# A timeline-based framework for aggregating and summarizing electronic health records
Debek et al. identify the major challenges associated with utilizing Electronic Health Records (EHRs) as the lack of usability for the end users, the inefficient navigation tools available and the massive increase in the amount of clinical data being collected .
As a result, the authors propose a framework to 1.) aggregate different data elements of an EHR, 2.) highlight significant data which leads to the discovery of contributing factors, 3.) `oom in and out on a timeline and 4.) summarize temporal information.
A timeline of an individual patient's history as shown in Figure 1, contains a group of summary nodes, each with significant events depicted by glyphs as shown in Figure 2.
Each node comes with a detail window with corresponding patient events as shown in Figure 3, clicking on any event will direct the user to the view shown in Figure 4, which presents the related textual information.
Filtering of data is supported through a context menu with checkboxes and a date range picker.
Figure 6 shows that users are able to expand and collapse nodes based on time.
Horizon charts visualizing lab test results are useful for identifying patterns and trends in the data as shown in Figure 7.
Figure 8 shows a customized sunburst chart visualizing a patient's diagnoses for a given timeframe. This chart was designed with domain experts who work closely with the authors.
The technique of displaying the overview of a patient's history is inspired by Krause et al. .
Single patient visualized in the examples.
- Tree graph
- Horizon chart
- Electronic Health Record Visualization
Dabek, F., Jimenez, E., & Caban, J. J. (2018). A timeline-based framework for aggregating and summarizing electronic health records. 2017 IEEE Workshop on Visual Analytics in Healthcare, VAHC 2017, 55–61. https://doi.org/10.1109/VAHC.2017.8387501 ↩︎
M. Monroe, R. Lan, H. Lee, C. Plaisant, and B. Shneiderman. Temporal event sequence simpliﬁcation. IEEE transactions on visualization and computer graphics, 19(12):2227–2236, 2013. ↩︎
K. Wongsuphasawat, J. A. Guerra G´omez, C. Plaisant, T. D. Wang, M. Taieb-Maimon, and B. Shneiderman. Lifeﬂow: visualizing an overview of event sequences. In Proceedings of the SIGCHI conference on human factors in computing systems, pp. 1747–1756. ACM, 2011. ↩︎
A. Perer and D. Gotz. Visualizations to support patient-clinician communication of care. In ACM CHI Workshop on Patient-Clinician Communication. Paris, France, 2013. ↩︎
S. Ozturk, M. Kayaalp, and C. J. McDonald. Visualization of patient prescription history data in emergency care. In AMIA Annual Symposium Proceedings, vol. 2014, p. 963. American Medical Informatics Association, 2014. ↩︎
E. R. Tufte. The visual display of quantitative information. Journal for Healthcare Quality, 7(3):15, 1985. ↩︎
S. M. Powsner and E. R. Tufte. Graphical summary of patient status. The Lancet, 344(8919):386–389, 1994. ↩︎
J. Krause, N. Razavian, E. Bertini, and D. Sontag. Visual inspection of longitudinal electronic medical records. 2016 Workshop on Visual Analytics in Healthcare, 2016. ↩︎