Establishing standards: harmonising coding principles for a minimal cancer dataset in the OMOP Common Data Model

• Published in: ESMO Real World Data and Digital Oncology Vol. 9; p. 100179
• Main Authors: Ajmal, A., Bouissou, O., Brash, J., Cheeseman, S., Banduge, P.G., Gomes, A.L., Revie, L., Ross, E., Theophanous, S., Thonnard, J., Van Maanen, A., Vengadeswaran, A., Wolf, A., Fernandez, X.M.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.09.2025; Elsevier
• Subjects: cancer research; Common Data Model (CDM); Observational Health Data Sciences and Informatics (OHDSI); Observational Medical Outcomes Partnership (OMOP); Oncology; Observational Medical Outcomes Partnership (OMOP); Observational Health Data Sciences and Informatics (OHDSI); Common Data Model (CDM); cancer research
• ISSN: 2949-8201; 2949-8201
• DOI: 10.1016/j.esmorw.2025.100179

Analysing clinical information across a network poses challenges due to heterogeneity of data collection, storage and availability. The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) provides a standardised framework for clinical data, allowing network-level comparisons and the combination of data to enhance analytical power and increase research robustness. To capture specific oncology information across the Digital Oncology Network for Europe (DigiONE) using OMOP, we developed the Minimal Essential Description of Cancer (MEDOC) framework. MEDOC was developed through several iterations and was then utilised in DigiONE pilot studies. This was a community-driven process, made possible by discussions to distinguish differences in hospital data and by conducting deep-dive sessions to solve specific issues in aligning source data with the MEDOC structure. The initial version of MEDOC has been utilised in two DigiONE observational cancer studies to date with a further two studies in progress, and training resources including the implementation guide have been developed. Lessons learned in the development of our MEDOC to OMOP alignment include challenges in establishing diagnosis date, confirming metastasis location and tumour classification code due to granularity of available data, among other challenges specific to individual centres. The utility of MEDOC has been evidenced in research applications and will be continually developed in line with both learnings from centres and developments in the field of oncology. The implementation of MEDOC in line with the OMOP CDM is timely, given European initiatives to harmonise health care data systems. •MEDOC standardises clinical oncology data.•MEDOC enables observational cancer studies with OMOP.•Curating MEDOC data faced challenges due to varied practices and data granularity.