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Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 39

IoB-TMAF: Internet of Body-based Telemedicine Adoption Framework

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DOI: http://dx.doi.org/10.15439/2024F4805

Citation: Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 39, pages 343–353 ()

Full text

Abstract. Saudi healthcare organizations are increasingly using Telemedicine (TM) services to reduce expenses and improve the effectiveness of healthcare delivered. Population aging and the growth of the costs of chronic diseases management has an urgent problem that requires the use of technical solutions that contribute to expanding and improving healthcare services and addressing these issues. Consequently, the growing investments in developing TM products and services have made user acceptance of technology crucial in ensuring effective use. The purpose of this study is to explore the factors influencing Saudi patients and healthcare providers to adopt Internet of Body (IoB) technologies to support diagnosis in TM settings. The Technology Acceptance Model (TAM) is employed in this study as the foundational theoretical framework, extending it with additional constructs to fit the context. The IoB-TMAF model identifies factors influencing the adoption intentions of patients and providers for IoB-based TM system. The influencing factors stem from users' individual contexts (social influence, self-efficacy, attitude, and perceived trust), technological contexts (perceived usefulness, perceived ease of use, task fit, reliability, perceived cost, and perceived privacy control), organizational contexts (facilitating conditions), and health contexts (perceived health risk). This study adds to the existing literature by introducing a comprehensive model to explore the motivational factors driving the effective adoption of IoB-based TM in the Kingdom of Saudi Arabia (KSA). Thus, formulating a strategy for the proper execution aligned with the viewpoints of its users.

References

  1. I. Grand View Research. (2023, Dec) Telemedicine market size to reach $380.3 billion by 2030. [Online]. Available: "https://www.grandviewresearch.com/press-release/global-telemedicine-industry"
  2. A. Albahri, J. Alwan, Z. Taha, S. Fawzi, R. Amjed, A. Zaidan, O. Albahri, B. Bahaa, A. Alamoodi, and M. Alsalem, “Iot-based telemedicine for disease prevention and health promotion: State-of-the-art,” Journal of Network and Computer Applications, 10 2020.
  3. F. Albejaidi and K. Nair, “Building the health workforce: Saudi arabia’s challenges in achieving vision 2030,” The International Journal of Health Planning and Management, vol. 34, 08 2019.
  4. M. Mohaya, M. Almaziad, K. Al-Hamad, and M. Mustafa, “Telemedicine among oral medicine practitioners during covid-19 pandemic and its future impact on the specialty,” Risk Management and Healthcare Policy, vol. Volume 14, pp. 4369–4378, 10 2021.
  5. S. Sood, V. Mbarika, S. Jugoo, R. Dookhy, C. Doarn, N. Prakash, and R. Merrell, “What is telemedicine? a collection of 104 peer-reviewed perspectives and theoretical underpinnings,” Telemedicine journal and e-health : the official journal of the American Telemedicine Association, vol. 13, pp. 573–90, 11 2007.
  6. M. Ong, P. Romano, S. Edgington, H. Aronow, A. Auerbach, J. Black, T. Marco, J. Escarce, L. Evangelista, B. Hanna, T. Ganiats, B. Greenberg, S. Greenfield, S. Kaplan, A. Kimchi, H. Liu, D. Lombardo, C. Mangione, B. Sadeghi, and G. Fonarow, “Effectiveness of remote patient monitoring after discharge of hospitalized patients with heart failure,” JAMA Internal Medicine, vol. 176, 02 2016.
  7. A. Di Cerbo, J. C. Morales Medina, B. Palmieri, and T. Iannitti, “Narrative review of telemedicine consultation in medical practice,” Patient Preference and Adherence, vol. 9, pp. 65–75, 01 2015.
  8. A. Neamah, M. Ghani, A. Ahmad, E. Alomari, and R. R. Nuiaa, “E-health state in middle east countries: An overview,” pp. 2974–2990, 09 2018.
  9. T. Ghiwaa, I. Khan, M. White, and N. Beloff, “Telemedicine adoption for healthcare delivery: A systematic review,” International Journal of Advanced Computer Science and Applications, vol. 14, 01 2023.
  10. M. Al-rawashdeh, P. Keikhosrokiani, B. Belaton, M. Alawida, and A. Zwiri, “Iot adoption and application for smart healthcare: A systematic review,” Sensors, vol. 22, p. 5377, 07 2022.
  11. C. Amato, “Internet of bodies: Digital content directive, and beyond,” JIPITEC – Journal of Intellectual Property, Information Technology and E-Commerce Law, vol. 12, 2021.
