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

Ajmal, A.; Bouissou, O.; Brash, J.; Cheeseman, S.; Banduge, PG.; Gomes, AL.; Revie, L.; Ross, E.; Theophanous, S.; Thonnard, J.; Van Maanen, A.; Vengadeswaran, A.; Wolf, A.; Fernandez, XM.

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

All Authors

Ajmal, A.

Bouissou, O.

Brash, J.

Cheeseman, S.

Banduge, PG.

Gomes, AL.

Revie, L.

Ross, E.

Theophanous, S.

Thonnard, J.

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LTHT Author

Cheeseman, Sue
Theophanous, Stelios

LTHT Department

Leeds Cancer Centre
Oncology
Research & Innovation
Real World Evidence Alliance
Medical Physics & Engineering

Non Medic

Physicist

Publication Date

2025

Item Type

Journal Article

Subject

HOSPITALISATION , DATA INTERPRETATION, STATISTICAL , DATA COLLECTION , BIOMEDICAL RESEARCH

Abstract

Background: 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. Materials and methods: 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. Results: 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. Conclusion: 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.