Using Graphical Representations to Support the Calculation of Infusion Parameters

  • Sandy J. J. Gould
  • Anna L. Cox
  • Duncan P. Brumby
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8120)

Abstract

A variety of medical procedures require arithmetic calculations to be performed. These calculations can be complex and induce errors that can have serious consequences on the ward. In this paper, we consider whether a graphical representation might make these calculations easier. The results of a laboratory experiment are reported in which participants were asked to solve a number of infusion parameter problems that were represented either graphically or textually. Results show that participants were faster but no more accurate in solving graphical problems than they were textual problems. We discuss the need for situated work to be conducted that builds on these initial findings to determine whether the advantages of graphical representations transfer to actual workplace settings.

Keywords

Graphical reasoning infusion pumps re-representation calculation 

References

  1. 1.
    Bindler, R., Bayne, T.: Medication Calculation Ability of Registered Nurses. Journal of Nursing Scholarship 23, 221–224 (1991)CrossRefGoogle Scholar
  2. 2.
    McMullan, M., Jones, R., Lea, S.: Patient safety: numerical skills and drug calculation abilities of nursing students and Registered Nurses. Journal of Advanced Nursing 66, 891–899 (2010)CrossRefGoogle Scholar
  3. 3.
    Harvey, S., Murphy, F., Lake, R., Jenkins, L., Cavanna, A., Tait, M.: Diagnosing the problem: Using a tool to identify pre-registration nursing students’ mathematical ability. Nurse Education in Practice 10, 119–125 (2010)CrossRefGoogle Scholar
  4. 4.
    Schulmeister, L.: Chemotherapy medication errors: descriptions, severity, and contributing factors. Oncol. Nurs. Forum. 26, 1033–1042 (1999)Google Scholar
  5. 5.
    Harne-Britner, S., Kreamer, C.L., Frownfelter, P., Helmuth, A., Lutter, S., Schafer, D.J., Wilson, C.: Improving medication calculation skills of practicing nurses and senior nursing students: a pilot study. J. Nurses Staff Dev. 22, 190–195 (2006)CrossRefGoogle Scholar
  6. 6.
    Hutton, M., Coben, D., Hall, C., Rowe, D., Sabin, M., Weeks, K., Woolley, N.: Numeracy for nursing, report of a pilot study to compare outcomes of two practical simulation tools – An online medication dosage assessment and practical assessment in the style of objective structured clinical examination. Nurse Education Today 30, 608–614 (2010)CrossRefGoogle Scholar
  7. 7.
    Nemeth, C.P., O’Connor, M., Cook, R.I.: The Infusion Device as a Source of Healthcare Resilience. In: Nemeth, C.P., Hollnagel, E., Dekker, S. (eds.) Preparation and Restoration, p. 215. Ashgate Publishing, Ltd. (2009)Google Scholar
  8. 8.
    Furniss, D., Blandford, A., Mayer, A.: Unremarkable errors: low-level disturbances in infusion pump use. In: Proceedings of the 25th BCS Conference on Human-Computer Interaction, pp. 197–204. BCS, Swinton (2011)Google Scholar
  9. 9.
    Eastwood, K.J., Boyle, M.J., Williams, B., Fairhall, R.: Numeracy skills of nursing students. Nurse Education Today 31, 815–818 (2011)CrossRefGoogle Scholar
  10. 10.
    Thimbleby, H., Williams, D.: Using nomograms to reduce harm from clinical calculations. In: IEEE International Conference on Healthcare Informatics (accepted)Google Scholar
  11. 11.
    Larkin, J.H., Simon, H.A.: Why a Diagram is (Sometimes) Worth Ten Thousand Words. Cog. Sci. 11, 65–100 (1987)CrossRefGoogle Scholar
  12. 12.
    Zhang, J., Norman, D.A.: Representations in distributed cognitive tasks. Cog. Sci. 18, 87–122 (1994)CrossRefGoogle Scholar
  13. 13.
    Ainsworth, S., Th Loizou, A.: The effects of self-explaining when learning with text or diagrams. Cog. Sci. 27, 669–681 (2003)CrossRefGoogle Scholar
  14. 14.
    Peebles, D., Cheng, P.C.-H.: Modeling the Effect of Task and Graphical Representation on Response Latency in a Graph Reading Task. J. Hum. Factor Ergon. 45, 28–46 (2003)CrossRefGoogle Scholar
  15. 15.
    Shah, P., Freedman, E.G.: Bar and Line Graph Comprehension: An Interaction of Top-Down and Bottom-Up Processes. TopiCS 3, 560–578 (2011)Google Scholar
  16. 16.
    Kirchner, W.K.: Age differences in short-term retention of rapidly changing information. J. Exp. Psych., 1–17 (1958)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sandy J. J. Gould
    • 1
  • Anna L. Cox
    • 1
  • Duncan P. Brumby
    • 1
  1. 1.UCL Interaction CentreUniversity College LondonLondonUK

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