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Health Information Technology
Published in Kelly H. Zou, Lobna A. Salem, Amrit Ray, Real-World Evidence in a Patient-Centric Digital Era, 2023
Joseph P. Cook, Gabriel Jipa, Claudia Zavala, Lobna A. Salem
Technology may not always be adopted in a timely fashion, despite the value provided even if available, and AI is no different. Technology Acceptance Model (Davis, 1989), derived from theory of planned behavior (TPB; Ajzen, 1991), was used to explain the success or failure of multiple technology adoption studies, based on motivational states as perceived usefulness and perceived ease of use that effects the Behavioral Intention to be used. TAM was developed later in other variants and adapted to specific areas of research as learning, social media, internet and mobile application (Tamilmani et al., 2021). In practice, that suggests that technology alone cannot provide the value without proper methodologies. Some companies provide guidance in a selection of AI platforms, taking into account multiple factors as ease of use, scalability or integration, as well as considering ethical considerations (Char, Shah and Magnus, 2018; Taulli, 2021b).
The Impact of Digital Technologies, Data Analytics and AI on Nursing Informatics:
Published in Connie White Delaney, Charlotte A. Weaver, Joyce Sensmeier, Lisiane Pruinelli, Patrick Weber, Nursing and Informatics for the 21st Century – Embracing a Digital World, 3rd Edition, Book 4, 2022
Charlene H. Chu, Aaron Conway, Lindsay Jibb, Charlene E. Ronquillo
The fields of information science and implementation science provide frameworks and models to better understand nurses' substantial influence in the success or failure of digital health technologies. The Technology Acceptance Model (TAM) and its various iterations (Davis, 1989; Davis et al., 1989) and the Unified Theory of Acceptance and Use of Technology (UTAUT; Venkatesh & Bala, 2008; Venkatesh, 2015) are among commonly used models that nurses can use to examine individual-level factors (e.g., attitudes toward technologies, perceived usefulness of technologies, perceived computer self-efficacy), as predictors of individuals' acceptance and adoption of digital health technologies. Meanwhile, implementation science models can identify the structural and contextual factors that influence technology implementation such as the Consolidated Framework for Implementation Research (Damschroder et al., 2009) and the Reach, Efficacy/Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) framework, among others (Minichiello et al., 2013). Theoretical understanding requires upstream approaches, including technology-focused education in undergraduate and graduate programs to promote digital professionalism and literacy (O'Connor et al., 2020; O'Connor & LaRue, 2021).
Feedback from French nursing staff in gerontology
Published in Maria Łuszczyńska, Marvin Formosa, Ageing and COVID-19, 2021
Pauline Gouttefarde, Chloé Gaulier, Sébastien Rabier, Vincent Augusto, Caroline Dupré, Solène Dorier, Jessica Guyot, Nathalie Barth
Several models attempt to capture the modalities inherent in the acceptability (and ultimately acceptance) of using a new tool or embracing a new organisation. For example, the Technology Acceptance Model (TAM) deals with behavioural intention, a factor that leads people to use a technology (Davis 1985). But another model offers a better explanatory capacity for behavioural intention: the ‘Unified Theory of Acceptance and Use of Technology’ (UTAUT) (Venkatesh et al. 2003:425). This model proposes 4 main factors influencing the behavioural intention and use of a new feature (tool, organisation, system, etc.): performance expectancy; effort expectancy (i.e., the degree of ease associated with use); facilitating conditions, and lastly, social influence (i.e., the influence of the people around the individual and their perception of the new feature) (Bobillier-Chaumon & Dubois 2009: 355–382; Lewin 1946: 34–46; Venkatesh et al. 2003:425).
Healthcare professionals’ acceptance Electronic Health Records system: Critical literature review (Jordan case study)
Published in International Journal of Healthcare Management, 2020
Mohammad Rasmi, Malik B. Alazzam, Mutasem K. Alsmadi, Ibrahim A. Almarashdeh, Raed A. Alkhasawneh, Sanaa Alsmadi
Performance expectancy is grounded on the concept of Perceived Usefulness (PU) in (TAM) model Davis F. D., (1993) Morris et al. [16] pertaining a person’s perception of the perceived enhanced benefits towards job performance when an IT system is used. It signifies the extent to which a person/user is convinced that the utilization of a particular IT-supported product or service will assist in performance enhancement. Scholars amongst others have merged it with four particular moderators (gender, age, deliberateness of use, and experience) that have the potential to affect behavioral intention (BI) and observable use behavior. Previous research demonstrates that BI is intensifying and positively affected by the performance expectancy construct. The notion of performance expectancy (PE) proposes that technology users will be more receptive to embrace the technology, if they are convinced of the benefits of the technology and that it is able to deliver advantages to health or assists in health self-management (autonomy) as expounded by several authors [23–30].
Video delivery of toxicology educational content versus textbook for asynchronous learning, using acetaminophen overdose as a topic
Published in Clinical Toxicology, 2019
Timothy Vo, Caroline Ledbetter, Matthew Zuckerman
A higher proportion of students expressed satisfaction with the activity in the video group than the textbook group. Students also overall expressed comfort using new technology to obtain new medical education. This may relate to perceived usefulness and perceived ease of use, which inform acceptance and actual usage rate in the Technology Acceptance Model [16]. Additionally, all students in the video group completed the activity, whereas four students did not complete the activity in the textbook group. Most students elected to complete the activity from home, and a greater number of students in the textbook group gave up on the activity partway through. A greater number of students in the video group completed the activity in full. This suggests a greater level of engagement in the activity with the video than the textbook.
The Potential of Information Technology to Navigate Caregiving Systems: Perspectives from Dementia Caregivers
Published in Journal of Gerontological Social Work, 2019
Nicole Ruggiano, Ellen L. Brown, Shanae Shaw, David Geldmacher, Peter Clarke, Vagelis Hristidis, Jessica Bertram
The technology acceptance model (TAM) is a useful framework for understanding how technologies can adequately reflect the realities, needs, and attitudes of caregivers for adoption of use. TAM posits that users are more likely to accept technologies when they are (1) perceived useful and (2) perceived as being easy to use (Davis, 1989). TAM is useful in understanding the likelihood that caregivers would use a technology-based intervention, such as this. In terms of perceived usefulness, Venkatesh and Davis (2000) extended this concept to include perceptions of job relevance and output quality. TAM has been used extensively by researchers to show the acceptance of technology in health care (Holden & Karsh, 2010), and has been used as a framework for understanding caregivers’ acceptance of technology use in relationship to hospice (Oliver et al., 2015; Whitten, Holtz, & Nazione, 2009) and AD/RD care settings (Kramer, 2013; Mao, Chang, Yao, Chen, & Huang, 2015). TAM was used by the researchers to inform the beta test design of the investigation, as well as the questions on the interview guide.