Measuring Data Quality Through a Source Data Verification Audit in a Clinical Research Setting.

Area of Science:

• Health Informatics
• Clinical Research Data Management
• Data Quality Assurance

Background:

• Health data quality and integrity are critical but lack standardized measurement methods.
• Existing methods for assessing data quality in clinical trials are insufficient.
• No universally accepted 'gold standard' exists for data quality and error rate measurement.

Purpose of the Study:

• To develop and evaluate a straightforward source data verification (SDV) audit method for clinical trial datasets.
• To assess data quality and identify error rates within a prospective clinical trial.
• To establish a quality assurance rule for ongoing data monitoring.

Main Methods:

• Conducted a 100% manual verification of a 10% random sample of participant files from a prospective clinical trial dataset.
• Implemented a quality assurance rule: if >5% of data variables were incorrect, a second 10% sample was audited.
• Coded errors as correct, incorrect (valid/invalid), not recorded, or not entered.

Main Results:

• The initial audit (Audit-1) revealed a total error rate of 33%, with a second audit (Audit-2) showing 36% error.
• The physiological data section was the only area with <5% error.
• Data not recorded on case report forms significantly impacted error calculations; a strong association (p=0.00) was found between audit results and data correctness.

Conclusions:

• A practical SDV audit method with defined error coding and an audit rule was successfully developed.
• The SDV audit effectively identified significant data quality and integrity issues in the clinical research setting.
• Implementing this SDV audit approach is recommended for future clinical research to enhance data quality and integrity.