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Assuring analytical quality through process planning and quality control.

J O Westgard1

  • 1Department of Pathology and Laboratory Medicine, University of Wisconsin Medical School, Madison.

Archives of Pathology & Laboratory Medicine
|July 1, 1992
PubMed
Summary
This summary is machine-generated.

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Quality assurance in laboratory testing uses quality control (QC) and proficiency testing to meet performance standards. Statistical QC planning ensures reliable analytical quality, guaranteeing accurate routine test results.

Area of Science:

  • Clinical Chemistry
  • Laboratory Medicine
  • Quality Management Systems

Background:

  • Governmental regulations mandate quality assurance (QA) in laboratory testing.
  • QA integrates quality control (QC) and proficiency testing to ensure routine tests meet performance criteria.

Purpose of the Study:

  • To translate Health Care Financing Administration proficiency testing criteria into actionable process specifications.
  • To establish a quantitative approach for guaranteeing analytical quality assurance in routine laboratory testing.

Main Methods:

  • Defining analytical "total error" requirements from proficiency testing criteria.
  • Translating error requirements into specifications for allowable imprecision and inaccuracy.
  • Designing QC procedures, including decision rules and control measurements, to detect critical errors.

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Main Results:

  • The probability of detecting errors is directly related to the chosen QC procedure.
  • Statistical QC can verify the achievement of desired quality requirements with a specified probability.
  • Process planning and QC provide a quantitative method for analytical quality assurance.

Conclusions:

  • A planned, quantitative approach to analytical quality assurance is essential for reliable laboratory testing.
  • Effective QC procedures are crucial for detecting errors that could compromise test quality.
  • Statistical QC offers a robust framework for guaranteeing analytical quality in clinical laboratories.