Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems.
The management of hospitals within fixed-input health systems such as the U.S. Military Health System (MHS) can be challenging due to the large number of hospitals, as well as the uncertainty in input resources and achievable outputs. This paper introduces a stochastic multi-objective auto-optimizat...
| Publicado en: | Health Care Management Science Vol. 20; no. 2; pp. 246 - 265 |
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| Autores principales: | , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2017
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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=122835412&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 122835412 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13869620 BSE jtl: Health Care Management Science issn: 13869620 maglogo: N pubinfo: dt: Jun2017 vid: 20 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 122835412 122835412 NLM26742504 10.1007/s10729-015-9350-2 NLM26742504 122835412 ppf: 246 ppct: 19 formats: tig: atl: Stochastic multi-objective auto-optimization for resource allocation decision-making in fixed-input health systems. aug: au: Bastian, Nathaniel Ekin, Tahir Kang, Hyojung Griffin, Paul Fulton, Lawrence Grannan, Benjamin Bastian, Nathaniel D Griffin, Paul M Fulton, Lawrence V Grannan, Benjamin C affil: Department of Industrial & Manufacturing Engineering , The Pennsylvania State University , 362 Leonhard Building University Park 16802 USA sug: subj: Hospitals Resource Allocation Decision Making Organizational Efficiency Uncertainty Scales ab: The management of hospitals within fixed-input health systems such as the U.S. Military Health System (MHS) can be challenging due to the large number of hospitals, as well as the uncertainty in input resources and achievable outputs. This paper introduces a stochastic multi-objective auto-optimization model (SMAOM) for resource allocation decision-making in fixed-input health systems. The model can automatically identify where to re-allocate system input resources at the hospital level in order to optimize overall system performance, while considering uncertainty in the model parameters. The model is applied to 128 hospitals in the three services (Air Force, Army, and Navy) in the MHS using hospital-level data from 2009 - 2013. The results are compared to the traditional input-oriented variable returns-to-scale Data Envelopment Analysis (DEA) model. The application of SMAOM to the MHS increases the expected system-wide technical efficiency by 18 % over the DEA model while also accounting for uncertainty of health system inputs and outputs. The developed method is useful for decision-makers in the Defense Health Agency (DHA), who have a strategic level objective of integrating clinical and business processes through better sharing of resources across the MHS and through system-wide standardization across the services. It is also less sensitive to data outliers or sampling errors than traditional DEA methods. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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