Evolving Medical Students' Digital Health Perceptions and Intentions: Insights From a Prepandemic and Postpandemic Survey Study.

Background: Digital health (dHealth) technologies, such as telehealth, artificial intelligence (AI), and mobile apps, are increasingly essential in medical practice. However, despite their growing significance, medical curricula often lack structured dHealth training, leaving students underprepared for digitally integrated health care environments. Objective: This study investigates the factors influencing medical students' intentions to integrate dHealth technologies into their future practice and examines changes in their perceptions over time. Methods: We conducted a 2-phase survey at a large Canadian medical school to assess changes in perceptions before (N=184) and after (N=177) the COVID-19 pandemic. A mixed methods approach combined component-based structural equation modeling and fuzzy-set qualitative comparative analysis. The model was grounded in the technology acceptance model and Triandis' theory of interpersonal behavior, examining constructs such as individual background, facilitating conditions, perceived usefulness, and beliefs about AI. Results: Across both phases, over 85% (306/361) of students agreed that dHealth education should be a mandatory component of medical training. Mean ratings for intention to use dHealth in future practice increased significantly between t0 and t1 for patient communication (3.4 to 4.2, P<.001), monitoring (3.3 to 4.0, P<.001), and diagnosis/treatment (3.6 to 4.2, P<.001). Experience with AI tools increased from 1.3 to 1.5 (P<.001), and telehealth from 1.2 to 1.6 (P<.001), while exposure to hospital IT systems and mobile apps remained unchanged. Results confirmed that perceived usefulness (β=.37 at t0; β=.34 at t1) and beliefs about AI (β=.39 at t0; β=.27 at t1) were strong predictors of intention to integrate dHealth (P<.001). The explanatory power of the structural equation modeling model declined postpandemic (R²=0.53 at t0 vs R²=0.25 at t1), suggesting increasing complexity in influencing factors. Fuzzy-set qualitative comparative analysis revealed multiple configurations leading to high intention, with consistency values exceeding 0.88 and overall solution coverage of 0.74 postpandemic. Core conditions across high-intention profiles included strong beliefs in the role of AI and perceived importance of dHealth education. Conversely, gender appeared as a recurring core condition in non-high-intention configurations, suggesting persistent disparities in dHealth adoption. Conclusions: The study advocates for the integration of formal dHealth training in medical curricula to better prepare future physicians for the demands of an increasingly digital health care landscape. While the COVID-19 pandemic may have contributed to shifting perceptions, other factors, such as recent AI advancements, likely played a role. These findings highlight the urgent need for medical education to adapt to the changing dHealth environment.