Toward Order Set Optimization Using Click Cost Criteria in the Pediatric Environment

Order sets as part of computerized provider order entry (CPOE) have the potential to improve care delivery by making it faster and easier for physicians to enter orders and by guiding care according to known best practices. Currently, order sets are not utilized to their full extent due to factors such as user inexperience, lack of updated content with evolving best practices, and inability to modify an order set to include relevant items. This exploratory study uses order data from Asthma and Appendectomy patients at a large pediatric healthcare institution to examine the optimization of current ordering patterns using direct and cognitive click-through costs as evaluation criteria. We examine four models where modifications to current ordering practices are analyzed: improving order set usage through removal of inexperienced-user effect, changed default setting based on scientific evidence, and newly designed order sets through K-means clustering. While improving current ordering practice was found to reduce cost across all diagnoses and severity levels, the most significant decrease in cost was realized when clustering individual items into new order sets, pointing to a promising new approach for order set optimization.