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Related Concept Videos

Quality Assurance01:19

Quality Assurance

167
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...
167
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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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...
177
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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Data Validation01:15

Data Validation

194
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
194
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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Advances in internal quality control.

Tze Ping Loh1, Chun Yee Lim2, Sunil Kumar Sethi1

  • 1Department of Laboratory Medicine, National University Hospital, Singapore, Singapore.

Critical Reviews in Clinical Laboratory Sciences
|May 17, 2023
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Summary
This summary is machine-generated.

Patient-based quality control offers superior error detection and continuous monitoring compared to traditional methods. Laboratories should adopt these advanced strategies to enhance patient safety and improve the clinical relevance of results.

Keywords:
Average of normalmoving averagespatient-based quality controlpatient-based real time quality controlquality control

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Area of Science:

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

Background:

  • Modern laboratory quality control has evolved from statistical error assessment to considering measurement capability (sigma metrics) and patient risk.
  • Conventional internal quality control (IQC) faces limitations including non-commutable materials, infrequent testing, and high costs.
  • Patient-based quality control (PBQC) has advanced with improved algorithms for error detection and validation protocols.

Purpose of the Study:

  • To highlight the limitations of conventional internal quality control.
  • To emphasize the advantages and advancements in patient-based quality control.
  • To encourage the adoption of patient-based quality control in laboratories.

Main Methods:

  • Review of advancements in quality control methodologies.
  • Comparison of conventional internal quality control with patient-based quality control.
  • Discussion of algorithmic improvements in patient-based quality control.

Main Results:

  • Patient-based quality control offers continuous, commutable data superior to conventional IQC.
  • Advanced algorithms in PBQC enable sensitive error detection with minimal patient data.
  • PBQC enhances laboratory understanding of the clinical impact of results.

Conclusions:

  • Patient-based quality control overcomes limitations of conventional IQC, providing more reliable and clinically relevant quality assurance.
  • Laboratories are encouraged to implement PBQC for improved patient safety and result integrity.
  • Regulatory recognition and informatics advancements are needed for wider PBQC adoption.