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The Role of Digital Twinning, the Next Generation of EMR/EHR in Healthcare in a Society 5.0: Collecting Patient Data from Birth to the Grave

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Society 5.0 and Next Generation Healthcare

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Abstract

The U.S. FDA estimates that drug treatments may be ineffective in 38–75% of patients. This clearly demonstrates the importance of personalized medicine. Personalized medicine requires vast amounts of data, and a digital twin is an easy way to represent and use this data. A digital twin is a virtual copy of an individual generated using large amounts of highly descriptive data specific to that individual. To generate the most accurate digital twin, information should be collected from the individual’s birth, and this record must be kept up to date. This digital twin will then be a digital version of the individual containing their full medical history, genetic information, family history, biometric data, demographic information, and details concerning their environment and exposure to risk factors for various diseases. Epigenomic, transcriptomic, proteomic, metabolomic, and microbiomic data should be collected at various times to identify potential risk biomarkers that have developed. When an individual requires medical treatment, the digital twin can be updated using the latest “omics” data. These digital twins can then be used as accurate virtual models to test patient responses to various treatment, or to monitor patients at risk, which will improve early diagnosis and ensure early treatment. In this way, the digital twin could contribute to the lifetime healthcare goals of healthcare in Society 5.0, leading to the goals of improving the life expectancy and vitality of an individual through personalized healthcare from the cradle to the grave. Digital twins can also be used to improve health delivery and the healthy layout of cities and attain a multitude of other sustainable development goals, through virtual modeling and optimization based on the use of these models. Despite the promise of digital twins in healthcare, there are barriers to their use and implementation. These include ethical issues, violation of privacy, abuse, and the creation of a population of hypochondriacs as digital twins can be used to overdiagnose conditions.

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References

  • Adel A (2022) Future of industry 5.0 in society: human-centric solutions, challenges and prospective research areas. J Cloud Comput (Heidelb) 11:40

    Article  Google Scholar 

  • Armstrong D (1995) The rise of surveillance medicine. Sociol Health Illness 17:393–404

    Article  Google Scholar 

  • Autiosalo J, Siegel J, Tammi K (2021) Twinbase: open-source server software for the digital twin web. IEEE Access 9:140779–140798

    Article  Google Scholar 

  • Bagaria N, Laamarti F, Badawi H, Albraikan A, Martinez R, El Saddik, A (2020, January) Connected health in smart cities

    Google Scholar 

  • Barabási AL, Gulbahce N, Loscalzo J (2011) Network medicine: a network-based approach to human disease. Nat Rev Genet 12:56–68

    Article  Google Scholar 

  • Batch KE, Yue J, Darcovich A, Lupton K, Liu CC, Woodlock DP, El Amine MAK, Causa-Andrieu PI, Gazit L, Nguyen GH, Zulkernine F, Do RKG, Simpson AL (2022) Developing a cancer digital twin: supervised metastases detection from consecutive structured radiology reports. Front Artif Intell 5:826402–826402

    Article  Google Scholar 

  • Benson M (2021) Digital twins will revolutionise healthcare. IET Digital Library 16:50–53

    Google Scholar 

  • Björnsson B, Borrebaeck C, Elander N, Gasslander T, Gawel DR, Gustafsson M, Jörnsten R, Lee EJ, Li X, Lilja S, Martínez-Enguita D, Matussek A, Sandström P, Schäfer S, Stenmarker M, Sun XF, Sysoev O, Zhang H, Benson M (2019) Digital twins to personalize medicine. Genome Med 12:4

    Article  Google Scholar 

  • Bornman MS, Aneck-Hahn NH, De Jager C, Wagenaar GM, Bouwman H, Barnhoorn IEJ, Patrick SM, Vandenberg LN, Kortenkamp A, Blumberg B, Kimmins S, Jegou B, Auger J, Digangi J, Heindel JJ (2017) Endocrine disruptors and health effects in Africa: a call for action. Environ Health Perspect 125:085005

    Article  Google Scholar 

  • Boschert S, Heinrich C, Rosen R (2018) Next generation digital twin. In: Proceedings of TMCE 2018. Las Palmas de Gran Canaria, Spain, pp 7–11

    Google Scholar 

  • Bruynseels K, Santoni De Sio F, Van Den Hoven J (2018) Digital twins in health care: ethical implications of an emerging engineering paradigm. Front Genet 9:31

