Willingness of elderly patients to use artificial intelligence robots and its influencing factors_West China Medical Journal

Authors：

ZHOU Yulan ¹ , SHU Xiao ¹ , YANG Zijing ¹ ,  CHEN Qian ^1,2

1. West China School of Nursing, Sichuan University / Department of Geriatrics, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
2. Sichuan Key Laboratory of Nursing, Chengdu, Sichuan 610041, P. R. China;

Corresponding?author：

CHEN Qian, Email: chen_qian@scu.edu.cn

Keywords：

Elderly people; artificial intelligence robots; usage willingness; influence factors

DOI：

10.7507/1002-0179.202112114

Video：

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Objective To broaden the current understanding of the usage willingness about artificial intelligence (AI) robots and relevant influence factors for elderly patients. Methods The elderly patients in the inpatient ward, outpatient department and physical examination of the Department of Geriatrics, West China Hospital of Sichuan University were selected by convenient sampling for investigation between February and April 2020, to explore the willingness of elderly patients to use AI robots and related influencing factors. Results A total of 446 elderly patients were included. There were 244 males and 202 females. The willingness to use AI robots was (14.40±3.62) points. There were statistically significant differences among the elderly patients with different ages, marital status, living conditions, educational level, current health status, current vision status, current hearing status, self-care ability and family support in their willingness to use AI robots (P<0.05). Multiple linear regression analysis showed that age, education level and family support were the influencing factors of use intention (P<0.05). Among the elderly patients, 60.76% had heard of AI robots, but only 28.03% knew the medical application of AI robots, and only 13.90% had used AI robot services. Most elderly patients (>60%) thought that some adverse factors may reduce their usage willingness, like “the price is too expensive” and “the use is complex, or I don’t know how to use”. Conclusions Elderly patients’ cognition of AI robots is still at a low level, and their willingness to use AI robots is mainly affected by age, education level and family support. It is suggested to consider the personalized needs of the elderly in terms of different ages, education levels and family support, and promote the cheap and user-friendly AI robots, so as to improve the use of AI robots by elderly patients.

Citation： ZHOU Yulan, SHU Xiao, YANG Zijing, CHEN Qian. Willingness of elderly patients to use artificial intelligence robots and its influencing factors. West China Medical Journal, 2022, 37(9): 1340-1346. doi: 10.7507/1002-0179.202112114 Copy

