Clinicians’ Role in the Adoption of an Oncology Decision Support App in Europe and Its Implications for Organizational Practices: Qualitative Case Study
Bibliographic record
Abstract
BACKGROUND: Despite the existence of adequate technological infrastructure and clearer policies, there are situations where users, mainly physicians, resist mobile health (mHealth) solutions. This is of particular concern, bearing in mind that several studies, both in developed and developing countries, showed that clinicians' adoption is the most influential factor in such solutions' success. OBJECTIVE: The aim of this study was to focus on understanding clinicians' roles in the adoption of an oncology decision support app, the factors impacting this adoption, and its implications for organizational and social practices. METHODS: A qualitative case study of a decision support app in oncology, called ONCOassist, was conducted. The data were collected through 17 in-depth interviews with clinicians and nurses in the United Kingdom, Ireland, France, Italy, Spain, and Portugal. RESULTS: This case demonstrates the affordances and constraints of mHealth technology at the workplace, its implications for the organization of work, and clinicians' role in its constant development and adoption. The research findings confirmed that factors such as app operation and stability, ease of use, usefulness, cost, and portability play a major role in the adoption decision; however, other social factors such as endorsement, neutrality of the content, attitude toward technology, existing workload, and internal organizational politics are also reported as key determinants of clinicians' adoption. Interoperability and cultural views of mobile usage at work are the key workflow disadvantages, whereas higher efficiency and performance, sharpened practice, and location flexibility are the main workflow advantages. CONCLUSIONS: Several organizational implications emerged, suggesting the need for some actions such as fostering a work culture that embraces new technologies and the creation of new digital roles for clinicians both on the hospitals or clinics and on the development sides but also more collaboration between health care organizations and digital health providers to enable electronic medical record integration and solving of any interoperability issues. From a theoretical perspective, we also suggest the addition of a fourth step to Leonardi's methodological guidance that accounts for user engagement; embedding the users in the continuous design and development processes ensures the understanding of user-specific affordances that can then be made more obvious to other users and increase the potential of such tools to go beyond their technological features and have a higher impact on workflow and the organizing process.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, not a consensus.
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".