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Managing data quality for a drug safety surveillance system
Abraham G Hartzema1, Christian G Reich, Patrick B Ryan
1College of Pharmacy, University of Florida, Gainesville, FL, USA, Hartzema@ufl.edu.
Drug Safety
|October 30, 2013
Summary
Ensuring data quality in observational studies requires a robust assurance program. This study details methods to identify and resolve data anomalies from disparate sources, crucial for reliable drug safety monitoring.
Area of Science:
- Health Informatics
- Data Science
- Pharmacovigilance
Background:
- Methodological research for drug safety monitoring necessitates standardized data quality processes across disparate databases.
- Current lack of consensus on evaluating source data and extract-transform-load (ETL) procedures hinders data integrity.
Purpose of the Study:
- To present a data quality assurance program for disparate data sources integrated into a Common Data Model.
- To identify and resolve data quality issues encountered during data processing and analysis.
Main Methods:
- A framework for comprehensive data quality assurance throughout data processing and analysis was proposed.
- Data anomaly management involved characterizing data sources, detecting anomalies, determining causes, and implementing remediation.
Main Results:
- Identified data anomalies included incomplete records (e.g., missing race, year of birth) and implausible data (e.g., future birth year, inverted dates).
- Extract-transform-load (ETL) errors involved incorrect zip code loading, rounded drug quantities, and flawed calculation of drug/condition exposure lengths.
Conclusions:
- Obtaining complete and reliable observational data is challenging.
- Continuous, transparent data quality assurance processes are essential due to regular data updates and the need for ongoing assessment.
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