Related Experiment Video

Updated: May 4, 2026

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management
05:35

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

Published on: January 19, 2024

1.9K

Types and frequency of preanalytical errors in haematology lab

Shashi Upreti1, Sanjay Upreti2, Rani Bansal3

  • 1Assistant Professor , Department of Pathology, Subharti Medical College , Meerut, U.P., India .

Journal of Clinical and Diagnostic Research : JCDR
|January 7, 2014
PubMed

Insights

Preanalytical errors in haematology labs occur in approximately 1% of samples. Misidentification was the most frequent error, leading to sample rejection.

Area of Science:

  • Clinical Laboratory Science
  • Hematology
  • Quality Management

Background:

  • Preanalytical errors significantly impact laboratory test results and patient care.
  • Identifying and quantifying these errors is crucial for improving laboratory efficiency and accuracy.

Purpose of the Study:

  • To determine the frequency of preanalytical errors in a clinical haematology laboratory.
  • To identify the most common types of preanalytical errors leading to sample rejection.

Main Methods:

  • A retrospective analysis of one year of data from a haematology laboratory.
  • Inclusion of data from both inpatient (IPD) and outpatient (OPD) departments.
  • Systematic scanning and categorization of preanalytical variables.

Main Results:

  • A total of 135,808 samples were analyzed.
  • Preanalytical errors were identified in 1,339 samples, representing approximately 1% of all samples.
  • The highest rejection rate was due to sample misidentification (0.35%), while sample dilution accounted for the least (0.04%).

Conclusions:

  • Preanalytical errors represent a significant challenge in haematology laboratory workflows.
  • Sample misidentification is a primary driver of preanalytical errors and sample rejections.
  • Targeted interventions focusing on sample identification are essential for reducing preanalytical error rates.
Abstract

Keywords:
HaematologyLaboratoryPre–analytical errors

More Related Videos

Evaluation of a Point-of-Care Testing Analyzer for Measuring Peripheral Blood Leukocytes05:58

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

Published on: March 22, 2022

3.8K
An In Vitro Hemodynamic Loop Model to Investigate the Hemocytocompatibility and Host Cell Activation of Vascular Medical Devices08:44

An In Vitro Hemodynamic Loop Model to Investigate the Hemocytocompatibility and Host Cell Activation of Vascular Medical Devices

Published on: August 21, 2020

8.0K

Related Concept Videos

Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

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...
8.7K
Errors occurring during blood pressure monitoring01:25

Errors occurring during blood pressure monitoring

Blood pressure monitoring is a crucial clinical procedure in diagnosing and managing various cardiovascular conditions. Despite its significance, the accuracy of blood pressure measurements can be compromised by multiple factors, potentially leading to either falsely high or low readings. These inaccuracies are critical as they can significantly impact patient care. So, it is vital to understand these challenges deeply and adopt strategic approaches to minimize errors.
Several factors...
1.6K
Contaminants and Errors01:16

Contaminants and Errors

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...
666
Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
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...
8.7K
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
11.2K
Random and Systematic Errors01:20

Random and Systematic Errors

974