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Healthcare robots and human generations: Consequences for nursing and healthcare

Published:February 01, 2022DOI:https://doi.org/10.1016/j.colegn.2022.01.008

      Summary of relevance

      • Problem: Not much is known about the influence of healthcare robots on human generational dependency.
      • What is already known: Healthcare robots were used mostly for older people care. As its use continues to progress, healthcare robots will be used for different generations to enhance broader purposes.
      • What this paper adds: Human generations appreciate healthcare robots differently. Professional nurses and multidisciplinary researchers need to develop healthcare robots for their effective use and participation in the practice of nursing and healthcare.

      Abstract

      Background

      Intelligent machines reinforce global technological reliance, maintaining their primacy in healthcare environments. Healthcare robots as intelligent machines foster proficient healthcare, although not much is known about these outcomes. Generation Z, Millennials, and Baby Boomers are expected to value healthcare robots more significantly.

      Aim

      This opinion paper explores healthcare reliance on technologies particularly intelligent healthcare robots and examines the utilitarian functionalities of technologies impacted by human generations.

      Methods

      Discursive contents were derived from relevant literature surveyed during the past decade regarding healthcare robots, intelligent machines, and human generational consequences.

      Findings

      Generational effects of healthcare robots existed. Despite being technology-natives, Generation “Z” were more adaptive, from uncertainty and emotional attachments to fearful engagements; Millennials were more trusting of robots while Baby boomers as target markets of robots showed broad-minded acceptance, especially after experiencing them. Three theories of nursing are advanced to explain the primacy of healthcare robot technologies; Transactive Relationship Theory of Nursing, Model for the Intermediary Role of Nurses in Transactive Relationships with Healthcare Robots and Technological Competency as Caring in Nursing.

      Discussion

      Reliance on technologies is a significant and egalitarian consequence of technologies, and AI offers inimitable generational consequences. Technology-dependency reflective of generational focus on effectiveness and impact on healthcare foster responsive nursing practice. Prioritising healthcare robot efficiencies in practice engenders nursing as integral to human health and well-being.

      Conclusion

      Consequences of healthcare robots in practice should distinguish generational positions. Professional nurses and multidisciplinary researchers need to legitimise healthcare robots as integral to nursing and human healthcare.

