Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Introduction to Statistical Process Control

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...

Quality Control

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...

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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, controlled...

Biostatistics: Overview

Biostatistics: Overview

Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...

Overview of Biostatistics in Health Sciences

Overview of Biostatistics in Health Sciences

Biostatistics involves the application of statistical techniques to scientific research in health-related fields, including biology and public health. These techniques are essential for designing studies, collecting data, and analyzing it to draw meaningful conclusions. Given the complexity of biological processes, particularly in studies involving human subjects, biostatistical methods are crucial for effectively organizing and interpreting data that might otherwise obscure underlying patterns...

Statistical Software for Data Analysis and Clinical Trials

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...

You might also read

Related Articles

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

Sort by

Same author

Winter, spring, summer or fall: temporal patterns in placenta-mediated pregnancy complications-an exploratory analysis.

Archives of gynecology and obstetrics·2023

Same author

Analysis of antibiotic use patterns in Danish hospitals 2015-2021 using an adapted version of the who aware classification.

BMJ open quality·2022

Same author

Sustainability of healthcare improvements for patients admitted with community-acquired pneumonia: follow-up data from a quality improvement project.

BMJ open quality·2022

Same author

Nonspecific symptoms dominate at first contact to emergency healthcare services among cases with invasive meningococcal disease.

BMC family practice·2021

Same author

The increasing importance of <i>Haemophilus influenzae</i> in community-acquired pneumonia: results from a Danish cohort study.

Infectious diseases (London, England)·2020

Same author

Improved treatment of community-acquired pneumonia through tailored interventions: Results from a controlled, multicentre quality improvement project.

PloS one·2020

Search research articles

Related Experiment Video

Updated: Jun 23, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

[Statistical process control in healthcare].

Jacob Anhøj¹, Brian Bjørn

¹Hvidovre Hospital, Enhed for Patientsikkerhed. jacob.anhoej@regionh.dk

Ugeskrift for Laeger

|May 21, 2009

Summary

Statistical process control (SPC) uses methods to study process variation. Run charts help identify special cause variation, indicating unpredictable process changes.

Area of Science:

Statistics
Process Improvement

Background:

Statistical process control (SPC) is essential for understanding process variation.
Variation is categorized into common cause (inherent) and special cause (unpredictable).

Purpose of the Study:

To introduce the run chart as a tool for analyzing process variation.
To demonstrate how run chart analysis can detect special cause variation.

Main Methods:

Utilizing run charts for process variation analysis.
Interpreting patterns and anomalies in run charts.

Main Results:

Run charts provide a visual method for assessing process stability.
Anomalies in run charts can signal shifts or unusual patterns.

Related Experiment Videos

Last Updated: Jun 23, 2026

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

Published on: August 29, 2025

Conclusions:

Run charts are effective for identifying special cause variation.
SPC methods, including run charts, aid in process monitoring and improvement.