  12. M. Zayyad and M. Toycan, “Factors affecting sustainable adoption of e-health technology in developing countries: An exploratory survey of nigerian hospitals from the perspective of healthcare professionals,” PeerJ, vol. 6, p. e4436, 03 2018.
  13. F. Alanezi, “Factors affecting the adoption of e-health system in the kingdom of saudi arabia,” International Health, vol. 13, 11 2020.
  14. W. H. O. (WHO). (1998, Dec) A health telematics policy in support of who’s health-for-all strategy for global health development. [Online]. Available: https://apps.who.int/gb/ebwha/pdf_files/EB101/pdfangl/angid9.pdf
  15. R. Wootton, “Telemedicine support for the developing world,” Journal of telemedicine and telecare, vol. 14, pp. 109–14, 02 2008.
  16. A. Alaboudi, A. Atkins, B. Sharp, A. Balkhair, M. Alzahrani, and T. Sunbul, “Barriers and challenges in adopting saudi telemedicine network: The perceptions of decision makers of healthcare facilities in saudi arabia,” Journal of Infection and Public Health, vol. 9, 09 2016.
  17. S. Zailani, M. Gilani, D. Nikbin, and M. Iranmanesh, “Determinants of telemedicine acceptance in selected public hospitals in malaysia: Clinical perspective,” Journal of medical systems, vol. 38, p. 111, 09 2014.
  18. M. Douglas, J. Xu, A. Heggs, G. Wrenn, D. Mack, and G. Rust, “Assessing telemedicine utilization by using medicaid claims data,” Psychiatric Services, vol. 68, pp. 173–178, 02 2017.
  19. H. Nadri, B. Rahimi, H. Lotfnezhad Afshar, M. Samadbeik, and A. Garavand, “Factors affecting acceptance of hospital information systems based on extended technology acceptance model: A case study in three paraclinical departments,” Appl Clin Inform, vol. 09, pp. 238–247, 04 2018.
  20. H. Al-Samarraie, S. Ghazal, A. Alzahrani, and L. Moody, “Telemedicine in middle eastern countries: Progress, barriers, and policy recommendations,” International Journal of Medical Informatics, vol. 141, p. 104232, 07 2020.
  21. A. Alqudah, M. Al-Emran, and K. Shaalan, “Technology acceptance in healthcare: A systematic review,” Applied Sciences, vol. 11, p. 10537, 11 2021.
  22. E. M. Rogers, Diffusion of Innovations, 4th Edition. New York: the Free Press, 1995.
  23. M. Fishbein, I. Ajzen, and A. Belief, “Belief, attitude, intention, and behavior: An introduction to theory and research,” Contemporary Sociology, vol. 6, 03 1977.
  24. I. Ajzen, “The theory of planned behaviour: Reactions and reflections,” Psychology & health, vol. 26, pp. 1113–27, 09 2011.
  25. V. Venkatesh and H. Bala, “Technology acceptance model 3 and a research agenda on interventions,” Decision Sciences - DECISION SCI, vol. 39, pp. 273–315, 05 2008.
  26. V. Venkatesh and F. Davis, “A theoretical extension of the technology acceptance model: Four longitudinal field studies,” Management Science, vol. 46, pp. 186–204, 02 2000.
  27. V. Venkatesh, M. Morris, G. Davis, and F. Davis, “User acceptance of information technology: Toward a unified view,” MIS Quarterly, vol. 27, pp. 425–478, 09 2003.
  28. I. M. Rosenstock, “Historical origins of the health belief model,” Health Education Monographs, vol. 2, no. 4, pp. 328–335, 1974. [Online]. Available: https://doi.org/10.1177/109019817400200403
  29. C. Barrette, “Usefulness of technology adoption research in introducing an online workbook,” System, vol. 49, 04 2015.
  30. W. Ben Arfi, I. Nasr, T. Khvatova, and Y. Ben Zaied, “Understanding acceptance of ehealthcare by iot natives and iot immigrants: An integrated model of utaut, perceived risk, and financial cost,” Technological Forecasting and Social Change, vol. 163, 11 2020.
  31. R. Holden and B.-T. Karsh, “The technology acceptance model: Its past and its future in health care,” Journal of biomedical informatics, vol. 43, pp. 159–72, 08 2009.
  32. J. Monthuy-Blanc, S. Bouchard, C. Maïano, and M. Seguin, “Factors influencing mental health providers’ intention to use telepsychotherapy in first nations communities,” Transcultural psychiatry, vol. 50, 05 2013.
