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Related Experiment Video

Updated: Jun 29, 2026

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes

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Quality control issues in point of care testing.

Cameron L Martin¹

¹Pathology Queensland, Clinical and State Wide Services, Royal Brisbane & Women's Hospital, Herston, Qld 4006, Australia. Cameron_martin@health.qld.gov.au

The Clinical Biochemist. Reviews

|October 15, 2008

Summary

Implementing intelligent system analysis simplifies quality control (QC) in Point of Care Testing (PoCT). A tailored QC program ensures adequate system control without excessive cost or complexity.

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Area of Science:

Clinical Laboratory Science
Medical Diagnostics
Biotechnology

Background:

Quality Control (QC) in Point of Care Testing (PoCT) is often perceived as complex.
Intelligent system analysis offers a method to simplify PoCT QC.
The goal is an effective QC program that balances control with operational costs and complexity.

Purpose of the Study:

To outline strategies for simplifying and enhancing PoCT quality control.
To develop a QC program that is both effective and cost-efficient.
To guide the selection and implementation of appropriate QC measures for diverse PoCT systems.

Main Methods:

Emphasize pre-analytical work: documentation, operator training, monitoring, and analyser maintenance.
Analyze analyser characteristics: type, test capabilities, calibration, self-checks, and reagent requirements.
Establish the location and components of the analysis system.
Tailor QC to specific components while assessing the entire system.
Implement common QC approaches: cartridge checks, monthly QA samples, and daily checks if no electronic verification exists.
Consider non-conventional QC models when necessary.

Main Results:

Intelligent analysis can significantly simplify PoCT QC.
Effective pre-analytical work and analyser-specific QC are crucial.
A comprehensive QC strategy should check individual components and the system as a whole.
Conventional QC models may not suffice for all PoCT systems.

Conclusions:

A well-controlled PoCT system can be achieved through intelligent analysis and tailored QC programs.
Balancing QC effectiveness with operational efficiency is key.
Flexibility in QC approaches, including non-conventional methods, is essential for optimal PoCT management.