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Setting Staffing Levels in an Emergency Department: Opportunities and Limitations of Stationary Queueing Models

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  • M. Defraeye
  • I. Van Nieuwenhuyse

Abstract

Within the operations management literature, the field of healthcare operations management provides one of the most intensively researched areas of the past two decades. Healthcare systems (like many other service systems) tend to exhibit characteristics which complicate the direct application of traditional models. One of these typical characteristics is the time-varying demand for service: in an emergency department (ED) for instance, patient arrival rates tend to vary throughout the day. This severely complicates the process of determining appropriate staffing levels in order to ensure timely service to the patients. Indeed, an important goal of an ED is to strive for patient waiting times that are sufficiently low for all patients, independent of the arrival time. In this paper, we highlight the different approaches which have been put forward in the literature in order to (1) approximate the behavior of time-varying systems by traditional (stationary) queuing models, and (2) determine appropriate staffing levels for these systems, in order to meet given waiting time related targets. These models provide a means to evaluate waiting time related performance measures, and can be used as a basis to determine appropriate staffing levels in view of reaching specific performance targets. The applicability and appropriateness of each method in the specific context of an emergency department is discussed, along with the main advantages and drawbacks.

Suggested Citation

  • M. Defraeye & I. Van Nieuwenhuyse, 2011. "Setting Staffing Levels in an Emergency Department: Opportunities and Limitations of Stationary Queueing Models," Review of Business and Economic Literature, Intersentia, vol. 56(1), pages 73-101, March.
  • Handle: RePEc:sen:rebelj:v:56:i:1:y:2011:p:73-101
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    Cited by:

    1. Ran Liu & Xiaolan Xie, 2018. "Physician Staffing for Emergency Departments with Time-Varying Demand," INFORMS Journal on Computing, INFORMS, vol. 30(3), pages 588-607, August.

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