Clinical decision support systems (CDSSs) are designed to enhance patient safety by providing alerts to prescribers about potential medication issues. However, a significant proportion of these alerts are ignored, which can compromise patient safety. This study explores the feasibility of using subgroup discovery, a machine learning method, to identify determinants influencing physicians’ medication-related CDSS alert handling. By analyzing CDSS log data from the electronic health record, this research shows the feasibility of the use of subgroup discovery on this data, and its potential to uncover behavioral patterns and factors that affect how alerts are managed. This can ultimately contribute to the design of more effective CDSS alerts and improving patient safety.