Establishing Risk Management Through Quality Control in the Laboratory
Establishing risk management through quality control (QC) in the laboratory is important, but not always easy. EP23, the guideline released by the Clinical and Laboratory Standards Institute in 2011, was intended to help with that task and the transition period to the Individualized Quality Control Plan (IQCP), the CMS’ new voluntary QC program.
In interviews with CAP TODAY, experts and core laboratory directors were questioned as to how they are developing optimum QC strategies that integrate key risk management concepts. According to the responses received, laboratories are taking a more systematic approach to their QC programs.
Mayo Clinic, for example, has consciously opted for a happy medium in setting its QC standards, according to Nikola Baumann, Ph.D., co-director of Mayo’s central clinical laboratory and central processing laboratory. This laboratory at the Rochester, Minn., facility reports more than 5.5 million billable tests per year serving its two hospitals and large outpatient clinic. The laboratory performs routine chemistry, immunoassay, hematology, and coagulation testing.
According to Dr. Baumann, there are two extremes for QC, “the minimum CLIA requirement of two concentrations per day for quantitative tests, and then the perfect but impractical world where you run QC with every patient sample so that you never report a result in error.” Because Mayo’s volumes are so high and the results are used almost immediately, “most of our QC frequency is between every two hours and every eight hours. The lower-volume, more robust tests would have QC scheduled every eight hours. At certain times of day, we analyze more than 600 samples per hour and we run QC every two hours for high-volume tests.”
During her interview, Dr. Baumann discussed that most core laboratories tend to run QC when the test volume is lower and people have more time to ensure minimal disruption to workflow.
“But when you think about the purpose of QC, that’s the wrong logic. It should really be run at a defined frequency and customized to each test. The frequency should be defined with the goal of detecting errors, minimizing the number of patients affected if an out-of-control situation occurs, and making sure that patient results reported in error can be corrected as soon as possible. Traditionally, that’s not how we’ve designed QC plans in the core lab, mostly because it’s difficult logistically.”
Dr. Baumann is the leader of a task force within her department that is looking at implementing the EP23 guidelines on risk-based QC. Mayo’s 50-plus testing laboratories have been using informal risk assessment to design their QC plans for a long time, but without documenting it. But now, Dr. Baumann and her team are developing tools to help the laboratories make risk assessment more of a formalized and easily documented process.
“It made us think there might be more requirements down the road, but also it is just good lab practice. We shouldn’t just keep doing QC as it has always been done, but rather respond to changes and advances in technology, automation, laboratory workflow, and even hospital and patient workflow," said Dr. Baumann.
Dr. Baumann also believes that identifying errors is also important in the QC process. She said, "We like to review the data and ask questions. What kinds of errors is QC catching? Detecting errors means QC is working; if you aren’t seeing out-of-control QC, it is likely you are missing something. Which types of errors is QC not detecting? And how do we modify our QC plan to be better moving forward? I think sometimes those questions are not being asked in a lot of the discussions we are seeing.”
“There are suggestions for balancing error detection and false rejection rates. The way we’ve designed our program in my lab, we have a very high error detection rate. We also have a relatively high false rejection rate, which means we do a lot of unnecessary repeat patient testing because QC was out of control but the changes in patient results were not clinically significant. That’s good for the patient—and I would rather err on that side than sacrifice error detection for lower false rejection rates—but it also means there’s room to optimize how we design our QC strategy. It’s always a work in progress.” she says.
Read the full article to learn about other laboratories' QC programs.