Assessment of the Generalizability of an eConsult Service through Implementation in a New Health Region
Bibliographic record
Abstract
INTRODUCTION: Excessive wait times for specialist care are a significant issue in many countries. Electronic consultation (eConsult) services have demonstrated the ability to improve access to specialist care. In this article, we evaluated the implementation of a successful eConsult service in a new jurisdiction to test its generalizability. METHODS: We used a multimethod approach to evaluate the Champlain Building Access to Specialists through eConsultation eConsult service's implementation in the South East Local Health Integration Network of Ontario, Canada. Our quantitative analysis drew on use data collected automatically by the service and survey responses completed between February 1, and June 15, 2017. For our qualitative analysis, we conducted a thematic analysis of 3 focus groups with primary care providers and specialists participating in the pilot study. RESULTS: Forty-nine out of the potential 219 primary care providers in Kingston submitted 301 cases to 24 specialty groups during the study period. Monthly case volume grew from 15 in February to 90 in May. The most frequently requested specialties included dermatology (n = 59), cardiology (n = 27), and gastroenterology (n = 26). Specialists responded in a median of 2 days, and a referral was originally contemplated but ultimately avoided in 40% of cases. Providers spoke positively of the service, citing high levels of satisfaction, enhanced collegiality, increased trust, and improved patient flow. CONCLUSIONS: Adoption of the eConsult service in the South East Local Health Integration Network was successful. The service exceeded all adoption targets, and the number of completed cases demonstrated a consistently upward trend, suggesting continued growth beyond the study's duration. The service's rate of adoption, high levels of satisfaction, and use data similar to other regions all demonstrate eConsult's generalizability.
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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 | 0.033 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".