Is source data verification a valid tool to improve data quality of tumour documentation data? A critical assessment

Area of Science:

• Oncology
• Health Information Management
• Data Quality Assurance

Background:

• Accurate tumour documentation is crucial for cancer research and patient care.
• Challenges exist in healthcare management regarding data quality.
• Tumour documentation data requires robust verification methods.

Purpose of the Study:

• To evaluate the effectiveness of source data verification (SDV) in improving tumour documentation quality.
• To assess the resource implications of SDV.
• To focus on accuracy, completeness, and correctness of tumour data.

Main Methods:

• A source data verification (SDV) was performed on tumour documentation data (2016-2020) from a German University Hospital.
• An external audit group (RE) compared documented data (TD) against source documents for 240 cases.
• Errors were cross-validated by a third group (CO).

Main Results:

• Error frequencies were visualized by diagnosis year and organ entity.
• Potential errors were identified and feedback was provided.
• Uncertainties in error identification questioned the overall efficacy of SDV.

Conclusions:

• SDV identified errors but faced challenges with ambiguous source data, auditor bias, and cost-effectiveness.
• SDV may be suitable for small sample validation but not scalable for large datasets.
• Alternative methods like data exchange interfaces and plausibility checks are recommended for improving tumour documentation quality.