CFP last date
22 July 2024
Reseach Article

Patient Postoperative Care Data Visualization

by Olga Pilipczuk, Dmitri Eidenzon, Olena Kosenko
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 156 - Number 7
Year of Publication: 2016
Authors: Olga Pilipczuk, Dmitri Eidenzon, Olena Kosenko

Olga Pilipczuk, Dmitri Eidenzon, Olena Kosenko . Patient Postoperative Care Data Visualization. International Journal of Computer Applications. 156, 7 ( Dec 2016), 27-33. DOI=10.5120/ijca2016912469

@article{ 10.5120/ijca2016912469,
author = { Olga Pilipczuk, Dmitri Eidenzon, Olena Kosenko },
title = { Patient Postoperative Care Data Visualization },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2016 },
volume = { 156 },
number = { 7 },
month = { Dec },
year = { 2016 },
issn = { 0975-8887 },
pages = { 27-33 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2016912469 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:01:58.239437+05:30
%A Olga Pilipczuk
%A Dmitri Eidenzon
%A Olena Kosenko
%T Patient Postoperative Care Data Visualization
%J International Journal of Computer Applications
%@ 0975-8887
%V 156
%N 7
%P 27-33
%D 2016
%I Foundation of Computer Science (FCS), NY, USA

This paper describes the problem of hospital patient care data visualization. Although a significant amount of different kinds of tools have been created to improve health care still there is luck of tools supports patient care decision making. We briefly describe the method for visualization and analysis of multidimensional data implemented in the NovoSpark® Visualizer software (NV). An example of problem salvation based on data of patient state observation after the laparoscopic gallbladder removal is provided as well. The experiment results indicate that it is possible to visualize all the data from nurse report in single image, to identify the anomalies, trends and regularities in it using multidimensional visualization. The decision time, error rate and amount of invested mental effort were analyzed. The conducted experiments demonstrate the significant difference between decision time and mental effort amounts for paper report analysis and visualizations analysis in favor of the visualizations.

