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

Background: Accurate documentation of tumours presents significant opportunities for advancing cancer research and improving patient care, yet it also poses challenges for healthcare management.

Objective: This study aimed to assess the effectiveness and resource implications of source data verification (SDV) in enhancing the quality of tumour documentation data, focusing on accuracy, completeness and correctness.

Method: Using tumour documentation data from a large German University Hospital, an SDV was conducted by an external audit group (group RE), comparing the data initially documented by the centre's tumour documentalists (group TD) to available source documents for the years 2016-2020. The analysis set comprised 240 cases, with exemplary data fields strategically selected across various organ entities and other tumour features. Identified errors were cross-validated by a third group (group CO).

Results: Visualisations depicted error frequencies by diagnosis year and organ entity. Potential errors were identified, providing feedback to the tumour documentation unit. However, uncertainties in error identification raised questions about the efficacy of SDV.

Conclusion: While effective in identifying errors, SDV faced challenges due to ambiguous source data and potential bias from external auditors, as well as being deemed uneconomical. The study suggests SDVs suitability for small sample validation but questions its scalability for large datasets.Implications for health information management:Alternative methods, such as data exchange interfaces to subsystems or plausibility checks, are recommended for enhancing data quality. This study emphasises the need to explore alternatives for improving data quality in tumour documentation.