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Related Concept Videos

Quality Control01:05

Quality Control

216
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...
216
Quality Assurance01:19

Quality Assurance

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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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Methods of Documentation V: CBE01:23

Methods of Documentation V: CBE

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Charting by Exception, or CBE, is a method of documentation used in healthcare, particularly in nursing, that focuses on documenting only significant or abnormal findings rather than recording every detail. This approach aims to streamline the documentation process, improve efficiency, and ensure that healthcare providers can quickly identify deviations from normalcy in patient assessments.
In CBE, healthcare professionals establish predefined standards of practice that define what constitutes...
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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
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Controls in Experiments01:13

Controls in Experiments

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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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Developing an evidence-based approach to quality control.

Tony Badrick1, Tze Ping Loh2

  • 1RCPA Quality Assurance Programs, St Leonards, Sydney, Australia.

Clinical Biochemistry
|February 3, 2023
PubMed
Summary
This summary is machine-generated.

This opinion piece questions current analytical error detection methods in clinical biochemistry quality control. It suggests existing models overlook crucial errors related to quality control materials and irregular issues, impacting patient safety.

Keywords:
Error detectionPatient-based real-time quality controlQuality control strategySigma metric

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Area of Science:

  • Clinical Biochemistry
  • Analytical Chemistry
  • Laboratory Medicine

Background:

  • Effective quality control (QC) is fundamental in clinical biochemistry.
  • Current QC systems rely on detecting systematic and random analytical errors.
  • Existing theoretical models for error detection are being questioned.

Purpose of the Study:

  • To analyze the underlying assumptions of current quality control systems.
  • To highlight limitations in existing models for analytical error detection.
  • To discuss the impact of undetected errors on patient safety.

Main Methods:

  • Critical analysis of established quality control theories.
  • Review of analytical error types and detection methodologies.
  • Discussion of quality control material-related errors and irregular errors.

Main Results:

  • Current quality control models primarily recognize systematic and random errors.
  • The study identifies at least two additional error types: QC material-related and irregular errors.
  • Existing detection methods may fail to identify all significant analytical errors.

Conclusions:

  • The theoretical basis of current analytical error detection models requires re-evaluation.
  • Incorporating QC material and irregular errors into QC systems is crucial.
  • Enhancing QC strategies is essential to prevent patient harm from analytical errors.