  1. Zerhouni, E., A. 2008. Strategic Vision for the Future,
  2. Inoue, M., Kuroda, M., Suekuni, C., Dannoue, H., Tsuru, S., Nakanishi, M. 2006. Structural visualization of expert nursing: Expert nursing care for a patient undergoing outpatient radiotherapy. Stud Health Technol Inform 122, 931.
  3. Matsumoto, C., Uto, Y., Muranaga, F., Kumamoto, I. 2013. DPC in acute-phase inpatient hospital care. Visualization of amount of nursing care provided and accessibility to nursing care. Methods Inf Med 52 (6), 522-535.
  4. Rudin, R. S., Bates, D.W. 2014. Let the left hand know what the right is doing: a vision for care coordination and electronic health records. J Am Med Inform Assoc 21 (1), 13–16.
  5. Roque F. S., Slaughter L., Tkatchenko A. 2010. A Comparison of Several Key Information Visualization Systems for Secondary Use of Electronic Health Record Content, Proceedings of the NAACL HLT 2010 Second Louhi Workshop on Text and Data Mining of Health Documents, Los Angeles, California, USA, 76–83.
  6. Soulakis, N. D., et al. 2015. Visualizing collaborative electronic health record usage for hospitalized patients with heart failure. J Am Med Inform Assoc 22 (2), 299-311.
  7. Tai-Seale, M., Wilson, C.J., Panattoni, L., et al. 2014. Leveraging electronic health records to develop measurements for processes of care. Health Serv Res. 49 (2), 628–644.
  9. Faiola, A., Hillier, S. 2006. Information Visualization, Tenth International Conference on, 460 – 468.
  10. Shneiderman, B., Plaisant, C, Hesse, B. W. 2013. Improving health and healthcare with interactive visualization methods HCIL Technical Report. IEEE Computer Special Issue on Challenges in Information Visualization.
  11. Weaver, C. A., Delaney, C. W., Weber, P., Carr, R. L. 2010. Nursing and Informatics for the 21st Century An International Look at Practice, Education and EHR Trends Second Edition, Healthcare Information and Management Systems Society (HIMSS).
  12. Klimov, D., Shahar, Y., Taieb-Maimon, M. 2010. Intelligent visualization and exploration of time-oriented data of multiple patients, Artificial Intelligence in Medicine 49, 11–31.
  13. Bui, A., Aberle, D.R., Kangarloo, H. 2007. TimeLine: Visualizing Integrated Patient Records. IEEE Transactions on Information Technology in Biomedicine 11(4), 462-473.
  14. Kosara, R., Miksch, S., 2001. Metaphors of movement: a visualization and user interface for time-oriented, skeletal plans. Artif Intell Med 22, 111-131.
  15. Kielman, J., Thomas, J. 2009. Special Issue: Foundations and Frontiers of Visual Analytics, Information Visualization, Volume 8, Number 4, (Winter 2009), 239-314.
  16. Shortliffe, E. H., Cimino, J. J. 2013. Biomedical Informatics: Computer Applications in Healthcare and Biomedicine: 4th Edition, Springer, New York (2013).
  17. Hallett, C. 2008. Multi-modal presentation of medical histories, IUI '08: Proceedings of the 13th international conference on Intelligent user interfaces, ACM-89, 80-89.
  18. Wang, T.D., Plaisant, C., Shneiderman, B., Spring, N., Roseman, D., Marchand, G., Mukherjee, V., Smith, M., 2009. Temporal Summaries: Supporting Temporal Categorical Searching, Aggregation and Comparison. IEEE J VCG 15, 1049-1056.
  19. Wongsuphasawat, K., Gomez, J. A. G., Plaisant, C., Wang, T. D., Shneiderman, B., Taieb-Maimon, M. 2011. LifeFlow: Visualizing an overview of event sequences, Proc. ACM SIGCHI Conference, ACM Press, New York (May 2011), 1747-1756.
  20. Ward, M. O., Grinstein, G., Keim, D. A., 2010. Interactive Data Visualization: Foundations, Techniques, and Application, A. K. Peters, Ltd.
  21. Keim, D.A., Mansmann, F., Schneidewind, J., Thomas, J., Ziegler, H., 2008. Visual analytics: Scope and challenges. Visual Data Mining: Theory, Techniques and Tools for Visual Analytics, Springer, 76-90.
  22. Donaldson, N., Brown, D. S., Aydin, C. E., et al. 2005. Leveraging nurse-related dashboard benchmarks to expedite performance improvement and document excellence. J Nurs Adm 35, 163-172.
  23. Aydin, C.E., Burnes Bolton, L., Donaldson, N., Brown, D., Mukerji, A., 2008. Beyond Nursing Quality Measurement: The Nation’s First Regional Nursing Virtual Dashboard, In Henriksen, K., Battles, J.B., Keyes, M.A., Grady, M.L., Eds. Advances in Patient Safety: New Directions and Alternative Approaches, Vol. I Assessment. AHRQ Publication No. 08-0034-1. Rockville, MD: Agency for Healthcare Research and Quality; August 2008, 217-234.
  24. Wilbanks, B. A, Langford, P. A., 2014. A review of dashboards for data analytics in nursing, Comput Inform Nurs 32 (11), 545-549.
  25. Carroll, C., Flucke, N., Barton, A. J., Thompson, T. L. 2013. Information Technology and the Clinical Nurse Specialist: The Use of Dashboards to Monitor Quality of Care, Clinical Nurse Specialist: The Journal for Advanced Nursing Practice 27 (2), 61 – 62.
