LAUSR

Background Risk management strategies have been proposed for applications in clinical laboratories to reduce patient risks; however, effective and visual risk-monitoring tools are currently lacking in medical laboratories. In this study, we constructed a risk quality control (QC) chart based on risk management strategies. Methods We calculated the risk levels of QC materials based on Bayes' theorem by combining the total allowable error, QC results, and the maximum number of unacceptable errors in the laboratory. Then, we constructed a risk QC chart by presenting the Z values and corresponding risk levels of QC materials simultaneously. Finally, we evaluated the risk-monitoring capabilities of the risk QC charts by simulating different long-term errors in the laboratory. Results The risk levels of QC materials increased as the QC results moved further away from the set mean. Larger sigma values led to fewer risks obtained for the same QC results. The constructed risk QC charts intuitively showed specific risk levels and could warn lab staff out-of-control, without the need for QC rules to make judgments. The risk levels of erroneous results differed for items with different sigma performance. Conclusions Risk-based QC charts allowed visualization of the QC results and specific risk levels simultaneously, providing more intuitive results than those obtained from traditional QC charts.