Association of physician-delivered virtual care near the end of life with healthcare use outcomes: A national population-based study of Canadians
Bibliographic record
Abstract
BACKGROUND: The last 90 days of life are marked by high healthcare utilization in acute care settings, often conflicting with the preference to remain at home. The COVID-19 pandemic accelerated the adoption of virtual care, but its impact on healthcare utilization near the end-of-life remains unclear. This study assessed the association between physician-delivered virtual care use near the end-of-life and acute healthcare utilization, before and during the COVID-19 pandemic across four Canadian provinces. METHODS: A retrospective population-based cohort study using linked health administrative data from January 1, 2018, to December 31, 2021, across British Columbia (BC), Alberta (AB), Ontario (ON), and Newfoundland & Labrador (NFLD). The study included 548,955 adult decedents who died within the study period. Virtual care use in the last 90 days of life, categorized by pre-pandemic and pandemic periods, was the primary exposure. Primary outcomes were rates of ED visits, hospitalizations, and in-hospital deaths during the last 90 days of life. Modified Poisson regression models were used to measure associations, adjusting for demographic and clinical characteristics. RESULTS: Among the 548,955 decedents, virtual care utilization during the pandemic varied by province, ranging from 53% in NFLD to 78% in BC. During the pandemic, virtual care was associated with higher ED visits (adjusted rate ratios [aRateRs] ranging from 1.12 to 1.72) and hospitalizations (aRateRs: ranging from 1.01 to 1.59) in most provinces. Virtual care was linked to a higher risk of in-hospital death in AB (adjusted risk ratios [aRiskR]: 1.11; 95% CI: 1.08-1.14; P < 0.001) and ON (aRiskR: 1.04; 95% CI: 1.03-1.05; P < 0.001). Pre-pandemic, associations were weaker, with virtual care linked to lower in-hospital death rates in ON, AB and BC. CONCLUSION: Virtual care during the pandemic was linked to increased acute healthcare utilization, contrasting with pre-pandemic patterns when it appeared more selective and associated with fewer in-hospital deaths. Findings highlight the evolving role of virtual care and the need for region-specific policies to optimize end-of-life care delivery.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".