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Systematic Error: Methodological and Sampling Errors01:15

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Contaminants and Errors01:16

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Data analytics for error detection in clinical laboratories.

Clarence W Chan1

  • 1Department of Pathology, Pritzker School of Medicine, The University of Chicago, Chicago, Illinois, USA.

Critical Reviews in Clinical Laboratory Sciences
|September 19, 2025
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Summary
This summary is machine-generated.

Errors in laboratory medicine are unavoidable. This review details methods for detecting errors and assessing test performance limitations, crucial for quality management and patient care.

Keywords:
Data analyticsaverage of normalsclinical laboratory and laboratory medicinedelta checkserror detectionerror propagationmoving averagesrandom error and imprecisionregressionsystematic error and bias

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Area of Science:

  • Clinical Laboratory Science
  • Quality Management in Healthcare
  • Medical Diagnostics

Background:

  • Laboratory medicine relies on accurate results for patient care.
  • Errors can occur in pre-analytical, analytical, and post-analytical phases of testing.
  • Detecting and assessing errors is vital for quality management.

Purpose of the Study:

  • To review standard concepts and methods for quantifying uncertainty and error in clinical laboratories.
  • To present method validation and verification as tools for preemptive error assessment.
  • To highlight data analytic approaches and emerging AI/ML for error detection.

Main Methods:

  • Review of standard error quantification concepts.
  • Discussion of method validation and verification studies.
  • Overview of data analytic, statistical, machine learning, and artificial intelligence approaches.

Main Results:

  • Standard methods for error quantification and assessment are introduced.
  • Method validation/verification are key for proactive error identification.
  • Data analytics, ML, and AI show promise for advanced error detection.

Conclusions:

  • Effective error detection and management are essential in laboratory medicine.
  • Proactive assessment through validation and verification is crucial.
  • Emerging technologies like AI and ML offer future advancements in error detection.