Remote Follow-up of Self-isolating COVID-19 Patients with a Patient Portal: Protocol for a Mixed-method Pilot Study (The Opal-COVID Study) (Preprint)
Notice bibliographique
Résumé
BACKGROUND Individuals diagnosed with COVID-19 are instructed to self-isolate at home. However, during self-isolation, they may experience anxiety and insufficient care. Some patient portals can allow patients to self-monitor and share their health status with healthcare professionals for remote follow-up, but little data is available on the feasibility of their use. OBJECTIVE This manuscript presents the protocol of the Opal-COVID Study which has four objectives: 1) assess the implementation of using the Opal patient portal for distance monitoring of COVID-19 patients self-isolating at home; 2) identify influences on the intervention’s implementation; and describe 3) service and 4) patient outcomes of this intervention. METHODS This mixed-method pilot study aims to recruit 50 COVID-19 patient participants tested at the McGill University Health Centre (MUHC, Montreal, Canada) for 14 days of remote follow-up. With access to questionnaires through the Opal patient portal smartphone app, configured for this study, patients will complete a daily self-assessment of symptoms, vital signs, and mental health, monitored by a nurse, and receive subsequent teleconsultations, as needed. Study questionnaires will be administered to collect data on sociodemographic characteristics, medical background, implementation outcomes (acceptability, usability, and respondent burden) and patient satisfaction. Coordinator logbook entries will inform on feasibility outcomes, namely, recruitment/retention rates and fidelity, as well as on the frequency and nature of contacts with healthcare professionals via Opal. The statistical analyses for Objectives 1 (implementation outcomes), 3 (service outcomes), and 4 (patient outcomes) will evaluate the effects of time and sociodemographic characteristics on the outcomes. For Objectives 1 (implementation outcomes) and 4 (patient outcomes), the statistical analyses will also examine the attainment of predefined success thresholds. As to the qualitative analyses, for Objective 2 (influences on implementation), semi-structured qualitative interviews will be conducted with four groups of stakeholders (i.e., patient participants, healthcare professionals, technology developers and study administrators) and submitted to content analysis, guided by the Consolidated Framework for Implementation Research to help identify barriers and facilitators of implementation. For Objective 3 (service outcomes), reasons for contacting healthcare professionals through Opal will also be submitted to content analysis. RESULTS Between December 2020 and March 2021, 51 patient-participants were recruited. Qualitative interviews were conducted with 39 involved stakeholders, from April to September 2021. Delays in the study process were experienced due to implemented measures at the MUHC to address COVID-19 but the quantitative and qualitative analyses are currently underway. CONCLUSIONS This protocol is designed to generate multidisciplinary knowledge on the implementation of a patient portal-based COVID-19 care intervention and will lead to a comprehensive understanding of feasibility, stakeholder experience, and influences on implementation that may prove useful for scaling up similar interventions. CLINICALTRIAL ClinicalTrials.gov identifier NCT04978233.
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 | 0,031 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,003 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,051 | 0,010 |
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 ».