Healthcare queueing models.

Healthcare systems differ intrinsically from manufacturing systems. As such, they require a distinct modeling approach. In this article, we show how to construct a queueing model of a general class of healthcare systems. We develop new expressions to assess the impact of service outages and use the resulting model to approximate patient flow times and to evaluate a number of practical applications. We illustrate the devastating impact of service interruptions on patient flow times and show the potential gains obtained by pooling hospital resources. In addition, we present an optimization model to determine the optimal number of patients to be treated during a service session.Operations research; Health care evaluation mechanisms; Organizational efficiency; Management decision support systems; Time management; Queueing theory;

(R1975) MAP/PH(1), PH(2)/2 Queue with Multiple Vacation, Optional Service, Consultations and Interruptions

Two types of services are explored in this paper: regular server and main server, both of which provide both regular and optional services. Customers arrive using the Markovian Arrival Process (MAP), and service time is allocated based on phase type. The regular server uses the main server as a resource. Customers’ service at the primary server is disrupted as a result. When the queue size is empty, the main server can take several vacations. This system has been represented as a QBD Process that investigates steady state with the use of matrix analytic techniques, employing finite-dimensional block matrices. Our model’s waiting time distribution has been examined in more detail during the busy times. The system’s key parameters are assessed, and a few graphs and numerical representations are constructed

Modeling a healthcare system as a queueing network:The case of a Belgian hospital.

The performance of health care systems in terms of patient flow times and utilization of critical resources can be assessed through queueing and simulation models. We model the orthopaedic department of the Middelheim hospital (Antwerpen, Belgium) focusing on the impact of outages (preemptive and nonpreemptive outages) on the effective utilization of resources and on the flowtime of patients. Several queueing network solution procedures are developed such as the decomposition and Brownian motion approaches. Simulation is used as a validation tool. We present new approaches to model outages. The model offers a valuable tool to study the trade-off between the capacity structure, sources of variability and patient flow times.Belgium; Brownian motion; Capacity management; Decomposition; Health care; Healthcare; Impact; Model; Models; Performance; Performance measurement; Queueing; Queueing theory; Simulation; Stochastic processes; Structure; Studies; Systems; Time; Tool; Validation; Variability;

An analytical comparison of the patient-to-doctor policy and the doctor-to-patient policy in the outpatient clinic

Outpatient clinics traditionally organize processes such that the doctor remains in a consultation room, while patients visit for consultation, we call this the Patient-to-Doctor policy. A different approach is the Doctor-to-Patient policy, whereby the doctor travels between multiple consultation rooms, in which patients prepare for their consultation. In the latter approach, the doctor saves time by consulting fully prepared patients. We compare the two policies via a queueing theoretic and a discrete-event simulation approach. We analytically show that the Doctor-to-Patient policy is superior to the Patient-to-Doctor policy under the condition that the doctor’s travel time between rooms is lower than the patient’s preparation time. Simulation results indicate that the same applies when the average travel time is lower than the average preparation time. In addition, to calculate the required number of consultation rooms in the Doctor-to-Patient policy, we provide an expression for the fraction of consultations that are in immediate succession; or, in other words, the fraction of time the next patient is prepared and ready, immediately after a doctor finishes a consultation.We apply our methods for a range of distributions and parameters and to a case study in a medium-sized general hospital that inspired this research

Analytical models to determine room requirements in outpatient clinics

Outpatient clinics traditionally organize processes such that the doctor remains in a consultation room while patients visit for consultation, we call this the Patient-to-Doctor policy (PtD-policy). A different approach is the Doctor-to-Patient policy (DtP-policy), whereby the doctor travels between multiple consultation rooms, in which patients prepare for their consultation. In the latter approach, the doctor saves time by consulting fully prepared patients. We use a queueing theoretic and a discrete-event simulation approach to provide generic models that enable performance evaluations of the two policies for different parameter settings. These models can be used by managers of outpatient clinics to compare the two policies and choose a particular policy when redesigning the patient process.We use the models to analytically show that the DtP-policy is superior to the PtD-policy under the condition that the doctor’s travel time between rooms is lower than the patient’s preparation time. In addition, to calculate the required number of consultation rooms in the DtP-policy, we provide an expression for the fraction of consultations that are in immediate succession; or, in other words, the fraction of time the next patient is prepared and ready, immediately after a doctor finishes a consultation. We apply our methods for a range of distributions and parameters and to a case study in a medium-sized general hospital that inspired this research

