# rind2017visual

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

Concept

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].

Classification

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.

Challenges

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

61

Years Spanned

1997-2016

Application Domain

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

Reference


  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