AI May Cut ‘Wrong Blood in Tube’ Errors

Machine learning-based multianalyte delta checks may be effective at identifying ‘wrong blood in tube’ (WBIT) errors, according to a proof-of-concept study published in the American Journal of Clinical Pathology. The authors say that these technology-enabled multianalyte delta check will be more effective at identifying errors and improving patient safety than traditional single analyte delta checks. Because laboratories usually manage the analytic phase of testing with internal quality control measures, the pre- and postanalytic phases may be the most vulnerable to errors, experts say. These preanalytic errors can include improper specimen collection and transport, or WBIT mislabeling errors. WBIT errors can negatively impact clinical care diagnostic or treatment decisions are based on test results corresponding to the wrong patient. “Although only a very small proportion of specimens are presumably affected by WBIT errors, WBIT errors in aggregate may not be rare due to high test volume,” , writes coauthor Matthew Rosenbaum, M.D., from Massachusetts General Hospital in Boston. “For example, even if only one in 1,000 (0.1 percent) specimens were impacted by a WBIT error, a hospital testing a million specimens per year might report 1,000 sets of erroneous results every year.” Given the impossibility of eliminating all human error, processes […]