Related Experiment Video
Updated: Apr 25, 2026

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes
Published on: March 22, 2022
Use of quality indicators to compare point-of-care testing errors in a neonatal unit and errors in a STAT central
Insights
Quality indicators reveal significant preanalytical errors in point-of-care testing (POCT), similar to central laboratory (CL) testing. Monitoring these quality indicators is crucial for reducing POCT errors.
Area of Science:
- Clinical Laboratory Science
- Point-of-Care Diagnostics
- Quality Management in Healthcare
Background:
- Point-of-care testing (POCT) is susceptible to errors, similar to central laboratory (CL) testing.
- Quality indicators (QIs) are recommended for evaluating error rates, but data for POCT is limited.
- Understanding POCT quality error rates is essential for improving diagnostic accuracy.
Purpose of the Study:
- To investigate and quantify quality error rates in POCT.
- To compare POCT quality error rates with those of CL testing.
- To identify specific areas within the testing process contributing to errors.
Main Methods:
- Standardized quality indicators (QIs) were used to compare POCT and CL testing.
- Error rates were assessed for sample requests, collection, and handling.
- External quality assessment programs (EQAP) and internal quality control (IQC) results were analyzed.
Main Results:
- Patient identification errors were significantly higher in POCT (45.3%) versus CL (0.02%).
- Samples without results were more frequent in POCT (15.8%) compared to CL (3.3%).
- Analytical phase QIs showed better performance in POCT than CL testing for EQAP and IQC.
Conclusions:
- The preanalytical phase presents the primary challenge for both POCT and CL testing.
- Continuous monitoring of quality indicators is a highly effective strategy for minimizing errors in POCT.
- Implementing robust QI monitoring can enhance the reliability of point-of-care diagnostic results.
Background:
Point-of-care testing (POCT), like other laboratory tests, can be affected by errors throughout the total testing process. To evaluate quality error rates, the use of quality indicators (QIs) is recommended; however, little information is available on the quality error rate associated with POCT. The objective of this study was to investigate quality error rates related to POCT and compare them with central laboratory (CL) testing.
Methods:
We studied standardized QIs for POCT in comparison to CL testing. We compared error rates related to requests, collection, and handling of samples and results from external quality assessment program (EQAP) and internal quality control (IQC).
Results:
The highest difference between POCT and CL testing was observed for QI related to patient identification, 45.3% vs. 0.02% (p<0.001). Regarding specimen collection and handling, the QI related to samples without results was also higher in POCT than in CL testing, 15.8% vs. 3.3% (p<0.001). For the QI related to insufficient sample volume, we obtained 2.9% vs. 0.9% (p=0.27). Unlike QIs for the preanalytical phase, QIs for the analytical phase had better results in POCT than CL testing. We obtained 8.3% vs. 16.6% (p=0.13) for QI related to unacceptable results in EQAP and 0.8% vs. 22.5% (p<0.001) for QI related to unacceptable results in IQC.
Conclusions:
Our results show that the preanalytical phase remains the main problem in POCT like in CL testing and that monitoring of quality indicators is a very valuable tool in reducing errors in POCT.
Related Concept Videos
Systematic Error: Methodological and Sampling Errors
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Errors occurring during blood pressure monitoring
Several factors...
Types of Errors: Detection and Minimization
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
Accuracy and Errors in Hypothesis Testing
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
Contaminants and Errors
Another key consideration is determining the appropriate number of samples required to...
Random and Systematic Errors

