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)
Bibliographic record
Abstract
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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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 | 0.023 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".