Mining Electronic Health Records to Identify Key Factors Influencing Diagnostic Errors in the Emergency Department

Screening for diagnostic errors in the emergency department (ED) includes the use of electronic health record (EHR) triggers to identify patients with certain patterns of care, such as escalation of care or return ED visits. Once errors are identified, data analytics and machine learning techniques can be applied to evaluate factors associated with trigger positive and negative cases and evaluate the accuracy with confirmed diagnostic errors. Association rule mining (ARM) is a data mining technique that uses prior knowledge of frequent item set and aims to extract frequent item set, meaningful correlation, or causal structure within data. Our objectives were to extract rules that govern the relationship between the patient and systems factors and risk of being trigger-positive.