Assessing acceptance of AI nurses for outpatients with chronic diseases: From nurses’ perspective
Vol 9, Issue 5, 2024, Article identifier:
VIEWS - 382 (Abstract) 172 (PDF)
Abstract
The primary objective of this article is to investigate and forecast nurses’ attitudes toward using AI nurses for outpatients with chronic diseases. AI technology is used in hospitals in a disease-centric manner. However, it is desired by healthcare regulators to be used in an individual-centric and holistic manner. The research model was developed based on the Unified Theory of Accepting and Using Technology. In determining the causes and consequences of the attitudes, actions, ideas, and beliefs of the nurses, the screening technique of causal comparison was used. Research data was collected from registered nurses who work in research hospitals and use intelligent health technologies for inpatients. Based on 494 responses, this study conducted a dual-phase assessment using Partial Least Squares Structural Equation Modeling as well as the creation of an AI method known as deep learning (artificial neural network). According to the results, nurses are convinced that AI is a suitable tool for their nursing tasks and increases their efficiency and productivity. It has been determined that nurses have intentions to use AI nurses for outpatients with chronic diseases. However, nurses have concerns about the reliability of ambulatory patient data. The policies and strategies of regulators will affect the acceptance of AI technology, not only for nurses but for all healthcare professionals and patients.
Keywords
Full Text:
PDFReferences
1. International Council of Nurses. Nursing definitions. Available online: https://www.icn.ch/nursing-policy/nursing-definitions (accessed on 10 Feburary 2023).
2. World Health Organization. Recommendations on digital interventions for health system strengthening. Available online: https://www.who.int/publications/i/item/9789241550505 (accessed on 30 January 2023).
3. Bhattacharya S. Artificial intelligence, human intelligence, and the future of public health. AIMS Public Health. 2022, 9(4): 644-650. doi: 10.3934/publichealth.2022045
4. Schmitz B, Gatsios D, Peña-Gil C, et al. Patient-centered cardiac rehabilitation by AI-powered lifestyle intervention – the timely approach. Atherosclerosis. 2022, 355: 251. doi: 10.1016/j.atherosclerosis.2022.06.959
5. Haigh KZ, Kiff LM. The independent lifestyle assistant (TM) (ILSA): AI lessons learned. In: Proceeding of the 16th Conference on Innovative Applications of Artificial Intelligence; 27-29 July 2004; San Jose, California, USA. pp. 852-857.
6. Coco K, Kangasniemi M, Rantanen T. Care Personnel’s Attitudes and Fears Toward Care Robots in Elderly Care: A Comparison of Data from the Care Personnel in Finland and Japan. Journal of Nursing Scholarship. 2018, 50(6): 634-644. doi: 10.1111/jnu.12435
7. Lin SY, Mahoney MR, Sinsky CA. Ten Ways Artificial Intelligence Will Transform Primary Care. Journal of General Internal Medicine. 2019, 34(8): 1626-1630. doi: 10.1007/s11606-019-05035-1
8. Uymaz P, Uymaz AO, Akgül Y. Assessing the Behavioral Intention of Individuals to Use an AI Doctor at the Primary, Secondary, and Tertiary Care Levels. International Journal of Human–Computer Interaction. 2023, 1-18. doi: 10.1080/10447318.2023.2233126
9. Uymaz AO, Uymaz P, Akgül Y. The shift from disease-centric to patient-centric healthcare: Assessing physicians’ intention to use AI doctors. Environment and Social Psychology. 2024, 9(4). doi: 10.54517/esp.v9i4.2308
10. Holman HR. The Relation of the Chronic Disease Epidemic to the Health Care Crisis. ACR Open Rheumatology. 2020, 2(3): 167-173. doi: 10.1002/acr2.11114
11. Yach D, Leeder SR, Bell J, et al. Global Chronic Diseases. Science. 2005, 307(5708): 317-317. doi: 10.1126/science.1108656
12. World Health Organization. Noncommunicable diseases. Available online: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases (accessed on 25 January 2024).
13. Zager Kocjan G, Špes T, Svetina M, et al. Assistive digital technology to promote quality of life and independent living for older adults through improved self-regulation: a scoping review. Behaviour & Information Technology. 2022, 42(16): 2832-2851. doi: 10.1080/0144929x.2022.2149423
14. Cruz-Martínez RR, Wentzel J, Sanderman R, et al. Tailoring eHealth design to support the self-care needs of patients with cardiovascular diseases: a vignette survey experiment. Behaviour & Information Technology. 2021, 41(14): 3065-3086. doi: 10.1080/0144929x.2021.1971764
15. Guo C, Li H. Application of 5G network combined with AI robots in personalized nursing in China: A literature review. Frontiers in Public Health. 2022, 10. doi: 10.3389/fpubh.2022.948303
