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Reducing clinical workload in the care prescription process: optimization of order sets

Gartner, Daniel, Zhang, Yiye and Padman, Rema 2019. Reducing clinical workload in the care prescription process: optimization of order sets. IMA Journal of Management Mathematics 30 (3) , pp. 305-321. 10.1093/imaman/dpy018

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Order sets are a critical component in hospital information systems, designed to substantially reduce clinician workload and improve patient safety and health outcomes. Order sets represent clusters of order items, such as medications prescribed at hospital admission, that are administered to patients during their hospital stay. In prior research, we constructed order sets for defined time intervals during inpatient stay based on historical data on items ordered by clinicians across a large number of patients. In this study, we build on our prior work to formulate a mathematical program for optimizing order sets that are applicable across the entire duration of inpatient stay and are independent of the time intervals. Furthermore, due to the intractability of the problem, we develop a Greedy algorithm to tackle real-world test instances. We extract data sets for three clinical scenarios and conduct both cognitive and physical workload analyses. Finally, we extend a software application to facilitate the comparison of order sets by practitioners. Our computational results reveal that the optimization-based physical and cognitive workload models can solve small test instances to optimality. However, for real-world instances, the Greedy heuristic is more competitive, in particular when physical workload instead of cognitive workload is the optimization objective. Overall, the Greedy heuristic can solve the test instances within one minute and outperforms the mathematical program in 2/3 of the test instances within a time limit of ten minutes, demonstrating a feasible and promising approach to develop inpatient order sets that can subsequently be validated by clinical experts.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Mathematics
Data Innovation Research Institute (DIURI)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
R Medicine > RZ Other systems of medicine
Publisher: Oxford University Press: IMA
ISSN: 1471-678X
Date of First Compliant Deposit: 27 November 2018
Date of Acceptance: 23 November 2018
Last Modified: 24 Nov 2020 18:39

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