Patient Experiences With a Remote Monitoring Pathway for COVID-19
Bibliographic record
Abstract
INTRODUCTION: Due to the coronavirus disease 2019 (COVID-19) pandemic, a remote monitoring pathway was developed at Michael Garron Hospital to allow individuals with confirmed or presumed COVID-19 infection to successfully manage their illness at home. This study aims to understand patients' experiences on this remote monitoring pathway and to investigate the effectiveness of the pathway in preventing unnecessary emergency department (ED) visits and detecting severe infection. METHODS: A total of 35 semi-structured interviews were conducted over the phone. Researchers reviewed transcripts to come up with an index of nodes. Two researchers initially coded the same four transcripts to ensure high inter-rater reliability. The remaining 31 transcripts were coded by one researcher. RESULTS: Of patients, 80% (n = 28) had a positive experience on the pathway. Remote monitoring was effective in reassuring 22.9% of patients (n = 8) with mild-moderate symptoms that their symptoms were not significant enough to go to the ED and they were monitored at home. A total of 8.6% of patients (n = 3) were correctly identified as having severe symptoms while on the pathway and were asked to present to the hospital. For 8.6% of patients (n = 3), remote monitoring did not identify their severe COVID-19 illness. Of patients, 2.9% (n = 1) were incorrectly identified as having severe COVID-19 symptoms when they were clinically well. DISCUSSION: Remote monitoring is an effective tool to optimize healthcare resources during a pandemic. It reduces ER visits and provides a means for routine follow-up while minimizing virus exposure. Patients generally had a positive experience; however, more research needs to be done on optimizing the detection of severe infection.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".