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A comprehensive framework for data quality assessment in CER
The panel addresses the urgent need to ensure that comparative effectiveness research (CER) findings derived from diverse and distributed data sources are based on credible, high-quality data; and that the methods used to assess and report data quality are consistent, comprehensive, and available to...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Medical Informatics Association
201
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3845781/ https://www.ncbi.nlm.nih.gov/pubmed/24303241 |
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author | Holve, Erin Kahn, Michael Nahm, Meredith Ryan, Patrick Weiskopf, Nicole |
author_facet | Holve, Erin Kahn, Michael Nahm, Meredith Ryan, Patrick Weiskopf, Nicole |
author_sort | Holve, Erin |
collection | PubMed |
description | The panel addresses the urgent need to ensure that comparative effectiveness research (CER) findings derived from diverse and distributed data sources are based on credible, high-quality data; and that the methods used to assess and report data quality are consistent, comprehensive, and available to data consumers. The panel consists of representatives from four teams leveraging electronic clinical data for CER, patient centered outcomes research (PCOR), and quality improvement (QI) and seeks to change the current paradigm where data quality assessment (DQA) is performed “behind the scenes” using one-off project specific methods. The panelists will present their process of harmonizing existing models for describing and measuring clinical data quality and will describe a comprehensive integrated framework for assessing and reporting DQA findings. The collaborative project is supported by the Electronic Data Methods (EDM) Forum, a three-year grant from the Agency for Healthcare Research and Quality (AHRQ) to facilitate learning and foster collaboration across a set of CER, PCOR, and QI projects designed to build infrastructure and methods for collecting and analyzing prospective data from electronic clinical data . |
format | Online Article Text |
id | pubmed-3845781 |
institution | National Center for Biotechnology Information |
language | English |
publishDate |
201 |
publisher |
American Medical Informatics Association
|
record_format | MEDLINE/PubMed |
spelling | pubmed-38457812013-12-03 A comprehensive framework for data quality assessment in CER Holve, Erin Kahn, Michael Nahm, Meredith Ryan, Patrick Weiskopf, Nicole AMIA Jt Summits Transl Sci Proc Articles The panel addresses the urgent need to ensure that comparative effectiveness research (CER) findings derived from diverse and distributed data sources are based on credible, high-quality data; and that the methods used to assess and report data quality are consistent, comprehensive, and available to data consumers. The panel consists of representatives from four teams leveraging electronic clinical data for CER, patient centered outcomes research (PCOR), and quality improvement (QI) and seeks to change the current paradigm where data quality assessment (DQA) is performed “behind the scenes” using one-off project specific methods. The panelists will present their process of harmonizing existing models for describing and measuring clinical data quality and will describe a comprehensive integrated framework for assessing and reporting DQA findings. The collaborative project is supported by the Electronic Data Methods (EDM) Forum, a three-year grant from the Agency for Healthcare Research and Quality (AHRQ) to facilitate learning and foster collaboration across a set of CER, PCOR, and QI projects designed to build infrastructure and methods for collecting and analyzing prospective data from electronic clinical data . American Medical Informatics Association 2013 -03- 18 /pmc/articles/PMC3845781/ /pubmed/24303241 Text en ©2013 AMIA - All rights reserved. |
spellingShingle | Articles Holve, Erin Kahn, Michael Nahm, Meredith Ryan, Patrick Weiskopf, Nicole A comprehensive framework for data quality assessment in CER |
title |
A comprehensive framework for data quality assessment in CER
|
title_full |
A comprehensive framework for data quality assessment in CER
|
title_fullStr |
A comprehensive framework for data quality assessment in CER
|
title_full_unstemmed |
A comprehensive framework for data quality assessment in CER
|
title_short |
A comprehensive framework for data quality assessment in CER
|
title_sort | comprehensive framework for data quality assessment in cer |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3845781/ https://www.ncbi.nlm.nih.gov/pubmed/24303241 |
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