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01-10-2011 | Original Paper

Extracting Insights from Electronic Health Records: Case Studies, a Visual Analytics Process Model, and Design Recommendations

Authors: Taowei David Wang, Krist Wongsuphasawat, Catherine Plaisant, Ben Shneiderman

Published in: Journal of Medical Systems | Issue 5/2011

Abstract

Current electronic health record (EHR) systems facilitate the storage, retrieval, persistence, and sharing of patient data. However, the way physicians interact with EHRs has not changed much. More specifically, support for temporal analysis of a large number of EHRs has been lacking. A number of information visualization techniques have been proposed to alleviate this problem. Unfortunately, due to their limited application to a single case study, the results are often difficult to generalize across medical scenarios. We present the usage data of Lifelines2 (Wang et al. 2008), our information visualization system, and user comments, both collected over eight different medical case studies. We generalize our experience into a visual analytics process model for multiple EHRs. Based on our analysis, we make seven design recommendations to information visualization tools to explore EHR systems.

Wilcox, L., Morris, D., Tan, D., and Gatewood, J., Designing patient-centric information displays for hospitals. Proc. of the 28th ACM Int Conf on Human Factors in Comp Syst. 2123–2132. New York, NY, USA. ACM, 2010.

Wilcox, L., Lu, J., Lai, J., Feiner, S., and Jordan, D., Physician-driven management of patient progress notes in an intensive care unit. Proc. of the 28th ACM Int Conf on Human Factors in Comp Syst. 1879–1888. New York, NY, USA. ACM, 2010.

Zhou, X., Ackerman, M. S., and Zhen, K., Doctors and psychosocial information: Records and reuse in inpatient care. Proc. of the 28th ACM Int Conf on Human Factors in Comp Syst. 1767–1776. New York, NY, USA. ACM, 2010.

Chen, Y., Documenting transitional information in EMR, Proc. of the 28th ACM Int Conf on Human Factors in Comp Sys. 1787–1796. New York, NY, USA. ACM, 2010.

Sarcevic, A., “Who’s scribing?”: Documenting patient encounter during trauma resuscitation, Proc. of the 28th ACM Int Conf on Human Factors in Comp Syst. 1899–1908. New York, NY, USA. ACM, 2010.

Microsoft., http://www.microsoft.com/amalga/

Murphy, S., Mendis, M., Hackett, K., Kuttan, R., Pan, W., Phillips, L., and Gainer, V., et al., Architecture of the open-source clinical research chart from informatics for integrating biology and the bedside. Proc. Am. Med. Inform. Assoc. Ann. Symp. 598–602, 2007.

Viederman, M., The psychodynamic life narrative: A psychotherapeutic intervention useful in crisis situations. Psychiater. 46(3):236–246, 1983.

Greenhalgh, T., Narrative based medicine in an evidence based world. Brit. Med. J. 318(7179):323–325.

10.

Charon, R., A model for empathy, reflection, profession, and trust. J. Am. Med. Assoc. 286(15):1897–1902, 2001.CrossRef

11.

Powsner, S., and Tufte, E., Graphical summary of patient status. Lancet 344:386–389, 1994.CrossRef

12.

Plaisant, C., Mushlin, R., Snyder, A., Li, J., Heller, D., and Shneiderman, B., Using visualization to enhance navigation and analysis of patient records. Proc. Am. Med. Inform Assoc. Ann. Symp. 76–80, 1998.

13.

Bade, R., Schelchweg, S., and Miksch, S., Connecting time-oriented data and information to a coherent interactive visualization. Proc 22nd ACM Int Conf on Human Factors in Comp Syst. 105–112. New York, NY, USA, 2004.

14.

Pieczkiewicz, D. S., Finkelstine, S. M., and Hertz, M. I., Design and evaluation of a web-based interactive visualization system for lung transplant home monitoring data. Proc. Am. Med. Inform. Assoc. Ann. Symp. 598–602, 2007.

15.

Horn, W., Popow, C., and Unterasinger, L., Support for fast comprehension of ICU data: Visualization using metaphor graphics. Method Info. Med. 40(5):421–424, 2001.

16.

Wongsuphasawat, K., and Shneiderman, B., Finding comparable temporal categorical records: A similarity measure with an interactive visualization. Proc. IEEE Symp. Vis. Ana Sci. and Tech. 27–34, 2009.

17.

Post, A. R., and Harrison, J. H., Protempa: A method for specifying and identifying temporal sequences in retrospective data for patient selection. J. Am. Med. Inform. Assoc. 14(5):674–683, 2007.CrossRef

18.

Hinum, K., Miksch, S., Aigner, W., Ohmann, S., Popow, C., Pohl, M., and Rester, M., Gravi++: Interactive information visualization to explore highly structured temporal data. J. Univ Comp. Sci. – Spec. Iss. Vis. Data Mining 11(11):1792–1805, 2005.

19.

Klimov, D., Shahar, Y., and Taieb-Maimon, M., Intelligent selection and retrieval of multiple time-oriented records. J. Intel. Inform Syst. (Published Online), 2009.

20.

Chittaro, L., Combi, C., and Trapasso, G., Data mining on temporal data: A visual approach and its clinical applications to hemodialysis. J. Vis. Lang. Comput. 14:591–620, 2003.CrossRef

21.

Rind, A., Wang, T. D., Aigner, W., Miksh, S., Wongsuphasawat, K., Plaisant, C., and Shneiderman, B., Interactive information visualization for exploring and querying electronic health records: A systematic review. Technical Report HCIL-2010-19, Human-Computer Interaction Lab, University of Maryland at College Park, 2010.

22.

Shneiderman, B., and Plaisant, C., Strategies for evaluating information visualization tools: Multi-dimensional in-depth long-term case studies. Proc. Beyond Time and Errors: Novel Eval. Metho. Inform Vis. Workshop, 38–43, 2006.

23.

Card, S. K., Mackinlay, J. D., and Shneiderman, B., Readings in information visualization: Using vision to think. Morgan Kaufmann publishers, San Francisco, 1999.

24.

Pirolli, P., and Card, S. K., The Sensemaking process and leverage points for analyst technology as identified through cognitive task analysis. Proc. Inter. Conf on. Intel. Analy, 2–4, 2005.

25.

Thomas, J. J., and Cook, K. A., Illuminating the path: The research and development agenda for visual analytics. National Visualization and Analytics Center, 2004.

26.

Hansen, D. L., Rotman, D., Bonsignore, E., Milić-Frayling, N., Rodrigues, E. M., Smith, M., and Shneiderman, B., Do you know your way to NSA?: A process model for analyzing and visualizing social media data. Technical Report HCIL-2009-17, Human-Computer Interaction Lab, University of Maryland, 2009.

27.

Wang, T. D., Plaisant, C., Quinn, A. J., Stanchak, R., Murphy, S., and Shneiderman, B., Aligning temporal data by sentinel events: Discovering patterns in electronic health records. Proc. of the 26th ACM Int Conf on Human Factors in Comp Syst. 457–466. New York, NY, USA. ACM, 2008.

28.

Wang, T. D., Plaisant, C., Shneiderman, B., Spring, N., Roseman, D., Marchand, G., Mukherjee, V., and Smith, M., Temporal summaries: Supporting temporal categorical searching, aggregation, and comparison. IEEE Trans. Vis. Comput. Graph. 15(6):1049–1056, 2009.CrossRef

29.

Janszky, I., and Ljung, R., Shifts to and from daylight saving time to incidence of myocardial infarction. New Engl. J. Med 359(18):1966–1968, 2008.CrossRef

30.

Wang, T. D., Wongsuphasawat, K., Plaisant, C., and Shneiderman, B., Visual information seeking in multiple electronic health records: Design recommendations and a process model. Proc 1st ACM Int Health Infor Symp, 46–55. NY, USA. ACM, 2010.

31.

Ventsias, T., Health IT: An Rx for health care. Terp 7(1):20–23, 2009.

32.

Bederson, B. B., Grosejean, J., and Meyer, J., Toolkit design for interactive structured graphics. IEEE Trans. Softw. Eng. 30(8):535–546, 2004.CrossRef

33.

Wang, T. D., Deshpande, A., and Shneiderman, B. A., Temporal pattern search algorithm, IEEE Trans Knowl and Data Engin, Pre-print, doi.ieeecomputersociety.org/10.1109/TKDE.2010.257, 2010.

34.

Lee, B., Parr, C. C., Plaisant, C., Bederson, B., Veksler, V. D., Gray, W. D., and Kotfila, C., TreePlus: Interactive exploration of networks with enhanced tree layouts. IEEE Trans. Vis. Comput Graph Spec. Issue Vis. Anal. 12(6):1414–1426, 2006.

35.

Aris, A., and Shneiderman, B., Designing semantic substrates for visual network exploration. Info. Vis. 6(4):281–300, 2007.CrossRef

36.

Wongsuphasawat, K., Guerra Gómez, J., Plaisant, C., Wang, T. D., Taieb-Maimon, M., and Shneiderman, B., LifeFlow: Visualizing an overview of event sequences. To appear in Proc of the 29th ACM Int Conf on Human Factors in Comp Syst. New York, NY, USA. ACM, 2011.

37.

Vrotsou, K., Johansson, J., and Cooper, M., ActiviTree: Interactive visual exploration of sequences in event-based data using graph similarity. IEEE Trans. Vis. Comput. Graph. 15(6):945–952, 2009.CrossRef

38.

Stottler Henke. DataMontage., http://www.stottlerhenke.com/datamontage/

39.

Patrick, J., and Budd, P., Ockham’s razor of design: An heuristic for guiding design and development of a clinical information systems generator. Proc 1st ACM Int Health Infor Symp, 18–27. NY, USA. ACM, 2010.

Title: Extracting Insights from Electronic Health Records: Case Studies, a Visual Analytics Process Model, and Design Recommendations
Authors: Taowei David Wang
Krist Wongsuphasawat
Catherine Plaisant
Ben Shneiderman
Publication date: 01-10-2011
Publisher: Springer US
Published in: Journal of Medical Systems / Issue 5/2011
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI: https://doi.org/10.1007/s10916-011-9718-x

At a glance: The STEP trials

Springer Medicine

Extracting Insights from Electronic Health Records: Case Studies, a Visual Analytics Process Model, and Design Recommendations

Abstract

At a glance: The STEP trials

Springer Medicine

Abstract

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