COVIDCare@Home: Lessons from a Family Medicine Led Remote Monitoring Program
Bibliographic record
Abstract
Abstract Background Virtual care for patients with COVID-19 allows providers to monitor COVID-19 positive patients with variable trajectories while reducing the risk of transmission to others and managing healthcare capacity in acute care facilities. Objective To develop and test the feasibility of a family medicine-led remote monitoring model of care (COVIDCare@Home program) to manage patients with COVID-19 in the community. Methods This multi-faceted, family medicine-led, interprofessional team-based remote monitoring program was developed at Women’s College Hospital in Toronto, Ontario. A cross-sectional chart review of the first cohort of patients was conducted and learnings from the implementation of CovidCare@Home are described. Results During the study period, April 8 to May 11, 2020, there were 97 patients (average age 48.6, 62% female) with 424 recorded virtual visits with a median virtual length of stay of 8 days (IQR 5). 5.2% required escalation to an in-person visit with no patients requiring hospitalization. 16% of patients required support with mental and social health needs. Interpretations A family medicine-led, team-based remote monitoring program can safely be used to manage outpatients diagnosed with COVID-19. Attention to mental and social health needs is critical for this population. Future efforts should consider how to design programs to best support populations disproportionately impacted by COVID-19, something which primary care is well-positioned to do. Further analysis will describe the effectiveness, impact, and satisfaction with the program among patients and providers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".