Digital twins have recently gathered significant interest in the healthcare community. This concept promises to unlock various previously unavailable services such as remote monitoring, advanced visualization, simulation of medical procedures, predictive analytics, demographic studies, and so on. At present research in this area is localized and conducted independently. Thus, efective deployment of digital twins in healthcare is still a work in progress due to inconsistent data representation and isolated innovation without efective integration at large scale. Knowledge representation plays a vital role in structuring, integrating, and reasoning over heterogeneous healthcare data sources such as electronic health records, genomics data, clinical guidelines, reports, medical literature, and more. The process of digitization is relevant not only to patients but also to healthcare professionals, infrastructure facilities, devices, insurance providers, and even historical records. This work proposes to thoroughly highlight this research gap and the current initiatives addressing these issues. It aims to review and consolidate existing eforts in standardizing data structures for healthcare digital twins, with a focus on interoperability, representation and integration across diverse healthcare domains.
The uncivilized human species have solely depended on their biological immunity and
behavioural traits for treating physical ailments[
The concept of digital twins is being adopted by several industries[
Though several of previous studies have been conducted in this topic[
The healthcare industry has already started adopting the concept of digital twins in various
diferent ways. Till now the innovation has mostly been localized. The objective of this paper
is to propose a more global approach to make digital healthcare a reality. The applications of
digital twins in healthcare can be classified into several categories based on the application
scope, target areas and purpose that the digital twins would serve.
2.1. Scope
Digital twins is may be implemented at the level of a single patient[
The next obvious aspect of digital twins is to figure out what components of the healthcare industry can be represented using a digital twin.
• Multiomics: Multiomics modelling[
The final piece of the digital twin ecosystem is to define the purpose that is served by the twin.
We have already discussed about the various scopes and targets for digitization. These twins
would be useful in several scenarios.Digital twins may be used for training [24] of healthcare
personnel on new equipment or clinical procedures may carried out. Surgical simulations [25]
may be performed on patient specific twins of target organs. Demographic surveys allow us
to plan region specific healthcare services[ 26] and also create decision support systems for
epidemics and community healthcare. Past records may be used to forecast future outcomes[27]
of treatment protocols, health camps, and also anticipate maintenance needs and provide
supportive evidences for taking decisions. As discussed before, various vaccine[28] and drug
design rely on digital twins for simulating molecular interactions. Personalized medicine[
The proposed holistic healthcare framework is a hypothetical model that ideologically establishes the potential of the digital twin industry. The objective of this study is to recognise the challenges of integrating digital twins into the existing healthcare industry and to propose essential steps for initiating a globally connected healthcare industry that can exploit modern technologies such as deep learning, internet of things(IoT), 5G/6G communications and cloud computing. But before we proceed we must understand that a digital twin is not a nuclear computational module working in isolation. It is an ecosystem consisting of various stakeholders such as patients, healthcare facilities, healthcare personnel, technology developers, device manufacturers, regulatory bodies, ethics committees, cybersecurity service providers, educational & research institutions, financial support providers, government, advocacy groups and more. It is built upon a versatile and robust technological stack [29]that interacts with various external data repositories and under the supervision of ethics committees and regulatory bodies.
The necessary technological stack (as illustrated in fig. 2) for an ideal digital twin ecosystems would have multiple functional modules. Obviously the ecosystem would be built on top of the real world. This physical layer consists of the patients, healthcare professionals, medical facilities, equipment, and other relevant institutions. The virtual world would start with the data layer that would consist of the electronic data acquired from various sources such diagnostic equipment, patient records, reports, genomic profiles and so on. Once we have acquired the data the obvious next concerns are addressed by the communication layer and storage layer. Communication protocols may be defined as per implementation of the IoT infrastructure built upon 5G or 6G network backbone. The storage layer would deal with the necessary storage infrastructure. Blockchain techniques may be used for decentralization of information. The representation layer deals with the computational representation of the acquired data. The data would generally be acquired from diferent sources with diferent representation format. A common modelling language is needed for a consistent embedding. For a globally holistic healthcare framework common schema would be needed to assemble and integrate multi-modal information in a consistent data structure. The computation layer consists of the high performance computing infrastructure along with the advanced machine learning algorithms, image processing and language processing toolkit and models along with rendering engines. Finally the application layer would provide the necessary interface for visualization, simulation, and analysis. This can be carried out through standard modes of human computer interaction or even through augmented and virtual reality platform. Other than this there is a obvious needs of communication with external repositories such as electronic health records, government databases, insurance records, demographic information, pharmaceutical and disease repository, multiomics data banks and more. Additionally there is a need for cyber-security protocols across the stack and supervision from ethics and regulatory bodies.
For a digital twin ecosystem we need to operate with data acquired from various sources such as patients, equipment, healthcare facilities, and external data banks. All these data have vastly diferent formats and structure. The biggest challenge of this digital twin ecosystem is to progress towards the one species one schema concept. This refers to a single <ENTITYATTRIBUTE-RELATION> model that can represent various elements of the healthcare industries along with their relevant parameters and contexts. This would allow us to create the knowledge graphs for healthcare digitalization[30]. The need of a common data structure is graphically summarized in fig. 3.
