期刊文献+

广东省基层医生对人工智能辅助诊疗技术的应用意愿分析

Application willingness of artificial intelligence‐assisted diagnosis and treatment technology among primary care physicians in Guangdong Province
原文传递
导出
摘要 目的 探讨广东省基层医生对人工智能(artificial intelligence,AI)辅助诊疗技术的应用意愿及其影响因素。方法 采用方便抽样方法选取广东省的基层医生为研究对象,对其人口学特征以及AI辅助诊疗技术应用意愿进行问卷调查。拟合结构方程模型分析基层医生对AI辅助诊疗技术应用意愿的影响因素。结果 纳入广东省基层医生3 490名,平均年龄(32.99±15.89)岁,男性1 815名(52.01%)。基层医生支持AI辅助诊疗技术服务患者、认为AI可促进医疗技术进步和高精尖医疗技术普及、愿意尝试使用或继续使用AI辅助诊疗技术为患者提供服务的同意率分别为82.15%、78.83%、78.45%。结构方程模型结果显示,感知有用性、感知满意度、感知服务质量、感知信息质量以及较高学历对基层医生应用AI辅助诊疗技术产生正向影响,标化路径系数分别为0.354、0.268、0.121、0.270、0.035(P<0.05或P<0.01);较高职称对基层医生应用AI辅助诊疗技术产生负向影响,标化路径系数为-0.045(P<0.01)。结论 广东省基层医生对AI辅助诊疗技术的应用意愿总体较高,感知有用性、感知满意度、感知信息质量、感知服务质量、学历、职称是基层医生应用AI辅助诊疗技术的影响因素。 ObjectiveTo explore the application willingness of artificial intelligence(AI)-assisted diagnosis and treatment technology and its influencing factors among primary care physicians in Guangdong Province.MethodsPrima-ry care physicians in Guangdong Province were selected by convenient sampling method to conduct a questionnaire survey on their demographic characteristics and willingness to apply AI-assisted diagnosis and treatment technology.The structur-al equation modeling was fitted to analyze the influencing factors of primary care physicians′willingness to apply AI-assist-ed diagnosis and treatment technology.ResultsA total of 3490 primary care physicians from Guangdong Province were included,with an average age of(32.99±15.89)years,and 1815 were male(52.01%).The consent rates of primary care physicians who supported AI-assisted diagnosis and treatment technology to serve patients,believed that AI could promote the progress of medical technology and the popularization of advanced and sophisticated medical technology,and were will-ing to try or continue to use AI-assisted diagnosis and treatment technology to provide services for patients was 82.15%,78.83%,and 78.45%respectively.The results of structural equation modeling showed that perceived usefulness,per-ceived satisfaction,perceived service quality,perceived information quality,and a higher education level had a positive impact on the willingness of primary care physicians to apply AI-assisted diagnosis and treatment technology,and the stan-dardized path coefficients were 0.354,0.268,0.121,0.270,and 0.035 respectively(P<0.05 or P<0.01).The higher professional title had a negative impact on the willingness of primary care physicians to apply AI-assisted diagnosis and treatment technology,and the standardized path coefficient was-0.045(P<0.01).ConclusionsPrimary care physi-cians′willingness to apply AI-assisted diagnosis and treatment technology is generally high in Guangdong Province.Per-ceived usefulness,perceived satisfaction,perceived information quality,perceived service quality,education level,and professional title are the influencing factors of primary care physicians′willingness to apply AI-assisted diagnosis and treat-ment technology.
作者 万东华 江金女 潘波 梁华 吴琳 何志辉 陈燕铭 WAN Dong-hua;JIANG Jin-nu;PAN Bo;LIANG Hua;WU Lin;HE Zhi-hui;CHEN Yan-ming(Guangdong Provincial Institute of Public Health,Guangdong Provincial Center for Disease Control and Prevention,Guangzhou 511430,China;Guangdong Provincial Center for Disease Control and Prevention;The Third Affiliated Hospital,Sun Yat-sen University)
出处 《华南预防医学》 2023年第1期32-36,共5页 South China Journal of Preventive Medicine
基金 广州市重点领域研发计划项目(202007040003)。
关键词 基层医生 人工智能 辅助诊疗 意愿 结构方程模型 Primary care physician Artificial intelligence Assisted diagnosis and treatment Willingness Structural equation modeling
  • 相关文献

参考文献9

二级参考文献77

  • 1吕宛青.居民可持续遗产旅游参与行为研究——基于计划行为理论视角[J].社会科学家,2019,34(12):89-100. 被引量:22
  • 2石志恒,崔民,张衡.基于扩展计划行为理论的农户绿色生产意愿研究[J].干旱区资源与环境,2020,0(3):40-48. 被引量:43
  • 3Bollen KA. Structural equations with latent variables Toronto:John Wiley & Sons, 1989.318-403.
  • 4Cliff N. Some cautions concerning the application of causal modeling methods. Multivariate Behavioral Research, 1983,18 : 115-126.
  • 5Wall MM, Li R. Comparison of multiple regression to two latent variable techniques for estimation and prediction. Stat Med, 2003,22:3671-3685.
  • 6Mcdonald RP, Ho M-HR. Principles and practice in reporting structural equation analyses. Psychological Methods,2002,7:64-82.
  • 7West SG, Finch JF, Curran PJ. Structural equation models with non-normal variable. In: Hoyle GH, ed. Structural equation modeling:Concepts issues and application. Thousand Oaks:Sage, 1995.56-75.
  • 8Chou CP, Benter P. Estimates and tests in structural equation modeling. In : Hoyle GH, ed. Structural equation modeling: Concepts issues and application. Thousand Oaks:Sage, 1995. 167-244.
  • 9Satorra A, Bentler PM. Corrections totest statistic and standard errors in covariance structure analysis. In: VonEye A, Clogg CC.eds. Analysis of latent variable in developmental research. Newbury Park:Sage, 1994. 399-419.
  • 10SAS Institute, Inc. SAS/STAT users guide. Cary, NC: Author,1998. 370-432.

共引文献114

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部