16. Baysal AC. Attitude. In: Baysal AC, Tekarslan E (editors). Behavioral Science, 4th ed. Avciol; 2004. pp. 299-342.
17. Deranek K, Hewitt B, Gudi A, et al. The impact of exercise motives on adolescents’ sustained use of wearable technology. Behaviour & Information Technology. 2020, 40(7): 691-705. doi: 10.1080/0144929x.2020.1720295
18. Amisha, Malik P, Pathania M, et al. Overview of artificial intelligence in medicine. Journal of Family Medicine and Primary Care. 2019, 8(7): 2328. doi: 10.4103/jfmpc.jfmpc_440_19
19. Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minimally Invasive Therapy & Allied Technologies. 2019, 28(2): 73-81. doi: 10.1080/13645706.2019.1575882
20. van Melle W. MYCIN: a knowledge-based consultation program for infectious disease diagnosis. International Journal of Man-Machine Studies. 1978, 10(3): 313-322. doi: 10.1016/s0020-7373(78)80049-2
21. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthcare Journal. 2019, 6(2): 94-98. doi: 10.7861/futurehosp.6-2-94
22. Evans S. Challenges facing the distribution of an artificial-intelligence-based system for nursing. Journal of Medical Systems. 1985, 9(1-2): 79-89. doi: 10.1007/bf00992524
23. Tanaka N, Miyamoto K. The world needs more and better nurses. Here’s how the education sector can help. Available online: https://blogs.worldbank.org/education/world-needs-more-and-better-nurses-heres-how-education-sector-can-help (accessed on 10 Feburary 2023).
24. Bialous SA, Baltzell K. The World Needs 6 Million More Nurses: What Are We Waiting For? The American Journal of Tropical Medicine and Hygiene. 2020, 103(1): 1-2. doi: 10.4269/ajtmh.20-0451
25. Bal D. How Much Does Nursing School Cost? Available online: https://nursejournal.org/resources/how-much-does-nursing-school-cost/ (accessed on 25 January 2024).
26. Matheny M, Israni ST, Ahmed M, Whicher D (editors). Artificial Intelligence in Health Care: The Hope, the Hype, the Peril. The National Academies Press; 2019. doi: 10.17226/27111
27. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine. 2019, 25(1): 44-56. doi: 10.1038/s41591-018-0300-7
28. Roy S, Singh A, Choudhary C. Artificial intelligence in healthcare. In: Kaiser MS, Xie J, Rathore VS (editors). Information and Communication Technology for Competitive Strategies (ICTCS 2020). Springer; 2021. Volume 190. pp. 287-296. doi: 10.1007/978-981-16-0882-7_24
29. Ishii E, Ebner DK, Kimura S, et al. The advent of medical artificial intelligence: lessons from the Japanese approach. Journal of Intensive Care. 2020, 8(1). doi: 10.1186/s40560-020-00452-5
30. Ngiam KY. Braving the new world of artificial intelligence. Nature Medicine. 2019, 25(1): 13-13. doi: 10.1038/s41591-018-0317-y
31. Kwak Y, Seo YH, Ahn JW. Nursing students’ intent to use AI-based healthcare technology: Path analysis using the unified theory of acceptance and use of technology. Nurse Education Today. 2022, 119: 105541. doi: 10.1016/j.nedt.2022.105541
32. Kueper JK, Terry AL, Zwarenstein M, et al. Artificial Intelligence and Primary Care Research: A Scoping Review. The Annals of Family Medicine. 2020, 18(3): 250-258. doi: 10.1370/afm.2518
33. Khedkar P. AI can deliver better healthcare for all. Here’s how. Available online: https://www.weforum.org/agenda/2022/05/ai-can-deliver-better-healthcare-for-all-how/ (accessed on 7 Feburary 2023).
