J Biomed Inform - Improving hospital bed occupancy and resource utilization through queuing modeling and evolutionary computation.

Tópicos

{ health(3367) inform(1360) care(1135) }
{ patient(2837) hospit(1953) medic(668) }
{ framework(1458) process(801) describ(734) }
{ process(1125) use(805) approach(778) }
{ imag(1947) propos(1133) code(1026) }
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{ method(2212) result(1239) propos(1039) }
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{ data(1714) softwar(1251) tool(1186) }
{ use(2086) technolog(871) perceiv(783) }
{ visual(1396) interact(850) tool(830) }
{ group(2977) signific(1463) compar(1072) }
{ health(1844) social(1437) communiti(874) }
{ bind(1733) structur(1185) ligand(1036) }
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{ imag(2675) segment(2577) method(1081) }
{ take(945) account(800) differ(722) }
{ assess(1506) score(1403) qualiti(1306) }
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{ surgeri(1148) surgic(1085) robot(1054) }
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{ record(1888) medic(1808) patient(1693) }
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{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
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{ gene(2352) biolog(1181) express(1162) }
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{ patient(1821) servic(1111) care(1106) }
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{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
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{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
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Resumo

Scarce healthcare resources require carefully made policies ensuring optimal bed allocation, quality healthcare service, and adequate financial support. This paper proposes a complex analysis of the resource allocation in a hospital department by integrating in the same framework a queuing system, a compartmental model, and an evolutionary-based optimization. The queuing system shapes the flow of patients through the hospital, the compartmental model offers a feasible structure of the hospital department in accordance to the queuing characteristics, and the evolutionary paradigm provides the means to optimize the bed-occupancy management and the resource utilization using a genetic algorithm approach. The paper also focuses on a "What-if analysis" providing a flexible tool to explore the effects on the outcomes of the queuing system and resource utilization through systematic changes in the input parameters. The methodology was illustrated using a simulation based on real data collected from a geriatric department of a hospital from London, UK. In addition, the paper explores the possibility of adapting the methodology to different medical departments (surgery, stroke, and mental illness). Moreover, the paper also focuses on the practical use of the model from the healthcare point of view, by presenting a simulated application.

Resumo Limpo

scarc healthcar resourc requir care made polici ensur optim bed alloc qualiti healthcar servic adequ financi support paper propos complex analysi resourc alloc hospit depart integr framework queu system compartment model evolutionarybas optim queu system shape flow patient hospit compartment model offer feasibl structur hospit depart accord queu characterist evolutionari paradigm provid mean optim bedoccup manag resourc util use genet algorithm approach paper also focus whatif analysi provid flexibl tool explor effect outcom queu system resourc util systemat chang input paramet methodolog illustr use simul base real data collect geriatr depart hospit london uk addit paper explor possibl adapt methodolog differ medic depart surgeri stroke mental ill moreov paper also focus practic use model healthcar point view present simul applic

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