      Keywords

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      References

        • Abdi J.
        • Al-Hindawi A.
        • Ng T.
        • Vizcaychipi M.P.
        Scoping review on the use of socially assistive robot technology in elderly care.
        BMJ Open. 2018; 8 (e018815): 1-20https://doi.org/10.1136/bmjopen-2017-018815
        • Arthur C.
        • Shuhui R.
        China, robot delivery vehicles deployed to help with COVID-19 emergency.
        Available from. 2020; (Accessed 12 April 2020)
        • Beauchamp T.L.
        • Childress J.F.
        Principles of biomedical ethics.
        7th ed. University of Chicago Press, New York, NY2013
        • Betriana F.
        • Osaka K.
        • Matsumoto K.
        • Tanioka T.
        • Locsin R.C.
        Relating Mori’s, Uncanny Valley in generating conversations with artificial affective communication and natural language processing.
        Nursing Philosophy. 2020; 00: e12322https://doi.org/10.1111/nup.12322
        • Betriana F.
        • Tanioka T.
        • Osaka K.
        • Kawai C.
        • Yasuhara Y.
        • Locsin R.C.
        Interactions between healthcare robots and older people in Japan: A qualitative descriptive analysis study.
        Japan Journal of Nursing Science. 2021; 18: e12409https://doi.org/10.1111/jjns.12409
        • Beuscher L.M.
        • Fan J.
        • Sarkar N.
        • Dietrich M.S.
        • Newhouse P.A.
        • Miller K.F.
        • et al.
        Socially assistive robots: Measuring older adults` perceptions.
        Journal of Gerontological Nursing. 2017; 43 (https://dx.doi.org/10.3928/00989134-20170707-04): 35-43
        • Biswas M.
        • Romeo M.
        • Cangelosi A.
        • Jones R.B.
        Are older people any different from younger people in the way they want to interact with robots? Scenario based survey.
        Journal on Multimodal User Interfaces. 2020; 14: 61-72https://doi.org/10.1007/s12193-019-00306-x
        • Bradwell H.L.
        • Winnington R.
        • Thill S.
        • Jones R.B.
        Longitudinal diary data: Six months real world implementation of affordable companion robots for older people in supported living.
        in: Proceedings, HRI’20: Companion of the 2020 ACM/IEEE International Conference on Human Robot Interaction. 2020: 148-150https://doi.org/10.1145/3371382.3378256
        • Broadbent E.
        • Stafford R.
        • MacDonald B.
        Acceptance of healthcare robots for the older population: review and directions.
        International Journal of Social Robotics. 2009; 1: 319-330https://doi.org/10.1007/s12369-009-0030-6
        • Brown A.
        Everything you`ve wanted to know about gen Z but were afraid to ask.
        Available from. 2021; (Accessed 12 February 2021)
        • Chen K.
        • Lou V.W.
        • Tan K.C.
        • Wai M.Y.
        • Chan L.L.
        Changes in technology acceptance among older people with dementia: the role of social robot engagement.
        International Journal of Medical Informatics. 2020; 141104241https://doi.org/10.1016/j.ijmedinf.2020.104241
        • Davenport T.
        • Kalakota R.
        The potential for artificial intelligence in healthcare.
        Future Health Journal. 2019; 6: 94-98https://doi.org/10.7861/futurehosp.6-2-94
        • Dimock M.
        Defining generations: Where Millennials end and Generation Z begins.
        Available from. 2021; (Accessed 5 February 2021)
        • Fenech R.
        • Baguant P.
        • Abdelwahed I.
        Robotics and generation Z – apprehension or attachment?.
        International Journal of Business Performance Management. 2020; : 245-259https://doi.org/10.1504/IJBPM.2020.106110
        • IEEE
        IEEE unveils third annual ‘Generation of AI’ global study, illuminating trust millennial parents have in artificial intelligence for the health and wellness of their generation alpha kids.
        Available from. 2019; (Accessed date: 8 February 2021)
        • Ito H.
        • Miyagawa M.
        • Kuwamura Y.
        • Yasuhara Y.
        • Tanioka T.
        • Locsin R.
        Professional nurses’ attitudes towards the introduction of Humanoid Nursing Robots (HNRs) in health care settings.
        Journal of Nursing and Health Sciences. 2015; 9: 73-81
        • Kasasa
        • Boomers
        • Gen X.
        • Gen Y.
        • Gen Z.
        Boomers, Gen X, Gen Y, Gen Z, and Gen A explained.
        Available from. 2021; (Accessed 17 February 2021)
        • Lazanyi K.
        Generation Z and Y – are they different when it comes to trust in robots? Proceeding of the 23rd IEEE International Conference on Intelligent Engineering Systems.
        Hungary. 2019; (25 April 2019 through 27 April 2019): 191-194
        • Locsin R.C.
        • Ito H.
        • Tanioka T.
        • Yasuhara Y.
        • Osaka K.
        • Schoenhofer S.O.
        Humanoid nurse robots as caring entities: a revolutionary probability?.
        International Journal of Studies in Nursing. 2018; 3: 146-154https://doi.org/10.20849/ijsn.v3i2.456
        • Locsin R.C.
        Technological competency as caring in nursing: a model for practice.
        Sigma Theta Tau International Press, Indianapolis2005
        • Locsin R.C.
        • Purnell M.
        Advancing the theory of technological competency as caring in nursing: the universal technological domain.
        International Journal for Human Caring. 2015; 19: 50-54
        • Marín-Morales J.
        • Higuera-Trujillo J.L.
        • Greco A.
        • Guixeres J.
        • Llinares C.
        • Scilingo E.P.
        • et al.
        Affective computing in virtual reality: Emotion recognition from brain and heartbeat dynamics using wearable sensors.
        Scientific Reports. 2018; 8: 13657https://doi.org/10.1038/s41598-018-32063-4
        • Matthews K.
        The growing emergence of robots in healthcare: Key opportunities and benefits.
        Available from. 2019; (Accessed 13 November 2020)
        • Miyagawa M.
        • Yasuhara Y.
        • Tanioka T.
        • Locsin R.
        • Kongsuwan W.
        • Catangui E.
        • et al.
        The optimization of humanoid robot`s dialog in improving communication between humanoid robot and older adults.
        Intelligent Control and Automation. 2019; 10: 118-127https://doi.org/10.4236/ica.2019.103008
        • Mori M.
        The Uncanny Valley.
        Energy. 1970; 7: 33-35
        • Oracle
        Mental health at work requires attention, nuance, and swift action.
        Available from. 2021; 2 (Accessed 10 May, 2021): 2-9
      1. Orejana, J. R., MacDonald, B. A., Ahn, H. S., Peri, K., & Broadbent, E. (2015). Healthcare robots in homes of rural older adults. In: A. Tapus, E. André, J. C. Martin, F. Ferland, & M. Ammi (eds) Social robotics. ICSR 2015. Lecture notes in computer science, vol 9388. Springer, Cham. https://doi.org/10.1007/978-3-319-25554-5_51

