A human factors approach to observation chart design can trump health professionals' prior chart experience

Resuscitation. 2013 May;84(5):657-65. doi: 10.1016/j.resuscitation.2012.09.023. Epub 2012 Oct 4.


Aim: To determine whether experienced health professionals recognise patient deterioration more accurately and efficiently using (a) novel observation charts, designed from a human factors perspective, or (b) chart designs with which they have long-term experience.

Methods: Participants were 101 health professionals experienced in using either a multiple parameter track-and-trigger chart or a graphical chart with no track-and-trigger system. Participants were presented with realistic abnormal and normal patient observations recorded on six hospital observation charts of varying design quality, including the chart that participants were familiar with (or a very similar design). Across 48 trials, the participant was asked to specify if any of the vital sign observations were abnormal, or if all of the observations were normal. Participants' overall error rates (i.e., proportion of incorrect responses) and response times, the main outcome measures, were calculated for each observation chart.

Results: Participants made significantly fewer errors and responded significantly faster when using a novel user-friendly chart compared with all the other designs, including the charts that they were experienced with in a clinical setting.

Conclusions: The findings suggest that, at least in the contexts examined, superior observation chart design appears to trump familiarity. Hence, hospitals motivated to improve the detection of patient deterioration should implement charts designed from a human factors perspective, rather than simply maintaining the status quo of reliance on clinical experience.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Australia
  • Clinical Competence*
  • Female
  • Health Personnel / standards*
  • Humans
  • Male
  • Medical Errors / statistics & numerical data*
  • Medical Records / standards*
  • Medical Records / statistics & numerical data
  • Middle Aged