Digital twin technology in cardiovascular care: future perspectives for nursing practice, patient management, and clinical decision-making

Background and objective Cardiovascular diseases remain the leading cause of mortality globally and continue to impose substantial clinical, economic, and organisational burdens on healthcare systems due to increasing disease complexity, recurrent hospitalisations, and the need for long-term monitoring and personalised care. Rapid advances in artificial intelligence, computational modelling, wearable biosensors, telehealth, and digital health technologies have accelerated the emergence of Digital Twin Technology (DTT) as a promising innovation in precision cardiovascular healthcare. Although DTT has gained growing attention in medicine and engineering, its implications for cardiovascular nursing practice, patient management, and clinical decision-making remain insufficiently synthesised. This scoping review aimed to explore the current applications of DTT in cardiovascular healthcare and examine its implications for cardiovascular nursing practice, patient management, clinical decision-making, and future healthcare delivery. Methods Review was conducted using the Arksey and O'Malley methodological framework and reported according to the PRISMA-ScR guidelines. A comprehensive search of PubMed/MEDLINE, Scopus, Web of Science, CINAHL, Embase, IEEE Xplore, and Google Scholar was performed for studies published between 2020 and 2026. Eligible studies included literature related to digital twins, computational cardiovascular modelling, artificial intelligence-assisted cardiovascular care, wearable technologies, remote monitoring systems, and nursing implications in cardiovascular healthcare. Data were charted and synthesised narratively according to predefined research questions and thematic areas Results Thirty studies met the inclusion criteria. The findings demonstrated that DTT is increasingly applied in heart failure monitoring, arrhythmia prediction, telecardiology, smart ICU systems, virtual cardiac modelling, personalised rehabilitation, and population-level cardiovascular risk prediction. Artificial intelligence, machine learning, wearable biosensors, cloud computing, electronic health records, and predictive analytics were the most commonly reported technological components. The review further identified important implications for cardiovascular nursing, including transition from reactive to predictive care, expansion of telehealth nursing, enhanced data-driven clinical decision-making, and the growing need for digital competency. However, ethical concerns, cybersecurity risks, interoperability challenges,infrastructure costs, workforce preparedness, and limited nursing-focused evidence remain significant barriers. Conclusion DTT has the potential to transform cardiovascular healthcare through predictive, personalised, and continuously adaptive models of care. Strengthening nursing leadership, digital competency and interdisciplinary collaboration is essential for integration of DTT into future cardiovascular practice.