  33. M. Yamin and B. Alyoubi, “Adoption of telemedicine applications among saudi citizens during covid-19 pandemic: An alternative health delivery system,” Journal of Infection and Public Health, vol. 13, 10 2020.
  34. R. Evering, M. Postel, H. van Os-Medendorp, M. Bults, and M. den Ouden, “Intention of healthcare providers to use video-communication in terminal care: a cross-sectional study,” BMC Palliative Care, vol. 21, 11 2022.
  35. G. Pagaling, A. Espiritu, M. Dellosa, C. F. Leochico, and P. Pasco, “The practice of teleneurology in the philippines during the covid-19 pandemic,” Neurological Sciences, vol. 43, 11 2021.
  36. J. Kim and H.-A. Park, “Development of a health information technology acceptance model using consumers’ health behavior intention,” Journal of medical Internet research, vol. 14, p. e133, 10 2012.
  37. S. Kamal, M. Shafiq, and P. Kakria, “Investigating acceptance of telemedicine services through an extended technology acceptance model (tam),” Technology in Society, vol. 60, p. 101212, 11 2019.
  38. H. Wang, N. Yu, and X. Qu, “Understanding consumer acceptance of healthcare wearable devices: An integrated model of utaut and ttf,” International Journal of Medical Informatics, vol. 139, p. 104156, 04 2020.
  39. F. Dany and B. Römer, “Understanding dr. no - a comprehensive model explaining physicians’ acceptance of telemedical systems,” ECIS 2014 Proceedings - 22nd European Conference on Information Systems, 01 2014.
  40. W. Ben Arfi, I. Nasr, G. Kondrateva, and L. Hikkerova, “The role of trust in intention to use the iot in ehealth: Application of the modified utaut in a consumer context,” Technological Forecasting and Social Change, vol. 167, p. 120688, 06 2021.
  41. A. Esber, M. Teufel, L. Jahre, J. Schmitten, E.-M. Skoda, and A. Bäuerle, “Predictors of patients’ acceptance of video consultation in general practice during the coronavirus disease 2019 pandemic applying the unified theory of acceptance and use of technology model,” DIGITAL HEALTH, vol. 9, p. 205520762211493, 01 2023.
  42. M. I. Hossain, A. Fadhil, A. Hussin, N. Iahad, and A. Sadiq, “Factors influencing adoption model of continuous glucose monitoring devices for internet of things healthcare,” Internet of Things, vol. 15, p. 100353, 01 2021.
  43. N. Dogra, S. Bakshi, and A. Gupta, “Exploring the switching intention of patients to e-health consultations platforms: blending inertia with push–pull–mooring framework,” Journal of Asia Business Studies, vol. ahead-of-print, 01 2022.
  44. S. Kaphle, S. Chaturvedi, I. Chaudhuri, R. Krishnan, and N. Lesh, “Adoption and usage of mhealth technology on quality and experience of care provided by frontline workers: Observations from rural india,” JMIR mHealth and uHealth, vol. 3, p. e61, 05 2015.
  45. M. Mital, P. Choudhary, V. Chang, A. Papa, and A. Pani, “Adoption of internet of things in india: A test of competing models using a structured equation modeling approach,” Technological Forecasting and Social Change, vol. 136, 03 2017.
  46. A. Bhattacherjee and N. Hikmet, “Reconceptualizing organizational support and its effect on information technology usage: Evidence from the health care sector,” Journal of Computer Information Systems, vol. 48, pp. 69–76, 06 2008.
  47. J. Liu, G. Sorwar, M. Rahman, and M. Hoque, “The role of trust and habit in the adoption of mhealth by older adults in hong kong: a healthcare technology service acceptance (htsa) model,” BMC geriatrics, vol. 23, p. 73, 02 2023.
  48. C. Bakker, J. Huirne, F. Schaafsma, C. Geus, H. Bonjer, and J. Anema, “Electronic health program to empower patients in returning to normal activities after colorectal surgical procedures: Mixed-methods process evaluation alongside a randomized controlled trial (preprint),” 04 2018.
  49. A. Alomari and B. Soh, “Determinants of medical internet of things adoption in healthcare and the role of demographic factors incorporating modified utaut,” International Journal of Advanced Computer Science and Applications, vol. 14, 01 2023.
  50. D. Compeau and C. Higgins, “Computer self-efficacy: Development of a measure and initial test,” MIS Quarterly, vol. 19, pp. 189–211, 06 1995.
