Related Experiment Video
Updated: Jan 25, 2026
07:07
Errors as a Means of Reducing Impulsive Food Choice
Published on: June 5, 2016
9.1K
Pre- and postanalytical errors in haematology.
1West Herts Hospitals NHS Trust, UK NEQAS Haematology, Watford, UK.
International Journal of Laboratory Hematology
|May 10, 2019
Summary
Laboratory medicine errors predominantly occur in preanalytical and postanalytical phases. Addressing these, especially in haematology, is crucial for accurate automated counting and reliable patient results.
Area of Science:
- Laboratory Medicine
- Clinical Pathology
- Haematology
Background:
- Most laboratory medicine errors originate in the preanalytical and postanalytical phases.
- Preanalytical errors pose the greatest risk and may remain undetected until postanalytical review.
- Haematology laboratories must understand the specific impact of these errors on automated counting.
Purpose of the Study:
- To highlight the significance of pre- and postanalytical errors in laboratory medicine.
- To emphasize the unique challenges faced by haematology in relation to these errors.
- To discuss the difficulties in data collection for quality indicators.
Main Methods:
- Review of common error sources in laboratory medicine.
- Analysis of the impact of pre- and postanalytical errors on haematology automated counting.
- Discussion of challenges in standardizing reference intervals and interpreting results.
- Examination of quality indicators for error measurement.
Main Results:
- Preanalytical phase identified as the highest risk stage for errors.
- Postanalytical phase presents challenges in standardizing reference intervals, impacting interpretation (e.g., haemoglobin).
- Laboratories face difficulties in collecting high-quality data for error incidence measurement.
Conclusions:
- Minimizing pre- and postanalytical errors is essential for laboratory diagnostic accuracy.
- Standardization of reference intervals and robust data collection are key to improving laboratory services.
- Continuous monitoring and improvement are necessary to reduce error rates in laboratory medicine.
Related Concept Videos
01:15Systematic Error: Methodological and Sampling Errors
10.0K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal 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...
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...
10.0K
01:14Fundamental Attribution Error
13.7K
According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
13.7K
01:04Random Error
9.2K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
9.2K
01:27Margin of Error
7.1K
The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
7.1K
01:16Contaminants and Errors
354
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
Another key consideration is determining the appropriate number of samples required to...
354
01:13Standard Error of the Mean
11.8K
The sampling variability of a statistic is defined as how much the statistic varies from one sample to another. The sampling variability of a statistic is typically measured by measuring its standard error.
The standard error of the mean is an example of a standard error. It is a unique standard deviation known as the standard deviation of the sampling distribution of the mean. The standard error of the mean is a statistic that calculates how correctly a sample distribution represents a...
The standard error of the mean is an example of a standard error. It is a unique standard deviation known as the standard deviation of the sampling distribution of the mean. The standard error of the mean is a statistic that calculates how correctly a sample distribution represents a...
11.8K