Understanding Clinicians’ Adoption of Mobile Health Tools: A Qualitative Review of the Most Used Frameworks
Bibliographic record
Abstract
BACKGROUND: Although there is a push toward encouraging mobile health (mHealth) adoption to harness its potential, there are many challenges that sometimes go beyond the technology to involve other elements such as social, cultural, and organizational factors. OBJECTIVE: This review aimed to explore which frameworks are used the most, to understand clinicians' adoption of mHealth as well as to identify potential shortcomings in these frameworks. Highlighting these gaps and the main factors that were not specifically covered in the most frequently used frameworks will assist future researchers to include all relevant key factors. METHODS: This review was an in-depth subanalysis of a larger systematic review that included research papers published between 2008 and 2018 and focused on the social, organizational, and technical factors impacting clinicians' adoption of mHealth. The initial systematic review included 171 studies, of which 50 studies used a theoretical framework. These 50 studies are the subject of this qualitative review, reflecting further on the frameworks used and how these can help future researchers design studies that investigate the topic of mHealth adoption more robustly. RESULTS: The most commonly used frameworks were different forms of extensions of the Technology Acceptance Model (TAM; 17/50, 34%), the diffusion of innovation theory (DOI; 8/50, 16%), and different forms of extensions of the unified theory of acceptance and use of technology (6/50, 12%). Some studies used a combination of the TAM and DOI frameworks (3/50, 6%), whereas others used the consolidated framework for implementation research (3/50, 6%) and sociotechnical systems (STS) theory (2/50, 4%). The factors cited by more than 20% of the studies were usefulness, output quality, ease of use, technical support, data privacy, self-efficacy, attitude, organizational inner setting, training, leadership engagement, workload, and workflow fit. Most factors could be linked to one framework or another, but there was no single framework that could adequately cover all relevant and specific factors without some expansion. CONCLUSIONS: Health care technologies are generally more complex than tools that address individual user needs as they usually support patients with comorbidities who are typically treated by multidisciplinary teams who might even work in different health care organizations. This special nature of how the health care sector operates and its highly regulated nature, the usual budget deficits, and the interdependence between health care organizations necessitate some crucial expansions to existing theoretical frameworks usually used when studying adoption. We propose a shift toward theoretical frameworks that take into account implementation challenges that factor in the complexity of the sociotechnical structure of health care organizations and the interplay between the technical, social, and organizational aspects. Our consolidated framework offers recommendations on which factors to include when investigating clinicians' adoption of mHealth, taking into account all three aspects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".