Developing an adaptive policy for long-term care capacity planning.
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 sta...
| Publicado en: | Health Care Management Science Vol. 16; no. 3; pp. 271 - 280 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104205755&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104205755 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13869620 BSE jtl: Health Care Management Science issn: 13869620 maglogo: N pubinfo: dt: Sep2013 vid: 16 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104205755 NLM23512643 2012218989 10.1007/s10729-013-9229-z NLM23512643 104205755 ppf: 271 ppct: 9 formats: tig: atl: Developing an adaptive policy for long-term care capacity planning. aug: au: Zhang, Yue Puterman, Martin L affil: College of Business and Innovation, University of Toledo, 2801 W. Bancroft Street, Toledo, OH, 43606, USA, yue.zhang@utoledo.edu. sug: subj: Computer Simulation Health and Welfare Planning Administration Long Term Care ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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