Prerpints.org logo
Preprint
Article

“The State of the Art of Digital Twins in Health – A Quick Review of the Literature”

Altmetrics

Downloads

145

Views

67

Comments

0

A peer-reviewed article of this preprint also exists.

Submitted:

26 June 2024

Posted:

27 June 2024

You are already at the latest version

Alerts
Abstract
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.
Keywords: 
Subject: Public Health and Healthcare  -   Public Health and Health Services

I. Introduction

The incorporation of digital models as an indispensable part of the process of studying and developing physical objects is an established practice. The virtual prototyping approach combines the power of digital technology with the principles of engineering and design, allowing physical products and systems to be created and improved more efficiently and accurately. The ability to virtually simulate and analyze objects and systems prior to their physical production has proven to be an invaluable tool in a wide variety of sectors, including manufacturing, engineering, architecture and medicine, among others, providing benefits such as time savings, cost reductions and the possibility of optimizing the performance and functionality of products [3,21,30].
The advent of the fourth industrial revolution, known as Industry 4.0, characterized by the fusion of physical and digital technologies, creating intelligent and interconnected systems, has resulted in a reinvention of the concept of simulation, giving rise to what is defined as Digital Twins [15,21,39]. The continuous growth and evolution of technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning and Big Data has made it possible to collect, process and store large amounts of data in real time. These technologies are essential for the creation and operation of what we now know as the Digital Twin (DT), providing the basis for capturing data from the physical world and transforming it into accurate virtual replicas, allowing us to analyze various aspects of an object, a equipment, a process or even an industrial plant in their entirety, testing scenarios and possibilities before implementing them in their physical copy, making decision-making faster, more efficient and cheaper [21,27].
A digital twin is, conceptually, defined as a digital replica or virtual representation of an object, process or system. However, its definition can be even broader, considering it as a virtual model (data plus algorithms) with special characteristics not found in traditional models and simulations, which dynamically combines the physical and digital worlds, taking advantage of new technologies, such as intelligent sensing, analysis of large volumes of data and artificial intelligence (AI) in order to detect and prevent failures, improve performance and prospect opportunities for innovation in the entity studied [6,35]. In this way, its potential goes beyond virtual prototyping, being able to provide information and subsidies to anticipate situations before they occur, allowing scenarios to be planned in advance, reducing costs, minimizing losses and failures and mitigating risks and damages that compromise the active [24].
The increasing use of digital twin technology across industries highlights its potential to revolutionize the way we create, manage and operate even the most complex systems. The Health field is characterized by being a complex ecosystem that requires effective and efficient operations, optimizations, management and control in order to offer reliable, economical and quality healthcare actions and services. Your main challenge is to provide the best possible healthcare services to patients using limited healthcare resources. The use of digital twins for predictive analysis, process improvement, supply capacity planning, risk management, assertiveness in diagnosis, increased clinical safety, among others, will allow managerial and clinical decisions to improve the quality of health care offered in the future. [32,34,37].
The aim of this work is to compile the state of the art in the use of digital twins in healthcare, by reviewing the scientific literature on the subject, thus making it possible to assess the trends and challenges of this technology for the coming years. We hope that this work will make it possible to capture and anticipate future developments in the healthcare sector, in order to generate visions and options for a technology that is expanding in use and that brings opportunities for innovation in the ways of planning, producing and managing products and processes.
In addition to this introduction, this article presents five other sections. Section 2 describes the bibliographic research carried out on Digital Twins, Digital Health and Healthcare. Section 3 lists the methodology used for the rapid literature review. Section 4 presents the results. Section 5 discusses the results. Finally, section 6 presents the conclusion and the limitations of the work.

II. Literature Review

This section presents the theoretical basis used to support the discussion on the use of digital twins in healthcare. To this end, we sought to contextualize the topic, seeking to understand the process of digital transformation in the health sector and its relationship with innovative forms of service provision, especially in health care.

