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

Christopher‐John L. Farrell; John Giannoutsos

Study

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

June 29, 2022

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

View more articles from the same authors.

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

Remote Response Team and Customized Alert Settings Help Improve Management of Sepsis

Verification Screen That Includes Prominent Patient Photograph Significantly Reduces Errors Caused by Orders Placed in Wrong Chart

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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.

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