Journal
AMERICAN JOURNAL OF CLINICAL PATHOLOGY
Volume 150, Issue 6, Pages 555-566Publisher
OXFORD UNIV PRESS INC
DOI: 10.1093/AJCP/AQY085
Keywords
Delta check; WBIT; Wrong blood in tube; Preanalytic error; Machine learning; Patient safety
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Objectives: An unfortunate reality of laboratory medicine is that blood specimens collected from one patient occasionally get mislabeled with identifiers from a different patient, resulting in so-called wrong blood in tube (WBIT) errors and potential patient harm. Here, we sought to develop a machine learning-based, multianalyte delta check algorithm to detect WBIT errors and mitigate patient harm. Methods: We simulated WBIT errors within sets of routine inpatient chemistry test results to develop, train, and evaluate five machine learning-based WBIT detection algorithms. Results: The best-performing WBIT detection algorithm we developed was based on a support vector machine and incorporated changes in test results between consecutive collections across 11 analytes. This algorithm achieved an area under the curve of 0.97 and considerably outperformed traditional single-analyte delta checks. Conclusions: Machine learning-based multianalyte delta checks may offer a practical strategy to identify WBIT errors prior to test reporting and improve patient safety.
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