Access to orthopedic specialist service in Ontario via eConsult
Bibliographic record
Abstract
BACKGROUND: Increasing strain on public health resources in Canada, in particular with respect to accessing specialist care, necessitates the exploration of alternative models of care. The aim of this study was to assess the efficacy of electronic consultation (eConsult) in providing orthopedic surgery specialist service to patients in the Champlain Local Health Integration Network (LHIN) of Ontario. METHODS: This was a cross-sectional review of all 564 Champlain LHIN orthopedic surgery referral requests received via the Champlain Building Access to Specialist service through the eConsult (BASE) system in 2017. Primary outcome measures were impact on primary care provider (PCP) referral pattern and time to receive orthopedic consultation. RESULTS: eConsult prevented unnecessary in-person consultation 64% of the time, while PCP referral decisions were modified 51% of the time. Of all eConsults, 94% were rated as valuable to PCPs in their practice and 97% of eConsults resulted in actionable advice. eConsults took an average of 14.5 minutes of specialist time to complete, and the mean time from referral to response was 3.7 days. CONCLUSION: The eConsult system spares unnecessary consultation to orthopedic surgery; catches important referrals that would have otherwise been missed; saves time for patients, PCPs and orthopedic surgeons; and improves efficiency in a socialized health care system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".