Queueing Analysis in Healthcare

Green, Linda

doi:10.1007/978-0-387-33636-7_10

Cited by 145 publications

(84 citation statements)

References 17 publications

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“…Models to predict waiting times for patients with differing priorities, such as between elective and acute care, have been developed starting the 1970s (12,14,15,16). This situation is similar to our setting for the emergency department in which critical patients have priority over non-critical patients.…”

Section: Literature Reviewmentioning

confidence: 99%

Patient Choice in Acute Care

Çakıcı¹,

Dewan²,

Freimer³

2017

Proceedings of the Annual Hawaii International Conference on System Sciences

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Abstract-Consumer healthcare information plays a critical role in informing patients who participate in or make healthcare decisions for themselves without direct supervision of a healthcare professional. One such example is the choice of facility for acute care, prototypically between a fully equipped emergency care department (ED) at a hospital and a more convenient but less capable urgent care (UC) or retail clinic. We model a strategic patient making this decision taking into account the limited medical information and convenience factors that affect the patient's decision. This model is then used to inform the pricing decision made by the manager of the UC. We show that a separating equilibrium, in which all patients self-triaged as noncritical choose to go to the UC first, dominates pooling equilibria for moderate error rates in self-triage. We analyze the separating equilibrium to examine the effect of consumer health information (CHI) systems, and show that as the quality of the CHI decreases and the error rates go up, the co-pay for an UC decreases, the facility is smaller, and makes less profit.

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Section: Literature Reviewmentioning

confidence: 99%

Patient Choice in Acute Care

Çakıcı¹,

Dewan²,

Freimer³

2017

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…There are several good reviews of queueing theory applied in healthcare [27,29]. Queueing analysis is not only suitable for assessing the operational performance of a system, but is also used for determining capacity requirements, such as determining the required number of ORs for certain patient categories.…”

Section: Type Of Analysismentioning

confidence: 99%

Trade-offs in operating room planning for electives and emergencies: A review

Riet

Demeulemeester

2015

Operations Research for Health Care

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The planning of the operating rooms (ORs) is a difficult process due to the different stakeholders involved. The real complexity, however, results from various sources of variability entering the processes. These uncertain processes cannot be ignored since they greatly influence the trade-offs between the hospital costs and the patient waiting times. As a result, a need for policies guiding the OR manager in handling the trade-offs arises. Therefore, researchers have investigated different possibilities to incorporate non-elective patients in the schedule with the goal of maximizing both patient-and hospital-related measures. This paper reviews the literature on OR planning where both elective and non-elective patient categories are involved. It shows the various policies, the differences and similarities in the research settings and the resulting outcomes, whether they are beneficial or not. We find that the dedicated and the flexible policy are mostly pursued, but the setting and the assumptions of the reviewed papers vary widely. Decisions on both operational policies as well as on capacity are required to assure timely access and efficiency, which are the two main drivers for the problem at hand. Furthermore, the policy choice impacts the number of schedule disruptions and the OR utilization. However, results on the overtime and the patient waiting time are partly contradicting. The review shows that some policies have already received considerable attention, but the question of which policies are most appropriate is not yet fully answered. Neither has the full spectrum of policies been explored yet. Consequently, this topic provides several areas for future research, which are outlined throughout the paper.

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“…The multi-server queue is a well-studied object in operations research with widespread applications, for example in the modeling of call centres [11] and healthcare systems [9] . In many situations, the system needs to be designed in such a way that the service level offered is sufficiently high.…”

Section: Introductionmentioning

confidence: 99%

Efficient simulation of tail probabilities in a queueing model with heterogeneous servers

Kühn

Mandjes

2018

Stochastic Models

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This paper considers a multi-server queue with Markovmodulated Poisson input and server-dependent phase-type service times. We develop an efficient rare-event simulation technique to estimate the probability that the number of customers in this system reaches a high value. Relying on explicit bounds on the probability under consideration as well as the associated likelihood ratio, we succeed in proving that the proposed estimator is of bounded relative error. Simulation experiments illustrate the significant speed-up that can be achieved by the proposed algorithm.

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Queueing Analysis in Healthcare

Cited by 145 publications

References 17 publications

Patient Choice in Acute Care

Patient Choice in Acute Care

Trade-offs in operating room planning for electives and emergencies: A review

Efficient simulation of tail probabilities in a queueing model with heterogeneous servers

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