Analisys of hospital bed capacity via queuing theory and simulation

Pinto, Luiz Ricardo; Campos, Francisco Carlos Cardoso de; Perpétuo, Ignez Helena Oliva; Ribeiro, Yara Cristina Neves Marques Barbosa

doi:10.1109/wsc.2014.7019984

Cited by 12 publications

(7 citation statements)

References 15 publications

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“…Meanwhile, more modern works such as [28,29] consider more extensive sources of data to build their queuing models. Moreover, the output of a model is catered more towards being actionable -as is the prerogative of operational research.…”

Section: Queuing Modelsmentioning

confidence: 99%

Segmentation analysis and the recovery of queuing parameters via the Wasserstein distance: a study of administrative data for patients with chronic obstructive pulmonary disease

Wilde,

Knight,

Gillard

et al. 2020

Preprint

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This work uses a data-driven approach to analyse how the resource requirements of patients with chronic obstructive pulmonary disease (COPD) may change, quantifying how those changes impact the hospital system with which the patients interact. This approach is composed of a novel combination of often distinct modes of analysis: segmentation, operational queuing theory, and the recovery of parameters from incomplete data. By combining these methods as presented here, this work demonstrates that potential limitations around the availability of fine-grained data can be overcome. Thus, finding useful operational results despite using only administrative data.The paper begins by finding a useful clustering of the population from this granular data that feeds into a multi-class M/M/c model, whose parameters are recovered from the data via parameterisation and the Wasserstein distance. This model is then used to conduct an informative analysis of the underlying queuing system and the needs of the population under study through several what-if scenarios.The analyses used to form and study this model consider, in effect, all types of patient arrivals and how those types impact the system. With that, this study finds that there are no quick solutions to reduce the impact of COPD patients on the system, including adding capacity to the system. In this analysis, the only effective intervention to reduce the strain caused by those presenting with COPD is to enact external policies which directly improve the overall health of the COPD population before they arrive at the hospital.

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Section: Queuing Modelsmentioning

confidence: 99%

Segmentation analysis and the recovery of queuing parameters via the Wasserstein distance: a study of administrative data for patients with chronic obstructive pulmonary disease

Wilde,

Knight,

Gillard

et al. 2020

Preprint

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“…Por lo tanto, Pinto et al (7) consideran que una relación estimada para la planeación anual del número de camas es definida como la demanda al año de pacientes, multiplicada por el promedio de días de estancia entre 365 días al año, por la tasa de ocupación requerida, pero Kokangul (16) y Nguyen et al (18) consideran enfoques diarios y mensuales , esto se expresa a través de la Ecuación 2:…”

Section: Tabla 3 Límites Del Modelo Para El Estudio De Las Camas Hospitalariasunclassified

“…Esto, por lo tanto, les concierne a las áreas de planeación de la capacidad hospitalaria, la cual es un problema común en salud. (7).…”

Section: Introductionunclassified

Modelo de simulación sistémica para el dimensionamiento de camas hospitalarias en una institución de salud de alta complejidad utilizando la metodología de dinámica de sistemas

Uribe-Gómez

Gómez

2020

RGYPS

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Las clínicas, hospitales y centros de salud se encuentran diariamente en la necesidad de optimizar sus recursos profesionales, farmacéuticos, hospitalarios o de infraestructura mobiliaria, sin embargo, debido a múltiples factores aleatorios y complejos, la labor se torna demandante, por la falta de metodologías que ayuden a los gestores de salud a controlar las necesidades del proceso. A partir de todo esto, el objetivo es desarrollar una metodología sistémica y un modelo de planificación para el dimensionamiento de la cama hospitalaria, con el fin de mejorar la planeación y administración en una institución de salud. Inicialmente, se realizó una búsqueda de factores claves que afectan la planeación, las relaciones y componentes en la medición de las camas. Con esto se planteó un modelo causal y uno de simulación mediante dinámica de sistemas, que integre dichos factores. Finalmente, los resultados de la simulación indican que la necesidad de camas adicionales para la institución es del 9% o 16 camas hospitalarias en promedio. Se espera que el concepto presentado brinde una idea clara, holística y consecuente en la planeación de recursos hospitalarios.

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“…Furthermore, (Al-Araidah, Boran, and Wahsheh 2012) applied the tool in an ophthalmology laboratory in order to reduce patients' waiting and appointment time. Regarding planning and capacity analysis, (Pinto et al 2015) defined the ideal number of beds for a Brazilian hospital. For the balancing of staff work (Reynolds et al 2011) applied the simulation to reduce the workload of an English pharmacy and (Pongjetanapong et al 2019) used the tool to evaluate the effect of changes to staff levels in a cytology department.…”

Section: Literature Backgroundmentioning

confidence: 99%

Lean healthcare integrated with discrete event simulation and design of experiments: an emergency department expansion

Gabriel

Campos

Magacho

et al. 2019

Preprint

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Background. Discrete Event Simulation (DES) and Lean Healthcare are management tools that are efficient and assist in the quality and efficiency of health services. In this sense, the purpose of the study is to use lean principles jointly with DES to plan the expansion of a Canadian emergency department and to the demand that comes from small closed care centers. Methods. For this, we used simulation and modeling method. We simulated the emergency department in FlexSim Healthcare® software and, with the Design of Experiments (DoE), we defined the optimal number of locations and resources for each shift. Results. The results show that the ED cannot meet expected demand in the current state. Only 17.2% of the patients were completed treated, and the Length of Stay (LOS), on average, was 2213.7, with a confidence interval of (2131.8 - 2295.6) minutes. However, after changing decision variables, the number of treated patients increased to 95.7% (approximately 600%). Average LOS decreased to 461.2, with a confidence interval of (453.7 - 468.7) minutes, about 79.0%. In addition, the study shows that emergency department staff are balanced, according to Lean principles.

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Analisys of hospital bed capacity via queuing theory and simulation

Cited by 12 publications

References 15 publications

Segmentation analysis and the recovery of queuing parameters via the Wasserstein distance: a study of administrative data for patients with chronic obstructive pulmonary disease

Segmentation analysis and the recovery of queuing parameters via the Wasserstein distance: a study of administrative data for patients with chronic obstructive pulmonary disease

Modelo de simulación sistémica para el dimensionamiento de camas hospitalarias en una institución de salud de alta complejidad utilizando la metodología de dinámica de sistemas

Lean healthcare integrated with discrete event simulation and design of experiments: an emergency department expansion

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