dimensionality: static
  • Type: multivariate
  • Libraries used: Java

Visualization Techniques

  • Glyph
  • Tree
  • Time-series chart

Papers Cited

27

Years Spanned

1932-2011

Application Domain

  • Electronic Health Record Visualization
  • Medical focus: discuss with Bob

Reference


  1. Gschwandtner, T., Aigner, W., Kaiser, K., Miksch, S., & Seyfang, A. (2011). CareCruiser: Exploring and visualizing plans, events, and effects interactively. IEEE Pacific Visualization Symposium 2011, PacificVis 2011 - Proceedings, 43–50. https://doi.org/10.1109/PACIFICVIS.2011.5742371 ↩︎

  2. Plaisant, C., Mushlin, R., Snyder, A., Li, J., Heller, D., & Shneiderman, B. (1998). LifeLines: using visualization to enhance navigation and analysis of patient records. Proceedings / AMIA ... Annual Symposium. AMIA Symposium. ↩︎

  3. Fails, J. A., Karlson, A., Shahamat, L., & Shneiderman, B. (2006). A visual interface for multivariate temporal data: Finding patterns of events across multiple histories. IEEE Symposium on Visual Analytics Science and Technology 2006, VAST 2006 - Proceedings. https://doi.org/10.1109/VAST.2006.261421 ↩︎

  4. Martins, S. B., Shahar, Y., Goren-Bar, D., Galperin, M., Kaizer, H., Basso, L. V., … Goldstein, M. K. (2008). Evaluation of an architecture for intelligent query and exploration of time-oriented clinical data. Artificial Intelligence in Medicine. https://doi.org/10.1016/j.artmed.2008.03.006 ↩︎

  5. McLachlan, P., Munzner, T., Koutsofios, E., & North, S. (2008). LiveRAC: Interactive visual exploration of system management time-series data. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/1357054.1357286 ↩︎

  6. W. Aigner. Guideline overview tool (GOT), Asgaard-TR-2001-4. Technical report, Vienna University of Technology, Institute of Soft- ware Technology and Interactive Systems, Vienna, Austria, 2001. ↩︎

  7. Aigner, W., & Miksch, S. (2006). CareVis: Integrated visualization of computerized protocols and temporal patient data. Artificial Intelligence in Medicine. https://doi.org/10.1016/j.artmed.2006.04.002 ↩︎

  8. Bade, R., Schlechtweg, S., & Miksch, S. (2004). Connecting time-oriented data and information to a coherent interactive visualization. Conference on Human Factors in Computing Systems - Proceedings. ↩︎

Last Updated: 10/7/2019, 7:36:40 PM