Implementing a Patient Portal for the Remote Follow-Up of Self-Isolating Patients With COVID-19 Infection Through Patient and Stakeholder Engagement (the Opal-COVID Study): Mixed Methods Pilot Study (Preprint)
Notice bibliographique
Résumé
BACKGROUND The COVID-19 pandemic was an unprecedent challenge to public health systems, with 95% of cases in Quebec sent home for self-isolation. To ensure continuous care, we implemented an intervention supported by a patient portal (Opal) to remotely monitor at-home patients with COVID-19 via daily self-reports of symptoms, vital signs, and mental health that were reviewed by health care professionals. OBJECTIVE We describe the intervention’s implementation, focusing on the (1) process; (2) outcomes, including feasibility, fidelity, acceptability, usability, and perceived response burden; and (3) barriers and facilitators encountered by stakeholders. METHODS The implementation followed a co-design approach operationalized through patient and stakeholder engagement. The intervention included a 14-day follow-up for each patient. In the mixed methods study at the McGill University Health Centre in Montreal, Quebec, participants completed questionnaires on implementation outcomes on days 1, 7, and 14. All scores were examined against predefined success thresholds. Linear mixed models and generalized estimating equations were used to assess changes in scores over time and whether they differed by sex, age, and race. Semistructured interviews were conducted with expert patients, health care professionals, and coordinators for the qualitative analysis and submitted to thematic analysis guided by the Consolidated Framework for Implementation Research. RESULTS In total, 51 participants were enrolled between December 2020 and March 2021; 49 (96%) were included in the quantitative analysis. Observed recruitment and retention rates (51/52, 98% and 49/51, 96%) met the 75% feasibility success threshold. Over 80% of the participants found it “quite easy/very easy” to complete the daily self-report, with a completion rate (fidelity) of >75% and a nonsignificant decreasing trend over time (from 100%, 49/49 to 82%, 40/49; P=.21). Mean acceptability and usability scores at all time points exceeded the threshold of 4 out of 5. Acceptability scores increased significantly between at least 2 time points (days 1, 7, and 14: mean 4.06, SD 0.57; mean 4.26, SD 0.59; and mean 4.25, SD 0.57; P=.04). Participants aged >50 years reported significantly lower mean ease of use (usability) scores than younger participants (days 1, 7, and 14: mean 4.29, SD 0.91 vs mean 4.67, SD 0.45; mean 4.13, SD 0.89 vs mean 4.77, SD 0.35; and mean 4.24, SD 0.71 vs mean 4.72, SD 0.71; P=.004). In total, 28 stakeholders were interviewed between June and September 2021. Facilitators included a structured implementation process, a focus on stakeholders’ recommendations, the adjustability of the intervention, and the team’s emphasis on safety. However, Opal’s thorough privacy protection measures and limited acute follow-up capacities were identified as barriers, along with implementation delays due to data security–related institutional barriers. CONCLUSIONS The intervention attained targets across all studied implementation outcomes. Qualitative findings highlighted the importance of stakeholder engagement. Telehealth tools have potential for the remote follow-up of acute health conditions. INTERNATIONAL REGISTERED REPORT RR2-10.2196/35760
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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 | 0,023 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».