1.	Ji H, Ling J, McCarthy V L. Successful aging in the United States and China: a theoretical basis to guide nursing research, practice, and policy. J Transcult Nurs, 2015, 26(2): 129-136.
2.	Dikken J, Hoogerduijn J, Oeseburg B, et al. Educating for an aging society: competencies, knowledge and attitude and how to motivate students. J Adv Nurs, 2016, 72: 8.
3.	He X, Song M, Qu J, et al. Basic and translational aging research in China: present and future. Protein Cell, 2019, 10(7): 476-484.
4.	Wang Xr, Hui ECM, Sun Jx. Population aging, mobility, and real estate price: evidence from cities in China. Sustainability, 2018, 10(9): 3140.
5.	曹裕蓉. 老年社區的護理現狀與展望. 養生保健指南, 2019(40): 142,164.
6.	Niu Y, Li N, Jin C, et al. Activity outside the home, environmental barriers, and healthy aging for community-dwelling elderly individuals in China. Biosci Trends, 2017, 11(5): 603-605.
7.	Bao J, Tang Q, Chen Y. Individual nursing care for the elderly among China’s aging population. Biosci Trends, 2018, 11(6): 694-696.
8.	Li X, Fan L, Leng SX. The aging tsunami and senior healthcare development in China. J Am Geriatr Soc, 2018, 66(8): 1462-1468.
9.	蔡耀婷. 人工智能在醫療領域的應用現狀及發展前景. 護理研究, 2019, 33(15): 2640-2643.
10.	van Hartskamp M, Consoli S, Verhaegh W, et al. Artificial intelligence in clinical health care applications: viewpoint. Interact J Med Res, 2019, 8(2): e12100.
11.	Biot J. How will clinical practice be impacted by artificial intelligence?. Eur J Dermatol, 2019, 29(S1): 8-10.
12.	Das N, Topalovic M, Janssens W. Artificial intelligence in diagnosis of obstructive lung disease: current status and future potential. Curr Opin Pulm Med, 2018, 24(2): 117-123.
13.	查曉陽. 醫療領域中人工智能技術的應用分析. 電腦知識與技術, 2018, 14(6): 143-144.
14.	Davies N. Can robots handle your healthcare?. Eng Technol, 2016, 11(9): 58-61.
15.	Kajitani I, Wakita Y. An introduction to the development of transfer assistive robots in Japan. Stud Health Technol Inform, 2017, 242: 465-471.
16.	Sedenberg E, Chuang J, Mulligan D. Designing commercial therapeutic robots for privacy preserving systems and ethical research practices within the home. Int J Soc Robot, 2016, 8(4): 575-587.
17.	Holanda LJ, Silva PMM, Amorim TC, et al. Robotic assisted gait as a tool for rehabilitation of individuals with spinal cord injury: a systematic review. J Neuroeng Rehabil, 2017, 14(1): 126.
18.	新一代人工智能發展規劃. 科技導報, 2018, 36(17): 113.
19.	Zhou Z, Wang P, Fang Y. Loneliness and the risk of dementia among older Chinese adults: gender differences. Aging Ment Health, 2018, 22(4): 519-525.
20.	徐維維, 彭滬, 楊佳芳, 等. 人工智能在醫療健康領域的應用與發展前景分析. 中國醫療管理科學, 2019, 9(5): 37-41.
21.	Venkatesh V, Davis FD. A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag Sci, 2000, 46(2): 186-204.
22.	王苑蓉, 陳麗, 胡春艷. 老年患者移動醫療使用意愿及影響因素調查. 護理學雜志, 2019, 34(7): 73-76.
23.	Smilkstein G. The family APGAR: a proposal for a family function test and its use by physicians. J Fam Pract, 1978, 6(6): 1231.
24.	劉伶俐, 賀一墨, 劉祥德. 患者對人工智能醫療的認知及信任度調查. 中國醫學倫理學, 2019, 32(8): 986-990.
25.	王藝蓉, 王前, 李琳娜, 等. 糖尿病患者移動醫療服務的使用意愿及其影響因素調查. 中國全科醫學, 2017, 20(13): 1619-1625.
26.	程康耀, 呂偉波, 沈雪芹, 等. 老年糖尿病患者微信平臺使用意愿影響因素的路徑分析. 解放軍護理雜志, 2019, 36(4): 1-5.
27.	Contreras I, Vehi J. Artificial intelligence for diabetes management and decision support: literature review. J Med Internet Res, 2018, 20(5): e10775.
28.	Miller T. Explanation in artificial intelligence: insights from the social sciences. Artif Intell, 2017: 267.
29.	Xing L, Krupinski EA, Cai J. Artificial intelligence will soon change the landscape of medical physics research and practice. Med Phys, 2018, 45(5): 1791-1793.
30.	許燕梅, 林慧珠. 圖文式宣教手冊在老年住院患者中的應用. 臨床合理用藥雜志, 2016, 9(28): 146-147.
31.	周瀅, 李崢. 人工智能技術在老年護理中應用的研究進展. 中國護理管理, 2018, 18(6): 777-780.
32.	?abanovi? S, Chang WL. Socializing robots: constructing robotic sociality in the design and use of the assistive robot PARO. AI Soc, 2016, 31: 537-551.
33.	康凱寧. 人文關懷在空巢老年患者的護理價值的研究. 中國婦幼健康研究, 2016, 27(S1): 493.

