# rind2017visual

# Visual Analytics of Electronic Health Records with a Focus on Time


This survey reviews the existing Visual Analytics and Visualization work with a focus on EHRs [1] [2]. The authors structure the paper around technical challenges drawn from earlier work [3].


There is no clear classification available in the survey, nor a classification table. Systems are classified by their technical challenges listed in Section Challenge below.

The authors emphasize on VisuExplorer [4], Gnaeus [5], Gravi++[6], TimeCleanser [7] and LifeLine [8] with detailed descriptions of the systems.

They also briefly review 10 other state-of-the-art EHR systems with very limited context.


The authors identify 5 technical and societal challenges in existing implementations.

  1. Complexity of time-oriented data.
  2. Intertwining patient condition with treatment processes.
  3. Scale from single patients to cohorts.
  4. Data quality and uncertainty.
  5. User interaction and user-centered design.

Unsolved Problems

  1. Utilization of real-time sensor data is still unexplored.
  2. Collaborative analysis with multiple devices and screens.
  3. Patient involvement in managing health records.

Papers Cited


Years Spanned


Application Domain

Visual Analytics and Information Visualization of EHR. Visual Analytics of Time-Oriented Data.


  1. Rind, A., Federico, P., Gschwandtner, T., Aigner, W., Doppler, J., & Wagner, M. (2017). Visual Analytics of Electronic Health Records with a Focus on Time. In F. Capello, G. Rinaldi, & G. Gatti (Eds.), New Perspectives in Medical Records (pp. 65–77). https://doi.org/10.1007/978-3-319-28661-7 ↩︎

  2. Aigner, W., Federico, P., Miksch, S., & Rind, A. (2012). Challenges of Time-oriented Data in Visual Analytics for Healthcare. IEEE VisWeek Workshop on Visual Analytics in Healthcare. ↩︎

  3. Rind, A., Wang, T. D., Aigner, W., Miksch, S., Wongsuphasawat, K., Plaisant, C., & Shneiderman, B. (2011). Interactive Information Visualization to Explore and Query Electronic Health Records. Foundations and Trends® in Human–Computer Interaction, 5(3), 207–298. https://doi.org/10.1561/1100000039 ↩︎

  4. Rind, A., Miksch, S., Aigner, W., Turic, T., & Pohl, M. (2010). VisuExplore: gaining new medical insights from visual exploration. Proceedings of the 1st International Workshop on Interactive Systems in Healthcare (WISH@CHI2010). ↩︎

  5. Federico, Paolo & Unger, J. & Amor-Amorós, Albert & Sacchi, Lucia & Klimov, D. & Miksch, Silvia. (2015). Gnaeus: utilizing clinical guidelines for knowledge-assisted visualisation of EHR cohorts. 10.2312/eurova.20151108. ↩︎

  6. Hinum, K., Miksch, S., Aigner, W., Ohmann, S., Popow, C., Pohl, M., & Rester, M. (2005). Gravi++: Interactive information visualization to explore highly structured temporal data. Journal of Universal Computer Science. ↩︎

  7. Gschwandtner, T., Aigner, W., Miksch, S., Gärtner, J., Kriglstein, S., Pohl, M., & Suchy, N. (2014). TimeCleanser: A visual analytics approach for data cleansing of time-oriented data. ACM International Conference Proceeding Series. https://doi.org/10.1145/2637748.2638423 ↩︎

  8. Plaisant, C., Milash, B., Rose, A., Widoff, S., & Shneiderman, B. (1996). LifeLines: visualizing personal histories. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems Common Ground, 221-ff. https://doi.org/10.1145/238386.238493 ↩︎

🔄 Last Updated: 7/8/2020, 9:48:18 PM