Application of queuing theory to optimize the triage process in a tertiary emergency care (“ER”) department

Carrillo, Atilio Moreno; Arenas, Lina María Ávila; Fonseca, John Fredy; Caicedo, Camilo Andrés; Tovar, Sandra Verónica; Muñoz-Velandia, Óscar Mauricio

doi:10.4103/jets.jets_42_19

Cited by 12 publications

(3 citation statements)

References 10 publications

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“…Furthermore, studies like that of Kamali et al (2019) [12] addressed the role of communication and collaboration in improving triage effectiveness, recognizing the importance of a multidisciplinary approach in emergency care settings. Collectively, these studies form the foundation for the current research, informing the methodology and guiding the exploration of novel strategies to optimize triage systems and resource allocation in emergency care (Moreno-Carrillo et al, 2019) [14] . Despite the wealth of research on optimizing triage systems in emergency care, a super research gap exists in the integration of superior statistics analytics and device-gaining knowledge of algorithms to decorate the accuracy of affected person prioritization.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing triage systems in emergency care: A comprehensive analysis of patient outcomes and resource allocation

Alotaibi,

Alahmari

et al. 2024

Pharma Innovation

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This study affords a complete analysis of optimizing triage systems in emergency care with a focal point on improving affected person outcomes and aid allocation. In the quick-paced and dynamic surroundings of emergency departments, efficient triage is paramount to ensure timely and appropriate care. Through a radical exam of affected person information and operational metrics, our studies identify key factors influencing triage effectiveness. By using superior facts analytics and device learning algorithms, we advise a sophisticated triage device that complements the accuracy of affected person prioritization, leading to advanced results. The observation also investigates the impact of optimized useful resource allocation on typical emergency branch performance and price effectiveness. Results display a vast discount in patient wait times, multiplied workforce pleasure, and more sensible use of sources. Moreover, our findings underscore the ability to mitigate overcrowding and enhance affected person reviews via strategic allocation of personnel, equipment, and centers. This research contributes to the continued discourse on healthcare optimization, supplying realistic insights that can be applied to decorate emergency care shipping, in the end benefitting each patient and healthcare institution. The proposed version offers a scalable and adaptable framework for other healthcare settings looking to enhance triage systems and aid utilization in emergency conditions.

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Section: Literature Reviewmentioning

confidence: 99%

Optimizing triage systems in emergency care: A comprehensive analysis of patient outcomes and resource allocation

Alotaibi,

Alahmari

et al. 2024

Pharma Innovation

View full text Add to dashboard Cite

show abstract

“…8 In brief, queueing models aim to capture how a customer population moves through a queueing system via a series of processes dictated by probabilistic rates: arriving at a service site, receiving service, waiting in a queue if the server is busy, or leaving the queue before service is rendered when waiting times exceed a customer’s willingness to wait. Queueing theory is foundational to operations research and has been applied to many facets of healthcare operations, including the triage process in emergency care departments, 9 staffing needs in operating rooms, 10 hospital bed management, 11,12 and outpatient scheduling. 13 Additionally, it has been applied to COVID-19 vaccine distribution and capacity planning, 7,14–18 as well as the containment of disease outbreaks, bioterrorist attacks, and other public health emergencies.…”

Section: Introductionmentioning

confidence: 99%

“…Queueing theory is foundational to operations research and has been applied to many facets of healthcare operations, including the triage process in emergency care departments, 9 staffing needs in operating rooms, 10 hospital bed management, 11,12 and outpatient scheduling. 13 Additionally, it has been applied to COVID-19 vaccine distribution and capacity planning, 7,[14][15][16][17][18] as well as the containment of disease outbreaks, bioterrorist attacks, and other public health emergencies.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Mass Vaccination Programs with Queueing Theory and Spatial Optimization

Xie,

Rieders,

Changolkar

et al. 2024

Preprint

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BackgroundMass vaccination is a cornerstone of public health emergency preparedness and response. However, injudicious placement of vaccination sites can lead to the formation of long waiting lines orqueues, which discourages individuals from waiting to be vaccinated and may thus jeopardize the achievement of public health targets. Queueing theory offers a framework for modeling queue formation at vaccination sites and its effect on vaccine uptake.MethodsWe developed an algorithm that integrates queueing theory within a spatial optimization framework to optimize the placement of mass vaccination sites. The algorithm was built and tested using data from a mass canine rabies vaccination campaign in Arequipa, Peru. We compared expected vaccination coverage and losses from queueing (i.e., attrition) for sites optimized with our queue-conscious algorithm to those obtained from a queue-naive version of the same algorithm.ResultsSites placed by the queue-conscious algorithm resulted in 9-19% less attrition and 1-2% higher vaccination coverage compared to sites placed by the queue-naïve algorithm. Compared to the queue-naïve algorithm, the queue-conscious algorithm favored placing more sites in densely populated areas to offset high arrival volumes, thereby reducing losses due to excessive queueing. These results were not sensitive to misspecification of queueing parameters or relaxation of the constant arrival rate assumption.ConclusionOne should consider losses from queueing to optimally place mass vaccination sites, even when empirically derived queueing parameters are not available. Due to the negative impacts of excessive wait times on participant satisfaction, reducing queueing attrition is also expected to yield downstream benefits and improve vaccination coverage in subsequent mass vaccination campaigns.

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Pre-triage wait times for non-ambulance arrivals in the emergency department: A retrospective video audit

Sedgman

Aldridge

Miller

et al. 2022

Australasian Emergency Care

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Application of queuing theory to optimize the triage process in a tertiary emergency care (“ER”) department

Cited by 12 publications

References 10 publications

Optimizing triage systems in emergency care: A comprehensive analysis of patient outcomes and resource allocation

Optimizing triage systems in emergency care: A comprehensive analysis of patient outcomes and resource allocation

Enhancing Mass Vaccination Programs with Queueing Theory and Spatial Optimization

Pre-triage wait times for non-ambulance arrivals in the emergency department: A retrospective video audit

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