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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 source (direct Gemma or distilled Codex), 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".