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The aim of this research study is to develop a queue assessment model to evaluate the inflow of walk-in outpatients in a busy public hospital of an emerging economy, in the absence of appointment systems, and construct a dynamic framework dedicated towards the practical implementation of the proposed model, for continuous monitoring of the queue system.

The current study utilizes data envelopment analysis (DEA) to develop a combined queuing–DEA model as applied to evaluate the wait times of patients, within different stages of the outpatients' department at the Combined Military Hospital (CMH) in Lahore, Pakistan, over a period of seven weeks (23rd April to 28th May 2014). The number of doctors/personnel and consultation time were considered as outputs, where consultation time was the non-discretionary output. The two inputs were wait time and length of queue. Additionally, VBA programming in Excel has been utilized to develop the dynamic framework for continuous queue monitoring.

The inadequate availability of personnel was observed as the critical issue for long wait times, along with overcrowding and variable arrival pattern of walk-in patients. The DEA model displayed the “required” number of personnel, corresponding to different wait times, indicating queue build-up.

The current study develops a queue evaluation model for a busy outpatients' department in a public hospital, where “all” patients are walk-in and no appointment systems. This model provides vital information in the form of “required” number of personnel which allows the administrators to control the queue pre-emptively minimizing wait times, with optimal yet dynamic staff allocation. Additionally, the dynamic framework specifically targets practical implementation in resource-poor public hospitals of emerging economies for continuous queue monitoring.

In order to provide access to care in a timely manner, it is necessary to effectively manage the allocation of limited resources. such as beds. Bed management is a key to the effective delivery of high quality and low-cost healthcare. The purpose of this paper is to develop a discrete event simulation to assist in planning and staff scheduling decisions.

A discrete event simulation model was developed for a hospital system to analyze admissions, patient transfer, length of stay (LOS), waiting time and queue time. The hospital system contained 50 beds and four departments. The data used to construct the model were from five years of patient records and contained information on 23,019 patients. Each department’s performance measures were taken into consideration separately to understand and quantify the behavior of departments individually, and the hospital system as a whole. Several scenarios were analyzed to determine the impact on reducing the number of patients waiting in queue, waiting time and LOS of patients.

Using the simulation model, it was determined that reducing the bed turnover time by 1 h resulted in a statistically significant reduction in patient wait time in queue. Further, reducing the average LOS by 10 h results in statistically significant reductions in the average patient wait time and average patient queue. A comparative analysis of department also showed considerable improvements in average wait time, average number of patients in queue and average LOS with the addition of two beds.

This research highlights the applicability of simulation in healthcare. Through data that are often readily available in bed management tracking systems, the operational behavior of a hospital can be modeled, which enables hospital management to test the impact of changes without cost and risk.

This study is carried out in one public and one private health-care centers based on different probabilities of patient’s no-show rate. The present study aims to determine the optimal queuing system capacity so that the expected total cost is minimized.

In this study an M/M/1/K queuing model is used for analytical properties of optimal queuing system capacity and appointment window so that total costs of these cases could be minimized. MATLAB software version R2014a is used to code the model.

In this paper, the optimal queuing system capacity is determined based on the changes in effective parameters, followed by a sensitivity analysis. Total cost in public center includes the costs of patient waiting time and rejection. However, the total cost in private center includes costs of physician idle time plus costs of public center. At the end, the results for public and private centers are compared to reach a final assessment.

Today, determining the optimal queuing system capacity is one of the most central concerns of outpatient clinics. The large capacity of the queuing system leads to an increase in the patient’s waiting-time cost, and on the other hand, a small queuing system will increase the cost of patient’s rejection. The approach suggested in this paper attempts to deal with this mentioned concern.

The purpose of this paper is to carry out queuing analysis to analyse patient load in outpatient and inpatient services to facilitate more realistic resource planning.

