A scoping review of OMOP CDM adoption for cancer research using real world data
Abstract The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) supports large-scale research by enabling distributed network analyses. However, the breadth of its adoption in cancer research is not well understood. We conducted a scoping review to describe the adoption of the...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01581-7 |
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| _version_ | 1850202315789697024 |
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| author | Liwei Wang Andrew Wen Sunyang Fu Xiaoyang Ruan Ming Huang Rui Li Qiuhao Lu Heather Lyu Andrew E. Williams Hongfang Liu |
| author_facet | Liwei Wang Andrew Wen Sunyang Fu Xiaoyang Ruan Ming Huang Rui Li Qiuhao Lu Heather Lyu Andrew E. Williams Hongfang Liu |
| author_sort | Liwei Wang |
| collection | DOAJ |
| description | Abstract The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) supports large-scale research by enabling distributed network analyses. However, the breadth of its adoption in cancer research is not well understood. We conducted a scoping review to describe the adoption of the OMOP CDM in cancer research. A total of 49 unique articles were included in the review, with 30 on the data analysis theme, and 20 on the infrastructure theme. This review highlighted that while the OMOP CDM ecosystem has enabled successful data support for cancer research, particularly for collaborative studies, ongoing model development and iterative improvement remain needed to fulfill additional research data needs. Expanding disease sites, specifically for rare cancers, integrating more diverse types of data sources, improving data quality, adopting advanced analytics methodology, and increasing multisite evaluations serve as important opportunities to facilitate secondary usage of observational data in future cancer research. |
| format | Article |
| id | doaj-art-b7acef65f3cd466ea80c656071fab3de |
| institution | OA Journals |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-b7acef65f3cd466ea80c656071fab3de2025-08-20T02:11:47ZengNature Portfolionpj Digital Medicine2398-63522025-04-018111110.1038/s41746-025-01581-7A scoping review of OMOP CDM adoption for cancer research using real world dataLiwei Wang0Andrew Wen1Sunyang Fu2Xiaoyang Ruan3Ming Huang4Rui Li5Qiuhao Lu6Heather Lyu7Andrew E. Williams8Hongfang Liu9McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at HoustonMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at HoustonMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at HoustonMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at HoustonMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at HoustonMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at HoustonMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at HoustonDepartment of Surgical Oncology, Division of Surgery, The University of Texas MD Anderson Cancer CenterClinical and Translational Science Institute, Tufts Medical CenterMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at HoustonAbstract The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) supports large-scale research by enabling distributed network analyses. However, the breadth of its adoption in cancer research is not well understood. We conducted a scoping review to describe the adoption of the OMOP CDM in cancer research. A total of 49 unique articles were included in the review, with 30 on the data analysis theme, and 20 on the infrastructure theme. This review highlighted that while the OMOP CDM ecosystem has enabled successful data support for cancer research, particularly for collaborative studies, ongoing model development and iterative improvement remain needed to fulfill additional research data needs. Expanding disease sites, specifically for rare cancers, integrating more diverse types of data sources, improving data quality, adopting advanced analytics methodology, and increasing multisite evaluations serve as important opportunities to facilitate secondary usage of observational data in future cancer research.https://doi.org/10.1038/s41746-025-01581-7 |
| spellingShingle | Liwei Wang Andrew Wen Sunyang Fu Xiaoyang Ruan Ming Huang Rui Li Qiuhao Lu Heather Lyu Andrew E. Williams Hongfang Liu A scoping review of OMOP CDM adoption for cancer research using real world data npj Digital Medicine |
| title | A scoping review of OMOP CDM adoption for cancer research using real world data |
| title_full | A scoping review of OMOP CDM adoption for cancer research using real world data |
| title_fullStr | A scoping review of OMOP CDM adoption for cancer research using real world data |
| title_full_unstemmed | A scoping review of OMOP CDM adoption for cancer research using real world data |
| title_short | A scoping review of OMOP CDM adoption for cancer research using real world data |
| title_sort | scoping review of omop cdm adoption for cancer research using real world data |
| url | https://doi.org/10.1038/s41746-025-01581-7 |
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