Using social network analysis to model palliative care

In the grand scheme, the whole care system can be seen as a social network consisting of patients and care providers. This representation provides an opportunity to apply social network analysis techniques to enhance the topology of the system and improve its efficiency. In this paper, we propose a novel computational agent-based model to address this problem by extending the agent's capabilities using the benefits of the social network. We assume that each patient agent can cover its disabilities and perform its desired tasks through collaboration with other agents. The primary objective is to optimize a dynamic, personalized care pathway system that will support palliative care within a community eco-system. Testing the ability of the system to match social support agents with personal preferences, needs, and capabilities is the second goal of this research work. In addition, we are going to measure the impact of the system on perceived quality of life, social connectedness, caregiver burden, and care satisfaction. The performance and functionality of our proposed model have been evaluated using various synthetic and a real palliative networks. The results demonstrate a significant reduction in the operational costs and enhancement of the service quality.