Reducing provider cognitive workload in CPOE use: optimizing order sets.
Higher cognitive workload due to poor usability is a significant, unanticipated consequence of healthcare information technology (IT), resulting in new types of medical errors. An important example of this can be observed in the use of order sets, which allow safe and efficient provider order entry guided by known best practices. This paper aims to improve IT-enabled order entry by re-designing order sets using data-driven approaches to develop new order sets that match current usage and workflow, while incurring minimum cognitive workload. Applying optimization models embedded with clustering techniques, our methods identify items for constituting order sets that are relevant based on historical ordering data wherein items for a single patient are often placed together or in close temporal proximity during hospital stay. Results indicate that the new approaches dominate current solutions, significantly reducing cognitive workload, and improving order set content. Data driven methods thus offer a promising approach for designing order sets that are generalizable, evidence-based and up-to-date with current best practices.
The H. John Heinz III College, Carnegie Mellon University, Pittsburgh, PA, USA.
Yiye
Y
Zhang
Rema
R
Padman
James E
JE
Levin
D058245:Cognitive Reserve; D003628:Database Management Systems; D016208:Databases, Factual; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D050316:Medical Order Entry Systems; D014584:User-Computer Interface; D057188:Workflow; D016526:Workload