Journal of Clinical Monitoring and Computing

, Volume 19, Issue 3, pp 183–194

A Comparison of Graphical and Textual Presentations of Time Series Data to Support Medical Decision Making in the Neonatal Intensive Care Unit

  • Anna S. Law
  • Yvonne Freer
  • Jim Hunter
  • Robert H. Logie
  • Neil Mcintosh
  • John Quinn
Article

Abstract

Objective. To compare expert-generated textual summaries of physiological data with trend graphs, in terms of their ability to support neonatal Intensive Care Unit (ICU) staff in making decisions when presented with medical scenarios. Methods. Forty neonatal ICU staff were recruited for the experiment, eight from each of five groups – junior, intermediate and senior nurses, junior and senior doctors. The participants were presented with medical scenarios on a computer screen, and asked to choose from a list of 18 possible actions those they thought were appropriate. Half of the scenarios were presented as trend graphs, while the other half were presented as passages of text. The textual summaries had been generated by two human experts and were intended to describe the physiological state of the patient over a short period of time (around 40 minutes) but not to interpret it. Results. In terms of the content of responses there was a clear advantage for the Text condition, with participants tending to choose more of the appropriate actions when the information was presented as text rather than as graphs. In terms of the speed of response there was no difference between the Graphs and Text conditions. There was no significant difference between the staff groups in terms of speed or content of responses. In contrast to the objective measures of performance, the majority of participants reported a subjective preference for the Graphs condition. Conclusions. In this experimental task, participants performed better when presented with a textual summary of the medical scenario than when it was presented as a set of trend graphs. If the necessary algorithms could be developed that would allow computers automatically to generate descriptive summaries of physiological data, this could potentially be a useful feature of decision support tools in the intensive care unit.

Key Words

intensive care computerised monitoring decision making decision support 

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References

  1. 1.
    Green CA, Gilhooly KJ, Logie RH, Ross DG. Human factors and computerisation in Intensive Care Units: A review. Int J Clin Monit Comput 1991; 8: 167–178.CrossRefPubMedGoogle Scholar
  2. 2.
    Hanson CW, Marshall BE. Artificial intelligence applications in the intensive care unit. Crit Care Med 2001; 29: 427–435.CrossRefPubMedGoogle Scholar
  3. 3.
    Alberdi E, Gilhooly K, Hunter J, Logie R, Lyon A, McIntosh N, et al. Computerisation and decision making in neonatal intensive care: A cognitive engineering investigation. J Clin Mont Comput 2000; 16: 85–94.CrossRefGoogle Scholar
  4. 4.
    Ambroso C, Bowes C, Chambrin MC, Gilhooly K, Green C, Kari A, et al. INFORM: European survey of computers in Intensive Care Units. Int J Clin Monit Comput 1992; 9: 53–61.CrossRefPubMedGoogle Scholar
  5. 5.
    Cunningham S, Deere S, Symon A, Elton RA, McIntosh N. A randomized, controlled trial of computerized physiologic trend monitoring in an intensive care unit. Crit Care Med 1998; 26: 2053–2059.CrossRefPubMedGoogle Scholar
  6. 6.
    McIntosh N, Lyon A, Badger P. Time trend monitoring in the Neonatal Intensive Care Unit: Why doesn't it make a difference? Pediatrics 1996; 98:540.Google Scholar
  7. 7.
    Alberdi E, Becher JC, Gilhooly K, Hunter J, Logie RH, Lyon A et al. Expertise and the interpretation of computerized physiological data: implications for the design of computerized monitoring in neonatal intensive care. Int J Hum-Comput St 2001; 55: 191–216.CrossRefGoogle Scholar
  8. 8.
    Ewing G, Freer Y, Logie RH, Hunter J, McIntosh N, Rudkin S, et al. Role and experience determine decision support interface requirements in a neonatal intensive care environment. J Biomed Inform 2003; 36: 240–249.CrossRefPubMedGoogle Scholar
  9. 9.
    Freer Y, Ferguson L, Ewing G, Hunter J, Logie RH, Rudkin S et al. Mismatched concepts in a neonatal intensive care unit (NICU): Further issues for computer decision support? J Clin Monit Comput 2003; 17: 441–447.CrossRefGoogle Scholar
  10. 10.
    Hunter J. The Time Series Workbench: User Manual, University of Aberdeen Computing Science Technical Report, 2004Google Scholar
  11. 11.
    Ewing G, Ferguson L, Freer Y, Hunter J, McIntosh N. Observational data acquired on a Neonatal Intensive Care Unit. University of Aberdeen Computing Science Departmental Technical Report: TR 0205, 2002.Google Scholar
  12. 12.
    Hunter JRW, Ferguson L, Freer Y, Ewing G, Logie R, McCue P, et al. The NEONATE Database. Workshop on Intelligent Data Analysis in Medicine and Pharmacology and Knowledge-Based Information Management in Anaesthesia and Intensive Care, AIME-03, Cyprus, 2003, pp 21–24.Google Scholar
  13. 13.
    Benbasat I, Dexter AS, Todd P. The influence of color and graphical information presentation in a managerial decision simulation. Human-Computer Interaction 1986; 2: 65–92.Google Scholar
  14. 14.
    Wright P, Jansen C, Wyatt JC. How to limit clinical errors in interpretation of data. The Lancet 1998; 352: 1539– 1543.CrossRefGoogle Scholar
  15. 15.
    Somayajulu G, Sripada S, Reiter E, Hunter J, Yu J. Summarizing neonatal time series data. In: Proceedings of the research note sessions of the EACL03, Budapest, 2003, pp. 167–170.Google Scholar

Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  • Anna S. Law
    • 1
  • Yvonne Freer
    • 2
  • Jim Hunter
    • 3
  • Robert H. Logie
    • 1
    • 4
  • Neil Mcintosh
    • 2
  • John Quinn
    • 2
  1. 1.Department of PsychologyUniversity of EdinburghU.K.
  2. 2.Department of NeonatologyRoyal Infirmary of EdinburghU.K.
  3. 3.Department of Computing ScienceUniversity of AberdeenU.K.
  4. 4.Department of PsychologyUniversity of EdinburghEdinburghU.K.

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