Performance evaluation of respite care services through multi-agent based simulation

Abstract

Caregivers of patients with chronic diseases are undergoing a daily burnout in their lives. Although respite care seems a promising solution, no quantitative analysis has yet been provided to demonstrate its positive impact. In this article, we propose (i) a new model of caregivers' burnout evolution based on Markov chain and machine learning to model health state evolution, and (ii) a multi-agent based simulation approach to describe the burnout evolution of caregivers and the impact of respite structures on the system. Optimal capacity of respite structures is obtained through a design of experiment. Several management strategies are also tested (collaboration between structures, reservation of beds for emergent cases). Key performance indicators considered are quality of service and costs. Results show a positive impact of respite services on both quality of service and costs. The model also show a trade-off between quality of service and costs when bed reservation policies are used.