Implementation of a common data model in Health Data Research Network Canada: Lessons learned
Bibliographic record
Abstract
BackgroundThe Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) is a standardized structure for observational data that enables collaborative health research. Health Data Research Network (HDRN Canada) is conducting a pilot study to implement this standard at partner data linkage centers. ObjectiveOur objective is to describe the process used to implement the OMOP CDM, methodological and technical challenges, and the approach to evaluate CDM implementation at multiple sites. ApproachCDM working group members include technical staff from data linkage centers in four provinces and a national data coordinating center, HDRN Canada operational and executive leads, and OMOP CDM consultants. The evaluation aims to collect quantitative and qualitative information on Extract, Transform, and Load (ETL) processes, performance of the CDM in a multi-site observational case study, and data quality. ResultsInitial steps have included collaborative learning about the CDM structure, importing open-source tools and licensed coding standards (i.e., Systematized Nomenclature of Medicine-Clinical Terms [SNOMED-CT], RxNorm) at data linkage centres, and exploring the technical requirements for developing ETL processes and hosting OMOP data. Creation of select OMOP tables has been accompanied by discussions about models for mapping clinical and prescription drug coding standards. A scoping review of evaluation strategies is underway. Conclusions and ImplicationsThe benefits of standardizing health data for multi-site research and supporting the production of findable, accessible, interoperable and reusable (FAIR) national data underscores the importance of HDRN Canada embarking on this OMOP CDM implementation project. The lessons learned will benefit other multi-site data standardization initiatives.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.432 | 0.428 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.018 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.035 | 0.035 |
| Open science | 0.019 | 0.021 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".