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Related Experiment Video

Updated: May 15, 2026

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

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Published on: August 29, 2025

Validating the performance of QC procedures.

John Yundt-Pacheco¹, Curtis A Parvin

¹Quality Systems Division, Bio-Rad Laboratories, 3201 Technology Drive, Plano, TX 75074, USA.

Clinics in Laboratory Medicine

|January 22, 2013

Summary

This study introduces a method to calculate unreliable patient test results from out-of-control lab conditions. It helps design quality control strategies to minimize patient risk and meet laboratory standards.

Area of Science:

Clinical chemistry
Laboratory quality management
Medical diagnostics

Background:

Ensuring the accuracy of patient test results is critical in healthcare.
Laboratory quality control (QC) strategies aim to detect analytical errors.
Out-of-control conditions can lead to reporting unreliable patient results.

Purpose of the Study:

To develop a methodology for calculating the maximum expected number of unreliable patient results.
To present strategies for modifying the number of unreliable results produced and reported.
To establish design criteria for QC strategies based on risk assessment.

Main Methods:

A computational methodology was developed to determine the maximum expected unreliable results.
The study analyzed unreliable results reported before and after the last accepted QC evaluation.

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Published on: August 29, 2025

Design criteria were formulated using these metrics to align with laboratory risk tolerance.

Main Results:

The methodology quantifies the expected number of unreliable patient results under specific QC strategies.
Strategies are proposed to reduce the number of unreliable results.
The number of unreliable results before and after the last QC check serves as a key design parameter.

Conclusions:

The presented methodology allows for the quantitative assessment of risk associated with QC strategies.
Laboratories can use these findings to design QC protocols that minimize the impact of out-of-control conditions.
This approach supports the development of risk-based QC strategies tailored to specific laboratory needs.