William Professor and Director of the Department of Community Health and Epidemiology at Queens University and Director of the Division of Cancer Care and Epidemiology at Queens Cancer Research Institute gave a presentation on the development of standards for access to the radiotherapy. Its objectives were To report on the progress being made on the establishment of benchmarks based on evidence for radiotherapy; Demonstrate that wait times alone are an unsatisfactory able to determine access to care; Remind participants and the need to take effective corrective action because system queue software otherwise the measurement exercises and control will be useless. MacKillop began his presentation by describing the use and effectiveness of radiation queue service solution therapy for cancer treatment for localized cancer or to relieve symptoms in the case of terminally ill patients.

He gave an overview of the complex methodological approach used by the team, which includes the use of expert opinion, an analysis of existing landmarks, radio biological models based on the system queue software relationship between the tumor volume and the risk of recurrence, and the rate of growth of the tumor, direct observations on the relationship between waiting time and recurrence in clinical settings queue service solution and in the opinions of patients. Risks associated with radiation-related delays are fully described in the book by and colleague.

Emphasized that waiting times are only one aspect of access to care and they have limitations as a measure of accessibility to radiotherapy because they put undue emphasis on problems related to the offer d., availability, neglecting the problems related to the application d., awareness, spatial accessibility, affordability and installations. In addition, access to radiotherapy indicators include waiting time and utilization rates. The latter helps to identify issues related to demand, particularly in terms of spatial accessibility and awareness services. We must therefore consider the two sets of indicators. William Hodge Associate Professor of Ophthalmology, the Eye Institute of the University of Ottawa presented a comprehensive review of the situation of wait times for cataract treatment prepared by the Eye Institute University of Ottawa and the systematic study of Thomas C.

Center. The research team examined the issue of assets and liabilities wait times system queue software nationally and internationally, and studied how they are associated with specific system queue software outcomes ex., Visual acuity, quality indexs life and vision, adverse events, patient satisfaction with regard to deadlines and attitudes of family doctors with regard to deadlines. Hodge and colleagues found that there was little difference in how passive waiting time or waiting times measured for sight restoration are measured. They found that only two countries had active wait time measures or those set up by following a policy for active wait time Sweden where they were subsequently abandoned system queue software and UK where they are pending. Hodge then described two studies that suggest there is a link between wait times and outcome

measures.

There are also many variables that affect the result of waiting times, system queue software as operational difficulties or inability to work. Discussion One participant stressed that there is relatively little data on the effectiveness of radiation treatment for different types of cancer and stated that the use of radiotherapy could be reduced if we proceeded to an analysis review the number of required treatment. Replied by saying that the way radiation therapy is prescribed has an impact, but that the guidelines for radiotherapy are of a legislative nature; Therefore, the appropriateness of the use is very important The queue service solution discussions focused on the precise nature of care guarantees.

This is a logical consequence of the fact that queue management the length is geometrically determined. In this precise case, of all trains will have a length of one package, all other trains will consist of more packages. It is therefore the case that a maximum of of all arriving trains can have a minimum delay of one package, for the remaining trains, the length, and thus also the minimum possible delay, will be higher. Attached shows how the lengths of the trains will be divided. The most important finding is that it can be demonstrated very clearly on this graph that the waiting line discipline with reservation see this locations can indeed be considered as a middle way between the AP and FCFS waiting discipline.

We note that for waiting areas with reservation locations online queue management system the probability function of the package and train delays is closely linked to the probability function that forms for the AP waiting discipline. The more reservation locations are included in the queue, the greater the value on the x axis for which a difference is observed in the probability functions for the AP waiting discipline and the waiting line discipline with the reservation locations. When the values on the x axis become very large, the probability functions of the parcel and train delays for the guard cycles with reservation places will then lapse with the same proportion as is observed with the FCFS guarding discipline. In summary, it can therefore first be stated that for small values on the x axis, the probability functions of the waiting areas with reservation locations follow the characteristics of the AP waiting discipline, for large values on the x axis the waiting disciplines then follow the characteristics of the FCFS. Guardian discipline.

Secondly, it can be stated that the more reservation online queue management system locations are included in the queue, the longer the characteristics of the chance function of the waiting line discipline with reservation places are related to the characteristics of the probability function of the AP waiting discipline. This observation supra has already been done in an article by De Must, for an arrival process that is not determined by train arrivals. By carrying out a simulation with train tickets, it could already be established that the findings are also valid for an arrival process with train tickets. How the plotted graph evolves by changing the characteristics of the trains is examined in the following paragraphs. We will study these changes in function of the train delays.

In a first part of this section, we examine how a customer queue management system different distribution of the percentage type and type trains influences the results. In the left hand, the percentage of type and type trains was determined at and, respectively. In the right hand this distribution was adjusted to for both types. All other parameters concerning length and operating time were not adjusted. This adjustment changes the distance between the various probability functions for package and train delays. For type trains, the distance between the probability functions decreases as more type packages and trains arrive in the system. Because the number of type packages and trains decreases, the distance between these probability functions becomes greater. We can therefore conclude that the smaller the partial load of a type train, the greater the distance between the probability functions will be. In a second part, we examine how the average train length has an influence on the graph of the probability functions for parcel and train delays of the various guarding customer queue management system disciplines.