34. Gulshan V, Peng L, Coram M, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016, 316(22): 2402. doi: 10.1001/jama.2016.17216
35. Hosny A, Parmar C, Quackenbush J, et al. Artificial intelligence in radiology. Nature Reviews Cancer. 2018, 18(8): 500-510. doi: 10.1038/s41568-018-0016-5
36. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017, 69: S36-S40. doi: 10.1016/j.metabol.2017.01.011
37. Jheng YC, Kao CL, Yarmishyn AA, et al. The era of artificial intelligence–based individualized telemedicine is coming. Journal of the Chinese Medical Association. 2020, 83(11): 981-983. doi: 10.1097/jcma.0000000000000374
38. Ozmen Garibay O, Winslow B, Andolina S, et al. Six Human-Centered Artificial Intelligence Grand Challenges. International Journal of Human–Computer Interaction. 2023, 39(3): 391-437. doi: 10.1080/10447318.2022.2153320
39. Woensel WV, Scioscia F, Loseto G, et al. Explainable clinical decision support: towards patient-facing explanations for education and long-term behavior change. In: Michalowski M, Abidi SSR, Abidi S (editors). Artificial Intelligence in Medicine, Proceedings of the 20th International Conference on Artificial Intelligence in Medicine, AIME 2022; 14–17 June 2022; Halifax, NS, Canada. Volume 13263, pp. 57-62. doi: 10.1007/978-3-031-09342-5_6
40. Chen J, Chen C, Walther JB, Sundar SS. Do you feel special when an AI doctor remembers you? Individuation effects of AI vs. human doctors on user experience. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI EA 2021. 8–13 May 2021; Online Conference. doi: 10.1145/3411763.3451735
41. Ergin E, Karaarslan D, Şahan S, et al. Artificial intelligence and robot nurses: From nurse managers’ perspective: A descriptive cross‐sectional study. Journal of Nursing Management. 2022, 30(8): 3853-3862. doi: 10.1111/jonm.13646
42. Sqalli MT, Al-Thani D. AI-supported health coaching model for patients with chronic diseases. In: Proceedings of the 2019 16th International Symposium on Wireless Communication Systems (ISWCS); 27-30 August 2019; Oulu, Finland. pp. 452-456. doi: 10.1109/iswcs.2019.8877113
43. World Health Organization. Nursing and midwifery. Available online: https://www.who.int/news-room/fact-sheets/detail/nursing-and-midwifery (accessed on 25 January 2024).
44. Stokes F, Palmer A. Artificial Intelligence and Robotics in Nursing: Ethics of Caring as a Guide to Dividing Tasks Between AI and Humans. Nursing Philosophy. 2020, 21(4). doi: 10.1111/nup.12306
45. Tuisku O, Johansson-Pajala RM, Hoppe JA, et al. Assistant nurses and orientation to care robot use in three European countries. Behaviour & Information Technology. 2022, 42(6): 758-774. doi: 10.1080/0144929x.2022.2042736
46. Landi H. Nearly half of U.S. doctors say they are anxious about using AI-powered software: survey. Available online: https://www.fiercehealthcare.com/practices/nearly-half-u-s-doctors-say-they-are-anxious-about-using-ai-powered-software-survey (accessed on 29 January 2023).
47. Venkatesh, Thong, Xu. Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly. 2012, 36(1): 157. doi: 10.2307/41410412
48. Christensen L, Johnson R, Turner L. Research Methods, Design, and Analysis. Pearson; 2014.
49. Davis FD. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly. 1989, 13(3): 319. doi: 10.2307/249008
50. Venkatesh V, Davis FD. A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science. 2000, 46(2): 186-204. doi: 10.1287/mnsc.46.2.186.11926
51. Wang X, Zhou R. Impacts of User Expectation and Disconfirmation on Satisfaction and Behavior Intention: The Moderating Effect of Expectation Levels. International Journal of Human–Computer Interaction. 2022, 39(15): 3127-3140. doi: 10.1080/10447318.2022.2095479
52. Uymaz P, Uymaz AO. Assessing acceptance of augmented reality in nursing education. PLoS One. 2022, 17(2): e0263937. doi: 10.1371/journal.pone.0263937
53. Austin RR, Mathiason MA, Lu SC, et al. Toward Clinical Adoption of Standardized mHealth Solutions. CIN: Computers, Informatics, Nursing. 2022, 40(2): 71-79. doi: 10.1097/cin.0000000000000862
54. Huo W, Zhang Z, Qu J, et al. Speciesism and Preference of Human–Artificial Intelligence Interaction: A Study on Medical Artificial Intelligence. International Journal of Human–Computer Interaction. Published online February 14, 2023: 1-13. doi: 10.1080/10447318.2023.2176985
55. Sharma SK, Sharma H, Dwivedi YK. A Hybrid SEM-Neural Network Model for Predicting Determinants of Mobile Payment Services. Information Systems Management. 2019, 36(3): 243-261. doi: 10.1080/10580530.2019.1620504
56. Akgül Y, Uymaz AO. Facebook/Meta usage in higher education: A deep learning-based dual-stage SEM-ANN analysis. Education and Information Technologies. 2022, 27(7): 9821-9855. doi: 10.1007/s10639-022-11012-9
57. Akgul Y, Uymaz AO, Uymaz P. Understanding mobile learning continuance after the COVID-19 pandemic: Deep learning-based dual stage partial least squares-structural equation modeling and artificial neural network analysis. Environment and Social Psychology. 2024, 9(4). doi: 10.54517/esp.v9i4.2307
58. Lee MH, Siewiorek DP, Smailagic A, et al. Towards efficient annotations for a human-AI collaborative, clinical decision support system: A case study on physical stroke rehabilitation assessment. In: Porceedings of the IUI’22: 27th International Conference on Intelligent User Interfaces; 22-25 March 2022; Helsinki Finland. pp. 4-14, 2022, doi: 10.1145/3490099.3511112
59. Chong AYL. A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption. Expert Systems with Applications. 2013, 40(4): 1240-1247. doi: 10.1016/j.eswa.2012.08.067
60. Akgül Y. Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach. In: Patel HA, Senthil Kumar AV (editors). Applications of Artificial Neural Networks for Nonlinear Data. Engineering Science Reference; 2021. pp. 117-153. doi: 10.4018/978-1-7998-4042-8.ch006
61. Lu HP, Yang YW. Toward an understanding of the behavioral intention to use a social networking site: An extension of task-technology fit to social-technology fit. Computers in Human Behavior. 2014, 34: 323-332. doi: 10.1016/j.chb.2013.10.020
62. Park E, Baek S, Ohm J, Chang HJ. Determinants of player acceptance of mobile social network games: An application of extended technology acceptance model. Telematics and Informatics. 2014, 31(1): 3-15. doi: 10.1016/j.tele.2013.07.001
63. Hair JF, Hult GMT, Ringle CM, Sarstedt M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd ed. Sage Publication; 2016.
