An online short-term bed occupancy rate prediction procedure based on discrete event simulation

Zhu, Zhiwei

doi:10.5430/jha.v3n4p37

Cited by 10 publications

(11 citation statements)

References 17 publications

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“…Researchers in the UK have based their studies on the duration of stay of infants by analysing the duration of stay in premature infants, having been observed to be more intensive [31,13,32,33].…”

Section: Predicting the Length Of Staymentioning

confidence: 99%

Simulation Analysis of a Neonatal Unit With Complex Patient Flow Patterns – An Enhanced Model for Capacity Planning

Perera

Uslu

2022

SAJIE

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Steadily increasing demand requires neonatal units and networks to improve their overall capacities. Given the operational complexities involved, simulation is a popular choice for capacity planning studies. , For efficient model building, simulation exercises typically assume unidirectional flow of patients i.e., infants progressively move to lower levels of care. Patient flows are, however, much more complex i.e., time-to-time patients may require a higher level of care than the current level of care. This paper argues that bi-directional flow modelling is required to produce accurate capacity estimates thereby improving the quality decision making.

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Section: Predicting the Length Of Staymentioning

confidence: 99%

Simulation Analysis of a Neonatal Unit With Complex Patient Flow Patterns – An Enhanced Model for Capacity Planning

Perera

Uslu

2022

SAJIE

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“…Overlooked elements affected the demand for beds in the hospital per day. The researchers to understand and forecast the bed occupancy model like simulation (Developed using ARENA 10.0) and linear regression and simulation model is mostly preferred amongst all because it answers a lot about the availability and engagement of beds in a hospital [27,33].…”

Section: Bed Occupancymentioning

confidence: 99%

A Machine Learning Solution for Bed Occupancy Issue for Smart Healthcare Sector

Gochhait

Butt

De-la-Hoz-Franco

et al. 2021

Aut. Control Comp. Sci.

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The health care domain is a culmination and emergence of many other economic sectors that give different services from patient treatment to healing, protective, rehabilitation, and palliative care. The GDP consumes to facilitate health in terms of smart device development, clinical examinations, outsourcing, and Tele-Medication facilities. The Asian countries and less developed countries with a high population rate are facing health care services related issues. One of these countries is India. India has two types of health care services system; (i) public service system and (ii) private system. The public health system, i.e., the government, provides facilities to patients as primary health centers (PHCs) through limited secondary and tertiary health institutions like hospitals in rural areas while the private service is owned by local practitioners and institutions. Both of these service providers are facing bed occupancy issues for patients due to a highly populated country. To overcome this issue, we propose a Machine Learning solution for patient admission scheduling autonomously. The propose framework helps hospitals to enhance the decision process for bed occupancy for patients concerning their departments and their diseases. We have deployed our framework in real time environment and find that it facilitates the overall performance of bed allocation in the prescribed hospitals.

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“…Zhecheng [10] proposed and developed an online prediction procedure based on discrete-event simulation to predict the bed occupancy rate in a short term period. Simulation results showed that the predicted values were closer to the actual values with a narrower confidence interval compared to the offline approach.…”

Section: Zhao and Liementioning

confidence: 99%

Operations Research and Simulation in Healthcare

2021

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It is globally accepted to act against global warming through the reduction of carbon dioxide.Carbon footprint is historically defined as the total emissions caused by an individual, event, organization, or product, expressed as carbon dioxide equivalent. Healthcare system consumes large amount of energy in order to provide health services to patients who have to pass a series of treatment processes at each care unit, which each one of them needs different medical equipment in hospital that they cause consuming of electrical power, and the more electrical power consumption, the more greenhouse gases, specifically CO2. The discrete-event simulation has been applied to develop the model of the treatment process and the estimation of carbon dioxide in the treatment process. By the knowledge that the simulation is not an optimization method in itself, the OptQuest optimization method has applied to reduce greenhouse gases and carbon footprint in the patients' flow in the emergency department by considering leveling off the waiting time and length of stay as constraints to leveling up patient's satisfaction. The results comparison of simulation and OptQuest show that the OptQuest is an efficient technique for patient flow optimization.

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An online short-term bed occupancy rate prediction procedure based on discrete event simulation

Cited by 10 publications

References 17 publications

Simulation Analysis of a Neonatal Unit With Complex Patient Flow Patterns – An Enhanced Model for Capacity Planning

Simulation Analysis of a Neonatal Unit With Complex Patient Flow Patterns – An Enhanced Model for Capacity Planning

A Machine Learning Solution for Bed Occupancy Issue for Smart Healthcare Sector

Operations Research and Simulation in Healthcare

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