Simulation-based optimization to improve hospital patient assignment to physicians and clinical units

Hui Zhang (), Thomas J. Best (), Anton Chivu () and David O. Meltzer ()
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Hui Zhang: University of Chicago
Thomas J. Best: University of Chicago
Anton Chivu: University of Chicago
David O. Meltzer: University of Chicago

Health Care Management Science, 2020, vol. 23, issue 1, No 9, 117-141

Abstract: Abstract A fundamental activity in hospital operations is patient assignment, which we define as the process of assigning hospital patients to specific physician services and clinical units based on their diagnosis. When the preferred assignment is not possible, typically due to capacity limits, hospitals often allow for overflow, which is the assignment of patients to other services and/or units. Overflow accelerates assignment, but can also reduce care quality and increase length of stay. This paper develops a discrete-event simulation model to evaluate different assignment strategies. Using a simulation-based optimization approach, we evaluate and heuristically optimize these strategies accounting for expected hospital and physician profit, care quality and patient waiting time. We apply the model using data from the University of Chicago Medical Center. We find that the strategies that use heuristically optimized designation of overflow services and units increase expected profit relative to the capacity-based strategy in which overflow patients are assigned to a service and unit with the most available capacity. We also find further improvement in the strategy that uses heuristically optimized overflow services and units as well as a holding unit that holds patients until a bed in their primary or secondary unit becomes available. Additionally, we demonstrate the effects of these strategies on other performance measures such as patient concentration, waiting time, and outcomes.

Keywords: Hospital services; Optimization; Simulation; Decision support systems; Patient assignment; Patient overflow (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10729-019-09483-3

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