ArticleEGEMS (Washington, DC)2015
The Challenges of Data Quality Evaluation in a Joint Data Warehouse.
Article in EGEMS (Washington, DC), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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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
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Digital Health Data Quality Issues: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Temporal Variability in Diagnosis Code Distributions Across Extraction Time Points in a Multicenter Integrated EHR Database: A Snapshot Comparison Study.Journal of medical systems · 2026Observational
- Contrasting rule and machine learning based digital self triage systems in the USA.NPJ digital medicine · 2024Review
- Navigating electronic health record accuracy by examination of sex incongruent conditions.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- The challenges and opportunities of continuous data quality improvement for healthcare administration data.JAMIA open · 2024Article
- Advances in Machine Learning Approaches to Heart Failure with Preserved Ejection Fraction.Heart failure clinics · 2022Review
- Assessing the practice of data quality evaluation in a national clinical data research network through a systematic scoping review in the era of real-world data.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Use of electronic health records from a statewide health information exchange to support public health surveillance of diabetes and hypertension.BMC public health · 2019Article
- Short-term functional recovery after total joint arthroplasty is unaffected by bundled payment participation.Arthroplasty today · 2019Article
- The Burden of Chronic Heart Failure in Primary Care in Italy.High blood pressure & cardiovascular prevention : the official journal of the Italian Society of Hypertension · 2017Article
- DQEGEMS (Washington, DC) · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe use of clinically derived data from electronic health records (EHRs) and other electronic clinical systems can greatly facilitate clinical research as well as operational and quality initiatives. One approach for making these data available is to incorporate data from different sources into a joint data warehouse. When using such a data warehouse, it is important to understand the quality of the data. The primary objective of this study was to determine the completeness and concordance of common types of clinical data available in the Knowledge Program (KP) joint data warehouse, which contains feeds from several electronic systems including the EHR.
methodsA manual review was performed of specific data elements for 250 patients from an EHR, and these were compared with corresponding elements in the KP data warehouse. Completeness and concordance were calculated for five categories of data including demographics, vital signs, laboratory results, diagnoses, and medications.
resultsIn general, data elements for demographics, vital signs, diagnoses, and laboratory results were present in more cases in the source EHR compared to the KP. When data elements were available in both sources, there was a high concordance. In contrast, the KP data warehouse documented a higher prevalence of deaths and medications compared to the EHR. DISCUSSION: Several factors contributed to the discrepancies between data in the KP and the EHR-including the start date and frequency of data feeds updates into the KP, inability to transfer data located in nonstructured formats (e.g., free text or scanned documents), as well as incomplete and missing data variables in the source EHR.
conclusionWhen evaluating the quality of a data warehouse with multiple data sources, assessing completeness and concordance between data set and source data may be better than designating one to be a gold standard. This will allow the user to optimize the method and timing of data transfer in order to capture data with better accuracy.
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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.