The paper adopts an analytical approach based on real life data (e.g. not a priori or an academic one where data are mingled to fit a theoretical stance) in accordance with the service level prescribed by the hospital administration. A service level is usually specified in terms of admissible range of queuing characteristics, such as mean patient waiting time, reduction of inordinate delays, incidences of minimum delays, average queue length, etc. which the management of a health organisation may decide to aim and control.

Queuing analysis reported in this case study provides a basis for estimating medical staff size and number of beds, which are two very important resources for outpatient and inpatient services in a large hospital, and all other hospital resources in one way or another depend on them.

The study challenges and aims to replace thumb‐rule approaches, which can be very conveniently carried out with efficient computer aids available at present for any hospital. Queuing analysis provides valuable insights into a hospital system, though it may not be the best approach as several underlying assumptions may not always hold true. In hospitals, for example, there can be several interacting queues, many of which could be cyclic with interaction among them. Accordingly, treatment of each queue individually, independent of others may not be a valid assumption.

Medical staff (doctors) and beds are very basic hospital resources, which largely depend on the hospital load in terms of arrival rates of patients in outpatient and inpatient services. When hospitals are adequately staffed and equipped in terms of beds and other key resources, it is unlikely that patients will turn away to other hospitals for treatment and there will be all round satisfaction with the hospital performance.

The authors do not claim the findings to be novel or unique but rather more well‐documented and comprehensive in coverage than available in existing literature. The practice‐based themes such as this well‐documented case study may evoke global interest as a multiplier effect for using such methodologies for resource planning in hospitals.

The purpose of this study is twofold: exploring new queue-based variables enabled by process mining and evaluating their impact on the accuracy of waiting time prediction. Such queue-based predictors that capture the current state of the emergency department (ED) may lead to a significant improvement in the accuracy of the prediction models.

Alongside the traditional variables influencing ED waiting time, the authors developed new queue-based predictors exploiting process mining. Process mining techniques allowed the authors to discover the actual patient-flow and derive information about the crowding level of the activities. The proposed predictors were evaluated using linear and nonlinear learning techniques. The authors used real data from an ED.

As expected, the main results show that integrating the set of predictors with queue-based variables significantly improves the accuracy of waiting time prediction. Specifically, mean square error values were reduced by about 22 and 23 per cent by applying linear and nonlinear learning techniques, respectively.

Accurate estimates of waiting time can enable the ED systems to prevent overcrowding e.g. improving the routing of patients in EDs and managing more efficiently the resources. Providing accurate waiting time information also can lead to decreased patients’ dissatisfaction and elopement.

The novelty of the study relies on the attempt to derive queue-based variables reporting the crowding level of the activities within the ED through process mining techniques. Such information is often unavailable or particularly difficult to extract automatically, due to the characteristics of ED processes.

This paper aims to propose an approach by human and material resources combination to reduce hospitals crowding. Hospitals crowding is becoming a serious problem. Many research works present several methods and approaches to deal with this problem. However, to the best of the authors’ knowledge – after a deep reading of literature – in all the proposed approaches, human and material resources are studied separately while they must be combined (to a given number of material resources an optimal number of human resources must be assigned and vice versa) to reflect reality and provide better results.

Hospital inpatient unit is chosen as framework. This unit crowding reduction is carried out by its capacity increasing. Indeed, inpatient unit modeling is performed to find the adequate combinations of human and material resources numbers insuring this unit stability and providing optimal service rates. At first, inpatient unit is modeled using queuing networks and considering only two resources (beds and nurses). Then, the obtained service rate formula is improved by including other resources and parameters using Baskett, Chandy, Muntz and Palecios (BCMP) queuing networks. This work is applied to “Princess Lalla Meryem” hospital inpatient unit.

Results are patients’ average number reduction by an average (in each block) of three patients, patients’ average waiting time reduction by an average of 9.98 h and non-admitted patients (to inpatient wards) access percentage of 39.26 per cent on average.

