A Journey from New Scientist to QC Expert

As medical and clinical laboratory scientists, quality control is at the core of our work. However, as new scientists, we often learn to accept and reject quality control, but our troubleshooting skills are limited to retest, recalibrate, and retest. This results in hours of testing and calibrating, which can be frustrating, and management can be irritated with the wasted time and resources on 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. What do all these rules mean? How can we troubleshoot all these different rejections and warnings? Are our leaders unsure themselves, or have they been on the bench for so long that they have forgotten how to teach scientists how to read a Levy-Jennings chart or interpret the Westgard rules?

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?

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 and spent time researching our third-party quality control vendor. I quickly realized there was much to learn, and the quality control design had many errors. I learned the importance of reading documents and understanding 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, and 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. 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.

At Lab Connections, we understand the importance of education and being a resource for medical labs. Let us know how we can help you achieve quality results for your lab.

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