This schema for a global knowledge base must be built with some specific properties that ensures its sustainability. These properties, here abbreviated as M.U.S.C.L.E., refers to the aspects of • Multi-modality: Data may come from various sources but must be mapped to the same schema • Uniformity: Standardization of data representation is mandatory • Scalability: The schema should be able to grow with the growing complexities of the data • Consistency: The updates in the schema should be non-disruptive in nature • Longevity: The schema must be able to adapt and grow with time to remain relevant with the fast moving pace of medical research as it can be a costly afair to revamp the entire schema • Ethics: Each transactions in the knowledge based must be traceable to the responsible healthcare personnel or facility and must be explainable to avoid ethical or legal issues. 3.2.1. Knowledge Base Transactions The knowledge base that is built upon the schema should be accessed and altered through serialized transactions. Transactions to update the knowledge base may be at three diferent 1. Content : refers to nuclear updates that deals with specific parameters. These updates do not disrupt other nodes or edges. e.g. Updating the height and weight of a patient 2. Context : refers to updates that have efect in its immediate neighbourhood. These updates tends to display contextual significance. Updating diagnosis based on a test report result. Test report parameters will be updated which will trigger update in the diagnosis parameters 3. Concept: refers to the updates in the knowledge base that leads to addition, alteration or deletion of entire conceptual branches. A proposed treatment by a physician opens up a new branch for the healthcare facility that requires addition of several nodes and edges such as hospital beds, OT reservations, insurance parameters and so on.
For the realization of a global knowledge repository, the first and most important step is to develop a unique biological identifier. A unique globally standardized biological identifier must be created. This may be similar to other identification documents like passports, social security numbers, taxpayer identification numbers, Aadhar Card, VoterCard etc. However, a biological identifier must be associated to a biological signature of a being such as the fingerprints, retina scans or even genome sequence[31]. This must be standardized by the respective government as per uniform global standards. Most importantly it should be mandatory to associate with all healthcare related procedures similar to a taxpayer identification being connected with all ifnancial transactions.
Besides the UBID being necessary to unify healthcare transactions, a digital twin system would also require innovation tracking [32] and development of global policies, and standardization. We must understand that isolated innovation is the source of inconsistencies. Existing predictive models must be standardized and existing literature must digitized to create a viable ontology. Additionally an efort must be made to ensure every clinic, hospital, pharmacy, doctor, patients, equipment is connected to the common Healthcare Framework.
It is evident that revolution in global healthcare requires a migration to the digital twin ecosystem. However, several significant actions must be taken. From development of unique biological identifiers to moving towards a unified knowledge repository built on robust backbone schema. The proposed work tries to establish the need of "One Species One Schema" and the role that knowledge representation plays in the transformation of modern healthcare. The schema would need to be carefully designed to ensure long-term sustainability. The ultimate goal would be an unified knowledge graph that connects patients, healthcare facilities, professionals and all other stakeholder to ensure quality of medical services. [21] S. Alrashed, N. Min-Allah, I. Ali, R. Mehmood, Covid-19 outbreak and the role of digital twin, Multimedia Tools and Applications 81 (2022) 26857–26871. [22] X. Zhong, F. Babaie Sarijaloo, A. Prakash, J. Park, C. Huang, A. Barwise, V. Herasevich, O. Gajic, B. Pickering, Y. Dong, A multidisciplinary approach to the development of digital twin models of critical care delivery in intensive care units, International Journal of Production Research 60 (2022) 4197–4213. [23] Y. Peng, M. Zhang, F. Yu, J. Xu, S. Gao, Digital twin hospital buildings: an exemplary case study through continuous lifecycle integration, Advances in Civil Engineering 2020 (2020) 1–13. [24] M. W. Zackof, D. Davis, M. Rios, R. D. Sahay, B. Zhang, I. Anderson, M. NeCamp, I. Rogue, S. Boyd, A. Gardner, et al., Tolerability and acceptability of autonomous immersive virtual reality incorporating digital twin technology for mass training in healthcare, Technical Report, LWW, 2023. [25] K. Hagmann, A. Hellings-Kuß, J. Klodmann, R. Richter, F. Stulp, D. Leidner, A digital twin approach for contextual assistance for surgeons during surgical robotics training, Frontiers in Robotics and AI 8 (2021) 735566. [26] F. Pilati, R. Tronconi, G. Nollo, S. S. Heragu, F. Zerzer, Digital twin of covid-19 mass vaccination centers, Sustainability 13 (2021) 7396. [27] A. Allen, A. Siefkas, E. Pellegrini, H. Burdick, G. Barnes, J. Calvert, Q. Mao, R. Das, A digital twins machine learning model for forecasting disease progression in stroke patients, Applied Sciences 11 (2021) 5576. [28] T. Zohdi, Machine-learning and digital-twins for rapid evaluation and design of injected vaccine immune-system responses, Computer Methods in Applied Mechanics and Engineering 401 (2022) 115315. [29] S. N. G. Gourisetti, S. Bhadra, D. J. Sebastian-Cardenas, M. Touhiduzzaman, O. Ahmed, A theoretical open architecture framework and technology stack for digital twins in energy sector applications, Energies 16 (2023) 4853. [30] A. Carbonaro, A. Marfoglia, F. Nardini, S. Mellone, Connected: leveraging digital twins and personal knowledge graphs in healthcare digitalization, Frontiers in Digital Health 5 (2023) 1322428. [31] C. Lippert, R. Sabatini, M. C. Maher, E. Y. Kang, S. Lee, O. Arikan, A. Harley, A. Bernal, P. Garst, V. Lavrenko, et al., Identification of individuals by trait prediction using wholegenome sequencing data, Proceedings of the National Academy of Sciences 114 (2017) 10166–10171. [32] J. Barlow, Managing innovation in healthcare, World scientific publishing company, 2016.