BACKGROUND To continuously improve medical quality and provide clinicians with more accurate blood test reports, this study collected blood quality control data in 2017 from a medical examination laboratory in a teaching level hospital located in Taoyuan City, Taiwan. METHODS The quality control data were arranged and analyzed from daily complete blood count (CBC), including white blood cells (WBC), red blood cells (RBC), hemoglobin (Hb), and platelets (PLT) recorded by a laboratory blood analyzer. Using the empirical Bayesian method, we estimated the variation of concentrations of the last and current batches to establish a novel control chart with adjusted upper and lower limits for the current batch, and then compared results with the traditional Shewhart method. The average run length (ARL) and sensitivity of the empirical Bayesian method were explored. RESULTS The study found that ARL showed a qualified capability for the four blood routine tests when using the empirical Bayesian method. Compared to the Levey-Jennings control chart, the novel control chart presents an alert earlier when a deviation occurs and shows a fake alert later when there is no deviation. CONCLUSIONS The parallel tests showed that the longer the time is, the better the test's proficiency. We concluded that the empirical Bayesian method could be applied effectively to improve the capability of daily control in CBC laboratory tests.