Solving general multi-class closed queuing networks using parametric decomposition

Nursing homes face ever-tightening healthcare budgets and are searching for ways to increase the efficiency of their healthcare processes without losing sight of the needs of their residents. Optimizing the allocation of care workers plays a key role in this search as care workers are responsible for the daily care of the residents and account for a significant proportion of the total labor expenses. In practice, the lack of reliable data makes it difficult for nursing home managers to make informed staffing decisions. The focus of this study lies on the 'care on demand' process in a Belgian nursing home. Based on the analysis of real-life 'call button' data, a queueing model is presented which can be used by nursing home managers to determine the number of care workers required to meet a specific service level. Based on numerical experiments an 80/10 service level is proposed for this nursing home, meaning that at least 80 percent of the clients should receive care within 10 minutes after a call button request. To the best of our knowledge, this is the first attempt to develop a quantitative model for the 'care on demand' process in a nursing home.

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“…In that case, a viable option is to rely on relations between open and closed queueing systems, as in e.g. [30,36].…”

Section: Remarkmentioning

confidence: 99%

Care on demand in nursing homes: a queueing theoretic approach

Eeden¹,

Moeke

Bekker³

2014

Health Care Manag Sci

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“…When the stationary distribution does not have a product-form, the exact analysis may not be computationally tractable, and we may turn to approximations [2,16,4,18], bounds [20,8], or simulation [1].…”

Section: Introductionmentioning

confidence: 99%

Perfect Sampling for Multiclass Closed Queueing Networks

Bouillard¹,

Bušíć²,

Rovetta³

2015

Quantitative Evaluation of Systems

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In this paper we present an exact sampling method for multiclass closed queuing networks. We consider networks for which stationary distribution does not necessarily have a product form. The proposed method uses a compact representation of sets of states, that is used to derive a bounding chain with significantly lower complexity of one-step transition in the coupling from the past scheme. The coupling time of this bounding chain can be larger than the coupling time of the exact chain, but it is finite in expectation. Numerical experiments show that coupling time is close to that of the exact chain. Moreover, the running time of the proposed algorithm outperforms the classical algorithm.

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“…Fortin et al (2005) mentioned the scarcity of research in the area of multi-comorbidity patients in comparison to specific diseases, even though the behavior of these patients can significantly affect the efficiencies of healthcare systems. The queuing method that can be used to show the differences between multi-comorbidities is multi-class closed queuing networks, which provides a convenient framework with which to evaluate the impact of population constraints on the stochastic interactions between different classes at various nodes of the network (Satyam et al, 2013 Satyam et al (2013) mentioned that "this approach is based on parametric characterization of the traffic processes in the network, which uses two-moment approximations to estimate performance measures at individual nodes" (Satyam et al, 2013).…”

Section: Optimizing Panel Size Using Queuing Theorymentioning

confidence: 99%

“…The service time distribution of a class r at a node j is characterized by two parameters: the mean, rj  , and squared coefficient of variation, 2 .As a result, the service rate, is equal to  −1 (Satyam et al, 2013). The arrival process is described by the mean and SCV parameters ( , −1 2 ) (Satyam et al, 2013), and the arrival time distribution and service time distribution for a multi-class queuing network can be estimated.…”

Section: Optimizing Panel Size Using Queuing Theorymentioning

confidence: 99%

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Modeling Clinic Utilization by Considering Panel size, Multicomorbidities and Patient Scheduling

Kiani¹

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Modeling Clinic Utilization by Considering Panel size, Multi-comorbidities and Patient Scheduling Mahsa Kiani Many appointment-based clinical systems experience long waiting times. Consequently, these systems experience higher rates of cancellation or no-show. This problem creates dissatisfaction among customers, as well as inefficiencies in healthcare systems, but more importantly, increases medical complications due to postponement of care. As an added complication, sometimes no-showing patients will reschedule appointments and the rate and reschedule discipline can have significant effects on overall patient satisfaction and system efficiency. In this study, a one server, multi-class queuing network model is proposed in which patients have a probability of no-showing as well as a rescheduling rate. No-show and rescheduling rates are computed based on the current backlog of the system. This model categorizes patients into different classes, based on number of comorbidities, with individual service times and arrival rates. In addition to considering the differences of various classes of patients, the model also decreases the under-utilization of resources by considering the noshow and rescheduling rate of customers. The purpose of the model is to determine the number of patients representing the panel size allocated to a specific physician, with recommendations for adding physicians to alleviate increasing backlogs based on increasing rates of comorbidity. In the second section of the study, the appointment system is simulated, and its results are compared with those generated by queuing theory. A preference model is then introduced which gives patients an option of choosing among all available appointments. The simulation results suggest that allowing patients to choose their favorite appointment time does not affect overall system utilization.

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Solving general multi-class closed queuing networks using parametric decomposition

Cited by 11 publications

References 17 publications

Care on demand in nursing homes: a queueing theoretic approach

Care on demand in nursing homes: a queueing theoretic approach

Perfect Sampling for Multiclass Closed Queueing Networks

Modeling Clinic Utilization by Considering Panel size, Multicomorbidities and Patient Scheduling

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