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Are we ready to integrate advanced artificial intelligence models in clinical laboratory?

Slavica Dodig, Ivana Čepelak, Matko Dodig

Reviews, Volume 35, Articles ahead of print

KEYWORDS: artificial intelligence; machine learning; deep learning; healthcare; clinical laboratory

https://doi.org/10.11613/BM.2025.010501

A survey on the practice of phlebotomy in Lithuania and adherence to the EFLM-COLABIOCLI recommendations: continuous training and clear standard operating procedures as tools for better quality

Ricardas Stonys, Dalius Vitkus

Original articles, Volume 34, June, Issue 2

KEYWORDS: preanalytical phase; phlebotomy; blood specimen collection; healthcare quality assurance

https://doi.org/10.11613/BM.2024.020702

Utilization of a healthcare failure mode and effects analysis to identify error sources in the preanalytical phase in two tertiary hospital laboratories

Adolfo Romero, Juan Gómez-Salgado, Adolfo Romero-Arana, Carlos Ruiz-Frutos

Original Papers, Volume 28, June, Issue 2

KEYWORDS: preanalytical phase; Healthcare Failure Mode and Effects Analysis; quality assessment; errors

https://doi.org/10.11613/BM.2018.020713

Timeliness “at a glance”: assessing the turnaround time through the six sigma metrics

Cristiano Ialongo, Sergio Bernardini

Original papers, Volume 26, February, Issue 1

KEYWORDS: turnaround time; statistical data analysis; quality control; healthcare quality indicator; postanalytical phase

https://doi.org/10.11613/BM.2016.010

The most common nonconformities encountered during the assessments of medical laboratories in Hong Kong using ISO 15189 as accreditation criteria

Bella Ho, Eric Ho

Original papers, Volume 22, June, Issue 2

KEYWORDS: accreditation; quality improvement; healthcare quality assurance; clinical pathology

https://doi.org/10.11613/BM.2012.027

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