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Team Formation in Community-Based Palliative Care

机译:基于社区的姑息治疗的团队形成

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摘要

In this paper, a novel knowledge-based evolutionary algorithm is proposed to assemble a team of care providers for patients in community-oriented palliative care. The main objective of this research is to optimize the patient's care services and human resource allocation process. From a system perspective in palliative care, there exists a group of patients with needs who are not able to perform some of their ordinary life activities due to their limited capability, as a consequence of their disease or disorders. On the other hand, we have a group of care providers who are capable, skilled, and ready to provide a wide range of services to the patients to fulfill those needs. This poses the challenge of assigning members to a team of care providers in an optimal manner to help the patient satisfy their needs, while taking into consideration the communication, distance and contact costs. To deal with this problem, we propose a novel algorithm based on a cultural algorithm (CA) as the basis for our model for assembling an optimal team of care providers. The overall goals are to minimize the costs and increase the patient's satisfaction rate. We have evaluated our model using multiple synthetic networks and conducted comparative analysis with other existing methods. The results show that our proposed model can overcome the shortcomings posed by the existing approaches.
机译:在本文中,提出了一种新颖的基于知识的进化算法,以组装社区痛苦护理中的患者的护理提供者团队。本研究的主要目标是优化患者的护理服务和人力资源分配过程。从姑息治疗中的系统透视中,由于其疾病或疾病的结果,存在一组不得能够由于其能力而无法执行其一些普通生活活动的需求。另一方面,我们有一组有能力,熟练,准备为患者提供广泛服务的一组护理提供者,以满足这些需求。这使得将成员分配给护理提供者团队的挑战以最佳的方式帮助患者满足他们的需求,同时考虑到通信,距离和接触成本。为了解决这个问题,我们提出了一种基于文化算法(CA)的新型算法,作为我们组装最佳护理提供者团队的模型的基础。总体目标是最大限度地降低成本并提高患者的满意度。我们使用多个合成网络评估了我们的模型,并与其他现有方法进行了比较分析。结果表明,我们的拟议模型可以克服现有方法所带来的缺点。

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