Hospital capacity management based on the queueing theory

Bittencourt, Otavio Neves da Silva; Verter, Vedat; Yalovsky, Morty

doi:10.1108/ijppm-12-2015-0193

Cited by 29 publications

(20 citation statements)

References 33 publications

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“…With the continuous advancement and implementation of healthcare reform, scientifically and reasonably shortening the average hospitalization day, optimizing bed utilization has become a core issue that hospital management is paying close attention to. In the current medical environment, major domestic hospitals are exploring management models suitable for their own development, including refined management, target management, clinical path management, day wards, medical prepayment systems, and pay-by-case systems [ 4 – 6 ]. Relevant scholars have explored the use of their beds, mainly using prospective cohort studies [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Markov Queuing Model in Hospital Bed Resource Allocation

Chen

et al. 2020

Journal of Healthcare Engineering

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Bed resources are the platform in which most medical and health resources in the hospital play a role and carry the core functions of the health service system. How to improve the efficiency of the use of bed resources through scientific management measures and methods and ultimately achieve the optimization of overall health resources is the focus of hospital management teams. This paper analyzes the previous research models of knowledge related to queuing theory in medical services. From the perspective of the hospital and the patient, several indicators such as the average total number of people, the utilization rate of bed resources, the patient stop rate, and the patient average waiting time are defined to measure the performance of the triage queue calling model, which makes the patient queue more reasonable. According to the actual task requirements of a hospital, a Markov queuing strategy based on Markov service is proposed. A mathematical queuing model is constructed, and the process of solving steady-state probability based on Markov theory is analyzed. Through the comparative analysis of simulation experiments, the advantages and disadvantages of the service Markov queuing model and the applicable scope are obtained. Based on the theory of the queuing method, a queuing network model of bed resource allocation is established in principle. Experimental results show that the queuing strategy of bed resource allocation based on Markov optimization effectively improves resource utilization and patient satisfaction and can well meet the individual needs of different patients. It does not only provide specific optimization measures for the object of empirical research but also provides a reference for the development of hospital bed resource allocation in theory.

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Section: Introductionmentioning

confidence: 99%

Optimization of Markov Queuing Model in Hospital Bed Resource Allocation

Chen

et al. 2020

Journal of Healthcare Engineering

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“…Using an (Markovian patient's arrival distribution and finite resources) M/M/m and (Markovian distribution of patient's arrival, general resources usage, infinite resources) M/G/∞ to reflect the probability distribution of total patients in each ward, the study confirmed the Little's law of queuing theory. Two years later, in 2018, [25] used different queuing theory models' variations to determine operation performances like beds utilization rate, waiting times and demand behavior assessment. The model was based on six months data recorded in University hospital and concluded as results that the balance between admissions and type of pathologies can be a possible way to optimize resources usage.…”

Section: Related Workmentioning

confidence: 99%

Modeling and Validation of the Hospital’s Ambulatory and Inpatients Operations Using a Non-Homogenous Discrete Time Markovian Chains

et al. 2021

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Overcrowded hospitals in its different process's levels is a very common issue all over the globe. Research through different works have studied this problem from different angles. Most of those focused on a very specific part of the internal flows. In this study, we present a mathematical model aiming to reflect patient flow and resources usage. This model presents a new perspective on how it is possible to organize a hospital flow for inpatients of different pathologies. It takes into consideration the patient's pathology, survival function and the current beds distribution and discharges. To model the different variations of a patient's flow, we used a non-homogenous discrete time Markov chain. Then, we tested our model using discrete event simulation using the National Health Service (NHS) dataset. The hospital episode statistics of the NHS used in this study included more than 12 million admission treated at NHS hospitals or care centers funded by the NHS in the period between March 2020 and February 2021. The results of the simulation model was then compared to the dataset using the chi-square goodness of fit test.

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“…This is the main reason behind the wide use of the model in practice. Others models only improved methods of measurements or inputs for the model, as demonstrated by studies of Bittencourt et al (2018) and Ventura et al (2018) who focused on hospital capacity management to improve organisational performance and deal with increased demand in the healthcare sector. Their research determined operation measures, such as utilisation rate, waiting probability, estimated bed capacity, capacity simulations and demand behaviour assessment.…”

Section: Literature Reviewmentioning

confidence: 99%

Benchmarking of contributory organisations within the framework of technical efficiency

Rylková

Šebestová

2019

Engineering Management in Production and Services

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Organisations should evaluate their goals in the areas of customer service provision, overall organisational strategy, finance, and human resource management. The performance of specific services provided to the client should be monitored and evaluated in greater detail. The comparison should be made between similar organisations aiming to improve services and technical efficiency. Most organisations, profit and non-profit alike, do not know how to evaluate and compare their efficiency. Retirement homes were selected for evaluation. The review focused on the technical efficiency for the years 2015-2017. To achieve the goal, Data Envelopment Analysis (DEA) was used as a specialised model tool for assessing the technical efficiency, performance or productivity of a group of homogeneous or comparable production units based on selected inputs and outputs. Due to different types of inputs and outputs, the method was selected from among multi-criteria decision-making methods. Two models, Model X and Model Y, including specific inputs and outputs, were designed to evaluate and compare the technical efficiency of selected retirement homes. According to the results, the output-oriented model (Model Y) was more effective for retirement homes compared to the input-oriented model (Model X). The value added could be seen in the model combination and comparison between different studies, which helps to understand the transferability of the results. The analysis confirmed the necessity to combine the DEA method with the quality of service assessment to be able to benchmark the real efficiency of service of a selected type of an organisation.

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Hospital capacity management based on the queueing theory

Cited by 29 publications

References 33 publications

Optimization of Markov Queuing Model in Hospital Bed Resource Allocation

Optimization of Markov Queuing Model in Hospital Bed Resource Allocation

Modeling and Validation of the Hospital’s Ambulatory and Inpatients Operations Using a Non-Homogenous Discrete Time Markovian Chains

Benchmarking of contributory organisations within the framework of technical efficiency

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