Remote Follow-up of Self-isolating Patients With COVID-19 Using a Patient Portal: Protocol for a Mixed Methods Pilot Study (Opal-COVID Study)
Bibliographic record
Abstract
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.
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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 | 0.051 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.008 |
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".