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Validation of multisource electronic health record data: an application to blood transfusion data

Overview of attention for article published in BMC Medical Informatics and Decision Making, July 2017
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Title
Validation of multisource electronic health record data: an application to blood transfusion data
Published in
BMC Medical Informatics and Decision Making, July 2017
DOI 10.1186/s12911-017-0504-7
Pubmed ID
Authors

Loan R. van Hoeven, Martine C. de Bruijne, Peter F. Kemper, Maria M.W. Koopman, Jan M.M. Rondeel, Anja Leyte, Hendrik Koffijberg, Mart P. Janssen, Kit C.B. Roes

Abstract

Although 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. The 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. Application 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%. This 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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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 97 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 97 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 21%
Student > Master 14 14%
Researcher 10 10%
Unspecified 8 8%
Other 5 5%
Other 13 13%
Unknown 27 28%
Readers by discipline Count As %
Medicine and Dentistry 14 14%
Computer Science 13 13%
Business, Management and Accounting 8 8%
Unspecified 8 8%
Nursing and Health Professions 6 6%
Other 14 14%
Unknown 34 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 July 2017.
All research outputs
#20,434,884
of 22,988,380 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,815
of 2,003 outputs
Outputs of similar age
#272,401
of 312,506 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#39
of 43 outputs
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So far Altmetric has tracked 2,003 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 43 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.