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Reducing emergency department waiting times by adjusting work shifts considering patient visits to multiple care providers

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  • David Sinreich
  • Ola Jabali
  • Nico Dellaert

Abstract

Reducing Emergency Department (ED) overcrowding in the hope of improving the ED's operational efficiency and health care delivery ranks high on every health care decision maker's wish list. The current study concentrates on developing efficient work shift schedules that make the best use of current resource capacity with the objectives of reducing patient waiting time and leveling resource utilization as much as possible. The study introduces two iterative heuristic algorithms, which combine simulation and optimization models for scheduling the work shifts of the ED resources: physicians, nurses and technicians. The algorithms are distinctive because they account for patients being treated by multiple care providers, possibly over the course of several hours, often with interspersed waiting. In such instances, patient arrival time is not a good indicator of when the various care providers are needed. The algorithms were tested using a detailed simulation based on data from five general hospital EDs. A patient's Length of Stay (LOS) is measured as the time a patient spends in the ED until being admitted to the hospital or discharged. The first algorithm achieved an average reduction of between 20 and 45% in the total patient waiting time, which led to a reduction of between 7 and 17% in the combined average patient LOS. By allowing a restructure of the ED resource capacities, the second algorithm achieved an average reduction of between 20 and 64% in the total patient waiting time, leading to an 11 to 29% reduction in the combined average patient LOS.

Suggested Citation

  • David Sinreich & Ola Jabali & Nico Dellaert, 2012. "Reducing emergency department waiting times by adjusting work shifts considering patient visits to multiple care providers," IISE Transactions, Taylor & Francis Journals, vol. 44(3), pages 163-180.
  • Handle: RePEc:taf:uiiexx:v:44:y:2012:i:3:p:163-180
    DOI: 10.1080/0740817X.2011.609875
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    Citations

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    Cited by:

    1. Shujing Sun & Susan F. Lu & Huaxia Rui, 2020. "Does Telemedicine Reduce Emergency Room Congestion? Evidence from New York State," Information Systems Research, INFORMS, vol. 31(3), pages 972-986, September.
    2. Andersen, Anders Reenberg & Nielsen, Bo Friis & Reinhardt, Line Blander, 2017. "Optimization of hospital ward resources with patient relocation using Markov chain modeling," European Journal of Operational Research, Elsevier, vol. 260(3), pages 1152-1163.
    3. Hainan Guo & Haobin Gu & Yu Zhou & Jiaxuan Peng, 2022. "A data-driven multi-fidelity simulation optimization for medical staff configuration at an emergency department in Hong Kong," Flexible Services and Manufacturing Journal, Springer, vol. 34(2), pages 238-262, June.
    4. Andersen, Anders Reenberg & Nielsen, Bo Friis & Reinhardt, Line Blander & Stidsen, Thomas Riis, 2019. "Staff optimization for time-dependent acute patient flow," European Journal of Operational Research, Elsevier, vol. 272(1), pages 94-105.
    5. Omar EL-Rifai & Thierry Garaix & Vincent Augusto & Xiaolan Xie, 2015. "A stochastic optimization model for shift scheduling in emergency departments," Health Care Management Science, Springer, vol. 18(3), pages 289-302, September.
    6. Davide Duma & Roberto Aringhieri, 2020. "An ad hoc process mining approach to discover patient paths of an Emergency Department," Flexible Services and Manufacturing Journal, Springer, vol. 32(1), pages 6-34, March.
    7. Miguel Angel Ortíz-Barrios & Juan-José Alfaro-Saíz, 2020. "Methodological Approaches to Support Process Improvement in Emergency Departments: A Systematic Review," IJERPH, MDPI, vol. 17(8), pages 1-41, April.

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