Clinical Telemedicine Utilization in Ontario over the Ontario Telemedicine Network
Bibliographic record
Abstract
INTRODUCTION: Northern Ontario is a region in Canada with approximately 775,000 people in communities scattered across 803,000 km(2). The Ontario Telemedicine Network (OTN) facilitates access to medical care in areas that are often underserved. We assessed how OTN utilization differed throughout the province. MATERIALS AND METHODS: We used OTN medical service utilization data collected through the Ontario Health Insurance Plan and provided by the Ministry of Health and Long Term Care. Using census subdivisions grouped by Northern and Southern Ontario as well as urban and rural areas, we calculated utilization rates per fiscal year and total from 2008/2009 to 2013/2014. We also used billing codes to calculate utilization by therapeutic area of care. RESULTS: There were 652,337 OTN patient visits in Ontario from 2008/2009 to 2013/2014. Median annual utilization rates per 1,000 people were higher in northern areas (rural, 52.0; urban, 32.1) than in southern areas (rural, 6.1; urban, 3.1). The majority of usage in Ontario was in mental health and addictions (61.8%). Utilization in other areas of care such as surgery, oncology, and internal medicine was highest in the rural north, whereas primary care use was highest in the urban south. CONCLUSIONS: Utilization was higher and therapeutic areas of care were more diverse in rural Northern Ontario than in other parts of the province. Utilization was also higher in urban Northern Ontario than in Southern Ontario. This suggests that telemedicine is being used to improve access to medical care services, especially in sparsely populated regions of the province.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".