Remote Follow-up of Self-isolating Patients With COVID-19 Using a Patient Portal: Protocol for a Mixed Methods Pilot Study (Opal-COVID Study)
Notice bibliographique
Résumé
BACKGROUND: People with COVID-19 are instructed to self-isolate at home. During self-isolation, they may experience anxiety and insufficient care. Patient portals can allow patients to self-monitor and remotely share their health status with health care professionals, but little data are available on their feasibility. OBJECTIVE: This paper presents the protocol of the Opal-COVID Study. Its objectives are to assess the implementation of the Opal patient portal for distance monitoring of self-isolating patients with COVID-19, identify influences on the intervention's implementation, and describe service and patient outcomes of this intervention. METHODS: This mixed methods pilot study aims to recruit 50 patient participants with COVID-19 tested at the McGill University Health Centre (Montreal, Canada) for 14 days of follow-up. With access to an existing patient portal through a smartphone app, patients will complete a daily self-assessment of symptoms, vital signs, and mental health monitored by a nurse, and receive 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, on recruitment, retention, and fidelity, as well as on the frequency and nature of contacts with health care professionals. 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 for the qualitative analyses, for objective 2 (influences on implementation), semistructured qualitative interviews will be conducted with 4 groups of stakeholders (ie, patient participants, health care professionals, technology developers, and study administrators) and submitted for content analysis, guided by the Consolidated Framework for Implementation Research to help identify barriers to and facilitators of implementation. For objective 3 (service outcomes), reasons for contacting health care professionals through Opal will also be submitted for content analysis. RESULTS: Between December 2020 and March 2021, a total of 51 patient participants were recruited. Qualitative interviews were conducted with 39 stakeholders from April to September 2021. Delays were experienced owing to measures taken at the McGill University Health Centre to address COVID-19. The quantitative and qualitative analyses began in May 2022. As of June 2022, a total of 2 manuscripts (on the implementation and the patient outcomes) were being prepared, and 3 conference presentations had been given on the study's methods. 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. TRIAL REGISTRATION: ClinicalTrials.gov NCT04978233; https://clinicaltrials.gov/ct2/show/NCT04978233. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/35760.
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 | 0,051 | 0,029 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,004 | 0,003 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,036 | 0,008 |
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 ».