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Feasibility and utility of applications of the common data model to multiple, disparate observational health databases
Objectives To evaluate the utility of applying the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) across multiple observational databases within an organization and to apply standardized analytics tools for conducting observational research. Materials and methods Six deide...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4457111/ https://www.ncbi.nlm.nih.gov/pubmed/25670757 http://dx.doi.org/10.1093/jamia/ocu023 |
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author | Voss, Erica A Makadia, Rupa Matcho, Amy Ma, Qianli Knoll, Chris Schuemie, Martijn DeFalco, Frank J Londhe, Ajit Zhu, Vivienne Ryan, Patrick B |
author_facet | Voss, Erica A Makadia, Rupa Matcho, Amy Ma, Qianli Knoll, Chris Schuemie, Martijn DeFalco, Frank J Londhe, Ajit Zhu, Vivienne Ryan, Patrick B |
author_sort | Voss, Erica A |
collection | PubMed |
description | Objectives To evaluate the utility of applying the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) across multiple observational databases within an organization and to apply standardized analytics tools for conducting observational research. Materials and methods Six deidentified patient-level datasets were transformed to the OMOP CDM. We evaluated the extent of information loss that occurred through the standardization process. We developed a standardized analytic tool to replicate the cohort construction process from a published epidemiology protocol and applied the analysis to all 6 databases to assess time-to-execution and comparability of results. Results Transformation to the CDM resulted in minimal information loss across all 6 databases. Patients and observations excluded were due to identified data quality issues in the source system, 96% to 99% of condition records and 90% to 99% of drug records were successfully mapped into the CDM using the standard vocabulary. The full cohort replication and descriptive baseline summary was executed for 2 cohorts in 6 databases in less than 1 hour. Discussion The standardization process improved data quality, increased efficiency, and facilitated cross-database comparisons to support a more systematic approach to observational research. Comparisons across data sources showed consistency in the impact of inclusion criteria, using the protocol and identified differences in patient characteristics and coding practices across databases. Conclusion Standardizing data structure (through a CDM), content (through a standard vocabulary with source code mappings), and analytics can enable an institution to apply a network-based approach to observational research across multiple, disparate observational health databases. |
format | Online Article Text |
id | pubmed-4457111 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-44571112016-05-01 Feasibility and utility of applications of the common data model to multiple, disparate observational health databases Voss, Erica A Makadia, Rupa Matcho, Amy Ma, Qianli Knoll, Chris Schuemie, Martijn DeFalco, Frank J Londhe, Ajit Zhu, Vivienne Ryan, Patrick B J Am Med Inform Assoc Special Focus on Standards Objectives To evaluate the utility of applying the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) across multiple observational databases within an organization and to apply standardized analytics tools for conducting observational research. Materials and methods Six deidentified patient-level datasets were transformed to the OMOP CDM. We evaluated the extent of information loss that occurred through the standardization process. We developed a standardized analytic tool to replicate the cohort construction process from a published epidemiology protocol and applied the analysis to all 6 databases to assess time-to-execution and comparability of results. Results Transformation to the CDM resulted in minimal information loss across all 6 databases. Patients and observations excluded were due to identified data quality issues in the source system, 96% to 99% of condition records and 90% to 99% of drug records were successfully mapped into the CDM using the standard vocabulary. The full cohort replication and descriptive baseline summary was executed for 2 cohorts in 6 databases in less than 1 hour. Discussion The standardization process improved data quality, increased efficiency, and facilitated cross-database comparisons to support a more systematic approach to observational research. Comparisons across data sources showed consistency in the impact of inclusion criteria, using the protocol and identified differences in patient characteristics and coding practices across databases. Conclusion Standardizing data structure (through a CDM), content (through a standard vocabulary with source code mappings), and analytics can enable an institution to apply a network-based approach to observational research across multiple, disparate observational health databases. Oxford University Press 2015-05 2015-02-10 /pmc/articles/PMC4457111/ /pubmed/25670757 http://dx.doi.org/10.1093/jamia/ocu023 Text en © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Special Focus on Standards Voss, Erica A Makadia, Rupa Matcho, Amy Ma, Qianli Knoll, Chris Schuemie, Martijn DeFalco, Frank J Londhe, Ajit Zhu, Vivienne Ryan, Patrick B Feasibility and utility of applications of the common data model to multiple, disparate observational health databases |
title | Feasibility and utility of applications of the common data model to multiple, disparate observational health databases |
title_full | Feasibility and utility of applications of the common data model to multiple, disparate observational health databases |
title_fullStr | Feasibility and utility of applications of the common data model to multiple, disparate observational health databases |
title_full_unstemmed | Feasibility and utility of applications of the common data model to multiple, disparate observational health databases |
title_short | Feasibility and utility of applications of the common data model to multiple, disparate observational health databases |
title_sort | feasibility and utility of applications of the common data model to multiple, disparate observational health databases |
topic | Special Focus on Standards |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4457111/ https://www.ncbi.nlm.nih.gov/pubmed/25670757 http://dx.doi.org/10.1093/jamia/ocu023 |
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