|Error Rates in the Total Testing Process|
For over a decade, the prevailing wisdom has been that analytical errors rarely happen and that pre-analytical and post-analytical errors are more important. A 2011 study of 5 years of laboratory data calls this emphasis into question. Perhaps some errors are not more equal than others.
Sten Westgard, MS
|Laboratory Process / quality indicator||Average of median error rate %||Sigma-metric|
|Reports from referred tests exceed delivery time
|Undetected requests with incorrect patient name
(preanalytic within laboratory)
|External control exceeds acceptance limits
|Total incidences in test requests (preanalytic)||3.4%||3.4|
|Patient data missing (preanalytic)||3.4%||3.4|
Given these error rates in just the usual % error format, it can be hard to know if (for example) a 0.2% rate for insufficient sample (ESR) is good or bad. On the Sigma scale, that transforms into a 4.4 Sigma, which is considered good. Surprisingly, the error rate for Hemolyzed serum samples, often considered the leading cause of pre-analytical errors, was actually pretty good in these laboratories. Just 0.6% samples (average of median laboratory rates) werer hemolyzed, for a Sigma-metric of 4.1.
The study found only 2 laboratory processes below 3 Sigma, which is considered the threshhold for acceptable performance in other industries. The majority of the other processes were between 3 and 5-Sigma.
What's interesting here is the fact that the analytical process is in the top 5 (in a three-way tie for third place) worst processes in the laboratory.
Now, when we look at the definition of the analytical quality indicator - External control exceeds acceptance limits - we find even more interesting information: "This indicator reflects the number of external control results that are more [than] 2 SD from the group mean of participants using the same method. It allows comparison of the performance of each laboratory with respect to the other laboratories operating under the same conditions." This analytical indicator is really measuring whether the individual laboratory is failing in comparison with an EQA group. This number is not truly reflective of the analytical outliers within the laboratories, nor are the limits on performance set based on the quality required by the tests. This is more of a consensus assessment - how many labs didn't get the same results as their fellow labs. Thus, this number may in fact be optimistically low. If you applied the proper QC design to these laboratories, you might find even more of these labs are exceeding the appropriate QC limits. [Of course, part of the reason why the study may not have tracked the internal analytical performance is the challenge it would face in determining the correct control limits for each laboratory and getting accurate reporting on the number of outliers.]
Nevertheless, this is a significant study in the literature, not only because it is tracking at least one measure of analytical quality (even though the indicator is less than ideal, it is an accomplishment that some level of analytical performance was tracked), but also for the fact that it converts these error rates into Six Sigma metrics. The benefit of transforming the error rates into Six Sigma metrics is that it makes plain which processes need improvement and which processes are acceptable.
Again, here's the full reference for the study:
Quality Indicators and specifications for key analytical-extraanalytical processes in the laboratory. Five years' experience using the Six Sigma concept. Antonia Llopis, Gloria Trujillo, Isabel Llovet, Ester Tarres, Merce Ibarz, Carme Biosca, Rose Ruiz, Jesus Alsina Kirchner, Virtudes Alvarez, Gloria Busquets, Vicenta Domenech, Carme Figueres, Joana Minchinela, Rosa Pastor, Carmen Perich, Carmen Ricos, Mireia Sansalvador, and Margarita Simon Palmada, Clin Chem Lab Med 2011;49(3):463-470
The lesson here - and the reason why this article is included with our Basic QC Practices lessons - is that analytical performance cannot be taken for granted. We cannot assume analytical errors don't occur, or that the errors that occur are minor and can be ignored. We have to pay as much attention to analytical errors as we pay to pre-analytical and post-analytical errors.
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