A Digital Twin (DT) can be understood as a representation of a real asset, a virtual replica of a physical object, process, or even a system. They have been used in managing healthcare facilities, streamlining care processes, personalizing treatments, and enhancing patient recovery. The potential impact of this tool on our society and its well-being is quite significant. A quick review of the literature was carried out using the terms (‘Digital Twins’) and (‘Digital Health’), and (Health Care) with a time interval of up to 5 years (2018–23). Using the PRISMA Method, the search was conducted in six academic databases: IEEE Xplore, Dimensions, Scopus, Web of Science, PubMed, and ACM. After applying the search strings and the exclusion criteria, a total of 13 publications were identified and listed to constitute and support the discussion of this article. The selected studies were categorized into 2 groups according to their application in healthcare: A group of clinical applications, subdivided into topics on personalized care and reproduction of biological structures and another group of operational applications, subdivided into topics such as optimization of operational processes, reproduction of physical structures, and development of devices and drugs. The use of DT in healthcare presents important challenges related to data integration, privacy, and interoperability. However, trends indicate exciting potential in personalizing treatment, prevention, remote monitoring, informed decision-making, and process management, which can result in significant improvements in quality and efficiency in healthcare.

A digital twin can be understood as a representation of a real asset, in other words, a virtual replica of a physical object, process or even a system. Virtual models can integrate with all the latest technologies, such as the Internet of Things (IoT), cloud computing, and artificial intelligence (AI). Digital twins have applications in a wide range of sectors, from manufacturing and engineering to healthcare. They have been used in managing healthcare facilities, streamlining care processes, personalizing treatments, and enhancing patient recovery. By analysing data from sensors and other sources, healthcare professionals can develop virtual models of patients, organs, and human systems, experimenting with various strategies to identify the most effective approach. This approach can lead to more targeted and efficient therapies while reducing the risk of collateral effects. Digital twin technology can also be used to generate a virtual replica of a hospital to review operational strategies, capabilities, personnel, and care models to identify areas for improvement, predict future challenges, and optimize organizational strategies. The potential impact of this tool on our society and its well-being is quite significant. This article explores how digital twins are being used in healthcare. This article also introduces some discussions on the impact of this use and future research and technology development projections for the use of digital twins in the healthcare sector.

The COVID-19 pandemic, a transformative event in modern society, has disrupted routine, work, behavior, and human relationships. Organizations, amidst the chaos, have innovatively adapted to the evolving situation. However, many countries were unprepared for the magnitude of the challenge, revealing the fragility of health responses due to inadequate leadership, insufficient resources, and poor information system integration. Structural changes in health systems are imperative, particularly in leadership, governance, human resources, financing, information systems, technology, and health service provision. This research utilizes the Technological Roadmapping method to analyze the health sector, focusing on public health, drawing on articles from SCOPUS and PubMed databases, and creating a roadmap extending to 2050. The research presents three long-term scenarios based on the literature-derived roadmap and explores various alternatives, including integrated care, telemedicine, Big Data utilization, nanotechnology, and Big Tech’s AI services. The results underscore the anticipation of post-pandemic public health with high expectations, emphasizing the importance of integrating health history access, encouraging self-care, and leveraging technology for streamlined treatment. Practical implications include insights for decision makers and stakeholders to inform strategic planning and adapt to evolving industry demands, recognizing the significance of preventive services and the humanizing potential of technology.

Background: This paper presents the application of simulation to assess the functionality of a proposed Digital Twin (DT) architecture for immunisation services in primary healthcare centres. The solution is based on Industry 4.0 concepts and technologies, such as IoT, machine learning, and cloud computing, and adheres to the ISO 23247 standard.Methods: The system modelling is carried out using the Unified Modelling Language (UML) to define the workflows and processes involved, including vaccine storage temperature monitoring and population vaccination status tracking. The proposed architecture is structured into four domains: observable elements/entities, data collection and device control, digital twin platform, and user domain. To validate the system's performance and feasibility, simulations are conducted using SimPy, enabling the evaluation of its response under various operational scenarios.Results: The system facilitates the storage, monitoring, and visualisation of data related to the thermal conditions of ice-lined refrigerators (ILR) and thermal boxes. Additionally, it analyses patient vaccination coverage based on the official immunisation schedule. The key benefits include optimising vaccine storage conditions, reducing dose wastage, continuously monitoring immunisation coverage, and supporting strategic vaccination planning.Conclusion: The paper discusses the future impacts of this approach on immunisation management and its scalability for diverse public health contexts. By leveraging advanced technologies and simulation, this digital twin framework aims to improve the performance and overall impact of immunization services.

Introduction: This paper presents a proposal for the modelling and reference architecture of a digital twin for immunisation services in primary health care centres. The system leverages Industry 4.0 concepts and technologies, such as the Internet of Things (IoT), machine learning, and cloud computing, to improve vaccination management and monitoring.Methods: The modelling was conducted using the Unified Modelling Language (UML) to define workflows and processes such as temperature monitoring of storage equipment and tracking of vaccination status. The proposed reference architecture follows the ISO 23247 standard and is structured into four domains: observable elements/entities, data collection and device control, digital twin platform, and user domain.Results: The system enables the storage, monitoring, and visualisation of data related to the immunisation room, specifically concerning the temperature control of ice-lined refrigerators (ILRs) and thermal boxes. An analytic module has been developed to monitor vaccination coverage, correlating individual vaccination statuses with the official vaccination calendar.Discussion: The proposed digital twin improves vaccine temperature management, reduces vaccine dose wastage, monitors the population’s vaccination status, and supports the planning of more effective immunisation actions. The article also discusses the feasibility, potential benefits, and future impacts of deploying this technology within immunisation services.

A Digital Twin can be understood as a representation of a real asset, in other words, a virtual replica of a physical object, process or even a system. As virtual models, they can integrate with all the latest technologies, such as the Internet of Things (IoT), Cloud Computing and Artificial Intelligence (AI), among others. Digital twins have applications in a wide range of sectors, from manufacturing and engineering to healthcare. They have been used in managing healthcare facilities , streamlining care processes, personalizing treatments , and enhancing patient recovery . By analyzing data from sensors and other sources, healthcare professionals can develop virtual models of patients, organs, and human systems, experimenting with various strategies to identify the most effective approach. This approach can lead to more targeted and efficient therapies while reducing the risk of collateral effects . Digital twin technology can also be used to generate a virtual replica of a hospital to review operational strategies, capabilities, personnel, and care models to identify areas for improvement, predict future challenges, and optimize organizational strategies. The potential impact of this tool on our society and its well-being is quite significant. This article explores how digital twins are being used in healthcare. The article also introduces some discussions on the impact of this use and future research and technology development projections for the use of digital twins in the healthcare sector.