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Discrepancies in Aggregate Patient Data between Two Sources with Data Originating from the Same Electronic Health
Allen J Yiu1,2,3, Graham Stephenson1, Emilie Chow4
1Department of Emergency Medicine, University of California, Irvine, California, United States.
Electronic health record (EHR) data extraction tools and clinical data warehouses yield different patient demographic results. This highlights the need for data standardization and user education to ensure accurate research and quality improvement initiatives.
Area of Science:
- Health Informatics
- Data Science
- Clinical Research
Background:
- Electronic health records (EHRs) offer self-service tools for data extraction, aiding quality improvement and research.
- Discrepancies between data extracted by self-service tools and institutional data warehouses raise concerns about data completeness and accuracy.
Purpose of the Study:
- To compare aggregate data from an EHR self-service tool and a clinical data warehouse (CDW).
- To identify factors contributing to any observed data discrepancies.
Main Methods:
- Aggregate demographic data for influenza vaccinations (August-December 2020) were extracted from both EHR tool and CDW.
- Data were analyzed by demographics and vaccination sites to identify differences.
- Underlying data models and sources were examined.
Main Results:
- Significant discrepancies in patient volumes and demographic data (age, race, ethnicity, primary language) were found between the two sources.
- These variations can impact research outcomes and interpretations.
Conclusions:
- Data quality must be rigorously examined, emphasizing the need for user education on accurate data extraction.
- Enhanced data standardization and validation are crucial for reliable research and informed decision-making.
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