Advertisement

Data Visualization

  • J. Michael SchmidtEmail author
  • John M. Irvine
  • Sarah Miller
Chapter

Abstract

The development of neurocritical care informatics is essential to improving patient care for critically ill neurological patients. The wealth of data available in the neurointensive care unit, however, creates a challenge for healthcare professionals who must turn this mass of data into useful information that will lead to more informed healthcare decisions. Data visualization is essential to achieving this but ultimately must be combined with data analysis to facilitate specific treatment decisions and provide clinicians with situational awareness regarding patient state. Utilizing cognitive work analysis methodologies to develop and test data visualizations for clinical decision support enhances its usability reduces the likelihood of failed implementation due to non-technology or human factors.

The best visualization tools will find an effective balance among clinical, analytic, and usability factors to enable optimal performance at the bedside.

Keywords

Data visualization Informatics Cognitive support Workflow Electronic health record 

References

  1. 1.
    Morris A, Gardner R. Computer applications. In: Hall J, Schmidt G, Wood L, editors. Principles of critical care. New York: McGraw-Hill; 1992. p. 500–14.Google Scholar
  2. 2.
    Imhoff M, Fried R, Gather U, Lanius V. Dimension reduction for physiological variables using graphical modeling. AMIA Annu Symp Proc. 2003;2003:313–7.PubMedCentralGoogle Scholar
  3. 3.
    Imhoff M. Detecting relationships between physiological variables using graphical modeling. In: Proc AMIA Symp, vol. 2002; 2002. p. 340–4.Google Scholar
  4. 4.
    De Turck F, Decruyenaere J, Thysebaert P, Van Hoecke S, Volckaert B, Danneels C, Colpaert K, De Moor G. Design of a flexible platform for execution of medical decision support agents in the intensive care unit. Comput Biol Med. 2007;37(1):97–112.CrossRefPubMedGoogle Scholar
  5. 5.
    Jennings D, Amabile T, Ross L. Informal assessments: data-based versus theory-based judgments. In: Kahnemann D, Slovic P, Tversky A, editors. Judgments under uncertainity: heuristics and biases. Cambridge: Cambridge University Press; 1982. p. 211–30.CrossRefGoogle Scholar
  6. 6.
    Ordóñez P, desJardins M, Lombardi M, Lehmann CU, Fackler J. An animated multivariate visualization for physiological and clinical data in the ICU. New York: ACM; 2010. p. 771–9.Google Scholar
  7. 7.
    Simon HA. Identifying basic abilities underlying intelligent performance of complex tasks. In: Resnick LB, editor. The nature of human intelligence. Hillsdale, NJ: Lawrence Erlbaum Associates; 1976. p. 5.Google Scholar
  8. 8.
    Woods DD, Patterson ES, Roth EM. Can we ever escape from data overload? A cognitive systems diagnosis. Cogn Tech Work. 2002;4(1):22–36.CrossRefGoogle Scholar
  9. 9.
    Zhang J, Norman DA. Representations in distributed cognitive tasks. Cogn Sci. 1994;18(1):87–122.CrossRefGoogle Scholar
  10. 10.
    Tufte ER. Visual explanations: images and quantities, evidence and narrative. In: Envisioning information. Cheshire, CT: Graphics Press; 2006.Google Scholar
  11. 11.
    Tufte ER, Goeler NH, Benson R. Envisioning information, vol. 21. Cheshire, CT: Graphics Press; 1990.Google Scholar
  12. 12.
    Chan WW-Y. A survey on multivariate data visualization, vol. 8(6). Hong Kong: Department of Computer Science and Engineering, Hong Kong University of Science and Technology; 2006. p. 1–29.Google Scholar
  13. 13.
    Keim DA. Designing pixel-oriented visualization techniques: theory and applications. IEEE Trans Vis Comput Graph. 2000;6(1):59–78.CrossRefGoogle Scholar
  14. 14.
    Healey CG. Perception in visualization. Raleigh, NC: Department of Computer Science, North Carolina State University; 2005. http://www.csc.ncsu.edu/faculty/healey/PP/.Google Scholar
