The delta check methods are methods for detection of random errors in clinical laboratory tests including specimen abnormalities, specimen mix-up, problems in analysis processes, and clerical errors. Methodologically, it is known that the multivariate delta check methods are more superior to the univariate delta check methods. However, due to some problems in reality including technical difficulties, it is hard to put the multivariate delta check methods into practice. Since the univariate delta check methods are methods at hand, there has been a need for an efficient and effective univariate delta check method. In order to meet such a need, we propose "the multi-item univariate delta check (MIUDC) method". By the multi-item univariate delta check (MIUDC) method, we mean a method in which univariate delta checks are performed on multiple items and specimens with the positive univariate delta check in at least k items are put under a detailed investigation. Our research objectives are the determination of an appropriate value of such k and identification of test items deserving of more interest. Through real data and simulation studies, we concluded that an appropriate value of k is 4 because, with k = 4, we can have light checking-out volumes and high efficiency. Also, we identified total cholesterol, albumin, and total protein as items deserving of more interest because the false positive rate associated with them in the MIUDC was zero in a simulation study. We present the MIUDC method as a quality control method that is easy-to-implement and efficient.