Improving Access to Chronic Pain Services Through eConsultation: A Cross-Sectional Study of the Champlain BASE eConsult Service
Bibliographic record
Abstract
OBJECTIVE: To describe the impact of the Champlain BASE (Building Access to Specialists through eConsultation) eConsult service on access to specialist care for patients with chronic pain. DESIGN: A cross-sectional descriptive study SETTING: The Champlain Local Health Integration Network, comprising Ottawa, Canada, and the surrounding region. SUBJECTS: All eConsult cases submitted to chronic pain specialists by primary care providers between April 15, 2011 and June 30, 2015. METHODS: Usage data and provider responses to a mandatory closeout survey were analyzed to determine response times, case outcomes, and provider satisfaction. RESULTS: Ninety-three primary care providers submitted 199 eConsults to four chronic pain specialists during the study period. Submitted cases had median response times of 1.9 days. Thirty-six percent of cases resulted in an unnecessary referral being avoided, and over 90% of cases were rated by primary care providers as being of high or very high value for their patients and themselves. CONCLUSION: The eConsult service improved access to specialist care for patients with chronic diseases. By facilitating prompt communication between primary care providers and specialists, eConsult can help mitigate the negative effects of long wait times experienced by patients with chronic pain.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".