Detecting Blood Laboratory Errors Using a Bayesian Network

Abstract

To detect errors in blood laboratory results using a Bayesian network (BN), to compare results with an established method for detecting errors based on frequency patterns (LabRespond) and logistic regression model. In Experiment 1 and 2 using a sample of 5,800 observations from the National Health and Nutrition Examination Survey dataset, large, medium and small errors were randomly generated and introduced to liver enzymes (ALT, AST, and LDH) of the dataset. Experiment 1 examined systematic errors, while Experiment 2 investigated random errors. The outcome of interest was the correct detection of liver enzymes as "error" or "not error." With the BN, the outcome was predicted by exploiting probabilistic relationships among AST, ALT, LDH, and gender. In addition to AST, ALT, LDH, and gender, LabRespond required more information on related analytes to achieve optimal prediction. We assessed performance by examining the area under the receiver operating characteristics curves using a 10-fold cross validation method, as well as risk stratification tables. In Experiment 1, the BN significantly outperformed both LabRespond and logistic regression in detecting large (both at p < 0.001), medium (p = 0.01 and p < 0.001, respectively), and small (p = 0.03 and, p = 0.05, respectively) systematic errors. In Experiment 2, the BN performed significantly better than LabRespond and multinomial logistic regression in detecting large (p = 0.04 and p < 0.001, respectively) and medium (p = 0.05 and p < 0.001, respectively) random errors. A Bayesian network is better at detection and can detect errors with less information than existing automated models, suggesting that Bayesian model may be an effective means for reducing medical costs and improving patient safety.