A. Digital Twins

A digital twin can be understood as technology capable of mapping the real world to the digital world through interaction between the two in real time. By reflecting the composition and structure of the entities studied, the relationship between them and the external environment and their behavior in the digital world, this technology makes it possible to appropriate the current state of these entities, predict subsequent changes and provide guidance in their operation. Digital twins can be used to simulate a variety of entities with increasing levels of complexity, from everyday consumer objects to complex systems such as transportation, cities, ecosystems and even the human body [1,5,42].
Although the concept was mentioned as early as the 1990s in David Gelernter’s book Mirror Worlds, it was Michael Grieves, at a conference for industry at the University of Michigan in 2002, who proposed an architecture for digital twins. The model mentioned both the concepts of real and virtual space and a flow of data between them. Although it had an initial idea of dealing with reality and virtual existence in the context of Digital Twins, the model did not explain exactly how these two elements would interact, nor did it indicate which technology could be used for this interaction [6,20]. It wasn’t until 2010 that the concept of Digital Twins was formally introduced by John Wicks in a NASA strategy report, marking the official starting point for the development of the idea of digital twins. The United States space agency began using the term “Digital Twins” in its technological strategy in 2012, signaling its commitment to exploring and improving this innovation.
Advances in computer processing capacity, storage and data transmission speed have triggered the emergence of innovative technologies, which include Machine Learning, connectivity (4G and 5G) and management of large volumes of data (Big Data), cloud computing (Cloud Computing) and the Internet of Things (IoT). These technologies represented a significant transformation in the industrial landscape, being the main drivers of the so-called Fourth Industrial Revolution, also known as Industry 4.0. Machine Learning empowers systems to learn and adapt from data, driving automation and intelligent decision-making. Connectivity, in turn, allows devices, machines and systems to communicate in real time, enabling more effective coordination and better process integration. Big Data deals with the ability to collect, store and analyze significant volumes of data, providing valuable insights to improve processes and make decisions with more confidence. Cloud computing enables access to scalable and flexible computing resources, reducing costs and improving efficiency. IoT, in turn, involves the interconnection of physical devices on the network, allowing remote monitoring and control. Together, these technologies are redefining the industry, promoting greater efficiency, personalization, automation and the creation of new business models. In this context, Digital Twin technology has recently been added [6,18,22,24,36,43].
The concept of digital twins appears intrinsically related to this perspective of technological innovation, aiming to create a detailed and as faithful digital representation as possible of a real-world entity. This digital representation makes it possible to experiment with scenarios and possibilities even before they are implemented in the physical world, resulting in more agile, effective and economically advantageous decision-making. Through digital twins, it is possible to accurately model objects, systems and processes in a virtual environment. This digital replica not only mirrors the physical characteristics, but also the dynamics and behavior of the real world. Thus, it is possible to carry out more detailed simulations and test different approaches without incurring the costs associated with manipulating objects or modifying physical systems. The ability to virtually test scenarios and changes makes decision-making more informed, as the impact of choices can be assessed before implementing them. Furthermore, this approach contributes to the early identification of problems, allowing for more economical and efficient corrections [2,30,42].
In practice, a Digital Twin uses data, such as physical models, sensor updates and operation histories, and integrates several simulation processes from different perspectives. This allows you to map the object or system in virtual space, reflecting its entire life cycle or execution time. Digital replicas offer two major advantages: on the one hand, they help to create hypotheses and scenarios to anticipate any type of error and adverse event and, on the other, they contribute to optimizing the functioning of different types of processes and operations.
A digital twin is made up of three main parts: (1) the physical product in real space, (2) the virtual product in virtual space and (3) the data and information link that connects the real and virtual spaces. The virtual part not only stores the history of the physical part, but can also provide optimization and prediction for it, so that convergence between the parts is always sought [27,39]. They are characterized by three functions: (1) data fusion of various characteristics of physical objects and high-fidelity mapping of physical objects in real time; (2) coexistence and evolution throughout the life cycle of physical objects; and (3) description, optimization and control of physical objects.
Healthcare is a field in which digital twins are being explored and applied. Digital twins are highly relevant for the development and consequent advancement of digital health. There is a growing demand from key players in the health sector in relation to the use of new digital health solutions, which are technologically viable and capable of actively and intelligently using patient data. These replicas can be used for a variety of purposes, including analyzing existing situations, but also predicting the results of interventions. The use of digital twins (DTs) in healthcare can help understand and diagnose different pathologies, simulate therapies or medical treatments and predict their results. Such predictions can be made for a specific individual or for an entire population.
To date, most studies [2,8,12,14,16,32,37,38,44,45,46] in the industry focus on the virtualization of individual assets, considering the point of view of an application specific, such as body parts, organs or body systems aiming to personalize care or in the reproduction of health equipment and facilities such as hospitals. However, in the real world, these assets are often interconnected and play roles in common processes and ecosystems. Certainly, the greatest potential for its future use is to virtualize contexts and situations that involve several interrelated strategic assets of a macro healthcare organization. This results in the creation of digital twin ecosystems based on a “digital twin as a service” perspective. The future of healthcare is inextricably linked to our ability to effectively improve healthcare delivery and the patient experience through well-informed and consistent new technologies, and digital twins can play this role.

B. Digital Health

According to the World Health Organization, Digital Health encompasses the field of knowledge and practice related to the development and use of digital technologies to support health. Digital health generally encompasses a broader spectrum of technologies and applications, while “e-Health” or “ eHealth ” often focuses on the digital delivery and consumption of healthcare services. This definition expands the original concept of e-Health to include the latest technological advances such as the Internet of Things, artificial intelligence, big data and robotics [19].
Digital Health would then comprise the use of Information and Communication Technology (ICT) resources in order to produce and make reliable information about health status available to citizens, health professionals and public managers [9]. It represents a revolution in healthcare, taking advantage of technological advances to improve access, efficiency and quality of healthcare services. It is a rapidly growing field that encompasses a wide range of technologies and applications, all aimed at improving the well-being of patients and simplifying the work process for healthcare professionals. The accelerated incorporation of technology has profoundly changed the way medical care is being provided, as well as the management of health care.
The evolution of the concept of digital health and its interconnection with digital transformation demonstrate the profound influence of technology on the provision of medical care. Digital health interventions are increasingly being integrated into healthcare workflows with the aim of improving efficiency and effectiveness in patient care. As a result, continued patient engagement with these digital interventions can maximize benefits in terms of health outcomes. However, the journey is long to complete, and the next decades promise a continuous redefinition in the way healthcare services are provided, making it more patient-focused, accessible and effective.
While Digital Health has extraordinary potential, it faces significant challenges and risks. One such notable challenge is the ‘Digital Health Paradox’. While this technology has the potential to increase access to healthcare, it can also be perceived as a “form of barrier” where people who benefit most often face more difficulties accessing it due to a lack of resources or digital skills. This makes them more susceptible to the “infodemic”, misinformation and the risk of having their personal data exploited [17,26]. digital transformation: i) access to infrastructure and connectivity, ii) competence in digital literacy, and iii) encouraging the use of digital technologies [7].
The incorporation of digital health into day-to-day healthcare services is essential for an accessible, patient-centered healthcare system. Its widespread adoption can meet individual and collective health needs and can also offer a harmonious therapeutic relationship through unique therapeutic projects and the reconfiguration of the bond between patients and health equipment [47].
The ability to collect, analyze and share data effectively is enabling unprecedented personalization in healthcare, improving the assertiveness of treatments, reducing costs and putting patients in control of their own health [25]. This digital revolution is shaping the future of healthcare, promising to bring even more disruptive changes in the years to come. But for this to become a reality, it is necessary to bring together efforts in initiatives that boost the analytical intelligence of healthcare organizations, in order to enable the integration, mining and interoperability of data that enable the prescription, prediction and personalization of centered care. patient and value-based.

