Data-driven quality assurance to prevent erroneous test results

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.

Keywords:

• Quality assurance
• laboratory data
• liquid chromatography
• mass spectrometry
• automated chemistry

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Disclosure statement

No potential conflict of interest was reported by the authors.