Characterizing the use of virtual care in primary care settings during the COVID-19 pandemic: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: In March 2020, Canada implemented restrictions to curb viral transmission of COVID-19, which resulted in abrupt disruptions to conventional (in-person) clinical care. To retain continuity of care the delivery of primary care services shifted to virtual care. This study examined the nature of virtual visits, characterizing the use and users of virtual care in primary care settings from March 14/20 to June 30/20 of the COVID-19 pandemic. METHODS: Retrospective cohort study of primary care providers in Manitoba, Canada that participate in the Manitoba Primary Care Research Network (MaPCReN) and offered ≥ 1 virtual care visit between 03/14/20 and 06/30/20 representing 142,616 patients. Tariff codes from billing records determined the visit type (clinic visit, virtual care). Between 03/14/20, and 06/30/20, we assessed each visit for a follow-up visit between the same patient and provider for the same diagnosis code. Patient (sex, age, comorbidities, visit frequency, prescriptions) and provider (sex, age, clinic location, provider type, remuneration, country of graduation, return visit rate) characteristics describe the study population by visit type. Generalized estimating equation models describe factors associated with virtual care. RESULTS: There were 146,372 visits provided by 154 primary care providers between 03/14/20 and 06/30/20, of which 33.6% were virtual care. Female patients (OR 1.16, CI 1.09-1.22), patients with ≥ 3 comorbidities (OR 1.71, CI 1.44-2.02), and patients with ≥ 10 prescriptions (OR 2.71, 2.2-1.53) had higher odds of receiving at least one virtual care visit compared to male patients, patients with no comorbidities and patients with no prescriptions. There was no significant difference between the number of follow-up visits that were provided as a clinic visit compared to a virtual care visit (8.7% vs. 5.8%) (p = 0.6496). CONCLUSION: Early in the pandemic restrictions, approximately one-third of visits were virtual. Virtual care was utilized by patients with more comorbidities and prescriptions, suggesting that patients with chronic disease requiring ongoing care utilized virtual care. Virtual care as a primary care visit type continues to evolve. Ongoing provision of virtual care can enhance quality, patient-centered care moving forward.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".