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An iconic language for the graphical representation of medical concepts

BACKGROUND: Many medication errors are encountered in drug prescriptions, which would not occur if practitioners could remember the drug properties. They can refer to drug monographs to find these properties, however drug monographs are long and tedious to read during consultation. We propose a two-...

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Autores principales: Lamy, Jean-Baptiste, Duclos, Catherine, Bar-Hen, Avner, Ouvrard, Patrick, Venot, Alain
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2413217/
https://www.ncbi.nlm.nih.gov/pubmed/18435838
http://dx.doi.org/10.1186/1472-6947-8-16
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author Lamy, Jean-Baptiste
Duclos, Catherine
Bar-Hen, Avner
Ouvrard, Patrick
Venot, Alain
author_facet Lamy, Jean-Baptiste
Duclos, Catherine
Bar-Hen, Avner
Ouvrard, Patrick
Venot, Alain
author_sort Lamy, Jean-Baptiste
collection PubMed
description BACKGROUND: Many medication errors are encountered in drug prescriptions, which would not occur if practitioners could remember the drug properties. They can refer to drug monographs to find these properties, however drug monographs are long and tedious to read during consultation. We propose a two-step approach for facilitating access to drug monographs. The first step, presented here, is the design of a graphical language, called VCM. METHODS: The VCM graphical language was designed using a small number of graphical primitives and combinatory rules. VCM was evaluated over 11 volunteer general practitioners to assess if the language is easy to learn, to understand and to use. Evaluators were asked to register their VCM training time, to indicate the meaning of VCM icons and sentences, and to answer clinical questions related to randomly generated drug monograph-like documents, supplied in text or VCM format. RESULTS: VCM can represent the various signs, diseases, physiological states, life habits, drugs and tests described in drug monographs. Grammatical rules make it possible to generate many icons by combining a small number of primitives and reusing simple icons to build more complex ones. Icons can be organized into simple sentences to express drug recommendations. Evaluation showed that VCM was learnt in 2 to 7 hours, that physicians understood 89% of the tested VCM icons, and that they answered correctly to 94% of questions using VCM (versus 88% using text, p = 0.003) and 1.8 times faster (p < 0.001). CONCLUSION: VCM can be learnt in a few hours and appears to be easy to read. It can now be used in a second step: the design of graphical interfaces facilitating access to drug monographs. It could also be used for broader applications, including the design of interfaces for consulting other types of medical document or medical data, or, very simply, to enrich medical texts.
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spelling pubmed-24132172008-06-06 An iconic language for the graphical representation of medical concepts Lamy, Jean-Baptiste Duclos, Catherine Bar-Hen, Avner Ouvrard, Patrick Venot, Alain BMC Med Inform Decis Mak Research Article BACKGROUND: Many medication errors are encountered in drug prescriptions, which would not occur if practitioners could remember the drug properties. They can refer to drug monographs to find these properties, however drug monographs are long and tedious to read during consultation. We propose a two-step approach for facilitating access to drug monographs. The first step, presented here, is the design of a graphical language, called VCM. METHODS: The VCM graphical language was designed using a small number of graphical primitives and combinatory rules. VCM was evaluated over 11 volunteer general practitioners to assess if the language is easy to learn, to understand and to use. Evaluators were asked to register their VCM training time, to indicate the meaning of VCM icons and sentences, and to answer clinical questions related to randomly generated drug monograph-like documents, supplied in text or VCM format. RESULTS: VCM can represent the various signs, diseases, physiological states, life habits, drugs and tests described in drug monographs. Grammatical rules make it possible to generate many icons by combining a small number of primitives and reusing simple icons to build more complex ones. Icons can be organized into simple sentences to express drug recommendations. Evaluation showed that VCM was learnt in 2 to 7 hours, that physicians understood 89% of the tested VCM icons, and that they answered correctly to 94% of questions using VCM (versus 88% using text, p = 0.003) and 1.8 times faster (p < 0.001). CONCLUSION: VCM can be learnt in a few hours and appears to be easy to read. It can now be used in a second step: the design of graphical interfaces facilitating access to drug monographs. It could also be used for broader applications, including the design of interfaces for consulting other types of medical document or medical data, or, very simply, to enrich medical texts. BioMed Central 2008-04-24 /pmc/articles/PMC2413217/ /pubmed/18435838 http://dx.doi.org/10.1186/1472-6947-8-16 Text en Copyright © 2008 Lamy et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Lamy, Jean-Baptiste
Duclos, Catherine
Bar-Hen, Avner
Ouvrard, Patrick
Venot, Alain
An iconic language for the graphical representation of medical concepts
title An iconic language for the graphical representation of medical concepts
title_full An iconic language for the graphical representation of medical concepts
title_fullStr An iconic language for the graphical representation of medical concepts
title_full_unstemmed An iconic language for the graphical representation of medical concepts
title_short An iconic language for the graphical representation of medical concepts
title_sort iconic language for the graphical representation of medical concepts
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2413217/
https://www.ncbi.nlm.nih.gov/pubmed/18435838
http://dx.doi.org/10.1186/1472-6947-8-16
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