The implementation and evaluation of the Ontario COVID@Home Clinical Primary Care Pathway
Bibliographic record
Abstract
BACKGROUND: The COVID@Home Clinical Care Pathway (the Pathway) was developed and implemented as an evidence-based remote monitoring clinical care pathway for the integrated management of coronavirus disease 2019 (COVID-19) in the province of Ontario, Canada. We examine its effectiveness and rapid large-scale implementation. METHODS: Using a prospective longitudinal study design, we used electronic medical record clinical data, provider and patient surveys, web analytics, healthcare and provincial utilization, and government holdings data to evaluate reach, effectiveness, adoption, implementation, and maintenance outcomes, including patient mortality and health equity. RESULTS: The Pathway was widely accessed (19 474 Ontario unique users), contributed 28 816 oxygen saturation monitors, and achieved coverage across income levels and geography. Two-thirds of patients had > 1 encounter, monitored for a median of 4 days (Range: 1-57). Fifty percent of patients had > 1 chronic condition. Patients receiving Pathway care were less likely to die by 0.44% (20/4556), two times lower compared to the total mortality of a population-based representative patient cohort over a parallel time period in Ontario of 0.86% (1820/212 326, P = .0023). Patients were very satisfied with their care, and felt care was accessible, safe, and clear. Providers were very satisfied with the Pathway resources and reported strengthened relationships across the health system. CONCLUSIONS: Primary care (PC) rapidly implemented a clinical care pathway during the COVID-19 crisis. The Pathway demonstrated the beneficial role and effectiveness of PC when patients are provided with timely, accessible, and comprehensive care. Public health responses should explicitly collaborate with PC to address population health.
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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 | 0.021 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".