13 research outputs found
How to overcome the AI chasm in ophthalmology? An international expert interview study on AI adoption and workflow integration
No full textExploring Predictors of AI Chatbot Usage Intensity Among Students: Within- and Between-Person Relationships Based on the Technology Acceptance Model
No full textThe current research investigated the factors associated with the intensity of AI chatbot usage among university students, applying the Technology Acceptance Model (TAM) and its extended version, TAM3. A daily diary study over five days was conducted among university students, distinguishing between inter-individual (between-person) and intra-individual (within-person) variations. Multilevel structural equation modeling (SEM) was used to analyze the data. In Study 1 (N = 72), results indicated that AI chatbot anxiety was associated with perceived ease of use (PEOU) and perceived usefulness (PU), which serially mediated the link with AI chatbot usage intensity. Study 2 (N = 153) supported these findings and further explored the roles of facilitating conditions and subjective norm as additional predictors of PEOU and PU. Results from both studies demonstrated that, at the between-person level, students with higher average levels of PEOU and PU reported more intensive AI chatbot usage. In Study 1, the relationship between PEOU and usage intensity was mediated through PU at the within-person level, while the mediation model was not supported in Study 2. Post-hoc comparisons highlighted much higher variability in PEOU and PU in Study 1 compared to Study 2. The results have practical implications for enhancing AI chatbot adoption in educational settings. Emphasizing user-friendly interfaces, reducing AI-related anxiety, providing robust technical support, and leveraging peer influence may enhance the usage intensity of AI chatbots. This study underscores the necessity of considering both stable individual differences and dynamic daily influences to better understand AI chatbot usage patterns among students
Exploring the Predictors of AI Chatbot Usage Intensity Among Students: Within- and Between-Person Relationships Using the Technology Acceptance Model
No full text
Sociotechnical influences on the adoption and use of AI-enabled clinical decision support systems in ophthalmology: a theory-based interview study
No full textAbstract Background Artificial intelligence (AI) has the potential to ease the increasing workload in ophthalmology by supporting ophthalmologists’ clinical decision-making. However, despite regulatory approvals, the adoption and use of AI-enabled clinical decision support systems (AI-CDSS) in ophthalmology remains limited. Critical obstacles that innovative healthcare technologies such as AI-CDSS face on their path to widespread clinical use are nonadoption and abandonment by their intended users, which prevent broader dissemination and real clinical benefit. This study explores how to overcome nonadoption and prevent abandonment of ophthalmic AI-CDSS by identifying ophthalmology professionals’ requirements for adoption and continued use of such tools in clinical practice. Methods We conducted semi-structured interviews with 22 ophthalmology professionals from Germany, Austria, and Switzerland, representing a range of professional roles, clinical settings, and extent of AI-CDSS experience. To explore sociotechnical factors shaping ophthalmology professionals’ adoption decisions and their ability to derive added value from ophthalmic AI-CDSS, we conducted a qualitative content analysis combining deductive and inductive coding. The Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) framework deductively guided the development of higher-level code categories, representing seven sociotechnical domains relevant for the implementation of healthcare technologies. These were further refined into subcategories through inductive coding of the interview material. Results Most participants expressed general openness to ophthalmic AI-CDSS. However, actual adoption decisions and the ability to derive clinical value from these tools were shaped not just by individual attitudes but also by a range of other sociotechnical influences. Specifically, we inductively identified 29 code categories, representing sociotechnical influences and requirements from all seven NASSS domains, including technological, user, organizational, and societal aspects. Our findings also suggest that while many sociotechnical influences and challenges are shared between AI-based and traditional healthcare technologies, a key distinction of (ophthalmic) AI-CDSS lies in the users’ psychological appraisal of such tools. Conclusions Our findings highlight the complex and context-specific nature of integrating AI-CDSS into ophthalmic practice. This study also informs AI and healthcare researchers on the applicability of the NASSS framework for studying AI implementation and provides actionable insights for AI developers and implementers aiming to address user needs more effectively
Balancing cognitive and environmental constraints when deciding to switch tasks: exploring self-reported task-selection strategies in self-organised multitasking
No full textSociotechnical Influences on the Adoption and Use of AI-enabled Clinical Decision Support Systems in Ophthalmology: a Theory-Based Interview Study
No full textNavigating the complexity of AI adoption in psychotherapy by identifying key facilitators and barriers
No full textArtificial intelligence (AI) technologies in mental healthcare offer promising opportunities to reduce therapists’ burden and enhance healthcare delivery, yet adoption remains challenging. This study identified key facilitators and barriers to AI adoption in mental healthcare, precisely psychotherapy, by conducting six online focus groups with patients and therapists, using a semi-structured guide based on the NASSS (Nonadoption, Abandonment, Scale-up, Spread, and Sustainability) framework. Data from N = 32 participants were analyzed using a combined deductive and inductive thematic analysis. Across the seven NASSS domains, 36 categories emerged. Sixteen categories were identified as factors facilitating adoption, including useful technology elements, the customization to user needs, and cost coverage. Eleven categories were perceived as barriers to adoption, encompassing the lack of human contact, resource constraints, and AI dependency. Further nine, such as therapeutic approach and institutional differences, acted as both facilitators and barriers depending on the context. Our findings highlight the complexity of AI adoption in mental healthcare and emphasize the importance of addressing barriers early in the development of AI technologies