C. Health Care

The health sector has made historic advances with basic sanitation, discoveries of vaccines and medicines, with the rapid incorporation of medical technologies for diagnosis and treatments, with the improvement and expansion of health care, among others. However, social and demographic factors such as intense urban mobility, accelerated population growth with increased life expectancy and high health costs have increased the level of difficulty in providing health responses for national health systems. In addition to this, the emergence of new diseases with a global reach dramatically encourages the health sector to be better prepared in the coming decades.
Providing quality healthcare means guaranteeing and respecting the dignity of each citizen who forms part of and builds a given society. There has been much discussion about the best way to manage health in countries. The World Health Organization—WHO defines a health system as the set of activities whose primary purpose is to promote, restore and maintain the health of a population to achieve an optimal level of health in an equitable manner, adequate risk protection for the entire population. population, the provision of safe and effective services, the humanized reception of citizens and the provision of efficient services. Health care systems constitute social responses organized to respond to the needs (and preferences) of societies [29].
Among the current and future challenges for global health, we highlight the greater use of health systems resulting from the demographic and epidemiological transitions of the world population, the increasing incorporation of new technologies and the expansion of self-care managed by individuals, the increase in expectations of individuals mainly due to the association with other consumption patterns and the inability of supply/wealth to grow in the same proportion as needs due to the inelastic behavior of demand, unlike the needs for health care, tending to infinity [13]. Although the amounts spent on health, as a proportion of the Gross Domestic Product (GDP), are becoming increasingly higher, discrepancies continue between countries and continents when considering per capita spending. Therefore, health underfunding has also been repeatedly identified as one of the main causes of precarious health services in many countries [41].
However, perhaps the biggest challenge of all is the shift in the focus of the workforce in the Health sector, which is no longer centered on acting essentially on the disease and becomes centered on the person, involving and committing individuals to your own health. In the disease-centered model, the main purpose is the treatment, and if possible, the cure of the disease. Its emphasis is on identifying the signs and symptoms necessary to reach a diagnosis and then begin the most appropriate treatment for that pathology. It is worth noting that in this model, the health professional is the one who defines the object of the work, with the patient being just a supporting role. In the person-centered care model, the purpose goes beyond the disease, because, no matter how assertive the treatment, the main objective is to promote the individual’s autonomy and their participation as protagonists of their health status. In this new paradigm of health care, the provision of services would necessarily occur in a way that respects autonomy, responding to the needs, preferences and values of the person assisted, with the guarantee that such values guide all clinical decisions, thus resulting in strong engagement of the individual with their health status. Person-centered care helps users develop the knowledge, skills and confidence they need to manage and make informed decisions about their own health and healthcare more effectively [33,40].
Established as a global priority in 2005 by the World Health Organization (WHO—World Health Organization ) [19], the digital transition has significantly impacted the health domain by creating the conditions for redefining the care model, in the sense of becoming more integrated, more participatory and more personalized.
Currently, the delivery of healthcare services continues to occur in a fragmented and timely manner for the majority of patients. It becomes extremely difficult for healthcare professionals and patients to monitor all important events, correlations and exposures that negatively affect the provision of healthcare, both in terms of cost and quality. In this context, digital twins offer remarkable potential to significantly improve healthcare delivery.
The correlation between healthcare and digital twins becomes intricate and complex. For example, digital twins have the potential to improve medical diagnosis [25]. Healthcare professionals can use these virtual representations to simulate medical conditions, test different treatment approaches, and evaluate results before applying them to real patients. This can lead to more accurate diagnoses and more effective treatments. Additionally, digital twins can improve communication and collaboration within the healthcare team. By enabling healthcare professionals to access centralized, up-to-date patient information, care coordination becomes more efficient. Doctors can share insights and treatment decisions based on hard data. The potential of digital twins in telemedicine is also notable. Patients can have a digital “avatar” that represents their health status, allowing doctors to monitor health remotely. This is especially valuable in chronic care situations or in places where access to medical care is limited.
The combination of healthcare and digital twins opens the door to a revolution in healthcare delivery. With the potential to improve diagnoses, treatments, care coordination and remote monitoring, this correlation promises to offer significant benefits for patients, doctors and the entire healthcare system. The journey towards more effective and personalized healthcare begins with the amalgamation of digital innovation and healthcare delivery, resulting in unique, highly customized therapeutic projects.

