Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Quality Control01:05

Quality Control

230
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...
230
Quality Assurance01:19

Quality Assurance

174
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...
174
Introduction to Statistical Process Control01:15

Introduction to Statistical Process Control

210
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...
210
The R Chart01:02

The R Chart

122
In statistical process control, control charts, particularly R charts, are instrumental in monitoring process variations and identifying non-random patterns that run charts might miss. R charts track the variability within process subgroups, which is crucial when standard deviation use is impractical or unknown process variations exist.
R charts are pivotal for pinpointing shifts in process variability. Stability is indicated when all data points remain within the defined upper and lower...
122
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

156
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,...
156
Interpreting R Charts01:22

Interpreting R Charts

98
R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum...
98

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Immunalysis tapentadol assay reformulation resolves tramadol interference.

Journal of analytical toxicology·2025
Same author

On-Cell Stability of Digoxin, Lithium, Phenytoin, Valproic Acid, and Vancomycin for Therapeutic Drug Monitoring.

The journal of applied laboratory medicine·2025
Same author

Estimating the incidence of transfusion-associated circulatory overload using active surveillance: A systematic review and meta-analysis.

Transfusion·2025
Same author

<i>TPMT</i> and <i>NUDT15</i> genotyping, TPMT enzyme activity and metabolite determination for thiopurines therapy: a reference laboratory experience.

Pharmacogenomics·2025
Same author

Is the 99th Percentile Cutoff Still Relevant? A Single-Center Assessment of Different Thresholds for Diagnosing Antiphospholipid Syndrome.

The journal of applied laboratory medicine·2024
Same author

Alkaline Phosphatase Activity Inconsistent with Patient's Clinical Presentation: A Cautionary Tale.

Clinical chemistry·2024

Related Experiment Video

Updated: Aug 15, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K

Risk Analysis for Quality Control Part 3: Practical Application of the Precision Quality Control Model.

Robert L Schmidt1,2, Ryleigh A Moore3, Brandon S Walker2

  • 1Department of Pathology, University of Utah, Salt Lake City, UT.

The Journal of Applied Laboratory Medicine
|January 7, 2023
PubMed
Summary

The Precision Quality Control (PQC) framework effectively minimizes quality costs by establishing optimal control limits. This method, applied to analytes like cadmium and CDT, proves practical for real-world laboratory applications.

More Related Videos

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

Author Spotlight: Microbial Control and Monitoring Strategies for Cleanroom Environments and Cellular Therapies

Published on: March 17, 2023

3.6K
Author Spotlight: Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method
09:16

Author Spotlight: Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method

Published on: May 12, 2023

1.2K

Related Experiment Videos

Last Updated: Aug 15, 2025

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.2K
Author Spotlight: Microbial Control and Monitoring Strategies for Cleanroom Environments and Cellular Therapies
09:30

Author Spotlight: Microbial Control and Monitoring Strategies for Cleanroom Environments and Cellular Therapies

Published on: March 17, 2023

3.6K
Author Spotlight: Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method
09:16

Author Spotlight: Optimization of Processing Technology for Tiebangchui with Zanba Based on CRITIC Combined with Box-Behnken Response Surface Method

Published on: May 12, 2023

1.2K

Area of Science:

  • Clinical Chemistry
  • Laboratory Medicine
  • Quality Management

Background:

  • A theoretical framework, Precision Quality Control (PQC), was developed to minimize the cost of quality.
  • The practical applicability of the PQC framework for determining optimal control limits was previously unknown.

Purpose of the Study:

  • To evaluate the practical application of the PQC framework in establishing optimal laboratory control limits.
  • To assess different visualization methods for interpreting PQC results.

Main Methods:

  • Applied the PQC framework using data for two analytes: cadmium and carbohydrate-deficient transferrin (CDT).
  • Explored three visualization approaches: risk trade-off, cost-risk trade-off, and cost minimization.
  • Analyzed analytes with differing sigma values to determine their impact on control limits.

Main Results:

  • The PQC framework successfully generated three distinct visualizations for suggesting control limits.
  • Risk-based analysis was simple to apply but challenging to interpret.
  • Cost-risk and cost minimization methods offered varying degrees of interpretability and ease of application.

Conclusions:

  • The Precision Quality Control (PQC) method is a viable approach for determining laboratory control limits.
  • PQC effectively minimizes the overall cost of quality in laboratory settings.