Ontario Primary Care Practitioners and Access to Ophthalmologists Through Electronic Consultation
Bibliographic record
Abstract
Purpose: In Canada, accessing specialty services remains a significant challenge, leading to growing wait times for specialist care. To address this concern, the Champlain Building Access to Specialists through eConsultation (BASE) service was introduced. The Champlain BASE eConsult service is a secure web-based program that enables access to medical specialists by PCPs. The primary objective is to investigate the types of questions primary care providers (PCP) are asking ophthalmologists through the BASE service. Patients and Methods: Descriptive and retrospective analysis of 116 eConsults sent from PCPs to the ophthalmology specialty between January and December 2022, within the Champlain region, covering ~ 1.3 million people in Eastern Ontario. Using two validated taxonomies, the “content” and “type” of questions asked were coded. A closeout survey was used to determine PCP’s subsequent course of action, referral outcomes, and perceived helpfulness of eConsult responses. Results: 116 eConsults (37 pediatric; 79 adult) were reviewed with an average patient age of 36.8 years. The most common types of questions asked related to general management (61%) and referral appropriateness (43%). The most common content questions related to other non-specified content – adults (28%), lid lesions (24%) and other non-specified content – pediatrics (12%). The ophthalmologist’s median response time was 0.67 days (16.1 hours) after eConsult creation, with 84% of responses being received within seven days. PCPs received a new or additional course of action in 47% of cases. Unnecessary in-person referrals were avoided in 44% of cases. Over 88% of cases were rated at least 4/5 in value, and in 94% of eConsults, the ophthalmologists’ recommendations were accepted. Conclusion: The use of the eConsult service improves access to ophthalmologists by providing quicker, helpful, and generally accepted specialist advice while decreasing the requirement for patients to attend in-person consultations. Plain Language Summary: In Canada, long wait times to see specialists like eye doctors are a significant concern for patients. Our study looked at how family doctors and nurse practitioners used an eConsult service to ask eye doctors questions about how to manage their patients’ eye issues. We examined how many in-person referrals were prevented, how helpful the service was to the family doctors and nurse practitioners, and how quickly they received responses. We found that the online service helped patients get faster advice from eye doctors and reduced the need for in-person visits. The family doctors and nurse practitioners felt that the service was useful for their own learning and improved their ability to manage eye issues for future patients. Keywords: family medicine, specialist, healthcare access, eConsult
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".