ABSTRACT
BACKGROUND: Prospective physicians are expected to find artificial intelligence (AI) to be a key technology in their future practice. This transformative change has caught the attention of scientists, educators, and policy makers alike, with substantive efforts dedicated to the selection and delivery of AI topics and competencies in the medical curriculum. Less is known about the behavioral perspective or the necessary and sufficient preconditions for medical students' intention to use AI in the first place. OBJECTIVE: Our study focused on medical students' knowledge, experience, attitude, and beliefs related to AI and aimed to understand whether they are necessary conditions and form sufficient configurations of conditions associated with behavioral intentions to use AI in their future medical practice. METHODS: We administered a 2-staged questionnaire operationalizing the variables of interest (ie, knowledge, experience, attitude, and beliefs related to AI, as well as intention to use AI) and recorded 184 responses at t0 (February 2020, before the COVID-19 pandemic) and 138 responses at t1 (January 2021, during the COVID-19 pandemic). Following established guidelines, we applied necessary condition analysis and fuzzy-set qualitative comparative analysis to analyze the data. RESULTS: Findings from the fuzzy-set qualitative comparative analysis show that the intention to use AI is only observed when students have a strong belief in the role of AI (individually necessary condition); certain AI profiles, that is, combinations of knowledge and experience, attitudes and beliefs, and academic level and gender, are always associated with high intentions to use AI (equifinal and sufficient configurations); and profiles associated with nonhigh intentions cannot be inferred from profiles associated with high intentions (causal asymmetry). CONCLUSIONS: Our work contributes to prior knowledge by showing that a strong belief in the role of AI in the future of medical professions is a necessary condition for behavioral intentions to use AI. Moreover, we suggest that the preparation of medical students should go beyond teaching AI competencies and that educators need to account for the different AI profiles associated with high or nonhigh intentions to adopt AI.
ABSTRACT
BACKGROUND: The COVID-19 crisis has drastically changed care delivery with teleconsultation platforms experiencing substantial spikes in demand, helping patients and care providers avoid infections and maintain health care services. Beyond the current pandemic, teleconsultation is considered a significant opportunity to address persistent health system challenges, including accessibility, continuity, and cost of care, while ensuring quality. OBJECTIVE: This study aims at identifying the determinants of patients' intention to continue using a teleconsultation platform. It extends prior research on information technology use continuance intention and teleconsultation services. METHODS: Data was collected in November 2018 and May 2019 with Canadian patients who had access to a teleconsultation platform. Measures included patients' intention to continue their use; teleconsultation usefulness; teleconsultation quality; patients' trust toward the digital platform, its provider. and health care professionals; and confirmation of patients' expectations toward teleconsultation. We used structural equation modeling employing the partial least squares component-based technique to test our research model and hypotheses. RESULTS: We analyzed a sample of 178 participants who had used teleconsultation services. Our findings revealed that confirmation of expectations had the greatest influence on continuance intention (total effects=0.722; P<.001), followed by usefulness (total effects=0.587; P<.001) and quality (total effects=0.511; P<.001). Usefulness (ß=.60; P<.001) and quality (ß=.34; P=.01) had direct effects on the dependent variable. The confirmation of expectations had direct effects both on usefulness (ß=.56; P<.001) and quality (ß=.75; P<.001) in addition to having an indirect effect on usefulness (indirect effects=0.282; P<.001). Last, quality directly influenced usefulness (ß=.34; P=.002) and trust (ß=.88; P<.001). Trust does not play a role in the context under study. CONCLUSIONS: Teleconsultation is central to care going forward, and it represents a significant lever for an improved, digital delivery of health care in the future. We believe that our findings will help drive long-term teleconsultation adoption and use, including in the aftermath of the current COVID-19 crisis, so that general care improvement and greater preparedness for exceptional situations can be achieved.