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• W2983642203 abstract "Increasing laboratory automation and efficiency requires quality assurance (QA) approaches to ensure that reported results are precise and accurate. Prerequisites for designing optimal QA strategies include an in-depth understanding of the laboratory processes, the expected results, and of the mechanisms that can cause erroneous results. Oftentimes, a laboratory's own data, extracted from the laboratory information system, electronic medical record, and/or clinical data warehouse are necessary to master the aforementioned requirements. Data-driven QA utilizes retrospective and/or prospective laboratory results to minimize errors in the clinical laboratory due to pre-analytical or analytical vulnerabilities. Additionally, exploitation of this data may improve result interpretation. The objective of this review is to illustrate specific examples of data-driven QA approaches for several areas of the clinical laboratory and for different phases of the testing cycle." @default.
• W2983642203 created "2019-11-22" @default.
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• W2983642203 date "2019-11-02" @default.
• W2983642203 modified "2023-10-17" @default.
• W2983642203 title "Data-driven quality assurance to prevent erroneous test results" @default.
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• W2983642203 doi "https://doi.org/10.1080/10408363.2019.1678567" @default.
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