Logistics performances of health care system using queue analysis

As there is a very high demand for health service that exceeds the available capacity, the public healthcare centers are overwhelmed with the long queues or they are delivering the service with relatively very low consultation time. In the existing conditions, patients go as early as they can to the healthcare facilities, waiting in queue, even before the opening and had to wait long time for examination, consultation and diagnosis. However, due to high number of patients at the outpatient departments relative to the number of physicians, it results in an increased workload on the physicians and it shortens the patient consultation time, which has an impact on the patients’ health. The main objective of this research was to study the logistic performances of the healthcare system using queuing analysis. This research used three key performance indicators namely, patient queue length, patient waiting time and consultation time length. The performance evaluation was conducted based on data from patients who visited 69 clinical, surgical and diagnosis departments at the outpatient clinics of the hospital. Queue analysis was performed to determine the operational characteristics using a queue scenario with Poisson arrival, exponential service, infinite population, First Comes First Served (FCFS) discipline and multiple server arrangement. The study showed that the patients’ arrival rate highly exceeded the service rate, in each respective clinical department. The outpatient clinics at the SPHMMC achieved an average total waiting time of 92 minutes to get consultation and nearly 70% of the patients waited for more than 95 minutes. The consultation time was as low as 5.71 minute at the Medical clinic and 6.16 minute at the Ophthalmology clinic and around 60% of the patients saw the doctor for a time less than 10 minutes. Therefore, this research recommends addressing the gaps in human resources and logistical supplies, to implement and enforce a staggered patient scheduling and appointment system and to have serious intervention and control on the dual practice, to ensure a smooth clinic process and to reduce waiting times

Reduce fluctuations in capacity to improve the accessibility of radiotherapy treatment cost-effectively

This paper is motivated by a case study to reduce the throughput times for radiotherapy treatment. The goal is to find a cost-effective way to meet future throughput targets. A combination of queuing theory and computer simulation was used. First, computer simulation to detect the bottleneck(s) in a multi-step radiotherapy process. Despite, the investment in an additional linear accelerator, the main bottleneck turned out to be the outpatient department (OPD). Next, based on queuing theory, waiting times were improved by reducing the fluctuations in the OPD capacity. Computer simulation was used again to quantify the effect on the total throughput time of a radiotherapy patient. The results showed a reduction in both access times as well as waiting times prior to the consecutive steps: the preparation phase and actual treatment. The paper concludes with practical suggestions on how to reduce the fluctuations in capacity, and seems of interest for other radiotherapy departments or other multi-step situations in a hospital

Queueing System Analysis A case study

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáThe application of simulation tools in the construction of models that represent areal system is increasingly important in the analysis and optimization of production and management processes. This study aims to analyze and optimize a queuing system in a Health Care Unit in the district of Bragança, Portugal. In particular, the check-in process of patients/ customers in the health care unit is analyzed to carry out complementary diagnostic tests, treatments and external consultations in different medical specialities. The health care unit is faced with longer check-in waiting times than desired, so it is intended with this work to find solutions to increase the efficiency of the system. Thus, quantitative models for the management of queues will be approached and studied to indicate the expected performance of the system without having to quantify the waiting cost. Given the complexity of the system, the simulation technique will be used to develop different types of mathematical and logical models that reproduce the behaviour of the system under study. The system was modelled using the Simio R software, which is a tool for modelling discrete events by simulation, based on intelligent objects. A validation model was used to simulate the real system as a parameter for comparing the results obtained from the analysis of 4 alternative scenarios that present solutions for optimizing the queues. The results presented in this study can be used as a decision method and implemented following the reality of the Health Care Unit.A aplicação de ferramentas de simulação na construção de modelos que representem um sistema real tem se mostrado cada vez mais importante na análise e otimização de processos produtivos e administrativos. Esse estudo tem como objetivo a análise e otimização de um sistema de filas de espera numa unidade de saúde do distrito de Bragança, Portugal. Em particular, é analisado o processo de check-in dos pacientes/utentes na unidade de saúde para a realização de exames complementares de diagnóstico, tratamentos e consultas externas nas diferentes especialidades médicas. A unidade de saúde depara-se com tempos de espera de check-in superiores ao desejado, pelo que se pretende com este trabalho encontrar soluções que permitam aumentar a eficiência do sistema. Assim, serão abordados e estudados modelos quantitativos para a gestão de filas de espera com o propósito de indicar o desempenho esperado do sistema sem que seja necessário quantificar o custo de espera. Dada a complexidade do sistema, será usada a técnica de simulação para o desenvolvimento de diferentes tipos de modelos matemáticos e lógicos que reproduzam o comportamento do sistema em estudo. O sistema foi modelado utilizando o software Simio R , que é uma ferramenta de modelagem de eventos discretos por simulação, baseada em objetos inteligentes. Para simular o sistema real foi construído um modelo de validação utilizado como parâmetro de comparação dos resultados obtidos a partir da análise de 4 cenários alternativos que apresentam soluções para otimização das filas de espera. Os resultados apresentados nesse estudo podem ser utilizados como método de decisão e implementados em acordo com a realidade da unidade de saúde

