Abstract
The health sector still lags behind in development of data visualization tools due to the complex nature of health data. Furthermore, due to the volume, velocity and veracity of health data consolidated from various sources, re-presenting them in a way that promotes decision-making while supporting various aspects of human interaction becomes even more challenging. With the plethora of research on improving visualization of integrated health data, focus is shifting from simple charts to novel ways of data re-presentation. Literature also suggests the need for an in-depth exploration on aligning visualizations to tasks, context, and appropriate cognition aspects. We conducted a field study at the Kenya National Health Data Warehouse (KNHDW) in the month of July 2017 to identify the techniques and practices used to visualize data. Two salient tasks performed in the KNHDW were identified in order to explore possibilities of visualizing the data. We then adopted a task-based approach in developing recommendations based on categorical data. These recommendations include (1) use of visualization approaches that promote proper space utilization, and (2) use of leverage points that influence aspects of human cognition process. In addition, the proposed visualizations enable potential users to get a new experience with the data and explore possibilities for visualization. Nevertheless, these recommendations are by no means exhaustive but aim at encouraging best practice in health data visualization in the KNHDW.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
O. Ola and K. Sedig, “Beyond simple charts: Design of visualizations for big health data.,” Online J. Public Health Inform., vol. 8, no. 3, p. e195, 2016.
MeSH Consortium, “Kenya-HIV-CBS-SWOT-Case-Study_MeSH-Consortium (3),” 2016.
L. N. Carroll, A. P. Au, L. T. Detwiler, T. chieh Fu, I. S. Painter, and N. F. Abernethy, Visualization and analytics tools for infectious disease epidemiology: A systematic review, vol. 51. Academic Press, 2014, pp. 287–298.
L. Zhang and S. Mittelst, “Visual Analytics for the Big Data Era – A Comparative Review of State-of-the-Art Commercial Systems,” in Visual Analytics Science and Technology (VAST), 2012 IEEE Conference on, 2012, pp. 173–182.
A. Rind, “Interactive Information Visualization to Explore and Query Electronic Health Records,” Found. Trends® Human–Computer Interact., vol. 5, no. 3, pp. 207–298, 2013.
R. Kosara and S. Miksch, “Visualization methods for data analysis and planning in medical applications.,” Int. J. Med. Inform., vol. 68, no. 1–3, pp. 141–53, Dec. 2002.
O. E. Sheta and A. N. Eldeen, “The Technology of Using a Data Warehouse to Support Decision-Making in Health Care,” Int. J. Database Manag. Syst., vol. 5, no. 3, pp. 75–86, Jun. 2013.
S. Faisal, A. Blandford, and H. W. Potts, “Making sense of personal health information: Challenges for information visualization,” Health Informatics J., vol. 19, no. 3, pp. 198–217, Sep. 2013.
V. L. West, D. Borland, and W. E. E. Hammond, “Innovative information visualization of electronic health record data: a systematic review,” J. Am. Med. Informatics Assoc., vol. 22, no. 2, pp. 330–9, 2014.
E. Y. Gorodov, V. V. Gubarev, V. V. Gubarev, and evich, “Analytical Review of Data Visualization Methods in Application to Big Data,” J. Electr. Comput. Eng., vol. 2013, pp. 1–7, Nov. 2013.
M. Q. Wang Baldonado, A. Woodruff, and A. Kuchinsky, “Guidelines for using multiple views in information visualization,” in Proceedings of the working conference on Advanced visual interfaces - AVI ’00, 2000, pp. 110–119.
B. Shneiderman, C. Plaisant, and B. W. Hesse, “Improving healthcare with interactive visualization,” Computer (Long. Beach. Calif)., vol. 46, no. 5, pp. 58–66, May 2013.
M. Blevins, F. H. Wehbe, P. F. Rebeiro, Y. Caro-Vega, C. C. McGowan, and B. E. Shepherd, “Interactive data visualization for HIV cohorts: Leveraging data exchange standards to share and reuse research tools,” PLoS One, vol. 11, no. 3, p. e0151201, Mar. 2016.
Measure Evaluation, “Data Visualization That Works Facilitating HIV Program Targeting : Case Examples and Considerations Data Visualization That Works Targeting : Case Examples and,” Meas. Eval., no. April, 2016.
H. Helwig, “The Future of Big Data,” in The Future of Big Data Visualization, 2017, no. February.
K. Sedig and P. Parsons, Design of Visualizations for Human-Information Interaction: A Pattern-Based Framework, vol. 4, no. 1. 2016.
R. E. Patterson et al., “A human cognition framework for information visualization,” Comput. Graph., vol. 42, pp. 42–58, Aug. 2014.
Centres for disease control and prevention Kenya, “Annual Report 2015,” 2015.
M. Mauri, T. Elli, G. Caviglia, G. Uboldi, and M. Azzi, “RAWGraphs: A Visualisation Platform to Create Open Outputs,” in Proceedings of the 12th Biannual Conference on Italian SIGCHI Chapter, 2017, p. 28:1–28:5.
F. Bendix, R. Kosara, and H. Hauser, “Parallel sets: visual analysis of categorical data,” in IEEE Symposium on Information Visualization, 2005. INFOVIS 2005., pp. 133–140.
M. Rosvall and C. T. Bergstrom, “Mapping Change in Large Networks,” PLoS One, vol. 5, no. 1, p. e8694, Jan. 2010.
Acknowledgements
This work was supported in part by the NORHED program (Norad: Project QZA-0484). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Norwegian Agency for Development Cooperation. All authors report no conflicts of interest.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Gesicho, M., Babic, A. (2019). Task-Based Approach Recommendations to Enhance Data Visualization in the Kenya National Health Data Warehouse. In: Lhotska, L., Sukupova, L., Lacković, I., Ibbott, G.S. (eds) World Congress on Medical Physics and Biomedical Engineering 2018. IFMBE Proceedings, vol 68/1. Springer, Singapore. https://doi.org/10.1007/978-981-10-9035-6_86
Download citation
DOI: https://doi.org/10.1007/978-981-10-9035-6_86
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-9034-9
Online ISBN: 978-981-10-9035-6
eBook Packages: EngineeringEngineering (R0)