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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. 

Patient misidentification errors are common and potentially catastrophic. Patient identification incidents reported in one hospital were examined to identify errors and contributory factors. Of the 293 reported incidents, the most common errors were missing wristbands, wrong charts or notes in files, administrative issues, and wrong labeling. The most frequent contributory factors include absence of patient identity control, patient transfer, and emergency context.

This commentary reveals insights from a physician who was involved in a misidentified specimen incident. The author explains how the organization performed a root cause analysis to determine safety gaps in specimen handling processes and recommends four key steps to prevent errors.

This article uses data from a large database of voluntarily reported errors to characterize errors in laboratory medicine. Most errors occurred at the preanalytic phase (before the specimen arrived in the laboratory), with many errors arising from misidentification of specimens. The vast majority of the more than 30,000 errors analyzed did not lead to patient harm. A pathology misidentification error that resulted in disclosure of an incorrect diagnosis to a patient is discussed in this AHRQ WebM&M commentary.

In response to multiple incidents of registration-associated patient misidentification (eg, assigning a new patient an existing patient's medical record number), an interdisciplinary team used plan-do-study-act methodology to investigate the root cause of such errors and formulate solutions. Several system problems were identified, ranging from inadequate training of registrars to the lack of a true master list of patients' medical record numbers. The authors describe the iterative process used to identify and address sources of error at several points within the patient registration process.