Use of virtual care near the end of life before and during the COVID-19 pandemic: A population-based cohort study
Bibliographic record
Abstract
BACKGROUND AND AIMS: The expanded use of virtual care may worsen pre-existing disparities in use and delivery of end-of-life care among certain groups of people. We measured the use of virtual care in the last three months of life before and after the introduction of virtual care fee codes that funded care delivery at the start of COVID-19 on March 14, 2020, and identified changes in the characteristics of people using it. METHODS: We used linked clinical and administrative datasets to study use of virtual care in the last three months of life among 411,564 adults who died between January 25, 2018, and November 30, 2022. Modified Poisson regression was used to measure the association of the use of virtual care in the last three months of life with the pandemic study period and its association with each person- and physician-level factor. RESULTS: 14,261 people (8%) used virtual care in the last three months of life before the pandemic, and 161,000 people (69%) used it during the pandemic (relative risk [RR] 8.76; 95% CI 8.48-9.05). Several individual patient characteristics were associated with statistically significant increases in the use of virtual care after March 14, 2020 (following the introduction of virtual care fee codes), compared to before such as among older adults, ethnic minorities, multiple chronic comorbid health conditions and higher frailty groups. CONCLUSIONS: The introduction of new fee codes broadening technology and funding for end-of-life care at the start of pandemic combined with pandemic-related effects was associated with a substantial increase in the use of virtual care near the end of life among certain groups and a general leveling of pre-existing disparities in its use. Virtual end-of-life care delivery may strengthen person-centredness for individuals with limited ability to attend in-person appointments and by providers who may not have previously engaged in such care.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".