Sustainable Virtual Care in Ontario’s Health System: A Quality Metrics Comparison
Bibliographic record
Abstract
Objective: The lack of pre-existing public virtual care platforms in Ontario at the start of the COVID-19 pandemic led to the generation of many private companies employing physicians in corporate practice groups. The quality of care provided remains unknown. This study assesses the quality of care provided by physicians through a virtual care platform that focuses on independent physicians providing coordinated care within the public sector. Methodology: A prospective self-directed survey was distributed to Ontario patients who received virtual care visits through the Rocket Doctor platform between October 18, 2022 and November 30, 2022. Quality indicators were selected and benchmarked against Health Quality Ontario’s (HQO) quality framework. Additional indicators of quality were measured using internal platform metrics. Results: A total of 2276 patients completed the survey out of 14,324 who completed virtual care encounters. Among the respondents, 40% reported using physicians on Rocket Doctor as their primary care provider. Compared with in-person primary care clinics, patients were able to book appointments on the same day or the next day, 74% versus 41% of the time. In addition, the platform was able to book an appointment on the same day or the next day with a specialist 86% of the time. Physicians were able to resolve or provide follow-up on 94% of encounters, with only 1% of patients referred to the emergency department. Conclusion: Quality of care metrics overall met and surpassed comparable HQO indicators. Services provided were more timely, efficient, and effective. High-quality virtual care can be effectively delivered through a private technology platform built on public health insurance and integrated into Canada’s healthcare system. Plain Language Summary Delivery of care through a virtual platform in Canada is novel and expanded rapidly in response to the COVID-19 pandemic. While the medical care provided is publicly funded, private companies manage the virtual care platforms. The quality of care provided in this unique, novel system was currently unknown and much needed to help inform Canada’s health policies on virtual care. This patient survey study looked at the quality of care provided on one of these virtual care platforms and compared it with provincial quality standards. Patients were able to be seen effectively, timely, and efficiently above provincial quality standards. Virtual care platforms can help provide high-quality clinical care and sustainably integrate into the Canadian healthcare system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".