III. Methodology

The objective of the research was to analyze scientific publications with the purpose of investigating the use of digital twins in health and the trends and challenges in their use, providing an insight into the possible paths of health care. The research sought to answer two guiding questions: “How are digital twins being used in the healthcare sector?” and “What are the challenges and perspectives of using digital twins in the healthcare sector?”
The elaboration of the research questions and the bibliographic search were based on the PICO strategy (acronym for P: population/patients; I: intervention; C: comparison/control; O: outcome/ outcome ). The PICO strategy works as an important auxiliary tool in the construction of research questions and bibliographic search, allowing the researcher to locate, accurately and quickly, the best scientific information available.
Table 1.  
Table 1.  
Preprints 110461 i001
The search locations defined for the research originated from the automatic search in the digital library of six academic databases: IEEE Xplore, Dimensions, Scopus, Web of Science, PubMed and ACM. The six databases were chosen with the aim of covering the Engineering and Health research areas. IEEE Xplore and ACM serve the purpose of containing the Engineering/Technology area, whereas the Pubmed database is focused on the Health area. Scopus, Web of Science and Dimensions incorporate a large number of fields of knowledge, and were included to identify any other records relevant to this study that were not in the specific databases. We tried to restrict the search by using specific keywords to find publications of interest.
The search strategy was based on three main terms: Digital Twins, Digital Health and Healthcare. Another filter applied in the search strategy was publications within a time range of up to 5 years (2018 to 2023).
The resulting search query is shown below:
Table 2.  
Table 2.  
Preprints 110461 i002
After applying the search strings, a total of 86 results were reported. After analyzing possible duplicate references in the Zotero library, 28 duplicate results were excluded. At this stage, a total of 58 publications were reached. This last material, then, went through a data survey, collection and analysis process, divided into three stages:
  • Pre-analysis evaluating title, abstract and keywords;
  • Exploration of the selected material;
  • Treatment of results and interpretations.
Exclusion criteria included works in languages other than English; articles unavailable or only partially available for download in the chosen databases. Furthermore, all articles that did not explicitly mention the use of Digital Twins in Health, as well as items selected in the search, which were books or book chapters, were also excluded to guide the discussion.
The only inclusion criterion was confluent studies that dealt with topics related to Digital Twins, Digital Health and Health Care. Figure 1 illustrates the entire process:
At the end of applying the methodology, 13 articles were obtained that met the eligibility criteria proposed for discussion.

IV. Results

The studies included for discussion can be categorized according to the application of digital twins in the Health sector into 2 groups: The clinical applications group, with 7 records and the operational applications group with 6 records. The full spectrum of articles is detailed below in Table 3.
Table 3. findings and results.
Table 3. findings and results.
Preprints 110461 i003
When carrying out the content analysis of the articles, we can also verify a subdivision within the groups. In the clinical applications group, we have five articles that are focused on the theme of personalized care/precision medicine, signaling the development of digital technologies based on real-time patient data for the management of specific diseases or conditions, one article adressing the reproduction of biological structures creating avatars of organs or even the human body and another that focuses on ethics issues of using DTs in healthcare. In the operational applications group, we have a subgroup, with 5 articles, that discusses the application of digital twins supporting the optimization of operational processes by using real-time data integration, advanced analytics and virtual simulations to improve patient care and another subgroup with one article that relies on the construction of virtual structures such as hospital.
In summary, in the sample of selected articles, the main benefits of using Digital Twin technology for healthcare predominantly included increased personalization of care, improved quality of care with the increasingly consistent use of precision medicine and gains in operational efficiency of health facilities, equipment and services.
However, some included studies also highlighted key challenges related to DTs in healthcare, such as interoperability, processing of large volumes of data, patient confidentiality and data security, listing them as the biggest obstacles at the moment. for the large-scale implementation of this technology.

