New insights in preanalytical quality

The negative impact of preanalytical errors on the quality of laboratory testing is now universally recognized. Nonetheless, recent technological advancements and organizational transformations in healthcare - catalyzed by the still ongoing coronavirus disease 2019 (COVID-19 pandemic) - have introduced new challenges and promising opportunities for improvement. The integration of value-based scoring systems for clinical laboratories and growing evidence linking preanalytical errors to patient outcomes and healthcare costs underscore the critical importance of this phase. Emerging topics in the preanalytical phase include the pursuit of a "greener" and more sustainable environment, innovations in self-sampling and automated blood collection, and strategies to minimize patient blood loss. Additionally, efforts to reduce costs and enhance sustainability through patient blood management have gained momentum. Digitalization and artificial intelligence (AI) offer transformative potential, with applications in sample labeling, recording collection events, and monitoring sample conditions during transportation. AI-driven tools can also streamline the preanalytical workflow and mitigate errors. Specific challenges include managing hemolysis and developing strategies to minimize its impact, addressing issues related to urine collection, and designing robust protocols for sample stability studies. The rise of decentralized laboratory testing presents unique preanalytical hurdles, while emerging areas such as liquid biopsy and anti-doping testing introduce novel complexities. Altogether, these advancements and challenges highlight the dynamic evolution of the preanalytical phase and the critical need for continuous innovation and standardization. This collective opinion paper, which summarizes the abstracts of lectures delivered at the two-day European Federation of Laboratory Medicine (EFLM) Preanalytical Conference entitled "New Insight in Preanalytical Quality" (Padova, Italy; December 12-13, 2025), provides a comprehensive overview of preanalytical errors, offers some important insights into less obvious sources of preanalytical vulnerability and proposes efficient opportunities of improvement.