Future outlook on the materialisation, expectations and implementation of Digital Twins in healthcare

In this paper we examine how experts in digital transformation of healthcare envision the application of Digital Twins. The concept of a Digital Twin refers to a digital replica of potential and actual physical assets, processes, people, places, systems and devices that can be used for various purposes including scientific experiments, simulations and prediction of intervention outcomes. Digital Twins are an emerging technological vision and while evocative as a term it holds different promises and connotations for different application areas and may evolve in very different directions. In order to examine how these directions can develop and impact healthcare we used the Delphi method to reach a consensus among experts on three different research questions we have put forward, namely how experts see the materialisation, expectations and implementation of a Digital Twin in healthcare. Our main conclusion is that Digital Twins are seen as enabling preventive healthcare and trial-and-error approaches to support personalised medicine and/or patient centred care.

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Author and article information

Contributors

Christel De Maeyer

Panos Markopoulos

Conference

Publication date: July 2021

Publication date (Print): July 2021

Pages: 180-191

Affiliations

[0001]Department of Industrial Design

Eindhoven University of Technology

Article

DOI: 10.14236/ewic/HCI2021.18

SO-VID: ea7704cc-ef72-41ff-ac69-311b0093a3e3

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This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

Conference name: 34th British HCI Conference

Conference acronym: HCI2021

Conference number: 34

Conference location: London, UK

Conference date: 20th - 21st July 2021

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Post-pandemic HCI – Living Digitally

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1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/HCI2021.18

Self URI (journal page): https://ewic.bcs.org/

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Comments

Comment on this article

[1] AI HLEG, (2019). Ethics Guidelines For Trustworthy AI.https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthy-ai.

[2] N. Bagaria (2020). Health 4.0: Digital Twins for Health and Well-Being. pp. 143-152.

[3] M. A. Barrett (2013, September). Big Data and Disease Prevention: From Quantified Self to Quantified Communities. Big Data, pp. 168-175.

[4] B. R. Barricelli (2019). A Survey on Digital Twin: Definitions, Characteristics, Applications, and Design Implications. IEEE Xplore, 7, pp. 167653-167671.

[5] C. Bartneck (2020). An Introduction to Ethics in Robotics and AI. SpringerBriefs in Ethics.

[6] K. Bruynseels (2018). Digital Twins in Health Care: Ethical Implications of an Emerging Engineering Paradigm. Frontiers in Genetics, 31.

[7] Byteflies. (n.d.). Retrieved from https://www.byteflies.com/

[8] C. De Maeyer (2020). Are Digital Twins becoming our Personal (predictive) Advisors? 'Our Digital Mirror of Who We Were, Who We Are and Who We Will Become’. HCI International 2020. 12208. Copenhagen: Q. Gao, J. Zhou (eds) Human Aspects of IT for the Aged Population. Healthy and Active Aging. HCII 2020. Lecture Notes in Computer Science. Springer, Cham.

[9] L. El-Alti (2019). Person Centered Care and Personalized Medicine: Irreconcilable Opposites or Potential Companions? Health Care Anal, pp. 45-59.

[10] H. Elayan (2021). Digital twin for intelligent context-aware iot healthcare systems. Ieee Internet of Things Journal.

[11] H. Etzkowitz (2017). The Triple Helix: University–Industry–Government Innovation and Entrepreneurship (2nd ed.). Routledge.

[12] Fibricheck. (n.d.). Retrieved from https://www.fibricheck.com/

[13] F. Fukuyama (2002). Our Posthuman Future: Consequences of the Biotechnology Revolution. Published by Farrar, Straus and Giroux. Macmillan.

[14] A. F. Fuller (2020). Digital Twin: Enabling Technology, Challenges and Open Research. IEEE Access, 8, pp. 108952-108971.

[15] T. J. Gordon (1994). The Delphi Method.

[16] M. Grieves (2014, January 3). Digital Twin: Manufacturing Excellence through Virtual Factory Replication. USA.

[17] M. Grieves (2017). Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems. FJ. Kahlen, S. Flumerfelt, A. Alves (eds) Transdisciplinary Perspectives on Complex Systems.

[18] Healthouse, Retrieved from https://www.health-house.be/en/

[19] O. Helmer (1950-60). Retrieved from https://www.rand.org/

[20] A. Justice (n.d.). Algoritmic Justice League: Unmasking AI harms and Biases. Retrieved from https://www.ajl.org/

[21] W. Kritzinger (2018). Digital Twin in manufacturing: A categorical literature review and classification. IFAC-PapersOnLine, 51(11), pp. 1016-1022.

[22] H. Lakki (2019). Prototyping a Digital Twin for Real Time Remote Control Over Mobile Networks: Application of Remote Surgery. IEEE Access.

[23] D. Lewis (2021). Contact-Tracing Apps Help to Reduce Covid Infections. Nature.

[24] D. Lupton (2014). Self-Tracking Modes: Reflexive Self-Monitoring and Data Practices. SSRN Electronic Journal.

[25] D. Lupton (2019). Data selves, More-than-Human Perspectives. Wiley.

[26] S. Malakuti (2018). Architectural aspects of digital twins in IIoT systems. Proceedings of the 12th European Conference on Software Architecture: Companion Proceedings ECSA '18, Association for Computing Machinery, N (pp. 1-2). ACM Digital Library.

[27] P.M. Mullen (2003). Delphi:myths and reality, Journal of Health Organization and Management, Vol 17 No 1, pp 37-52

[28] C. O' Neill (2016). Weapons of math destruction. Crown Books.

[29] Persistent. (2020). Digital Twin Whitepaper. Retrieved from https://www.persistent.com/wp-content/uploads/2020/01/digital-twins-whitepaper.pdf

[30] A. Rasheed (2020). Digital Twin: Values, Challenges and Enablers From a Modeling Perspective. IEEE Access, 21980-22012.

[31] A.B. Renzi (2015). Delphi method to explore future scenario possibilities on technology and HCI. A. Marcus (eds) Design, User Experience, and Usability: Design Discourse. Lecture Notes in Computer Science. Springer, Cham.

[32] L. F. Rivera (2019). Towards continuous monitoring in personalized healthcare through digital twins. Proceedings of the 29th Annual International Conference on Computer Science and Software Engineering (CASCON '19) (pp. 329-335). IBM Corp.

[33] P. Rodríguez (2021). A population-based controlled experiment assessing the epidemiological impact of digital contact tracing. Nat Commun 12, 587.

[34] T. Saaty (2008). Decision making with the analytic hierarchy process. Int. J. Services Sciences, Vol. 1, No. 1.

[35] A. Saddik (2018). Digital Twins: The Convergence of Multimedia Technologies. IEEE MultiMedia,, 87-92.

[36] M. Searl (2010). It is time to talk about people: a human-centered healthcare system. Health Res Policy Syst.

[37] G. Smith (2016). Surveillance, Data and Embodiment: On the Work of Being Watched. Body & Society, Vol.22, 108-139.

[38] Schwartz (2020). Digital Twins and the Emerging Science of Self: Implications for Digital Health Experience Design and “Small” Data. Frontiers in Computer Science, 31.

[39] World Economic Forum. (2020, April 7). Retrieved from What can smart cities tell us about covid-19: https://www.weforum.org/agenda/2020/04/smart-cities-technology-coronavirus-covid19/

[40] C. Yang (2019). Exploring Design Guidelines of Using User-Centered Design in Gamification Development: A Delphi Study. International Journal of Human–Computer Interaction, 1170-1181.

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