V. Discussion

Digital twins, at their core, represent highly detailed and interactive replicas of objects, processes, dynamic structures and even human entities. They operate integrated with their real-world counterparts, synchronously and synergistically. This conception has deep roots in engineering, specifically in the practice of prototyping. However, with the increasing advancement of technology, digital twins have become a promising tool by opening up space for innovation and the development of solutions in various areas.
They have become viable thanks to developments in sensing, notably the Internet of Things (IoT) and Artificial Intelligence. These technologies are capable of capturing information from the physical world and transmitting it to be recreated in a virtual environment. The key to the functionality of a digital twin is constant synchronization with the real world, a task carried out through communications via the Internet, 4G, WiFi and, mainly, 5G, making this possible in real time.
Creating a digital twin allows you to anticipate future scenarios or review past events, all without the need to experience these situations in reality. This saves costs and minimizes risks. Digital twins have been used in healthcare to build digital representations from large volumes of health data, such as hospital environments, test results, human physiology, lines of care, etc., through computational models. To build virtual twins, several categories of data are used that cover individual, population and even environmental characteristics.
Considering the results found in the selected literature, 4 main axes of application of digital twins in health are presented:
Axis 1: Use of Digital Twins for Virtual Representation of Biological Structures
The use of digital twins in the virtual representation of biological structures has enabled a new approach in medical education and clinical practice. The evolution of imaging technologies, such as magnetic resonance imaging and computed tomography, has allowed the creation of extremely precise 3D models of organs and tissues. These models provide an interactive representation that not only increases understanding of anatomy but also improves knowledge retention for medical students. The ability to “navigate” organs and systems in a virtual environment has a lasting impact on the training of future doctors, providing an immersive learning experience that can be difficult to achieve with traditional methods.
The application of digital twins to simulate medical procedures is another exciting facet of this axis. These models allow healthcare professionals to practice surgical interventions and medical procedures in a virtual environment that accurately replicates the human body. This not only reduces the risk to patients during actual procedures, but also improves the skill and confidence of healthcare professionals. Furthermore, three-dimensional visualization of organs and pathological structures in digital twins aids in understanding complex clinical cases and making informed clinical decisions.
Perhaps the biggest change is in the path that health treatments will follow, moving from being organized by a standard to being based on the genetic, phenotypic, structural, physical and psychosocial characteristics of the individual, being referred to as precision medicine or even more broad as personalized care. Essentially, patients would be treated as individuals and not according to some norm or standard of care (providing the right treatment, at the right time, to the right person).
Axis 2: Use of Digital Twins to Improve Healthcare Processes
The application of digital twins to improve healthcare processes is driving significant advances in several areas. In oncology, for example, digital twins play a fundamental role in modeling tumors and simulating treatments. This approach allows physicians and oncologists to adjust treatment strategies based on the evolution of a patient’s disease, resulting in more personalized and less invasive interventions. Furthermore, continuous monitoring of tumors in digital twins enables a more agile response to changes in the clinical picture, providing better results for patients.
In the area of geriatric care, digital twins enable the creation of care plans adapted to the complex needs of older adults. With the population aging, this personalization becomes crucial to guarantee the quality of life of the elderly. Digital twins can model a patient’s health conditions, taking into account comorbidities and risk factors associated with advanced age. This results in more comprehensive and effective care that improves seniors’ quality of life and reduces unplanned hospital admissions.
Similarly, in women’s health, digital twins are used to monitor gynecological conditions such as endometriosis and fibroids and to manage pregnancy more effectively. The ability to simulate scenarios and monitor the progress of the pregnancy in a virtual environment offers patients more personalized and safer care. This also makes it possible to identify complications early, which is essential to ensure maternal and fetal health.
Axis 3: Use of Digital Twins in reproducing Healthcare Structures and Improving Operational Efficiency
The use of the digital twin in the healthcare sector has stood out as a powerful tool to improve the management of healthcare units and, as a consequence, the system in which they are inserted. This technology allows the creation of a virtual copy of the unit and even the system, providing a detailed analysis of operations, capabilities, human resources and service models. This makes it possible to identify areas for improvement, anticipate future challenges and improve organizational strategies.
Operational efficiency in healthcare is a constant concern, and digital twins play a crucial role in optimizing healthcare systems. One of the main benefits is the improvement in resource allocation in hospitals and other healthcare units. Digital twins allow a detailed analysis of the use of beds, personnel and equipment, enabling more efficient and effective planning. This not only reduces patient waiting times, but also improves assertiveness in the provision of care, resulting in a more successful patient journey and a more positive experience for everyone involved in the care process.
Taking a hospital as an example, by using historical and real-time data from operations, as well as information from the surrounding environment, such as cases of notifiable diseases and traffic accidents, the digital twin allows the hospital unit manager to make informed decisions, how to identify a lack of beds, optimize team schedules and manage room occupancy more effectively. This approach not only increases resource efficiency, but also improves hospital and staff performance while reducing costs.
By building a Digital Twin of the patient’s journey, the healthcare unit can predict patient activity and plan operational capacity according to demand, resulting in significant improvement in services delivered to the patient, in addition to more safety, more volume and a better patient experience.
Modeling and forecasting demand for healthcare services are also areas where digital twins offer great potential. By analyzing real-time data and simulating scenarios, healthcare systems can make more informed decisions about resource distribution and staffing, ensuring care is available where and when it is needed. These capabilities are critical to improving operational efficiency and ensuring the healthcare system is prepared for future challenges.
Axis 4: Use of Digital Twins for the Development of Medicines and Health Devices
In relation to drug development, digital twins enable detailed simulation of molecules, pharmacological interactions and virtual clinical trials. This saves time and resources by accelerating the identification of promising drug candidates. Furthermore, digital twins enable the development of personalized medicines, adapted to the individual needs of patients, which is a major advance in precision medicine. The digital twin has application in the analysis of data related to the use of medications and their effects. By integrating information from patients, healthcare professionals and other sources, the digital twin enables a comprehensive and dynamic view of a medicine’s safety profile.
The increasing use of personal health monitoring devices, such as mobile apps and integrated sensors, enables active surveillance of the user’s key health parameters, such as electrocardiogram (ECG), blood pressure, heart rate and glucose level, minimizing potential inaccuracies in data recording. Such devices have the ability to collect and transmit information anonymously to the cloud, where it can be compared with disease symptom histories, or even alert competent healthcare professionals when necessary. Digital twins are proving to be important for the design and testing of new products. They allow detailed simulations of the functioning of devices such as prosthetics, monitoring devices and medical equipment. This helps identify design issues and ensure devices are safe and effective before they are manufactured and deployed. Furthermore, digital twins are useful for training healthcare professionals in the use of new devices, improving the safety and quality of treatments.
Finally, the bibliography presented by the research also exposes some of the main challenges in the use of digital twins in healthcare and its future, which we highlight:
  • Data Integration: Healthcare management involves a vast amount of information and clinical data. One of the main challenges is the efficient integration of this data into digital twins, ensuring that all relevant information is available in one place. This can be complicated by the diversity of health record systems and data standards.
  • Privacy and Security: Maintaining the privacy and security of health data is a critical concern. Digital twins contain highly sensitive information, and it is essential to ensure that they are protected from unauthorized access and data breaches.
  • Interoperability: Healthcare systems often use different technologies and standards. For digital twins to be effective, they need to be interoperable, i.e., able to communicate and share information effectively between different systems.
Primary Health Care (PHC) can be understood as the level of care that represents the entrance door to the health system. PHC services provide care to the person over time, dealing with the most common problems of the community through health promotion, prevention, treatment and rehabilitation. It should be noted that despite the health sector’s rising costs, increased demand for services and the already established role of Primary Health Care in improving a population’s health levels, no study was found in the selected literature on the specific use of digital twins in it. The closest study found was the one that signaled the use of digital twins to monitor the elderly and chronic diseases, however without mentioning that it was a line of care in Primary Health Care. Studies carried out on the use of DT in hospital environments have been increasingly frequent, both in terms of building “smart hospitals” and trying to improve the patient journey at hospital level, although there are still few in the literature.
As it is the main axis of changes in health systems to improve health levels, it is extremely necessary that Primary Health Care can receive due attention from the scientific community and the industrial complex as a promising field for the use of twin’s digital technology in healthcare and in the operation of its units. As its main attributes include first contact/access, longitudinality of care, comprehensiveness, and coordination, PHC is, by far, the most promising area in health to leverage the use of digital twins in the sector. In addition, because PHC works in a territorialized way and with a recognizable population, it has the possibility of using digital twins to consolidate and infer scenarios through predictive analyses in relation to the health status of its population.

