Use and Acceptance of Innovative Digital Health Solutions Among Patients and Professionals: Survey Study
Bibliographic record
Abstract
Background: Digital solutions are gaining increasing importance and present a challenge regarding their introduction and acceptance into professional medical environments. Significant advances have been made regarding the availability, safety, and ease of use of data generated by a multitude of devices and wearables. However, data security and data protection are delaying factors. The underlying analysis focuses on the use and acceptance of digital solutions, and their respective differences between health care professionals and patients. Objective: This study examines the current use and acceptance of digital solutions among health care professionals and patients. In addition, it derives an outlook on future developments and expectations in the setting of innovative technologies able to penetrate the health market. Methods: An anonymous web-based survey of 23 multiple-choice and 3 open-text questions was conducted among medical professionals and patients between April and September 2023. In this study, quantitative analysis was performed using Python, with Pandas for data processing and Matplotlib for visualization. Chi-square tests were used to analyze binary categorical data, while Mann-Whitney U tests were used to evaluate ordinal data. Additionally, a qualitative analysis was conducted to summarize the results of the open-ended questions. Results: During 178 days, the survey garnered 2058 clicks, resulting in 1389 participants (67.5% response rate). A total of 1002 participants completed the entire questionnaire, while 387 (27.9%) did not finish. Incomplete responses were excluded from the comprehensive analysis. The sample comprised 271 (27%) physicians and 731 (73%) patients. The study found significant agreement between both groups in adopting and foreseeing the use of digital health tools and telemedicine. Both groups recognized the future importance of digital health without substantial differences. Conclusions: Overall, attitudes toward digital health and telemedicine were consistent, reflecting a uniform acceptance and expectation of these technologies among health care professionals and patients. The consensus on telemedicine's future role over the next 5 years indicates a unified vision for digital health paradigms. These consistencies between the 2 groups might be future drivers for improvements in accessibility, convenience, and efficiency in health care delivery.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".