A Machine-Learning-Based Approach for Identifying Diagnostic Errors in Electronic Medical Records

Diagnostic error refers to a missed, delayed, or wrong diagnosis, which seriously affects diagnostic reliability and safety. Identifying diagnostic errors is crucial for diagnostic error research. As an important method of identifying diagnostic errors, electronic triggers (e-triggers) have several limitations, including the performance improvement bottleneck, the inability to predict diagnostic errors, and the narrow trigger range caused by inadequate data utilization. This article proposes a novel approach for identifying diagnostic errors in electronic medical records (EMRs) based on machine learning (ML) techniques. First, we design four stages for our approach, i.e., EMR utilization, ML, evaluation, and prediction. By learning the implicit identification pattern of diagnostic errors from historical EMRs, the proposed approach can find potential diagnostic error cases and predict the probability of upcoming diagnostic errors. Second, in the evaluation stage, we introduce various metrics to evaluate its performance accurately and effectively. Metrics for measuring the abilities to identify diagnostic errors and resist false negatives are introduced. Third, the study utilizes real-world EMRs of leukemia patients to illustrate and verify the proposed approach. The experimental results indicate that our approach accurately identifies most diagnostic error cases and efficiently reduces the number of false negatives, which will help protect diagnostic reliability and safety in the clinic.