Using Machine Learning-Based Multianalyte Delta Checks to Detect Wrong Blood in Tube Errors.

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.