VI. Conclusions

In this paper, we provide a literature review on the use of digital twins in health, and their possible trends based on the content analysis of articles on the subject, selected through a specific search in the IEEE Xplore, Dimensions, Scopus, Web of Science, PubMed and the ACM databases.
The work was able to contribute in some way to broadening discussions about the future of healthcare, from the perspective of incorporating new technologies, notably Digital Twins. The two guiding questions: “How are digital twins being used in the health sector?” and “What are the challenges and perspectives of using digital twins in the health sector?”, were answered to some extent.
The use of digital twins, especially in healthcare management, presents major challenges related to data integration, privacy and interoperability. However, trends indicate great potential in treatment personalization, predictive capacity for unwanted events, remote monitoring, accuracy of procedures and treatments, and data-driven decision making, which could result in significant improvements in quality and efficiency for the healthcare sector. As technologies and practices evolve, digital twins can play an increasingly leading role.
It is important to highlight the complexity of the various clinical, technical, organizational, ethical and legal issues that, due to their incipience, still make it difficult to fully realize the benefits associated with the adoption of digital twins in healthcare, which, therefore, can generate a limitation in its large-scale implementation. The growing relevance of using digital technologies is aligned with investment in strategies to promote health and well-being, as well as interventions that help people self-manage their biopsychosocial condition. In this context, the use of digital twins has great potential to transform healthcare by establishing a communicative and informative connection between two worlds, the virtual and the real, making it challenging to manage in the coming years. As these actions are closely related to the scope and breadth of Primary Health Care, it is crucial that the application of digital twin technology can be directed as a common effort to leverage this level of care in favor of increasingly effective health responses.
More in-depth future studies should be carried out to explore the possible consolidation of the use of digital twins in Health, especially in processes linked to healthcare and Primary Health Care, or even clarify which initiatives should be implemented or even strengthened to sustain the advances made so far.

