Literaturnachweis - Detailanzeige
| Autor/inn/en | Lin, Hui-Chen; Tu, Yun-Fang; Hwang, Gwo-Jen; Huang, Hsin |
|---|---|
| Titel | From Precision Education to Precision Medicine: Factors Affecting Medical Staff's Intention to Learn to Use AI Applications in Hospitals |
| Quelle | In: Educational Technology & Society, 24 (2021) 1, S.123-137 (15 Seiten)Infoseite zur Zeitschrift
PDF als Volltext |
| Sprache | englisch |
| Dokumenttyp | gedruckt; online; Zeitschriftenaufsatz |
| ISSN | 1436-4522 |
| Schlagwörter | Medical Services; Artificial Intelligence; Allied Health Personnel; Individual Needs; Intention; Attitudes; Influences; Technology Integration; Value Judgment; Usability; Difficulty Level; Social Attitudes; Behavior; Hospitals; Social Influences; Peer Influence; Foreign Countries; Taiwan |
| Abstract | Precision medicine has become an essential issue in the medical community as the quality of medical care is being emphasized nowadays. The technological data analysis and predictions made by Artificial Intelligence (AI) technologies have assisted medical staff in designing personalized medicine for patients, making AI technologies an important path to precision medicine. During the implementation of the new emerging technology, medical staff's learning intentions will have a great influence on its effectiveness. With reference to the Technology Acceptance Model, this study explored medical staff's attitudes, intentions, and relevant influencing factors in relation to AI application learning. A total of 285 valid questionnaires were collected. Five major factors, perceived usefulness (PU), perceived ease of use (PEU), subjective norms (SN), attitude towards AI use (ATU), and behavioral intention (BI), were used for analyzing the AI learning of medical staff in a hospital. Based on the SEM analytical results and the research model, the four endogenous constructs of PU, PEU, SN, and ATU explained 37.4% of the changes in BI. In this model, SN and PEU were the determining factors of BI. The total effects of SN and PEU were 0.448 and 0.408 respectively, followed by PU, with a total effect of 0.244. As a result, the intentions of medical staff to learn to use AI applications to support precision medicine can be predicted by SN, PEU, PU, and ATU. Among them, subjective norms considering the influences of both supervisors and peers, such as encouragement, communication, and sharing, may assist precision education in supporting the learning attitudes and behavior regarding precision medicine. The research results can provide recommendations for examining medical staff's intention to use AI applications. (As Provided). |
| Anmerkungen | International Forum of Educational Technology & Society. Available from: National Yunlin University of Science and Technology. No. 123, Section 3, Daxue Road, Douliu City, Yunlin County, Taiwan 64002. e-mail: journal.ets@gmail.com; Web site: https://www.j-ets.net/ |
| Erfasst von | ERIC (Education Resources Information Center), Washington, DC |
| Update | 2024/1/01 |
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