Machine learning models outperform manual result review for the identification of wrong blood in tube errors in complete blood count results.

Journal: International journal of laboratory hematology

Published Date: Jun 1, 2022

Abstract

INTRODUCTION: Wrong blood in tube (WBIT) errors are a significant patient-safety issue encountered by clinical laboratories. This study assessed the performance of machine learning models for the identification of WBIT errors affecting complete blood count (CBC) results against the benchmark of manual review of results by laboratory staff.

Authors

Christopher-John L Farrell

Department of Biochemistry, New South Wales Health Pathology, Nepean Blue Mountains Pathology Service, Penrith, Australia.
John Giannoutsos

New South Wales Health Pathology, Nepean Hospital, Penrith, NSW, Australia.

Keywords

Blood Cell Count Computer Simulation Humans Logistic Models Machine Learning Neural Networks, Computer

External Resources

View on PubMed Access via DOI PubMed (35274468)

Machine learning models outperform manual result review for the identification of wrong blood in tube errors in complete blood count results.

Abstract

Authors

Keywords

External Resources

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