Drug Safety

, Volume 37, Issue 5, pp 351–359 | Cite as

A Comparison of the Effects of Different Typographical Methods on the Recognizability of Printed Drug Names

  • Calvin K. L. OrEmail author
  • Hailiang Wang
Original Research Article



Tall Man lettering is a text enhancement method currently recommended by various organizations for distinguishing look-alike drug names to prevent medication errors. However, the literature has suggested that other typographic styles may be more effective.


Our objective was to examine the effects of text enhancements and orthographic similarity on the accuracy of look-alike drug name differentiation.


We conducted two experiments that were based on a two-way, repeated-measures design with text enhancement (Tall Man, boldface, boldface plus Tall Man, colored text [red] and contrast with lowercase as a ‘no text enhancement’ control) and orthographic similarity (low, medium, and high) as factors. Engineering students without a pharmacy background in experiment 1 (n = 40) and student pharmacists in experiment 2 (n = 40) participated in a computer-based drug name differentiation task in which they determined whether the two drug names in each of the 336 name pairs were the same or different. Only the data generated from the pairs in which the two names were different were used in the analysis. The differentiation accuracy was measured as the proportion of correct responses.


In both experiments, all five text enhancements significantly improved accuracy compared with the lowercase condition; and boldface plus Tall Man and color significantly outperformed Tall Man lettering. A high degree of orthographic similarity yielded the least accuracy, followed by medium and low degrees.


Highlighting the differing portions of confusing drug name pairs using enhanced text clearly renders differentiation easier, although Tall Man lettering may not be the most effective choice for this method.


Visual Search Medication Error Pair Stimulus Differentiation Accuracy Stroke Width 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by the matching fund granted by the Faculty of Engineering and the Department of Industrial and Manufacturing Systems Engineering at the University of Hong Kong (Grant #: 006010013, PI: Calvin Or). The authors are grateful to all the participants for their time.

Conflict of interest

Calvin K. L. Or and Hailiang Wang have no conflicts of interest that are directly relevant to the content of this study.

Supplementary material

40264_2014_156_MOESM1_ESM.docx (24 kb)
Supplementary material 1 (DOCX 24 kb)
40264_2014_156_MOESM2_ESM.docx (69 kb)
Supplementary material 2 (DOCX 69 kb)


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Industrial and Manufacturing Systems EngineeringThe University of Hong KongPokfulamHong Kong
  2. 2.Department of Industrial and Manufacturing Systems EngineeringThe University of Hong KongPokfulamHong Kong

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