Improving hospital bed occupancy and resource utilization through queuing modeling and evolutionary computation

Abstract

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.

Dimensionamento das Entradas Ambulatoriais em uma Maternidade em Função do Número de Leitos Disponíveis no Alojamento Conjunto

“…Erlang-B para dimensionamento de serviços de maternidade e de outras unidades de internação de hospitais (BELCIUG; GORUNESCU, 2015;ESSEN;HOUDENHOVE, 2015;KEMBE et al, 2014;JONES, 2011). Para seu cálculo, assume-se que a taxa de chegada de pacientes por dia respeita a distribuição de Poisson.…”

“…O presente estudo tem pontos fortes e aplicabilidade prática, uma vez que o cálculo foi construído com base em modelo teórico previamente testado por outros autores (BELCIUG; GORUNESCU, 2015;BRUIN et al, 2009;ESSEN;HOUDENHOVE, 2015;JONES, 2011;KEMBE; AGADA; OWUNA, 2014) e em dados de 10 meses obtidos do serviço avaliado. Pode contribuir para a melhora das condições de trabalho e do gerenciamento de leitos na unidade.…”

A hybrid genetic algorithm-queuing multi-compartment model for optimizing inpatient bed occupancy and associated costs

“…The task of finding an optimum balance between the number of beds (both occupied and ready for use) and the best health service can be approached by using GAs, seen as optimization algorithms [21]. For the GA-based optimization, we implemented a standard GA [22,23] in Java.…”

“…£720) -columns 2 and 3; • The same holding and penalty costs, but significantly different bed allocations (156 vs. M a n u s c r i p t 21 To conclude, depending on a predefined rejection threshold, the managers can simulate different scenarios regarding beds stock and afferent costs, eventually choosing the feasible optimum value of g.…”

“…Indifference curvesDeveloped in economics, an indifference curve displays various combinations of two items yielding equal satisfaction. Inspired by this model, applied for inventory optimization[34] and hospital management[10],[21], we have used the paradigm in our experiment. We have explored the circumstances in which we are 'indifferent' between neighboring values of the control variables c and L vis-à-vis the rejection probability P N and cost function g.Otherwise stated, we have sought combinations of the two parameters c and L providing nearly equal rejection and cost, respectively.…”