Related Experiment Video
Updated: Jun 27, 2026

Detection of Targetable Alterations in Non-small Cell Lung Cancer using Next-generation Sequencing
Published on: October 10, 2025
Causes, consequences, detection, and prevention of identification errors in laboratory diagnostics.
Giuseppe Lippi1, Norbert Blanckaert, Pierangelo Bonini
1University of Verona, Verona, Italy.
Patient misidentification in laboratory diagnostics, a critical step in healthcare, occurs in about 1% of cases. This can lead to serious patient harm, emphasizing the need for robust prevention and detection strategies.
Area of Science:
- Clinical diagnostics
- Laboratory medicine
- Patient safety
Background:
- Laboratory diagnostics are crucial for clinical decision-making.
- The preanalytical phase is prone to manual errors, impacting patient safety.
- Patient misidentification errors carry severe clinical consequences, including misdiagnosis and incorrect treatment.
Purpose of the Study:
- To highlight the prevalence and causes of patient misidentification in laboratory diagnostics.
- To analyze the adverse outcomes associated with these errors.
- To propose guidelines for preventing and detecting misidentification.
Main Methods:
- Review of literature on laboratory specimen misidentification.
- Analysis of error causes and clinical consequences.
- Development of prevention and detection strategies.
Main Results:
- Patient misidentification in general laboratory specimens occurs at approximately 1%.
- Errors can lead to significant patient harm if not detected promptly.
- Direct-positive identification, IT, automation, multiple identifiers, and delta checks are key strategies.
Conclusions:
- Patient misidentification is a significant risk in laboratory diagnostics.
- Implementing proposed guidelines can mitigate risks.
- Specimen rejection and recollection are crucial upon detection of misidentification.
Related Concept Videos
Errors occurring during blood pressure monitoring
Several factors...
Automated Microbial Diagnostics
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...
Rapid Identification of Pathogens
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...
Data Validation
Key parameters for method validation include:
