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Simulation modelling of patient flow and capacity planning for regional long-term care needs: a case study

Author

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  • Ki-Hwan Bae
  • Molly Jones
  • Gerald Evans
  • Demetra Antimisiaris

Abstract

The need for Long-Term Care (LTC) arises in the elderly population, especially those reaching age 65 each year. This elderly population will grow tremendously in the United States over the next decade, resulting in short- and long-term challenges of matching resource capacity with uncertain demand for hospitals and other healthcare providers. This paper describes research involving the development of a simulation model of patient flow in order to understand the relationship between capacity and demand, and to investigate the impacts on performance measures such as average wait times for LTC patients. We propose an aggregate capacity model to consider patient flow among various types of care providers by integrating hospitals, nursing homes, assisted living facilities, and home health care. Using the data including patient demographics and service provider information, we forecast patient demand for LTC. The computational results demonstrate the efficacy of a simulation-based optimisation solution approach for capacity planning.

Suggested Citation

  • Ki-Hwan Bae & Molly Jones & Gerald Evans & Demetra Antimisiaris, 2019. "Simulation modelling of patient flow and capacity planning for regional long-term care needs: a case study," Health Systems, Taylor & Francis Journals, vol. 8(1), pages 1-16, January.
  • Handle: RePEc:taf:thssxx:v:8:y:2019:i:1:p:1-16
    DOI: 10.1080/20476965.2017.1405873
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    Cited by:

    1. Mahsa Pahlevani & Majid Taghavi & Peter Vanberkel, 2024. "A systematic literature review of predicting patient discharges using statistical methods and machine learning," Health Care Management Science, Springer, vol. 27(3), pages 458-478, September.

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