Paré Guy, Raymond Louis, Pomey Marie-Pascale, Grégoire Geneviève, Castonguay Alexandre, Ouimet Antoine Grenier
HEC Montréal, Montreal, Canada.
Université du Québec à Trois-Rivières, Trois-Rivieres, Canada.
Digit Health. 2022 Jul 21;8:20552076221114195. doi: 10.1177/20552076221114195. eCollection 2022 Jan-Dec.
We aimed to explore the factors that influence medical students' intention to integrate dHealth technologies in their practice and analyze the influence of the COVID-19 pandemic on their perceptions and intention.
We conducted a two-phased survey study at the University of Montreal's medical school in Canada. The study population consisted of 1367 medical students. The survey questionnaire was administered in two phases, that is, an initial survey (t) in February 2020, before the Covid-19 pandemic, and a replication survey (t) in January 2021, during the pandemic. Component-based structural equation modeling (SEM) was used to test seven research hypotheses.
A total of 184 students responded to the survey at t (13%), whereas 138 responded to the survey at t (10%). Findings reveal that students, especially those who are in their preclinical years, had little occasion to experiment with dHealth technologies during their degree. This lack of exposure may explain why a vast majority felt that dHealth should be integrated into medical education. Most respondents declared an intention to integrate dHealth, including AI-based tools, into their future medical practice. One of the most salient differences observed between t and t brings telemedicine to the forefront of medical education. SEM results confirm the explanatory power of the proposed research model.
The present study unveils the specific dHealth technologies that could be integrated into existing medical curricula. Formal training would increase students' competencies with these technologies which, in turn, could ease their adoption and effective use in their practice.
我们旨在探究影响医学生将数字健康技术融入其临床实践的意向的因素，并分析新冠疫情对他们的认知及意向的影响。
我们在加拿大蒙特利尔大学医学院开展了一项分两阶段的调查研究。研究对象包括1367名医学生。调查问卷分两个阶段进行发放，即2020年2月新冠疫情之前的初始调查（t1），以及疫情期间2021年1月的重复调查（t2）。基于成分的结构方程模型（SEM）用于检验七个研究假设。
共有184名学生回复了t1阶段的调查（13%），而138名学生回复了t2阶段的调查（10%）。研究结果显示，学生，尤其是处于临床前阶段的学生，在其学位课程期间几乎没有机会试用数字健康技术。这种缺乏接触的情况或许可以解释为什么绝大多数人认为数字健康应融入医学教育。大多数受访者表示有意将包括基于人工智能的工具在内的数字健康技术融入其未来的医疗实践。在t1和t2之间观察到的最显著差异之一是远程医疗成为医学教育的前沿。结构方程模型结果证实了所提出的研究模型的解释力。
本研究揭示了可融入现有医学课程的特定数字健康技术。正规培训将提高学生使用这些技术的能力，这反过来又可以促进他们在实践中采用和有效使用这些技术。
