Data driven order set development using metaheuristic optimization

Abstract

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 datadriven order sets have the potential to dominate existing, consensus order sets in terms of usability and cognitive workload.