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Comparison of two VA laboratory data repositories indicates that missing data vary despite originating from the same
Kathleen A McGinnis1, Melissa Skanderson, Forrest L Levin
1Center for Health Equity Research and Promotion, VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania, USA.
Comparing two VA data systems revealed significant overlap in laboratory results for most tests, but missing data highlights the need for validation. Implementing data validation is crucial for accurate research and quality improvement projects.
Area of Science:
- Health Informatics
- Clinical Research Data Management
- HIV/AIDS Research
Background:
- The Veterans Affairs (VA) Healthcare System utilizes a robust electronic medical information system.
- Two independent data repositories, the Immunology Case Registry and the Decision Support System, extract data from the same source.
- This presents an opportunity to assess the accuracy and completeness of data extraction methods.
Purpose of the Study:
- To compare overlapping laboratory data between the Immunology Case Registry and the Decision Support System within the VA Health Information System.
- To evaluate the consistency and completeness of data for key laboratory tests in HIV+ veterans.
Main Methods:
- The study analyzed data from 22,647 HIV-positive veterans from the Virtual Cohort for fiscal year 2002.
- Overlap and Pearson's correlation coefficients were calculated for nine laboratory tests, including hemoglobin, CD4 counts, and viral load.
- Data from the Immunology Case Registry and Decision Support System were compared.
Main Results:
- Six out of nine laboratory tests showed substantial overlap, ranging from 68% to 72%.
- Lower overlap (<31%) was observed for CD4 counts, viral load, and glycosylated hemoglobin.
- Correlations were strong for overlapping results, with the exception of CD4 counts.
Conclusions:
- Significant overlap in laboratory data exists between the two VA systems for most tests, but both repositories contain missing data.
- Validation of laboratory data is essential before its use in quality and efficiency initiatives.
- Alternative validation methods should be employed when direct database comparison is not feasible.
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