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Related Concept Videos

Quality Control01:05

Quality Control

Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
Quality Assurance01:19

Quality Assurance

Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
Control Systems01:10

Control Systems

Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...
Introduction to Statistical Process Control01:15

Introduction to Statistical Process Control

Statistical Process Control (SPC) is a method used to monitor and control quality within processes, particularly in manufacturing and service delivery, by employing statistical methods. SPC aims to distinguish between natural (common cause) variation and variation due to specific changes or events (special cause), allowing for timely improvements and sustained quality. The control chart, a pivotal tool in SPC, visually displays data over time alongside a central line of upper and lower control...
Healthcare Associated Infections II: Preventive Measures01:22

Healthcare Associated Infections II: Preventive Measures

Essential infection prevention measures are based on the knowledge of the infection chain, the modes of transmission in healthcare settings, and the use of the best practices in all healthcare settings. Compulsory public reporting of healthcare-associated infection rates is needed to allow individuals and the community to make informed choices regarding selecting a healthcare facility.
The best practices for preventing healthcare-associated infections include hand hygiene, patient risk...
Controls in Experiments01:13

Controls in Experiments

When conducting an experiment, it is crucial to have control to reduce bias and accurately measure the dependent variables. It also marks the results more reliable. Controls are elements in an experiment that have the same characteristics as the treatment groups but are not affected by the independent variable. By sorting these data into control and experimental conditions, the relationship between the dependent and independent variables can be drawn. A randomized experiment always includes a...

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Related Experiment Video

Updated: May 7, 2026

Microbial Control and Monitoring Strategies for Cleanroom Environments and Cellular Therapies
09:30

Microbial Control and Monitoring Strategies for Cleanroom Environments and Cellular Therapies

Published on: March 17, 2023

Internal quality control: best practice.

Helen Kinns1, Sarah Pitkin, David Housley

  • 1Department of Clinical Biochemistry, Luton and Dunstable University Hospital NHS Trust, , Luton, UK.

Journal of Clinical Pathology
|September 28, 2013
PubMed
Summary
This summary is machine-generated.

Implementing effective internal quality control (IQC) in clinical biochemistry labs optimizes error detection and reduces false rejections. This practical guide details IQC system stages for reliable patient results.

Keywords:
Laboratory ManagementQuality AssuranceQuality Control

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Area of Science:

  • Clinical Biochemistry
  • Laboratory Medicine
  • Quality Management

Background:

  • Internal quality control (IQC) implementation and review vary widely in laboratory practice.
  • Ineffective IQC can lead to incorrect patient result validation or unnecessary rejection of analytical runs.

Purpose of the Study:

  • To provide a practical approach for clinical biochemistry laboratories to implement an efficient IQC system.
  • To optimize error detection and minimize false rejections in routine laboratory operations.

Main Methods:

  • Consideration of each IQC system stage: material selection, rule selection, and post-rejection actions.
  • Grouping assays based on performance to assign appropriate IQC rules.
  • Utilizing tools to categorize assay performance for IQC rule assignment.

Main Results:

  • Assay-specific IQC systems reduce inappropriate sample-run rejections compared to universal rules.
  • A limited number of IQC rules (3-4) are sufficient for routine biochemistry assays when grouped by performance.
  • Implementation requires time and education but yields significant cost and labor reductions.

Conclusions:

  • An optimized IQC system ensures day-to-day analytical consistency and reliable patient result release.
  • Tailoring IQC rules to specific analyte performance is more effective than a one-size-fits-all approach.
  • Strategic implementation of IQC leads to substantial operational efficiencies and cost savings.