Accumulating priority queues versus pure priority queues for managing patients in emergency departments

Cildoz, Marta; Ibarra, Amaia; Giménez, Ferran

doi:10.1016/j.orhc.2019.100224

Cited by 22 publications

(13 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, the arrival rate function λ(t) is roughly constant over periods of few hours [27,14,16,11]. Lognormal distributions for service times have already been reported in the literature [14,15,16,12]. The fact that the distribution of the sum of few independent, but not necessarily identical, lognormal random variables, could be approximated by a lognormal distribution [28], may explain the experimental findings when we only use two fitting parameters.…”

Section: Resultsmentioning

confidence: 81%

“…In this case, MFPT gives us the average time to request an ambulance from another operational zone to answer the next emergency call when the primary equipment is busy (interdistrict dispatches). MFPT can also be a useful KPI in the decision-making process of emergency departments [11,12], where MFPT provides the average time to a shortage of intensive therapy beds when the FCFS discipline is used after the patient's triage.…”

Section: Introductionmentioning

confidence: 99%

“…However, experimental evidence from an emergency service indicates that service time distributions are well fitted by lognormal distributions [12,[14][15][16]. Hence, our objective is also to numerically evaluate the deviation between analytical and simulated results for MFPT.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Time to Critical Condition in Emergency Services

Pury

2021

MCA

View full text Add to dashboard Cite

Providing uninterrupted response service is of paramount importance for emergency medical services, regardless of the operating scenario. Thus, reliable estimates of the time to the critical condition, under which there will be no available servers to respond to the next incoming call, become very useful measures of the system’s performance. In this contribution, we develop a key performance indicator by providing an explicit formula for the average time to the shortage condition. Our analytical expression for this average time is a function of the number of parallel servers and the inter-arrival and service times. We assume exponential distributions of times in our analytical expression, but for evaluating the mean first-passage time to the critical condition under more realistic scenarios, we validate our result through exhaustive simulations with lognormal service time distributions. For this task, we have implemented a simulator in R. Our results indicate that our analytical formula is an acceptable approximation under any situation of practical interest.

show abstract

Section: Resultsmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Time to Critical Condition in Emergency Services

Pury

2021

MCA

View full text Add to dashboard Cite

show abstract

“…Aghsami et al (2021) applied a queueing-inventory model to a hospital blood inventory management case study. Due to enhancing the emergency health care systems, Cildoz et al (2019) designed a novel queue policy, the accumulative priority queue with the finite horizon (APQ-h) based on APQ. Their method considers the acuity level of patients and their waiting time, and the stage of healthcare treatment.…”

Section: Introductionmentioning

confidence: 99%

Prioritizing and queueing the emergency departments’ patients using a novel data-driven decision-making methodology, a real case study

Alipour-Vaezi¹,

Aghsami²,

Jolai³

2022

Expert Systems with Applications

View full text Add to dashboard Cite

One of the principal problems in epidemic disruptions like the COVID-19 pandemic is that the number of patients needing hospitals’ emergency departments’ services significantly grows. Since COVID-19 is an infectious disease, any aggregation has to be prevented accordingly. However, few aggregations cannot be prevented, including hospitals. To the best of our knowledge, COVID-19 is a life-threatening disease, especially for people in poor health conditions. Therefore, it sounds reasonable to optimize the health care queuing systems to minimize the infection rate by prioritizing patients based on their health condition so patients with a higher risk of infection will leave the queue sooner. In this paper, relying on data mining models and expert’s opinions, we propose a method for patient classification and prioritizing. The optimal number of servers (treatment systems) will be determined by benefiting from a mixed-integer model and the grasshopper optimization algorithm.

show abstract

“…During the waiting time, the state of the low priority patient can become worse and he/she will need urgent treatment (becomes high priority customer). It is worth noting that in the recent paper [13] devoted to analysis of patients managing in emergency departments, the following is stated: "disciplines with changing priorities have been studied in the literature from a queuing theory point of view, which requires assumptions rarely found in real emergency departments, such as homogeneity in the patient arrival pattern and only one service stage." Assumptions about the BMMAP arrival process and phase type service (probably consisting of an arbitrary finite number of sequentially stages) made in our paper a better fit for modeling real emergency departments.…”

Section: Introductionmentioning

confidence: 99%

Queuing System with Two Types of Customers and Dynamic Change of a Priority

et al. 2020

View full text Add to dashboard Cite

The use of priorities allows us to improve the quality of service of inhomogeneous customers in telecommunication networks, inventory and health-care systems. An important modern direction of research is to analyze systems in which priority of a customer can be changed during his/her stay in the system. We considered a single-server queuing system with a finite buffer, where two types of customers arrive according to a batch marked Markov arrival process. Type 1 customers have non-preemptive priority over type 2 customers. Low priority customers are able to receive high priority after the random amount of time. For each non-priority customer accepted into the buffer, a timer, which counts a random time having a phase type distribution, is switched-on. When the timer expires, the customer with some probability leaves the system unserved and with the complimentary probability gains the high priority. Such a type of queues is typical in many health-care systems, contact centers, perishable inventory, etc. We describe the behavior of the system by a multi-dimensional continuous-time Markov chain and calculate a number of the stationary performance measures of the system including the various loss probabilities as well as the distribution function of the waiting time of priority customers. The illustrative numerical examples giving insights into the system behavior are presented.

show abstract

Accumulating priority queues versus pure priority queues for managing patients in emergency departments

Cited by 22 publications

References 37 publications

Time to Critical Condition in Emergency Services

Time to Critical Condition in Emergency Services

Prioritizing and queueing the emergency departments’ patients using a novel data-driven decision-making methodology, a real case study

Queuing System with Two Types of Customers and Dynamic Change of a Priority

Contact Info

Product

Resources

About