Tallman lettering as a strategy for differentiation in look-alike, sound-alike drug names: The role of familiarity in differentiating drug doppelgangers. (January 2016)

• Record Type: Journal Article
• Authors: DeHenau, Carly; Becker, Mark W.; Bello, Nora M.; Liu, Sichang; Bix, Laura
• Is Part Of: Applied ergonomics. Volume 52(2016:Jan.)
• Page Start: 77
• Page End: 84
• Publication Date: 2016-01
• Subjects: Medication error -- Tallman lettering -- Drug labeling -- Look-alike sound-alike drug names -- Text differentiation -- Name differentiation; US Food and Drug Administration FDA -- Institute of Medicine IoM -- Institute for Safe Medication Practices ISMP -- National Center for Patient Safety NCPS; Human engineering -- Periodicals; 620.82
• DOI: 10.1016/j.apergo.2015.06.009
• Languages: English
• ISSNs: 0003-6870

Abstract

Abstract: Tallman lettering, capitalizing the dissimilar portions of easily confused drug names, is one strategy for reducing medication errors. We assessed the efficacy of Tallman lettering in a visually complex environment using a change detection method with healthcare providers and laypeople. In addition, the effect of familiarity with the drug name was assessed using a subset of responses collected from healthcare providers. Both healthcare providers and laypeople detected changes in confusable pairs of drug names more often (P < 0.0001) and more quickly (P < 0.05) when changes were presented in Tallman lettering, though the benefits were more pronounced for healthcare providers (p < 0.05). Familiarity with both drug names in a confusable pair mitigated the benefit of Tallman lettering. Results are discussed in terms of bottom-up and top-down attentional systems for processing of information in the context of the varied healthcare environments. Highlights: Tallman lettering resulted in quicker detection of confusable pairs. Tallman lettering resulted in more frequent detection of confusable pairs. Were more pronounced for healthcare providers. Prior familiarity discussed within an attentional allocation framework.