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Case Studies and Error-Rate Statistics

One study example, conducted by Makary, sought to quantify pre-analytic errors, such as patient identification (ID) errors. In studying the pre-analytic phase problems at one US academic hospital, they detected an average of 4.3 errors per 1,000 surgical specimens. In another pre-analytic phase study performed at Johns Hopkins, they discovered that the average pre-analytic ID error rate resulted in approximately 182 mislabeled specimens per year. When they correlated the error rate findings with patient harm, it was estimated that about 39-45% of these detected pre-analytic errors had been associated with patient harm.
Misidentification errors are unfortunately not limited to occurrence only in the pre-analytic phase. Slide misidentification (mislabeling) errors are by far the MOST frequent analytic phase error in the histology laboratory, representing approximately 67% of all detected analytic phase errors that occur. The misidentification errors that typically occur during the pre-analytic and analytic phase of histology processing are greatly aided by appropriate automation, in particular barcode technology. The impact of barcoding on histology and mislabeling is well illustrated by a case study performed at the Henry Ford Hospital in Detroit, Michigan. In this study, the Henry Ford Hospital was able to successfully reduce the incidence of slide mislabeling events by 85%, while simultaneously increasing slide throughput during microtomy by 125% through use of a combination of process re-design and barcode technology.
Henry Ford Results From Process Improvement + Barcode Technology
Error SourcePre Barcode ImplementationPost Barcode Implementation
Surgical case 45%18%

Facilities where automation and technology are used in combination with process improvement techniques are showing impressive results that can be used to assist histology laboratories to achieve improved efficiency and patient safety goals. Standardization of processes alone has not been sufficient to achieve the consistency, efficiency, and accuracy demanded by current conditions, but is a great starting point. Simply automating an outdated or faulty process will not provide the most benefit. The entire process must be examined, placing a focus on elimination of wasteful and error prone steps. Once a clear, efficient, and standardized process has been achieved, automation can then be added to enhance an already effective process. Barcoding technology in particular is very useful for reducing misidentification errors, since it reduces opportunities for mislabeling that existed within the previously manual handling steps.