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Cognitive workload reduction in hospital information systems

Author

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  • Daniel Gartner

    (Cardiff University)

  • Yiye Zhang

    (Cornell University)

  • Rema Padman

    (Carnegie Mellon University)

Abstract

Order sets are a critical component in hospital information systems that are expected to substantially reduce physicians’ physical and cognitive workload and improve patient safety. Order sets represent time interval-clustered order items, such as medications prescribed at hospital admission, that are administered to patients during their hospital stay. In this paper, we develop a mathematical programming model and an exact and a heuristic solution procedure with the objective of minimizing physicians’ cognitive workload associated with prescribing order sets. Furthermore, we provide structural insights into the problem which lead us to a valid lower bound on the order set size. In a case study using order data on Asthma patients with moderate complexity from a major pediatric hospital, we compare the hospital’s current solution with the exact and heuristic solutions on a variety of performance metrics. Our computational results confirm our lower bound and reveal that using a time interval decomposition approach substantially reduces computation times for the mathematical program, as does a K −means clustering based decomposition approach which, however, does not guarantee optimality because it violates the lower bound. The results of comparing the mathematical program with the current order set configuration in the hospital indicates that cognitive workload can be reduced by about 20.2% by allowing 1 to 5 order sets, respectively. The comparison of the K −means based decomposition with the hospital’s current configuration reveals a cognitive workload reduction of about 19.5%, also by allowing 1 to 5 order sets, respectively. We finally provide a decision support system to help practitioners analyze the current order set configuration, the results of the mathematical program and the heuristic approach.

Suggested Citation

  • Daniel Gartner & Yiye Zhang & Rema Padman, 2018. "Cognitive workload reduction in hospital information systems," Health Care Management Science, Springer, vol. 21(2), pages 224-243, June.
    • Handle: RePEc:kap:hcarem:v:21:y:2018:i:2:d:10.1007_s10729-017-9406-6
    DOI: 10.1007/s10729-017-9406-6
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    References listed on IDEAS

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    Cited by:

    1. Min-Chi Chiu & Tin-Chih Toly Chen, 2020. "Assessing sustainable effectiveness of the adjustment mechanism of a ubiquitous clinic recommendation system," Health Care Management Science, Springer, vol. 23(2), pages 239-248, June.

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