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 deid...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 22; no. 3; pp. 553 - 565 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
May2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109744659&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109744659 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: May2015 vid: 22 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 109744659 NLM25670757 2013041014 10.1093/jamia/ocu023 NLM25670757 PMC4457111 109744659 ppf: 553 ppct: 12 formats: tig: atl: Feasibility and utility of applications of the common data model to multiple, disparate observational health databases. aug: au: Voss, Erica A Makadia, Rupa Matcho, Amy Ma, Qianli Knoll, Chris Schuemie, Martijn DeFalco, Frank J Londhe, Ajit Zhu, Vivienne Ryan, Patrick B sug: ab: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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