Smart health practices: Strategies to improve healthcare efficiency through digital twin technology

Abstract

A digital twin (DT) is a virtual representation of a real-world object that has dynamic, bidirectional connections between the real-world object and its digital domain. With the advent of Industry 4.0, DT technology was initially applied in the engineering and manufacturing sectors, but recent research indicates DT may also be useful within the healthcare sector. The purpose of this study was to determine the potential applications of DT technology in the healthcare sector and offer suggestions for its effective implementation by healthcare institutions to increase service efficiency. Based on a review of the literature, we developed a model to demonstrate the applications of DTs on public and personal health. A questionnaire with five points Likert scale was then designed based on this model. Data were collected through an online survey conducted with 306 participants. To verify our hypothesized correlations among the constructs, structural equation modeling was used. The findings suggested that explainable artificial intelligence-based early diagnosis, simulation model-based vaccination, artificial intelligence location technology, sensor-based real-time health monitoring, and in silico personalized medicine are potential applications of DT that can increase healthcare efficiency. We also considered the moderating influence of (a) security and privacy and (b) certification and regulatory issues, acknowledging their pivotal roles in ensuring the successful implementation and widespread acceptance of DT technology in the field of healthcare. This study contributes to the body of knowledge in academia and offers useful insights for technologists, policymakers, and healthcare professionals who want to fully utilize DT technology to build an effective healthcare system that can adapt to the changing needs of communities and individuals.