The journey to pre-analytical quality

Mario Plebani

doi:10.1515/cclm-2025-0057

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The journey to pre-analytical quality

Mario Plebani

Published/Copyright: January 28, 2025

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From the journal Clinical Chemistry and Laboratory Medicine (CCLM) Volume 63 Issue 7

The performance of a clinical laboratory can be measured by the indices of its products, the data (i.e. the quality of analytical results and laboratory information) and there is no doubt that the worst outcome of a laboratory test is an incorrect result/information, which should ultimately lead to a diagnostic error and harm to the patient.

Therefore, the primary aim of laboratory processes and procedures should be to avoid any risk of error in the total testing process (TTP), and in particular errors with a high risk to patient safety. Early studies in the field of error in laboratory medicine were devoted to identifying analytical errors, the analytical phase being the ‘core’ of laboratory work and analytical processes under the control of the laboratory staff. An analysis of the data collected and reported in the literature, from the 1947 paper by Belk and Sunderman to the present day, shows that the analytical error rate has decreased from 162,116 per million laboratory tests (parts per million, ppm) to 447 ppm [1], 2].

Although there is still a need to improve analytical quality, a body of evidence collected over the last decades highlights the vulnerability of the extra-analytical phases [3], [4], [5]. In particular, it is widely agreed that 60–70 % of the errors that occur in laboratory medicine are attributable to the preanalytical phase [6]. The term “preanalytical phase” first appeared in the literature in a paper by Statland and Winkel published in 1977 [7] highlighting awareness of this phase as a major contributor to laboratory errors. In this issue of the Journal, a paper by John GK and Coll. deals with a scoping review on the 50-year timeline of the evolution of studies on pre-analytical errors (PAEs) and on the role of laboratory professionals in preventing this type of errors [8]. Using a Covidence Review tool to formulate keywords for a comprehensive literature search, the authors evaluated 379 articles, which were screened for eligibility and 83 were finally included in the paper. As highlighted by the Authors, the “increased emphasis on PAEs reflects the ongoing effort in understanding and improving the whole laboratory testing process in pursuit of better patient care” [8]. However, despite increased awareness of the importance of PAEs and the need to reduce them, there are still significant barriers to adopting effective strategies and methods, including a lack of available digital tools, inadequate access to health data and staff skills. Other fundamental challenges are the importance of grading errors according to their severity and the need for reliable quality indicators to identify key areas for improvement in pre-analytical steps. In their work, the authors emphasize the importance of harmonizing not only the list of quality indicators (QI), but also data collection and the identification of reliable performance specifications, highlighting the work of the Working Group on Laboratory Errors and Patient Safety (WG-LEPS) of the International Federation of Clinical Chemistry and Laboratory Medicine to promote the model of quality indicators (MQI) [9].

According to the data reported, the most common errors are related to incorrect procedures for specimen collection, leading to haemolysis and clotting, but inaccurate patient identification and wrong tube labeling still occur and are referred to as “titanic errors” because they put the patient at risk of harm. The role of artificial intelligence (AI) in improving the pre-analytical phase is a very interesting and innovative part of the paper, as AI promises to drive improvements in the management and reduction of PAEs, but its role in addressing this issue is still in its infancy. In fact, improvements in the pre-analytical phase are related to the reduction of manual operations, traceability throughout all procedures and staff training, highlighting the need for further projects aimed at increasing awareness and interest of laboratory professionals in pre-analytical quality. The traditional pre-analytical phase has been divided into two parts, recognizing a pre-pre-analytical phase which consists of all the steps before samples enter the laboratory walls, while the pre-analytical phase is essentially related to sample preparation that takes place in the laboratory [1], 10]. However, in their work the authors state that “pre-pre-analytical errors involve inappropriate test selection (only)” [8]. The separation between the pre-analytical and pre-pre-analytical phases is not just a terminological issue, as it recognizes the need to work outside the silo with clinicians, healthcare professionals and even patients to manage the early stages of the testing process that are particularly at risk for errors and errors related to patent harm. This issue must therefore be clarified in order to steer future studies in the right direction to reduce the risk of error even in procedures and processes that fall in the border area between the clinic and the laboratory, as we know that “The Devil is in the boundary”. The take-home message is that the road to preanalytical quality is still a long one and therefore the EFLM has decided to organize the 6th Conference on the Preanalytical Phase on 12-13 December 2025 (https://www.eflm.eu/site accessed January 16th, 2025) to further raise awareness of this fundamental aspect of the whole testing process: so we are all welcome to participate in this scientific event that will be held in Padua!

Corresponding author: Mario Plebani, Honorary Professor of Clinical Chemistry and Clinical Molecular Biology, University of Padova-Italy, Padua, Italy, E-mail: mario.plebani@unipd.it

Research ethics: Not applicable.
Informed consent: Not applicable.
Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.
Use of Large Language Models, AI and Machine Learning Tools: None declared.
Conflict of interest: The author states no conflict of interest.
Research funding: None declared.
Data availability: Not applicable.

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Published Online: 2025-01-28

Published in Print: 2025-06-26

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