  15. 15.
    Vicente KJ. Cognitive work analysis: toward safe, productive, and healthy computer-based work. Hillsdale, NJ: Lawrence Erlbaum; 1999.Google Scholar
  16. 16.
    Abraham J, Kannampallil TG, Patel VL. Bridging gaps in handoffs: a continuity of care based approach. J Biomed Inform. 2012;45(2):240–54.CrossRefPubMedGoogle Scholar
  17. 17.
    Shneiderman B. Tree visualization with tree-maps: 2-d space-filling approach. ACM Trans Graph. 1992;11(1):92–9.CrossRefGoogle Scholar
  18. 18.
    Roth EM, Patterson ES, Mumaw RJ. Cognitive engineering: issues in user-centered system design. In: Marciniak JJ, editor. Encyclopedia of software engineering. New York: Wiley; 2002. p. 163–79.Google Scholar
  19. 19.
    Bisantz AM, Carayon P, Miller A, Khunlertkit A, Arbaje A, Xiao Y. Using human factors and systems engineering to improve care coordination. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting. Thousand Oaks, CA: SAGE Publications; 2012. p. 855–9.Google Scholar
  20. 20.
    Jiancaro T, Jamieson GA, Mihailidis A. Twenty years of cognitive work analysis in health care: a scoping review. J Cogn Eng Decis Making. 2013;8(1):3–22.CrossRefGoogle Scholar
  21. 21.
    Nemeth C, O’Connor M, Klock PA, Cook R. Discovering healthcare cognition: the use of cognitive artifacts to reveal cognitive work. Organ Stud. 2006;27(7):1011–35.CrossRefGoogle Scholar
  22. 22.
    Nemeth CP, Cook RI. Artifact analysis as a way to understand cognition. In: Lee JD, Kirlik A, editors. The Oxford handbook of cognitive engineering. Oxford: Oxford University Press; 2013. p. 302.Google Scholar
  23. 23.
    Nemeth CP, Cook RI, O’Connor M, Klock PA. Using cognitive artifacts to understand distributed cognition. IEEE Trans Syst Man Cybernet A Syst Humans. 2004;34(6):726–35.CrossRefGoogle Scholar
  24. 24.
    Collins SA, Mamykina L, Jordan D, Stein DM, Shine A, Reyfman P, Kaufman D. In search of common ground in handoff documentation in an intensive care unit. J Biomed Inform. 2012;45(2):307–15.CrossRefPubMedGoogle Scholar
  25. 25.
    Phipps D, Meakin G, Beatty P, Nsoedo C, Parker D. Human factors in anaesthetic practice: insights from a task analysis. Br J Anaesth. 2008;100(3):333–43.CrossRefPubMedGoogle Scholar
  26. 26.
    West E. Sociological contributions to patient safety. In: Patient safety. Maidenhead, UK: Open University Press; 2005. p. 19.Google Scholar
  27. 27.
    Donchin Y, Gopher D, Olin M, Badihi Y, Biesky M, Sprung C, Pizov R, Cotev S. A look into the nature and causes of human errors in the intensive care unit. Qual Saf Health Care. 2003;12(2):143–7.CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Nielsen J, Hackos JT. Usability engineering, vol. 125184069. Boston: Academic; 1993.Google Scholar
  29. 29.
    Shneiderman B. Designing the user interface: strategies for effective user interface interaction. Reading: Addison Wesley; 1998.Google Scholar
  30. 30.
    Kushniruk A. Evaluation in the design of health information systems: application of approaches emerging from usability engineering. Comput Biol Med. 2002;32(3):141–9.CrossRefPubMedGoogle Scholar
  31. 31.
    Kushniruk AW, Patel VL, Cimino JJ. Usability testing in medical informatics: cognitive approaches to evaluation of information systems and user interfaces. Proc AMIA Annu Fall Symp. 1997;1997:218–22.Google Scholar
  32. 32.
    Wongsuphasawat K, Guerra Gómez JA, Plaisant C, Wang TD, Taieb-Maimon M, Shneiderman B. LifeFlow: visualizing an overview of event sequences. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York: ACM; 2011. p. 1747–56.Google Scholar
  33. 33.
    Zhang J, Johnson TR, Patel VL, Paige DL, Kubose T. Using usability heuristics to evaluate patient safety of medical devices. J Biomed Inform. 2003;36(1):23–30.CrossRefPubMedGoogle Scholar
