Mislabeled specimens have been a problem since the dawn of modern medical care. Numerous articles have been published about the scope of labeling errors in the clinic over the last 20 years, some of which propose practical solutions, including barcode/RFID labels and automation. Here are some of the most recent articles describing the landscape of medical errors, providing an update to previous literature on the topic and giving an idea of what healthcare professionals currently require to help eliminate the risk of mislabeled specimens.
Carmack et al, 20241
The most extensive study of the year comes from Heather Carmack at the Mayo Clinic in Rochester, Minnesota, who performed a systematic review and meta-analysis of literature related to lost, mislabeled, and mishandled surgical and clinical pathology specimens during the preanalytical stage. Their review analyzed 44 articles published between 1990 and 2021, with all but 8 articles reporting from the US. Most of these articles were related to general surgical pathology or general clinical pathology and focused on mislabeled specimens.
Within the time frame studied the authors show that the number of articles increased in the late 2000s. However, they do not quantify the rates of errors reported in these articles and are thus unable to show whether they have increased or decreased over time. The authors note that many interventions were studied in these papers, including initiatives for education/training, checklists, labels, improved workflows, new protocols for submission requisition, and Lean strategies, with success rates ranging from 70-100% in the reduction of global/institutional and department-specific errors. Though these rates are encouraging, the authors urge caution as these methodologies were mostly centered only in the pathology laboratory or in a specific department, and they may not address global deficiencies in mitigating the risk of error.
Overall, this review and meta-analysis, while lacking concrete numbers, is likely to serve as a starting point for discerning the true scope of the problem and devising a systematic approach to reducing errors in pathology.
Raymond et al, 20242
The second relevant paper to be published in 2024 is from a team led by Caitlin Raymond from the University of Texas. This group acknowledged that mislabeled samples are extremely difficult to identify as there is often no direct indication that the sample was labeled with the wrong patient identifiers and that either a series of “clues” or performing a delta check, which involves verifying the results against internal controls derived from previous patient samples, might point to the fact that there was an error. They note that delta checks can be flawed, however, as variations in patient samples are often more likely due to their changing clinical condition rather than a mislabeled tube. Thus, they aimed to determine the overall rate of unidentified samples (i.e., occult mislabeled samples) despite the systems in place to avoid them.
To identify the rate of occult mislabeled samples, they tested samples for markers that were unique to the patient and unlikely to change within 24 hours, namely complete blood counts (CBCs) and blood type. They also removed all samples identified as mislabeled, including missing, incomplete, or compromised samples at receipt as well as those that failed delta checks, as these represent identified mislabeled samples, not occult.
The authors reported that the rate of occult mislabeled samples (3.17 per 1000) was nearly 3 times greater compared to the rate of identified mislabeled samples (1.15 per 1000). They use this data to estimate that at least 3 in 1000 mislabeled samples go undetected daily, despite quality assessment protocols that include two-factor identification and electronic patient identification. As a caveat, they note that their institution is not yet outfitted with mobile barcode printers for point of care sample collection, which may result in an increased mislabeling rate. Altogether, they recognize that their study represents a starting point for future studies that can both help assess other labs to determine the actual mislabeled sample rate and find new methods of minimizing it to enhance patient safety as well.
Microsystems to improve mislabeled specimen rates3
Speaking of finding new methods of preventing mislabeled samples, a group from Lewiston, PA, decided to implement a clinical microsystems (CM) solution to remedy their community hospital’s long-standing issues with mislabeled specimens. CM is a framework that includes the Purpose, Professionals, Patients, Process, and Patterns (5Ps), Plan-Do-Study-Act (PDSA), and other tools to initiate effective conversations about the context of the employee’s role and an understanding about how to improve. They utilized CM to devise a meeting, clarify the problem, and identify solutions.
Using the CM framework, the medical team focused on preventing errors during urine collection, the most common complaint among those in their emergency department. They created a commitment form, which was to be signed by each nurse to ensure they understood the proper identification protocols and a series of signs were posted in patient rooms reminding patients to take their urine samples with them from the washroom.
After one month, they found that the processes were not working, prompting a change in the original protocol based on anonymous feedback from the nurses. Additionally, the hospital received bedside scanners approximately one year into the study. Though change was not immediately apparent afterward, they decreased steadily from year to year and dropped dramatically once bedside scanners were integrated into the workflow.
It’s clear that strides are continually being made to mitigate errors and reduce the rate of mislabeled specimens. Though there is still much work to do, most institutions will only become safer as new methodologies and technologies are introduced into the clinic.
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References:
- Raymond C, et al. How many mislabelled samples go unidentified? Results of a pilot study to determine the occult mislabelled sample rate. J Clin Pathol. 2024;77(9):647-650.
- Carmack HJ, et al. Lost, mislabeled, and mishandled surgical and clinical pathology specimens: A systematic review of published literature. Am J Clin Pathol. 2024;aqae055.
- Yanni G, et al. Utilizing Clinical Microsystems to Improve Mislabeled Specimen Occurrences in the Emergency Department. J Emerg Nurs. 2023;49(5):694-702.
