Healthcare Professional Perspectives on the Use of Remote Patient-Monitoring Platforms during the COVID-19 Pandemic: A Cross-Sectional Study
Bibliographic record
Abstract
The COVID-19 pandemic created an urgent need to act to reduce the spread of the virus and alleviate congestion in healthcare services, protect health professionals, and help them maintain satisfactory quality and safety of care. Remote monitoring platforms (RPM) emerged as potential solutions. In this study, we evaluate, from health professionals' perspectives, the capacity and contribution of two different digital platforms to maintain quality, safety, and patient engagement in care. A cross-sectional study was conducted using a survey in which a total of 491 health professionals participated. The results show that, in general, user perceptions of the quality and safety of care provided through the platforms were positive. The ease of access to health professionals' services in general and shorter waiting times for patients were the two main features that were highly appreciated by most participants. However, some problems were encountered during the use of these two platforms, such as a lack of training and/or direct support for users. To improve the two platforms and maximize their use, the areas for improvement and the issues identified should be addressed as part of a collaborative process involving health professionals and patients as well as health system leaders, decision-makers, and digital platform providers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".