Abstract
This paper describes a refined methodology for determining long-term care (LTC) capacity levels over a multi-year planning horizon based on a previous study. The problem is to find a capacity level in each year during the planning horizon to meet a wait time service level criterion. Instead of a static policy for capacity planning, we proposal an adaptive policy, where the capacity level required in this year depends on the achieved service level in the last year as the state of the LTC system. We aggregate service levels into a few groups for tractability. Our methodology integrates a discrete event simulation for describing the LTC system and an optimization algorithm to find required capacity levels. We illustrate this methodology through a case study. The results show that the refined methodology overcomes the problems observed in the previous study. It also improves resource utilization greatly. To execute this adaptive policy in practice requires availability of surge or temporary capacity.
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References
Canadian Institute for Health Information (2009). http://secure.cihi.ca/cihiweb/
Canadian Union of Public Employees (2009) Residential long-term care in Canada: our vision for better seniors’ care http://www.cupe.ca/updir/CUPE-long-term-care-seniors-care-vision.pdf
Cohen M, Jeremy T, Baumbusch J (2009) An uncertain future for seniors: BC’s restructuring of home and community health care, 2001-2008. http://www.policyalternatives.ca/reports/2009/04/uncertain_future
Fu MC (1994) Optimization via simulation: a review. Ann Oper Res 53:199–247
Gibson D, Liu Z (2008) Planning ratios and population growth: will there be a shortfall in residential aged care by 2021. Australas J Ageing 14(2):57–62
Green LV, Kolesar PJ, Soares J (2001) Improving the SIPP approach for staffing service systems that have cyclic demand. Oper Res 49(4):549–564
Hare WL, Alimadad A, Dodd H, Ferguson R, Rutherford A (2009) A deterministic model of home and community care client counts in British Columbia. Health Care Manag Sci 12(1):80–98
Henderson SG, Nelson BL (2006) Handbook in operations research and management science: simulation, vol 13. North Holland
Klein JP, Moeschberger ML (2003) Survival analysis, techniques for censored and truncated data, 2nd edn. Springer-Verlag, New York
Miller RG (1981) Simultaneous statistical inference, 2nd edn. Springer-Verlag, New York
Prince Edward Island Department of Health (2009) Prince Edward Island’s healthy aging strategy. http://www.gov.pe.ca/photos/original/doh_agingstrat.pdf
Statistics Canada (2009) http://www12.statcan.ca/census-recensement/2006/index-eng.cfm
Tekin E, Sabuncuoglu I (2004) Simulation optimization: a comprehensive review on theory and applications. IIE Trans 36:1067–1081
Wiener JM, Stevenson DG, Goldenson SM (1998) Controlling the supply of long-term care providers at the state level, Urban Institute. Occasional Paper Number 22
World Health Organization (2004) http://www.who.int/mediacentre/news/releases/2004/pr60/en/
Zhang Y, Puterman ML, Nelson M, Atkins D (2012) A simulation optimization approach to long-term care capacity planning. Oper Res 60(2):249–261
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Zhang, Y., Puterman, M.L. Developing an adaptive policy for long-term care capacity planning. Health Care Manag Sci 16, 271–279 (2013). https://doi.org/10.1007/s10729-013-9229-z
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DOI: https://doi.org/10.1007/s10729-013-9229-z