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Psychosocial Factors Affecting Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study

BACKGROUND: Poor quality primary health care is a major issue in China, particularly in blindness prevention. Artificial intelligence (AI) could provide early screening and accurate auxiliary diagnosis to improve primary care services and reduce unnecessary referrals, but the application of AI in me...

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Autores principales: Ye, Tiantian, Xue, Jiaolong, He, Mingguang, Gu, Jing, Lin, Haotian, Xu, Bin, Cheng, Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6913088/
https://www.ncbi.nlm.nih.gov/pubmed/31625950
http://dx.doi.org/10.2196/14316
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author Ye, Tiantian
Xue, Jiaolong
He, Mingguang
Gu, Jing
Lin, Haotian
Xu, Bin
Cheng, Yu
author_facet Ye, Tiantian
Xue, Jiaolong
He, Mingguang
Gu, Jing
Lin, Haotian
Xu, Bin
Cheng, Yu
author_sort Ye, Tiantian
collection PubMed
description BACKGROUND: Poor quality primary health care is a major issue in China, particularly in blindness prevention. Artificial intelligence (AI) could provide early screening and accurate auxiliary diagnosis to improve primary care services and reduce unnecessary referrals, but the application of AI in medical settings is still an emerging field. OBJECTIVE: This study aimed to investigate the general public’s acceptance of ophthalmic AI devices, with reference to those already used in China, and the interrelated influencing factors that shape people’s intention to use these devices. METHODS: We proposed a model of ophthalmic AI acceptance based on technology acceptance theories and variables from other health care–related studies. The model was verified via a 32-item questionnaire with 7-point Likert scales completed by 474 respondents (nationally random sampled). Structural equation modeling was used to evaluate item and construct reliability and validity via a confirmatory factor analysis, and the model’s path effects, significance, goodness of fit, and mediation and moderation effects were analyzed. RESULTS: Standardized factor loadings of items were between 0.583 and 0.876. Composite reliability of 9 constructs ranged from 0.673 to 0.841. The discriminant validity of all constructs met the Fornell and Larcker criteria. Model fit indicators such as standardized root mean square residual (0.057), comparative fit index (0.915), and root mean squared error of approximation (0.049) demonstrated good fit. Intention to use (R(2)=0.515) is significantly affected by subjective norms (beta=.408; P<.001), perceived usefulness (beta=.336; P=.03), and resistance bias (beta=–.237; P=.02). Subjective norms and perceived behavior control had an indirect impact on intention to use through perceived usefulness and perceived ease of use. Eye health consciousness had an indirect positive effect on intention to use through perceived usefulness. Trust had a significant moderation effect (beta=–.095; P=.049) on the effect path of perceived usefulness to intention to use. CONCLUSIONS: The item, construct, and model indicators indicate reliable interpretation power and help explain the levels of public acceptance of ophthalmic AI devices in China. The influence of subjective norms can be linked to Confucian culture, collectivism, authoritarianism, and conformity mentality in China. Overall, the use of AI in diagnostics and clinical laboratory analysis is underdeveloped, and the Chinese public are generally mistrustful of medical staff and the Chinese medical system. Stakeholders such as doctors and AI suppliers should therefore avoid making misleading or over-exaggerated claims in the promotion of AI health care products.
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spelling pubmed-69130882020-01-02 Psychosocial Factors Affecting Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study Ye, Tiantian Xue, Jiaolong He, Mingguang Gu, Jing Lin, Haotian Xu, Bin Cheng, Yu J Med Internet Res Original Paper BACKGROUND: Poor quality primary health care is a major issue in China, particularly in blindness prevention. Artificial intelligence (AI) could provide early screening and accurate auxiliary diagnosis to improve primary care services and reduce unnecessary referrals, but the application of AI in medical settings is still an emerging field. OBJECTIVE: This study aimed to investigate the general public’s acceptance of ophthalmic AI devices, with reference to those already used in China, and the interrelated influencing factors that shape people’s intention to use these devices. METHODS: We proposed a model of ophthalmic AI acceptance based on technology acceptance theories and variables from other health care–related studies. The model was verified via a 32-item questionnaire with 7-point Likert scales completed by 474 respondents (nationally random sampled). Structural equation modeling was used to evaluate item and construct reliability and validity via a confirmatory factor analysis, and the model’s path effects, significance, goodness of fit, and mediation and moderation effects were analyzed. RESULTS: Standardized factor loadings of items were between 0.583 and 0.876. Composite reliability of 9 constructs ranged from 0.673 to 0.841. The discriminant validity of all constructs met the Fornell and Larcker criteria. Model fit indicators such as standardized root mean square residual (0.057), comparative fit index (0.915), and root mean squared error of approximation (0.049) demonstrated good fit. Intention to use (R(2)=0.515) is significantly affected by subjective norms (beta=.408; P<.001), perceived usefulness (beta=.336; P=.03), and resistance bias (beta=–.237; P=.02). Subjective norms and perceived behavior control had an indirect impact on intention to use through perceived usefulness and perceived ease of use. Eye health consciousness had an indirect positive effect on intention to use through perceived usefulness. Trust had a significant moderation effect (beta=–.095; P=.049) on the effect path of perceived usefulness to intention to use. CONCLUSIONS: The item, construct, and model indicators indicate reliable interpretation power and help explain the levels of public acceptance of ophthalmic AI devices in China. The influence of subjective norms can be linked to Confucian culture, collectivism, authoritarianism, and conformity mentality in China. Overall, the use of AI in diagnostics and clinical laboratory analysis is underdeveloped, and the Chinese public are generally mistrustful of medical staff and the Chinese medical system. Stakeholders such as doctors and AI suppliers should therefore avoid making misleading or over-exaggerated claims in the promotion of AI health care products. JMIR Publications 2019-10-17 /pmc/articles/PMC6913088/ /pubmed/31625950 http://dx.doi.org/10.2196/14316 Text en ©Tiantian Ye, Jiaolong Xue, Mingguang He, Jing Gu, Haotian Lin, Bin Xu, Yu Cheng. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 17.10.2019. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Ye, Tiantian
Xue, Jiaolong
He, Mingguang
Gu, Jing
Lin, Haotian
Xu, Bin
Cheng, Yu
Psychosocial Factors Affecting Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study
title Psychosocial Factors Affecting Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study
title_full Psychosocial Factors Affecting Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study
title_fullStr Psychosocial Factors Affecting Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study
title_full_unstemmed Psychosocial Factors Affecting Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study
title_short Psychosocial Factors Affecting Artificial Intelligence Adoption in Health Care in China: Cross-Sectional Study
title_sort psychosocial factors affecting artificial intelligence adoption in health care in china: cross-sectional study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6913088/
https://www.ncbi.nlm.nih.gov/pubmed/31625950
http://dx.doi.org/10.2196/14316
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