Data-driven optimization methodology for admission control in critical care units.
The decision of whether to admit a patient to a critical care unit is a crucial operational problem that has significant influence on both hospital performance and patient outcomes. Hospitals currently lack a methodology to selectively admit patients to these units in a way that patient health risk...
| Publicado en: | Health Care Management Science Vol. 22; no. 2; pp. 318 - 336 |
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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=136274723&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136274723 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: 136274723 136274723 NLM29536293 10.1007/s10729-018-9439-5 NLM29536293 136274723 ppf: 318 ppct: 18 formats: tig: atl: Data-driven optimization methodology for admission control in critical care units. aug: au: Meisami, Amirhossein Deglise-Hawkinson, Jivan Cowen, Mark E. Van Oyen, Mark P. affil: Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, USA sug: subj: Models, Theoretical Patient Admission Standards Intensive Care Units Administration Length of Stay Health Facility Administration Methods Hospital Mortality Decision Making Critical Care Family Needs Inventory ab: The decision of whether to admit a patient to a critical care unit is a crucial operational problem that has significant influence on both hospital performance and patient outcomes. Hospitals currently lack a methodology to selectively admit patients to these units in a way that patient health risk metrics can be incorporated while considering the congestion that will occur. The hospital is modeled as a complex loss queueing network with a stochastic model of how long risk-stratified patients spend time in particular units and how they transition between units. A Mixed Integer Programming model approximates an optimal admission control policy for the network of units. While enforcing low levels of patient blocking, we optimize a monotonic dual-threshold admission policy. A hospital network including Intermediate Care Units (IMCs) and Intensive Care Units (ICUs) was considered for validation. The optimized model indicated a reduction in the risk levels required for admission, and weekly average admissions to ICUs and IMCs increased by 37% and 12%, respectively, with minimal blocking. Our methodology captures utilization and accessibility in a network model of care pathways while supporting the personalized allocation of scarce care resources to the neediest patients. The interesting benefits of admission thresholds that vary by day of week are studied. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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