Previous works focus their studies on either human resources or material resources. Only a few works study both resources types, but separately. The context of those studies does not meet the real hospital context (where human resources are combined with material resources). Therefore, the provided results are not very reliable. In this paper, an approach by human and material resources combination is proposed to increase inpatient unit care capacity. Indeed, this approach consists of developing inpatient unit service rate formula in terms of human and material resources numbers.

Overwork and overcrowding in some periods was an important issue for the out‐patient department of a local hospital in Chia‐Yi in Taiwan. The hospital administrators wanted to manage the patient flow effectively. Describes a study which focused on the utilization of doctors and staff in the out‐patient department, the time spent in the hospital by an out‐patient, and the length of the out‐patient queue. Explains how a computer simulation model was developed to study how changes in the appointment system, staffing policies and service units would affect the observed bottleneck. The results show that the waiting time was greatly reduced and the workload of the doctor was also reduced to a reasonable rate in the overwork and overcrowding periods.

Notes that patients attending public outpatient departments in Hong Kong spend a long time waiting for a short consultation, that clinics are congested and that both staff and patients are dissatisfied. Points out that experimentation of management changes in a busy clinical environment can be both expensive and difficult. Demonstrates computerized simulation modelling as a potential tool for clarifying processes occurring within such systems, improving clinic operation by suggesting possible answers to problems identified and evaluating the solutions, without interfering with the clinic routine. Adds that solutions can be implemented after they had proved to be successful on the model. Demonstrates some ways in which managers in health care facilities can benefit from the use of computerized simulation modelling. Specifically, shows the effect of changing the duration of consultation and the effect of the application of an appointment system on patients’ waiting time.

Discusses the methods used in the NHS to bring demand into balance with supply. People with minor illnesses try self‐treatments and alternative medicine. Systematic programs to identify ill people are applied to only a few illnesses. Waiting lists for elective surgery cause some richer people to take their demand to private hospitals. An analysis of such waiting lists shows that, other than this, queues are not a method of rationing but are just the effect of bad management of the actual methods, which are then discussed. The same methods are used to ration access to specialist physicians. Providing extra resources would eliminate queues only if another condition was satisfied. It is argued that providing fully adequate medical care for patients of working age, although expensive, might produce a net economic gain, whereas all care for pensioners, including medical care, gives a net economic loss. Therefore it may not be sensible for people to have inadequate medical care for the first 65 years of their lives just because it is economically impracticable for them to have fully adequate medical care when they are pensioners.

Pharmacy services start right from prescribing medicines and continue as the medication’s effect is monitored. Hospital and community pharmacy staff promote rational prescribing and medicine use. Consequentially, pharmacy is a complex and busy field. Often there are peak workload hours when patients must wait, which is associated with patient dissatisfaction that may negatively affect patient experience and the organisation’s reputation. The purpose of this paper is to enlist techniques, methods and technological advancements that have been successfully employed to reduce patient waiting time.

A database search was conducted in 2017 to locate articles addressing methods and technologies that reduce pharmacy waiting time. The literature revealed various techniques and technologies like queuing theory, tele-pharmacy, evidence-based pharmacy design, automated pharmacy systems (robotics), system modelling and simulation and the Six Sigma method for identifying potential problems associated with increased wait time.

The authors conclude that various techniques and methods, including automated queuing technology, tele-pharmacy, automated pharmacy devices/machines for quick and accurate filling and dispensing, computer simulation modelling, evidence-based pharmacy infrastructure for smooth workflow and Six Sigma can maintain customer satisfaction, reduce waiting time, attract new customers, decrease workload and improve the organisation’s reputation.

The authors conclude that various techniques and methods, including automated queuing technology, tele-pharmacy, automated pharmacy devices/machines for quick and accurate filling and dispensing, computer simulation modelling, evidence-based pharmacy infrastructure for smooth workflow and Six Sigma methodology can maintain customer satisfaction, reduce waiting time, attract new customers, decrease workload and improve the organisation’s reputation.

The authors carried out a literature search and identified the techniques that have been successfully implemented to reduce pharmacy patient waiting time and methods that can identify potential process behind medication dispensation delays.