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Distributed Cognition Artifacts on Clinical Research Data Collection Forms

Medical record abstraction, a primary mode of data collection in secondary data use, is associated with high error rates. Cognitive factors have not been studied as a possible explanation for medical record abstraction errors. We employed the theory of distributed representation and representational...

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Detalles Bibliográficos
Autores principales: Nahm, Meredith, Nguyen, Vickie D., Razzouk, Elie, Zhu, Min, Zhang, Jiajie
Formato: Texto
Lenguaje:English
Publicado: American Medical Informatics Association 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041537/
https://www.ncbi.nlm.nih.gov/pubmed/21347145
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author Nahm, Meredith
Nguyen, Vickie D.
Razzouk, Elie
Zhu, Min
Zhang, Jiajie
author_facet Nahm, Meredith
Nguyen, Vickie D.
Razzouk, Elie
Zhu, Min
Zhang, Jiajie
author_sort Nahm, Meredith
collection PubMed
description Medical record abstraction, a primary mode of data collection in secondary data use, is associated with high error rates. Cognitive factors have not been studied as a possible explanation for medical record abstraction errors. We employed the theory of distributed representation and representational analysis to systematically evaluate cognitive demands in medical record abstraction and the extent of external cognitive support employed in a sample of clinical research data collection forms. We show that the cognitive load required for abstraction in 61% of the sampled data elements was high, exceedingly so in 9%. Further, the data collection forms did not support external cognition for the most complex data elements. High working memory demands are a possible explanation for the association of data errors with data elements requiring abstractor interpretation, comparison, mapping or calculation. The representational analysis used here can be used to identify data elements with high cognitive demands.
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spelling pubmed-30415372011-02-23 Distributed Cognition Artifacts on Clinical Research Data Collection Forms Nahm, Meredith Nguyen, Vickie D. Razzouk, Elie Zhu, Min Zhang, Jiajie Summit on Translat Bioinforma Articles Medical record abstraction, a primary mode of data collection in secondary data use, is associated with high error rates. Cognitive factors have not been studied as a possible explanation for medical record abstraction errors. We employed the theory of distributed representation and representational analysis to systematically evaluate cognitive demands in medical record abstraction and the extent of external cognitive support employed in a sample of clinical research data collection forms. We show that the cognitive load required for abstraction in 61% of the sampled data elements was high, exceedingly so in 9%. Further, the data collection forms did not support external cognition for the most complex data elements. High working memory demands are a possible explanation for the association of data errors with data elements requiring abstractor interpretation, comparison, mapping or calculation. The representational analysis used here can be used to identify data elements with high cognitive demands. American Medical Informatics Association 2010-03-01 /pmc/articles/PMC3041537/ /pubmed/21347145 Text en ©2010 AMIA - All rights reserved. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose
spellingShingle Articles
Nahm, Meredith
Nguyen, Vickie D.
Razzouk, Elie
Zhu, Min
Zhang, Jiajie
Distributed Cognition Artifacts on Clinical Research Data Collection Forms
title Distributed Cognition Artifacts on Clinical Research Data Collection Forms
title_full Distributed Cognition Artifacts on Clinical Research Data Collection Forms
title_fullStr Distributed Cognition Artifacts on Clinical Research Data Collection Forms
title_full_unstemmed Distributed Cognition Artifacts on Clinical Research Data Collection Forms
title_short Distributed Cognition Artifacts on Clinical Research Data Collection Forms
title_sort distributed cognition artifacts on clinical research data collection forms
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041537/
https://www.ncbi.nlm.nih.gov/pubmed/21347145
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