Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Holve, Erin, Kahn, Michael, Nahm, Meredith, Ryan, Patrick, Weiskopf, Nicole
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Informatics Association 201
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3845781/
https://www.ncbi.nlm.nih.gov/pubmed/24303241
_version_ 1782293365277065216
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
work_keys_str_mv AT holveerin acomprehensiveframeworkfordataqualityassessmentincer
AT kahnmichael acomprehensiveframeworkfordataqualityassessmentincer
AT nahmmeredith acomprehensiveframeworkfordataqualityassessmentincer
AT ryanpatrick acomprehensiveframeworkfordataqualityassessmentincer
AT weiskopfnicole acomprehensiveframeworkfordataqualityassessmentincer
AT holveerin comprehensiveframeworkfordataqualityassessmentincer
AT kahnmichael comprehensiveframeworkfordataqualityassessmentincer
AT nahmmeredith comprehensiveframeworkfordataqualityassessmentincer
AT ryanpatrick comprehensiveframeworkfordataqualityassessmentincer
AT weiskopfnicole comprehensiveframeworkfordataqualityassessmentincer