  26. Barton, A. J., Thompson, T. L. 2009. Decision Support and the Clinical Nurse Specialist Clinical Nurse Specialist Clinical Nurse Specialist: The Journal for Advanced Nursing Practice, 23(1), 9-10.
  27. Kim, H., Schulze, J., Cone, A., Sosinsky, G., Martone, M. 2010. Dimensionality Reduction on Multi-Dimensional Transfer Functions for Multi-Channel Volume Data Sets, Information Visualization 9 (3), 167-180.
  28. Maaten, van der L.J.P., Postma, E.O., Herik, van der H. J. 2009. Dimensionality Reduction: A Comparative Review, Journal of Machine Learning Research 10, 66-71.
  29. Coorprider, N., Burton, R. P., 2007. Extension of star coordinates into three dimensions. In Proceedings of the SPIE, 6495.
  30. Reitsma, R., Trubin, S., 2007. Information Space Partitioning Using Adaptive Voronoi Diagrams, Information Visualization 6 (2), 123-138.
  31. Johansson, J., Forsell K., Lind M., Cooper M. Perceiving Patterns in Parallel Coordinates: Determining Thresholds for Identification of Relationships, Information Visualization 7 (2), 152-162.
  32. Slingsby, A., Dykes, J., Wood, J. Using Treemaps for Variable Selection in Spatio-Temporal Visualisation, Information Visualization 7 (3-4), 210-224.
  33. Sun, Y., Tang J., Tang, D., Xiao, W. 2008. Advanced star coordinates. In Web-Age Information Magagement, 2008. WAIM 08. The Ninth International conference, 165–170.
  34. Schreck, T., Bernard J., von Landesberger T., Kohlhammer J. Visual Cluster Analysis of Trajectory Data with Interactive Kohonen Maps, Information Visualization, 8 (1), 14-29.
  35. Shneiderman, B., Plaisant, C. 2009. Treemaps for space-constrained visualization of hierarchies.
  36. Cao, H., Yichuan, S. 2010. A multi-perspective and interactive method for multidimensional data visualization. International Conference on Environmental Science and Information Application Technology (ESIAT), 3, 207 – 210.
  37. Hao, M., Dayal, U., Sharma, R., Keim, D., Janetzko H. 2010. Variable binned scatter plots, Information Visualization, 9(3), 194-203.
  38. Heilig, M., Huber, S., Demarmels, M., Reiterer, H. 2010. Scattertouch: a multi touch rubber sheet scatter plot visualization for co-located data exploration. In Proceedings ACM ITS’10, 263–264.
  39. Keim, D., Hao, M., Dayal, U., Janetzko, H., & Bak, P., 2010. Generalized scatter plots, Information Visualization; 9(4), 301-311.
  40. Lu, L. F., Zhang, J.W., Huang, M. L., Fu, L. 2010. A new concentric-circle visualization of multi-dimensional data and its application in network security. Journal of Visual Languages and Computing table of contents archive, 21(4), 194-208.
  41. Chen, Y., Cheng, X., Chen, H. 2011. A multidimensional data visualization method based on parallel coordinates and enhanced ring. International Conference on Computer Science and Network Technology (ICCSNT), 4, 2224 – 2229.
  42. Domańska, D., Wojtylak, M., & Kotarski, W. 2012. Visualization of multidimensional data in explorative forecast, Lecture Notes in Computer Science, 7594, 63-70.
  43. Buschmann, S., Nocke, T., & Tominski, C. 2013. Towards visualizing geo-referenced climate networks. Workshop GeoViz Interactive Maps that Help People Think, Hamburg, Germany,
  44. Chung, D., Legg, P., Parry, M., Bown, R., Griffiths, I., Laramee, R., Chen, M., 2013. Glyph sorting: Interactive visualization for multi-dimensional data, arXiv preprint arXiv:1304.2889.
  45. Li, X., Hu, S. 2013. Poisson coordinates visualization and computer graphics, IEEE Transactions on, 19(2), 344 – 352.
  46. Liu, Z., Jiang B., Heer, J. 2013. imMens: Real-time visual querying of big data, Computer Graphics Forum (Proceedings of the EuroVis), 32(3), 421-430.
  47. Eidenzon, D., Volovodenko V, 2009. Method for visualization of multidimensional data, Patent Application 20090252436, USA.
  48. Eidenzon, D., Volovodenko, V., Shamroni, D. 2013. Method and system for multidimensional data visualization, Saarbrücken: Lambert Academic Publishing.
  49. Eidenzon, D., Pilipczuk, O. 2015. Multidimensional data visualization, Encyclopedia of Information Science and Technology, IGI-Global, Hershey, 1600-1610.
  50. Andrews, D. 1972. Plots of High Dimensional Data, Biometrics 28, 125–136.
  51. Inselberg, A. 1996. Parallel Coordinates: A guide for the Perplexed, in Hot Topics Proc. of IEEE Conference on Visualization, IEEE Computer Society, Los Alamitos, CA, 35-38.
  52. Kostrzewa-Michalik, A. 2010. Obserwacja pacjenta na sali pooperacyjnej, Magazyn Pielęgniarki i Położnej, 4.
  53. De Waard, D. 1996. The Measurement of Drivers' Mental Workload., University of Groningen, Groningen.
  54. Borland D., Taylor R. M. 2007. Rainbow color map (still) considered harmful, IEEE CG&A 27 (2), 14-17.
Index Terms

Computer Science
Information Sciences


Multidimensional visualization patient care medical report medical data visualizations