End-of-life care reimagined: the promise of digital health technologies for aging populations – systematic review

Purpose This study aims to systematically examine how digital health technologies support end-of-life (EOL) care for older adults, focusing on the essential requirements, benefits, challenges and emerging technological opportunities that shape equitable and person-centered care. Design/methodology/approach Following PRISMA guidelines, a systematic review was conducted using studies published from 2010 onward across PubMed, ScienceDirect and MEDLINE. Twenty-eight studies met the eligibility criteria. Data extraction and thematic synthesis were performed to identify four overarching domains: key implementation requirements, benefits of digital health interventions, challenges and gaps in current EOL practice and advancements in AI, VR and robotics. The review analyzed diverse study designs, including randomized trials, qualitative studies, feasibility studies and systematic reviews, to capture a holistic understanding of digital health use in EOL contexts. Findings This study identified several requirements such as technological infrastructure, digital literacy, privacy protections and integration with existing care models. Key benefits include enhanced communication and symptom monitoring, increased access to care and reinforced caregiver and bereavement support. Challenges are digital divide issues, financial constraints and limited long-term evidence. Some emerging technology such as AI predictive analytics, VR-based therapies and social robotics, though still needs substantial evidence, to advance comfort, personalization and relief for caregivers. Originality/value This study offers a multidisciplinary and wide-ranging synthesis of digital health technologies within EOL care. It pushes the boundaries of knowledge and incorporates the cultural, emotional and accessibility alongside technological elements. It identifies clear gaps and sets the groundwork for future digital health research focused on specific regions and population aging near EOL to be longitudinal and scalable.