ArticleBMC medical research methodology2021
Facilitating harmonized data quality assessments. A data quality framework for observational health research data collections with software implementations in R.
Article in BMC medical research methodology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
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- A Practical Approach to Assessing the Completeness of Electronic Health Records for Medical Research: Data Quality Study.JMIR medical informatics · 2026Article
- Enhancing Data Quality in Clinical Trials: Cross-Company Validation of the Open-Source Clinical Trial Anomaly Spotter (CTAS).Therapeutic innovation & regulatory science · 2026Article
- Sharing EHR data of patients with rare diseases for research - The role of quality assessments in a national federated research data infrastructure.BMC medical informatics and decision making · 2026Article
- Integrative Harmonization of Phenotypic and Genomic Data Improves Bone Mineral Density Prediction in Multi-Study Osteoporosis Research.Genetic epidemiology · 2026Article
- Feasibility and effectiveness of an interprofessional educational program on perinatal mental health: perspectives of health students in two African countries.BMC medical education · 2026Article
- Dual-informant clinical outcome assessment in the inclusive orchestra: measurement architecture for neurodevelopmental disorders prior to empirical evaluation.Frontiers in neurology · 2026Article
- Genetics to Improve Outcomes in Schizophrenia (GENios): A within-case molecular genetic study protocol.PloS one · 2026Article
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- Assessing Data Quality in Heterogeneous Health Care Integration: Simulation Study of the AIDAVA Framework.JMIR medical informatics · 2025Article
- Synthetic data, synthetic trust: navigating data challenges in the digital revolution.The Lancet. Digital health · 2025Review
- A Multi-Dimensional Framework for Data Quality Assurance in Cancer Imaging Repositories.Cancers · 2025Article
- Practice of oxygenation and respiratory support during fibreoptic bronchoscopy: the OxyFOB study protocol.BMJ open · 2025Article
- Design aspects for prognostic factor studies.BMJ open · 2025Review
- Medical Science Data Value Evaluation Model: Mixed Methods Study.JMIR medical informatics · 2025Article
- Sleep in the academic sphere: identifying sleep profiles and their influencing factors using latent profile analysis in German university students.BMC psychology · 2025Article
- The MUnich SArcopenia Registry (MUSAR) : Paving the way for increased visibility, more frequent diagnoses and innovative new treatment.Zeitschrift fur Gerontologie und Geriatrie · 2025Article
- Low-dose ionizing radiation and the exposure-lag response: protocol for a prospective cohort study on The Health Effects of Chongqing Occupational Radiation Workers.Frontiers in public health · 2025Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNo standards exist for the handling and reporting of data quality in health research. This work introduces a data quality framework for observational health research data collections with supporting software implementations to facilitate harmonized data quality assessments.
methodsDevelopments were guided by the evaluation of an existing data quality framework and literature reviews. Functions for the computation of data quality indicators were written in R. The concept and implementations are illustrated based on data from the population-based Study of Health in Pomerania (SHIP).
resultsThe data quality framework comprises 34 data quality indicators. These target four aspects of data quality: compliance with pre-specified structural and technical requirements (integrity); presence of data values (completeness); inadmissible or uncertain data values and contradictions (consistency); unexpected distributions and associations (accuracy). R functions calculate data quality metrics based on the provided study data and metadata and R Markdown reports are generated. Guidance on the concept and tools is available through a dedicated website.
conclusionsThe presented data quality framework is the first of its kind for observational health research data collections that links a formal concept to implementations in R. The framework and tools facilitate harmonized data quality assessments in pursue of transparent and reproducible research. Application scenarios comprise data quality monitoring while a study is carried out as well as performing an initial data analysis before starting substantive scientific analyses but the developments are also of relevance beyond research.
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