Presented by Krystal S. Glaze, LCDR, MSC, USN, MLS(ASCP)CM
Tuesday, May 8
9:15 a.m.–9:45 a.m.
Go to www.clma.org/knowledgelab for more information
Medical errors are among the top five leading causes of preventable death or patient harm in the United States.1 Included in the broad category of medical errors are specimen-labeling errors comprising mislabeled, incomplete, and unlabeled laboratory specimens. Notwithstanding the potential for patient harm, these medical errors are estimated to cost health care facilities approximately $200 to $400 million per year, or roughly $700 per error reconciliation.2
Despite reinforced patient safety goals and standards initiated by accrediting organizations such as The Joint Commission and the College of American Pathologists (CAP), labeling errors remain one of the leading types of pre-analytical errors associated with ancillary services. Even with the implementation of technologies such as bar code scanning to help mitigate human error, some laboratories still see labeling error rates higher than the estimated national average of between 0.2% to 0.3% of all tests ordered.3 Research further suggests the majority of these errors are caused by a lack of attention to detail, worker interruptions, insufficient technician experience, and ineffective workflow or machine processes.
Regardless of whether the drivers are economic, financial, or social, health care organizations are now obligated to actively address the leading contributors to medical errors. Gone are the days when retraining was seen as the sole acceptable solution. Today’s tools enable constant data analysis, process improvement, and active leadership as we strive to match and exceed our industrial counterparts with high reliability, best practices, and zero errors or mishaps. While an error rate of 0.2% may seem acceptable to the layman, we as health care professionals understand that one compromised patient is one too many. As preventable labeling errors result in patient care delays and potential harm, in addition to staff rework, we organized a case study to review labeling practices and address what we considered an unacceptable error rate.
Over a two-year period, the team noted that laboratory personnel were responsible for half of all labeling errors, and the clinic’s patient safety team reported an average of three labeling errors per month for non-laboratory generated labeling errors. As the primary stakeholders for labeling, it is crucial for laboratorians to set the benchmark for labeling within their health care organization. Such errors erode the confidence of our clinical colleagues and patients. Additionally, these errors further jeopardize the significant portion of medical decision-making that is based on the quality and veracity of our testing procedures and results.
This presentation depicts one ambulatory clinical laboratory’s journey toward maintaining zero labeling errors for laboratory personnel for more than one year. The investigative process used to determine the root causes of labeling errors, as well as the best practices implemented to collectively address those errors, will be discussed in detail. Attendees are offered the lessons and key quality management tools that specifically attributed to this laboratory’s successes. The presentation also will detail the laboratory’s continued quest to help fellow clinical colleagues in reducing bedside labeling errors and specimen rejection rates.
1. Johns Hopkins Medicine. Medical errors now third leading cause of death in United States. ScienceDaily. www.sciencedaily.com/releases/2016/05/160504085309.htm. Accessed January 29, 2018.
2. Morrison AP, Tanasijevic MJ, Goonan EM, et al. Reduction in Specimen Labeling Errors After Implementation of a Positive Patient Identification System in Phlebotomy. American Journal of Clinical Pathology. 2010;33(6):870–877. https://doi.org/10.1309/AJCPC95YYMSLLRCX. Accessed January 29, 2018.
3. Astion M. Right Patient, Wrong Sample. U.S. Department of Health and Human Services: Patient Safety Network. https://psnet.ahrq.gov/webmm/case/142. Accessed January 29, 2018.