Title: Remote monitoring program for patients with COVID-19 after hospital discharge: Exploring user's experience and perspectives on two telehealth platforms. (Preprint)
Bibliographic record
Abstract
BACKGROUND As Covid-19 pandemic circumstances created the need to act to reduce the spread of the virus and alleviate healthcare services from congestions, protect healthcare providers and support them in maintaining a satisfactory quality and safety of care, Covid19 patient remote monitoring platforms quickly emerged. OBJECTIVE This study aimed to evaluate the capacity and contribution of two different platforms' services to monitor remotely patients with Covid-19. The first is a platform of telecare calls (Telecare-Covid), and the second platform is a telemonitoring app (Tactio-Covid). The study sought to examine the differences in acceptability, usefulness, and conviviality of those two different platforms services from users' perspectives and evaluate their contribution in maintaining the quality and safety of care, and engaging patients in their care. METHODS We performed a retrospective cross-sectional study using a survey. The data were collected through phone calls between May and August 2020. The data were analyzed using descriptive statistics, and t-test analysis. The participants' responses and comments on open-ended questions were analyzed using content analysis. The research approach through descriptive statistics allowed us to examine the differences in acceptability, usefulness, and conviviality of those two different platforms services from users' perspectives and determine their contributions to maintaining the quality and safety of care and promoting patient engagement. Whereas the content analysis of the general comments enabled the identification of certain stakes and challenges and improvements paths of the platforms. RESULTS In total, 51 patients participated in the study. 18 participants have used the Tactio-Covid platform and 33 participants have used the Telecare-Covid platform. Overall, the satisfaction rate regarding the quality and safety of the care services provided through the two platforms was 80%. Over 88% of users on each platform considered the services offered by the two platforms as engaging, useful, convivial, and meet their needs. The survey identified very few significant differences in users' perceptions regarding certain aspects on each platform. The survey identified four well-appreciated domains by the platforms’ users: (1) the ease of access and the proximity of care teams, and (2) the conviviality of the platform features (3) the continuity of care, and (4) the multitude of services. Certain stakes and limits such as the importance of maintaining human contact and confidentiality have been also identified and suggestions for improvement have been formulated. CONCLUSIONS This study provided preliminary evidence suggesting that the two remote monitoring platforms were well-received by users by users with very few significant differences between users' experience and perspectives over the two platforms. This type of program can be considered in a post-pandemic era and for other post-hospitalization clienteles. To maximize efficiency, the areas for improvement and the issues identified should be considered in a patient-centered manner. CLINICALTRIAL NA
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 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".