Paving the COWpath: data-driven design of pediatric order sets

Abstract

OBJECTIVE: Evidence indicates that users incur significant physical and cognitive costs in the use of order sets, a core feature of computerized provider order entry systems. This paper develops data-driven approaches for automating the construction of order sets that match closely with user preferences and workflow while minimizing physical and cognitive workload. MATERIALS AND METHODS: We developed and tested optimization-based models embedded with clustering techniques using physical and cognitive click cost criteria. By judiciously learning from users' actual actions, our methods identify items for constituting order sets that are relevant according to historical ordering data and grouped on the basis of order similarity and ordering time. We evaluated performance of the methods using 47,099 orders from the year 2011 for asthma, appendectomy and pneumonia management in a pediatric inpatient setting. RESULTS: In comparison with existing order sets, those developed using the new approach significantly reduce the physical and cognitive workload associated with usage by 14-52%. This approach is also capable of accommodating variations in clinical conditions that affect order set usage and development. DISCUSSION: There is a critical need to investigate the cognitive complexity imposed on users by complex clinical information systems, and to design their features according to 'human factors' best practices. Optimizing order set generation using cognitive cost criteria introduces a new approach that can potentially improve ordering efficiency, reduce unintended variations in order placement, and enhance patient safety. CONCLUSIONS: We demonstrate that data-driven methods offer a promising approach for designing order sets that are generalizable, data-driven, condition-based, and up to date with current best practices.

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