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)
Bibliographic record
Abstract
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.
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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 | 0.031 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.051 | 0.010 |
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".