  51. V. Venkatesh, “Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model,” Information Systems Research, vol. 11, pp. 342–365, 12 2000.
  52. S. RMarikyan, D. Papagiannidis, THEORYHUB BOOK, A THEORY RESOURCE FOR STUDENTS AND RESEARCHERS ALIKE. Newcastle upon Tyne, 2022. [Online]. Available: https://open.ncl.ac.uk/
  53. Y. Sun, N. Wang, X. Guo, and J. Peng, “Understanding the acceptance of mobile health services: A comparison and integration of alternative models,” Journal of Electronic Commerce Research, vol. 14, pp. 183–200, 01 2013.
  54. H. Cho, G. Sanabria, M. Saylor, M. Gradilla, and R. Schnall, “Use of the fitt framework to understand patients’ experiences using a real-time medication monitoring pill bottle linked to a mobile-based hiv self-management app: A qualitative study,” International Journal of Medical Informatics, vol. 131, 08 2019.
  55. L. Russo, I. Campagna, B. Ferretti, E. Agricola, E. Pandolfi, E. Carloni, A. D’Ambrosio, F. Gesualdo, and A. Tozzi, “What drives attitude towards telemedicine among families of pediatric patients? a survey,” BMC Pediatrics, vol. 17, 01 2017.
  56. V. Brunelli, J. Fox, and D. Langbecker, “Disparity in cancer survivorship care: A cross-sectional study of telehealth use among cancer nurses in australia,” Collegian, vol. 28, 01 2021.
  57. P. Chau and P. Hu, “Investigating health care professionals decisions to accept telemedicine technology: An empirical test of competing theories,” Information & Management, vol. 39, pp. 297–311, 01 2002.
  58. S. Chakraborty and V. Bhatt, “Mobile iot adoption as antecedent to care-service efficiency and improvement: Empirical study in healthcare-context,” Journal of International Technology and Information Management, vol. 28, p. 2019, 12 2019.
  59. L. Liu and L. Shi, “Chinese patients’ intention to use different types of internet hospitals: Cross-sectional study on virtual visits,” Journal of Medical Internet Research, vol. 23, p. e25978, 08 2021.
  60. H. Hoaas, H. Andreassen, L. Lien, A. Hjalmarsen, and P. Zanaboni, “Adherence and factors affecting satisfaction in long-term telerehabilitation for patients with chronic obstructive pulmonary disease: a mixed methods study,” BMC Medical Informatics and Decision Making, vol. 16, 02 2016.
  61. W. Luo and M. Najdawi, “Trust-building measures: A review of consumer health portals,” Commun. ACM, vol. 47, pp. 108–113, 01 2004.
  62. D. McKnight, M. Carter, J. Thatcher, and P. Clay, “Trust in a specific technology: An investigation of its components and measures.” ACM Trans. Management Inf. Syst., vol. 2, p. 12, 01 2011.
  63. J. Nord, A. Koohang, and J. Paliszkiewicz, “The internet of things: Review and theoretical framework,” Expert Systems with Applications, vol. 133, 05 2019.
  64. D. Zulman, M. Kirch, K. Zheng, and L. An, “Trust in the internet as a health resource among older adults: Analysis of data from a nationally representative survey,” Journal of medical Internet research, vol. 13, p. e19, 02 2011.
  65. F. Davis and F. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information technology,” MIS Quarterly, vol. 13, pp. 319–, 09 1989.
  66. R. Saravanakumar, P. Bedi, O. Hemakesavulu, T. Natarajan, E. Poornima, L. Thangavelu, and D. Jayadevappa, “Iob: Sensors for wearable monitoring and enhancing health care systems,” IEEE Instrumentation & Measurement Magazine, vol. 25, pp. 63–70, 05 2022.
  67. M. Nguyen, J. Fujioka, K. Wentlandt, N. Onabajo, I. Wong, R. Bhatia, O. Bhattacharyya, and V. Stamenova, “Using the technology acceptance model to explore health provider and administrator perceptions of the usefulness and ease of using technology in palliative care,” BMC palliative care, vol. 19, p. 138, 09 2020.
  68. S. Marhefka, D. Turner, and E. Lockhart, “Understanding women’s willingness to use e-health for hiv-related services: A novel application of the technology readiness and acceptance model to a highly stigmatized medical condition,” Telemedicine and e-Health, vol. 25, 08 2018.
