Adoption, feasibility and safety of a family medicine–led remote monitoring program for patients with COVID-19: a descriptive study
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Virtual care for patients with coronavirus disease 2019 (COVID-19) allows providers to monitor COVID-19-positive patients with variable trajectories while reducing the risk of transmission to others and ensuring health care capacity in acute care facilities. The objective of this descriptive analysis was to assess the initial adoption, feasibility and safety of a family medicine-led remote monitoring program, COVIDCare@Home, to manage the care of patients with COVID-19 in the community. METHODS: COVIDCare@Home is a multifaceted, interprofessional team-based remote monitoring program developed at an ambulatory academic centre, the Women's College Hospital in Toronto. A descriptive analysis of the first cohort of patients admitted from Apr. 8 to May 11, 2020, was conducted. Lessons from the implementation of the program are described, focusing on measure of adoption (number of visits per patient total, with a physician or with a nurse; length of follow-up), feasibility (received an oximeter or thermometer; consultation with general internal medicine, social work or mental health, pharmacy or acute ambulatory care unit) and safety (hospitalizations, mortality and emergency department visits). RESULTS: The COVIDCare@Home program cared for a first cohort of 97 patients (median age 41 yr, 67% female) with 415 recorded virtual visits. Patients had a median time from positive testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to first appointment of 3 (interquartile range [IQR] 2-4) days, with a median virtual follow-up time of 8 (IQR 5-10) days. A total of 4 (4%) had an emergency department visit, with no patients requiring hospitalization and no deaths; 16 (16%) of patients required support with mental and social health needs. INTERPRETATION: A family medicine-led, team-based remote monitoring program can safely manage the care of outpatients diagnosed with COVID-19. Virtual care approaches, particularly those that support patients with more complex health and social needs, may be an important part of ongoing health system efforts to manage subsequent waves of COVID-19 and other diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it