ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2015
A Data Quality Ontology for the Secondary Use of EHR Data.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 4 of them syntheses that pooled it.
What it found
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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
39 citing papers in PubMed, 4 syntheses or guidelines pooled 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
- Digital Health Data Quality Issues: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Assessment of the integrity of real-time electronic health record data used in clinical research.PloS one · 2026Article
- The Venus score for the assessment of the quality and trustworthiness of biomedical datasets.BioData mining · 2025Article
- Beware the Little Foxes that Spoil the Vines: Small Inconsistencies in Clinical Data Can Distort Machine Learning Findings.Fortune journal of health sciences · 2025Article
- Navigating electronic health record accuracy by examination of sex incongruent conditions.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Investigation on the preferences for data quality assessment indicators of electronic health records: user-oriented perspective.JAMIA open · 2024Article
- The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic review.NPJ digital medicine · 2024Review
- Development of a quantitative index system for evaluating the quality of electronic medical records in disease risk intelligent prediction.BMC medical informatics and decision making · 2024Article
- A Standard Framework for Evaluating Large Health Care Data and Related Resources.MMWR supplements · 2024Article
- Factors impacting clinical data and documentation quality in Australian aged care and disability services: a user-centred perspective.BMC geriatrics · 2024Article
- Rare Diseases in Hospital Information Systems-An Interoperable Methodology for Distributed Data Quality Assessments.Methods of information in medicine · 2023Article
- Automating Electronic Health Record Data Quality Assessment.Journal of medical systems · 2023Review
- Data-driven identification of heart failure disease states and progression pathways using electronic health records.Scientific reports · 2022Article
- Developing a systematic approach to assessing data quality in secondary use of clinical data based on intended use.Learning health systems · 2022Article
- Identifying Data Quality Dimensions for Person-Generated Wearable Device Data: Multi-Method Study.JMIR mHealth and uHealth · 2021Article
- Quality of Hospital Electronic Health Record (EHR) Data Based on the International Consortium for Health Outcomes Measurement (ICHOM) in Heart Failure: Pilot Data Quality Assessment Study.JMIR medical informatics · 2021Article
- Linking a Consortium-Wide Data Quality Assessment Tool with the MIRACUM Metadata Repository.Applied clinical informatics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The secondary use of EHR data for research is expected to improve health outcomes for patients, but the benefits will only be realized if the data in the EHR is of sufficient quality to support these uses. A data quality (DQ) ontology was developed to rigorously define concepts and enable automated computation of data quality measures. The healthcare data quality literature was mined for the important terms used to describe data quality concepts and harmonized into an ontology. Four high-level data quality dimensions ("correctness", "consistency", "completeness" and "currency") categorize 19 lower level measures. The ontology serves as an unambiguous vocabulary, which defines concepts more precisely than natural language; it provides a mechanism to automatically compute data quality measures; and is reusable across domains and use cases. A detailed example is presented to demonstrate its utility. The DQ ontology can make data validation more common and reproducible.
Indexed as
Identifiers
26958293PMC4765682What OpenQuestion 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 OpenQuestion graph.