1. Ji H, Ling J, McCarthy V L. Successful aging in the United States and China: a theoretical basis to guide nursing research, practice, and policy. J Transcult Nurs, 2015, 26(2): 129-136.
2. Dikken J, Hoogerduijn J, Oeseburg B, et al. Educating for an aging society: competencies, knowledge and attitude and how to motivate students. J Adv Nurs, 2016, 72: 8.
3. He X, Song M, Qu J, et al. Basic and translational aging research in China: present and future. Protein Cell, 2019, 10(7): 476-484.
4. Wang Xr, Hui ECM, Sun Jx. Population aging, mobility, and real estate price: evidence from cities in China. Sustainability, 2018, 10(9): 3140.
5. 曹裕蓉. 老年社區的護理現狀與展望. 養生保健指南, 2019(40): 142,164.
6. Niu Y, Li N, Jin C, et al. Activity outside the home, environmental barriers, and healthy aging for community-dwelling elderly individuals in China. Biosci Trends, 2017, 11(5): 603-605.
7. Bao J, Tang Q, Chen Y. Individual nursing care for the elderly among China’s aging population. Biosci Trends, 2018, 11(6): 694-696.
8. Li X, Fan L, Leng SX. The aging tsunami and senior healthcare development in China. J Am Geriatr Soc, 2018, 66(8): 1462-1468.
9. 蔡耀婷. 人工智能在醫療領域的應用現狀及發展前景. 護理研究, 2019, 33(15): 2640-2643.
10. van Hartskamp M, Consoli S, Verhaegh W, et al. Artificial intelligence in clinical health care applications: viewpoint. Interact J Med Res, 2019, 8(2): e12100.
11. Biot J. How will clinical practice be impacted by artificial intelligence?. Eur J Dermatol, 2019, 29(S1): 8-10.
12. Das N, Topalovic M, Janssens W. Artificial intelligence in diagnosis of obstructive lung disease: current status and future potential. Curr Opin Pulm Med, 2018, 24(2): 117-123.
13. 查曉陽. 醫療領域中人工智能技術的應用分析. 電腦知識與技術, 2018, 14(6): 143-144.
14. Davies N. Can robots handle your healthcare?. Eng Technol, 2016, 11(9): 58-61.
15. Kajitani I, Wakita Y. An introduction to the development of transfer assistive robots in Japan. Stud Health Technol Inform, 2017, 242: 465-471.
16. Sedenberg E, Chuang J, Mulligan D. Designing commercial therapeutic robots for privacy preserving systems and ethical research practices within the home. Int J Soc Robot, 2016, 8(4): 575-587.
17. Holanda LJ, Silva PMM, Amorim TC, et al. Robotic assisted gait as a tool for rehabilitation of individuals with spinal cord injury: a systematic review. J Neuroeng Rehabil, 2017, 14(1): 126.
18. 新一代人工智能發展規劃. 科技導報, 2018, 36(17): 113.
19. Zhou Z, Wang P, Fang Y. Loneliness and the risk of dementia among older Chinese adults: gender differences. Aging Ment Health, 2018, 22(4): 519-525.
20. 徐維維, 彭滬, 楊佳芳, 等. 人工智能在醫療健康領域的應用與發展前景分析. 中國醫療管理科學, 2019, 9(5): 37-41.
21. Venkatesh V, Davis FD. A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag Sci, 2000, 46(2): 186-204.
22. 王苑蓉, 陳麗, 胡春艷. 老年患者移動醫療使用意愿及影響因素調查. 護理學雜志, 2019, 34(7): 73-76.
23. Smilkstein G. The family APGAR: a proposal for a family function test and its use by physicians. J Fam Pract, 1978, 6(6): 1231.
24. 劉伶俐, 賀一墨, 劉祥德. 患者對人工智能醫療的認知及信任度調查. 中國醫學倫理學, 2019, 32(8): 986-990.
25. 王藝蓉, 王前, 李琳娜, 等. 糖尿病患者移動醫療服務的使用意愿及其影響因素調查. 中國全科醫學, 2017, 20(13): 1619-1625.
26. 程康耀, 呂偉波, 沈雪芹, 等. 老年糖尿病患者微信平臺使用意愿影響因素的路徑分析. 解放軍護理雜志, 2019, 36(4): 1-5.
27. Contreras I, Vehi J. Artificial intelligence for diabetes management and decision support: literature review. J Med Internet Res, 2018, 20(5): e10775.
28. Miller T. Explanation in artificial intelligence: insights from the social sciences. Artif Intell, 2017: 267.
29. Xing L, Krupinski EA, Cai J. Artificial intelligence will soon change the landscape of medical physics research and practice. Med Phys, 2018, 45(5): 1791-1793.
30. 許燕梅, 林慧珠. 圖文式宣教手冊在老年住院患者中的應用. 臨床合理用藥雜志, 2016, 9(28): 146-147.
31. 周瀅, 李崢. 人工智能技術在老年護理中應用的研究進展. 中國護理管理, 2018, 18(6): 777-780.
32. ?abanovi? S, Chang WL. Socializing robots: constructing robotic sociality in the design and use of the assistive robot PARO. AI Soc, 2016, 31: 537-551.
33. 康凱寧. 人文關懷在空巢老年患者的護理價值的研究. 中國婦幼健康研究, 2016, 27(S1): 493.

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