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Data-driven quality assurance to prevent erroneous test results.

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 mecha...

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Published in:Critical Reviews in Clinical Laboratory Sciences Vol. 57; no. 3; pp. 146 - 161
Main Authors: Crews, Bridgit O., Drees, Julia C., Greene, Dina N.
Format: review tables/charts Journal Article
Published: Taylor & Francis Ltd May2020
Online Access:View this record in EBSCOhost
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      dt: May2020
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      pub: Taylor & Francis Ltd
      place: Philadelphia, Pennsylvania
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        10.1080/10408363.2019.1678567
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        atl: Data-driven quality assurance to prevent erroneous test results.
      aug:
        au:
          Crews, Bridgit O.
          Drees, Julia C.
          Greene, Dina N.
        affil: Department of Pathology and Laboratory Medicine, University of California Irvine, Irvine, CA, USA
      sug:
        subj:
          Diagnostic Errors Prevention and Control
          Clinical Laboratories Administration
          Quality Assurance
          Data Quality
          Literature Review
          Clinical Laboratory Information Systems
          Data Warehouse
          Electronic Health Records
          Data Analysis
      ab: 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.
      pubtype: Review
      doctype:
        review
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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