Supporting Strategic Planning with Interactive Visualization - A Case Study of Patient Flow through a Large Hospital

Dominique Brodbeck, Markus Degen, Andreas Walter

Abstract

Hospitals collect large amounts of data during their daily operation. Next to its immediate primary purpose, this data also contains implicit information that can be used to improve clinical and administrative processes. We present a case study of how strategic infrastructure planning can be supported by the analysis of enriched patient flow through a hospital. Data from various hospital information systems was collected, enriched with topographical and organizational data, and integrated into a coherent data store. Common analysis tools and methods do not support exploration and sense-making well for such large and complex problems. We therefore developed a highly interactive visual analytics application that offers various views onto the data, and provides fast access to details in order to show them in context. The analysts were able to validate their experiences, confirm hypotheses and generate new insights. As a result, several sub-systems of clinics were identified that will play a central role on the future hospital campus. This approach was successful enough that we envision to extend it towards other process optimization tasks in hospitals.

References

  1. Alapont, J., Bella-Sanjuán, A., Ferri, C., Hernández-Orallo, J., Llopis-Llopis, J. D., and Ramírez-Quintana, M. J. (2005). Specialised tools for automating data mining for hospital management. In In Proc. First East European Conference on Health Care Modelling and Computation, pages 7-19.
  2. Buja, A., McDonald, J. A., Michalak, J., and Stuetzle, W. (1991). Interactive data visualization using focusing and linking. In Proceedings of the 2nd conference on Visualization 7891, VIS 7891, pages 156-163, Los Alamitos, CA, USA. IEEE Computer Society Press.
  3. Chalmers, M. (1996). A linear iteration time layout algorithm for visualising high-dimensional data. In Proceedings of the 7th conference on Visualization 7896, VIS 7896, pages 127-ff., Los Alamitos, CA, USA. IEEE Computer Society Press.
  4. Chaudhry, B., Wang, J., Wu, S., Maglione, M., Mojica, W., Roth, E., Morton, S. C., and Shekelle, P. G. (2006). Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med, 144(10):742-752.
  5. Cuzzocrea, A., Song, I.-Y., and Davis, K. C. (2011). Analytics over large-scale multidimensional data: the big data revolution! In Proceedings of the ACM 14th international workshop on Data Warehousing and OLAP, DOLAP 7811, pages 101-104, New York, NY, USA. ACM.
  6. Jha, A. K., DesRoches, C. M., Campbell, E. G., Donelan, K., Rao, S. R., Ferris, T. G., Shields, A., Rosenbaum, S., and Blumenthal, D. (2009). Use of electronic health records in U.S. hospitals. N Engl J Med, 360(16):1628-1638.
  7. Kandel, S., Heer, J., Plaisant, C., Kennedy, J., van Ham, F., Riche, N. H., Weaver, C., Lee, B., Brodbeck, D., and Buono, P. (2011). Research directions in data wrangling: Visualizations and transformations for usable and credible data. Information Visualization, 10(4):271-288.
  8. Keim, D. A., Kohlhammer, J., Ellis, G., and Mansmann, F., editors (2010). Mastering The Information Age - Solving Problems with Visual Analytics. Eurographics.
  9. Krzywinski, M. I., Schein, J. E., Birol, I., Connors, J., Gascoyne, R., Horsman, D., Jones, S. J., and Marra, M. A. (2009). Circos: An information aesthetic for comparative genomics. Genome Research.
  10. Laney, D. (2001). 3d data management: Controlling data volume, velocity, and variety. application delivery strategies. Available online at http://blogs.gartner.com/douglaney/files/2012/01/ad949-3D-Data-ManagementControlling-Data-Volume-Velocity-and-Variety.pdf Visited on August, 22th, 2012.
  11. North, C. and Shneiderman, B. (2000). Snap-together visualization: Can users construct and operate coordinated views? Intl. Journal of Human-Computer Studies, Academic Press, 53(5):715-739.
  12. Shneiderman, B. (1996). The eyes have it: A task by data type taxonomy for information visualizations. In Proceedings of the 1996 IEEE Symposium on Visual Languages, VL 7896, pages 336-, Washington, DC, USA. IEEE Computer Society.
  13. Van der Aalst, W. M. P. (2012). Process mining. Communications of the ACM, 55(8):7683.
  14. Wong, P. C. and Thomas, J. (2004). Visual analytics. Computer Graphics and Applications, IEEE, 24(5):20 - 21.
  15. Zikopoulos, P. and Eaton, C. (2011). Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data. McGraw-Hill Companies,Incorporated.
Download


Paper Citation


in Harvard Style

Brodbeck D., Degen M. and Walter A. (2013). Supporting Strategic Planning with Interactive Visualization - A Case Study of Patient Flow through a Large Hospital . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2013) ISBN 978-989-8565-37-2, pages 85-93. DOI: 10.5220/0004244000850093


in Bibtex Style

@conference{healthinf13,
author={Dominique Brodbeck and Markus Degen and Andreas Walter},
title={Supporting Strategic Planning with Interactive Visualization - A Case Study of Patient Flow through a Large Hospital},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2013)},
year={2013},
pages={85-93},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004244000850093},
isbn={978-989-8565-37-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2013)
TI - Supporting Strategic Planning with Interactive Visualization - A Case Study of Patient Flow through a Large Hospital
SN - 978-989-8565-37-2
AU - Brodbeck D.
AU - Degen M.
AU - Walter A.
PY - 2013
SP - 85
EP - 93
DO - 10.5220/0004244000850093