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Understanding data granularity is crucial for multi-center observational studies. We assessed concept granularity in 22 data sources, finding three levels (low, moderate, high) but noting limitations for small datasets.

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Area of Science:

  • Health Informatics
  • Observational Research
  • Data Science

Background:

  • Multi-center observational studies face challenges reconciling patient data from diverse sources.
  • Differences in coding practices and data capture lead to varying concept granularity, impacting population definition.
  • Ensuring appropriate concept granularity is vital for accurate research on specific patient populations.

Purpose of the Study:

  • To evaluate concept granularity across 22 data sources within the OHDSI network.
  • To develop and assess a composite granularity score for clinical datasets.
  • To investigate the relationship between data source characteristics and concept granularity.

Main Methods:

  • Studied concept granularity in 22 OHDSI network data sources.
  • Calculated a composite granularity score for each dataset.
  • Applied three alternative SNOMED-based approaches to score granularity.

Main Results:

  • Classified data sources into low, moderate, and high granularity levels with consistent SNOMED-based approaches.
  • Observed correlations between granularity levels, data provenance, and country of origin.
  • Identified limitations in ordering data sources within groups and for small datasets.

Conclusions:

  • SNOMED-based approaches can classify data sources by granularity, correlating with origin.
  • Current methods show limitations in fine-grained ordering and small data source assessment.
  • Further research is needed to refine approaches for evaluating data source granularity in observational studies.