Modeling patients' acceptance of provider-delivered e-health.

By E. Vance Wilson, Nancy K. Lankton. JAMIA Journal of the American Medical Informatics Association, 2004

375 citations recorded by the source corpus.

Subfields: General Health Professions, Health Information Management, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/6a11c72d6f4d8fdb7d66016f5601b5e525fdbc36

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In General Health Professions

In Health Information Management

In Information Systems and Management

Citations in the corpus

Comparison of user groups' perspectives of barriers and facilitators to implementing electronic health records: a systematic review (2011, background): “EHRs can enable a better quality of care as patients have their essential health data accessible to their different health providers [2,3].”
Factors affecting home care patients' acceptance of a web-based interactive self-management technology (2011, methodology): “Healthcare knowledge, which refers to “the amount of knowledge participants feel they have regarding their health condition and care for their disease,” was also examined based on Wilson and Lankton.23 Patients who think that they have little knowledge about caring for their health problems may be m”
Dead Or Alive? The Development, Trajectory And Future Of Technology Adoption Research (2007, methodology): “…calculator (Mathieson 1991), digital libraries (Hong et al. 2001), spreadsheets (Mathieson 1991; Venkatesh and Davis 1996), and e-health systems (Wilson and Lankton 2004).1 TAM has also been tested in different countries such as the U.S. (e.g., Davis 1989; Venkatesh and Davis 2000), Japan…”
Managing Citizens' Uncertainty in E-Government Services: The Mediating and Moderating Roles of Transparency and Trust (2016, methodology): “Citizens’ need for government service was measured using three items on a seven-point Likert scale ranging from 1 (“strongly disagree”) to 7 (“strongly agree”), adapted from Wilson and Lankton (2004).”
Mobile health technology adoption across generations: Narrowing the digital divide (2018, background): “Control variables included gender, nationality, education, job status, and health care need (Wilson & Lankton, 2004).”
Review Paper: A Systematic Review of Patient Acceptance of Consumer Health Information Technology (2009, background): “Human–Technology Interaction Factors Davis’ Technology Acceptance Model(37) has been one of the most influential theories for studying acceptance with diverse types of information technologies and population (including CHITs and patient population).(6,9,87,108) The model posits perceived usefulness ”