Keeping pace with the ebbs and flows in daily nursing home operations.
Nursing homes are challenged to develop staffing strategies that enable them to efficiently meet the healthcare demand of their residents. In this study, we investigate how demand for care and support fluctuates over time and during the course of a day, using demand data from three independent nursi...
| Publicado en: | Health Care Management Science Vol. 22; no. 2; pp. 350 - 364 |
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| Autores principales: | , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2019
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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=136274725&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136274725 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13869620 BSE jtl: Health Care Management Science issn: 13869620 maglogo: N pubinfo: dt: Jun2019 vid: 22 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 136274725 136274725 NLM29532197 10.1007/s10729-018-9442-x NLM29532197 136274725 ppf: 350 ppct: 14 formats: tig: atl: Keeping pace with the ebbs and flows in daily nursing home operations. aug: au: Bekker, René Moeke, Dennis Schmidt, Bas affil: Department of Mathematics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands sug: subj: Personnel Staffing and Scheduling Administration Nursing Homes Administration Appointments and Schedules Time Factors Netherlands Models, Theoretical Quality of Health Care Impact of Events Scale ab: Nursing homes are challenged to develop staffing strategies that enable them to efficiently meet the healthcare demand of their residents. In this study, we investigate how demand for care and support fluctuates over time and during the course of a day, using demand data from three independent nursing home departments of a single Dutch nursing home. This demand data is used as input for an optimization model that provides optimal staffing patterns across the day. For the optimization we use a Lindley-type equation and techniques from stochastic optimization to formulate a Mixed-Integer Linear Programming (MILP) model. The impact of both the current and proposed staffing patterns, in terms of waiting time and service level, are investigated. The results show substantial improvements for all three departments both in terms of average waiting time as well as in 15 minutes service level. Especially waiting during rush hours is significantly reduced, whereas there is only a slight increase in waiting time during non-rush hours. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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