Quality Control and Quality Assurance

Quality control (QC) and quality assurance (QA) are the complementary practices by which a clinical laboratory keeps its results trustworthy. Quality control is the day-to-day statistical monitoring of analytical performance using control materials; quality assurance is the broader management system - policies, procedures, audits, and external assessment - that ensures the whole testing process consistently produces fit-for-use results.

Definition

Quality control is the statistical process of analyzing control specimens of known expected value to detect analytical error in real time, while quality assurance is the encompassing system of planned activities - including external assessment and continual monitoring - that ensures results meet defined quality requirements across the entire testing process.

Scope

This topic covers internal quality control (control materials, control charts, and decision rules), external quality assessment or proficiency testing, and the quality-management framework that surrounds them, including quality indicators that track performance across the testing process. It is a methodological reference within laboratory medicine and does not provide instructions for operating a particular assay or laboratory.

Core questions

• How does a laboratory detect, in real time, that an analytical run has gone out of control?
• What distinguishes internal quality control from external quality assessment?
• How are control rules chosen to balance error detection against false rejection?
• How do quality indicators extend assurance beyond the analytical step?

Key concepts

• Internal quality control (IQC)
• Control material and control chart (Levey-Jennings)
• Westgard multi-rules
• Error detection vs. false rejection probability
• External quality assessment (EQA) / proficiency testing
• Quality indicators
• Total quality management and ISO 15189 accreditation
• Random and systematic analytical error

Mechanisms

Internal quality control works by analyzing control materials of known, stable composition alongside patient specimens and plotting the results on a control chart, typically the Levey-Jennings chart, on which limits are set in standard-deviation units around the established mean. Control rules decide when a run is rejected: a single rule such as a result beyond two standard deviations is sensitive but generates frequent false alarms, so Westgard's multi-rule scheme combines several rules to raise true error detection while keeping false rejection low. External quality assessment complements this by sending the same blinded specimens to many laboratories and comparing their results, revealing systematic bias that internal control cannot. Around both sits a quality-management system that defines requirements, audits performance, and tracks quality indicators across the pre-analytical, analytical, and post-analytical phases.

Clinical relevance

QC and QA determine whether the numbers clinicians receive are reliable enough to act on; a laboratory that fails to detect a calibration shift can report systematically wrong results across many patients. This topic describes how laboratories guard against that; it characterizes laboratory practice and is not guidance for interpreting an individual patient's result.

Evidence & guidelines

Routine internal quality control rests on Westgard's multi-rule framework, which built on the Levey-Jennings adaptation of Shewhart control charts to the clinical laboratory. ISO 15189 specifies quality and competence requirements for medical laboratories, and consensus work has sought to harmonize quality indicators so that performance can be compared across the testing process. Discipline-specific guidelines, such as those for tumor-marker testing, set quality requirements for particular analytes.

History

The control chart entered the clinical laboratory in 1950 when Levey and Jennings adapted Shewhart's industrial charts to monitor analytical performance. Over the following decades single-rule charts proved either insensitive or prone to false rejection, and in 1981 Westgard and colleagues introduced a multi-rule scheme that combined several control rules to improve error detection. Quality thinking subsequently expanded from the analytical step to whole-process quality assurance, formalized in accreditation standards and in harmonized quality indicators.

Debates

How should control limits and rules be set?

Tighter limits and more rules catch more true errors but also reject more good runs; the choice of QC strategy is a trade-off between error detection and false-rejection rate, ideally matched to the analytical quality required for the test's clinical use.

Key figures

• James O. Westgard
• Stanley Levey
• E. R. Jennings
• Mario Plebani

Seminal works

• levey-jennings-1950
• westgard-1981
• plebani-2014

Frequently asked questions

What is the difference between quality control and quality assurance?

Quality control is the real-time statistical monitoring of analytical performance using control specimens, while quality assurance is the broader management system - including external assessment, audits, and quality indicators - that ensures the whole testing process meets defined quality requirements.

Why use multiple control rules instead of a single limit?

A single rule, such as flagging any result beyond two standard deviations, is either too insensitive or rejects too many acceptable runs. Combining several rules, as in the Westgard scheme, raises true error detection while keeping the false-rejection rate low.