ArticleHealth information management : journal of the Health Information Management Association of Australia2026
Is source data verification a valid tool to improve data quality of tumour documentation data? A critical assessment.
Article in Health information management : journal of the Health Information Management Association of Australia, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- The Relevance of Curative-Intent Metastasectomy in Colorectal Cancer Patients: Real-World Insights From a Certified Comprehensive Cancer Center in Germany.International journal of cancer · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate documentation of tumours presents significant opportunities for advancing cancer research and improving patient care, yet it also poses challenges for healthcare management.
objectiveThis 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.
methodUsing 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).
resultsVisualisations 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.
conclusionWhile 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.