A. Ajmal , O. Bouissou , J. Brash , S. Cheeseman , P.G. Banduge , A.L. Gomes , L. Revie , E. Ross , S. Theophanous , J. Thonnard , A. Van Maanen , A. Vengadeswaran , A. Wolf , X.M. Fernandez
{"title":"建立标准:在OMOP公共数据模型中协调最小癌症数据集的编码原则","authors":"A. Ajmal , O. Bouissou , J. Brash , S. Cheeseman , P.G. Banduge , A.L. Gomes , L. Revie , E. Ross , S. Theophanous , J. Thonnard , A. Van Maanen , A. Vengadeswaran , A. Wolf , X.M. Fernandez","doi":"10.1016/j.esmorw.2025.100179","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>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.</div></div><div><h3>Materials and methods</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>","PeriodicalId":100491,"journal":{"name":"ESMO Real World Data and Digital Oncology","volume":"9 ","pages":"Article 100179"},"PeriodicalIF":0.0000,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Establishing standards: harmonising coding principles for a minimal cancer dataset in the OMOP Common Data Model\",\"authors\":\"A. Ajmal , O. Bouissou , J. Brash , S. Cheeseman , P.G. Banduge , A.L. Gomes , L. Revie , E. Ross , S. Theophanous , J. Thonnard , A. Van Maanen , A. Vengadeswaran , A. Wolf , X.M. Fernandez\",\"doi\":\"10.1016/j.esmorw.2025.100179\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>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.</div></div><div><h3>Materials and methods</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusion</h3><div>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.</div></div>\",\"PeriodicalId\":100491,\"journal\":{\"name\":\"ESMO Real World Data and Digital Oncology\",\"volume\":\"9 \",\"pages\":\"Article 100179\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ESMO Real World Data and Digital Oncology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2949820125000682\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ESMO Real World Data and Digital Oncology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949820125000682","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Establishing standards: harmonising coding principles for a minimal cancer dataset in the OMOP Common Data Model
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.