ArticleBMC medical informatics and decision making2021
A method for interoperable knowledge-based data quality assessment.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 4 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Common data models and data standards for tabular health data: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Data quality assessment in healthcare, dimensions, methods and tools: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Challenges for Data Quality in the Clinical Data Life Cycle: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Electronic health record data quality assessment and tools: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- 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
- Multi-center disease-specific management system: toward standardized and harmonized clinical pathways in integrated care networks.Frontiers in public health · 2026Article
- Proposal for Using AI to Assess Clinical Data Integrity and Generate Metadata: Algorithm Development and Validation.JMIR medical informatics · 2025Article
- Uncovering Harmonization Potential in Health Care Data Through Iterative Refinement of Fast Healthcare Interoperability Resources Profiles Based on Retrospective Discrepancy Analysis: Case Study.JMIR medical informatics · 2024Article
- Rare Diseases in Hospital Information Systems-An Interoperable Methodology for Distributed Data Quality Assessments.Methods of information in medicine · 2023Article
- Targeted Data Quality Analysis for a Clinical Decision Support System for SIRS Detection in Critically Ill Pediatric Patients.Methods of information in medicine · 2023Article
- Automating Electronic Health Record Data Quality Assessment.Journal of medical systems · 2023Review
- Metadata integrity in bioinformatics: Bridging the gap between data and knowledge.Computational and structural biotechnology journal · 2023Review
- A data driven learning approach for the assessment of data quality.BMC medical informatics and decision making · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAssessing the quality of healthcare data is a complex task including the selection of suitable measurement methods (MM) and adequately assessing their results.
objectivesTo present an interoperable data quality (DQ) assessment method that formalizes MMs based on standardized data definitions and intends to support collaborative governance of DQ-assessment knowledge, e.g. which MMs to apply and how to assess their results in different situations.
methodsWe describe and explain central concepts of our method using the example of its first real world application in a study on predictive biomarkers for rejection and other injuries of kidney transplants. We applied our open source tool-openCQA-that implements our method utilizing the openEHR specifications. Means to support collaborative governance of DQ-assessment knowledge are the version-control system git and openEHR clinical information models.
resultsApplying the method on the study's dataset showed satisfactory practicability of the described concepts and produced useful results for DQ-assessment.
conclusionsThe main contribution of our work is to provide applicable concepts and a tested exemplary open source implementation for interoperable and knowledge-based DQ-assessment in healthcare that considers the need for flexible task and domain specific requirements.
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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 OpenQuestion graph.