Pre-Analytical, Analytical, And Post-Analytical Errors In Clinical Laboratory Testing: A Systematic Review Of Causes And Prevention Strategies
DOI:
https://doi.org/10.63278/jicrcr.vi.3220Abstract
Background:
Clinical laboratory testing plays a critical role in medical decision-making, with errors in the pre-analytical, analytical, and post-analytical phases potentially compromising diagnostic accuracy and patient safety. Pre-analytical errors, such as issues in sample collection and patient identification, are the most common, followed by analytical and post-analytical errors that arise from instrumentation failures and result misinterpretation.
Aim:
This systematic review aims to evaluate the prevalence, causes, and prevention strategies of errors across the three phases of laboratory testing and to assess the effectiveness of implemented error reduction measures.
Method:
A comprehensive literature search was conducted across PubMed, ScienceDirect, Cochrane Library, Web of Science, and Google Scholar to identify studies published from 2020 to 2024. Ten primary research papers were selected based on predefined criteria and were assessed for quality using the Critical Appraisal Skills Program (CASP) tool. Data on error types, causes, and prevention strategies were extracted and synthesized thematically.
Results:
The review identified that pre-analytical errors were the most prevalent, comprising up to 70% of all laboratory errors, primarily due to incorrect sample collection and delays in transport. Analytical errors were less frequent but critical, often due to equipment malfunctions. Post-analytical errors, such as delayed reporting and data entry mistakes, were also significant. Prevention strategies, including staff training, automation, and standardization, were effective in reducing errors across all phases.
Conclusion:
Reducing errors in clinical laboratory testing requires a multifaceted approach that includes improved protocols, ongoing training, and the integration of automated systems. Future research should focus on technological advancements and quality assurance in low-resource settings to further minimize errors.
