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Reducing Provider Cognitive Workload in CPOE Use: Optimizing Order Sets

机译:降低CPOE中的提供者认知工作量使用:优化订单集

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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-enabted 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.
机译:由于可用性差的认知工作量较高是医疗信息技术(IT)的重要,意外的后果,导致新的医疗错误。在使用订单集中可以观察到这一点的一个重要示例,这允许通过已知的最佳实践引导的安全和有效的提供商订单进入。本文旨在通过使用数据驱动方法重新设计订单集来改进IT-Enabit订单输入,以开发与当前使用和工作流程匹配的新订单集,同时产生最小的认知工作负载。应用嵌入聚类技术的优化模型,我们的方法识别基于历史排序数据的构成秩序集的项目,其中单个患者的物品通常在住院期间保持在一起或密切关仓。结果表明,新方法主导了当前解决方案,显着减少了认知工作量,提高了秩序集内容。因此,数据驱动方法提供了一个有希望的方法,用于设计概遍,基于证据和最新的订单集的订单集,并使用当前的最佳实践。

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