ArticleBMC medical informatics and decision making2017
Validation of multisource electronic health record data: an application to blood transfusion data.
Article in BMC medical informatics and decision making, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses 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.
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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.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The validity of electronic health data for measuring smoking status: a systematic review and meta-analysis.BMC medical informatics and decision making · 2024Pooled it
- Digital Health Data Quality Issues: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Model-based algorithms to ascertain smoking in administrative health data: a registry-based validation study.BMC medical research methodology · 2026Article
- Characterization and validation of electronic medical record data for pharmacoepidemiologic research.Frontiers in pharmacology · 2026Article
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- Analyzing real world data of blood transfusion adverse events: Opportunities and challenges.Transfusion · 2022Article
- Assessing the quality of clinical and administrative data extracted from hospitals: the General Medicine Inpatient Initiative (GEMINI) experience.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- How to validate a diagnosis recorded in electronic health records.Breathe (Sheffield, England) · 2019Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough data from electronic health records (EHR) are often used for research purposes, systematic validation of these data prior to their use is not standard practice. Existing validation frameworks discuss validity concepts without translating these into practical implementation steps or addressing the potential influence of linking multiple sources. Therefore we developed a practical approach for validating routinely collected data from multiple sources and to apply it to a blood transfusion data warehouse to evaluate the usability in practice.
methodsThe approach consists of identifying existing validation frameworks for EHR data or linked data, selecting validity concepts from these frameworks and establishing quantifiable validity outcomes for each concept. The approach distinguishes external validation concepts (e.g. concordance with external reports, previous literature and expert feedback) and internal consistency concepts which use expected associations within the dataset itself (e.g. completeness, uniformity and plausibility). In an example case, the selected concepts were applied to a transfusion dataset and specified in more detail.
resultsApplication of the approach to a transfusion dataset resulted in a structured overview of data validity aspects. This allowed improvement of these aspects through further processing of the data and in some cases adjustment of the data extraction. For example, the proportion of transfused products that could not be linked to the corresponding issued products initially was 2.2% but could be improved by adjusting data extraction criteria to 0.17%.
conclusionsThis stepwise approach for validating linked multisource data provides a basis for evaluating data quality and enhancing interpretation. When the process of data validation is adopted more broadly, this contributes to increased transparency and greater reliability of research based on routinely collected electronic health records.
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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.