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A Computational Adverse Event Detection Matrix

Harms caused during healthcare encounters are pervasive and occur at an alarming rate; therefore, building a set of computational detection methodologies in the adverse event area is urgently needed to address this problem. To understand the entire range of adverse event detection methods currently...

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Detalles Bibliográficos
Autores principales: GADDE, Mary, PENNING, Melody
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7928019/
https://www.ncbi.nlm.nih.gov/pubmed/32570358
http://dx.doi.org/10.3233/SHTI200134
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author GADDE, Mary
PENNING, Melody
author_facet GADDE, Mary
PENNING, Melody
author_sort GADDE, Mary
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description Harms caused during healthcare encounters are pervasive and occur at an alarming rate; therefore, building a set of computational detection methodologies in the adverse event area is urgently needed to address this problem. To understand the entire range of adverse event detection methods currently in practice we have developed a computational adverse event detection matrix. This structure is made of methods used presently at US hospitals to detect patient safety events. It contains adverse event 1) concepts and 2) synthesized detection strategies as well as calculations of overlap of coded data in the subset of algorithms implemented completely computationally. Most importantly, this matrix provides a clear picture of coverage gaps in the detection of adverse events.
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spelling pubmed-79280192021-03-03 A Computational Adverse Event Detection Matrix GADDE, Mary PENNING, Melody Stud Health Technol Inform Article Harms caused during healthcare encounters are pervasive and occur at an alarming rate; therefore, building a set of computational detection methodologies in the adverse event area is urgently needed to address this problem. To understand the entire range of adverse event detection methods currently in practice we have developed a computational adverse event detection matrix. This structure is made of methods used presently at US hospitals to detect patient safety events. It contains adverse event 1) concepts and 2) synthesized detection strategies as well as calculations of overlap of coded data in the subset of algorithms implemented completely computationally. Most importantly, this matrix provides a clear picture of coverage gaps in the detection of adverse events. 2020-06-16 /pmc/articles/PMC7928019/ /pubmed/32570358 http://dx.doi.org/10.3233/SHTI200134 Text en http://creativecommons.org/licenses/by-nc/4.0/ This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
spellingShingle Article
GADDE, Mary
PENNING, Melody
A Computational Adverse Event Detection Matrix
title A Computational Adverse Event Detection Matrix
title_full A Computational Adverse Event Detection Matrix
title_fullStr A Computational Adverse Event Detection Matrix
title_full_unstemmed A Computational Adverse Event Detection Matrix
title_short A Computational Adverse Event Detection Matrix
title_sort computational adverse event detection matrix
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7928019/
https://www.ncbi.nlm.nih.gov/pubmed/32570358
http://dx.doi.org/10.3233/SHTI200134
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