Using social network analysis to model palliative care

Abstract

Palliative and end-of-life care are special types of healthcare that focus on improving the quality of life of patients who are living with life-threatening illness or nearing their end of life. The primary goal here is to provide various support services to help the patients to maintain an active life and dignity. Assuming there are cost and resource limitations for delivering care within the system, where each care provider can support a limited number of patients, the problem can be defined as finding a set of suitable care providers with a minimum overall cost to match the needs of the maximum number of patients.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.