Standardized Observational Cancer Research Using the OMOP CDM Oncology Module

Rimma Belenkaya; Michael Gurley; Dmitry Dymshyts; Sonia Araujo; Andrew Williams; RuiJun Chen; Christian Reich

doi:10.3233/SHTI190670

Standardized Observational Cancer Research Using the OMOP CDM Oncology Module

Stud Health Technol Inform. 2019 Aug 21:264:1831-1832. doi: 10.3233/SHTI190670.

Authors

Rimma Belenkaya¹, Michael Gurley², Dmitry Dymshyts³, Sonia Araujo⁴, Andrew Williams⁵, RuiJun Chen⁶, Christian Reich⁷

Affiliations

¹ OHDSI Oncology Workgroup, Memorial Sloan Kettering Cancer Center, New York City, NY, USA.
² Clinical and Translational Sciences Institute, Northwestern University, Chicago, IL, USA.
³ Odysseus Data Science Inc, Cambridge, MA, USA.
⁴ Real World Analytics Solution, IQVIA, London, UK.
⁵ Maine Medical Center Research Institute, Center for Outcomes Research and Evaluation, Portland, ME, USA.
⁶ Biomedical Informatics Department, Columbia University Medical Center, New York City, NY, USA.
⁷ Real World Analytics Solution, IQVIA, Cambridge, MA, USA.

PMID: 31438365
DOI: 10.3233/SHTI190670

Abstract

Observational research in cancer requires substantially more detail than most other therapeutic areas. Cancer conditions are defined through histology, affected anatomical structures, staging and grading, and biomarkers, and are treated with complex therapies. Here, we show a new cancer module as part of the OMOP CDM, allowing manual and automated abstraction and standardized analytics. We tested the model in EHR and registry data against a number of typical use cases.

Keywords: Oncology; Research; Standardized.

MeSH terms

Databases, Factual
Electronic Health Records*
Models, Theoretical*
Neoplasms*
Registries
Research*