Evaluation of performance in preanalytical phase EQA: can laboratories mitigate common pitfalls? DOI
Anna Linko-Parvinen, Jonna Pelanti,

Tanja Vanhelo

et al.

Clinical Chemistry and Laboratory Medicine (CCLM), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 14, 2024

Abstract Objectives Preanalytical phase is an elemental part of laboratory diagnostics, but prone to humane errors. The aim this study was evaluate performance in preanalytical external quality assessment (EQA) cases. We also suggest preventive actions for risk mitigation. Methods included 12 EQA rounds (Labquality Ltd.) with three patient cases (36 cases, 54–111 participants, 7–15 countries) published 2018–2023. graded according percentage correct responses each case as ≥900 % excellent, 70–89 good, 50–69 satisfactory, 30–49 fair and <30 poor. Performance simultaneously failed ≥10 leading harmful events. Results Overall excellent 7, good 12, satisfactory 10, 4 poor 3 Additionally, 7 showed performance. Routine requests incorrect sample tubes or handling were detected Lower seen sudden abnormal results, rare requests, false identification (never-events) test requests. Information technology (IT) solutions (preanalytical checklists, autoverification rules specific notifications) could have prevented 33 36 Conclusions While most common errors performance, samples those requiring individualised consideration are vulnerable human misinterpretation. In many instances, should been identified rejected before reaching the being directed analysis. Optimising IT effectively detect these allows focus on infrequent events demanding accessible professional consultation. may help education occasions.

Language: Английский

Digital competency among pediatric healthcare workers and students: a questionnaire survey DOI

Sangsang Ren,

Weize Xu, Zhi Chen

et al.

World Journal of Pediatrics, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 3, 2025

Language: Английский

Citations

1
From errors to excellence: the pre-analytical journey to improved quality in diagnostics. A scoping review DOI Creative Commons
George John, Emmanuel J. Favaloro,

S. Bryn Austin

et al.

Clinical Chemistry and Laboratory Medicine (CCLM), Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 27, 2025

Abstract This scoping review focuses on the evolution of pre-analytical errors (PAEs) in medical laboratories, a critical area with significant implications for patient care, healthcare costs, hospital length stay, and operational efficiency. The Covidence Review tool was used to formulate keywords, then comprehensive literature search performed using several databases, importing results directly into (n=379). Title, abstract screening, duplicate removal, full-text screening were done. retrieved studies (n=232) scanned eligibility (n=228) included (n=83), summarised PRISMA flow chart. highlights role professionals preventing PAEs specimen collection processing, as well analyses. also discusses use advancements artificial intelligence (AI) machine learning reducing identifies inadequacies standard definitions, measurement units, education strategies. It demonstrates need further research ensure model validation, address regulatory validation Risk Probability Indexation (RPI) models consider regulatory, safety, privacy concerns. suggests that effectiveness AI software platforms real-world settings their implementation are lacking, presenting opportunities advance care improve management PAEs.

Language: Английский

Citations

1
Data flow in clinical laboratories: could metadata and peridata bridge the gap to new AI-based applications? DOI
Andrea Padoan, Janne Cadamuro, Glynis Frans

et al.

Clinical Chemistry and Laboratory Medicine (CCLM), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 5, 2024

In the last decades, clinical laboratories have significantly advanced their technological capabilities, through use of interconnected systems and software. Laboratory Information Systems (LIS), introduced in 1970s, transformed into sophisticated information technology (IT) components that integrate with various digital tools, enhancing data retrieval exchange. However, current capabilities LIS are not sufficient to rapidly save extensive data, generated during total testing process (TTP), beyond just test results. This opinion paper discusses qualitative types TTP proposing how divide laboratory-generated two categories, namely metadata peridata. Being both peridata derived from process, it is proposed first useful describe characteristics while second for interpretation Together standardizing preanalytical coding, subdivision or might enhance ML studies, also by facilitating adherence laboratory-derived Findability, Accessibility, Interoperability, Reusability (FAIR) principles. Finally, integrating can improve usability, support utility, advance AI model development healthcare, emphasizing need standardized management practices.

Language: Английский

Citations

8
Lights and shadows of artificial intelligence in laboratory medicine DOI Creative Commons
Giuseppe Lippi, Mario Plebani

Advances in Laboratory Medicine / Avances en Medicina de Laboratorio, Journal Year: 2025, Volume and Issue: 6(1), P. 1 - 3

Published: Feb. 24, 2025

Language: Английский

Citations

0
Luces y sombras de la inteligencia artificial en la medicina de laboratorio DOI Creative Commons
Giuseppe Lippi, Mario Plebani

Advances in Laboratory Medicine / Avances en Medicina de Laboratorio, Journal Year: 2025, Volume and Issue: 6(1), P. 4 - 6

Published: March 1, 2025

Citations

0
Determining the minimum blood volume required for laboratory testing in newborns DOI
Janne Cadamuro, Martin Wald,

Cornelia Mrazek

et al.

Clinical Chemistry and Laboratory Medicine (CCLM), Journal Year: 2025, Volume and Issue: unknown

Published: March 10, 2025

Language: Английский

Citations

0
New insights in preanalytical quality DOI
Mario Plebani, Sheri Scott, Ana-Maria Šimundić

et al.

Clinical Chemistry and Laboratory Medicine (CCLM), Journal Year: 2025, Volume and Issue: unknown

Published: April 23, 2025

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

Language: Английский

Citations

0
Beyond test results: the strategic importance of metadata for the integration of AI in laboratory medicine DOI Creative Commons
Fabio Del Ben

Clinical Chemistry and Laboratory Medicine (CCLM), Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 23, 2025

Language: Английский

Citations

0
Manual tilt tube method for prothrombin time: a commentary on contemporary relevance DOI
Emmanuel J. Favaloro

Clinical Chemistry and Laboratory Medicine (CCLM), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 22, 2025

Language: Английский

Citations

0
Evaluation of performance in preanalytical phase EQA: can laboratories mitigate common pitfalls? DOI
Anna Linko-Parvinen, Jonna Pelanti,

Tanja Vanhelo

et al.

Clinical Chemistry and Laboratory Medicine (CCLM), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 14, 2024

Abstract Objectives Preanalytical phase is an elemental part of laboratory diagnostics, but prone to humane errors. The aim this study was evaluate performance in preanalytical external quality assessment (EQA) cases. We also suggest preventive actions for risk mitigation. Methods included 12 EQA rounds (Labquality Ltd.) with three patient cases (36 cases, 54–111 participants, 7–15 countries) published 2018–2023. graded according percentage correct responses each case as ≥900 % excellent, 70–89 good, 50–69 satisfactory, 30–49 fair and <30 poor. Performance simultaneously failed ≥10 leading harmful events. Results Overall excellent 7, good 12, satisfactory 10, 4 poor 3 Additionally, 7 showed performance. Routine requests incorrect sample tubes or handling were detected Lower seen sudden abnormal results, rare requests, false identification (never-events) test requests. Information technology (IT) solutions (preanalytical checklists, autoverification rules specific notifications) could have prevented 33 36 Conclusions While most common errors performance, samples those requiring individualised consideration are vulnerable human misinterpretation. In many instances, should been identified rejected before reaching the being directed analysis. Optimising IT effectively detect these allows focus on infrequent events demanding accessible professional consultation. may help education occasions.

Language: Английский

Citations

0