    Article  Google Scholar 

  • Bunnik EM, Janssens AC, Schermer MH (2015) Personal utility in genomic testing: is there such a thing? J Med Ethics 41:322–326

    Article  Google Scholar 

  • Callcut M, Cerceau Agliozzo J-P, Varga L, Mcmillan LJS (2021) Digital twins in civil infrastructure systems. Sustainability 13:11549

    Article  Google Scholar 

  • Canzoneri M, De Luca A, Harttung J (2021) Digital twins: a general overview of the biopharma industry. Adv Biochem Eng Biotechnol 177:167–184

    Google Scholar 

  • Cho SW, Byun SH, Yi S, Jang WS, Kim JC, Park IY, Yang BE (2021) Sagittal relationship between the maxillary central incisors and the forehead in digital twins of Korean adult females. J Pers Med 11

    Google Scholar 

  • Costanza R, Daly L, Fioramonti L, Giovannini E, Kubiszewski I, Mortensen LF, Pickett KE, Ragnarsdottir KV, De Vogli R, Wilkinson R (2016) Modelling and measuring sustainable wellbeing in connection with the UN Sustainable Development Goals. Ecol Econ 130:350–355

    Article  Google Scholar 

  • De Benedictis A, Mazzocca N, Somma A, Strigaro C (2022) Digital twins in healthcare: an architectural proposal and its application in a social distancing case study. IEEE J Biomed Health Inform. https://doi.org/10.1109/JBHI.2022.3205506

    Book  Google Scholar 

  • Deren L, Wenbo Y, Zhenfeng SJCUS (2021) Smart city based on digital twins. Comput Urban Sci 1:1–11

    Article  Google Scholar 

  • Digitwins Consortium (2022) DigiTwins consortium. [Online]. http://www.digitwins.org/consortium

  • El Azzaoui A, Kim TW, Loia V, Park JH (2021) Blockchain-based secure digital twin framework for smart healthy city. In: Advanced multimedia and ubiquitous engineering. Springer

    Google Scholar 

  • Falter M, Budts W, Goetschalckx K, Cornelissen V, Buys R (2019) Accuracy of apple watch measurements for heart rate and energy expenditure in patients with cardiovascular disease: cross-sectional study. JMIR mHealth uHealth 7:e11889

    Article  Google Scholar 

  • Forward (2022) Healthcare [Online]. https://www.altair.com/healthcare?_ga=2.117750793.1878170733.1671062788-1355503050.1668856147&_gac=1.220449386.1671062995.CjwKCAIAheacBhB8EiwAItVO254s-Yhrc1AXcVATr0cq94nppmgS2C43RQ5Aoui9vAJeblAfgCxHzhoC5GUQAvD_BwE. Accessed 14 December 2022

  • Fuller A, Fan Z, Day C, Barlow CJIA (2020) Digital twin: enabling technologies, challenges and open research. IEEE Access 8:108952–108971

    Article  Google Scholar 

  • Garg H (2021) Digital twin technology: revolutionary to improve personalized healthcare. Sci Prog 1:32–34. https://doi.org/10.52152/spr/2021.105

    Article  Google Scholar 

  • GE Digital (2022) DIGITAL TWIN SOFTWARE. Available: https://www.ge.com/digital/applications/digital-twin. Accessed 18 November 2022

  • Gerber D, Nguyen B, Gaetani I (2019) Digital twin: towards a meaningful framework. New York, Arup, p 160

    Google Scholar 

  • Glaessgen E, Stargel D (2012) The digital twin paradigm for future NASA and US Air Force vehicles. In: 53rd AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and materials conference. 20th AIAA/ASME/AHS adaptive structures conference 14th AIAA, 1818

    Google Scholar 

  • Grieves MJWP (2014) Digital twin: manufacturing excellence through virtual factory replication. White Paper 1:1–7

    Google Scholar 

  • Harvey A, Brand A, Holgate ST, Kristiansen LV, Lehrach H, Palotie A, Prainsack B (2012) The future of technologies for personalised medicine. New Biotechnol 29:625–633

    Article  Google Scholar 

  • Hernandez-Boussard T, Macklin P, Greenspan EJ, Gryshuk AL, Stahlberg E, Syeda-Mahmood T, Shmulevich I (2021) Digital twins for predictive oncology will be a paradigm shift for precision cancer care. Nat Med 27:2065–2066

