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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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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 |
collection | PubMed |
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. |
format | Online Article Text |
id | pubmed-7928019 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
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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