Clinical and economic impact of a community-based, hybrid model of in-person and virtual care in a Canadian rural setting: a cross-sectional population-based comparative study
Bibliographic record
Abstract
OBJECTIVES: To determine the clinical and economic impact of a community-based, hybrid model of in-person and virtual care by comparing health-system performance of the rural jurisdiction where this model was implemented with neighbouring jurisdictions without such a model and the broader regional health system. DESIGN: A cross-sectional comparative study. SETTING: Ontario, Canada, with a focus on three largely rural public health units from 1 April 2018 until 31 March 2021. PARTICIPANTS: All residents of Ontario, Canada under the age of 105 eligible for the Ontario Health Insurance Plan during the study period. INTERVENTIONS: An innovative, community-based, hybrid model of in-person and virtual care, the Virtual Triage and Assessment Centre (VTAC), was implemented in Renfrew County, Ontario on 27 March 2020. MAIN OUTCOME MEASURES: Primary outcome was a change in emergency department (ED) visits anywhere in Ontario, secondary outcomes included changes in hospitalisations and health-system costs, using per cent changes in mean monthly values of linked health-system administrative data for 2 years preimplementation and 1 year postimplementation. RESULTS: Renfrew County saw larger declines in ED visits (-34.4%, 95% CI -41.9% to -26.0%) and hospitalisations (-11.1%, 95% CI -19.7% to -1.5%) and slower growth in health-system costs than other rural regions studied. VTAC patients' low-acuity ED visits decreased by -32.9%, high-acuity visits increased by 8.2%, and hospitalisations increased by 30.0%. CONCLUSION: After implementing VTAC, Renfrew County saw reduced ED visits and hospitalisations and slower health-system cost growth compared with neighbouring rural jurisdictions. VTAC patients experienced reduced unnecessary ED visits and increased appropriate care. Community-based, hybrid models of in-person and virtual care may reduce the burden on emergency and hospital services in rural, remote and underserved regions. Further study is required to evaluate potential for scale and spread.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 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".