    Article  Google Scholar 

  • Horak I, Horn S, Pieters R (2021) Agrochemicals in freshwater systems and their potential as endocrine disrupting chemicals: a South African context. Environ Pollut 268:115718

    Article  Google Scholar 

  • Jacoby M, Usländer TJAS (2020) Digital twin and internet of things—current standards landscape. Appl Sci 10:6519

    Article  Google Scholar 

  • Kamel Boulos MN, Al-Shorbaji NM (2014) On the internet of things, smart cities and the WHO healthy cities. Int J Health Geogr 13:10

    Article  Google Scholar 

  • Krumm J (2007) Inference attacks on location tracks. In: International conference on pervasive computing. Springer, pp 127–143

    Chapter  Google Scholar 

  • Kuchemüller KB, Pörtner R, Möller J (2021) Digital twins and their role in model-assisted design of experiments. Adv Biochem Eng Biotechnol 177:29–61

    Google Scholar 

  • Kurakova NG, Tsvetkova LA, Polyakova YV (2022) Digital twins in surgery: achievements and limitations. Khirurgiia (Mosk):97–110

    Google Scholar 

  • Kurlansik SL, Ibay AD (2012) Seasonal affective disorder. Am Fam Physician 86:1037–1041

    Google Scholar 

  • Kwong JCC, Khondker A, Tran C, Evans E, Cozma AI, Javidan A, Ali A, Jamal M, Short T, Papanikolaou F, Srigley JR, Fine B, Feifer A (2022) Explainable artificial intelligence to predict the risk of side-specific extraprostatic extension in pre-prostatectomy patients. Can Urol Assoc J 16:213–221

    Article  Google Scholar 

  • Laubenbacher R, Niarakis A, Helikar T, An G, Shapiro B, Malik-Sheriff RS, Sego TJ, Knapp A, Macklin P, Glazier JA (2022) Building digital twins of the human immune system: toward a roadmap. npj Digit Med 5:64

    Article  Google Scholar 

  • Lehrach H, Ionescu A, Benhabiles N (2016) The future of health care: deep data, smart sensors, virtual patients and the Internet-of-humans, vol 11. European Union

    Google Scholar 

  • Liu Y, Zhang L, Yang Y, Zhou L, Ren L, Wang F, Liu R, Pang Z, Deen MJ (2019) A novel cloud-based framework for the elderly healthcare services using digital twin. J IEEE Access 7:49088–49101

    Article  Google Scholar 

  • Lloyd-Price J, Abu-Ali G, Huttenhower C (2016) The healthy human microbiome. Genome Med 8:51

    Article  Google Scholar 

  • Majumder S, Mondal T, Deen MJ (2017) Wearable sensors for remote health monitoring. Sensors (Basel) 17

    Google Scholar 

  • Mandl KD, Manrai AK (2019) Potential excessive testing at scale: biomarkers, genomics, and machine learning. JAMA 321:739–740

    Article  Google Scholar 

  • Marcus GM (2020) The apple watch can detect atrial fibrillation: so what now? Nat Rev Cardiol 17:135–136

    Article  Google Scholar 

  • Masison J, Beezley J, Mei Y, Ribeiro H, Knapp AC, Sordo Vieira L, Adhikari B, Scindia Y, Grauer M, Helba B, Schroeder W, Mehrad B, Laubenbacher R (2021) A modular computational framework for medical digital twins. Proc Natl Acad Sci U S A 118

    Google Scholar 

  • Natakusumah K, Maulina E, Muftiadi A, Purnomo M (2022) Digital transformation of health quality services in the healthcare industry during disruption and society 5.0 era. Front Public Health 10:971486

    Article  Google Scholar 

  • Neethirajan S, Kemp B (2021) Digital twins in livestock farming. Animals 11(4):1008

    Article  Google Scholar 

  • Parker FR (2005) Department of Health and Human Services, US Food and Drug Administration: authority and responsibility. In: FDA administrative enforcement manual. CRC Press, Boca Raton, FL

    Chapter  Google Scholar 

  • Parmar R, Leiponen A, Thomas LDJBH (2020) Building an organizational digital twin. Bus Horizons 63:725–736

