Data Quality
CPCSSN applies a defined and structured approach to data quality to ensure that primary care data is suitable for research and system-level analysis.
This includes the transformation, standardization, and evaluation of data across multiple EMR systems and jurisdictions.
Common Data Structure
Data from multiple EMR systems is transformed into a standardized structure using consistent ontologies and terminologies. This allows data from different sources to be combined and compared across regions.
1
Defined Data Quality Framework
Data quality is assessed using a formal framework across five dimensions:
- Relevance
- Accuracy and reliability
- Timeliness and punctuality
- Comparability and coherence
- Accessibility and clarity
2
Validation and Consistency Checks
Data undergoes validation processes to assess:
- Agreement between related data fields
- Clinical plausibility (e.g., expected value ranges)
- Consistency across sites, regions, and EMR systems
- Expected data distributions
3
Assessment of Completeness and Variation
Data quality processes include evaluation of:
- Completeness of data elements
- Variation in documentation practices
- Differences across networks and systems
4
Use of Structured Data
Data is derived from structured EMR fields, including diagnoses, medications, laboratory results, and encounters. This supports consistency, comparability, and privacy across datasets.
