Title: Remote monitoring program for patients with COVID-19 after hospital discharge: Exploring user's experience and perspectives on two telehealth platforms. (Preprint)
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».