    Article  Google Scholar 

  • Patrick B (2020) Meet Boston’s digital twin. Esri Blog

    Google Scholar 

  • Patrick SM, Bornman MS, Joubert AM, Pitts N, Naidoo V, De Jager C (2016) Effects of environmental endocrine disruptors, including insecticides used for malaria vector control on reproductive parameters of male rats. Reprod Toxicol 61:19–27

    Article  Google Scholar 

  • Paul S, Riffat M, Yasir A, Mahim MN, Sharnali BY, Naheen IT, Rahman A, Kulkarni A, Networks A (2021) Industry 4.0 applications for medical/healthcare services. J Sensor 10:43

    Google Scholar 

  • Pesapane F, Rotili A, Penco S, Nicosia L, Cassano E (2022) Digital twins in radiology. J Clin Med 11

    Google Scholar 

  • Piplani S, Singh PK, Winkler DA, Petrovsky N (2021) In silico comparison of SARS-CoV-2 spike protein-Ace2 binding affinities across species and implications for virus origin. Sci Rep 11:13063

    Article  Google Scholar 

  • Popa, E. O., Van Hilten, M., Oosterkamp, E., Bogaardt, M.-J. & Policy 2021. The use of digital twins in healthcare: socio-ethical benefits and socio-ethical risks. Life Sci Soc, 17, 1–25

    Google Scholar 

  • Portela RMC, Varsakelis C, Richelle A, Giannelos N, Pence J, Dessoy S, Von Stosch M (2021) When is an in silico representation a digital twin? A biopharmaceutical industry approach to the digital twin concept. Adv Biochem Eng Biotechnol 176:35–55

    Google Scholar 

  • Prainsack B (2017) Personalized medicine. New York University Press, New York

    Google Scholar 

  • Rao DJ, Mane S (2019) Digital twin approach to clinical DSS with explainable AI. Artif Intell. https://doi.org/10.48550/arXiv.1910.13520

  • Sahal R, Alsamhi SH, Brown KN (2022) Personal digital twin: a close look into the present and a step towards the future of personalised healthcare industry. Sensors (Basel) 22:5918

    Article  Google Scholar 

  • Sandborn P (2007) Software obsolescence – complicating the part and technology obsolescence management problem. IEEE Trans Compon Packaging Technol 30:886–888

    Article  Google Scholar 

  • Schwartz SM, Wildenhaus K, Bucher A, Byrd B (2020) Digital twins and the emerging science of self: implications for digital health experience design and “small” data. Front Comput Sci 2:31

    Article  Google Scholar 

  • Scoles S (2016) A digital twin of your body could become a critical part of your health care

    Google Scholar 

  • Semarx (2022) Human digital twin (HDT) [online]. SEMARX, Lorton, VA. https://www.semarx.com/human-twin. Accessed 15 December 2022

  • Sifat, M. M. H., Choudhury, S. M., Das, S. K., Ahamed, M. H., Muyeen, S., Hasan, M. M., Ali, M. F., Tasneem, Z., Islam, M. M., Islam, M. R. & Ai 2022. Towards electric digital twin grid: technology and framework review. Energy, 11, 100213

    Google Scholar 

  • Singh M, Fuenmayor E, Hinchy EP, Qiao Y, Murray N, Devine D (2021a) Digital twin: origin to future. Appl Syst Innov 4:36

    Article  Google Scholar 

  • Singh RD, Koshta K, Tiwari R, Khan H, Sharma V, Srivastava V (2021b) Developmental exposure to endocrine disrupting chemicals and its impact on cardio-metabolic-renal health. Front Toxicol 3

    Google Scholar 

  • Sorell T, Rajpoot N, Verrill C (2022) Ethical issues in computational pathology. J Med Ethics 48:278–284

    Article  Google Scholar 

  • Subramanian K (2020) Digital twin for drug discovery and development—the virtual liver. J Indian Inst Sci 100:653–662

    Article  Google Scholar 

  • Sun T, He X, Song X, Shu L, Li Z (2022) The digital twin in medicine: a key to the future of healthcare? Front Med (Lausanne) 9:907066

    Article  Google Scholar 

  • Swedish Digital Twin Consortium (2022). https://www.sdtc.se/#consortium. Accessed 20 November 2022

  • Tao F, Liu W, Zhang M, Hu T, Qi Q, Zhang H, Sui F, Wang T, Xu H, Huang Z (2019a) Five-dimension digital twin model and its ten applications. Comput Integr Manuf Syst 25:1–18