References

  1. A. Fuller, Z. Fan, C. Day and C. Barlow, “Digital Twin : Enabling Technologies, Challenges and Open Research ,” in IEEE Access , vol. 8, pp. 108952-108971, 2020. keywords: { Smart cities;Data analysis;Manufacturing;Data models;Internet of Things;Computational modeling;Digital twins;applications;enabling technologies;industrial Internet of Things ( IIoT ); Internet of Things ( IoT ); machine learning;deep learning;literature review}. [CrossRef]
  2. A. Karakra , F. Fontanili , E. Lamine, J. Lamothe , and A. Taweel , “ Pervasive computing integrated discrete event simulation for a hospital digital twin ,” in 2018 IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA). IEEE, 2018, pp. 1 6.
  3. Ahram , Tareq ; Karwowski , Waldemar. & Falcão, Christianne. 2015. Applications of Systems Engineering for Testing and Evaluation : A Human Systems Integration Perspective. In: Boehm-Davis, Deborah A., Durso , Frank, & Lee, John D. (eds.). APA Handbook of Human Systems Integration . APA handbooks in psychology . Washington, DC: American Psychological Association . pp. 101-111. [CrossRef]
  4. Aluvalu , Rajanikanth , et al. “The novel emergency hospital services for patients using digital twins .” Microprocessors and Microsystems 98 (2023): 104794. [CrossRef]
  5. Armentano, R., Bhadoria , R. S., Chatterjee , P., & Deka , G. C. (2017). The Internet of Things : Foundation for Smart Cities , EHealth , and Ubiquitous Computing . Chapman and Hall/CRC.
  6. Barricelli BR, Casiraghi E., Fogli D. A Survey on Digital Twin : Definitions , Characteristics , Applications , and Design Implications . IEEEAccess. 2023; 7:167653–167671—IT HURTS. [CrossRef]
  7. Barros, Maria & Lima, Presleyson & Oliveira, Dione & Arcanjo, Cláudio & Oliveira, Luis & Pereira, Sara. (2023). Digital Inclusion and Education: equity and access. International Journal of Scientific Studies. 1. 124-149. [CrossRef]
  8. Björnsson B., Borrebaeck C., Elander N., Gasslander T., Gawel DR, Gustafsson M., Jörnsten R., Lee EJ, Li X., Lilja S., et al. digital twins to personalize medicine. Genome Med. 2019; 12:4. [CrossRef]
  9. Brazil. Ministry of Health. Executive Secretariat. SUS Information Technology Department. Digital Health Strategy for Brazil 2020-2028 [electronic resource] / Ministry of Health, Executive Secretariat, SUS Information Technology Department. – Brasília: Ministry of Health, 2020. 128 p. : il. Access mode: World Wide Web: http://bvsms.saude.gov.br/bvs/publicacoes/estrategia_saude_digital_Brasil.pdf ISBN 978-85-334-2841-6.
  10. Chaudhari , Prabhanjan , Chaitali Gangane , and Akshay Lahe . “Digital Twin in Industry 4.0 A Real-Time Virtual Replica of Objects Improves Digital Health Monitoring System.” International Conference on Information Systems and Management Science. Call : Springer International Publishing , 2021.
  11. Cheng , Weibin , Wanmin Lian , and Junzhang Tian. “Building the intelligent hospital twins for all-scenario intelligence health care .” Digital Health 8 (2022): 20552076221107894. [CrossRef]
  12. Elkefi , Safa, and Onur Asan . “Digital Twins for Managing Health Care Systems: Rapid Literature Review.” Journal of medical Internet research 24.8 (2022): e37641. [CrossRef]
  13. El-Warrak, L.; Nunes, M.; Luna, G.; Barbosa, CE; Lyra, A.; Argôlo , M.; Lima, Y.; Salazar, H.; de Souza, JM Towards the future of Public Health: Roadmapping Trends and Scenarios in the Post-COVID Healthcare Era. Healthcare 2023, 11, 3118. [CrossRef]
  14. F. Michelfeit , ‘‘ Exploring the possibilities offered by digital twins in medical technology , ‘‘ Siemens Healthcare GmbH- Communc . RSNA, 2018. [Online]. Available : https://static.healthcare.siemens.com/ siemens_hwem - hwem_ssxa_websites - context -root/ wcm / idc / groups / public /@global/@press/documents/download/mda4/nzm4/ ∼ edisp/ exploring-the -possibilities-offered-by-digital-twins-in-medicaltechnology-05899262.pdf.
  15. F. Pires, A. Cachada, J. Barbosa, AP Moreira and P. Leitão, “Digital Twin in Industry 4.0: Technologies, Applications and Challenges ,” 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), Helsinki, Finland , 2019, pp. 721-726. [CrossRef]
  16. Gabrielli, Silvia, Enrico Maria Piras, and Oscar Mayora Ibarra. “Digital Twins in the Future Design of Digital Therapeutics .” Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing . 2023.
  17. Garcia, Leila Posenato and Duarte, Elisete. Infodemic: excess quantity to the detriment of quality of information about COVID-19. Epidemiol . Serv. Health [online]. 2020, vol.29, n.4 [cited 2024-02-21], and 2020186. Available at: <http://scielo.iec.gov.br/scielo.php?script=sci_arttext&pid=S167949742020000400001&lng=pt&nrm=iso >. Epub03-Sep-2020.ISSN1679-4974. [CrossRef]
  18. Georgios Tsaramirsis , Antreas Kantaros , Izzat Al- Darraji , Dimitrios Piromalis , Charalampos Apostolopoulos , Athanasia Pavlopoulou , Muath Alrammal , Zamhar Ismail, Seyed M. Buhari , Milos Stojmenovic , Hatem Tamimi , Princy Randhawa , Akshet Patel , Fazal Qudus Khan, “A Modern Approach towards an Industry 4.0 Model: From Driving Technologies to Management”, Journal of Sensors , vol. 2022, Article ID 5023011, 18 pages , 2022. [CrossRef]
  19. Global strategy on digital health 2020-2025. Geneva: World Health Organization ; 2021. License : CC BY-NC-SA 3.0 IGO.
  20. Grieves , M.: Digital twin : manufacturing excellence through virtual factory replication . White Paper , NASA (2014).
  21. Gunal , MM Simulation for Industry 4.0. (2019). [CrossRef]
  22. Guo J, Lv Z. Application of Digital Twins in multiple fields . Multimed Tools Appl . 2022;81(19):26941-26967. Epub 2022 Feb 16. [CrossRef] [PubMed] [PubMed Central]
  23. Huang, Pei-hua , Ki -hun Kim, and Maartje Schermer . “ Ethical issues of digital twins for personalized health care service : preliminary mapping study .” Journal of Medical Internet Research 24.1 (2022): e33081. keywords : {Digital Twin;Industry 4.0;Virtualisation;Simulation}. [CrossRef]
