The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Feasibility and utility of applications of the common data model to multiple, disparate observational health databases
Open accessAbstract
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.
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A review of uses of health care utilization databases for epidemiologic research on therapeutics
Schneeweiss, S., & Avorn, J. (2005) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2004.10.012 · Cited by 1,277
Validation of a common data model for active safety surveillance research
Overhage, J. M., Ryan, P. B., Reich, C. G., Hartzema, A. G., & Stang, P. E. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000376 · Cited by 548
Self-citationStang, P. E., Ryan, P. B., Racoosin, J. A., Overhage, J. M., Hartzema, A. G., Reich, C., Welebob, E., Scarnecchia, T., & Woodcock, J. (2010) · Annals of Internal Medicine · doi:10.7326/0003-4819-153-9-201011020-00010 · Cited by 441
Self-citationConfounding Control in Healthcare Database Research: Challenges and Potential Approaches
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Self-citationCOX-2 Inhibitors — Lessons in Drug Safety
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Evaluating the Impact of Database Heterogeneity on Observational Study Results
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Self-citationTransforming the Premier Perspective® hospital database to the OMOP Common Data Model
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Reps, J. M., Schuemie, M. J., Suchard, M. A., Ryan, P. B., & Rijnbeek, P. R. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy032 · Cited by 205
Self-citationLi, X., Ostropolets, A., Makadia, R., Shoaibi, A., Rao, G., Sena, A. G., Martinez-Hernandez, E., Delmestri, A., Verhamme, K., Rijnbeek, P. R., et al. (2021) · BMJ · doi:10.1136/bmj.n1435 · Cited by 219
Self-citationFinding factors that predict treatment-resistant depression: results of a cohort study
Cepeda, M. S., Reps, J., & Ryan, P. (2018) · Depression and Anxiety · doi:10.1002/da.22774 · Cited by 80
Self-citationPrats-Uribe, A., Sena, A. G., Lai, L. Y. H., Ahmed, W.-U.-R., Alghoul, H., Alser, O., Alshammari, T. M., Areia, C., Carter, W., Casajust, P., et al. (2021) · BMJ · doi:10.1136/bmj.n1038 · Cited by 68
Self-citationCepeda, M. S., Reps, J., Fife, D., Blacketer, C., Stang, P., & Ryan, P. (2018) · Depression and Anxiety · doi:10.1002/da.22705 · Cited by 50
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Metadata
- Authors
- Voss, E. A., Makadia, R., Matcho, A., Ma, Q., Knoll, C., Schuemie, M., DeFalco, F. J., Londhe, A., Zhu, V., & Ryan, P. B.
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-02-10
- Volume
- 22
- Issue
- 3
- Pages
- 553-564
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- OpenAlex
- W2157365260
- Cited by
- 339
- References
- 19
- FWCI
- 13.19
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Voss, E. A., Makadia, R., Matcho, A., Ma, Q., Knoll, C., Schuemie, M., DeFalco, F. J., Londhe, A., Zhu, V., & Ryan, P. B. (2015). Feasibility and utility of applications of the common data model to multiple, disparate observational health databases. Journal of the American Medical Informatics Association. https://doi.org/10.1093/jamia/ocu023
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