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Related Concept Videos

Quality Control01:05

Quality Control

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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...
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Quality Assurance01:19

Quality Assurance

161
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...
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Introduction to Statistical Process Control01:15

Introduction to Statistical Process Control

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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...
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Control Systems01:10

Control Systems

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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...
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The X̄ Chart00:58

The X̄ Chart

154
The  x̄ chart is a statistical tool for monitoring the means in a process.
The x̄ chart, often known as the individual control chart, is a crucial tool in statistical process control. It is designed to monitor process behavior and performance over time and is widely used in various industries to ensure that processes are operating at their optimum capacity and within specified limits.
A x̄ chart is constructed by plotting individual measurements of a quality...
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Interpreting X̄ Charts01:13

Interpreting X̄ Charts

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Interpreting x̄ charts, a type of control chart used in statistical process control helps monitor the variation in processes over time. The x̄ chart is based on the sample mean and allows for monitoring variations in the process mean over time. These charts are pivotal for quality assurance in manufacturing and other sectors.
An x̄ chart plots the values of individual measurements over time against control limits calculated from historical data. The central line...
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Internal quality control - past, present and future trends.

Carmen Ricós1, Pilar Fernandez-Calle1,2, Carmen Perich1

  • 1External Quality Programs Committee and Analytical Quality Commission, Spanish Society of Laboratory Medicine (SEQCML), Barcelona, Spain.

Advances in Laboratory Medicine
|June 26, 2023
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Summary
This summary is machine-generated.

Internal quality control (IQC) models have evolved significantly, from standard deviation limits to biological variation and sigma metrics, enhancing patient safety in laboratory diagnostics.

Keywords:
control with patients resultsinternal quality controlperformance specifications

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Area of Science:

  • Clinical Laboratory Science
  • Quality Management in Healthcare

Background:

  • Internal quality control (IQC) is essential for ensuring the accuracy and reliability of laboratory test results.
  • The evolution of IQC models reflects advancements in analytical technology and a growing understanding of clinical needs.

Purpose of the Study:

  • To provide a historical overview of IQC models from the mid-20th century to the present.
  • To analyze the strengths and weaknesses of various IQC approaches.
  • To project future directions for IQC to enhance patient safety.

Main Methods:

  • Critical analysis of published literature on IQC procedures.
  • Historical review of IQC model development.
  • Discussion of the strengths and limitations of different IQC strategies.

Main Results:

  • Early IQC models relied on standard deviation limits with control materials.
  • Later models incorporated biological variation and patient-specific data.
  • Modern approaches include sigma metrics and workload-adjusted IQC, focusing on patient harm reduction.

Conclusions:

  • IQC models have progressively improved, offering more sophisticated quality assessment.
  • Addressing the weaknesses of current IQC practices is crucial for optimal laboratory performance.
  • Future IQC strategies should prioritize patient safety by minimizing analytical errors.