Transforming Access to Specialist Care for Inflammatory Bowel Disease: The PACE Telemedicine Program
Bibliographic record
Abstract
BACKGROUND: There are significant geographic disparities in the delivery of IBD healthcare in Ontario which may ultimately impact health outcomes. Telemedicine-based health services may potentially bridge gaps in access to gastroenterologists in remote and underserved areas. METHODS: We conducted a needs assessment for IBD specialist care in Ontario using health administrative data. As part of a separate initiative to address geographic disparities in access to care, we described the development and implementation of our Promoting Access and Care through Centres of Excellence (PACE) Telemedicine Program. Over the first 18 months, we measured wait times and potential cost savings. RESULTS: We found substantial deficiencies in specialist care early in the course of IBD and continuous IBD care in regions where the number of gastroenterologists per capita were low. The PACE Telemedicine Program enabled new IBD consultations within a median time of 17 days (interquartile range [IQR], 7-32 days) and visits for active IBD symptoms with a median time of 8.5 days (IQR, 4-14 days). Forty-five percent of new consultations and 83% of patients with active IBD symptoms were seen within the target wait time of two weeks. Telemedicine services resulted in an estimated cost savings of $47,565 among individuals who qualified for Ontario's Northern Travel Grant. CONCLUSIONS: The implementation of telemedicine services for IBD is highly feasible and can reduce wait times to see gastroenterologists that meet nationally recommended targets and can lead to cost savings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".