Yiye Zhang

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.

Cognitive workload reduction in hospital information systems

Order sets are a critical component in hospital information systems that are expected to substantially reduce physicians’ physical and cognitive workload and improve patient safety. Order sets represent time interval-clustered order items, such as medications prescribed at hospital admission, that are administered to patients during their hospital stay. In this paper, we develop a mathematical programming model and an exact and a heuristic solution procedure with the objective of minimizing physicians’ cognitive workload associated with prescribing order sets. Furthermore, we provide structural insights into the problem which lead us to a valid lower bound on the order set size. In a case study using order data on Asthma patients with moderate complexity from a major pediatric hospital, we compare the hospital’s current solution with the exact and heuristic solutions on a variety of performance metrics. Our computational results confirm our lower bound and reveal that using a time interval decomposition approach substantially reduces computation times for the mathematical program, as does a K −means clustering based decomposition approach which, however, does not guarantee optimality because it violates the lower bound. The results of comparing the mathematical program with the current order set configuration in the hospital indicates that cognitive workload can be reduced by about 20.2% by allowing 1 to 5 order sets, respectively. The comparison of the K −means based decomposition with the hospital’s current configuration reveals a cognitive workload reduction of about 19.5%, also by allowing 1 to 5 order sets, respectively. We finally provide a decision support system to help practitioners analyze the current order set configuration, the results of the mathematical program and the heuristic approach.

Data Driven Order Set Development Using Metaheuristic Optimization

An unanticipated negative consequence of using healthcare information technology for clinical care is the cognitive workload imposed on users due to poor usability characteristics. This is a widely recognized challenge in the context of computerized provider order entry (CPOE) technology. In this paper, we investigate cognitive workload in the use of order sets, a core feature of CPOE systems that assists clinicians with medical order placement. We propose an automated, data-driven algorithm for developing order sets such that clinicians’ cognitive workload is minimized. Our algorithm incorporates a two-stage optimization model embedded with bisecting K-means clustering and tabu search to optimize the content of order sets, as well as the time intervals where specific order sets are recommended in the CPOE. We evaluate our algorithm using real patient data from a pediatric hospital, and demonstrate that data-driven order sets have the potential to dominate existing, consensus order sets in terms of usability and cognitive workload.