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

Farrell CL, Giannoutsos J. Machine learning models outperform manual result review for the identification of wrong blood in tube errors in complete blood count results. Int J Lab Hematol. 2022;44(3):497-503. doi: 10.1111/ijlh.13820

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June 29, 2022
Farrell C‐JL, Giannoutsos J. Int J Lab Hematol. 2022;44(3):497-503.

Wrong blood in tube (WBIT) errors can result in serious diagnostic and treatment errors, but may go unrecognized by clinical staff. In this study, machine learning was used to identify potential WBIT errors which were then compared to manual review by laboratory staff. The machine learning models showed higher accuracy, sensitivity, and specificity compared to manual review. 

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Farrell CL, Giannoutsos J. Machine learning models outperform manual result review for the identification of wrong blood in tube errors in complete blood count results. Int J Lab Hematol. 2022;44(3):497-503. doi: 10.1111/ijlh.13820