Related Experiment Video
Updated: Mar 15, 2026

Detection of Targetable Alterations in Non-small Cell Lung Cancer using Next-generation Sequencing
Published on: October 10, 2025
Preanalytical errors in medical laboratories: a review of the available methodologies of data collection and analysis
Jamie West1, Jennifer Atherton2, Seán J Costelloe3
11 Department of Clinical Biochemistry and Immunology, Peterborough City Hospital, Peterborough, UK.
Abstract:
Preanalytical errors have previously been shown to contribute a significant proportion of errors in laboratory processes and contribute to a number of patient safety risks. Accreditation against ISO 15189:2012 requires that laboratory Quality Management Systems consider the impact of preanalytical processes in areas such as the identification and control of non-conformances, continual improvement, internal audit and quality indicators. Previous studies have shown that there is a wide variation in the definition, repertoire and collection methods for preanalytical quality indicators. The International Federation of Clinical Chemistry Working Group on Laboratory Errors and Patient Safety has defined a number of quality indicators for the preanalytical stage, and the adoption of harmonized definitions will support interlaboratory comparisons and continual improvement. There are a variety of data collection methods, including audit, manual recording processes, incident reporting mechanisms and laboratory information systems. Quality management processes such as benchmarking, statistical process control, Pareto analysis and failure mode and effect analysis can be used to review data and should be incorporated into clinical governance mechanisms. In this paper, The Association for Clinical Biochemistry and Laboratory Medicine PreAnalytical Specialist Interest Group review the various data collection methods available. Our recommendation is the use of the laboratory information management systems as a recording mechanism for preanalytical errors as this provides the easiest and most standardized mechanism of data capture.
Insights
Preanalytical errors significantly impact laboratory quality and patient safety. Laboratory information management systems offer the most standardized method for capturing preanalytical error data.
Area of Science:
- Clinical Laboratory Science
- Quality Management in Healthcare
- Patient Safety
Background:
- Preanalytical errors are a major source of laboratory mistakes and patient safety risks.
- ISO 15189:2012 mandates consideration of preanalytical processes within laboratory Quality Management Systems.
- Existing preanalytical quality indicators lack standardized definitions and collection methods.
Purpose of the Study:
- To review available data collection methods for preanalytical errors.
- To recommend a standardized approach for capturing preanalytical quality data.
- To support harmonization and continual improvement in laboratory quality management.
Main Methods:
- Review of various data collection methods for preanalytical errors.
- Analysis of quality management processes applicable to preanalytical data.
- Consideration of recommendations from the Association for Clinical Biochemistry and Laboratory Medicine PreAnalytical Specialist Interest Group.
Main Results:
- Significant variation exists in the definition and collection of preanalytical quality indicators.
- Laboratory Information Management Systems (LIMS) provide a standardized mechanism for data capture.
- Harmonized definitions and data collection support interlaboratory comparisons.
Conclusions:
- Laboratory Information Management Systems are recommended for recording preanalytical errors due to ease and standardization.
- Standardized data capture facilitates quality improvement and patient safety in laboratory settings.
- Adoption of harmonized preanalytical quality indicators is crucial for laboratory accreditation and performance.
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...
Contaminants and Errors
Another key consideration is determining the appropriate number of samples required to...
Data Validation
Key parameters for method validation include:
Data Validation
Nursing assessment guides are generally based on holistic models rather than medical...
Errors occurring during blood pressure monitoring
Several factors...
Development of Analytical Methods