  34. 34.
    Basseville ME, Nikiforov IV. Detection of abrupt changes: theory and application. Upper Saddle River, NJ: Prentice-Hall; 1993.Google Scholar
  35. 35.
    Brodsky BE, Darkhovsky BS. Nonparametric methods in change point problems, vol. 243. New York: Springer; 1993.CrossRefGoogle Scholar
  36. 36.
    Chen J, Gupta A. Parametric statistical change point analysis. Boston, MA: Birkhause; 2000.CrossRefGoogle Scholar
  37. 37.
    Van Dobben de Bruyn C. Cumulative sum tests: theory and practice. London: Griffin; 1968.Google Scholar
  38. 38.
    Wu Y. Inference for change point and post change means after a CUSUM test, vol. 180. New York: Springer; 2005.Google Scholar
  39. 39.
    Woods D. The cognitive engineering of problem representations. In: Human-computer interaction and complex systems, vol. 169. Boston: Academic; 1991. p. 188.Google Scholar
  40. 40.
    Tufte E, editor. Envisioning information. Cheshire, CT: Graphic Press; 1990.Google Scholar
  41. 41.
    Tufte ER. The visual display of quantitative information, vol. 7. Cheshire, CT: Graphics press; 1983.Google Scholar
  42. 42.
    Woods DD. Visual momentum: a concept to improve the cognitive coupling of person and computer. Int J Man Mach Stud. 1984;21(3):229–44.CrossRefGoogle Scholar
  43. 43.
    Powsner SM, Tufte ER. Graphical summary of patient status. Lancet. 1994;344(8919):386–9.CrossRefPubMedGoogle Scholar
  44. 44.
    Koch S, Staggers N, Weir C, Agutter J, Liu D, Westenskow D. Integrated information displays for ICU nurses: field observations, display design, and display evaluation. Thousand Oaks, CA: SAGE Publications; 2010. p. 932–6.Google Scholar
  45. 45.
    Balas EA. Interactive computer graphics support of medical decision-making. Salt Lake City, UT: Department of Medical Informatics, University of Utah; 1991.Google Scholar
  46. 46.
    Elson RB, Connelly DP. The impact of anticipatory patient data displays on physician decision making: a pilot study. Proc AMIA Annu Fall Symp. 1997;1997:233–7.Google Scholar
  47. 47.
    Rogers J, Haring O. The impact of a computerized medical record summary system on incidence and length of hospitalization. Med Care. 1979;17(6):618–30.CrossRefPubMedGoogle Scholar
  48. 48.
    Plaisant C, Milash B, Rose A, Widoff S, Shneiderman B. LifeLines: visualizing personal histories. New York: ACM; 1996. p. 221–7.Google Scholar
  49. 49.
    Alonso D, Rose A, Plaisant C, Norman K. Viewing personal history records: a comparison of tabular format and graphical presentation using LifeLines. Behav Inform Technol. 1997;17(5):249–62.CrossRefGoogle Scholar
  50. 50.
    Faiola A, Newlon C. Advancing critical care in the ICU: a human-centered biomedical data visualization systems. In: Ergonomics and health aspects of work with computers. Cham: Springer; 2011. p. 119–28.CrossRefGoogle Scholar
  51. 51.
    Zhang J. Human-centered computing in health information systems Part 1: Analysis and design. J Biomed Inform. 2005;38(1):1–3.CrossRefPubMedGoogle Scholar
  52. 52.
    Effken JA, Loeb RG, Kang Y, Lin ZC. Clinical information displays to improve ICU outcomes. Int J Med Inform. 2008;77(11):765–77.CrossRefPubMedGoogle Scholar
  53. 53.
    M.I.T. Laboratory of Computational Physiology. Physionet challenge. 2009. http://physionet.org/challenge/2009/.
  54. 54.
    Moody GB, Lehman L. Predicting acute hypotensive episodes: the 10th annual physioNet/computers in cardiology challenge. Comput Cardiol. 2009;2009:541–4.Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2020

Authors and Affiliations

  • J. Michael Schmidt
    • 1
    Email author
  • John M. Irvine
    • 2
  • Sarah Miller
    • 3
  1. 1.NationBuilderLos AngelesUSA
  2. 2.Information and Decisions SystemsDraper LaboratoryCambridgeUSA
  3. 3.Watson Health at IBMCambridgeUSA

Personalised recommendations