The primary care COVID-19 integrated pathway: a rapid response to health and social impacts of COVID-19
Bibliographic record
Abstract
BACKGROUND: The first wave of COVID-19 in Calgary, Alberta accelerated the integration of primary care with the province's centrally managed health system. This integration aimed to deliver wraparound in-community patient care through two interventions that combined to create the COVID-19 Integrated Pathway (CIP). The CIP's interventions were: 1) a data sharing platform that ensured COVID-19 test results were directly available to family physicians (FPs), and 2) a clinical algorithm that supported FPs in delivering in-community follow up to improve patient outcomes. We describe the CIP function and its capacity to facilitate FP follow-up with COVID-19 patients and evaluate its impact on Emergency Department (ED) visits and hospitalization. METHOD: We generated descriptive statistics by analyzing data from a Calgary Zone hub clinic called the Calgary COVID-19 Care Clinic (C4), provincially maintained records of hospitalization, ED visits, and physician claims. RESULTS: Between Apr. 16 and Sep. 27, 2020, 7289 patients were referred by the Calgary Public Health team to the C4 clinic. Of those, 48.6% were female, the median age was 37.4 y. 97% of patients had at least one visit with a healthcare professional, where follow-up was conducted using the CIP's algorithm. 5.1% of patients visited an ED and 1.9% were hospitalized within 30 days of diagnosis. 75% of patients had a median of 4 visits with their FP. DISCUSSION: Our data suggest that information exchange between Primary Care (PC) and central systems facilitates primary care-based management of patients with COVID-19 in the community and has potential to reduce acute care visits.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.009 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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".