# roque2010comparison

# A Comparison of Several Key Information Visualization Systems for Secondary Use of Electronic Health Record Content

Concept

Roque et al. review six information visualization systems for exploring EHR data[1].

  1. LifeLine [2]
  2. LifeLine2 [3]
  3. Timelines [4]
  4. KNAVE2 [5]
  5. AsbruView [6]
  6. CLEF [7]

The authors state that temporal visualization if the primary visual design type for assisting the user in exploring EHR data. There is also a focus on pre-processed EHR data, specifically numeric data such as lab tests, heart rate, and blood pressure.

Classification

The authors follow the categorization of visual analytics approaches by Bertini and Lalanne [8]:

  1. Pure visualization (VIS)
  2. Computationally-enhanced visualization (V++)
  3. Visually enhanced mining (M++)
  4. Integrated visualization and mining (VM)

Based on the categorization, the authors derive their classification table of the six reviewed systems as follows:

The authors also compare the six EHR systems by their target user, goals and tasks.

Challenges

  1. The rapid pace of changing in medicine and technology sectors is making it difficult for researchers to make use of EHR data.
  2. There is a lack of a uniform identifier to integrate different EHR data sources. (a standardized terminology)
  3. Existing research methods do not cope well with the rapid increase in data size. (scalability)

Unsolved Problems

  1. Tools are often developed without user feedback and evaluation under clinical settings.
  2. Existing tools do not provide enough automation enhancements for visualizing EHR.
  3. EHR contain missing and inconsistent data that require additional data processing to handle.

Papers Cited

19

Years Spanned

1996-2009

Application Domain

Information Visualization of EHR.

Reference

  1. Roque, F. S., Slaughter, L., & TkatΕ‘enko, A. (2010). A Comparison of Several Key Information Visualization Systems for Secondary Use of Electronic Health Record Content. Proceedings of the NAACL HLT 2010 Second Louhi Workshop on Text and Data Mining of Health Documents, (June), 76–83. Retrieved from http://portal.acm.org/citation.cfm?id=1867735.1867747 β†©οΈŽ

  2. Plaisant C, Milash B, Rose A, et al. LifeLines: visualizing personal histories. SIGCHI Conference on Human Factors in Computing Systems Proceedings; 1996:221–227 β†©οΈŽ

  3. Plaisant C, Mushlin R, Snyder A, et al. LifeLines: using visualization to enhance navigation and analysis of patient records. AMIA Symposium Proceedings; 1998:76–80 β†©οΈŽ

  4. B. Harrison, R. Owen, and R. Baecker. Timelines: An interactive system for the collection and visualization of temporal data. In Grahpics Interface, 1994. β†©οΈŽ

  5. Martins SB, Shahar Y, Goren-Bar D, et al. Evaluation of an architecture for intelligent query and exploration of time-oriented clinical data. ArtifIntellMed. 2008;43:17–34 β†©οΈŽ

  6. Kosara, R., & Miksch, S. (2001). Metaphors of movement: A visualization and user interface for time-oriented, skeletal plans. Artificial Intelligence in Medicine. https://doi.org/10.1016/S0933-3657(00)00103-2 β†©οΈŽ

  7. Hallett, C. (2008). Multi-modal presentation of medical histories. International Conference on Intelligent User Interfaces, Proceedings IUI. https://doi.org/10.1145/1378773.1378785 β†©οΈŽ

  8. Bertini, E., & Lalanne, D. (2009). Surveying the complementary role of automatic data analysis and visualization in knowledge discovery. Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery, VAKD ’09. https://doi.org/10.1145/1562849.1562851 β†©οΈŽ

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