  24. Kritzinger , W., M. Karner , G. Traar , J. Henjes , and W. Sihn . 2018. Digital twin in manufacturing : A categorical literature review and classification ‘. IFAC- PapersOnLine 51 (11): 1016–1022. [CrossRef]
  25. Majumder S., Deen M.J. Smartphone Sensors for Health Monitoring and Diagnosis . Sensors . 2019; 19:2164. [CrossRef]
  26. Millstone, E., & Zwanenberg , P. van (2000). The crisis of trust : For science , scientists or for institutions ? Nature Medicine, 6, 1307-1308. [CrossRef]
  27. Mussomeli , A. et al. Expecting digital twins . Copyright© Deloitte Insights, published on: 02 May. 2018. Available at : . Accessed on: 06 Oct. 2023.
  28. N. Wickramasinghe et al., “A Vision for Leveraging the Concept of Digital Twins I’m Support the Provision of Personalized Cancer Care,” in IEEE Internet Computing, vol. 26, no. 5, pp. 17-24, 1 Sept.- Oct. 2022, keywords: {Digital twins;Mathematical model;Computational modeling;Medical services;Cancer;Numerical models;Internet;Context awareness }. [CrossRef]
  29. WHO - World Health Organization. Why Are health systems important? WHR 2000. WHO. Available at: https://www.who.int/whr/2000/en/whr00_ch1_es.p.
  30. Q. Lu, X. Xie, AK Parlikad , JM Schooling , and E. Konstantinou , “ Moving from building information models to digital twins for operation and maintenance ,” Proceedings of the institution of Civil Engineers — Smart Infrastructure and Construction , pp. 1–9, 2020.
  31. Ricci, Alessandro, Angelo Croatti , and Sara Montagna. “ Pervasive and connected digital twins — a vision for digital health .” IEEE Internet Computing 26.5 (2021): 26-32. [CrossRef]
  32. Rivera, Luis F., et al. “ Towards continuous monitoring in personalized healthcare through digital twins .” Proceedings of the 29th annual international conference on computer science and software engineering . 2019.
  33. Rodrigues, Juliana Loureiro da Silva de Queiroz; PORTELA, Margareth Crisóstomo; MALIK, Ana Maria. Agenda for research on patient-centered care in Brazil. Ciência & Saúde Coletiva, [SL], v. 24, no. 11, p. 4263-4275, nov. 2019. Available at: https://www.scielo.br/j/csc/a/NbjdSZYLx5yxyLtZ963R7LC/?format=pdf&lang=pt. Accessed on: 21September. 2023.
  34. S. Scharff . (Sep. 10, 2010). From Digital Twin I’m Improved Patient Experience. Siemens Healthineers . Accessed : Oct. 16, 2023. [Online]. Available : https://www.siemens-healthineers.com/perspectives/mso-digital-twin-mater.html.
  35. Semeraro, Digital twin paradigm: a systematic literature review, Computing Ind , № 130, с. 103469. [CrossRef]
  36. Schwartz SM, Wildenhaus K, Bucher A and Byrd B (2020) Digital Twins and the Emerging Science of Self: Implications for Digital Health Experience Design and “ Small ” Data. Front. Computer. Sci . 2:31. [CrossRef]
  37. Sun T, He X, Li Z. Digital twin in healthcare: Recent updates and challenges . DigitHealth . 2023 Jan 3; 9:20552076221149651. [CrossRef] [PubMed] [PubMed Central]
  38. T.Marshall. (Sep. 6, 2016). VPH: The Ultimate Stage Before Your Own Medical Digital Twin . Accessed : Oct. 16, 2019. [Online]. Available : https://www.linkedin.com/pulse/vph-ultimate-stage-before-your-ownmedical-digital-twin-marchal/?trk=mp-reader-car.
  39. Tao, F.; Zhang, M. Digital twin shop- floor : a new shop- floor paradigm towards smart manufacturing . IEEE Access, IEEE, v. 5, p. 20418–20427, 2017.
  40. The Health Foundation. Person- centered care made simple: what everyone should know about person-centered care. Quick guide. London: The Health Foundation, 2016. Available at https://www.health.org.uk/publications/person-centred-care-made-simple. Accessed on: 20Sept. 2023.
  41. US National Science Foundation. Science and Engineering Indicators 2018. Alexandria, VA: National Science Foundation, 2018. Available from: https://www.nsf.gov/statistics/2018/nsb20181/assets/nsb20181.pdf.
  42. VQ Lu, AK Parlikad , and P. Woodall , “ Developing a dynamic digital twin at a building level : using Cambridge campusascasestudy ,” in Proceedings of the 2019 International Conference on Smart Infrastructure and Construction 2019 (ICSIC) Driving Data- Informed Decision -Making, Cambridge, UK, 2019.
  43. V. Stojanovic , M. Trapp , R. Richter, B. Hagedorn , J. D¨ ollner , and M. Alamaniotis , “ Towards the generation of digital twins for facility management based on 3D point clouds,” in Proceedings of the 34th Annual ARCOM Conference , Belfast, UK, 2018.
  44. Vallée A Digital twin for healthcare systems. Front. Digit . Health 5:1253050. (2023). [CrossRef]
  45. Venkatesh KP, Raza MM, Kvedar JC Health digital twins as tools for precision medicine: Considerations for computation , implementation , and regulation . Npj Digit . Med. 2022;5:150. [CrossRef]
  46. Viceconti, Marco, et al. “ From the digital twins in healthcare to the Virtual Human Twin: a moon -shot project for digital health research.” (2023). arXiv:2304.06678.
  47. WHO - WORLD HEALTH ORGANIZATION. WHO global strategy on integrated people-centered health services 2016-2026 - Executive Summary: Placing people and communities at the center of health services. [ Sl ]: Draft for consultation , Version 2015. Available at https://interprofessional.global/wp-content/uploads/2019/11/WHO-2015-Global-strategy-onintegrated-people-centered-health-services-2016 -2026.pdf. Accessed on: 21 September. 2023.
Preprints 110461 g001
Figure 1. scheme of the applied methodology.
Figure 1. scheme of the applied methodology.
Preprints 110461 g001
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Prerpints.org logo

Preprints.org is a free preprint server supported by MDPI in Basel, Switzerland.

facebook logotwitter logolinkedin logo
MDPI Initiatives
Important Links
Subscribe

Disclaimer

Terms of Use

Privacy Policy

© 2024 MDPI (Basel, Switzerland) unless otherwise stated