Data harmonization processes of cancer data into the observational medical outcomes partnership common data model

Abstract

Abstract Cancer data is inherently complex and heterogeneous, originating from diverse sources with differing formats, terminologies, and structures, leading to significant interoperability challenges. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), provided by Observational Health Data Sciences and Informatics (OHDSI) initiative, has been adopted as a standardized framework to mitigate data fragmentation and enhance evidence generation. However, harmonizing cancer data into OMOP CDM remains challenging due to granular data, unstructured formats, and lack of cancer-specific harmonization approaches. This study develops a generic harmonization process for integrating cancer data into the OMOP CDM by examining existing methodologies and identifying patterns and challenges. Following the Design Science Research Methodology (DSRM), the process was informed by literature and refined through expert feedback. The proposed process consists of five steps: Initiation, Requirement Analysis, Design Planning, Technical Implementation, and Maintenance. Each step incorporates cancer-specific considerations. It addresses challenges including source data quality and complexity, mapping issues, and maintenance, supporting oncology research and evolving technologies.