Low-priority Clients Research Articles

Waiting list management practices for home-care occupational therapy in the province of Quebec, Canada.

Referral prioritisation is commonly used in home-based occupational therapy to minimise the negative impacts of waiting, but this practice is not standardised. This may lead to inequities in access to care, especially for clients considered as low priority, who tend to bear the brunt of lengthy waiting lists. This cross-sectional study aimed to describe waiting list management practices targeting low-priority clients in home-based occupational therapy in the province of Quebec, Canada, and to investigate the association between these practices and the length of the waiting list. A structured telephone interview was conducted in 2012-2013 with the person who manages the occupational therapy waiting list in 55 home care programmes across Quebec. Questions pertained to strategies aimed at servicing low-priority clients, the date of the oldest referral and the number of clients waiting. Results were analysed using descriptive statistics and non-parametric tests. The median wait time for the oldest referral was 18 months (range: 2-108 months). A variety of strategies were used to service low-priority clients. Programmes that used no strategies to service low-priority clients (n = 16) had longer wait times (P < 0.0001) and a greater number of people on the waiting list (P = 0.006) compared with programmes that applied a maximum wait time target (n = 12). In conclusion, diverse strategies exist to allocate services to low-priority clients in home-based occupational therapy programmes. However, in programmes where none of these strategies are used, low-priority clients may be denied access to services indefinitely.

Incorporating mutation probability into priority-aware protection in optical networks

A persisting major challenge for optical network operators is to meet the various availability requirements of the different subscribed services through the deployment of effective protection strategies. Priority-aware shared protection is a promising scheme that has been proposed in the open literature as a potential approach to tackling this challenge. However, the priority-aware protection strategy is rigid in the sense that it privileges the high priority connections regardless of the low priority ones. Hence, this letter proposes to improve priority-aware protection by introducing the mutation probability parameter. This parameter expresses the likelihood that a high-priority connection be relegated temporarily to a lower priority level during recovery. In this way, the mutation-based protection strategy offers optical operators the possibility to increase the availability of their low-priority clients without violating the availability requirements of their high-priority ones. Performance of this novel protection strategy is analyzed in this letter by precisely calculating the connection availabilities resulting from its deployment.

A hybrid priority-based video-on-demand resource sharing scheme

Video-on-demand (VoD) environments frequently batch video requests to decrease I/O demand and increase throughput. Since users may leave due to waiting too long, a good video scheduling policy has to consider not only the batch size, but also the user defection probabilities and waiting times. Moreover, a practical VoD resource sharing scheme should endeavor to provide some free streams to serve a high-priority clients requests immediately, since the high-priority clients might pay for the requested video. To tackle these problems, this work proposes a hybrid resource sharing model that integrates controlled multicasting and batching. The proposed hybrid model applies a bandwidth borrowing and reserving scheme to give high-priority clients a prompt service, while still providing low-priority clients with a reasonable service. Furthermore, a novel probability model-based scheduling policy is proposed to alleviate the user defection behavior and unfairness issue. Experimental results demonstrate that the proposed resource sharing scheme is effective and feasible in terms of blocking probability of high-priority clients, the defection probability, service delay time and fairness to low-priority users.

Performance evaluation of an admission control algorithm: dynamic threshold with negotiation

An admission control algorithm for a multimedia server is responsible for determining if a new request can be accepted without violating the QoS requirements of the existing requests in the system. Most admission control algorithms treat every request uniformly and hence optimize the system performance by maximizing the number of admitted and served requests. In practice, requests might have different levels of importance to the system. Requests offering high contribution or reward to the system performance deserve priority treatment. Failure of accepting a high-priority request would incur high penalty to the system. A novel threshold-based admission control algorithm with negotiation for two priority classes of requests is proposed in our previous study. The server capacity is divided into three partitions based on the threshold values: one for each class of requests and one common pool shared by two classes of requests. Reward and penalty are adopted in the proposed system model. High-priority requests are associated with higher values of reward as well as penalty than low-priority ones. In this paper, given the characteristics of the system workload, the proposed analytical models aim to finds the best partitions, optimizing the system performance based on the objective function of the total reward minus the total penalty. The negotiation mechanism reduces the QoS requirements of several low-priority clients, by cutting out a small fraction of the assigned server capacity, to accept a new high-priority client and to achieve a higher net earning value. Stochastic Petri-Net model is used to find the optimal threshold values and two analytical methods are developed to find sub-optimal settings. The experiment results show that the sub-optimal solutions found by the proposed analytical methods are very close to optimal ones. The methods enable the algorithm to dynamically adjust the threshold values, based on the characteristics of the system workload, to achieve higher system performance.