A Priority-based Fair Queuing (PFQ) Model for Wireless Healthcare System

Healthcare is a very active research area, primarily due to the increase in the elderly population that leads to increasing number of emergency situations that require urgent actions. In recent years some of wireless networked medical devices were equipped with different sensors to measure and report on vital signs of patient remotely. The most important sensors are Heart Beat Rate (ECG), Pressure and Glucose sensors. However, the strict requirements and real-time nature of medical applications dictate the extreme importance and need for appropriate Quality of Service (QoS), fast and accurate delivery of a patient’s measurements in reliable e-Health ecosystem. As the elderly age and older adult population is increasing (65 years and above) due to the advancement in medicine and medical care in the last two decades; high QoS and reliable e-health ecosystem has become a major challenge in Healthcare especially for patients who require continuous monitoring and attention. Nevertheless, predictions have indicated that elderly population will be approximately 2 billion in developing countries by 2050 where availability of medical staff shall be unable to cope with this growth and emergency cases that need immediate intervention. On the other side, limitations in communication networks capacity, congestions and the humongous increase of devices, applications and IOT using the available communication networks add extra layer of challenges on E-health ecosystem such as time constraints, quality of measurements and signals reaching healthcare centres. Hence this research has tackled the delay and jitter parameters in E-health M2M wireless communication and succeeded in reducing them in comparison to current available models. The novelty of this research has succeeded in developing a new Priority Queuing model ‘’Priority Based-Fair Queuing’’ (PFQ) where a new priority level and concept of ‘’Patient’s Health Record’’ (PHR) has been developed and integrated with the Priority Parameters (PP) values of each sensor to add a second level of priority. The results and data analysis performed on the PFQ model under different scenarios simulating real M2M E-health environment have revealed that the PFQ has outperformed the results obtained from simulating the widely used current models such as First in First Out (FIFO) and Weight Fair Queuing (WFQ). PFQ model has improved transmission of ECG sensor data by decreasing delay and jitter in emergency cases by 83.32% and 75.88% respectively in comparison to FIFO and 46.65% and 60.13% with respect to WFQ model. Similarly, in pressure sensor the improvements were 82.41% and 71.5% and 68.43% and 73.36% in comparison to FIFO and WFQ respectively. Data transmission were also improved in the Glucose sensor by 80.85% and 64.7% and 92.1% and 83.17% in comparison to FIFO and WFQ respectively. However, non-emergency cases data transmission using PFQ model was negatively impacted and scored higher rates than FIFO and WFQ since PFQ tends to give higher priority to emergency cases. Thus, a derivative from the PFQ model has been developed to create a new version namely “Priority Based-Fair Queuing-Tolerated Delay” (PFQ-TD) to balance the data transmission between emergency and non-emergency cases where tolerated delay in emergency cases has been considered. PFQ-TD has succeeded in balancing fairly this issue and reducing the total average delay and jitter of emergency and non-emergency cases in all sensors and keep them within the acceptable allowable standards. PFQ-TD has improved the overall average delay and jitter in emergency and non-emergency cases among all sensors by 41% and 84% respectively in comparison to PFQ model