scholarly journals Admission control and resource allocation strategies for IEEE 802.15.5

Author(s):  
Samar Sindian ◽  
Matthieu Crussière ◽  
Jean-François Hélard ◽  
Abed Ellatif Samhat ◽  
Ayman Khalil
Author(s):  
Samar Sindian ◽  
Ayman Khalil ◽  
Abed Ellatif Samhat ◽  
Matthieu Crussiere ◽  
Jean-Francois Helard

2010 ◽  
Vol E93-B (3) ◽  
pp. 721-724
Author(s):  
Abhishek ROY ◽  
Navrati SAXENA ◽  
Jitae SHIN

Author(s):  
G.J. Melman ◽  
A.K. Parlikad ◽  
E.A.B. Cameron

AbstractCOVID-19 has disrupted healthcare operations and resulted in large-scale cancellations of elective surgery. Hospitals throughout the world made life-altering resource allocation decisions and prioritised the care of COVID-19 patients. Without effective models to evaluate resource allocation strategies encompassing COVID-19 and non-COVID-19 care, hospitals face the risk of making sub-optimal local resource allocation decisions. A discrete-event-simulation model is proposed in this paper to describe COVID-19, elective surgery, and emergency surgery patient flows. COVID-19-specific patient flows and a surgical patient flow network were constructed based on data of 475 COVID-19 patients and 28,831 non-COVID-19 patients in Addenbrooke’s hospital in the UK. The model enabled the evaluation of three resource allocation strategies, for two COVID-19 wave scenarios: proactive cancellation of elective surgery, reactive cancellation of elective surgery, and ring-fencing operating theatre capacity. The results suggest that a ring-fencing strategy outperforms the other strategies, regardless of the COVID-19 scenario, in terms of total direct deaths and the number of surgeries performed. However, this does come at the cost of 50% more critical care rejections. In terms of aggregate hospital performance, a reactive cancellation strategy prioritising COVID-19 is no longer favourable if more than 7.3% of elective surgeries can be considered life-saving. Additionally, the model demonstrates the impact of timely hospital preparation and staff availability, on the ability to treat patients during a pandemic. The model can aid hospitals worldwide during pandemics and disasters, to evaluate their resource allocation strategies and identify the effect of redefining the prioritisation of patients.


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