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The Challenges of Data Quality Evaluation in a Joint Data Warehouse
Charles J Bae1, Sandra Griffith1, Youran Fan1
1Cleveland Clinic.
EGEMS (Washington, DC)
|August 21, 2015
Summary
Data quality in a joint data warehouse is crucial for research. This study found high agreement between sources when data existed, but completeness varied, highlighting the need for careful data transfer methods.
Area of Science:
- Health Informatics
- Clinical Data Management
- Biomedical Research Data
Background:
- Electronic health records (EHRs) and clinical systems provide valuable data for research and quality initiatives.
- Joint data warehouses integrate data from multiple sources, but data quality assessment is essential.
- This study evaluated the completeness and concordance of clinical data within the Knowledge Program (KP) joint data warehouse.
Purpose of the Study:
- To determine the completeness and concordance of common clinical data types in the KP joint data warehouse.
- To compare data quality between the source EHR and the KP data warehouse.
- To identify factors influencing data discrepancies.
Main Methods:
- Manual review of specific data elements for 250 patients.
- Comparison of data between the source EHR and the KP data warehouse.
- Calculation of completeness and concordance for demographics, vital signs, laboratory results, diagnoses, and medications.
Main Results:
- Demographics, vital signs, diagnoses, and laboratory results were generally more complete in the source EHR than in the KP.
- High concordance was observed when data elements were present in both the EHR and the KP.
- The KP data warehouse showed a higher prevalence of documented deaths and medications compared to the EHR.
Conclusions:
- Discrepancies are attributed to data feed update frequency, inability to transfer unstructured data, and missing variables in the source EHR.
- Assessing completeness and concordance between datasets and sources is more informative than designating a single gold standard.
- Optimizing data transfer methods and timing is key to improving data accuracy in joint data warehouses.
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