An alternative scheduling approach for improving emergency department performance

Amir Elalouf, Guy Wachtel

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

Crowding in hospitals and its effects on emergency departments (ED) and on patients have received increasing attention in recent years. The objective of this paper is to propose an approach for optimizing the scheduling of patients' ED examinations. Referred to as the "floating patients" method, this approach entails sending patients, if possible, to receive treatment in other hospital departments (instead of in the ED) in order to reduce their waiting time for examinations and treatment. Specifically, we assume that the ED determines a maximal (fixed or dynamic) value for patients' length of stay, and that patients who cannot be evaluated in the ED in a timely fashion are redirected for treatment to other hospital departments. In order to demonstrate the use of this method we use an algorithmic approach. We first solve a basic problem in which the ED physician and the triage have full information on patients' conditions and on how long their evaluations are expected to take. We then extend this problem to incorporate real-life uncertainty; specifically, we assume that the physician carries out initial examinations to obtain information on patient attributes and, at each point in time, decides whether to continue to examine patients or to stop the process (halting rule) and "float" the remaining patients to other departments. Next, the physician determines the optimal schedule for the full evaluations of the examined patients. The proposed algorithms are demonstrated using a simulation with real-life data.

Original languageEnglish
Pages (from-to)65-71
Number of pages7
JournalInternational Journal of Production Economics
Volume178
DOIs
StatePublished - 1 Aug 2016

Bibliographical note

Publisher Copyright:
© 2016 Elsevier B.V. All rights reserved.

Keywords

  • Approximation algorithm
  • Emergency department
  • Optimal stopping
  • Patients scheduling

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