The potential for AI to the monitoring and support for caregivers: an urgent tech-social challenge

Italy stands at the forefront of a group of high and middle-income countries currently experiencing a relatively swift progression of population ageing. Meeting the social challenges and seizing the opportunities connected with population ageing represents a complex task. The increasing gap between long-term care needs of an older population, and available formal and informal care resources is perhaps one of the most critical challenges posed by the process of population ageing to our social fabric. The actual institutional arrangements characterising long-term care provision in Italy are ill-equipped to face such a challenge. It is in this context that solutions based on both assistive technologies and artificial intelligence appear as a necessary avenue to increase the future social and economic sustainability of population ageing. “Care Sustainability in an Ageing Society” (CaSAS) is part of the larger Age-It national project, which is dedicated to better equipping and preparing Italian society through institutional, economic, social, medical, and technological solutions to face the challenges and meet the opportunities presented by rapid population ageing. Prior research has predominantly focused on utilizing artificial intelligence (AI) and assistive technology (AT) to enhance the capacity and intensity of monitoring the health conditions and activities of care receivers. Some AI applications were also implemented to assist caregivers with advice to provide care tasks or to remember caregiving routines. CaSAS seeks to complement this approach by shifting the focus toward caregivers’ skills, information, and, most importantly, their physical and mental well-being. Both informal and formal caregivers require tailored, specific advice, education, and information to better cope with caregiving tasks and the associated burden. The potential of AI and AT tools is substantial in expanding existing protocols, interventions, and best practices from occasional small-scale experiences to interventions that impact the general population, potentially yielding ground-breaking social impact. The preliminary phases of implementation of the CaSAS research program led to the formulation of five recommendations when planning and utilizing AI and AT solutions in the context of the caregiver-care receiver relation.