64. Henseler J, Ringle CM, Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science. 2014, 43(1): 115-135. doi: 10.1007/s11747-014-0403-8
65. Fornell C, Larcker DF. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research. 1981, 18(1): 39. doi: 10.2307/3151312
66. Shmueli G, Sarstedt M, Hair JF, et al. Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. European Journal of Marketing. 2019, 53(11): 2322-2347. doi: 10.1108/ejm-02-2019-0189
67. Haykin S. Neural Networks: A Comprehensive Foundation, 2nd ed. Prentice Hall; 2004.
68. Singh AK, Liébana-Cabanillas F. An SEM-neural network approach for predicting antecedents of online grocery shopping acceptance. International Journal of Human–Computer Interaction. 2022, 1-23. doi: 10.1080/10447318.2022.2151223
69. Chiang W yu K, Zhang D, Zhou L. Predicting and explaining patronage behavior toward web and traditional stores using neural networks: a comparative analysis with logistic regression. Decision Support Systems. 2006, 41(2): 514-531. doi: 10.1016/j.dss.2004.08.016
70. Lee VH, Hew JJ, Leong LY, et al. Wearable payment: A deep learning-based dual-stage SEM-ANN analysis. Expert Systems with Applications. 2020, 157: 113477. doi: 10.1016/j.eswa.2020.113477
71. Monteith S, Glenn T, Geddes J, et al. Commercial Use of Emotion Artificial Intelligence (AI): Implications for Psychiatry. Current Psychiatry Reports. 2022, 24(3): 203-211. doi: 10.1007/s11920-022-01330-7
72. Kim ED, Kuan KKY, Vaghasiya MR, et al. Passive resistance to health information technology implementation: the case of electronic medication management system. Behaviour & Information Technology. 2022, 42(13): 2308-2329. doi: 10.1080/0144929x.2022.2117081
73. Kim J, Park E. Understanding social resistance to determine the future of Internet of Things (IoT) services. Behaviour & Information Technology. 2020, 41(3): 547-557. doi: 10.1080/0144929x.2020.1827033
74. Heath M, Porter TH, Dunegan K. Obstacles to continued use of personal health records. Behaviour & Information Technology. 2020, 41(3): 574-587. doi: 10.1080/0144929x.2020.1829051
75. Ingram K. Constructing AI: Examining how AI is shaped by data, models and people. The International Review of Information Ethics. 2021, 29. doi: 10.29173/irie415
76. Müller S. Is there a civic duty to support medical AI development by sharing electronic health records? BMC Medical Ethics. 2022, 23(1). doi: 10.1186/s12910-022-00871-z
77. Yun JH, Lee E, Kim DH. Behavioral and neural evidence on consumer responses to human doctors and medical artificial intelligence. Psychology & Marketing. 2021, 38(4): 610-625. doi: 10.1002/mar.21445
78. van Berkel N, Tag B, Goncalves J, et al. Human-centred artificial intelligence: a contextual morality perspective. Behaviour & Information Technology. 2020, 41(3): 502-518. doi: 10.1080/0144929x.2020.1818828
DOI: https://doi.org/10.54517/esp.v9i5.2309
(382 Abstract Views, 172 PDF Downloads)
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Ali Osman Uymaz, Pelin Uymaz, Yakup Akgül
License URL: https://creativecommons.org/licenses/by/4.0/