        • Osaka K.
        Development of the model for the intermediary role of nurses in transactive relationships with healthcare robots.
        International Journal for Human Caring. 2020; 24: 265-274https://doi.org/10.20467/HumanCaring-D-20-00014
        • Papadopoulos I.
        • Koulouglioti C.
        • Ali S.
        Views of nurses and other health and social care workers on the use of assistive humanoid and animal-like robots in health and social care: a scoping review.
        Contemporary Nurse. 2018; 54: 425-442https://doi.org/10.1080/10376178.2018.1519374
        • Pepito J.A.
        • Ito H.
        • Betriana F.
        • Tanioka T.
        • Locsin R.C.
        Intelligent humanoid robots expressing artificial humanlike empathy in nursing situations.
        Nursing Philosophy. 2020; 21: e12318https://doi.org/10.1111/nup.12318
        • Picard R.W.
        Affective computing.
        MIT Press, Cambridge, MA, USA1997
        • Pinto C.
        • van Gool C.
        • Cascini F.
        • Marono F.
        • Hämäläinen H.
        • Csizmadia I.
        • et al.
        Why health is a special case for data governance.
        Available from. 2021; (Accessed 17 December 2021)
      2. Pinsker, J. (2020). Oh no, they`ve come up with another generation label. Retrieved from https://www.theatlantic.com/family/archive/2020/02/generation-after-gen-z-named-alpha/606862/on Accessed February 19, 2021.

      3. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation). Retrieved from https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:02016R0679-20160504 (Accessed on 17 December, 2021).

        • Rich K.L.
        Introduction to bioethics and ethical decision making.
        in: Butts J.B. Rich K.L. Nursing ethics: Across the curriculum and into practice. Jones & Barlett Learning, Burlington, MA2020 (Fifth edition)
        • Rogers E.M.
        Diffusion of innovations.
        Free Press, New York, NY2003
        • Ruspini E.
        The Robot and Us: An “Antidisciplinary” perspective on the scientific and social impacts of robotics.
        Journal of Tourism Futures. 2019; 5: 297-298https://doi.org/10.1108/JTF-09-2019-089
        • Sheetz K.H.
        • Claflin J.
        • Dimick J.B.
        Trends in the adoption of robotic surgery for common surgical procedures.
        JAMA Network Open. 2020; 3e1918911https://doi.org/10.1001/jamanetworkopen.2019.18911
        • Tanioka T.
        The development of the Transactive Relationship Theory of Nursing (TRETON): a nursing engagement model for person and humanoid nursing robots.
        International Journal of Nursing and Clinical Practices. 2017; 4: 233https://doi.org/10.15344/2394-4978/2017/223
        • Tanioka T.
        • Betriana F.
        • Yokotani T.
        • Osaka K.
        • Locsin R.C.
        • King B.
        • et al.
        The experience of older persons with mental health conditions who interact with healthcare robots and nurse intermediaries: the qualitative case studies.
        Belitung Nursing Journal. 2021; 7: 346-353https://doi.org/10.33546/bnj.1541
        • Tanioka T.
        • Locsin R.C.
        • Betriana F.
        • Kai Y.
        • Osaka K.
        • Baua E.
        • et al.
        Intentional observational clinical research design for complex clinical research using advanced technology.
        International Journal of Environmental Research and Public Health. 2021; 18: 11184https://doi.org/10.3390/ijerph182111184
        • Tanioka T.
        • Osaka K.
        • Locsin R.
        • Yasuhara Y.
        • Ito H.
        Recommended design and direction of development for humanoid nursing robots perspective from nursing researchers.
        Intelligent Control and Automation. 2017; 8: 96-110https://doi.org/10.4236/ica.2017.82008
        • van Wynsberghe A.
        Designing robots for care: Care centered value-sentitive design.
        Science and Engineering Ethics. 2012; 19: 407-433https://doi.org/10.1007/s11948-011-9343-6
      4. Western Governors University. (2019). Who is gen Z and how will they impact the workplace?Retrieved fromhttps://www.wgu.edu/blog/who-is-gen-z-how-they-impact-workplace1906.html on November 13, 2020.

        • Wu Y.H.
        • Wrobel J.
        • Cornuet M.
        • Kerhervé H.
        • Damnée S.
        • Rigaud A.S.
        Acceptance of an assistive robot in older adults: a mixed-method study of human-robot interaction over a 1-month period in the Living Lab setting.
        Clinical Interventions in Aging. 2014; 9 (https://dx.doi.org/10.2147/CIA.S56435): 801-811