  69. Y. Gao, H. Li, and Y. Luo, “An empirical study of wearable technology acceptance in healthcare,” Industrial Management & Data Systems, vol. 115, pp. 1704–1723, 10 2015.
  70. A. Asma, R. Crowder, and G. Wills, “Barriers to the adoption of ehr systems in the kingdom of saudi arabia: an exploratory study using a systematic literature review,” Journal of Health Informatics in Developing Countries, vol. 11, 07 2017.
  71. A. Kakar, “A user-centric typology of information system requirements,” Journal of Organizational and End User Computing, vol. 28, pp. 32–55, 01 2016.
  72. D. Goodhue, “Understanding user evaluations of information systems,” Management Science, vol. 41, pp. 1827–1844, 12 1995.
  73. E. Locke and G. Latham, “A theory of goal setting & task performance,” The Academy of Management Review, vol. 16, 04 1991.
  74. D. Kieras and P. Polson, “Approach to the formal analysis of user complexity,” International Journal of Man-Machine Studies, vol. 51, pp. 365–394, 04 1985.
  75. A. Mubaraki, A. Alrabie, A. Sibyani, R. Aljuaid, A. Bajaber, and M. Mubaraki, “Advantages and disadvantages of telemedicine during the covid-19 pandemic era among physicians in taif, saudi arabia,” Saudi Medical Journal, vol. 42, pp. 110–115, 01 2021.
  76. M. Alsaleh, V. Watzlaf, D. DeAlmeida, and A. Saptono, “Evaluation of a telehealth application (sehha) used during the covid-19 pandemic in saudi arabia: Provider experience and satisfaction,” Perspectives in health information management, vol. 18, p. 1b, 10 2021.
  77. W. Wilkowska and M. Ziefle, “Understanding trust in medical technologies,” 01 2018, pp. 62–73.
  78. T. Hess, A. McNab, and K. Basoglu, “Reliability generalization of perceived ease of use, perceived usefulness, and behavioral intentions,” MIS Quarterly, vol. 38, p. 2014, 03 2014.
  79. W. Delone and E. McLean, “Information systems success: The quest for the dependent variable,” Information Systems Research, vol. 3, pp. 60–95, 03 1992.
  80. V. Venkatesh, J. Thong, and X. Xu, “Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology,” MIS Quarterly, vol. 36, pp. 157–178, 03 2012.
  81. E. Park, Y. Cho, J. Han, and S. Kwon, “Comprehensive approaches to user acceptance of internet of things in a smart home environment,” IEEE Internet of Things Journal, vol. PP, pp. 1–1, 09 2017.
  82. J. Nagy, J. Oláh, E. Erdei, D. Máté, and J. Popp, “The role and impact of industry 4.0 and the internet of things on the business strategy of the value chain—the case of hungary,” Sustainability, vol. 10, p. 3491, 09 2018.
  83. A. Alhasan, L. Audah, I. Ibrhim, A. Alsharaa, A. Al-Ogaili, and M. Jabiry, “A case-study to examine doctors’ intentions to use iot healthcare devices in iraq during covid-19 pandemic,” International Journal of Pervasive Computing and Communications, vol. ahead-of-print, 11 2020.
  84. M. Abdellatif and W. Mohamed, “Telemedicine: An iot based remote healthcare system,” International Journal of Online and Biomedical Engineering (iJOE), vol. 16, p. 72, 05 2020.
  85. X. Zhang, S. Liu, X. Chen, L. Wang, B. Gao, and Q. Zhu, “Health information privacy concerns, antecedents, and information disclosure intention in online health communities,” Information & Management, vol. 55, 05 2018.
  86. H. Li, A. Gupta, J. Zhang, and R. Sarathy, “Examining the decision to use standalone personal health record systems as a trust-enabled fair social contact,” Decision Support Systems, vol. 57, p. 376–386, 01 2014.
  87. S. Mustafa, W. Zhang, M. Shehzad, A. Anwar, and G. Rubakula, “Does health consciousness matter to adopt new technology? an integrated model of utaut2 with sem-fsqca approach,” Frontiers in Psychology, vol. 13, 02 2022.
  88. N. Abolhassani, B. Santos-Eggimann, A. Chiolero, V. Santschi, and Y. Henchoz, “Readiness to accept health information and communication technologies: A population-based survey of community-dwelling older adults,” International Journal of Medical Informatics, vol. 130, 08 2019.
  89. J. Mou and J. Cohen, “Trust, risk barriers and health beliefs in consumer acceptance of online health services,” 01 2014.