    Google Scholar 

  • Tao F, Qi Q, Wang L, Nee AJE (2019b) Digital twins and cyber–physical systems toward smart manufacturing and industry 4.0. Correlation Comparison 5:653–661

    Google Scholar 

  • Telenti A, Pierce LC, Biggs WH, Di Iulio J, Wong EH, Fabani MM, Kirkness EF, Moustafa A, Shah N, Xie C, Brewerton SC, Bulsara N, Garner C, Metzker G, Sandoval E, Perkins BA, Och FJ, Turpaz Y, Venter JC (2016) Deep sequencing of 10,000 human genomes. Proc Natl Acad Sci U S A 113:11901–11906

    Article  Google Scholar 

  • Voigt I, Inojosa H, Dillenseger A, Haase R, Akgün K, Ziemssen T (2021) Digital twins for multiple sclerosis. Front Immunol 12:669811

    Article  Google Scholar 

  • Walker MJ, Rogers W (2017) Defining disease in the context of overdiagnosis. Med Health Care Philos 20:269–280

    Article  Google Scholar 

  • Walsh, J. R., Smith, A. M., Pouliot, Y., Li-Bland, D., Loukianov, A. & Fisher, C. K. 2020. Generating digital twins with multiple sclerosis using probabilistic neural networks. arXiv preprint arXiv:2002.02779

    Google Scholar 

  • Wickramasinghe N, Jayaraman PP, Forkan ARM, Ulapane N, Kaul R, Vaughan S, Zelcer J (2021) A vision for leveraging the concept of digital twins to support the provision of personalized cancer care. IEEE Internet Comput 26:17–24

    Article  Google Scholar 

  • Wickramasinghe N, Ulapane N, Andargoli A, Ossai C, Shuakat N, Nguyen T, Zelcer J (2022) Digital twins to enable better precision and personalized dementia care. JAMIA Open 5:ooac072

    Article  Google Scholar 

  • World Health Organisation (2023) Health topics: health and energy [Online]. https://www.who.int/health-topics/energy-and-health#tab=tab_1. Accessed 17 January 2023

  • Zhou X, Menche J, Barabási AL, Sharma A (2014) Human symptoms-disease network. Nat Commun 5:4212

    Article  Google Scholar 

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Authors and Affiliations

  1. SAMRC Precision Oncology Research Unit (PORU), DSI/NRF SARChI Chair in Precision Oncology and Cancer Prevention (POCP), Pan African Cancer Research Institute (PACRI), University of Pretoria, Hatfield, Pretoria, South Africa

    Rodney Hull & Zodwa Dlamini

  2. Department of Plastic and Reconstructive Surgery, Faculty of Health Sciences, Steve Biko Academic Hospital, University of Pretoria, Hatfield, Pretoria, South Africa

    Nkhensani Chauke-Malinga

  3. Department of Urology, Level 7, Bridge C, Steve Biko Academic Hospital, Faculty of Health Sciences, University of Pretoria, Arcadia, Pretoria, South Africa

    Guy Roger Gaudji

  4. Department of Internal Medicine, Section of Medical Oncology, School of Medicine, Yale Cancer Center, Yale University, New Haven, CT, USA

    Kim R. M. Blenman

  5. Department of Computer Science, School of Engineering and Applied Science, Yale University, New Haven, CT, USA

    Kim R. M. Blenman

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  1. Rodney Hull
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  2. Nkhensani Chauke-Malinga
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  3. Guy Roger Gaudji
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  1. SAMRC Precision Oncology Research Unit (PORU), DSI/NRF SARChI Chair in Precision Oncology and Cancer Prevention (POCP), Pan African Cancer Research Institute (PACRI), University of Pretoria, Pretoria, South Africa

    Zodwa Dlamini

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Hull, R., Chauke-Malinga, N., Gaudji, G.R., Blenman, K.R.M., Dlamini, Z. (2023). The Role of Digital Twinning, the Next Generation of EMR/EHR in Healthcare in a Society 5.0: Collecting Patient Data from Birth to the Grave. In: Dlamini, Z. (eds) Society 5.0 and Next Generation Healthcare. Springer, Cham. https://doi.org/10.1007/978-3-031-36461-7_8

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