Volume 38 Number 2 | April 2024
April German, MLS(ASCP)CM, Westgard Sigma-QM, ASCLS Today Volunteer Contributor
As medical and clinical laboratory scientists, quality control is at the core of our work. However, as new scientists entering the field, we often learn to accept and reject quality control. Our troubleshooting skills are limited to retest, recalibrate, and retest. This results in hours of testing and calibrating, which can be frustrating. It can also lead to costly wasted time and resources on unnecessary retesting. There seems to be a disconnect between what is taught in the classroom and what is practiced on the bench.
One of the biggest challenges occurs when fresh graduates are taught to focus on points on a graph rather than the bigger picture. In the event of a rule failure, we teach to “just rerun the quality control.” Have we forgotten or never learned how to troubleshoot all these different rejections and warnings? Have we been on the bench for so long that we have forgotten how to teach new scientists? Quality time is not spent on teaching how to read and interpret a Levy-Jennings chart or Westgard multirules. Other problems with quality control go deeper. Are the rules set correctly for the analyte based on performance and the frequency of patient samples tested between quality control events?
“Achieving quality takes a lot of time and effort. It is worth the investment to ensure we produce quality patients results.”
As a new scientist, I was also guilty of the mindset, “Get the QC to pass.” However, my knowledge was limited until I became a technical supervisor. Working in a small rural lab, I was often the only scientist on my shift. I spent my off hours researching our third-party quality control vendor. I quickly realized there was much to learn, and the quality control design implemented in our lab had many errors. I learned the importance of reading through technical documents to fully understand the software. What did all those quality control terms mean? What is a Westgard rule, and how does it work? What is allowable total error (TEa), coefficient variant, and the standard deviation index? How do they apply to what our QC is telling us about the accuracy and precision of our testing?
As I dug deeper and learned more, I realized that using manufacturer ranges was not wise, and the quality control should be set at the lab’s own mean and standard deviations. The lab’s data is crucial to getting closer to the true mean. Peer data is utilized to measure your lab’s accuracy against other labs. Achieving quality takes a lot of time and effort. It is worth the investment to ensure we produce quality patients results. Staff, the analyzer, and the manufacturer of products all contribute to the stability of testing. By working with academic experts in clinical quality control design, I was able to find answers and connect what I learned in the classroom to what I was doing on the bench.
It has become my goal to pass on the knowledge I have gained. Labs are facing shortages of qualified staff and limited time to train and prepare our next generation to take over. If we don’t learn how to pool our time and resources, quality will suffer. As a leader in laboratory sciences, ask yourself what you can do to connect with your peers. What skills can you contribute to the betterment of our field? It’s time for us to come out from behind our microscopes and analyzers to build a stronger and united community.
April German is Principal Consultant at Lab Connections in Joplin, Missouri.